The Role of Insulin and IGF2 Signalling on Metabolic Pathways in Prostate Cancer … · 2012-03-07 · The Role of Insulin and IGF2 Signalling on Metabolic Pathways in Prostate Cancer

The Role of Insulin and IGF2 Signalling on Metabolic Pathways in Prostate

Cancer Progression

Amy Anne Lubik

Bachelors of Molecular Biology and Biochemistry (Hon), Simon Fraser University,

British Columbia, Canada

Institute of Health and Biomedical Innovation

Faculty of Science and Technology

Australian Prostate Cancer Research Centre – Queensland (APCRC-Q)

Queensland University of Technology, Brisbane, Australia

Submitted for the fulfilment of the Requirements for the Degree of

Doctor of Philosophy

~2011~

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Key Words

Prostate Cancer, Metabolic Syndrome, Insulin, Insulin-like Growth Factor (IGF) 2,

Steroidogenesis, Lipogenesis, Sterol Response Element Binding Protein (SREBP),

Metformin, Simvastatin

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Abstract

Prostate cancer (CaP) is the most commonly diagnosed cancer in males in

Australia, North America, and Europe. If found early and locally confined, CaP can

be treated with radical prostatectomy or radiation therapy; however, 25-40%

patients will relapse and go on to advanced disease. The most common therapy in

these cases is androgen deprivation therapy (ADT), which suppresses androgen

production from the testis. Lack of the testicular androgen supply causes cells of the

prostate to undergo apoptosis. However, in some cases the regression initially seen

with ADT eventually gives way to a growth of a population of cancerous cells that

no longer require testicular androgens. This phenotype is essentially fatal and is

termed castrate resistant prostate cancer (CRPC). In addition to eventual regression,

there are many undesirable side effects which accompany ADT, including

development of a metabolic syndrome, which is defined by the U.S. National

Library of Medicine as “a combination of medical disorders that increase the risk of

developing cardiovascular disease and diabetes.” This project will focus on the

effect of ADT induced hyperinsulinemia, as mimicked by treating androgen

receptor positive CaP cells with insulin in a serum (hormone) deprived

environment. While this side effect is not widely explored, in this thesis it is

demonstrated for the first time that insulin upregulates pathways important to CaP

progression.

Our group has previously shown that during CaP progression, the enzymes

necessary for de novo steroidogenesis are upregulated in the LNCaP xenograft

model, total steroid levels are increased in tumours compared to pre castrate levels,

and de novo steroidogenesis from radio-labelled acetate has been demonstrated.

Because of the CaP dependence on AR for survival, we and other groups believe

that CaP cells carry out de novo steroidogenesis to survive in androgen deprived

conditions. Because (a) men on ADT often develop metabolic syndrome, and (b)

men with lifestyle-induced obesity and hyperinsulinemia have worse prognosis and

faster disease progression, and because (c) insulin causes steroidogenesis in other

cell lines, the hypothesis that insulin may contribute to CaP progression through

upregulation of steroidogenesis was explored.

Insulin upregulates steroidogenesis enzymes at the mRNA level in three AR

positive cell lines, as well as upregulating these enzymes at the protein level in two

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cell lines. It has also been demonstrated that insulin increases mitochondrial

(functional) levels of steroid acute regulatory protein (StAR). Furthermore, insulin

causes increased levels of total steroids in and induction of de novo steroid

synthesis by insulin has been demonstrated at levels induced sufficient to activate

AR.

The effect of insulin analogs on CaP steroidogenesis in LNCaP and VCaP cells has

also been investigated because epidemiological studies suggest that some of the

analogs developed may have more cancer stimulatory effects than normal insulin.

In this project, despite the signalling differences between glargine, X10, and

insulin, these analogs did not appear to induce steroidogenesis any more potently

that normal insulin. The effect of insulin of MCF7breast cancer cells was also

investigated with results suggesting that breast cancer cells may be capable of de

novo steroidogenesis, and that increase in estradiol production may be exacerbated

by insulin.

Insulin has also been long known to stimulate lipogenesis in the liver and

adipocytes, and has been demonstrated to increase lipogenesis in breast cancer

cells; therefore, investigation of the effect of insulin on lipogenesis, which is a

hallmark of aggressive cancers, was investigated. In CaP progression sterol

regulatory element binding protein (SREBP) is dysregulated and upregulates fatty

acid synthase (FASN), acetyl CoA-carboxylase, and other lipogenesis genes.

SREBP is important for steroidogenesis and in this project has been shown to be

upregulated by insulin in CaP cells. Fatty acid synthesis provides building blocks of

membrane growth, provides substrates for acid oxidation, the main energy source

for CaP cells, provides building blocks for anti-apoptotic and proinflammatory

molecules, and provides molecules that stimulate steroidogenesis. In this project it

has been shown that insulin upregulates FASN and ACC, which synthesize fatty

acids, as well as upregulating hormone sensitive lipase (HSL), diazepam-binding

inhibitor (DBI), and long-chain acyl-CoA synthetase 3 (ACSL3), which contribute

to lipid activation of steroidogenesis. Insulin also upregulates total lipid levels and

de novo lipogenesis, which can be suppressed by inhibition of the insulin receptor

(INSR). The fatty acids synthesized after insulin treatment are those that have been

associated with CaP; furthermore, microarray data suggests insulin may upregulate

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fatty acid biosynthesis, metabolism and arachidonic acid metabolism pathways,

which have been implicated in CaP growth and survival.

Pharmacological agents used to treat patients with hyperinsulinemia/

hyperlipidemia have gained much interest in regards to CaP risk and treatment;

however, the scientific rationale behind these clinical applications has not been

examined. This thesis explores whether the use of metformin or simvastatin would

decrease either lipogenesis or steroidogenesis or both in CaP cells. Simvastatin is a

3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitor, which blocks

synthesis of cholesterol, the building block of steroids/ androgens. It has also been

postulated to down regulate SREBP in other metabolic disorders. It has been shown

in this thesis, in LNCaP cells, that simvastatin inhibited and decreased insulin

induced steroidogenesis and lipogenesis, respectively, but increased these pathways

in the absence of insulin. Conversely, metformin, which activates AMP-activated

protein kinase (AMPK) to shut down lipogenesis, cholesterol synthesis, and protein

synthesis, highly suppresses both steroidogenesis and lipogenesis in the presence

and absence of insulin.

Lastly, because it has been demonstrated to increase steroidogenesis in other cell

lines, and because the elucidation of any factors affecting steroidogenesis is

important to understanding CaP, the effect of IGF2 on steroidogenesis in CaP cells

was investigated. In patient samples, as men progress to CRPC, IGF2 mRNA and

the protein levels of the receptors it may signal through are upregulated. It has also

been demonstrated that IGF2 upregulates steroidogenic enzymes at both the mRNA

and protein levels in LNCaP cells, increases intracellular and secreted

steroid/androgen levels in LNCaPs to levels sufficient to stimulate the AR, and

upregulated de novo steroidogenesis in LNCaPs and VCaPs. As well, inhibition of

INSR and insulin-like growth factor 1 receptor (IGF1R), which IGF2 signals

through, suggests that induction of steroidogenesis may be occurring predominantly

through IGF1R.

In summary, this project has illuminated for the first time that insulin is likely to

play a large role in cancer progression, through upregulation of the steroidogenesis

and lipogenesis pathways at the mRNA and protein levels, and production levels,

and demonstrates a novel role for IGF-II in CaP progression through stimulation of

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steroidogenesis. It has also been demonstrated that metformin and simvastatin

drugs may be useful in suppressing the insulin induction of these pathways. This

project affirms the pathways by which ADT- induced metabolic syndrome may

exacerbate CaP progression and strongly suggests that the monitoring and

modulation of the metabolic state of CaP patients could have a strong impact on

their therapeutic outcomes

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List of Abbreviations

3-hydroxy-3-methylglutaryl (HMG)-CoA reductase (HMGR)

3-hydroxy-3-methylglutaryl (HMG)-CoA synthase (HMGS)

5 α reductase 1 and/or 2 (SRD5A)

acetyl CoA carboxylase (ACC)

acyl-CoA dehydrogenase, C-2 to C-3 short chain (ACADS)

acyl-CoA oxidase and/or pristanoyl-CoA oxidase (ACOX)

acyl-CoA synthetase 3 (ACSL)

aldo-keto reductase family 1 members (AKR1Cs)

American Type Culture Collection (ATCC)

AMP-activated protein kinase (AMPK)

androgen dependent (AD)

androgen deprivation therapy (ADT)

androgen receptor (AR)

before castration (preCx)

benign prostatic hyperplasia (BPH)

bicinchoninic acid (BCA)

body mass index (BMI)

biotinidase (BTD)

bovine serum albumin (BSA)

carbohydrate-responsive element-binding protein (ChREBP)

cyclooxygenase (COX)- 2

carbonyl reductase 1 (CBR1)

castrate resistant CaP (CRPC)

charcoal stripped FBS (CSS)

cytochrome p450 member (CYP)

D-bifunctional protein (DBP)

degrees Celsius (oC)

dehydroepiandrosterone (DHEA)

diazepam-binding inhibitor/acyl-CoA-binding protein (DBI)

digital rectal exam (DRE)

dihydrotestosterone (DHT)

dimethyl sulfoxide (DMSO)

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Dulbecco's Modified Eagle Medium (DMEM)

endoplasmic reticulum (ER)

enzyme-linked immunosorbent assay (ELISA)

epigallocatechin gallate (EGCG)

epithelial mesenchymal transition (EMT )

ethanoloacetate (EtOAc)

estrogen receptor alpha (ER-α)

estrogen receptor beta (ER-β)

farnesyl diphosphate synthase (FDPS)

fatty acid methyl esterification (FAME)

fatty acid synthase (FASN)

fatty acid transport protein (FATP) [also SLC27A]

fetal bovine serum (FBS)

forkhead/winged helix box gene, group O (FoxO)

glyceraldehyde-3-phosphate dehydrogenase (GAPDH)

grams (g)

high performance liquid chromatography-mass spectrometry (HPLC-MS)

hormone sensitive lipase (HSL)

human soluble epoxide hydrolase gene 2 (EPHX2)

hybrid receptors (HR)

hydroxyacyl-CoA dehydrogenase (HADHA)

hydroxysteroid dehydrogenase family (HSDs)

IGF-binding protein (IGFBP)

ingenuity pathway analysis (IPA)

insulin receptor (INSR)

insulin receptor substrate (IRS)

insulin response element (IRE)

Insulin-like growth factor-1 receptor (IGF1R)

large ribosomal protein 32 (rpl32)

leukotriene synthases ( LTC[n]S)

linear models for microarray data (LIMMA)

lipooxygenase (LOX)

Liquid Chromatography-Mass Spectrometry (LC/MS/MS)

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luteinizing hormone releasing hormone (LHRH)

lymph node metastases prostate cancer cell line (LNCaP)

malonyl-CoA:ACP acyltransferase (MCAT)

methanol (MeOH)

methyl tert-butyl ether (MTBE)

micrograms/ millilitre (µg/ml)

microlitre (µl)

micromolar (µM)

millimolar (mM)

millilitre (mL)

mitogen-activated protein kinase (MAPK)

nadir (N)

nanogram/ millilitre (ng/ml)

nanomolar (nM)

neuroblastoma/glioblastoma derived oncogene homolog (HER2/neu)

neutral protamine hagedorn insulin (NPH)

omega 3 (n3)

omega 6 (n6)

peroxisome proliferators activator receptor (PPAR)

phosphoenolpyruvate carboxykinase (PEPCK)

phosphatidylinositol 3-kinase (PI3K)

picomolar (pM)

prostatic intraepithelial neoplasia (PIN)

poly-ADP-ribose-polymerase (PARP)

polycystic ovary syndrome (PCOS)

polyunsaturated fatty acids (PUFA)

propionyl-CoA (PCCB)

prostaglandin D synthase (PTGDS)

prostaglandin-endoperoxide synthase (PTGS) [also COX- 2]

prostate cancer (CaP)

prostate specific antigen (PSA)

pyruvate dehydrogenase kinase (PDK1)

quantitative reverse transcriptase polymerase chain reaction (QRT-PCR)

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radioimmunoprecipitation assay buffer (RIPA)

retinol 11-cis dehydrogenase (RDH5)

room temperature (RT)

Roswell Park Memorial Institute medium (RPMI)

sodium dodecyl sulphate (SDS)

solute carrier family 27 (SLC27A) 3,4,5

SREBP cleavage activating protein (SCAP)

stearoyl-CoA desaturase (SCD)

steroid acute regulatory protein (StAR)

sterol regulatory element (SRE)

sterol regulatory element binding protein (SREBP)

type 1 diabetes (T1D)

type 2 diabetes (T2D)

vertebrae metastasis cell line (VCaP)

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Statement of Authorship

The work contained in this thesis has not been previously submitted to meet the

requirements for an award at this or any other higher education institution. To the best

of my knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made

Signature: Amy Anne Lubik

Date: 21/09/2011

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Publications

Amy A Lubik, Jennifer H Gunter, Stephen C Hendy, Jennifer A Locke, Hans H

Adomat, Vanessa Thompson, Michael Pollak, Adrian Herington, Martin E Gleave,

Nelson CC. Insulin directly increases de novo steroidogenesis in prostate cancer cells.

Cancer Research (Published Online First July 11, 2011). (Chapter 1)

Sieh S, Lubik AA, Clements JA, Nelson CC, Hutmacher DW. Interactions between

human osteoblasts and prostate cancer cells in a novel 3D in vitro model.

Organogenesis. 2010 Jul-Sep;6(3):181-8. (Referred to in chapter 8).

Locke JA, Guns ES, Lehman ML, Ettinger S, Zoubeidi A, Lubik A, Margiotti K,

Fazli L, Adomat H, Wasan KM, Gleave ME, Nelson CC. Arachidonic acid activation

of intratumoral steroid synthesis during prostate cancer progression to castration

resistance. Prostate. 2010 Feb 15;70(3):239-51. (Referred to in chapter 5).

Locke JA, Fazli L, Adomat H, Smyl J, Weins K, Lubik AA, Hales DB, Nelson CC,

Gleave ME, Tomlinson Guns ES. A novel communication role for CYP17A1 in the

progression of castration-resistant prostate cancer. Prostate. 2009 Jun 15;69(9):928-

37. (Referred to in chapter 1).

Locke JA, Guns ES*, Lubik AA*, Adomat HH, Hendy SC, Wood CA, Ettinger SL,

Gleave ME, Nelson CC. Androgen levels increase by intratumoral de novo

steroidogenesis during progression of castration-resistant prostate cancer. Cancer

Research. 2008 Aug 1;68(15):6407-15. (Referred to in chapter 1-4, 6, 7)

*Both Authors Contributed Equally

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Acknowledgements

This work is dedicated in loving memory to Aunty Emmy Bennett, for her strength,

courage, humour and love. Thank you for lending your strength to the only other red-

head in the family.

I sincerely thank my principal supervisor, Professor Colleen Nelson, for all the

amazing opportunities and support she has given me since I was first a summer student

in her laboratory at the Prostate Centre at Vancouver General Hospital, in Canada. I

also express tremendous gratitude to my associate supervisor, Professor Adrian

Herington, for all his help and guidance. I am also especially grateful to Steven

Hendy, who is the “tech to be beat all techs” in Vancouver, Canada, for the amazing

help and support he has given me, and Hans Adomat, for all of his help and patience. I

also thank Dr. Morgan Pokorny for making some of my illustrations in chapter 1 look

lovely. I gratefully acknowledge QUT for offering the scholarship that allowed me to

do this project, and the Queensland Smart Futures Premier’s Fellowship and Prostate

Cancer Foundation of Australia for funding this project.

Thank you to the members of the APCRC-Q, especially Dr. Jennifer Gunter, Dr.

Lidija Jovanovic, Dr. Raja Vasireddy, Soon-to-be Dr. Shirly Sieh, and Dr. John Lai,

for excellent experimental assistance and/or advice, as well as help editing this thesis;

moreover, thank you for being such dear friends. And thank you to Dr. Inge Seim, for

editing assistance, and for being one of the funniest people I know.

I thank from the bottom of my heart, Richard Gauthier, my Sonitchko, who has been

supportive and patient from across the ocean for three long years. Thank you for being

my partner and the other side of my compass. Thank you to my amazing sisters for

their love, support, and humour, lots of tea and long emails. Thank you for letting me

borrow, or at least emulate, your business woman confidence when I needed it. Sarah,

thank you being my other half, closest confidant, and oldest (prenatal) friend. Jen,

thank you for being a wonderful partner in my favourite, non-laboratory, pursuits

(namely the science of cooking and baking, and all things outdoors!) Thank you to

Mom and Dad for their love and support and motivational quotations that have kept

me going and have been spread around Australia. A special thank you Nana for her

love, wit, and strength, and for being so inspirational; as well as for teaching me how

to knit – 8 billion scarfs, sweaters, afghans later, it has kept me sane when necessary.

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I sincerely appreciate the support and humour of my Canadian family and friends,

especially Rebecca and Mike, Shaun, Jonny, and Faran. Becca, thank you for being

you and for a monthly letter that I have always looked forward to so much.

I am extremely grateful to Daphne and John Gibbs, who have adopted me as family

while I have been in Queensland, for their love and support, and Alma Mullins, Ruth

Birch, and Liz Tibben for being some of the best friends I’ve ever had.

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Table of Contents

Key Words ................................................................................................................. 2

Abstract ...................................................................................................................... 3

List of Abbreviations ................................................................................................. 7

Statement of Authorship .......................................................................................... 11

Publications .............................................................................................................. 12

Acknowledgements .................................................................................................. 13

Table of Contents ..................................................................................................... 15

Table of Figures ....................................................................................................... 21

Chapter 1: Introduction and Literature Review ....................................................... 23

1.1 Introduction .................................................................................................... 25

1.2 Prostate cancer ................................................................................................ 25

1.3 Androgen receptor signalling in prostate cancer ............................................ 27

1.4 Steroidogenesis and prostate cancer ............................................................... 29

1.4.1 Steroidogenesis enzyme expression in prostate cancer ........................... 29

1.4.2 Intraprostatic androgens/ de novo steroidogenesis .................................. 32

1.4.3 AR and steroidogenesis inhibitors in prostate cancer treatment .............. 35

1.5 Lipid/ fatty acid contribution to prostate cancer and sterol response element binding protein...................................................................................................... 39

1.5.1 Lipogenesis and prostate cancer .............................................................. 39

1.5.2 Sterol regulatory element binding protein and prostate cancer ............... 40

1.5.3 Fatty acids and steroidogenesis ............................................................... 43

1.6 Metabolic syndrome and prostate cancer ....................................................... 44

1.6.1 Metabolic syndrome correlates with prostate cancer progression in epidemiological studies .................................................................................... 44

1.6.2 Insulin signalling ..................................................................................... 46

1.6.3 Insulin and analog effects on cancer ........................................................ 48

1.6.4 SREBP and insulin signalling .................................................................. 50

1.6.5 Insulin and steroidogenesis ...................................................................... 54

1.7 Metabolic syndrome drugs and cancer ........................................................... 55

1.7.1 Metformin ................................................................................................ 55

1.7.2 Statin drugs .............................................................................................. 57

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1.8 IGF2 and prostate cancer ................................................................................ 58

1.8.1 IGF axis signalling ................................................................................... 58

1.8.2 The IGF axis in prostate cancer ............... Error! Bookmark not defined.

1.8.3 IGF2 in cancer ......................................................................................... 62

1.8.4 IGF2 in prostate cancer ............................................................................ 64

1.8.5 IGF2 and steroidogenesis ........................................................................ 64

1.9 Summary and project relevance ..................................................................... 66

Chapter 2: Materials and Methods ........................................................................... 69

2.1 Introduction .................................................................................................... 71

2.2 General reagent, plates, and chemicals .......................................................... 71

2.3 Cell lines ......................................................................................................... 71

2.4 Cell culture ..................................................................................................... 72

2.5 RNA extraction .............................................................................................. 73

2.6 Reverse transcriptase polymerase chain reaction (RT-PCR) ......................... 73

2.7 Quantitative RT-PCR (QRT-PCR) ................................................................. 74

2.8 Protein extraction ........................................................................................... 74

2.9 Western blotting ............................................................................................. 75

2.10 Total steroid/ lipid/ androgen extraction ...................................................... 75

2.11 Estradiol extraction ...................................................................................... 77

2.12 14C- Steroid extraction .................................................................................. 78

2.13 Oil Red-O lipid extraction/ quantitation ....................................................... 79

2.14 Microarray gene expression profiling .......................................................... 79

Chapter 3: Insulin Directly Increases de novo Steroidogenesis in Prostate Cancer Cells ......................................................................................................................... 83

3.1 Introduction .................................................................................................... 85

3.2 Materials and Methods ................................................................................... 86

3.2.1 In vitro model .......................................................................................... 86

3.2.2 QRT-PCR ................................................................................................ 86

3.2.3 Western blotting ...................................................................................... 87

3.2.4 Mitochondrial fractionation assay ........................................................... 87

3.2.5 Steroid quantitation (total steroids) by LC/MS/MS ................................ 87

3.2.6 De novo steroid analysis using radiometric detection ............................. 88

3.2.7 Steroid analysis using DHT ELISA ......................................................... 88

3.2.8 PSA analysis of LNCaP media ................................................................ 88

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3.2.9 In vivo model: .......................................................................................... 88

3.2.10 Statistics ................................................................................................. 89

3.3 Results ............................................................................................................ 89

3.3.1 Insulin upregulates expression of enzymes necessary for steroidogenesis at the mRNA and protein levels ........................................................................ 89

3.3.2 Insulin increases intracellular steroids in prostate cancer cells ............... 96

3.3.3 Insulin increases secretion of steroids from prostate cancer cells ........... 96

3.3.4 PSA expression and secretion are increased by insulin ......................... 103

3.3.5 In LNCaP xenografts mice which showed an increase in both PSA and RDH5 expression at 28 days post castration also displayed an increase in INSR-A and IRS2 mRNA .............................................................................. 107

3.4 Discussion .................................................................................................... 109

Chapter 4: The Effect of Insulin Analogs on Steroidogenesis and Insulin Effect on Breast Cancer Steroidogenesis ............................................................................... 115

4.1 Introduction .................................................................................................. 117

4.2 Materials and Methods ................................................................................. 120

4.2.1 In vitro model ........................................................................................ 120

4.2.2 QRT-PCR .............................................................................................. 121

4.2.3 De novo steroid analysis using radiometric detection: .......................... 121

4.2.4 Total levels of MCF7 estradiol as measured by LC/MS/MS ................ 121

4.2.5 Statistics ................................................................................................. 122

4. 3 Results ......................................................................................................... 122

4.3.1 Insulin and analogs upregulate enzymes necessary for steroidogenesis at the mRNA level .............................................................................................. 122

4.3.1 Insulin analog effect on de novo steroidogenesis in LNCaP and VCaP medium ........................................................................................................... 124

4.3.3 Insulin effects on steroidogenesis in breast cancer ................................ 126

4.4 Discussion ........................................................................................................ 127

Chapter 5: Insulin increases Fatty Acid Synthesis in Prostate Cancer Cells ......... 131

5.1 Introduction .................................................................................................. 133


5.2.1 In vitro model ........................................................................................ 134

5.2.2 QRT-PCR .............................................................................................. 135

5.2.3 Western blotting ..................................................................................... 135

5.2.4 Oil Red-O lipid stain .............................................................................. 135

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5.2.5 Receptor inhibitor treatment: ................................................................. 135

5.2.6 14C-Fatty Acid Methyl Ester (FAME) derivatization and extraction ... 136

5.2.7 FAME analysis by gas chromatography-mass spectrometry (GC-MS):136

5.2.8 Microarray analysis ............................................................................... 137

5.2.9 Statistics: ................................................................................................ 137

5.3 Results .......................................................................................................... 137

5.3.1 Insulin upregulates lipid related genes in prostate cancer cells ............. 137

5.3.2 Insulin increases cellular lipid/ fatty acid content ................................. 141

5.3.3 Analysis of de novo fatty acid synthesis and lipid profile after insulin treatment ......................................................................................................... 145

5.3.4 Insulin effects on fatty acid metabolism, as demonstrated by microarray analysis ........................................................................................................... 148

5.4 Discussion .................................................................................................... 152

Chapter 6: Drugs Used in Metabolic Syndrome, Metformin and Simvastatin, Inhibit Fatty Acid Synthesis and Steroidogenesis in Prostate Cancer ............................... 159

6.1 Introduction .................................................................................................. 161


6.2.1 In vitro model ........................................................................................ 167

6.2.2 QRT-PCR .............................................................................................. 167

6.2.3 Western blotting .................................................................................... 167

6.2.4 Intracellular cholesterol assay ............................................................... 168

6.2.5 De novo steroid/ cholesterol analysis using radiometric detection ....... 168

6.2.6 Oil-Red O lipid stain: ............................................................................ 168

6.2.7 Statistics ................................................................................................. 168

6.3 Results .......................................................................................................... 169

6.3.1 Metformin inhibits insulin-induced cholesterol synthesis ..................... 169

6.3.2 Metformin inhibits steroidogenesis ....................................................... 171

6.3.3 Metformin decreases lipid content in CaP cells .................................... 175

6.3.4 Simvastatin inhibits insulin induced cholesterol synthesis .................... 179

6.3.5 Simvastatin inhibits insulin induced steroidogenesis ............................ 182

6.3.6 Simvastatin affects lipid content in CaP cells ....................................... 185

6.4 Discussion .................................................................................................... 186

Chapter 7: Insulin-like Growth Factor 2 Increases de novo Steroidogenesis in Prostate Cancer Cells ............................................................................................. 193

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7.1 Introduction .................................................................................................. 195


7.2.1 Laser capture microdissection (LCM) and microarray analysis ............ 197

7.2.2 Immunohistochemistry .......................................................................... 198

7.2.3 In vitro model ........................................................................................ 199

7.2.4 QRT-PCR .............................................................................................. 200

7.2.5 Western blotting ..................................................................................... 200

7.2.6 Steroid analysis in LNCaP cells ............................................................ 201

7.2.7 Radio-labelled acetate analysis of de novo steroidogenesis in LNCaP and VCaP cells ...................................................................................................... 201

7.2.8 Receptor inhibitor treatment: ................................................................. 201

7.2.9 Microarray analysis ............................................................................... 202

7.2.10 Statistics ............................................................................................... 202

7.3 Results .......................................................................................................... 202

7.3.1 IGF2 mRNA expression in men undergoing neoadujvant hormone therapy (NHT) ................................................................................................ 202

7.3.2 IGF2 effect on steroidogenesis enzymes ............................................... 204

7.3.3 IGF2 increases intracellular and secreted steroids ................................. 207

7.3.4 IGF2 effect on de novo steroidogenesis in LNCaP and VCaP medium 209

7.3.5 Receptor blockade of IGF2 effects ........................................................ 213

7.3.6 Insulin may increase expression of IGF2 in prostate cancer cells. ........ 213

7.4 Discussion .................................................................................................... 216

Chapter 8: General Discussion ............................................................................... 219

8.1 Overview ...................................................................................................... 221

8.2 Prostate cancer and metabolic syndrome: reducing risk by nutritional and pharmacological means ...................................................................................... 224

8.3 The effect of insulin on breast cancer: background and prospects for study/ intervention ......................................................................................................... 228

8.3.1 The similarities between breast and prostate cancer .............................. 228

8.3.2 Breast cancer and hyperinsulinemia ...................................................... 230

8.3.3 Insulin effects on steroidogenesis in breast cancer ................................ 231

8.4 IGF2, steroidogenesis, and bone metastases ................................................ 231

8.4.1 Androgens and bone metastases ............................................................ 231

8.4.2 IGF2 may promote steroidogenesis contributing to growth of bone metastases ....................................................................................................... 232

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8.4.3 Insulin and IGF2 may promote prostate cancer progression ................. 234

8.5 Final Conclusion and Summary ................................................................... 235

Appendices ............................................................................................................. 237

APPENDIX A: Primers Used ............................................................................ 239

APPENDIX B: Antibodies ................................................................................. 241

References .............................................................................................................. 242

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Table of Figures

Figure 1.1: Steroidogenesis pathway 30

Figure 1.2 Change in steroidogenic enzyme mRNA and tumour steroid

levels in LNCaP xenograft model to CRPC 34

Figure 1.3 SREBP regulation of lipogenesis and cholesterol synthesis

in prostate cancer cells 42

Figure 1.4: Proposed model of FASN increased activity and

expression in cancer. 52

Figure 1.5: The IGF signalling pathways 60


Figure 3.2: Insulin regulates expression of key steroidogenic enzymes at the

mRNA and protein level 93-94

Figure 3.3: Insulin treatment increases steroid production


Figure 3.4: Calculations and steroid spectra for insulin induction of

steroidogenesis

100-

101

Figure 3.5: Representative spectra of 14C steroids in VCaP and LNCaP cell

medium after 72hr. 102

Figure 3.6: Insulin treatment increases expression of PSA 104

Figure 3.7: Steroids in LNCaP cells after 48hr insulin treatment 106

Figure 3.8: In vivo tumour LNCaP xenograft model 108

Figure 4.1 Insulin and analogs upregulate enzymes necessary

for steroidogenesis at the mRNA level 123

Figure 4.2: Insulin analogs increase de novo steroidogenesis


Figure 4.3: The effect of insulin on steroidogenesis enzyme mRNA and

estradiol in MCF7 breast cancer cells 129

Figure 5.1: Insulin action on lipogenesis and lipid-mediated steroidogenesis 138

Figure 5.2: Insulin regulates expression of key lipogenic enzyme

mRNA 140

Figure 5.3: Insulin regulates expression of key lipogenic enzyme

protein level 142

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Figure 5.4: Change in intracellular lipid levels with insulin measured

by oil red-o staining assay 144

Figure 5.5: Effect of insulin on de novo lipogenesis and LNCaP lipid profile 146

Figure 5.6: Ingenuity lipogenesis/ fatty acid metabolism pathways:

Fatty acid biosynthesis (A) 149

Fatty acid metabolism (B) 150

Arachidonic acid metabolism (C) 151

Figure 6.1: The action of metformin and simvastatin on steroidogenesis

and lipogenesis pathways 166

Figure 6.2: Insulin treatment increases cholesterol production

in LNCaP cells in the absence of metformin 170

Figure 6.3: Metformin decreases LNCaP steroidogenesis 172

Figure 6.4: Metformin decreases LNCaP lipogenesis 176

Figure 6.5: Insulin treatment increases cholesterol production

in LNCaP cells in the absence of simvastatin 180

Figure 6.6: Simvastatin decreases LNCaP steroidogenesis in insulin

treated cells 184

Figure 6.7: Simvastatin differentially affects lipogenesis in the presence

and absence of insulin. 188

Figure 7.1: Tissue microarray analysis of IGF2 and receptor expression

in CaP 203


Figure 7.3: IGF2 regulates expression of steroidogenic enzymes

at the mRNA and protein level 206

Figure 7.4: IGF2 treatment increases steroid production in LNCaP cells 208

Figure 7.5: IGF2 increases de novo steroidogenesis in prostate cancer cells. 210

Figure 7.6: Radiometric spectra from de novo steroidogenesis with IGF2 in

LNCaP and VCaP cells 211

Figure 7.7: IGF2 increases de novo steroidogenesis in prostate cancer cells

through both IGF1R and INSR. 214

Figure 7.8 Insulin regulates IGF2 mRNA expression in LNCaP and 22RV1

cells. 215

23

Chapter 1: Introduction and Literature Review

24

25

1.1 Introduction

Prostate cancer (CaP) accounts for 25% of new cancer cases in men per year (Jemal

et al. 2008). Of these cases, 91% will present with locally confined tumours, for

which the treatment is surgery or radiation therapy, and the 5 year survival rate is

almost 100%; however, 25-40% of patients with non-localized disease will relapse

and go on to advanced disease (Rashid 2004). In these cases, most patients are

treated by androgen deprivation therapy (ADT), where testicular androgen supply is

cut off from the prostate. Androgens play an important role in development,

maintenance and management of normal prostate cells, as well as progression of

CaP. It is well established that 1) males castrated early do not get CaP (Pienta et al.

1993), 2) androgens exacerbate CaP, 3) androgen levels correlate with high

incidence of CaP, and 4) androgen blockade is successful in the majority of cases

(Swinnen et al. 2004). However, in some cases the regression initially seen with

ADT eventually gives way to a growth of a population of cancerous cells that no

longer requires testicular androgens. This phenotype is essentially fatal and is

termed castrate resistant CaP (CRPC) (Rashid et al. 2004; Scher et al. 2005; So et

al. 2005; Stanbrough et al. 2006). Furthermore, ADT is accompanied by many side

effects; specifically, this thesis will focus on metabolic syndrome, which is defined

by the U.S. National Library of Medicine as “a combination of medical disorders

that increase the risk of developing cardiovascular disease and diabetes” (Kapoor et

al. 2005; Braga-Basaria et al. 2006; Derweesh et al. 2007; Haidar 2007). It has

been demonstrated that 55% of CaP patients on ADT developed co-incident

metabolic syndrome after 12 months, compared to 22-20% of normal or non ADT

patients (Braga-Basaria et al. 2006). Furthermore, patients who develop metabolic

syndrome have shorter times to castrate resistance (Flanagan et al. 2010). The

association between the metabolic syndrome, hyperinsulinemia and prostate

cancer, as well as related growth factors and medications, will be explored in

this chapter and throughout this document.

1.2 Prostate cancer

CaP is the most common non-melanoma malignancy in men in Western countries

(Jemal et al. 2008). The reported incidence of CaP has increased in the last 20

years. In 2007 alone, 218890 new cases and 27050 deaths were reported in the US

(Trojan et al. 2006; Derweesh et al. 2007). Increase in diagnosis may be impacted

26

by increased screening for high/ increased serum levels of prostate specific antigen

(PSA) in western countries (Derweesh et al. 2007). Aberrant PSA levels signify

prostatic problems, including CaP and benign prostatic hyperplasia (BPH);

however, it is not a completely sound marker on its own, as noncancerous

inflammatory conditions can lead to high PSA (Oesterling 1991). Currently

researchers are screening for markers that will better distinguish between CaP and

other maladies, alone or in concert with PSA. CaP incidence is also on the rise in

Asian populations, where it was previously relatively rare, a fact which is blamed

on the increasing western influence on diet and lifestyle (Hsing et al. 2001).

CaP progression is partly characterised by dysregulation of androgen signalling.

Precursors of androgens are synthesized in the adrenal glands ( 5-androstenediol

and dehydroepiandrosterone [DHEA]) and testis (testosterone) and converted to

DHT, the main substrate of the androgen receptor (AR) (Evaul et al. 2010). The

research dogma of our group (APCRC-Q) is that there may be a small amount of de

novo androgen synthesis occurring in the prostate before castration, as all enzymes

necessary are present, though most androgens used by the cells at this stage are

acquired from exogenous supply (Locke et al. 2008). In the 1940s, Huggins et al.

first used orchiectomy (castration) to cut off the supply of androgens to the prostate

to bring about regression of CaP tumours, the first ADT (Huggins 1942). This was

effective in men who had local CaP, but not in those with bony metastasis. In

1979, Raynaud et al. introduced the idea of using non-steroidal anti-androgens to

counteract the effects of androgens by competing for binding to AR, thereby

blocking the activation of pathways which would otherwise lead to proliferation

and inhibit apoptosis of aberrant cells (Raynaud et al. 1979). The use of the anti-

androgens, usually combined with luteinizing hormone releasing hormone (LHRH)

agonists, which suppress testicular androgen production, with or without physical

castration, is termed total androgen ablation, as it theoretically inhibits peripheral

androgen supply to the prostate (Labrie et al. 1978). ADT is initially successful;

however, the positive effects do not last in advanced cases as demonstrated by

Huggins (Huggins 1942).

Early stage disease is often termed androgen dependent (AD) or pre-castrate CaP

and these cases usually respond favourably to androgen deprivation or endocrine

therapy. If the use of PSA screening can detect CaP early enough, when the cancer

27

is locally contained, it can be “cured” by radical prostatectomy or radiation therapy

(Stanbrough et al. 2006). Sadly, despite the initial success of these therapies, where

80-90% of patients experience tumour regression, most patients with advanced or

even clinically localized, disease eventually experience tumour recurrence, with a

phenotype that no longer responds to androgen ablation. This is termed castrate

resistant prostate cancer (CRPC) and is essentially fatal (Rashid et al. 2004;

Stanbrough et al. 2006).

Though it is the most successful therapy available, ADT also has major side effects,

both psychological and physical. Psychological side effects include decrease in

libido, hot flashes, fatigue, cognitive dysfunction, and depression. Serious physical

side effects can include anaemia, osteoporosis, and a metabolic syndrome

resembling diabetes; furthermore, adverse effects further infringe on the quality of

life due to the expense of the treatments (Braga-Basaria et al. 2006).

1.3 Androgen receptor signalling in prostate cancer

It is clear that the AR plays a substantial role in CRPC progression, as siRNA to

AR suppresses tumour growth, and upregulation of AR target genes and pathways

is seen in disease recurrence (Suzuki et al. 2003; Rashid et al. 2004; Stanbrough et

al. 2006). Between 80 and 90% of tumours are androgen dependent at diagnosis

(Niu et al. 2010). Advanced CaP does not respond well to antimitotics like other

cancers do, yet androgen ablation is usually initially successful (Knudsen et al.

2010). It is believed that after ADT, when tumour cells have progressed to CRPC,

the signalling of AR regulated pathways is more similar to that of an androgen

dependent CaP cell responding to testosterone than to castrate cells responding to

treatment (Sharifi 2010).

The recurrence of CaP is explained by two hypotheses, both of which involve AR

signalling 1) cells undergo clonal selection/ adaption under ADT, 2) AR sensitivity

increases when levels of exogenous androgens are low (So et al. 2005). In a healthy

prostate there is a balance between cells that require androgens (epithelial) and

those that do not (basal) and during the normal prostate development, basal cells

become luminal epithelial secretory cells which respond to androgens. During

cancer progression, the balance between these cells is lost, and those cells which

adapt to have no or low dependence on exogenous androgens are selected for

28

proliferation. Another factor that comes into play in 30% of tumour samples is

amplification of AR mRNA or genes may occur, leading to upregulation of AR

dependent genes, such as PSA. In these cases, there is often an increase in AR

stabilization (So et al. 2005). When AR is amplified, these tumours are more likely

to respond to androgen ablation (Suzuki et al. 2003; So et al. 2005). Androgens are

very important mediators of transcriptional pathways, controlling proliferation and

apoptosis in normal and neoplastic cells (Thomas et al. 2005). In CRPC, AR binds

to very different gene sites than it does in hormone ablation responsive cancers.

Resurgent AR mRNA in LNCaP and VCaP cells is equated with the expression of

epithelial to mesenchymal transition (EMT) markers, which correlates to

invasiveness of the cancer (Maitland et al. 2011).

During ADT, some CRPC tumours develop hypersensitive AR, which require four

orders of magnitude less DHT for activation than treatment naive AR (Gregory et

al. 2001) . There is increased nuclear localization of AR, which some believe may

be due to conversion of adrenal steroids to androgens in the prostate (Hofland et al.

2010), while others believe this is due to de novo synthesis of androgens which act

locally (Dillard et al. 2008; Locke et al. 2008). Moreover, after castration, AR is

not only more sensitive to androgens, but mutations to the ligand binding domain

contribute to making it promiscuous, as 10-20% of tumours have ARs activated by

progesterone, estradiol, adrenal androgens, cortisone, flutamide, nilutamide and

other AR-antagonists (Suzuki et al. 2003; So et al. 2005). Also, after ADT,

activation of AR pathways is assisted by over-expression of autocrine, endocrine,

and paracrine co-activators, such as insulin-like growth factor (IGF) 1, keratinocyte

growth factor, epidermal growth factor, LHRH, neuropeptides, neuroblastoma/

glioblastoma derived oncogene homolog (HER2/ neu) and interleukin-6. HER2/

neu can stimulate the AR through the Akt pathway and phosphorylates

phosphatidylinositol 3-kinase (PI3K), which phosphorylates AR, or the mitogen-

activated protein kinase (MAPK) pathway (Suzuki et al. 2003; So et al. 2005;

Knudsen et al. 2010). These signalling changes, as well as AR posttranslational

modifications, eg phosphorylation, acetylation, ubiquitylation, and sumoylation,

can bolster AR activity in a low ligand environment and can lead to poor outcomes

(Knudsen et al. 2010). AR gene amplification occurs in 50% of circulating prostate

tumour cells (Sharifi 2010).

29

1.4 Steroidogenesis and prostate cancer

1.4.1 Steroidogenesis enzyme expression in prostate cancer

Our group has shown that one of the pathways activated after ADT is the

steroidogenesis pathways (figure 1.1), which allows androgen starved prostate cells

to create steroids and androgens from precursors as far upstream as acetate and

cholesterol (Locke et al. 2008; Leon et al. 2010). In our first paper, Locke et al.

have described the steroidogenic pathway found in the prostate, adapted from

Payne and Hales, 2004 (Payne et al. 2004; Locke et al. 2008). Steroidogenic cells

import cholesterol bound to lipoprotein, which is then stored in the outer

mitochondrial membrane. When cholesterol is released, it is transported to steroid

acute regulatory protein (StAR), which transports cholesterol across the

mitochondrial inner wall where cytochrome p450 member (CYP) 11A1 converts

the cholesterol to pregnenolone, the first steroid in the pathway, which is converted

into various steroids by a variety of enzymes, depending on the needs of the cells.

In the male gonads, the testis, progesterone is converted to potent androgens by the

classical pathway, which converts testosterone to dihydrotestosterone (DHT), or the

backdoor pathway, which bypasses the testosterone intermediate (shown in figure

1.1) (Ghayee et al. 2007).

CYP17A1 is responsible for the conversion of pregnenolone, progesterone, and

pregnane3α-ol-20-one to their 17α hydroxylated derivatives and cleaves the C17-20

bond to make 17-OH progesterone into androstenedione (Locke et al. 2009).

Ketoconazole, which is a non-specific CYP inhibitor, impedes downstream

conversion to more potent steroids; furthermore, both ketoconazole and abiraterone,

a specific CYP17A1 inhibitor, have shown promise in increasing patient survival

times (Sharifi 2010). Our group has recently shown that CYP17A1 is expressed in

prostatic tissue of men with CaP and is excreted from CaP cells (Locke et al. 2009).

CYP17A1 is increased in the sera of men with CaP compared to control cohorts of

the same age. Enzymes needed for CYP17A1 function in sera, such as cytochrome

b5 (CYB5) which enhances the 17,20-lyase activity of CYP17A1 and circulatory

P450 oxidoreductase, are present in sera but it could not be shown in this study that

CYP17A1 was active. It could play another role in CaP related signalling, but

30

further study would be needed to examine if secreted CYP17A1 would be an

appropriate biomarker (Locke et al. 2009).

Figure 1.1: Steroidogenesis pathway adapted from Locke et al. 2008 (Locke et al.

2008). Inhibitors of steroidogenesis enzymes are ketoconazole, a non-specific CYP

family inhibitor (red and blue), abiraterone specific CYP17A1 inhibitor (blue), and

SRD5A inhibitors finasteride (specific to SRD5A2) and dutasteride (SRD5A1 and

2) (green). These inhibitors are used in clinical prostate cancer therapy (Sharifi

2010; Pal et al. 2011; Wu et al. 2011)

31

The rate limiting step of steroidogenesis in the adrenals and gonads is mediated by

StAR, without which steroidogenesis is reduced by 90% in Leydig cells (Ghayee et

al. 2007). StAR forms a complex with peripheral-type benzodiazepine receptor

(PBR) and its substrate, diazepam binding inhibitor (DBI), to import cholesterol to

the mitochondria (Hauet et al. 2005). DBI is known to promote steroidogenesis in

Leydig cells, and is upregulated by androgens (Swinnen et al. 1997; Swinnen et al.

1998). Expression of all of the enzymes necessary for de novo steroidogenesis has

been found to be expressed in the prostate (Vihko et al. 2005; Locke et al. 2008).

CaP progression and cancer pathology could be highly affected by synthesis of

androgens from precursors, such as cholesterol or adrenal steroids or by de novo

steroidogenesis from acetate and substrates further upstream of cholesterol (Leon et

al. 2010).

Most of the reactions in the steroidogenesis pathway are carried out by members of

the hydroxysteroid dehydrogenase family (HSD17Bs) and aldo-keto reductase 1

family (AKR1Cs). These are often reversible reactions, but favour a certain

direction (Ghayee et al. 2007). In normal cells and androgen dependent tumour

cells, these enzymes mainly favour oxidative/ deactivating reactions; however, in

CRPC, these enzymes tend to favour reductive/ activating reactions. Those HSDs

which are responsible for the conversion of androstenedione to testosterone,

HSD17B5 and HSD17B3, are also upregulated in CRPC, accompanied by a

decrease in the enzymes which deactivate DHT, such as HSD17B2 (Harkonen et al.

2003; Soronen et al. 2004; Vihko et al. 2005). Enzymes which activate estrogens

are also upregulated during progression (Carruba 2007).

In the healthy prostate, conversion of testicular testosterone to DHT is essential for

normal growth and function, but this is dysregulated in CaP and BPH. In the

prostate, this reaction is carried out by 5 reductase 1 and/ or 2 (SRD5A). In

normal tissue, SRD5A2 is more highly expressed and is correlated with cancer

aggressiveness; however, during cancer progression, SRD5A1 is expressed

preferentially compared to SRD5A2 (Penning et al. 2008; Thompson et al. 2008).

Both enzymes participate in the “backdoor” pathway to DHT, though in this

pathway they catalyse the conversion of progesterone to pregnan-3,20-dione for

shunting of substrates away from the classical pathway (Auchus 2004; Penning et

al. 2008). The backdoor pathway is not known to be associated with development

32

or sexual maturation, but has been related to diseases where there is a blockage in

the “classical” route to DHT (Penning et al. 2008). The backdoor pathway bypasses

testosterone, synthesizing DHT from androstanediol, and all enzymes involved in

the classical pathway are duplicated in this pathway, along with retinol 11-cis

dehydrogenase (RDH5) which converts androstanediol to DHT and is the only

enzyme that is involved solely in the backdoor pathway (Auchus 2004; Locke et al.

2008). Furthermore, in these cells, the enzymes favour an oxidative, or activating,

functionality, and they correspond to an increase in AR transactivation (Penning et

al. 2008). Supporting the theory that both the “backdoor” and “classical” pathways

play roles in prostate cancer progression, one study has demonstrated greater

efficacy in tumour volume decrease with dutasteride, a dual SRD5A inhibitor, than

with finasteride, which focuses on SRD5A2 exclusively (Thomas et al. 2005).

Dutasteride would theoretically inhibit enzymes in both the classical and backdoor

pathways.

1.4.2 Intraprostatic androgens/ de novo steroidogenesis

Evidence from our laboratory supports the hypothesis that CaP cells adapt to ADT

by expressing the genes to make their own androgens and further convert

precursors to active metabolites. All enzymes necessary for steroidogenesis are

expressed in prostate tumours, as has been examined by our laboratory using

clinical sample mRNA microarrays (unpublished data) and lymph node prostate

cancer cells (LNCaP) (Locke et al. 2008). Some groups believe that precursors

from the adrenals may play a dominant role in this process (Hofland et al. 2010),

however, in patients with advanced CaP who undergo adrenalectomy after ADT,

progression still occurs, suggesting adrenal precursors may have a limited role in

prostatic steroid synthesis and tumour progression (Bhanalaph et al. 1974).

Furthermore, it has been shown that testosterone levels in metastases are often 3-

times higher than local tumours (Twiddy et al. 2010). High cholesterol diets induce

tumour growth in mice with LNCaP and DU145 xenografts; cholesterol could be

used as a substrate for steroidogenesis (Venkateswaran et al. 2007; Dillard et al.

2008). Leon et al. demonstrated that proteins responsible for cholesterol synthesis

and uptake, including 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), are

upregulated during progression in the LNCaP xenograft tumour model, with a

corresponding increase in androgen synthesis (Leon et al. 2010).

33

Importantly, our group has demonstrated that all enzymes necessary for

steroidogenesis were present and upregulated in the LNCaP xenograft model,

though there was significant mouse to mouse variation (figure 1.2A) (Locke et al.

2008). In the LNCaP xenograft model of prostate cancer progression tumours are

implanted subcutaneously in 4 separate sites in the mouse and grown until

castration, when one tumour is excised and is representative of an androgen

dependent (AD) tumour. After castration, PSA levels decline to the lowest point of

PSA expression, nadir (N), when another tumour is taken. Shortly after nadir, there

is a resurgence of PSA, and at the this stage, CRPC, the tumours are no longer

dependent on external androgens (Locke et al. 2008). Using LC/MS/MS to

investigate steroid levels in these tumours, we have shown that androgen levels

decrease after castration, but are elevated again at CRPC, though not to the original

levels, in agreement with tumour PSA levels (figure 1.2B) (Locke et al. 2008).

Progesterone appears to increase at nadir, suggesting a role in transition to CRPC.

Furthermore, in this paper, we have demonstrated that CRPC tumours are capable

of de novo synthesis of cholesterol and detectable levels of progesterone, 4-

pregnen-17-ol-3,20-dione, and DHT using 14C-acetate labelling (Locke et al.

2008). Blockage of the “classical” or “backdoor” pathways to DHT after addition

of labelled progesterone shows progesterone to be converted to various downstream

steroids depending on the inhibited enzymes. This suggests that progesterone may

be used by either pathway to increase concentrations of more potent androgens

(Locke et al. 2009a) . Progesterone also increases CYP11A1, which, along with

StAR, initiates the rate-limiting step of steroidogenesis (unpublished data).

Furthermore, in CaP, inhibition of one steroidogenesis enzyme may lead to

steroidogenesis through a different set of enzymes, thereby altering steroidogenic

flow, but not abolishing androgen synthesis during progression to CRPC in LNCaP

xenografts (Locke et al. 2009a). In conclusion, our previous findings suggest de

novo synthesis of androgens in the prostate after CRPC may contribute to

prostate cancer progression.

34

Figure 1.2: Fold change in mRNA as determined by QRT and mean concentrations

of testosterone (T), dihydrotestosterone (DHT), and progesterone (P) in tumour

homogenates obtained from mice before castration (pre-Cx; 5, n = 5), 8 d after

castration (N; n = 5), and 35 d after castration (CRPC; n = 8). A) Fold change from

pre-Cx steroidogenic enzyme mRNA over progression to castrate resistance as

determined by QRT. B) Mean concentrations of steroids and PSA in tumours over the

androgen dependent (AD), nadir (N), castration resistant (CR) progression. Values are

shown plus SE (Locke et al. 2008).

A

B

35

1.4.3 AR and steroidogenesis inhibitors in prostate cancer treatment

To treat advanced CaP, manipulations of the AR/ androgen synthesis pathways are

the primary and secondary lines of treatment, first by androgen blockade, then often

by inhibition of enzymes in the steroidogenesis pathway (Nakabayashi et al. 2006;

Ryan et al. 2007). These findings support our theories of the importance of AR and

steroidogenesis in CaP. High risk, early stage cancers are often treated by

neoadjuvant or adjuvant therapy inhibiting these enzymes (Ettinger et al. 2004).

Effectiveness of medical castration (ADT) is measured by lowering of serum

testosterone levels; however, the decrease in serum testosterone is much more

significant than the decrease in intracellular concentrations of androgen, which may

be more important in the aggressiveness of disease by activating AR responsive

pathways in the prostate (Severi et al. 2006; Heracek et al. 2007; Mostaghel et al.

2007). Of patients who achieved castration levels of testosterone by LHRH agonists

or surgical castration, most displayed only a 75-80% decrease in prostatic androgen

levels, which is enough to maintain AR function (Sharifi 2010). Serum testosterone

levels may not be an accurate surrogate for androgen ablation in the prostate

microenvironment. As discussed above, changes in the microenvironment may lead

to adaptation and progression in a low androgen environment; furthermore, after

ADT, altered transcription, proliferation, apoptosis, carbohydrate and steroid

metabolism, cell structure and adhesion pathways may activate AR without

androgen, making cells more resistant to ADT (Suzuki et al. 2003; Mostaghel et al.

2007).

Treatment of CaP progression can be difficult and often requires a methodical

approach and changes in treatment and cotreatment over time (Sharifi 2010). AR

inhibitors can be used in conjunction with ADT. There are many on the market but

some such as cyproterone acetate and megestrol acetate are not as specific as would

be preferred as they affect other nuclear receptors (Attard et al. 2008; Sharifi 2010).

Bicalutamide, flutamide and nilutamide are non-steroidal and specific, though they

still have approximately 100 fold less affinity for AR than DHT (Sharifi 2010). AR

inhibitors, such as bicalutamide, often have early success, but the CaP cells become

resistant and can even use them as substrates to activate AR pathways (So et al.

2005). A new and important discovery in AR blockage is MDV3100 (Medivation,

San Francisco, CA, USA), a second generation AR inhibitor that is 5-8 fold more

36

potent than bicalutamide, which is a popular and effective treatment (Tyrrell et al.

1998). MDV3100 is also more effective because it prevents translocation of AR to

the nucleus and suppresses AR binding to PSA and transmembrane protease, serine

2 (TMPRESS 2) transcriptional binding sites (Sharifi 2010). Clinical studies have

demonstrated a 50% decline in PSA in 57% of patients who have not been treated

with docetaxel (a drug that interferes with cell division), and in 45% of patients

who experienced CaP progression on docetaxel. It has been previously believed that

cotreatment with AR antagonists during ADT was not more beneficial; however, in

light of the recent findings of the added properties of MDV3100, this may need to

be reconsidered (Attard et al. 2008; Sharifi 2010).

Inhibitors of the steroidogenesis pathway are also used to treat CaP progression

(figure 1.1). Ketoconazole is a general CYP inhibitor and an inhibitor of adrenal,

testicular, and prostatic steroid production (Vasaitis et al. 2010), which initially

works to slow cancer progression; however, the cells often evolve to become

resistant (Sharifi 2010). It has a great many side effects, including hepatotoxicity,

gastrointestinal toxicity, adrenal insufficiency, as well as drug interactions. Despite

these side effects, ketoconazole is widely used because it is effective at lowering

PSA in 55-65% of patients, and lengthens survival time in patients who respond to

it (Sharifi 2010).

A novel CaP treatment which is eagerly awaited by clinicians, abiraterone acetate,

is a specific CYP17A1 inhibitor. Abiraterone in mice has been shown to be much

more effective than ketoconazole at limiting CaP progression (Sharifi 2010). In

Phase 1 clinical trials abiraterone acetate caused 30, 50, and 90% decline in PSA in

66, 57, and 26% of patients, respectively, which lasted 69-578 days in

chemotherapy naive men. Treatment was also accompanied by a decrease in serum

testosterone and other steroids as well as estradiol (Attard et al. 2008).

Furthermore, in phase 3 trials, abiraterone reduced serum androgens in ADT

patients with advanced disease who were resistant to standard and experimental

treatments, including docetaxel, with a 50% PSA reduction in 51% of patients, as

well as a decrease in the size of the ventral prostate and seminal vesicle (Reid et al.

2010). It is hypothesized that abiraterone might block adrenal DHEA,

androstenediol, estradiol or de novo steroid synthesis. Interestingly, abiraterone is

successful in 47% of patients who had had CaP progression while on ketoconazole,

37

reducing PSA levels by 50%. A 50% decrease in PSA is also seen in 64% of

ketoconazole naive patients. Abiraterone is well tolerated, although side effects

include redirection of the steroid pathway to increase deoxycorticosterone and

corticosterone, increases in which contribute to hypertension, hypokalemia (low

serum potassium) and peripheral edema (swelling of the lower limbs by fluid

retention). These side effects can be managed with eplerenone, a selective

aldosterone blocker. CYP17A1 inhibition may also lower the production of

glucocorticoid in the adrenals, which may lower the conversion of cholesterol to

pregnenolone, decreasing steroid precursor levels (Sharifi 2010).

VN/ 124-1 (TOK-001), a 17α-Hydroxylase/ 17, 20 lyase inhibitor affects both AR

and CYP17A1, and stops growth of AR dependent tumours in xenografts (Sharifi

2010; Vasaitis et al. 2010). It may have this effect by stimulating endoplasmic

reticulum stress response and cytotoxicity, which could further hinder tumour

survival (Bruno et al. 2008). Compounds that block DHEA conversion to

testosterone would also be expected to impede AR activation; however, mutations

in the AR might lead to adaptation to these types of antagonists (Sharifi 2010). That

being stated, it has been demonstrated that VN/ 124-1 inhibits both wild-type AR

and the mutated AR seen in LNCaP cells (Vasaitis et al. 2010).

Some interest has been shown in 3BHSD inhibition, which may work by inhibition

of de novo steroidogenesis or by repressing growth induced by DHEA and

androstenediol. HSD3B is necessary for both the classical and backdoor pathways

of steroidogenesis (Sharifi 2010). Conversion of DHEA and androstenediol to

testosterone are necessary for androgen synthesis, and it has been speculated that

both could activate AR; however, Evaul et al. showed that both must be

metabolized to affect AR in LAPC4 and 22RV1 CaP cells (Evaul et al. 2010).

Inhibitors of any steps of steroidogenesis seem to be attractive therapeutic targets.

There is some interest in inhibition of HSD17B3 and 5 (AKR1C3), which are

overexpressed in CRPC (Sharifi 2010). Inhibitors block androstenedione

conversion to testosterone, but the most promising do not affect closely related

enzymes (Sharifi 2010). The HSD17B family of enzymes catalyse the reduction

and oxidation of not only steroids, but fatty acids and bile acids as well (Day et al.

2009). AKR1C3 is expressed in a wider variety of normal tissues than HSD17B3

38

and has a wider substrate range, from steroids to prostaglandins; however,

HSD17B3 appears to be more important to the conversion of androstenedione to

testosterone. Male humans born with HSD17B3 deficiency are often reared as

female because they have external female genitalia and no prostate; they do develop

testis and Wolffian duct-derived internal genitalia. Our group has shown that

HSD17B3 expression is increased during progression to CRPC in the LNCaP

xenograft model, and other groups have demonstrated over-expression in prostate

tumours (Gnatenko et al. 2005; Locke et al. 2008; Day et al. 2009). Though there

do not appear to be HSD17B3 inhibitors in clinical trials currently, there is much

activity in patenting HSD17B inhibitors for cancer and other diseases (Poirier

2010). Perhaps the most promising compounds are Bristol Myers Squibb

compounds 114-116, which inhibit HSD17B3 specifically at nanomolar

concentration in cell based assays, and the STX2171 compound, which has been

patented by Sterix (Poirier 2010). This compound caused a 99% decrease in cell

androstenedione induced proliferation at micromolar concentrations, and 74%

decrease in androstenedione induced proliferation at nanomolar concentrations.

STX2171 was not toxic to cells that were not treated with androstenedione (Day et

al. 2009). In vivo evidence demonstrated STX2171 reduced androstenedione

induced tumour growth by 81% in a LNCaP xenograft model (Poirier 2010).

As DHT is the most potent inducer of AR response, inhibition of SRD5A (type 1

and 2) has been popular. Both isoforms are present in normal prostate, BPH, and

cancer tissues (Schmidt et al. 2010). Originally, most interest was shown in

inhibiting SRD5A2, and it was shown in a phase 3 studies that 18.8% of men

treated with finasteride, specific to SRD5A2, developed CaP, compared to 24.4%

of the control group. Though this 25% decrease was significant, there was an

increase in the development of high grade tumours in the finasteride group, which

may be explained by the fact that both isoforms are present in cancers and SRD5A1

is more prevalent in CaP progression (Penning et al. 2008). Dutasteride, which

inhibits both isoforms, is approximately 60 times more potent than finasteride, as

studies have shown 90% decrease in serum DHT and a significant decrease in

prostate volumes after 1 year of treatment (Schmidt et al. 2010). The reduction in

cancer incidence in the dutasteride trial was similar to that of the finasteride trial;

however, there was still a non-significant trend toward higher grade cancers for

39

those who did develop CaP on the drug (Schmidt et al. 2010). Further analysis of

the study did not support the hypothesis that these inhibitors increased high grade

cancer; patients on SRD5A inhibitors did not have higher stage when determined in

radical prostatectomy specimens. Also, markers of CaP aggressiveness were seen

more often over a longer study in the placebo group than in the treatment group

(Barton 2011). Locke et al. have shown that tumours can adapt to their

microenvironment to bypass steroidogenic inhibitors (Locke et al. 2009a);

therefore, it is logical that there would still be a significant chance of CaP with

SRD5A inhibition. Furthermore, testosterone and other less potent androgens may

still affect AR signalling (So et al. 2005). It is interesting to note that use of

dutasteride with ketoconazole improved response and time to progression

considerably more than either agent alone (Schmidt et al. 2010). Though they were

first developed to be CaP prevention drugs, largely due to the findings of the first

study, physicians are largely unconvinced that the benefits of use of SDR5A

inhibitors outweigh the risks, especially in patients in whom other CaP risk factors

are unknown (Barton 2011). The interest in steroidogenesis enzyme inhibition

reflects the importance of steroidogenesis in CaP.

1.5 Lipid/ fatty acid contribution to prostate cancer and sterol response

element binding protein

1.5.1 Lipogenesis and prostate cancer

Contrary to most cells, those of the prostate are mainly dependent on lipids for

energy, instead of glucose (Greisen et al. 2001). In mammals, fatty acid synthase

(FASN) is the only enzyme that produces endogenous fatty acids; in most non-

malignant cells FASN expression is low, as cells in most tissues obtain exogenous

fatty acids primarily from the diet (Menendez et al. 2005c). However, cancer cells

proliferate rapidly and require increased fatty acid production for nutrition,

membrane synthesis, and lipid alteration of cell signalling molecules involved in

cancer growth, proliferation and survival (Menendez et al. 2006). Interestingly, up-

regulation of FASN has been observed in almost all cancers (Menendez et al. 2006;

Menendez et al. 2007), corresponding with poor prognosis in human breast and

CaP (Kuhajda 2006). Knock down of FASN, acetyl CoA carboxylase (ACC), and

40

other lipogenesis enzymes by siRNA leads to apoptosis in both breast cancer and

CaP cells (De Schrijver et al. 2003; Brusselmans et al. 2005).

Inhibition of FASN is also chemosensitizing in tumours (Menendez et al. 2004b).

Multiple FASN targeting molecules have been examined in attempts to develop

new therapeutics for CaP, namely cerulinin, C75, C93, and Triclosan (Pizer et al.

1996; Kuhajda et al. 2000); however, these inhibitors are not clinically useful in

their current incarnations due to toxicity, lack of bioavailability, and lack of

metabolic stability (Pizer et al. 1996; Kim et al. 2004). Despite the lack of

appropriate inhibitors, FASN and lipogenesis remain attractive targets for cancer

treatment (Liu et al. 2010).

1.5.2 Sterol regulatory element binding protein and prostate cancer

Sterol regulatory element binding protein (SREBP)-1 transcription factor has long

been considered a factor in the progression of CaP, as it is involved in the

lipogenesis and cholesterol synthesizing pathways, which are believed to be crucial

for CaP cell survival (Swinnen et al. 2004). In normal cells, lipid and cholesterol

synthesis are controlled by SREBP 1 and 2, respectively (Swinnen et al. 1997;

Ettinger et al. 2004; Swinnen et al. 2004). In CRCP, exogenous or dietary fatty

acids and cholesterol cease to control SREBP expression and activity, as they do in

non-cancerous cells, effectively leaving cholesterol synthesis nutritionally

unchecked. In CaP cells, lipogenesis is still upregulated by hormones and

androgens (Swinnen et al. 1997a; Ettinger et al. 2004). Androgens heavily

upregulate SREBP-1 and promote mature SREBP-1 relocation to the nucleus for

initiation of lipogenesis through promoting expression of FASN and related genes

(Swinnen et al. 1997a; Heemers et al. 2006). The androgen action on SREBP-1 is

largely mediated by its influence on SREBP cleavage activating protein (SCAP),

and Insig proteins, which disrupt the SREBP retention complex (Swinnen et al.

1997; Ettinger et al. 2004; Swinnen et al. 2004). Furthermore, androgen stimulation

of FASN and ACC, which are key lipogenic enzymes, is ineffective when SREBP-

1 binding sites on their promoters are mutated; moreover, a dominant negative

SREBP-1 abolishes lipogenesis (Heemers et al. 2006).

After castration, SREBP-1 and the genes it regulates decrease in expression;

however, they increase when CRPC is reached in in vivo xenograft models

41

(Ettinger et al. 2004). When PSA increases in this CaP progression model, there is

a concomitant increase in lipid and cholesterol synthesis and SREBP activation

which may result in an increase in membrane synthesis, transcription of

downstream genes, and an increase in energy metabolism (Ettinger et al. 2004;

Swinnen et al. 2006). SREBP-1 activates ACC to make precursors for fatty acid

synthesis and diazepam-binding inhibitor/ acyl-CoA-binding protein (DBI) which

participates in signalling for lipid synthesis and cholesterol import to the

mitochondria for steroidogenesis (Swinnen et al. 1998; Ettinger et al. 2004). DBI

not only protects acyl-CoA esters from degradation, but prevents them from

binding and inhibiting ACC (Swinnen et al. 1998). In the LNCaP xenograft

progression model lipogenesis gene levels (DBI and FASN), and cholesterol

synthesis genes (farnesyl diphosphate synthase [FDPS] and HMGR) follow the

same trend in expression as PSA; after castration they decrease, but increase with

progression to CRPC (Ettinger et al. 2004; Brown 2007). SREBP-1 upregulates

enzymes important for both cholesterol and fatty acid synthesis; therefore, it is an

important player in lipogenesis and steroidogenesis in CaP cells (Swinnen et al.

1998; Hauet et al. 2005). The primary actions and influences of SREBP over

lipogenesis and cholesterol synthesis are outlined in figure 1.3.

Literature suggests yet another role for SREBP in CaP; it may enhance the

transcription of CYP17A1, one of the key steroidogenic enzymes, as well as StAR

(Sewer et al. 2009).

42

Figure 1.3 Sterol regulatory element binding protein (SREBP) regulation of

lipogenesis and cholesterol synthesis in prostate cancer cells (Ettinger et al. 2004).

After activation by androgens or other hormones, including insulin, SREBP

cleavage activating protein (SCAP) influences SREBP translocation to the nucleus,

where it would bind to SREBP binding elements (SRE), and initiate transcription of

fatty acid synthase (FASN) for membrane synthesis and energy production,

diazepam-binding inhibitor (DBI) for lipid/ cholesterol transport and

steroidogenesis, and farnesyl diphosphate synthase (FDPS) and 3-hydroxy-3-

methylglutaryl-CoA reductase (HMGR) for cholesterol synthesis. Adapted from

Ettinger et al. 2004.

43

1.5.3 Fatty acids and steroidogenesis

In section 1.5.1, it was discussed that SREBP activates lipogenesis, which is

important for CaP progression. SREBP is also responsible for increased cholesterol

synthesis, which is the precursor to steroids. Combining the importance of both

steroidogenesis and lipogenesis in CaP Locke et al. have recently published a paper

demonstrating that fatty acid production is important in stimulation of

steroidogenesis in CaP (Locke et al. 2010). FASN expression is clinically

correlated with both grade and stage of the disease, and fatty acids are proposed to

be the main energy source for the CaP cells, as well as providing materials for

membrane synthesis and influencing the lipid and phospholipid ratios in the

membranes, thereby modulating signalling patterns and influencing sensitivity to

therapeutic treatments (Liu 2006; Bougnoux et al. 2010). Furthermore, FASN

produces eicosanoid lipids which are anti-apoptotic and activate pathways for

proliferation (Locke et al. 2010). Locke has observed that activation of AR

increased arachidonic acid levels in CaP cells, and inhibition of arachidonic acid

formation blocked steroidogenesis, suggesting that arachidonic acid may have a

role in CaP progression by activating the key enzymes for steroidogenesis (Locke et

al. 2009).

It has been elucidated by our group that hormone sensitive lipase (HSL) frees

cholesteryl esters from the membranes or lipid deposits, and the esters are then

converted to arachidonic acid, and activated by long-chain acyl-CoA synthetase 3

(ACSL3) (Locke et al. 2010). ACSL3 follows the same decrease and increase

pattern as other lipogenesis genes during the progression to CRPC in the LNCaP

xenograft model; as well, this paper demonstrates that ACSL3 is overexpressed in

the human prostate cancer cells as well as in LNCaP cells. Arachidonic acid-CoA

(activated) is transported to the mitochondria, where it undergoes a thioesterase

reaction to become a free fatty acid and activates StAR, which shuttles cholesterol

into the mitochondria to begin steroidogenesis (Arakane et al. 1998). In the

absence of androgens, activated mitochondrial StAR and cholesterol uptake into the

mitochondria is increased, suggesting that CaP cells use endogenously synthesized

arachidonic acid to initiate de novo steroid synthesis via StAR induction (Arakane

et al. 1998). Because the previously mentioned studies indicate that SREBP is in

control of lipogenesis, and insulin increases active SREBP, it may be that insulin

44

increases steroidogenesis through the above mentioned pathway. This concept will

be expanded on in the context of CaP in section 1.6, with emphasis on SREBP in

section 1.6.4. The effect of insulin on the enzymes and substrates in the pathway of

fatty acid activation of steroidogenesis will be explored in this thesis.

1.6 Metabolic syndrome and prostate cancer

1.6.1 Metabolic syndrome correlates with prostate cancer progression in

epidemiological studies

Established risk factors for prostate cancer are hypertension, obesity, dyslipidaemia,

hyperuricaemia, hyperinsulinaemia, and high serum alanine amino transferase,

which together indicate that there is a metabolic syndrome component in CaP

(Hammarsten et al. 2005). Metabolic syndrome is becoming increasingly prevalent

in Western society, with death and disability rising from side factors, such as heart

disease, which may be related to insulin resistance (Kapoor et al. 2005). Studies

show that metabolic syndrome, serum insulin levels, serum glucose levels (fasting),

insulin resistance and insulin receptor (INSR) gene polymorphisms are associated

with increased CaP risk (Hsing et al. 2007). Blood insulin levels are monitored by

assaying for C-peptide levels, which is the peptide that holds the insulin chains

together. C-peptide is examined because it fluctuates much less in serum

concentrations than insulin levels (Heding 1975). C-peptide, a surrogate for serum

insulin concentrations, has been positively correlated to age as well as BMI (Ma et

al. 2008). Furthermore, excess body weight was correlated with risk of CaP

progression, moderate risk for low grade cancers and much greater risk of higher

grade and terminal cancer (Neuhouser et al. 2010). As well, men with high C-

peptide levels after ADT were much more likely to have bone metastases (Flanagan

et al. 2010). Men with C-peptide in the highest quartile were 2.7-4 times more

likely to die of CaP (Ma et al. 2008; Cox et al. 2009). BMI was associated with

altered hormonal levels, such as testosterone, insulin, IGFs, and estrogens - all of

which are linked to CaP (Andersson et al. 1997; Renehan et al. 2006; Hsing et al.

2007; Pollak 2008c).

Obese patients, who often have concomitant hyperinsulinemia, had higher grade

cancers and higher Gleason scores, larger positive surgical margins and higher

45

incidence of biological recurrence than normal BMI counterparts (Jayachandran et

al. 2008a). Indeed, in two studies of 2000 men, those with high BMI were more

likely to be diagnosed with high grade, metastatic or fatal, disease than with local

disease (Jayachandran et al. 2008b). Adding to the problem may be the difficulty of

performing the digital rectal exam (DRE) on obese patients and the fact that obese

men have lower PSA, making prostate irregularities complicated to assess;

furthermore, men with high BMI were more likely not to have an abnormal biopsy

(Porter et al. 2005). Obesity is associated with a high fat diet, as well as a diet of

simple carbohydrates (Buschemeyer et al. 2007). Diet induced hyperinsulinemia

may lead to increased proliferation pathway activation and aggressiveness and

studies show that high carbohydrate diets contribute to serum insulin and are

associated with a 45% prostate tumour size increase in murine models

(Venkateswaran et al. 2007). Diet induced reduction of insulin may benefit patients

by decreasing cancer growth, as determined by epidemiological studies

(Venkateswaran et al. 2007; Cox et al. 2009). The idea that insulin/ metabolic

syndrome may increase cancer progression was corroborated in breast cancer

studies, where mice with insulin resistance have a higher incidence of mammary fat

pad tumours (Lann et al. 2008). These studies show that obesity and

hyperinsulinemia, which often accompany the metabolic syndrome, are highly

correlated with poor CaP prognosis and give credence to our hypothesis that insulin

may be exacerbating cancer progression.

The results of one study demonstrated that over 55% of ADT patients had

metabolic syndrome, compared to 22-20% of normal or non-ADT patients after 12

months (Braga-Basaria et al. 2006). Recent studies have specifically identified a

correlation between elevated insulin/ C-peptide levels, a surrogate measure of

insulin levels, with high grade CaP and worse patient prognosis (Smith et al. 2006;

Fowke et al. 2008; Isbarn et al. 2008; Ma et al. 2008; Nandeesha et al. 2008; Cox

et al. 2009). Major findings from recent studies of men receiving ADT

demonstrated a strong trend between elevated C-peptide levels and more

rapid progression to castrate resistance (Huggins 1942; Neuhouser et al. 2010).

46

1.6.2 Insulin signalling

Most insulin sensitive tissues respond to insulin loads by down-regulation of

INSR; however, it has been observed that tumour cells are less sensitive to down-

regulation, suggesting that cancer cells do not develop the insulin resistance that

other tissues do (Mountjoy et al. 1991). In MCF7 and T47D human breast cancer

cells, insulin did not cause any down-regulation of INSR, nor was there any

decrease in INSR binding affinity. In contrast, in HCT-8 human colon cancer cells,

INSR was down regulated at high concentrations of insulin with a paradoxical

increase in receptor affinity; this result was paralleled in rat hepatoma and

chondrosarcoma cells (Stevens et al. 1983; Crettaz et al. 1984). These papers

suggest that some cancer cells may be hypersensitive to increased exogenous

insulin levels due to various quirks in signalling and feedback controls. Cox et al.

found that INSR levels in both the membrane and cytosol were increased in

neoplastic prostate tissue specimens compared to non-neoplastic samples (Cox et

al. 2009). This suggests that high grade cancers may be more insulin sensitive. This

group also found that both INSR isoforms A and B were present in patient samples,

and insulin and IGF hybrid receptors were also present. Though INSR increased

with progression, IGF-IR did not seem to do so, though that may differ depending

on the degree of androgen independence in specific tumours. The difference

between the isoforms is that B is full-length and A is truncated and lacks exon 1.

Type A is mainly prenatally expressed; whereas, B is expressed in adults and has

higher affinity for IGF1. Type A shows a preference for insulin and IGF2 binding

compared to IGF1 (IGF1-R shares an 84% homology with the INSR kinase

domain).

A further point of interest in the insulin/ IGF axis is the presence of hybrid

receptors (HR), which further complicates the pathways of cascade induction. Two

forms of HR have been demonstrated: IGF-IR/ INSR-A has a higher affinity for

IGF1, but IGF-IR/ INSR-B has equal affinity for insulin and IGF1 (Meinbach et al.

2006). HR is widely expressed in many tissues and some cultured cell lines. High

serum insulin increases breast cancer risk, and an increase in an INSR-A/ IGF-IR

hybrid has been implicated in this risk (Meinbach et al. 2006). It was recently

demonstrated that HR expression is increased in skeletal muscles of patients with

type 2 diabetes. The expression of HR demonstrates the importance of the interplay

47

between the insulin and IGF pathways in cancer and the following study by Sakai et

al. is of particular interest concerning the HR (Sakai et al. 2002). Glucosamine has

been used in modelling of glucose induced insulin resistance, and glucosamine

infusion is known to increase HR expression in the rat skeleton. IGF1 induced

major glycogen synthesis, and insulin caused a significant increase, while together

they had an even stronger effect, which implies IGF1 enhances stimulation by

insulin. IGF1 could increase the HR affinity for insulin, or could act at another

point in the pathway to alter glucosamine/ insulin signalling (Sakai et al. 2002).

Insulin regulates the cellular processes of glucose transport, glycogen synthesis,

mitogenesis, and gene transcription (Mounier et al. 2006). Insulin positively and

negatively regulates approximately 150 genes; however, transcription factors can

act differently in different target tissues, meaning that insulin affects transcription

by modulation of the level, localization, and activity of transcription factors

differently in specific microenvironments (Mounier et al. 2006). Once INSR is

activated, it binds to an insulin receptor substrate (IRS), which is phosphorylated

and activates PI3K and MAPK. INSR and IGF-IR overlap in function, for INSR

can stimulate growth and IGF-IR can initiate metabolic response (Rother et al.

1998). In hepatocytes, it has been concluded that IGF1 is a major IRS-1 regulator.

Insulin typically works with insulin receptor substrate (IRS)-2 through the INSR,

and IRS-2 phosphorylation is required for optimal insulin action (Rother et al.

1998). IRSs were first thought to regulate insulin metabolism pathways; but they

are now known to play a part in other signal pathways including those involved in

mammary gland development, such as growth hormone and prolactin pathways

(Dearth et al. 2006). IRS-1 and 2 are essential for the PI3K pathway.

As mentioned above, insulin typically works with IRS-2 through the INSR, and

IRS2 activation is required for full insulin action (Rother et al. 1998). IRS2 has

been significantly associated with rectal cancer, pancreatic cancer and

hepatocellular cancer (Kornmann 1998; Slattery et al. 2005; Dearth et al. 2006). It

is of interest to this study, dietary risks for cancer progression showed significant

interaction with IRS-2 (Slattery et al. 2005). IRS proteins have also been shown by

some groups to be expressed in primary breast cancer and metastasis, correlating to

poor differentiation and lymph node migration (Dearth et al. 2006). It has been

48

suggested that IRS1 contributes to proliferation of breast cancer cells, likely

through IGF signalling, and IRS2 contributes to motility, demonstrated by the fact

that mice lacking IRS2 show less metastasis (Meinbach et al. 2006). The

importance of IRS in breast cancer is of particular interest because this is a

hormonally regulated cancer, similar to CaP. Moreover, our group has identified

interaction between IRS2 and CaP survival pathways, such as Akt and AR

signalling, in mRNA microarrays (unpublished).

Several cis-activating insulin response elements (IRE) have been identified, but

there is no consensus IRE (Mounier et al. 2006). Several IREs through which

insulin works have been identified. One such IRE upregulates SREBP, which has

been discussed as an important factor in CaP progression. A number of GC rich

regions in newly discovered IREs, which bind Sp1 transcription factor, are

considered major players in hepatic insulin regulation (Mounier et al. 2006).

Several transcription factors implicated in the insulin regulation of genes are also

themselves regulated by insulin, in a self-regulating manner (Mounier et al. 2006).

In transcription modulation, insulin can be either a positive or negative effector. To

control transcription, insulin may upregulate proteins which then displace

transcription factors while increasing the expression of these transcription factors at

the same time. This is the case for Sp1, which is displaced from the

phosphoenolpyruvate carboxykinase (PEPCK) promoter by SREBP. Both Sp1 and

SREBP are upregulated by insulin. Another mechanism for insulin modulation is

demonstrated by the down-regulation of IGF-binding protein 1 (IGFBP-1). For

IGFBP-1, insulin action causes phosphorylation of forkhead/ winged helix box

gene, group O (FoxO), along with a modification of other transcription factors,

which results in the disassembly of the transcription complex (Mounier et al. 2006).

In summary, insulin regulates different factors in different ways to affect metabolic

pathways.

1.6.3 Insulin and analog effects on cancer

Insulin and IGFs act in two ways: at the cellular level as growth factors and at the

whole body level as hormones that regulate growth and energy metabolism (Pollak

2008ba; Pollak 2008cb). It was first discovered in 1967 that insulin may play a role

in mammary gland cancer growth and that finding has been expanded upon since its

49

discovery (Heuson et al. 1967; Ceriani et al. 1972; Heuson et al. 1972; Campbell et

al. 2006). It has been shown that physiological levels of insulin increase thymidine,

uridine, and leucine incorporation into MCF7 breast cancer cells (Osborne et al.

1976; Rillema et al. 1977). Monaco et al. discovered that insulin increases fatty

acid/ lipid production in MCF7 cells in a specified growth pathway rather than

generally via macromolecule production, such as is induced by estrogens and other

hormones (Monaco et al. 1977). Interestingly, they discovered that leucine

incorporation occurred before nucleotide incorporation. In 1988, Wool et al.

demonstrated that insulin influences the translation of ribosomal (r) RNA and

ribosomal modifications, accounting for the increase in protein before mRNA

(Wool et al. 1968). An increase in long-chain fatty acids has also been

demonstrated in MCF7 cells in response to insulin, as well as an increase in ACC

activity. In line with increase in de novo lipogenesis, insulin stimulates an increase

in phospholipids, which may be involved in signalling modifications (Monaco et al.

1977). Follow up studies have determined that insulin stimulates growth of other

breast cancer cell lines, such as T47D (Monaco et al. 1983). Down-regulation of

INSR before examination of thymidine incorporation showed MCF7 cells

responded to INSR inhibition while the T47D cell line became more responsive,

suggesting that insulin may also signal through IGF1R. The growth promoting

properties of insulin occur mainly through INSR and slightly through IGF1R in

LNCaP models and mouse mammary GR2H6 cells (Shi et al. 1997; Pollak et al.

2010). High levels of insulin in the pancreas have also been demonstrated to

increase pancreatic cancer growth (Ding et al. 2000).

Insulin analogs have been of interest since the 1990s to achieve longer lasting

effects for type 1 diabetes patients and short effects, for meal times, for type 2

diabetics (Werner et al. 2011). Analogs are modified for action around the β-chain,

which should not affect insulin receptor binding; however, it has been demonstrated

that some acquire higher affinity for INSR, while others acquire stronger binding to

IGF1R. Noting that insulin has mitogenic and other growth promoting properties,

the effects these compounds may have on cancer, compared to insulin, is of great

importance. One of the first analogs was Asp B10, which had a higher affinity for

the IGF1R than insulin and increased growth of mammary tumours in mice

(Werner et al. 2011). Of note, glargine, which is one of the most prescribed insulin

50

analogs, has been shown to have almost 8 times more mitogenicity than regular

insulin, while other studies have shown similar worrisome results in colorectal

cancer and CaP cells (Kurtzhals et al. 2000; Kohn et al. 2007; Mayer et al. 2008;

Weinstein et al. 2009). Furthermore, population studies have showed mixed results

as to whether patients on glargine have more of a chance of cancer development

than with other insulin analogs (Rosenstock et al. 2008; Colhoun 2009; Rosenstock

et al. 2009; Mannucci et al. 2010). Interestingly, any higher risk diabetes patients

had of cancer, independent of their insulin/ analog schedule, was reversed by

metformin, a metabolic syndrome drug to be discussed in section 1.7 (Currie et al.

2009).

1.6.4 SREBP and insulin signalling

SREBP-1 is increased by insulin in liver and adipocytes, via activation of PI3K and

Akt (Mounier et al. 2006). Insulin may decrease the expression of Insig 2a, a

protein which binds the SREBP-1 preactivation-complex precursors to the

endoplasmic reticulum (ER). When released from this complex, SREBP-1 moves to

the Golgi where it is proteolytically cleaved, and activated. In the nucleus, SREBP-

1 activity seems to be increased after insulin induced phosphorylation of specific

serine and threonine residues (Mounier et al. 2006). Insulin induces SREBP, which

activates transcription of DBI, ACC, and FASN, which in turn increase lipogenesis.

As well, it stimulates other genes involved in glucose breakdown/ transport. These

genes are important in prostate cancer progression (Swinnen et al. 1997; Sul et al.

2000; Brusselmans et al. 2005). Constantly high levels of insulin, as in

hyperinsulinemia, may contribute to induction of these genes, despite the fact that

nutritional control of SREBP is lost in cancer (Swinnen et al. 1997a; Ettinger et al.

2004). In figure 1.4, Menendez et al. elegantly describe the dysregulation of

SREBP and FASN signalling in normal cells and cancer, which insulin and the

metabolic syndrome may contribute to (Menendez et al. 2004a).

Because the prostate is primarily dependent on lipids for energy, instead of glucose,

it may be that the purpose of insulin in that organ warrants more scrutiny (Greisen

et al. 2001). This pathway is used for citrate synthesis in the prostate as high levels

are used to produce prostatic fluid. This is presumably why glucose not used (Liu

2006). SREBP is well known to mediate insulin action on lipogenesis in the liver

51

and our laboratory has previously shown the importance of SREBP in CaP

progression (Ettinger et al. 2004). In that context, fatty liver disease, which is

coupled to hyperinsulinemia, is of particular interest to compare to CaP (Kohjima et

al. 2008). In animal studies, the total amount of SREBP in liver and adipose tissue

is reduced by fasting; this is coupled to insulin suppression, which is regained by

feeding (Horton et al. 2002). When rats are treated with streptozotocin, which

eliminates insulin secretion, liver SREBP is suppressed; however, insulin

administration can compensate by replacing endogenous insulin. When SREBP is

over-expressed in transgenic mice, however, a plunge in serum insulin levels

cannot decrease the expression of lipogenic enzymes, suggesting that SREBP

mediates insulin action on lipogenesis in the liver. This study shows that insulin

induction of SREBP-1 causes the fatty liver in insulin resistance states.

Interestingly, insulin continues to activate SREBP-1c transcription and cleavage in

the livers of insulin-resistant mice, despite insulin resistance in peripheral tissues

(Horton et al. 1998).

In a complementary study, hepatocytes were analysed by real-time RT-PCR, and

showed that insulin treatment did not affect the expression of SREBP-1c mRNA

after 3 hrs (Yellaturu et al. 2005). It was found that instead that insulin rapidly and

selectively stimulated processing of SREBP-1c by a mechanism that is independent

of sterol balance (which is usually the signal for SREBP cleavage and activation).

SREBP-1c phosphorylation was associated with the amount of proteolysis that

occurs before it is activated. This phosphorylation may stimulate its transport from

the ER to the Golgi to enhance the active protein levels. Furthermore, insulin

treatments lead to the nuclear accumulation of SREBP by way of the PI3K pathway

(Yellaturu et al. 2009). Though it may not act at the mRNA level, insulin appears to

increase active SREBP-1c in hepatocytes.

52

(Figure 1.4, page over)

Insulin INSR over-expression

A)

B)

53

Figure 1.4: Proposed model of FASN increased activity and expression in cancer.

(A) Regulation of SREBP and therefore FAS(N) are tightly controlled by dietary

lipids and fatty acids in hepatocytes and adipocytes. As well, exogenous hormones

can regulate transcription, where insulin induces FAS(N) expression and synthesis

and leptin decreases expression; the interplay between hormones and nutrition is

crucial for control of fatty acid synthesis.(B) FAS(N) expression in cancerous cells

is controlled by SREBP-1c, which is under the influence of constitutively active

Akt and MAPK pathways, and mainly insensitive to nutritional control, as well as

insulin and IGF1. Adapted from Menendez et al. 2004 with information from other

sources (Yeh et al. 1999; Pollak 2001; Menendez et al. 2004a; Menendez et al.

2007).

54

Because insulin increases SREBP-1 production and translocation, and that

transcription factor influences lipogenesis, it is of great interest to this project;

furthermore, SREBP-1 is suggested to have yet another role in CaP; it may enhance

the transcription of cytochrome p450 family member, CYP17A1, one of the key

steroidogenic enzymes, as well as StAR (Sewer et al. 2009), as well as fatty acids

and DBI which may activate steroidogenesis (Locke et al. 2009b).

1.6.5 Insulin and steroidogenesis

In Leydig cells, dose dependent increases in StAR protein and mRNA with

increasing insulin concentrations have been demonstrated (Le et al. 2006). In

culture media, progesterone decreases accordingly, indicating processing to other

steroids. Furthermore, insulin increases testosterone and progesterone in the media;

however, in concert with cAMP, there is a much greater increase in not only

hormones but StAR mRNA expression (Le et al. 2006).

In polycystic ovary syndrome (PCOS), insulin has actions on steroidogenesis

through its own receptor. Despite PCOS women having hyperinsulinemia and

insulin resistance; the ovary does not appear to be insulin resistant (Greisen et al.

2001). PCOS is a disease relevant to our study because it demonstrates a similar

phenotype to men with ADT induced metabolic syndrome – increased local

androgen production and hyperinsulinema, as well as cardiovascular issues

(Banaszewska et al. 2009). Insulin is known to increase growth of ovary cells in

PCOS through the PI3K pathway, however, wortmannin, a PI3K inhibitor, did not

decrease IGFBP-1 down-regulation (a normal action of insulin) but did decrease

PI3K activation. Wortmannin also did not inhibit progesterone production by

insulin stimulation, indicating that progesterone production may be PI3K

independent. Some studies have proposed insulin working though IGF1R; however,

signalling has been shown to be mainly through INSR (Poretsky et al. 2001). In

ovarian thecal cells taken from patients without PCOS, insulin increased oestrogen

after 2 days (Greisen et al. 2001), as well as stimulating production of enzymes

necessary for steroidogenesis and increasing local androgen production (Nestler

1997; Seto-Young et al. 2007). Furthermore, IRS-1 immunohistochemical staining

was decreased in granulosa cells of PCOS ovaries; whereas, IRS-2 was increased

and localized to the theca interna, a compartment actively engaged in production of

55

androgens (Wu et al. 2000). IRS-2 is thought to contribute more to the metabolic

signalling than mitogenic signalling of insulin (Wu et al. 2000) . Furthermore, IRS-

2 is considered a progesterone responsive gene (Cui et al. 2003), which, if insulin

increases steroidogenesis, may indicate a feed-forward loop of progesterone, IRS-2

and insulin signalling.

1.7 Metabolic syndrome drugs and cancer

1.7.1 Metformin

Where insulin has come into the spotlight for its tumourigenic potential, so have

metabolic syndrome drugs come into the spotlight as potential cancer therapeutics.

Currently, the best known drug for metabolic syndrome and diabetes mellitus type

2 is metformin. Long ago it was recognized that the French lilac, from which

biguanides such as metformin are derived, could have benefits to the prostate, as

medieval herbalists used it to treat polyuria (excessive urination) (Pollak 2010b).

Polyuria is also associated with diabetes and sometimes with prostate

abnormalities. Of the biguanides that have been developed, metformin has the best

safety rating, with less toxicity than its counterparts (Jalving et al. 2010).

Metformin is currently in clinical trials for many diseases including the prevention

of weight gain in combination with anti-psychotic medications, as an antimicrobial

and potentially as a treatment for AIDS related hyperinsulinemia and weight

fluctuation (Mulligan et al. 2007; Pollak 2010b).

While it is clearly effective, the mechanisms by which metformin acts are unclear;

it reduces hyperinsulinemia and hyperglycemia, disrupts ATP production and

increases liver kinase B1 (LKB1), which activates AMP-activated protein kinase

(AMPK) (Engelman et al. 2010). AMPK is a cellular energy signalling molecule

and controls pro-aging pathways, which overlap with tumour growth pathways.

LKB1 and AMPK also regulate whole body energy levels via the liver, and

metformin decreases gluconeogenesis, which represents the transport of energy

from the liver to the whole body, which decreases serum levels of glucose

(Engelman et al. 2010). AMPK phosphorylation and activation exert a direct effect

on some enzymes for ATP production while inhibiting pathways that are not

necessary for survival. AMPK down regulates protein synthesis, FASN, ACC, and

glycerol phosphate acyl-transferase (which regulates fatty acid and glucose

56

synthesis), as well as down regulating SREBP and carbohydrate-responsive

element-binding protein (ChREBP) (Luo et al. 2005). Where insulin increases fatty

acid synthesis in the liver, as discussed above, metformin has been demonstrated to

decrease fatty acid synthesis, as well as enzymes involved in cholesterol synthesis,

in the same organ (Fulgencio et al. 2001). In a colorectal cancer mouse xenograft

model, Algire demonstrated that metformin decreased intratumoural ACC, which

then decreases lipogenesis and may aid in stopping tumour progression (Algire et

al. 2010). Different treatments for metabolic syndrome may affect cancer risk, as

patients on insulin and analogs/ sulfonylureas had increased cancer development

risk compared to those on metformin or metformin combinations. In a nine year

study, diabetic patients taking metformin had 50% less risk of cancer than non-

users (Gallagher et al. 2010).

Interestingly, metformin was protective from cancer risk for mice on a high

carbohydrate diet (mimicking metabolic syndrome/ hyperinsulinemia), impeding

fatty acid synthesis in tumours (Anisimov et al. 2011). Metformin did not seem to

have the same effect on fatty acid synthesis in mice on a normal diet, which

indicates that the effectiveness of metformin might depend on the metabolic status

of the patient; however, tumour poly-ADP-ribose-polymerase (PARP) cleavage, an

indicator of apoptosis, was increased in both mouse diet groups. In a study of

female spontaneously hypertensive mice, Anisimov et al. discovered that

metformin seems to increase the lifespan of hypertensive mice on normal diets

compared to untreated control group mice on normal diets. Though treatment did

not make a difference to how many of each group developed cancer, the metformin

group had significantly less aggressive cancers (Anisimov et al. 2011). In humans,

it has been shown that women on metformin in conjunction with neoadjuvent

hormone therapy for breast cancer have higher complete response rates than those

on neoadjuvent therapy alone (Gallagher et al. 2010). In breast cancer tissue

culture, metformin and tamoxifen, an anti-oestrogen, have synergistic properties,

especially in those cells that are tamoxifen resistant (Berstein et al. 2010). In breast,

ovarian, endometrial and pancreatic cancers patients had better responses to

chemotherapy when combined with metformin (Gallagher et al. 2010).

Also, in PCOS, metformin has been shown to inhibit mitochondrial respiration as

well as ACC activation and FASN expression (Diamanti-Kandarakis et al. 2006).

57

Likely through a decrease in SREBP expression, metformin inhibits CYP17A1

lyase compared to hydroxylase activity, which reduces serum androgen levels, a

high proportion of which may come from ovarian origins (Cheang et al. 2009).

1.7.2 Statin drugs

There are research groups that believe statin drugs are the reason for decreases in

CaP mortality. From 2003-2004, 11.7% of US adults were taking statins, likely due

to the high prevalence of obesity in the US (Colli et al. 2008). In addition to

lowering cardiovascular events, the incidence of aggressive prostate cancer is also

lowered by statins. In a study by Loeb et al. men on statins were older and had high

BMI at time of radical prostatectomy, but had lower PSA, as well as lower biopsy

and Gleason score, despite the fact that clinical stage and rate of serum PSA rise

were similar to men not on statins. Patients on statins had lower tumour volume,

smaller surgical margins, lower stage and grade of cancer, and less risk of

biological recurrence (Loeb et al. 2009). Another study has shown a non-significant

decrease in CaP risk by statin users; however, a significant decrease in metastases

was determined (Yu et al. 2009). There are two classes of statins for lipid control;

hydrophobic statins travel through the blood stream to have whole body effects

while hydrophilic statins, such as pravastatin, work mainly in the liver.

Interestingly, the ability of pravastatin to decrease hepatic cholesterol may increase

cancer risk at distal tissues by provoking a compensatory response to lack of

cholesterol (Duncan et al. 2005).

The reason for the efficacy of statin drugs may be their method of action which is

via inhibition of -hydroxy-3-methylglutaryl-CoA reductase (HMGR), which makes

precursors to cholesterol. HMGR is upregulated in breast cancer and CaP tissue

compared to normal tissue, and some epidemiological studies show 50% reduction

in breast cancer risk with statins (Borgquist et al. 2008). Statins are used to treat

hypercholesterolemia and reduce cardiovascular disease. In cancer, this may mean

that they prevent androgen synthesis necessary for progression, as well the

synthesis of their precursors, which may play a role in signalling. Mevalonate,

downstream of HMGR, is necessary for prenylation of proteins involved in signal

transduction cascades for membrane receptors which participate in growth and

apoptosis (Yu et al. 2009). By disruption of cholesterol and mevalonate synthesis,

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statins may inhibit proliferation, inflammation, oxidative stress, angiogenesis, and

metastasis (Gallagher et al. 2010).

Statins may limit N-linked glycosylation of proteins, thereby inhibiting maturation

of INSR and IGF1R, and decreasing isoprenylation of small ATPases like Ras and

Raf (Kodaman et al. 2008). Cholesterol has been implicated in many cancers

including CaP, and it is increased in prostate adenomas (Twiddy et al. 2010).

Cholesterol may provide precursors for de novo steroidogenesis (Dillard et al.

2008). We and others have previously shown increasing steroid synthesis during

CaP progression (Gregory et al. 2001a; Locke et al. 2008; Montgomery et al. 2008;

Locke et al. 2009a), and testosterone levels in metastases are often three times

higher than in primary tumours (Twiddy et al. 2010). Some say adrenal precursors

might be the dominant factor driving intratumoural androgen levels, but in ADT

for advanced cancer, even with adrenalectomy, the CaP progression still returns,

suggesting limited contribution of adrenal precursors at this stage (Bhanalaph et al.

1974). In LNCaP and DU145 xenograft models, it has been demonstrated that high

cholesterol diets increase intratumoural cholesterol, and tumour proliferation also

increased (Twiddy et al. 2010). Statins decrease CYP11A1, HSD3B, CYP17A1 in

adrenocortical cells and the ovaries (Kodaman et al. 2008). As well, mevastatin

blocks MAPK and ERK1/2 phosporylation, pathways usually induced by

hyperinsulinemia, and data sets find that statins may decrease hyperandrogenism in

women with PCOS, also associated with high insulin levels (Kodaman et al. 2008).

Yet another study examined the relationship between statins and PSA in CaP cells,

finding that statins decrease PSA expression at the protein level but not mRNA

level, indicating PSA degradation in LNCaP and 22RV1 prostate cancer cells.

Furthermore, statins show growth inhibition in precastrate and castrate resistant cell

lines (Yokomizo et al. 2010).

1.8 IGF2 and prostate cancer

1.8.1 IGF axis signalling

The IGF system plays an import role in cell biology, regulating growth and

development, promoting proliferation, differentiation, and transformation, as well

as inhibiting apoptosis in endocrine, autocrine and paracrine manners (Pollak 2001;

Cardillo et al. 2003). The influence of IGF1 on CaP transformation is shown in

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figure 1.4. It is important to note that there are differences in the effects of IGF1

and 2 on cells. IGF1 mediates body growth effects after birth; whereas, IGF2

regulates foetal growth and has unclear effects on adult growth. Though its role is

less clear, the concentration of IGF2 in serum is approximately double that of IGF1

(Meinbach et al. 2006). IGF1 and 2 are endocrine growth factors produced mainly

in the liver and bone marrow, but in most other tissues as well (Meinbach et al.

2006; Sachdev et al. 2007). Synthesis of both IGFs occurs in many different tissues

in a highly regulated manner, and most of the differentiation and growth effects are

affected by the interactions between the IGFs and their receptors. IGF-IR is a

tyrosine kinase transmembrane receptor which binds IGF1, and to a lesser extent

IGF2. IGF2R is identical to the cation-independent mannose-6-phosphate receptor.

Unlike IGF-IR, IGF2R binds only to IGF2, any interactions with IGF1 being

unknown. IGF2R is a receptacle for IGF2, seeming to regulate free IGF2

concentration without downstream signalling (Fottner et al. 1998; Hu et al. 2005);

however, there is some evidence that IGF2 signalling through IGF2R may activate

ERK 1/2 through sphingosine kinase (El-Shewy et al. 2007). IGF2 can also bind

INSR, which has little affinity for IGF1, and the hybrid INSR-A/ IGF1R receptor

(Spicer et al. 2007). Activation of IGF-IR involves binding of one of its ligands,

IGF1, IGF2, or insulin, and activation of IRSs, which then accumulate coactivators,

activating the Ras-Raf and MAPK serine/ threonine kinases involved in regulating

cellular effectors and nuclear transcription factors (Fottner et al. 1998).

Activation of antiapoptotic pathways by the IGFs is initiated by binding of

receptors and subsequent activation and phosphorylation of IRS1 and 2 (figure 1.5).

IRS1 and 2 are essential for the PI3K pathway. PI3K has a p85 regulatory subunit

containing 2 SH2 subunits and 1 SH3 subunit. Akt is downstream of PI3K. Akt

binds the lipid products of PI3K then is activated through phosphorylation by

pyruvate dehydrogenase kinase (PDK1) and two PI3K dependent kinases, which

leads to proliferation (Yan et al. 2006).

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Figure 1.5: The IGF signalling pathways. Once IGF1 or 2 is bound to IGF1R, it

will activate signalling pathways through insulin response elements (IRS) and the

PI3K and MAPK signalling pathways, which may then stimulate sterol response

element binding proteins (SREBP – transcription factor), which may then

contribute to steroidogenesis by initiation of cholesterol transport into the

mitochondria. Both pathways may increase StAR phosphorylation, increasing its

activity, therefore increasing steroidogenesis. IGF2 has been demonstrated to

stimulate greater induction of steroidogenesis that IGF1 in literature (Fottner et al.

1998) and in studies by our laboratory (data not shown). Activation of these

pathways also hinders apoptosis. Figures are modified from:

http://www.abcam.co.jp/cms/displayImage.cfm?intImageID=15557 (2009).

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1.8.2 The IGF axis in prostate cancer

IGFs regulate proliferation, differentiation, apoptosis and transformation in

cancerous and normal prostate cells, as well as many other cells, as reviewed by

Gennigens et al. 2006; Monti et al. 2007; Pollak 2008a; Pollak 2008b. IGF1

increases DNA synthesis and mediates mitogenesis by increasing cyclin D

mediated cell cycle progression (Gennigens et al. 2006). It can also inhibit

apoptosis by increasing the Bcl/ Bax ratio, as well as stimulating angiogenesis and

cell migration (Gennigens et al. 2006).

Important to the interplay between CaP and obesity, obesity is associated with

higher circulating serum IGF1 levels and estrogens, which stimulate mitogenic

activity in CaP cells (Gennigens et al. 2006). PC3 cell (AR negative) tumours grew

faster in murine hosts that had high IGF levels compared to IGF1 deficient hosts

(Gennigens et al. 2006) and IGF1 increased cancer cell proliferation in a variety of

AR positive and negative CaP cell lines in vivo and in vitro (Monti et al. 2007).

IGF1 works in concert with androgens and other mediators to stimulate growth in

AR positive cell lines, such as LNCaPs (Gennigens et al. 2006; Monti et al. 2007).

There have been many epidemiological studies as to whether serum IGF1 levels

correlate with cancer risk, with contradictory results; however, very large studies

indicate elevated CaP risk (Monti et al. 2007). Interestingly, in patient tissue

samples, more dramatic upregulation of IGF2 is demonstrated than IGF1, indicating

it may play a more crucial role in local cancer stimulation (Monti et al. 2007). Most

studies also show an increase in IGF1R expression on CaP cells during progression

(Gennigens et al. 2006). It is also important to note that IGFs are very abundant in

bone, which is a major site of metastasis for CaP (Gennigens et al. 2006; Kimura et

al. 2010). IGFs stimulate tumour cell motility and cell-cell adhesion which may

influence metastatic potential (Gennigens et al. 2006). In the metastatic

environment, the interaction between molecules secreted from cancer cells and

receptors on osteoblasts can stimulate the cleavage of IGF binding proteins

(IGFBPs), which bind most of the IGFs in the body, thus increasing local IGF

concentrations and stimulating growth of both cell types (Gennigens et al. 2006).

The importance of IGFs in CaP is demonstrated by the array of inhibitors that have

been developed to target the IGF axis (Gennigens et al. 2006; Pollak 2008a). Some

62

studies have shown that downregulating IGF1R with interference RNA or antisense

oligonucleotides inhibits IGF1 stimulated CaP cell proliferation (Gennigens et al.

2006). These nucleotides work by binding to the mRNA to block transcription and/

or promoting mRNA degradation by RNase Though there remains the problem of

delivering these molecules into the intended cells in a clinical situation, they

represent an attractive strategy to target genes in many diseases. Inhibition of IGF

signalling by antisense oligonucleotides knockdown of IGF1R showed extensive

cancer cell apoptosis in glioma patient trials with marked clinical improvements in

8 of the 12 patients and there was a stimulation of the anticancer immune response

(Gennigens et al. 2006). This is a small but significant finding. Down-regulation of

IGF1R also sensitizes DU145 CaP cells to cisplatin, mitokantrone and paclitaxel

anti-cancer agents (Gennigens et al. 2006).

Monoclonal antibodies to IGF1R are also in trial, with some showing more promise

than others. A12 inhibits IGF mediated growth in many cell lines in vitro and in

vivo and has been shown to inhibit growth in both androgen sensitive and castrate

resistant CaP cell lines in xenograft models (Burtrum et al. 2003; Wu et al. 2005).

This antibody both blocks the receptor and increases its internalization and

degradation. The monoclonal antibody CP-751,871 also decreases proliferation of

cancer cells in vitro and in vivo by receptor inhibition and degradation and it works

synergistically with anticancer agents adriamycin, 5-fluorouracil, and tamoxifen

(Gennigens et al. 2006). This antibody is currently in phase II/ III clinical trials in

prostate and other cancers (Chitnis et al. 2008; Pollak 2008a).

1.8.3 IGF2 in cancer

IGF1, IGF2, IGF1R and the IGFBPs are known to play an important part in cancer.

Most studies have focussed on IGF1, which is known to stimulate cell proliferation

through activation of IGF-IR and downstream activators IRS-1 and IRS-2, and is

positively associated with a greater risk of prostate cancer (Meinbach et al. 2006).

The role of IGF2 in cancer, however, and in particular prostate cancer, has been

largely overlooked.

Diverging from IGF1 signalling, in leiomyosarcoma cells deficient for IGF1R,

IGF2 mediated invasiveness of tumours through INSR-A. IGF2 also promoted

mitogenesis through INSR much more potently than insulin did (Sciacca et al.

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2002). In choriocarcinoma, inhibition of IGF-IR and of IGF2R did not decrease

IGF2 induced cell invasion; therefore, another receptor had to be responsible which

proved to be INSR, especially type A, which has high affinity for, and can be

activated by, IGF2 (Diaz et al. 2007). Furthermore, the effects of IGF2, but not

IGF1, are diminished by INSR inhibition in these cells. The type A INSR is usually

seen during foetal growth and not in adults; however, it has been shown to be the

main INSR in breast, colon, and lung cancer and can be activated by IGF2, which it

binds with reasonably high affinity (Ulanet et al. 2010). This indicates that IGF2

and 1 have different mechanisms for mediating cell motility, and that IGF2 is very

important in malignancy in its own right (Spicer et al. 2007; Patalano et al. 2009).

Choriocarcinoma is a highly aggressive tumour derived from placental tissue and

arises in the gestational tract; however, its formation is not well understood. IGF2 is

expressed locally in tumourigenesis, where it is postulated to play an important role

in disease progression (Diaz et al. 2007). IGF1 is also found in these tumour cells at

sufficient levels to stimulate and saturate all known receptors of IGF1. In one

particular study, IGF1 action during invasion was enhanced by co-treatment with

IGF2, indicating that it uses a different receptor to assist in increased tumour

growth. In murine pancreatic neuroendocrine tumours, IGF2 is locally expressed

and has anti-apoptotic properties (Ulanet et al. 2010). In these tumours, IGF-IR is

highly expressed and correlated to aggressiveness; however, agonists that block this

receptor do not slow progression significantly. In breast tumours where IGF-IR is

down regulated, INSR expression is upregulated up to 7-fold (Ulanet et al. 2010).

Loss of INSR sensitizes these cancer cells to IGF1Rinhibition. In MCF7 breast

cancer cells and T47D cells, the IGF-IR to INSR ratio is high making them

sensitive to IGF-IR targeting. Contrarily, in MDA-MB-213 and 157 cells, the ratio

is low and anti-IGF-IR treatments are not effective without siRNA to INSR (Ulanet

et al. 2010).

IGF2 imprinting is lost in ovarian cancer. In patient sample arrays, IGF2 was 13-

fold more abundant than in normal epithelium (Sayer et al. 2005). Additionally, a

330-fold increase in IGF2 mRNA was detected, especially in poorly differentiated

tumours. IGF2 imprinting is also lost in breast, colorectal, and hepatocellular

carcinomas (Van Roozendaal et al. 1998; Cui et al. 2002; Cui et al. 2003; Poirier et

al. 2003; Sayer et al. 2005)

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1.8.4 IGF2 in prostate cancer

IGF2 may be an important marker in CaP, in concert with PSA. PSA was identified

as a marker for CaP in the 1970s; however, PSA levels cannot specifically

distinguish between BPH and CaP, with 30% of men screened having unnecessary

biopsies. At least one study has postulated that combined measurements of PSA

and IGF2 show significantly better preoperative staging than PSA alone and better

discrimination between BPH and CaP (Trojan et al. 2006). In contrast, a recent

review concluded that there is no direct association between prostate cancer and

circulating concentrations of IGF2 (Rowlands et al. 2009). In many cancers,

however, it is the local production of growth factors that may be the key issue

(Pollak 2008a; Pollak 2008b).

A very interesting study has shown upregulation of IGF2 in mouse and human

prostate tissue with aging (Fu et al. 2008). Epigenetic modifications including

histone chromatin changes and genomic imprinting are very sensitive to alterations

in cellular and bodily environment, such as cancer progression. IGF2 is imprinted

and expressed solely by either the paternal or maternal allele. IGF2 has enhancing

and imprint control regions to coordinate expression, and the chromatin insulator

protein CCCTC-binding factor (CTCF) binds to the imprinting control region and

represses the non-expressed allele. It was shown that hypermethylation or deletion

of the imprinting control region could leave both alleles expressed, which may be

tantamount to an early neoplastic switch (Fu et al. 2008). In three month old

sexually mature mice, imprinting was still detectable; however, the older mouse

cohorts showed progressively higher expression from the previously inactive IGF2

gene; in humans, loss of imprinting was significant with age due to allele

polymorphisms (Fu et al. 2008). Loss of imprinting was more extensive in men

with prostate cancer. In breast cancer, although total levels of IGF2 decreased, free

IGF2 increases in localized early disease; furthermore, there is a correlation

between free IGF2 and tumour size (Espelund et al. 2008).

1.8.5 IGF2 and steroidogenesis

IGF1 and 2 have also been associated with increased steroidogenesis in the testis

and adrenal gland (Berensztein et al. 2008), and steroidogenesis is increased in CaP

65

progression (Locke et al. 2008). Recent studies have also suggested that IGF2

augmentation of hormonal regulation may play a part in tumour progression (Yee

1994). Patalano et al. have shown that in adrenocortical cancer, IGF2 is over-

expressed (Patalano et al. 2009). This type of cancer occurs most frequently at two

ages: in early childhood, and in women between 40 and 50 years of age, both times

of significant hormonal change. Spicer found that IGF2 expressed in the ovarian

granulosa and theca cells increased progesterone at least two-fold in the absence

and presence of other growth factors (Spicer et al. 2007). IGF1 and 2 have also

been associated with increased steroidogenesis in the testis and adrenal gland

(Berensztein et al. 2008).

In human adult cultured adrenocortical cells, which express the IGF1 and 2

receptors, IGF2 increases basal levels of CYP17A1, and induces the expression of

CYP11A1, CYP17A1, and HSD3B, which results in an increase in aldosterone

(Kristiansen et al. 1997). It has been shown that insulin and IGFs increase

cholesterol uptake into these cells, resulting in significant pregnenolone production,

which is then converted to downstream androgens. In these cells, insulin and IGFs

can independently increase the mRNA expression of CYP17A1 and HSD3B,

though they require cAMP for increase of CYP11A1 mRNA, and insulin can

induce the expression of IGF1.

IGFs play a role in growth and differentiation of adrenal glands. In

adrenocarcinomas, both IGFs increased DHEA and its sulfate, DHEA-S, in a time

dependent manner. Treatment of adrenocorticoid cells with LH and IGF2 together

resulted in an increase in DHEA-S synthesis more quickly (24hr and 48hrs,

respectively) and to a greater extent than with LH and IGF1. Cortisol was also

increased with the LH and IGF2 combination and this was not seen with IGF1. The

increase of steroid expression with IGFs was paralleled by an increase in cAMP.

LH in combination with IGF2, but not IGF1, induced maximal cAMP expression.

Surprisingly, mutation studies show that in this case, IGF2 is acting through the

IGF-IR, not its own receptor (Fottner et al. 1998).

IGF2 is postulated to play a large, though somewhat unexplored, role in hormonally

active adrenocortical carcinomas, as elevated levels of IGF2 mRNA/ peptide are

found there. Serum concentrations of IGF2 are quite high; furthermore, IGF2 may

66

enhance the bioavailable IGF1 concentration by increasing proteolysis of inhibitory

IGFBPs (Fottner et al. 1998). The increase in bioavailable IGF1 could have a

synergistic action on steroidogenesis with IGF2 (Fottner et al. 1998). In this

document, the effect of IGF2 on steroidogenesis, which may increase

proliferation and survival in CaP cells, will be explored.

1.9 Summary and project relevance

In CaP, progression to CRPC is accompanied by a reactivation of androgen

dependent pathways working through AR, activation of which can be mediated by

various modifications and genetic variations (So et al. 2005). We have previously

found that during progression in LNCaP xenografts, all the enzymes for de novo

steroidogenesis are expressed and upregulated (Locke et al. 2008). ADT initially

stops CaP growth by cutting off supply of extra-prostatic androgens necessary for

growth and survival of prostate tissue. However, despite castrate levels of

androgens in the bloodstream, most men eventually experience terminal recurrence.

Under these conditions, selective pressure favours cells that can adapt to a low

androgen microenvironment. These cells have been demonstrated to harbour steroid

concentrations adequate to activate AR. We and others have shown that de novo

steroidogenesis by CaP cells contributes to intraprostatic steroid levels (Lock et al.

2008; Dillard et al. 2008). As steroid levels in metastases are three times those in

local tumours, it is important to investigate factors that influence steroidogenesis.

Many AR regulated pathways play a part in CaP progression, and one of the most

attractive targets for therapeutics is fatty acid synthesis/ lipogenesis. FASN was

first identified in breast cancer as an oncogene, expression of which is linked to

aggressiveness of cancer (Migita et al. 2009). In CaP, FASN is already upregulated

in pre-neoplastic lesions. FASN is expressed at low or undetectable levels in most

healthy tissues because there is enough fatty acid in the diet; however, nutritional

control is lost in cancer, where lipids are used in synthesizing and maintaining

membranes, signalling disruption, and other survival mechanisms. In CaP, fatty

acids are the main energy source as well, and activated fatty acids, especially

arachidonate, are implicated in initiation of steroidogenesis. Because it is not

necessary for most normal tissues, FASN is especially attractive as a target for

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inhibition in cancer. The regulation of FASN, and SREBP, its main transcription

factor, has been well studied in CaP; the effects of other growth factors on

lipogenesis have not been explored, but may be important to understand.

Hormone modification is the main treatment for many hormone dependent cancers,

such as breast, prostate and testicular cancers, and these therapies are often

accompanied by metabolic syndrome, with cardiovascular risks and

hyperinsulinemia, and they are associated with disease progression (Redig et al.

2010). In prostate cancer patients, 55% of ADT patients experience incidental

metabolic syndrome. Though it was originally thought to be an unfortunate side

effect, mounting epidemiological data suggests that the metabolic syndrome, and

the accompanying hyperinsulinemia, may promote cancer progression. Related

health concerns, such as type 2 diabetes and obesity are associated with higher

cancer risk and risk of more aggressive cancers. High levels of insulin or insulin

analogs have been shown to increase cancer growth, in in vitro and in vivo models.

Patient studies have shown insulin may upregulate steroidogenesis in some organs.

Additionally, insulin upregulates FASN in normal liver tissue and in vivo breast

cancer models, suggesting it may also upregulate FASN in CaP. The effect of

hyperinsulinemia on these pathways in CaP has not been studied, but may be

crucial to understanding the interplay between CaP and metabolic syndrome.

In agreement with the importance of metabolic syndrome in cancer, new emphasis

has been placed on the effect of metabolic syndrome drugs on cancer progression,

especially hormone dependent cancers. Metformin is an antidiabetic drug that

activates AMPK and down regulates pathways that are unnecessary for energy

conservation, such as protein synthesis and fatty acid synthesis. It has also been

noted to decrease steroidogenesis caused by insulin in thecal cells. Simvastatin halts

cholesterol production and down regulates SREBP in some cell lines and has been

associated with positive outcomes for hormone dependent cancer patients,

including CaP, where a substantial decrease in PSA is seen. Though insulin and

metabolic syndrome drugs have been studied epidemiologically, the pathways

modulated/ inhibited by these factors have not been investigated in CaP. We are

also interested in the role IGF2 may play in steroidogenesis, as it is noted to

increase adrenal steroidogenesis.

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This project will address each of these themes as well as the interplay of signalling

between them. The hypotheses of this study are:

1) Insulin increases steroidogenesis and lipogenesis in CaP cells and insulin analogs

may have differential effects on steroidogenesis.

2) Insulin increases fatty acid synthesis and lipid signalling in CaP cells.

3) Metformin and simvastatin diminish insulin signalling, thus decreasing

steroidogenesis and lipogenesis.

4) IGF2 increases steroidogenesis in CaP cells.

To investigate these hypotheses, the specific aims are:

1) To characterize the expression patterns of lipogenesis and steroidogenesis

enzymes in the presence and absence of insulin, and to investigate the effect of

insulin analogs and IGF2 on steroidogenesis enzymes.

2) To examine the change in steroid and lipid/ fatty acid levels in CaP cells in the

presence of insulin, and to examine changes in steroid levels in CaP cells with

insulin, insulin analogs and IGF2.

3) To investigate the effects of metformin and simvastatin on insulin induced

steroidogenesis and lipogenesis.

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Chapter 2: Materials and Methods

70

71

2.1 Introduction

This chapter will describe the experimental methodology that is common

throughout this project. Methods that are specific to a section or chapter will be

described therein.

2.2 General reagent, plates, and chemicals

Analytical grade chemicals used were obtained from Sigma (Castle Hill, NSW,

Australia). Tissue Culture plates used in Australia (protein, RNA, Oil-Red O, DHT

ELISA, Amplex Red Assay) came from In vitro (Noble Park North, VIC,

Australia). Tissue culture plates used in Canada (steroid extraction experiments,

fatty acid chromatography, and microarray assays) came from Corning (Fisher

Scientific, Pittsburgh, PA, USA).

2.3 Cell lines

The CaP cell lines used in this project were LNCaP (passage 38-50), 22RV1, and

VCAP. All cell lines used in this project were AR-positive cell lines, originally

obtained from American Type Culture Collection (ATCC) (Manassas, VA, USA).

LNCaP cell line was established in 1977 from a 50 year old Caucasian prostate

cancer patient. Cells were isolated by needle biopsy of the metastatic deposit in the

patient’s left supraclavicular lymph node (Horoszewicz et al. 1980). LNCaP cells

express both AR and estrogen receptor (ER), and are growth responsive to

androgens. The optimal concentration for stimulation under charcoal stripped serum

(CSS) conditions is 10nM DHT, with decrease in proliferation at higher

concentrations in foetal bovine serum (FBS) supplemented medium (Horoszewicz

et al. 1980; Horoszewicz et al. 1983). LNCaPs also express PSA in an androgen

dependent manner. The LNCaP AR has a Thr to Ala (T877A) single point mutation

to the ligand binding domain (Veldscholte et al. 1990b; Veldscholte et al. 1992).

This mutated and promiscuous AR can bind androgens, progestins, estrogens and

anti-androgens and activate/ suppress the induction of genes classically or non-

classically regulated by AR. This mutation is found in approximately 25% of CaP

patients with CRPC (Feldman et al. 2001).

22RV1 cells were established from a CWR22 xenograft, derived originally from a

primary site prostatic carcinoma from a patient with bone metastasis (Pienta et al.

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1993). CWR22 cells were androgen-dependent and formed PSA secreting,

androgen dependent tumours when injected into mice. These tumours were allowed

to progress before the mice were castrated and allowed to regress and subsequently

relapse. The resulting tumours were serially subcultured to establish a number of

secondary cell lines. The lineage used in this project was 22RV1, which is a

castrate resistant, but androgen responsive cell. PSA is stimulated by androgen, but

not highly (Pienta et al. 1993). 22RV1 cells also have an AR mutation in the ligand

binding domain, His to Tyr (H874), which has been reported in some CaP patients

with advanced disease after ADT and CRPC metastasis (McDonald et al. 2000).

This mutated AR is also thought to be stimulated by DHEA, estradiol,

progesterone and flutamide (McDonald et al. 2000). 22RV1 cells also express a

differentially spliced variant of AR, which are constitutively active (Tepper et al.

2002).

VCaP cells were established from a metastatic lesion on the vertebrae of a patient

with hormone refractory prostate cancer and grafted into immunocompromised

mice (Korenchuk et al. 2001). The xenograft tumours were then excised and

cultured in vitro. VCaP cells exhibit PSA expression in an androgen dependent

manner by activation of wild-type AR (Wu et al. 2011). They are a castrate

resistant, androgen sensitive cell line (van Bokhoven et al. 2003).

The only non-CaP cell line briefly investigated in this study was the MCF7 breast

cancer cell line. MCF7 cells are estrogen dependent (Dickson et al. 1986), and

express both ER and AR (Boccuzzi et al. 1993). MCF-7 cells were originally

derived from a breast tumour metastasised to a pleural effusion (a fluid filled cavity

around the lungs) from a breast cancer patient who had bilateral mastectomy

(Levenson et al. 1997).

2.4 Cell culture

Cells were maintained in an IR sensor Incubator (Sanyo, Quantum Scientific,

Brisbane, Australia) at 37oC with 5% CO2. LNCaP and 22RV1 cells were cultured

in Roswell Park Memorial Institute medium (RPMI, Invitrogen, Melbourne,

Australia), supplemented with 5% FBS. VCaP and MCF7 cells were cultured in

Dulbecco's Modified Eagle Media (DMEM, Invitrogen, Melbourne, Australia),

supplemented with 10% FBS. Culture medium was changed approximately every 4

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days, and cells were passaged when they reached confluence of approximately

80%. Cells were lifted by trypsinization (Invitrogen). For treatments, cells were

plated at 30% confluence and treated at 50-60% confluence.

Mycoplasma testing was performed once a month by nested PCR (primers from

Sigma Proligo, Castle Hill, NSW, Australia).

Specific treatments will be described in experimental chapters 3-7, with the

exception of MCF7 cell treatment, chapter 2.11.

2.5 RNA extraction

RNA was extracted from prostate cancer cells using TriReagent (Applied

Biosystems, Melbourne, Australia) or Trizol (Invitrogen). All RNA extractions

were done from cells grown in 6-well plates. For extraction, medium was removed

and 200µl Trizol was added per well. Cell lysates were pipetted until smooth, and

then let sit for 5 minutes at room temperature (RT). Lysates were removed to

Eppendorf tubes and 40µl of 1-bromo-3-chloropropane (20% of Trizol volume)

(Sigma) was added, tubes were inverted several times over 15 seconds and

incubated for two minutes before being centrifuged (15 minutes at 7700xg at 4oC).

The aqueous phase was moved to a new tube (approximately 50% of original Trizol

volume) and gently mixed with 100µl isopropanol (50% of original Trizol volume

used) (RNA grade, Sigma), before incubating for 10 minutes at 4oC. Samples were

centrifuged at 9500xg (4oC for 10 minutes). Supernatant was removed and

discarded, and RNA pellet washed with 80% EtOH (DNase Free). Tubes were

centrifuged at 5300xg for 5 minutes at 4oC, ethanol was removed, and RNA pellet

was left to air dry for 5 minutes before being resuspended in 25µl DNase/RNase

free water (Invitrogen). Tubes were incubated at 55oC for 10 minutes to ensure that

RNA was fully dissolved. RNA concentrations were measured by the Nanodrop

1000 Spectrophotometer (Thermo Scientific, Scoresby, BIC, Australia). Purity of

RNA was assessed by the A260/ 280 ratio, which was generally between 1.8 and

2.1. RNA was kept at -80ºC before use.

2.6 Reverse transcriptase polymerase chain reaction (RT-PCR)

For RT-PCR, nuclease free water was added to 2µg of RNA to get the total volume

of 8µl.To ensure there were no DNA contaminants, 2µl (2units) of 1:1 10x DNase I

Buffer and DNaseI amplification grade (Invitrogen) were added to RNA and

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incubated for 15 minutes at RT. The reaction was stopped with the addition of 1µl

of 25mM ETDA (final concentration 2.3mM) and incubation at 65oC for 10

minutes. RT-PCR reaction, 20µl total, was carried out by the addition of 4µl 5x

First Strand reaction buffer (diluted to 1x final concentration), 1µl 0.1M

dithiolthrietol (DTT) (final concentration 5mM), 1µl (40 units) RNaseOUT, 1µl

200ng random hexamers, 1µl 10mM dNTPs (0.5mM final concentration) and 1µl

(200 units) SuperScript III enzyme (all Invitrogen).

RT-PCR reaction was carried out in the PTC-200 Peltier Thermal Cycler DNA

Engine (BioRad, Gladesville, NSW, Australia), with PCR cycling conditions of: 5

minutes at 25oC; 1hr at 50oC; 15 minutes at 70oC; hold at 4oC until storage at -

30oC.

2.7 Quantitative RT-PCR (QRT-PCR)

QRT was done in 384-well plates using the 7900HT Fast Real-Time PCR system

(Applied Biosystems, Melbourne, Australia). Primers were designed by Primer 3

software (Applied Biosystems), unless otherwise noted (Appendix A), and NBCI

BLAST search was used to determine specificity. Reactions were carried out using

SYBR Green PCR mastermix (Applied Biosystems), diluted to 1x in reaction with

0.32µl cDNA, 0.4µM each forward and reverse primer and DNase/ RNase free

water; final reaction volume was 8µl (Invitrogen). The cycling conditions were:

50oC for 2 minutes; 95oC for 10 minutes; 95oC for 15 seconds; 60oC for 1 minute

(40 cycles), followed by dissociation stage. Manual threshold and base line were set

using SDS 2.3-2.4 software (Applied Biosystems). Relative gene expression was

compared to control using ∆∆Ct method (Livak et al. 2001).

2.8 Protein extraction

For cells grown in 10cm plates, cells were scraped and washed in PBS before being

pelleted at 2400xg for 4 minutes. PBS was aspirated and 100µl of RIPA buffer

with protease inhibitor was added (radioimmunoprecipitation assay buffer : 25mM

Tris-HCl pH 7.6, 150mM NaCl, 1% NP-40, 1% sodium deoxycholate, 0.1%

sodium dodecyl sulphate with EDTA-free Protease Inhibitor Cocktail [Roche

Diagnostic Australia Pty Ltd, Castle Hill, NSW, Australia]). Lysates were

incubated on ice for 30 minutes before cell debris was pelleted at 16100xg for 5

minutes. When grown in 6-well plates, cells were washed once with PBS and lysed

in 100µl per well RIPA buffer (with protease inhibitor) on a rocker at 4oC for 20

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minutes. Suspension was collected into Eppendorf tubes and spun to remove cell

debris. Protein concentrations were determined by BCA protein assay (Pierce,

Darra, QLD, Australia). Standard curve (four-parameters curve, 0.032-2µg/µl) was

created using bovine serum albumin (BSA) standards. Protein samples (25µl)

diluted in RIPA buffer was mixed with 200µl of BCA reagent working stock and

incubated for 30 minutes at 37oC. Absorbances were read at 560nm on FLUOstar

Omega (BMG Labtech, Mornington, VIC, Australia).

2.9 Western blotting

Protein samples were diluted in RIPA and 5x loading buffer to 15-30µg/ lane

before incubating at 95oC for 5 minutes. A pre-stained marker was used to

determine molecular weights and transfer success (Fermentas, Thermo Scientific,

Murarrie QLD, Australia). Proteins were separated using SDS-PAGE gels (sodium

dodecyl sulphate - polyacrylamide gels; stacking gel 4% polyacrylamide, separating

gel 8-12% polyacrylamide). Electrophoresis was performed at 100-135V in Protean

Mini Trans-Blot Cell (BioRad) using running buffer (0.0255M Tris pH 8.3, 0.25M

glycine 1% SDS). Proteins were transferred to PVDF-FL membrane (Millipore,

North Ryde, Australia) on the Trans-Blot® SD Semi-Dry Electrophoretic Transfer

Cell (BioRad) with transfer buffer (0.0255M Tris pH8.3, 0.25M glycine, and 20%

methanol). Depending on the number of blots, the transfer went for 30 to 60

minutes at 18V. Membranes were blocked for 1hr in Li-Cor blocking buffer (Li-

Cor Biosciences, Millennium Science, Surrey Hills, VIC, Australia). Primary

antibodies were added in a 1:1 solution of Li-Cor blocking buffer and 0.1% Tween-

20:PBS for the overnight incubation at 4oC. The following morning membranes

were washed and secondary antibody was added for 1hr incubation at room

temperature. Blots were visualized using the Li-Cor Odyssey Imager (Li-Cor

Biosciences). Densitometry and brightness adjustments were done using Odyssey

software (Li-Cor Biosciences). Antibodies used are outlined in Appendix B.

2.10 Total steroid/ lipid/ androgen extraction

For treatments, LNCaP cells were plated on 15cm plates in 5% FBS media. The

following day existing medium was replaced with 5% CSS medium for 48hr

starvation. After the starvation, cells were washed with PBS and brought to basal

levels by 24hr incubation in serum free RPMI. Cells were then treated as described

in Chapters 3 and 7. Cells were washed with PBS, centrifuged and weighed, and

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medium was collected. Two plates of cells were washed with PBS and pooled to

give one sample. Medium was collected and likewise combined.

Steroids were extracted from the cell pellet with an MTBE/ MeOH/ water

extraction as follows:

To the cell pellet, 450µl MeOH (23% final volume) and 10µl 1M NaOH (5nM final

concentration) were added and the cells were vortexed well, before addition of

1500µl methyl tert-butyl ether (MTBE) (76% final volume) and 3µl 10ng/ml d3T

deuterated testosterone standard (0.015ng/ml final concentration). Cell suspension

was then vortexed and rotated for 1hr at RT. In the next step, 380µl of water was

added and the sample was rotated for 10 minutes before being centrifuged.

Subsequently 1200µl of the top layer, which contained steroids and lipids, was

collected and transferred into a new glass tube for drying. To the original tube,

800µl MTBE, 200µl MeOH, and 400µl water was added (4:1:2 ratio), vortexed and

rotated for 1hr before spinning down. From the top steroid layer, 800µl was then

removed and added to the first extract and dried down over night. The residue was

resuspended in 500µl of acetonitrile and vortexed well, and then sonicated for 15

minutes. The sample was centrifuged for 5 minutes and the supernatant was

transferred to a 1.5 ml Eppendorf tube. The steroid sample was dried down again

for 1-2hr. The sample was resuspended in 55µl of 50% MeOH, vortexed well,

sonicated for 15 minutes, and centrifuged for 5 minutes at maximum speed

(16100xg) in a centrifuge.

At this time, 17µl of the 0.2M hydroxylamine-HCl derivatizing agent and 50µl of

steroid sample were combined and added to a tube with glass liquid

chromatography (LC) insert (0.05M hydroxylamine-HCl final concentration). A

control tube was prepared with 50µl of 50% MeOH and 17µl of the derivatizing

agent. Samples were then vortexed, incubated at 65oC for 1hr, then centrifuged.

Steroids were extracted from 3ml of media by the addition of 3ml of water-

equilibrated ethyl acetate (ethanoloacetate) (EtOAc) and 3µl of 10ng/ml d3T

deuterated testosterone standard. The sample was thoroughly vortexed and rotated

for 1hr at RT, before spinning down for 10 minutes. The top steroid/ lipid layer was

removed (2ml) to a fresh glass tube. The second extraction was done by adding the

removed volume of EtOAc, 800µl, back to the original tube. While the sample was

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vortexed and rotated at RT one more hour, the first extraction dried down. The

second steroid extract was then pooled with the first one and dried down over night.

When the sample was dry, 75µl of 50% MeOH was added, vortexed very well, and

sonicated for 10 minutes. The sample was centrifuged at 16100xg for 5 minutes and

50µl of the supernatant, containing steroids, was mixed with17µl of the 0.2M

hydroxylamine-HCl before being transferred to glass LC inserts. Samples were then

vortexed, incubated at 65ºC for 1hr, and centrifuged.

All samples were run on the Waters Acquity Liquid Chromatography system and

the Waters Quattro Premier LC/MS/MS. Steroids were identified using standards of

known retention times. The column used was a 2.1x100mm 1.7um BEH C18

column with a water/ acetonitrile gradient also containing 0.1% formic acid.

Gradient was 0min, 20%; 0.2min, 20%; 8min 80%, 9min, 100%; 10min 100%;

10.2min 20% (% acetonitrile) for a total run time of 12min at 0.3ml/min. The

analytes investigated were d3T, DHEA, androstenedione, 4-pregnan-17ol-3,20-

dione, testosterone, DHT, androsterone, OH-progesterone, pregnenolone,

progesterone, 5-pregnan-3,20-dione with RTs of 4.9, 4.55, 4.8, 4.8, 4.9, 5.1, 5.75,

5.8, 6.15, 6.1, 6.5min respectively.

The results were analysed using Quanlynx Software (Waters Corp, USA). Readings

were adjusted using cell pellet weight, d3T extraction efficiency, and normalized to

vehicle-treated samples.

2.11 Estradiol extraction

MCF7 cells were maintained in DMEM media with 10% FBS supplementation. For

insulin treatment cells were plated on 15cm plates in 10% FBS DMEM for 1 day,

before 48hr starvation in 5% CSS supplemented DMEM. Cells were washed once

with PBS and incubated in serum free DMEM, to bring them to basal hormone and

growth factor levels. Cells were then treated with 10nM insulin for 48hr (insulin

was refreshed after 24hr). Following the treatment, medium was collected, and the

cells were harvested. For collection, cells were washed with PBS, scraped in PBS,

centrifuged, and the cell pellet was weighed.

Steroids were extracted from the cell pellet with a 70/30 hexane/ EtOAc solution

and extraction efficiency was controlled with deuterated androstanediol. To the

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pellet, 3ml of hexane/ EtOAc and 1ml of distilled water were added. The cell pellet

was vortexed and rotated at RT for 1hr. The sample was then centrifuged to

separate the organic and inorganic phases. The organic layer was removed to a new

vial. For a second extraction, an equal volume of hexane/ EtOAc was added back to

the sample vial. After the second separation the steroid samples were pooled and

dried down in a Centrivap (Labconco, Kansas City, Missouri, USA) overnight,

16100xg at RT.

The same protocol was used to extract steroids from the cell media. The volume of

the starting sample was 3ml of medium, which were added to 3ml 70/30 hexane/

EtOAc.

Once the steroids extracted from cells and media dried, 500µl of 2-fluoro-1-methyl

pyridinium qp-toluenesulfonate (FMP) was added and samples were rotated at RT

for 1hr. To stop the reaction, 50µl of 100% MeOH was added. Samples were

incubated for 15 minutes at RT and dried down overnight. Samples were then

resuspended in 50µl of 50% MeOH and run on the Waters Acquity Liquid

Chromatography system and the Waters Quattro Premier LC/MS/MS. Steroids

were identified using standards of known retention times and analysed using

Quanlynx Software (Waters Corp, USA). The column used was a 2.1x100mm

1.7um BEH C18 column with a water/ acetonitrile gradient also containing 0.1%

formic acid, and the gradient was 0min, 10%; 0.5min, 10%; 7min, 30%; 13min,

35%; 13.5min 95%; 16min, 95%; 16.1min 10% for a total run time of 18min. RT's

for d3- androstanediol and estradiol were 7.9 and 6.2min. Readings were adjusted

using cell pellet weight and deuterated standard, and normalized to vehicle-treated

samples.

2.12 14C- Steroid extraction

Cells were treated in 6-well plates as described in chapters 3-7. De novo

steroidogenesis was then analysed by extraction of 14C- labelled steroids from 2ml

of medium. Cell pellets were weighed for normalization. A volume of 2ml of media

was added to 2ml of 75/25 hexane/ EtOAc and 10µl 1M HCL. This solution was

then vortexed well before 1hr rotation at RT. Samples were then centrifuged and

the top steroid-containing layer (1.3-1.5ml) was transferred to a fresh 1.5ml

Eppendorf. The removed volume of hexane/ EtOAc was then added back to the

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original vial for a second extraction, vortexed and rotated at RT for 1hr while the

first extraction dried down. The second extraction was then centrifuged and the top

layer was removed and pooled with the first extract. These samples were then dried

down for at least 2hr or overnight. The samples were then resuspended in 75µl of

50%MeOH, vortexed very well, sonicated for 10 minutes, and centrifuged at

16100xg for 5 minutes. Extracts were then transferred to glass LC inserts.

These samples were analysed on the Waters Alliance 2695 HPLC System and

Packard Radiometric Detector 150TR Flow Scintillation Analyzers. Peaks were

identified by comparison of retention times to Mix 10 steroid standard (Sigma).

2.13 Oil Red-O lipid extraction/ quantitation

Cells were treated as described in chapters 5 and 6 in 6-well plates. Serum free

medium was supplemented with 36µM sodium acetate to provide the cells with

lipid precursors. Cells were grown in treatment conditions for 48hr before medium

was aspirated. Cells were fixed with 5% paraformaldehyde at 4ºC for a least 1hr.

Following the fixation, cells were incubated with 700µl Oil Red-O solution

(5.4mM, 60% isopropanol, Sigma) for at least 20min before washing three times

with distilled water. Excess water was aspirated and stained lipid was released by

15 minute incubation in 300µl isopropanol. The absorbance was read at 490nm on

FLUOstar Omega (BMG Labtech). A background plate was stained in parallel to

subtract the nonspecific staining (Whitehead et al. 2004).

2.14 Microarray gene expression profiling

To examine expression profiles following insulin/ R1881 treatments, the LNCaP

model was utilized. For treatments, LNCaP cells were plated on 10cm tissue culture

plates in 5% FBS phenol-red free RPMI and left overnight to attach. The following

day cells were transferred to 5% CSS supplemented RPMI for 48hr. Following the

starvation, LNCaP cells were treated for 24hr with either 1nM R1881 (Sigma) in

CSS RPMI or equivolume 70% EtOH (vehicle). Cells were washed once with PBS

and, to bring them to basal growth factor levels, incubated in either serum free

RPMI with R1881or serum free RPMI EtOH for 24hr. Insulin, 10nM, (Sigma) or

vehicle control was then added to the plates and cells were incubated for 10hr

before Trizol extraction of RNA (chapter 2.5). All treatments were done in

triplicate.

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The microarray used was a custom-designed array from the Vancouver Prostate

Centre (VPC) - VPC v1 2x 105 ID032034 (Design ID 020026)

Melanie Lehman of the Vancouver General Hospital Prostate Centre has combined

information from both in-house and publicly available microarray and sequencing

data to design an Agilent custom 105K microarray (Agilent Technologies,

Wilmington, DE, USA). This microarray incorporates Agilent human gene

expression protein-coding probes and the non-coding probes designed by Dr.

Marcel Dinger and Dr. John Mattick. All probes on the microarray were then

classified based on their relation to the RNAs in the NCBI RefSeq

(http://www.ncbi.nlm.nih.gov/RefSeq/). This custom microarray is a cost-effective

method to rapidly measure the levels of protein and non-protein-coding RNAs

under a large number of conditions and treatments. This approach integrates the

information garnered from high-throughput sequencing with the convenience of

microarray expression profiling. Our goal in designing this microarray was to

identify prostate-expressed RNAs that have a regulatory role in prostate cancer

progression and may therefore be disease-specific targets for prostate cancer

treatment.

Microarray was done by Nadine Tomlinson at the Prostate Centre at Vancouver

General Hospital (Vancouver, Canada). Array analysis was done by Melanie

Lehman at the Prostate Centre at Vancouver General Hospital (Vancouver,

Canada).

The total RNA quality was assessed with the Agilent RNA 6000 Nano Kit (Agilent)

on an Agilent 2100 bioanalyzer. Samples with a RIN value (RNA integrity value)

equal to or greater than 8.0 were deemed to be acceptable for the microarray

analysis.

One-colour microarray-based gene expression analysis was performed following

Agilent's Quick Amp Labelling Kit (Agilent), Five hundred nanograms of total

RNA was reverse-transcribed with the addition of an oligo dT-T7 promoter primer

and the Affinity Script enzyme. The labelling procedure was performed using the

T7 RNA polymerase and cyanine 3-CTP dye. The reaction yields Cy-3 labelled

cRNA which was purified with Qiagen’s RNEasy mini spin columns (Qiagen,

Toronto, Ontario) and quantified using the Nanodrop-1000 (Thermo Scientific,

81

Wilmington, DE, USA). Samples were loaded onto the Agilent Custom Human

2x105K Gene Expression Microarrays (Design ID 020026) and allowed to

hybridize at 65°C for 17hr.

Microarrays were scanned with the Agilent High resolution Microarray Scanner,

and data was processed with the Agilent Feature Extraction 10.5.1 software

(Agilent). Data was normalized using quantile normalization method found in

Linear Models for Microarray Data (LIMMA) (open source software), an R

programming language package (Smyth 2005). The gene expression presented as

log10 was compared between 2 groups with t-test. Genes that were significantly

different between two groups were identified with p value ≤ 0.05, and average fold

changes ≥ 1.5.

Cells treated for microarray analysis were supplied by Amy Anne Lubik, the

microarray process was executed by Nadine Tomlinson at the Prostate Centre at

Vancouver General Hospital (Vancouver, Canada), and array analysis and input

into IPA was done by Melanie Lehman at the Prostate Centre at Vancouver General

Hospital (Vancouver, Canada). Analysis on IPA pathways was done by Amy Anne

Lubik.

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83

Chapter 3: Insulin Directly Increases de novo

Steroidogenesis in Prostate Cancer Cells

84

85

3.1 Introduction

Organ confined prostate cancer can be treated with radical prostatectomy or

radiation therapy; however, 25-40% of patients will experience biochemical

recurrence with a rise in serum levels of prostate specific antigen (PSA), used as a

marker of tumour growth, and continue to progress with metastatic disease (Rashid

et al. 2004). The most common therapy for progression is androgen deprivation

therapy (ADT) usually administered by LHRH agonists or antagonists which

disrupt the feedback loop within the hypothalamic gonadal axis to suppress

testosterone production from the testes. Initially, inhibition of testicular androgen

production causes the tumour cells to undergo apoptosis. However, this remission is

temporary and typically within 24 months patients recur with a rising PSA despite

castrate androgen levels in the serum. This is termed castrate resistant prostate

cancer (CRPC) and is currently viewed as incurable (Rashid et al. 2004; Scher

2005; So et al. 2005; Stanbrough et al. 2006). The underlying mechanisms for

progression to CRPC are complex; however, we have recently shown that one

major mechanism is that in the face of ADT, prostate tumours are capable of

synthesizing androgens de novo, reactivating androgen driven processes and

thereby promoting tumour growth (Locke et al. 2008). This de novo biosynthesis

appears to be further upregulated by androgens indicating a feed forward loop of

steroidogenesis. The key question remains as to what initiates and augments

steroidogenesis in prostate cancer following castration.

While ADT provides control of prostate cancer growth, the systemic non-cancer

effects include the induction of the metabolic syndrome with a key consistent

feature of hyperinsulinemia (Smith et al. 2006; Faris et al. 2010). We have

hypothesized that these high circulating levels of serum insulin may act directly on

prostate cancer cells and enhance steroidogenic pathways. Herein we demonstrate

that insulin treatment on prostate cancer cells increases the expression at both the

mRNA and protein levels of several key enzymes involved in de novo

steroidogenesis including cytochrome p450 family members (CYP)11A1 and

CYP17A1, RDH5 and hydroxysteroid dehydrogenase (HSD)3B2 in LNCaP, VCaP

and 22RV1 prostate cancer cell lines. Furthermore, in LNCaP cells, insulin

promotes the translocation of steroidogenic acute regulatory protein (StAR) to the

mitochondria, a rate-limiting step in steroidogenesis. We show that synthesis of

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steroid hormones including testosterone and DHT are increased in prostate cancer

cells with insulin treatment to levels sufficient enough to activate PSA production.

In vivo studies of LNCaP xenografts in mice show increased expression of insulin

receptor and insulin receptor substrate in prostate cells are associated with

progression to castrate resistance. We conclude from this study that insulin acts

directly on prostate cancer cells to increase de novo androgen synthesis in CRPC.

3.2 Materials and Methods

3.2.1 In vitro model: LNCaP cells (passage 36-48; American Type Culture

Collection) were cultured in phenol red-free RPMI 1640 (Invitrogen, Melbourne,

Australia) and 5% fetal calf serum (FBS; Hyclone, Sigma, Australia). 22RV1 cells

were cultured in phenol red-free RPMI with 10% FBS and VCaP cells were

cultured in DMEM (Invitrogen) containing 10% FBS. For modelling of androgen

deprivation, cells were cultured in charcoal-stripped serum (CSS; Hyclone) as

follows: cells were plated in FBS and at 60% confluence and changed to 5% CSS

media for 24hr, followed by 24hr in serum-free medium. Prostate cancer cells were

treated with 10nM insulin (Sigma) for various times (5, 10, 16, 24, and 48 hours).

Insulin and 10nM DHT (Sigma) was refreshed if treatment exceeded 24hr. For

DHT concentration course, cells were treated with concentrations ranging from

0.01pM to 10nM for 24hr and PSA mRNA expression was measured by QRT-PCR

to construct a linear regression curve to compare to PSA induction by insulin

expression (24hr). For bicalutamide treatment, cells were incubated in the presence

and absence of 25µM bicalutamide or vehicle control for 2hr prior to addition of

insulin or DHT.

3.2.2 QRT-PCR: QRT-PCR was carried out as follows: RNA was extracted from

prostate cancer cells using TriReagent (Applied Biosystems, Melbourne, Australia)

before reverse transcription with superscript III reverse transcriptase (Invitrogen,

Melbourne, Australia) as described in chapter 2.5-2.7. Subsequent QRT-PCR using

Applied Biosystems 7900HT Fast Real Time PCR System used SYBR Green

detection (Applied Biosystems). Primers were designed by Primer 3 software from

coding segments of genes, obtained from the NCBI data bank and ordered from

Sigma Proligo (Castle Hill, NSW, Australia). Primers used were: IRS2, SREBP,

StAR, CYP11A1, CYP17A1, HSD3B, AKR1C3, HSD17B3, RDH5, SRD5A1, and

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rpl32. For Primer sequences, see Appendix A. Gene expression was normalized to

the housekeeping gene rpl32, then expressed relative to the vehicle control at the

same time point. All reactions were run as described in chapter 2.7. Data was

analyzed with SDS 2.3 software by means of the ∆∆Ct method (Livak et al. 2001).

Experiments were repeated a minimum of 5 times.

3.2.3 Western blotting: Protein extraction and western blotting was carried out as

described in chapter 2.8-2.9: cells were lysed in RIPA buffer. SDS-polyacrylamide

gel electrophoresis was used to separate proteins (20ug/ lane), before transfer to

PVDF-FL membrane (Millipore, North Ryde, Australia). Membranes were blocked

for 1hr in Li-Cor blocking buffer (Li-Cor Biosciences, Lincoln, USA) and

antibodies were added to the blots in a 1:1 solution of Li-Cor blocking buffer and

0.1% Tween-20-PBS and incubated overnight at 4°C, before washing and

application of secondary antibody for 1hr at room temperature. Blots were

visualized using the Li-Cor Odyssey Imager (Li-Cor Biosciences, Lincoln, USA).

Experiments were repeated at least 3 times. Antibodies used were SREBP (Santa

Cruz), StAR (kind gift from Dr. B Hales, University of Chicago), CYP11A1

(Abcam), CYP17A1 (kind gift from Dr. B Hales, University of Chicago), HSD3B

(Santa Cruz), AKR1C3 (Abcam), HSD17B3 (Abnova), RDH5 (Abnova), SRD5A

(Novus) and GAPDH (Abcam) (loading control). For detailed antibody

information, please see Appendix B.

3.2.4 Mitochondrial fractionation assay: Mitosciences cell fractionation kit was

used for mitochondrial fractionation of LNCaP cells (Sapphire Biosciences,

Waterloo, NSW, Australia). GAPDH was used as a control for mitochondrial

isolation.

3.2.5 Steroid quantitation (total steroids) by LC/MS/MS: Steroid analysis was

performed as previously described (Locke et al. 2008), chapter 2.10. Briefly, cells

were grown in 15cm plates and treated with 10nM insulin as described above. Two

plates of cells were washed with PBS and pooled to give one sample. Medium was

collected and likewise combined. Steroids were extracted from the pellet with an

MTBE/ Methanol/ Water extraction, which was dried down and resuspended in

acetonitrile, sonicated, dried down and resuspended in 50% methanol, then

sonicated and spun to remove any particulates. Samples were derivatized in 0.2M

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hydroxylamine HCL. Water equilibrated ethyl acetate was used instead of MTBE/

Methanol/ Water for medium extractions. All samples were run on the Waters

Acquity Liquid Chromatography system and the Waters Quattro Premier

LC/MS/MS, identified using standards of known retention times (figure 3.1) and

analyzed using Quanlynx Software (Waters Corp, USA). Readings were adjusted

using cell pellet weight and normalized to vehicle-treated samples.

3.2.6 De novo steroid analysis using radiometric detection: LNCaP and VCaP

cells were grown in 6 well plates and treated as described above. At the time of

insulin treatment, 6µCi/ml 14C-acetate (PerkinElmer) was added to each plate for

co-incubation. Steroids from 2ml of media were extracted with 75/25 hexane/

ethanoloacetate (water-equilibrated ethyl acetate), dried down and resuspended into

75ul methanol (50%). These samples were analyzed on the Waters Alliance 2695

HPLC System and Packard Radiometric Detector 150TR Flow Scintillation

Analyzers. Peaks were identified by comparison of retention times to Mix 10

steroid standard (Sigma). Detailed method can be found in chapter 2.12.

3.2.7 Steroid analysis using DHT ELISA: DHT secreted into the medium by

22RV1 cells was evaluated using a DHT ELISA (BioCore Pty Limited) kit

according to the manufacturer’s instructions.

3.2.8 PSA analysis of LNCaP media: PSA levels of LNCaP cells treated with

insulin or dihydrotestosterone (DHT) were evaluated using ClinPro PSA kit as per

kit instructions (ClinPro International, USA).

3.2.9 In vivo model: LNCaP tumour progression to castrate resistance. All animal

experimentation was conducted in accordance with accepted standards of the

University of British Columbia Committee on Animal Care, as described previously

(Locke et al. 2007). Briefly, LNCaP xenograft tumours were grown in male

athymic nude mice at 4 subcutaneous sites. PSA levels were measured weekly from

tail vein serum samples. Six weeks following inoculation of tumours, mice were

castrated. Tumours were harvested from the same mouse before castration (preCx),

8 days after castration corresponding to a PSA nadir (N) and 28 days after

castration corresponding to CRPC (CR). Tumours were removed and homogenized

in Trizol (Invitrogen) and RNA was isolated for cDNA synthesis as described

above.

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3.2.10 Statistics: All statistical analysis was done using the two-tailed student’s

t-test assuming equal variance on Graphpad Prism 5 software. P-value of 0.05

or lower was considered significant.

3.3 Results

3.3.1 Insulin upregulates expression of enzymes necessary for steroidogenesis at

the mRNA and protein levels

A number of interlinked pathways can lead to the production of testosterone and

dihydrotestosterone from intracellular cholesterol synthesis (figure 3.1). In order to

investigate the direct effect of insulin on steroidogenesis within prostate cancer

Figure 3.1: Steroidogenesis pathway adapted from Locke et al. indicating enzymes

involved in both classical (left/ centre) and backdoor (far right, bold font) pathways

of DHT production. Many of the steroidogenic enzymes catalyze more than one

step in the pathway. Underlined steroids had standards available for analytical use

(Locke et al. 2008).

90

cells, QRT-PCR analysis was performed to determine the changes in expression of

key genes within this pathway following chronic exposure of insulin. Analysis of

LNCaP cells treated with 10nM insulin for 5, 10 and 16hrs showed enzymes

required for synthesis of androgens and other steroids were upregulated at the

mRNA level in the presence of insulin after 10hrs (figure 3.2A). A parallel and

significant 3.5-fold increase in IRS-2 (p<0.05) suggests increased signalling via the

insulin receptor (Takamoto et al. 2008). At this time point, Sterol regulatory

element binding protein (SREBP1) was also increased at the mRNA level in the

presence of insulin (p<0.05). SREBP transcription factors are responsible for co-

ordinately regulating the enzymes required for synthesis of cholesterol, its

importation into the mitochondria, and steroidogenesis (Hales 1992; Arakane et al.

1998; Shea-Eaton et al. 2001; Ozbay et al. 2006). In the presence of insulin, mRNA

levels of StAR, which chaperones cholesterol into the mitochondria, increased by

2.25-fold, and mRNA of CYP11A1, the rate limiting enzyme which commits

cholesterol to steroid synthesis, is upregulated 2-fold. Many of the steroidogenic

enzymes catalyze more than one step in the pathway to DHT synthesis. These

include the monoxygenase CYP17A1, oxidoreducatase / dehydrogenases, HSD3B2,

HSD17B3 and 5α reductase (SRD5A1). CYP17A1 was significantly increased 3.7-

fold (p<0.05), HSD3B2 also showed a significant 1.5-fold increase with insulin

(p<0.05), and HSD17B3 was upregulated at the mRNA level approximately 7.5

fold (p=0.11), while the aldo-ketoreductase (AKR)1C3 remained unchanged (data

not shown). The enzyme responsible for the conversion of testosterone to DHT,

SRD5A1, showed a significant 30% increase from base level. In the ‘backdoor’

pathway of steroidogenesis (Auchus 2004), 11-cis retinol dehydrogenase (RDH5)

converts androstenediol directly to DHT and was two-fold upregulated (p<0.05) at

the mRNA level with 10nM insulin.

These studies were extended into other prostate cancer cell lines with functioning

androgen receptors, VCaP and 22RV1s, at the single time point of 48 hours to

assess accumulated steroids synthesized de novo. Similarly to LNCaP cells,

increased mRNA in VCaP cells (figure 3.2B) was observed for CYP11A1 (3-fold),

CYP17A1 (1.5-fold), HSD17B3 (2.5-fold), and RDH5 (1.5-fold) (p<0.05),

following 48-hour insulin treatment. SREBP and StAR were expressed but were not

significantly different to control cells (figure 3.2B). In 22RV1 cells, similar

91

increases to LNCaP cells were demonstrated at the level mRNA with most enzymes

(figure 3.2C). SREBP and StAR were upregulated 2-fold (p<0.05), while

CYP11A1 was increased ~3-fold. CYP17A1 was upregulated 5-fold, while

HSD3B2 and HSD17B3 increased 2.3 and 2-fold, respectively (p<0.05). RDH5

increased 2.6 fold (p<0.05) (figure 3.2C). In contrast, IRS2 was expressed but not

significantly upregulated in VCaP or 22RV1 cells by insulin, under these

conditions. It should be noted that thought IRS2 mRNA is increased in LNCaP

cells, the phosphorylation of the receptor would be a more accurate measure of

activation, and may be occurring in VCaP cells and 22RV1 cells.

The three cell lines tested are all androgen responsive to differing degrees and

derived from prostate cancer patient metastases. However their response to

differing stimuli is variable and likely underpinned by differences in genomics and

other molecular characteristics of the lesions (Mayer et al. 2008). While the

magnitude and potential temporal upregulation of steroidogenesis enzyme mRNA

in these cells may be variably coordinated, posttranslational modifications and

subcellular localization also influence activity. Importantly, all enzymes necessary

for steroidogenesis are expressed in various CaP cell lines.

We have previously shown that prostate cancer cells can use alternative

steroidogenic pathways in a compensatory manner to synthesize DHT through

either the classical pathway, ‘backdoor’ pathway or a combination of the two

(Kohn et al. 2007). Furthermore, many of the enzymes in the steroidogenesis

pathway can function bidirectionally, allowing even greater opportunity for

alternative pathways of steroid synthesis (Miller 2007).

Protein levels of steroidogenic enzymes, as measured by western blot, were shown

to increase in LNCaP cells following insulin treatment (figure 3.2D). Specifically,

the protein levels of CYP11A1 and CYP17A1 were significantly increased with

insulin treatment by 2.5 and 2-fold respectively (p<0.05). Levels of HSD3B2

protein were also increased 3.6-fold; however, this did not reach statistical

significance. SRD5A, on the other hand, while more modestly increased following

10 hour insulin treatment at the mRNA level, increases greater than 3-fold at the

protein level (p<0.05), and expression remains elevated to a lesser extent (1.5 and 2

fold at 10 and 16hrs, respectively - data not shown). RDH5 protein was only

92

upregulated by insulin at the 10hr time point (p<0.05) and HSD17B3 was not

significantly increased until 16hrs insulin treatment (p<0.05), with steady

expression at 5 and 10hrs (not shown). AKRIC3 protein levels were unchanged,

however AKR1C3 is the predominant aldo-ketoreductase in prostate epithelial cells

and it is possible that the activity of the enzyme is regulated post-translationally,

without exceptional change to existing protein levels. It is important to note that

AKR1C3 is expressed in all cell lines investigated. In 22RV1 cells, the profile of

upregulated enzymes was similar to LNCaPs (figure 3.2E) with significant

increases in SREBP, CYP11A1 and CYP17A1 as well as HSD17B3 (p<0.05).

While our data suggests that insulin increases StAR expression, biologically the

more important effect is altered subcellular localization of StAR. Using a

mitochondrial separation assay, our data demonstrates a 1.7-fold increase in StAR

translocation into the mitochondria following 16hr treatment with insulin in LNCaP

cells (p<0.05; figure 3.2F).

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(Figure 3.2 see page 95)

94

(Figure 3.2 see page 95)

95


mRNA and protein level. RNA was extracted from CaP cells and used for QRT-

PCR analysis of steroidogenesis enzymes and related factors. Results are analyzed

by ΔΔCt method and normalized to RPL32 housekeeping gene, then normalized to

vehicle-treated (no insulin) for the equivalent time point. Error bars represent SE

(*p<0.05). Insulin (10nM) induced increased mRNA expression in (A) LNCaP

cells following 10 hour treatment, (B) VCaP cells following 48-hour insulin

treatment, (C) 22RV1 cells following 48-hour insulin treatment. Western blot

analysis of protein levels showed increased levels of steroidogenesis enzymes with

10nM insulin treatment in (D) LNCaP cells following 10nM insulin treatment

(5hrs), RDH5 at 10hrs (white*), or HSD17B3 at 16hrs (double white *) and (E)

22RV1 cells following 48 hour treatment. (F) Increased translocation of StAR

protein to the mitochondria, a rate limiting step in steroidogenesis occurred with 16

hours insulin treatment (10nM) compared to vehicle treated cells. Quantitation of

western blots was performed using Odyssey software, version 1.2 and normalized

to GAPDH loading control. Error bars represent SE (*p<0.05) n≥3.

96

3.3.2 Insulin increases intracellular steroids in prostate cancer cells

As shown above, all the necessary enzymes for steroidogenesis are present in

LNCaP, VCaP and 22RV1 cell lines and the levels of many increase in response to

insulin. To determine whether there is a parallel increase in steroid synthesis, we

first performed HPLC-MS on LNCaP cell extracts following 16hrs insulin

treatment to measure total steroid content of the cell. Insulin dramatically increased

intracellular LNCaP steroid levels (figure 3.3A). Insulin treatment (16 hours)

increased levels of the first steroid converted from cholesterol in the pathway,

pregnenolone, 2.5 fold (figure 3.3A; p<0.05), and we observed a 15-fold increase in

total intracellular 17-OH-progesterone levels (figure 3.3A; p<0.05), which is

converted from progesterone by CYP17A1, an enzyme shown in figure 3.2 to be

significantly increased with insulin. CYP17A1 also catalyses the final reaction in

the synthesis of DHEA, which was substantially increased, 18-fold, by insulin

(figure 3.3A; p<0.05). Notably, testosterone levels were also increased

approximately 60-fold in LNCaP cells following treatment with insulin (p<0.05).

Intracellular levels of testosterone were calculated to increase from approximately

0.011 to 0.65ng/g cells when treated with 10nM insulin for 16hrs (calculations and

spectra are shown in figure 3.4A,C). These levels are consistent with the

testosterone levels of our previous findings (Locke et al. 2008). Previously,

Gregory et al. have demonstrated DHT concentrations as low as 1x10−14 mol/L

(2.92 × 10−6 ng/g) to transactivate AR in prostate cancer cell lines (Gregory et al.

2001a). Titus et al. (2005b) report 1.25 pmol/g tissue (0.498ng/g) of DHT in

recurrent prostate cancer tissue specimens, and 0.4ng/g tissue was found in clinical

samples of prostate epithelium by Liu et al. (1985). Mosteghel found that DHT

levels in castrate patients are 0.2 to 1.78 ng/g (Drejer 1992); therefore, the

androgen concentrations detected in our study and increased by insulin treatment in

LNCaP cells were consistent with levels needed for activation of the AR.

3.3.3 Insulin increases secretion of steroids from prostate cancer cells

As steroidogenic cells differentially secrete specific steroids, we measured steroid

levels in the medium of prostate cancer cells cultured in serum-free medium versus

medium supplemented with 10nM insulin for 16 hours. HPLC-MS analysis of

steroids in LNCaP medium (figure 3.3B) was consistent with our intracellular

97

steroid data. As expected, it was the steroids in the latter part of the steroidogenic

pathway that appeared in the medium. Of greatest significance 17-OH progesterone

was 2-fold increased; p<0.05, testosterone 1.3-fold and DHT, 1.5-fold increased;

p<0.05, and androstenedione was increased 3-fold; p<0.05.

Importantly, these data indicate that insulin-stimulated intracellular steroidogenesis

by prostate cancer cells could provide steroids, including androgens, to the tumour

microenvironment. The concentrations of DHT and testosterone secreted into the

medium by LNCaP cells after 16 hours of insulin treatment in our studies were

calculated to be approximately 0.0249 and 0.037nM (calculations and spectra are

shown in figure 3.4B,C), with the baseline levels consistent with our previous

studies (Locke et al. 2008) and within the range necessary to activate AR (Gregory

et al. 2001a; Titus et al. 2005b).

To investigate de novo steroidogenesis, alongside steady state levels, LNCaP and

VCaP cells were treated with 14C labelled acetate in the presence and absence of

insulin for 72hrs before radiometric analysis of cell culture medium. Increased

steroids were persisting in samples treated with insulin compared to vehicle control,

consistent with steady-state data from LNCaP cells. In LNCaPs (figure 3.3C), we

demonstrated significant increases in testosterone (2-fold, p<0.05), androstenedione

(1.5-fold, p<0.05), and androsterone (2-fold), p<0.05), and pregnan-3,20-dione

(1.7-fold, p<0.05), as well as several other peaks within the steroid region which

did not correspond to Mix 10 standards (Spectrum shown in figure 3.5). Included in

these peaks was a peak with a retention time between that of progesterone and

pregnan-3,20-dione, 34 min, which was significantly increased (2.4-fold, p<0.05).

Steroid peaks were also significantly increased in VCaP extracts following insulin

treatment as well as a 4-fold increase in cholesterol synthesis (figure 3.3D).

Furthermore, steroids beyond androstenedione and androsterone in the pathways

were not detected in VCaP cells (spectra shown in figure 3.5) suggesting rates of

steroid synthesis differ between the cell lines. In 22RV1 cells, DHT secretion into

the medium was significantly increased from 0.23nM to 0.36nM (figure 3.3E),

following 48-hour treatment with 10nM insulin. This is comparable to DHT

secreted by LNCaP cells (calculations in figure 3.4) and sufficient to activate the

AR (Gregory et al. 2001a; Titus et al. 2005b).

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99

Figure 3.3: Insulin treatment increases steroid production in prostate cancer cells.

LNCaP cells were treated with 10nM insulin for 16 hours and steroids extracted.

LC-MS-MS was used to identify and quantitate (A) intracellular steroids showing a

significant increase in pregnanolone, 17-OH progesterone (17-OH P), DHEA and

testosterone (T) (*p<0.05). (B) Medium was collected and also analyzed to identify

and quantitate extracellular steroids. Statistically significantly increased steroid

levels were also seen in the medium for androstendione (Andr), 17-OH

progesterone (17-OH P), DHT and testosterone (T). Pregnanolone (Preg) was

increased, but did not reach significance. Steroid levels were adjusted to cell pellet

weight and recovery of deuterated testosterone used to calculate extraction

efficiency. Results were compared to the vehicle control. Error bars represent SE

(*p<0.05). De novo steroid synthesis was measured by incubating cells with

6µCi/ml radiolabelled acetate. 14C-labelled steroids were extracted from cell culture

medium after 72hr incubation with insulin (to allow accumulation to quantitative

levels) and HPLC and radiometric detection used to identify and quantitate

extracellular steroids from (C) LNCaP cells. Increased magnitude of persisting

steroid peaks was measured in insulin treated versus control samples with

significant increases in testosterone, androstenedione, androsterone and pregnan-

3,20-dione as well as a peak at 34 minutes which falls within the steroid range but

with no corresponding retention time amongst the standards. (D) In VCaPs,

androstendione, the step before testosterone in the classical steroid pathway,

increased approximately 3-fold (p<0.05); whereas, androsterone, a steroid of the

backdoor pathway, increased approximately 2-fold (p<0.05). The 34min steroid

peak which elutes at a time between progesterone and pregnan-3,20-dione, and

pregan-3,30-dione steroid increased 1.75 and 1.5-fold, respectively (p<0.05).

Cholesterol was detected in VCaP cells and furthermore, was increased

approximately 4-fold by insulin treatment. Intriguingly, steroids beyond

androstenedione and androsterone in the pathways were below the limit of

detection in VCaP cells at 72hrs. (E) DHT was measured by ELISA in medium

collected from 22RV1 cells after 48hr incubation with insulin and compared to

vehicle control. Statistically significantly increased DHT levels were demonstrated.

Error bars represent SE (*p<0.05) n≥3.

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101

Figure 3.4: Calculations and steroid spectra for insulin induction of

steroidogenesis. (A) Table of values from a representative LC/MS/MS spectrum

shown in ng/ml isolated from approximately 0.060g of LNCaP cells. Cellular

steroids were extracted by MTBE extraction into 95µl 0.2M hydroxylamine HCl,

and concentrations of that volume were given by the LC/MS/MS. The amount for

the whole sample (ng) was calculated, and then divided by approximately 0.06g to

give ng/g cell pellet. (B) For medium concentrations, 3mL of medium were

extracted into 95µl with MTBE. The total grams of steroid was calculated then

converted to moles and divided by the original volume to get the original

concentration of steroids in the medium. The concentrations of T (cells), and T and

DHT (medium) have been included in the paper. (C) This method measures

unlabelled steady-state steroid concentrations (in contrast to de novo synthesised

steroids measured in figure 3.3D and 3.3E), therefore a spectrum of peaks has been

provided. Spectrum from vehicle control appear in the top panel and insulin treated

in the bottom panel.

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Figure 3.5: Representative spectra of steroids isolated from 2mL of medium

following treatment with 14C acetate for 72hours in the +10nM insulin of VCaP

(A,B) or LNCaP cells (C, D). For VCaP cells, the control (A) was compared to

10nM insulin treated (B) to calculate fold change. The same was done for LNCaPs,

vehicle-treated control cells (C) being used to normalise insulin induced peaks (D).

Steroid retention times were comparable in both LNCaP and VCaP cells and are

identified from 1 to 7, with testosterone the first to elute from the column and

cholesterol the last. De novo synthesised testosterone was detected in LNCaPs. The

total amount of radiolabelled steroids after 72 hours insulin treatment in the

presence of 14C acetate was substantially lower in VCaPs. Peaks were also present

in the steroid region which could not be definitively identified (Peak 5; 34 mins) as

there was no corresponding peak in the Mix 10 standards.

103

3.3.4 PSA expression and secretion are increased by insulin

Serum PSA is the biomarker for CaP recurrence and for progression to CRPC

following ADT. We and others have shown that a key mechanism underlying

CRPC progression is the activation of androgen driven pathways through the

androgen receptor (AR) (Huang et al. 1999; Mostaghel et al. 2007); therefore, we

have used PSA as a functional surrogate of AR reactivation via increased androgen

production and measured the effect of insulin on PSA production from LNCaP

cells. To directly compare the level of insulin stimulation of PSA with DHT,

LNCaP cells were exposed to 10nM insulin or 10nM DHT for 16, 24, or 48 hours.

As shown in figure 3.6A, 24hr insulin treatment induced a 10-fold increase in PSA

mRNA, compared to a 20-fold increase by DHT, whereas the mRNA levels

decreased by 48hrs, likely due to the metabolism of these hormones. In contrast,

the non-metabolizable and more potent androgen R1881 continued to increase PSA

mRNA levels over this time course (figure 3.6B). To equate levels of response

between 10nM insulin and DHT, we performed a DHT titration; at 24 hours, as

shown in figure 3.4C, PSA induction by 10nM insulin was equivalent to the level

of induction seen with treatment of approximately 0.16(+/-0.29)nM DHT, as

calculated by linear regression. Mean concentration of PSA with vehicle did not

change over time (data not shown). PSA secreted into the medium was increased

due to insulin treatment after 16hrs (figure 3.6C), which suggests a lag in PSA

response which is both consistent with requisite steroid production and the lag in

PSA response profile seen directly with androgen treatment in figure 3.6A. These

data clearly show that PSA was increased at 16 hours and significantly accumulated

in the medium by 48hrs to 1.8 fold of baseline following insulin exposure (p<0.05).

This is supported by further data showing intracellular androgen levels induced by

insulin are sustained to 48hrs (figure 3.7). Induction of PSA expression following

24-hour insulin treatment also occurred in VCaP cells (figure 3.6E), with an

increase of approximately 40% from baseline (p<0.05). Furthermore, insulin

induced a 2-fold increase in PSA mRNA in 22RV1 cells at 48hr (figure 3.6F).

Treatment with the AR antagonist, bicalutamide attenuated the insulin induced

increase in PSA expression in LNCaP and 22RV1 cells (figure 3.6G-H) directly

implicating insulin activation is mediated by the AR.

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105

Figure 3.6: Insulin treatment increases expression of PSA. (A) Insulin-induced

changes in PSA mRNA expression from LNCaP cells were compared to 10nM

DHT at 16, 24 and 48hrs by QRT-PCR, ΔΔCt method as described, normalized to

rpl32 control gene. Changes in PSA expression by insulin are not detected until the

24 hour time point. The effects of DHT are reduced after 48 hours of culture. In

contrast (B), increased expression of PSA at 2 and 24 hours in LNCaP cells is

maintained at 48 hours by non-metabolizable AR agonist R1881. (C) To compare

the PSA response of insulin and DHT, LNCaP cells were treated with DHT

concentrations from 10nM to 0.1pM for 24hrs and the values compared to 10nM

insulin (normalized to rpl32 control gene). (D) Medium was collected from LNCaP

cells treated with insulin (10nM) for 5, 16 and 48 hours and 48hr control and PSA

analyzed by ELISA. Control is shown at 48hrs. Increased expression of PSA

mRNA was measured following insulin (10nM) compared to vehicle following (E)

24hr treatment of VCaP cells and (F) 48hrs treatment of 22RV1 cells. Treatment

with the AR-inhibitor bicalutamide (25µM) attenuated the insulin induced increase

in PSA expression in (G) LNCaP cells and (h) 22RV1 cells by ~50% (†p<0.05). All

results are shown +SE (*p<0.05) n≥3.

106

Figure 3.7: LNCaP cells were treated with 10nM insulin for 48hr and steroids were

extracted and prepared for quantitation by LC/MS/MS. Graphs depict the difference

in intracellular steroids, shown in ng/ml (controlled to weight of cell pellet), + SE,

n≥3, and show accumulation of androgens over time.

107

3.3.5 In LNCaP xenografts mice which showed an increase in both PSA and

RDH5 expression at 28 days post castration also displayed an increase in INSR-A

and IRS2 mRNA

CRPC progression can be modelled in vivo using LNCaP tumours injected

subcutaneously into immunocompromised male mice; tumour growth is followed

by monitoring tumour-derived PSA levels in the serum. Typically, after a 6-week

period of growth the mice are castrated, PSA levels fall to a Nadir (N) within 7

days. In most mice PSA levels will begin to increase again by day 28 post-

castration and this is referred to as castrate resistance (CR) in this model. However,

in some mice there is a greater lag of PSA production not arising until after 35

days. In a blinded study for PSA level following castration, tumours were grown

for 28 days post castration, and then analysed for the expression of markers relevant

to steroidogenesis. From the isolated LNCaP tumours, QRT-PCR was performed

on RNA for PSA, insulin receptor isoform A (INSR-A), IRS2, and RDH5. As

mentioned above, RDH5 is a key enzyme of the backdoor pathway to DHT

synthesis, and tumours expressing this enzyme may be more steroidogenic (Auchus

2004; Locke et al. 2008). Our previous studies have shown that the mRNA of most

steroidogenesis enzymes, of which RDH5 is an example, increase during

progression (Locke et al. 2008). In mice that exhibited a CRPC increase in PSA

levels 28 days following castration, PSA, RDH5 and IRS2 showed significantly

higher gene expression levels (figure 3.8A; p<0.05) and INSR-A isoform showed a

trend towards increased expression (figure 3.8A). This was in contrast to mice

bearing LNCaP tumours that did not show a serum PSA increase by 28 days (non-

progressed), where the genes were unchanged (figure 3.8B). Therefore, increased

steroidogenesis correlates to increased androgen activation (PSA production) in

vivo. Furthermore, the changes in key insulin signalling molecules suggest that

insulin may act via INSR-A and IRS2 in this model.

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Figure 3.8: In vivo tumour xenograft model. In LNCaP tumour xenografts were

collected from athymic nude mice at castration (pre-Cx), at the PSA nadir (8 days

post castration – N) and at castrate-resistant stage (28 days post castration – CR).

QRT-PCR analyses of PSA, IRS2, INSR-A, and RDH5 mRNA in tumours show

statistically significant increase in expression in mice that experienced PSA

recurrence (A). In contrast, the expression of these genes was not changed in mice

that did not progress to castrate resistance (B). Error bars represent SE (*p<0.05),

n=8.

INSR‐A

INSR‐A

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3.4 Discussion

Reactivation of the androgen receptor following ADT, defined by rising serum PSA

is a hallmark of castrate resistant prostate cancer progression. Several mechanisms

including hypersensitivity of the receptor to low steroid concentrations and ligand

promiscuity arising from mutations in the receptor ligand binding domain can play

a role AR reactivation (Gregory et al. 2001a; Mostaghel et al. 2007). We and

others have previously identified that intratumoural androgen production is also

associated with activation of AR (Heinlein et al. 2004; Titus et al. 2005b). We have

demonstrated that the LNCaP prostate cancer cell line expresses all of the enzymes

required for de novo androgen synthesis (Locke et al. 2008), and these observations

have been extended into VCaP and 22RV1 prostate cancer cells. Following ADT,

androgen levels continue to be substantial in prostate tissue, compared to the

dramatic and continued decrease of androgens in sera. These low levels are

sufficient to activate the AR (Stanbrough et al. 2006; Locke et al. 2008). These

studies indicate that the synthesis of androgens plays an important role in CRPC

progression; however, the biological factors inducing and regulating

steroidogenesis during prostate cancer progression have not been largely explored.

Androgen deprivation therapy is associated with a pattern of metabolic alterations

consistent with insulin resistance and the metabolic syndrome including an increase

in fat mass and fasting plasma insulin (hyperinsulinemia) (Basaria et al. 2006;

Smith et al. 2006). Emerging evidence suggests that both body mass index and high

serum insulin levels are independently predictive of poorer patient outcomes

including increased disease aggression and increased cancer mortality (Ma et al.

2008); obese men are more likely to have higher grade cancers, high recurrence

rates, and high prostate cancer-specific mortality (Buschemeyer et al. 2007).

However, recent studies have also identified a correlation between serum C-peptide

levels and prostate cancer mortality (Smith et al. 2006; Fowke et al. 2008; Isbarn et

al. 2008; Ma et al. 2008; Cox et al. 2009), suggesting a role for insulin in disease

progression. Moreover, mouse studies have shown that diet-induced

hyperinsulinemia leads to more aggressive tumour growth (Venkateswaran et al.

2007) and insulin has long been known to stimulate proliferation in breast and

prostate cancer cells (Lann et al. 2008; Pollak 2008b). In contrast, men with low

insulin levels due to diabetes appear to have a decreased risk of CaP development

110

(Zangeneh et al. 2006; Hsing et al. 2007; Kasper et al. 2008). Furthermore, recent

studies have shown increased insulin receptor (INSR) expression in neoplastic

prostate specimens as opposed to non-neoplastic prostate tissue (Cox et al. 2009)

suggesting increased insulin signalling in these cells.

Although there is mounting epidemiological evidence linking hyperinsulinemia and

CRPC, the direct action of insulin on prostate cancer cells has not been

investigated. Insulin is able to promote steroidogenesis through upregulation of

steroidogenic enzymes (Ogishima et al. 1989; Stattin et al. 2000; Tsilchorozidou et

al. 2003; Munir et al. 2004; Seto-Young et al. 2007; Diamanti-Kandarakis et al.

2008) in conditions such as polycystic ovarian syndrome (PCOS), and insulin

receptors have been reported on prostate cancer cell lines and prostate tumour tissue

(Cox et al. 2009). The ability for prostate cell lines to produce steroids has been

demonstrated (Nestler 1997; Soronen et al. 2004; Dillard et al. 2008; Locke et al.

2008; Leon et al. 2010). Therefore, we investigated whether insulin plays a role in

prostate cancer progression through the promotion of de novo steroidogenesis. We

show for the first time that insulin upregulates steroidogenesis in AR-responsive

prostate cancer cell lines, LNCaP, VCaP and 22RV1 cells, leading to increased cell

survival and likely exacerbation of CRPC progression.

We demonstrated that many enzymes required for steroidogenesis, via both the

classical and backdoor pathways, are upregulated following insulin treatment at

both the RNA and protein levels. Expression of the insulin signalling molecule,

IRS-2 is significantly increased at the RNA level in LNCaPs; increased expression

of IRS-2 has been associated with increased steroidogenesis in both ovarian thecal

and breast cancer cells (Wu et al. 2000; Cui et al. 2003). Importantly, we showed

an increase in expression of mRNA and protein for SREBP, the transcription factor

which is responsible for coordinating the initiation of cholesterol synthesis in

LNCaP cells, following 10 hours insulin treatment and 48hr treatment of 22RV1

cells. There was an increase in the level of CYP11A1 in all cell types and of StAR

in LNCaPs and 22RV1 cell lines; these enzymes are responsible for the importation

of cholesterol into the mitochondria for steroidogenesis and pregnenolone

synthesis. The enzymes which catalyse more than one step in the steroidogenesis

pathway including CYP17A1, HSD3B2, HSD17B3 and SRD5A1 (figure 3.1) were

all significantly upregulated by insulin treatment. All three prostate cancer cell lines

111

respond to insulin with upregulation of CYP17A1, HSD3B2 and HSD17B3.

Significantly increased expression of SRD5A1 was seen in LNCaP cells only but

RDH5 expression was increased in all cell lines; these enzymes convert

testosterone and androstenediol into DHT, respectively. Taken together our data

suggests both pathways of de novo androgen synthesis are upregulated in CaP cells

following insulin treatment, allowing for versatile means of synthesis of potent

androgens as seen in our previous studies (Kohn et al. 2007).

Insulin consistently stimulated an increase in intracellular steroids and steroids

released into the medium including androgens in all cell lines indicating the

enzymes are functionally active. The release of steroids by prostate cancer cells

may provide paracrine activity of the steroids within the microenvironment. Rising

PSA following ADT is considered the sentinel for CRPC progression most likely

driven by AR reactivation. We observed increased mRNA expression at 24hrs in

LNCaP and VCaP cells and 48hrs in 22RV1 cells, as well as increased PSA

secretion following 48hrs insulin treatment in LNCaPs which demonstrates there is

adequate AR activation in all 3 cell lines to stimulate PSA expression (Mostaghel et

al. 2007; Labrie et al. 2008; Locke et al. 2008) and this can be inhibited by

bicalutamide treatment. An increase in structurally related steroids may still be

relevant in cancer progression, in the cases where the AR has acquired mutations

leading to promiscuous activation by steroids and compounds other than

testosterone and DHT (Monge et al. 2006). The mutation of the LNCaP AR ligand

binding domain (T877A) also makes the AR susceptible to activation by non-

androgenic steroids, which may also contribute to activation by substrates and by

products of de novo synthesis (Hagedorn et al. 1936). Recent studies have

specifically identified a correlation between elevated insulin/ C-peptide levels, a

surrogate measure of insulin levels, with high grade CaP and worse patient

prognosis (Smith et al. 2006; Fowke et al. 2008; Isbarn et al. 2008; Ma et al. 2008;

Nandeesha et al. 2008; Cox et al. 2009). Major findings from recent studies of men

receiving ADT demonstrated a relationship between elevated C-peptide levels and

more rapid progression to castrate resistance (Huggins 1942; Neuhouser et al.

2010).

In summary, our research has shown that insulin increases steroidogenesis in AR

positive prostate cancer cell lines by increasing the mRNA and protein levels of

112

steroidogenic enzymes, leading to an increase in steroid production, including

androgens. Subsequent increases in PSA secretion suggest insulin can affect

prostate cancer cell survival and CRPC progression. Increased expression of the

insulin receptor in the LNCaP xenograft model during progression of cancer to

castrate resistance provides further evidence that insulin may be acting directly on

prostate cancer cells through the INSR. There are multiple studies correlating high

insulin levels and CaP progression (Hsing et al. 2001; Basaria et al. 2006; Hsing et

al. 2007; Venkateswaran et al. 2007; Nandeesha et al. 2008; Vieira et al. 2008;

Albanes et al. 2009). The significance of cholesterol synthesis (steroid precursor)

and steroidogenesis in CaP progression suggests treatments which target these

pathways are pertinent for the treatment of patients with CRPC, particularly in the

context of hyperinsulinaemia and the metabolic syndrome (Soronen et al. 2004;

Stanbrough et al. 2006; Mostaghel et al. 2007; Dillard et al. 2008; Labrie et al.

2008; Locke et al. 2008; Montgomery et al. 2008; Mostaghel et al. 2008; Leon et

al. 2010). Of note, Abiraterone, an inhibitor of CYP17A1, has shown promising

results in clinical trials with men who are no longer responsive to androgen ablation

(Goodwin et al. 2002; Allen et al. 2005). This is one pathway by which insulin may

contribute to cancer progression; however, in addition to upregulating

steroidogenesis, insulin is expected to activate multiple pathways in cancer cells

(Pollak 2008c). Further understanding of the direct action of insulin on prostate

cancer cells may provide important insight into new therapeutic strategies to

prevent progression of castrate resistant prostate cancer.

The results of our study suggest that the metabolic dysfunction of prostate cancer

patients should also be addressed. There are a number of pharmacological agents

currently available for the treatment of insulin resistance which can improve

(reduce) circulating insulin levels including metformin and recent studies suggest

targeting insulin resistance can have positive effects on cancer patient outcomes

including prostate cancer (Sahra et al. 2008; Algire et al.). Upstream inhibitors of

cholesterol synthesis such as the thiazolidinediones (TZDs) have been shown to be

effective insulin sensitizers in patients with metabolic syndrome. In a cancer

context, TZDs have been shown to decrease androgen production in H295 cells by

down-regulation of CYP17A1 and HSD3B2 (Kempná et al. 2007) and to reduce

proliferation of cancer cells (Krishnan et al. 2007; Luconi et al. 2010).

113

Furthermore, CaP patients who are undergoing cholesterol lowering treatment with

the class of HMG CoA inhibitors, statins, show markedly lower PSA and tumour

volumes than non-users (Loeb et al. 2009). Currently, ADT-induced

hyperinsulinaemia is not addressed in prostate cancer patients, despite an

significantly increased risk of cardio-vascular mortality in these patients (Redig et

al. 2010a); however, we provide further evidence that management of the metabolic

consequences of ADT may be as important as treatment of the cancer itself.

.

114

115

Chapter 4: The Effect of Insulin Analogs on

Steroidogenesis and Insulin Effect on Breast Cancer

Steroidogenesis

116

117

4.1 Introduction

Since 1922, insulin monotherapy has been used to treat type 1 diabetes (T1D) and

type 2 diabetes (T2D) (Carlbor 1938). Fredrich Banting and Charles Best first

isolated pancreatic insulin and administered it to diabetic dogs before successful

human treatment (Banting et al. 1922). Because regular insulin was so short lived,

interest soon followed to find a longer acting, more stable formulation. One of the

most crucial discoveries in this field happened at the University of Toronto, where

Scott and Fischer found that adding zinc acetate or sulfate and other metallic salts

to solution would change the chemical structure of insulin, decreasing the

solubility, slowing the absorption, and therefore prolonging the effect (Carlbor

1938; Scott et al. 1938). In 1936, Hagedorn et al. discovered that adding a basic

protein (protamine) to the formulation localized the insulin to the injection site,

which prolonged its action (Hagedorn et al. 1936); this was deemed Neutral

Protamine Hagedorn [insulin] (NPH). NPH begins action after an hour and a half

and takes 4-12hrs to reach peak serum concentrations.

Most insulin was obtained from porcine or bovine species until the 1980s, when

biosynthetic human insulins were produced by recombinant DNA technologies

(Gualandi-Signorini et al. 2001). This new era of insulin synthesis led to interest in

additional long or short acting insulin analogs, with the benefit of acting faster at

meal times in the case of T2D (short acting) or more constantly for T1D (long

acting) (Werner et al. 2011). Use of analogs in humans has only come about

relatively recently, the most utilized being Asp B28 (discovered 1990) and Lys Pro

(1992), which are short acting, and the long acting insulin glargine (2000), which

has residues of arginine inserted into the β-chain, and a glycine substituted for

histidine in the α-chain, and insulin determir (1997), which has a myristic acid

chain added to bind to albumin and slow absorption to areas beyond the injection

site (Werner et al. 2011).

Most of the modifications of insulin analogs occur around the β-chain, so as to not

alter insulin binding; however, it has recently become apparent that some insulin

analogs actually have a much higher affinity for the IGF1R than “normal” (wild-

type) insulin does (100 fold less than IGF1), which is of concern to some of the

medical community, as IGFs are known to increase risk of certain cancers, such as

breast and prostate (Kurtzhals et al. 2000; Pollak et al. 2010; Pollak 2010a). One of

118

the first insulin analogs, Asp B10, which differed from regular insulin by increased

affinity for IGF1R, was shown to have carcinogenic properties in female rats

(Werner et al. 2011).

T2D patients have an increased risk of cancer mortality (Currie et al. 2009), and

mice on high carbohydrate diets (T2D) had greater tumour growth and increased

IGF1R and insulin receptor (INSR) signalling. It has also been shown that an

increase in C-peptide levels correlates with poor outcome in breast and prostate

cancer (Pollak et al. 2010). Studies on the effect of insulin analogs on cancer have

been varied, with many conflicting opinions. In a large scale population study,

Hemkens et al. showed that patients treated with glargine have higher incidence of

cancers (Hemkens et al. 2009). Jonasson et al. demonstrated an increased incidence

of breast cancer in glargine treated patients (Jonasson et al. 2009). In a study in

Scotland, glargine was not associated with site specific cancer, but patients on

glargine did appear to have higher risk overall; however, this study had a small

sample size (Colhoun 2009). Another study showed glargine was associated with

cancer more than other insulins (Mannucci et al. 2010). Conversely, Rosenstock et

al. compared glargine to NPH in T2D patients over 4 years and saw no differences

in cancer incidence (Rosenstock et al. 2009). Analysis of Sanofi-Aventis’ clinical

data showed no difference in cancer incidence between glargine and other insulins

(Werner 2011).

Weinstein et al. (2009) are highly cited for their study on the differences in effects

of multiple analogs on various cancer cells, though some of the data has been

interpreted as incomplete (as most experiments were only done on colorectal cancer

cells), or super-physiological concentrations (100nM) (Weinstein et al. 2009). They

treated colorectal cancer (HCT-116), prostate cancer (PC3), or breast cancer

(MCF7) cells with long or short acting insulins, for 96hrs. Cell mitogenicity of

HCT-116 was increased 26% with 100nM IGF1 compared with glargine, lispro, or

detemir, 22, 20, or 17%, respectively. In PC3 cells, IGF1 increased mitogenicity by

25%, with glargine and determir at 16 and 14%, In the MCF7 breast cancer cell line

IGF1 displayed 22% increase, with glargine and determir increasing by 14% and

6% respectively compared to control. Insulin itself only increased mitogenicity by

7%. Both IGF1 and glargine increased cell proliferation in HCT-116 cells in a dose

dependent manner, whereas insulin did not. The remaining experiments, in HCT-

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116 cells, showed a decrease in apoptosis with IGF1, glargine and determir, though

not with insulin. Signalling studies showed most analogs caused signalling with

greater similarity to IGF1R activation than insulin mediated INSR signalling. In

other studies of MCF7 cells, Kurtzhals et al. saw that glargine had 7.83 times

greater mitogenic potential than “normal” (wild-type) insulin, and both Kohn et al.,

and Mayer et al. demonstrated a 4-fold increase in proliferation of human

mammary epithelial cells with glargine compared to insulin (Kurtzhals et al. 2000;

Kohn et al. 2007; Mayer et al. 2008). Kurtzhals et al. and Shukla et al.

demonstrated increased mitogenic potential occurs primarily through aberrant

IGF1R signalling (Kurtzhals et al. 2000; Shukla et al. 2009). Sciacca et al.

demonstrated that signalling responses to short acting insulin analogs were more

similar to normal insulin than those of long acting analogs (Sciacca et al. 2010).

Because of the interest in insulin analogs and cancer, and the fact that we have

recently shown (chapter 3) that insulin upregulates both steroidogenesis enzymes

and steroid hormones themselves (Lubik et al. 2011), it was important to

investigate whether there are differences in the steroidogenic potential of long-

acting insulin analogs and regular insulin at equimolar ratios (10nM). Analogs used

were Glargine (trade name Lantus), and insulin X10, also known as B10Asp. A

single aspartate is substituted for histidine in the dimer forming region of the

insulin X10 peptide, to prevent aggregation of insulin and prolong its systemic

effects (Brange et al. 1988), and it has twice the receptor affinity for INSR

compared to insulin, and approximately 10 times the affinity for IGF1R. Glargine

has had two amino acid substitutions to shift the solubility in order to make small

precipitates in the blood at the injection site, which would slow absorbance for

potentiating insulin effects with one daily dose (Wang et al. 2003). Glargine has

less affinity for INSR (0.86 compared to insulin), but more than 6-times greater

affinity than insulin for IGF1R (Kurtzhals et al. 2000). In this study, it has been

demonstrated that these analogs are not more steroidogenic than insulin.

Furthermore, the effect of insulin on steroidogenesis in breast cancer cells was also

investigated, because much of the data on the effects of insulin and analogs on

cancer growth was attained from breast cancer studies (Jonasson et al. 2009;

Kurtzhals et al. 2000; Kohn et al. 2007; Mayer et al. 2008). Also, breast cancer and

120

CaP are similar in hormonal dependence and treatment (Risbridger et al. 2010). In

breast cancer patients, obesity and hyperinsulinemia are associated with recurrence

and fatality (Goodwin et al. 2002). Also, high serum insulin levels are associated

with risk of developing breast cancer. Insulin receptor A (INSR-A) is almost

ubiquitously expressed and overexpressed in breast cancer, and is not down

regulated by exposure to ligand, as it is in normal tissue (Crettaz et al. 1984;

Goodwin 2008). There is much interest in the inhibition of many enzymes in the

steroidogenesis pathway for breast cancer therapy, not only aromatase, but the

HSD17B enzymes (Miyoshi et al. 2001; Day et al. 2008; Day et al. 2009; Poirier

2010), AKR1C3 (Rizner et al. 2006), and sulfotransferase, which bypasses

aromatase to convert less potent steroids to estrogens (Woo et al. 2010). There is

also interest in the use of abiraterone, which inhibits CYP17A1 (Risbridger et al.

2010). As of yet, it has been assumed that breast cancer can only synthesize

estrogens from exogenous steroids and precursors, as it was for CaP before the

ability of tumours to perform de novo steroidogenesis was demonstrated by our

group (Locke et al. 2008). It may be that breast cancer cells are also able to produce

de novo estrogens, and if so, the triggers for this would be essential to understand.

For these reasons, the expression of steroidogenesis enzymes in MCF7 AR and ER

positive breast cancer cells, in the presence and absence of insulin, has been

examined. Findings in this chapter demonstrate that breast cancer cells may in fact

be capable of de novo estrogen synthesis, which may contribute to breast cancer

progression. Moreover, it has been shown herein that insulin increases mRNA

levels of steroidogenesis enzymes and secreted levels of estradiol in MCF-7 breast

cancer cells.


4.2.1 In vitro model: LNCaP cells were cultured in phenol red-free RPMI 1640

(Invitrogen) and 5% fetal calf serum (FBS; Hyclone). VCaP and MCF7 cells were

cultured in DMEM and 10% FBS. For modelling of androgen deprivation, all cell

lines were cultured in charcoal-stripped serum (CSS; Hyclone) as follows: cells

were plated in FBS and at 60% confluence were changed to 5% CSS medium for

24hrs, followed by 24hr starvation in serum-free medium, after which, LNCaP and

VCaP cells were treated with 10nM insulin (Novo Nordisk), glargine (Sanofi-

Aventis), obtained from Vancouver General Hospital Pharmacy by prescription, or

121

X10 (Novo Nordisk, Denmark) for various times (48hrs for mRNA collection,

72hrs for steroid measurement) or with vehicle control. MCF7 cells were treated for

10 or 48hrs for mRNA collection and 48hr for estradiol extraction. Insulin was

refreshed every 24hrs.


prostate cancer cells using Trizol (Invitrogen, Burlington, Ontario, Canada), before

reverse transcription with Superscript III reverse transcriptase (Invitrogen) as

described in chapter 2.5-2.7. Subsequent QRT-PCR using Applied Biosystems

7900HT Fast Real Time PCR System used SYBR Green detection (Applied

Biosystems). Primers were designed by Primer 3 software from coding segments of

genes, obtained from the NCBI data bank and ordered from Integrated DNA

Technologies (San Diego, California, USA). Primers used for prostate cells were

SREBP, StAR, CYP11A1, CYP17A1, HSD3B2, AKR1C3, HSD17B3, and RDH5.

Additionally, HMGR, HMGS, and aromatase were used in MCF7 cells. For Primer

sequences, see Appendix A. Gene expression was normalized to the housekeeping

gene rpl32, then expressed relative to the vehicle control at the same time point.

Cycling conditions are described in chapter 2.7. Data was analyzed with SDS 2.3

software by means of the ∆∆Ct method (Livak et al. 2001). Experiments were

repeated a minimum of 5 times for CaP cells, 3 times for MCF7 cells.

4.2.3 De novo steroid analysis using radiometric detection: LNCaP cells were

grown in 6 well plates and treated as described in chapter 4.2.1. At the time of

insulin treatment, 6µCi/ml 14C-acetate (PerkinElmer, Woodbridge, Ontario) was

added to each plate for co-incubation. Steroids from 2ml of medium were extracted

with 75/25 hexane/ ethanoloacetate (water equilibrated), dried down and

resuspended into 75ul 50% methanol. Detailed method is described in chapter

2.12.These samples were analysed on the Waters Alliance 2695 HPLC System and

Packard Radiomatic Detector 150TR Flow Scintillation Analyzers. Peaks were

identified by comparison of retention times to Mix 10 steroid standard (Sigma).

4.2.4 Total levels of MCF7 estradiol as measured by LC/MS/MS:

Steroid analysis was performed as previously described, chapter 2.11. Briefly, cells

were grown in 15cm plates and treated with 10nM insulin for 10 and 48hr. Two

122

plates of cells were washed with PBS and pooled to give one sample. Medium was

collected and likewise combined. Steroids were extracted from the cell pellet and

medium with a 70/30 hexane/ EtOAc solution. Once the steroids extracted from

cells and medium and dried down, steroids were derivatized with 2-fluoro-1-methyl

pyridinium qp-toluenesulfonate (FMP). All samples were run on the Waters

Acquity Liquid Chromatography system and the Waters Quattro Premier

LC/MS/MS, identified using standards of known retention times and analyzed

using Quanlynx Software (Waters Corp, USA). Readings were adjusted using cell

pellet weight and normalized to vehicle-treated samples.

4.2.5 Statistics: All statistical analysis was carried out using the two-tailed

student’s t-test assuming equal variance on Graphpad Prism 5 software. P-value of

0.05 or less was considered significant.

4. 3 Results

4.3.1 Insulin and analogs upregulate enzymes necessary for steroidogenesis at

the mRNA level

In chapter 3, it was shown that insulin upregulates the mRNA transcripts of

steroidogenesis enzyme in LNCaP and VCaP cells (Lubik et al. 2011); therefore,

LNCaP and VCaP cells were treated with insulin, glargine, or X10 (figure 4.1A). In

VCaP cells, insulin significantly upregulated mRNA for SREBP, CYP11A1,

CYP17A1, HSD3B2, HSD17B3 and RDH5 (p<0.05), whereas X10 upregulated

SREBP (p<0.1), HSD3B2, and HSD17B3 significantly (p<0.05). Glargine did not

seem to induce mRNA expression of most enzymes; however, it did show

significant increase in CYP17A1 and HSD17B3 mRNA (p<0.05).

In LNCaP cells (figure 4.1B), insulin upregulated SREBP, StAR, CYP11A1,

CYP17A1, HSD3B2, and HSD17B3 (p<0.05). Induction with X10 only

demonstrated significant induction with CYP17A1, and HSD3B2 (p<0.05), but

trended toward induction of StAR, CYP11A1, and RDH5. Finally, glargine

increased the mRNA levels of StAR, HSD3B2, and RDH5 (p<0.05). Neither analog

showed extensive similarity to the insulin induction pattern of steroidogenic

enzyme mRNA.

123

Figure 4.1: Insulin and analogs regulate expression of steroidogenic enzymes at the

mRNA level. (A) Following 48 hour insulin (IN), X10, or glargine (Glar) treatment

(10nM), RNA was extracted from VCaP cells and used for QRT-PCR analysis of

steroidogenesis enzymes. Results are analyzed by ΔΔCt method and normalized to

RPL32 as a control gene, before normalization to vehicle-treated controls for the

equivalent time point.(B) Following 48 hour insulin, X10, or glargine (Glar)

treatment (10nM), RNA was extracted from LNCaP cells and used for QRT-PCR

analysis of steroidogenesis enzymes as per VCaP cells in (A). Error bars represent

SE, * = p<0.05 from control unless otherwise indicated in text n≥5. Brackets

represent statistical difference over all treatments. (B) LNCaP cells were analysed

as VCaPs.

124

4.3.1 Insulin analog effect on de novo steroidogenesis in LNCaP and VCaP

medium

By treating LNCaP or VCaP cells for 72hrs with 14C-labeled acetate and subsequent

HPLC and radiometric detection, increases in de novo steroidogenesis with insulin

and analogs were demonstrated (figure 4.2A). In VCaPs, androstenedione was

increased approximately 2.5 to 3.5-fold from control with all analogs (p<0.05), and

androsterone increased approximately 2-fold with insulin and glargine, though not

significantly with X10. There was also a 1.75-2.5-fold increase in pregnan-3,17-

dio-20-one across the insulins (p<0.05). A 1.5 to 1.8-fold increase in a steroid peak

with retention time of 34 minutes, most closely resembling progesterone in

retention time, was seen with insulin and X10 (p<0.05), though not with glargine.

In VCaPs, an increase in de novo cholesterol, which is the building block of

steroids, was also seen (4-10 fold, p<0.1) with all compounds; however, because

the extraction method utilized was not specific for cholesterol, increases are more

qualitative than quantitative.

In LNCaP cells (figure 4.2B) androsterone appeared to increase 1.5-fold with

insulin, but not with either of the analogs, where decrease was seen. Pregnan-3,17-

dio-20-one was static with insulin but decreased with the analogs. The

progesterone-resembling peak at 34 minutes was upregulated 2.5-fold with insulin

(p<0.1) and glargine 2-fold (p<0.1). There was no change with X10. Insulin

showed a trend toward increase in testosterone (4.5 fold), and a 7-fold increase was

demonstrated with X10 (p<0.1), though no induction was demonstrated with

glargine. The differences between the effects on steroids and precursors in VCaP

and LNCaP cells could be a result of their different lineages. It may be that

LNCaPs, where accumulation is of those steroids further along in the pathway to

androgens, process acetate and cholesterol to steroids/ androgens faster than

VCaPs, where acetate appears to accumulate in cholesterol. We have previously

demonstrated that cancer cells adapt to their microenvironment for their

steroidogenic needs (Locke et al. 2009a), which could be the reason for differences

in expression of steroidogenic enzyme mRNA and steroid induction between the

LNCaP and VCaP cells, as the metastases they were derived from came from

different microenvironments.

125

Figure 4.2: Insulin analogs increase de novo steroidogenesis in prostate cancer

cells. Medium was collected from VCaP and LNCaP cells after 72hr incubation

with 10nM insulin, X10, or glargine (Glar) and 6µCi/ml 14C-acetate before HPLC

and radiometric detection were used to identify and quantitate extracellular steroids.

(A) In VCaPs, all analogs demonstrated a similar steroid profile. In LNCaPs (B),

insulin and X10 showed similar inductions of testosterone, where insulin and

glargine had similar effects on a peak resembling progesterone. Error bars

represent SE (*p<0.1), n=3.

126

4.3.3 Insulin effects on steroidogenesis in breast cancer

The expression of steroidogenesis enzymes in MCF7 cells, including HMGR and

HMGS cholesterol synthesis enzymes, was investigated after 10 and 48hrs insulin

treatments (10nM) (figure 4.3A). At 10hrs, an increase in the cholesterol synthesis

enzymes was apparent, with approximately a 1.5 and 2-fold upregulation for

HMGR and HMGS mRNA, respectively, with little change in other steroidogenic

enzymes (data not shown). At 48hr, the presence of mRNA transcripts for all

necessary enzymes for steroidogenesis was detected with appreciable Ct values

(between 19- 33 cycles). No change was demonstrated for SREBP or StAR at these

times. This does not rule out a role for these factors in breast cell steroidogenesis as

activation; protein level and activation state maybe more informative for these

enzymes, as was the case in VCaP cell steroidogenesis (chapter 3). Significant 25%

increases in mRNA were demonstrated for CYP17A1 and HSD3B2. This may be of

interest to the groups studying the effects of abiraterone on breast cancer. A 2-fold

increase in HSD17B3 mRNA was apparent (p<0.05). There was little change in

SRD5A1 or RDH5. A striking increase in AKR1C3 was demonstrated (p<0.05),

which may indicate that targeting this enzyme in hyperinsulinemic breast cancer

patients would be beneficial, as AKR1C3 converts androstenedione to testosterone,

which can then be converted into estrogens (Ishikawa et al. 2006). Interestingly,

there appears to be an unanticipated decrease in aromatase; however, there are 2

enzymes responsible for the formation of estradiol, the most potent estrogen, and

the other is sulfotransferase. At the time of these experiments, this enzyme was not

examined, but in hyperinsulinemic patients it has been demonstrated that aromatase

inhibitors are not as effective (Goodwin 2008); therefore, a shift towards active

sulfotransferase may occur and should be investigated, as this may actually be the

driving force for estradiol synthesis in some breast cancers (Santen et al. 1986;

Pasqualini et al. 1996; Woo et al. 2010).

Of most importance, in treating steroid and insulin starved MCF7 cells with insulin

for 48hr in the presence of non-labelled acetate, a 50% decrease in intracellular

estradiol occurs in the presence of insulin, with a corresponding 2.3-fold (p<0.05)

increase in estradiol concentration in the medium (figure 4.3B,C). These are

sufficient concentrations to activate ER, at approximately 2.5pM; as 0.26pM

estradiol is required for minimum ER activation (Mattick et al. 1997).

127

4.4 Discussion

Much recent interest has been shown in the effects of insulin and analogs on cancer

(Pollak et al. 2010). Conflicting studies report that analogs may have more

carcinogenic potential than normal insulin, and most evidence supports the theory

that this may be through dysregulated IGF1R or INSR signalling; in fact, most

evidence of increases in mitogenic potential is demonstrated to be through IGF1R

(Kurtzhals et al. 2000; Shukla et al. 2009; Weinstein et al. 2009).

Herein it has been revealed that induction of steroidogenesis enzyme mRNA and

steroid levels in VCaP cells appears to be dissimilar between insulin, X10 and

glargine (long acting insulin). Interestingly, glargine, for which most evidence of

increased cancer risk has been reported, seems to show less steroidogenic potential

than the others. In LNCaPs, neither analog appears to have equal activity to insulin

for induction of steroidogenesis enzyme mRNA; X10 trends toward more similarity

than glargine. Significantly, though insulin induced androsterone production,

neither analog did. Increases in a progesterone-like peak were seen with insulin and

glargine, but not X10; whereas, both insulin and X10 seemed to increase

testosterone levels, with Glargine having no effect.

These findings indicate that clinical use of insulin analogs may not have any more

consequences for patients with prostate cancer than normal insulin, at least in terms

of stimulating steroidogenesis. It is interesting to note that X10 and insulin had

more effects on the cells, where glargine seemed to be less potent. As glargine has

been shown in breast cancer to implement signalling similar to IGF1, it may be that

glargine is affecting other proliferative and/ or mitogenic pathways (Varewijck et

al. 2010), which are beyond the scope of this study. It has been suggested that

insulin and analogs do not cause cancer transformation, but increase the growth of

precancerous lesions (Miller 2007), which would suggest these compounds should

all be used cautiously in patients predisposed to cancer. The difference in effects of

the insulin analogs on VCaP and LNCaP cells suggests that the analogs may have

differential effects on various cancer stages. It is important to note that the benefits

of insulin use for T1D and T2D patients far outweigh the risks (Pollak et al. 2010).

More studies will have to be done to determine if there is more cancer risk with

analogs, especially glargine. However, at the present time, the connection is

unconvincing. Interestingly, any increased risk of cancer growth with glargine or

128

other insulins in colorectal or pancreatic cancer was suppressed with metformin use

(Currie et al. 2009), a promising new finding for metabolic syndrome sufferers,

which will be further discussed in chapter 6.

Furthermore, metabolic syndrome has been shown to exacerbate breast cancer

progression, as well as CaP (Redig et al.2010a; Goodwin 2010a). Data shown in

this chapter supports the hypotheses that (a) breast cancer cells may be capable of

de novo steroidogenesis (b) and insulin may increase de novo steroidogenesis in

breast cancer cells, and exacerbate the conversion of exogenous precursors to

estradiol. These pathways should be further explored, in the presence and absence

of metformin; the effect of metformin on steroidogenesis will be demonstrated in

chapter 6.

129

0

1

2

3

4

5

6

AKR1C3

0

0.5

1

1.5

2

2.5

MCF7 mRNA Fold Steroid Enzyme Chan

ge from

Control (48hr)

Control

Insulin

0

0.5

1

1.5

2

2.5

MCF7 mRNA Fold Steroid Enzyme Change

from Control (10hr)

*

*

* *

**

0

0.5

1

1.5

2

2.5

3

Control Insulin

Change

in Ediolin M

edia (pM) (48h

rs) Ediol

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

Control Insulin

Chan

ge in In

tracellu

lar Ed

iol(ng/g pellet) (48hr)

Ediol *

A

B C


130


mRNA level and increases estradiol secretion. A) Following 10 and 48 hour insulin

treatment (10nM), RNA was extracted from MCF7 cells and used for QRT-PCR

analysis of steroidogenesis enzymes. Results are analyzed by ΔΔCt method and

normalized to RPL32 as a control gene, before normalization to vehicle-treated (no

insulin) for the equivalent time point. Error bars represent SE (*p<0.05). MCF7

cells were treated with 10nM insulin for 48 hours and estradiol (Ediol) was

extracted from B) cell pellet and C) media. LC-MS was used to quantitate

intracellular steroids showing a significant decrease in intracellular Ediol, and a

significant increase in secreted Ediol. Steroid levels were adjusted to cell pellet

weight and deuterated testosterone for extraction efficiency and compared to the

vehicle time point control. Error bars represent SE (*p<0.05), n=3.

131

Chapter 5: Insulin increases Fatty Acid Synthesis in

Prostate Cancer Cells.

132

133

5.1 Introduction

Humans can derive necessary fatty acids from either their diet or de novo synthesis.

In most diets, the exogenous fat intake is sufficient and fatty acid synthase (FASN),

which is responsible for converting malonyl-CoA and acetyl-CoA into long-

chained fatty acids, is expressed at low or undetectable levels in normal tissues

(Menendez et al. 2006). In normal cells, fatty acids are converted into triglycerides

for storage. In cancers, however, FASN over-expression has been noted for the

better part of a century (Menendez et al. 2006), and FASN over-expression has

been shown by immunohistochemistry in prostate epithelial tumours and prostate

intraepithelial neoplasia (PIN). Over-expression of FASN protein/ mRNA has also

been demonstrated in breast, colorectal, bladder, ovarian, oesophageal, stomach,

lung, thyroid, and endometrial cancers as well as oral squamous carcinoma, head

and neck carcinoma, mesothelioma, nephroblastoma, retinoblastoma, soft tissue

sarcomas and cutaneous melanocytic neoplasms (Menendez et al. 2006).

Furthermore, FASN over-expression is associated with more aggressive cancers

and bone metastasis, as well as poor patient outcomes in CaP (Swinnen et al. 2000;

Baron et al. 2004; Horiguchi et al. 2008). Finally, it has been suggested that it

may be a marker for poor differentiation in breast cancer (Alo et al. 1999).

In in vivo and in vitro cancer models, inhibition of FASN or enzymes associated

with lipogenesis, such as sterol regulatory element binding protein (SREBP) and

acetyl CoA carboxylase (ACC), has been shown to cause apoptosis or to halt cell

growth (Alo et al. 1999; Swinnen et al. 2006; Beckers et al. 2007; Ho et al. 2007;

Orita et al. 2007; Orita et al. 2008; Migita et al. 2009). SREBP, the transcriptional

factor that controls FASN expression, becomes dysregulated in prostate cancer

progression. During progression to castrate resistance it responds to androgen and

growth factor signalling, rather than traditional regulatory pathways, ultimately

resulting in increased FASN expression (Swinnen et al. 1996; Swinnen et al.

1997b; Baron et al. 2004; Ettinger et al. 2004).

Insulin stimulation of FASN was first investigated by Monaco in 1977 in MCF7

breast cancer cell lines because these cells responded similarly to normal mammary

gland tissue in response to insulin, androgens, estrogens, and glucocorticoids

(Monaco et al. 1977). Measuring a surrogate for total fatty acid synthesis and 14C-

acetate for de novo fatty acid synthesis, the authors showed that insulin at

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physiological concentrations increased fatty acid synthesis. In this study, total

cellular mRNA and protein production did not seem to correlate to the increase in 14C-acetate labelled lipids; however, the authors did not examine specific enzymes.

Interestingly, FASN, also called oncogenic antigen-519 or OA-519, was not

suggested as an oncogene until the 1990s (Epstein et al. 1995).

Lipids are a well-known major source of energy for prostate cancer cells, but they

also have a multitude of other functionalities (Liu 2006). It is thought that

increased lipid content is necessary for processes such as cell growth, cell

proliferation, and membrane expansion, as well as FASN-mediated supply of

palmitate and myristate for intracellular trafficking and changes in phospholipid

content in the cell membranes, which alters signalling pathways (Baron et al. 2004;

Swinnen et al. 2006). In CaP, it has also been shown that fatty acid activation is

important for initiation of steroidogenesis, through the action of hormone sensitive

lipase (HSL), long-chain acyl-CoA synthetase (ACSL), diazepam-binding inhibitor

(DBI) and sterol acute regulatory protein (StAR), ultimately resulting in activation

of the AR and CaP growth (Locke et al. 2010).

It is well established that insulin affects lipogenesis in the liver through SREBP

signalling (Horton et al. 2002), and that insulin upregulates fatty acids in breast

cancer cells (Monaco et al. 1983), and that FASN expression is modulated through

the ERK1/2 and PI3K pathways in cancer progression, both of which are regulated

by insulin (Baron et al. 2004; Menendez et al. 2006). Therefore, we have

investigated the effect of insulin on lipogenesis and the downstream effects on

related pathways in CaP cells.


5.2.1 In vitro model: LNCaP and 22RV1 cells were cultured in phenol red-free

RPMI 1640 (Invitrogen), and 5% fetal calf serum (FBS; Hyclone) for 24hrs before

two subsequent incubations in first 5% CSS medium, then in serum free medium.

LNCaP cells were treated with 10nM insulin (Sigma) for various times (5, 10, 16,

or 48 hrs) or with vehicle control. 22RV1 cells were treated for 48hrs. Insulin was

refreshed if treatment exceeded 24hrs.

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Sigma Proligo (Castle Hill, NSW, Australia). Primers used were for HSL, ACSL3,

DBI, ACC, and FASN. For primer sequences, see Appendix A. Gene expression

was normalized to the housekeeping gene rpl32, then expressed relative to the

vehicle control at the same time point. Cycling conditions are described in chapter

2.7. Data was analyzed with SDS 2.3 software by means of the ∆∆Ct method (Livak

et al. 2001). Experiments were repeated a minimum of 5 times.









visualized using the Li-Cor Odyssey Imager (Li-Cor Biosciences). Experiments

were repeated at least 3 times. Antibodies used were HSL (Abcam), ACSL3

(Abnova), ACC (Santa Cruz), and FASN (Santa Cruz). For antibody information,

please see Appendix B.

5.2.4 Oil Red-O lipid stain: Cells were treated as above, except that medium was

supplemented with 36µM sodium acetate. Oil Red-O lipid quantitation was

conducted as described in chapter 2.13.

5.3.5 Receptor inhibitor treatment: LNCaP cells were grown in 6-well plates for

24hr in FBS supplemented medium before incubation in 5% CSS medium for 24hr,

and a further 24hr incubation in serum free medium. For inhibition of lipogenesis/

lipid accumulation by receptor inhibition, cells were then incubated with either

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10µM Bristol-Myers Squibb-754807(BMS) receptor tyrosine kinase (RTK)

inhibitor of INSR and IGF1R, or 25µg/ml CP-751,871 IGF1R inhibitor antibody

for 2hrs before addition of both insulin and 36µM acetate for 48hrs, with medium

refreshed at 24hrs. All samples were treated with DMSO and PBS vehicle control

(BMS and CP-751,871, respectively), and with 0.2% bovine serum albumin, for

direct comparison purposes. Oil Red-O lipid quantitation was conducted as

described in chapter 2.13.

5.2.6 14C-Fatty Acid Methyl Ester (FAME) derivatization and extraction:

LNCaP cells were treated with insulin as described above for 48hrs. Three hours

before insulin treatment, cells were incubated with either 10µM BMS tyrosine

kinase (RTK) INSR/ IGF1R inhibitor, or 25µg/ml CP-751,871 IGF1Rinhibitor

antibody (kind gifts from Dr. Michael Pollak, McGill University, Canada). Cells

were then treated as in chapter 4.1 with the simultaneous addition of 36µM 14C-

acetate and insulin for de novo steroidogenesis analysis. All treatments were

normalized to vehicle control.

Cells were detached from plates by scraping into PBS, and pelleting on low speed

for 5 minutes. Pellets were collected into preweighed glass vials for normalization,

and extracted with MTBE, as described in chapter 2.10, and resuspended in 500µl

1% H2SO4 and 100µl hexane, vortexed, and incubated overnight at 37oC. Cells

were then added to 700µl hexane and 400µl distilled water and vortexed

thoroughly. Samples were then centrifuged in centrivap (Labconco) for 5 minutes

to separate phases, and the organic layer was removed. This layer was then dried

down and resuspended in 15µl 70:30 chloroform: MeOH, vortexed and sonicated,

before adding 75µl MeOH to final concentration 10:90 chloroform: MeOH. These

samples (75µl) were analysed on the Waters Alliance 2695 HPLC System and

Packard Radiometric Detector 150TR Flow Scintillation Analyzers.

5.2.7 FAME analysis by gas chromatography-mass spectrometry (GC-MS):

Analysis of unlabelled fatty acids was determined by MTBE extraction (chapter

2.10), with the exception that after the organic phase separation following

derivatization, samples were run directly on a Varian 210 ion trap mass

spectrometer in electron impact positive (EI+) mode with capillary voltage at 3kV,

source and desolvation temperatures of 120oC and 350oC, respectively and N2 gas

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flow of 450l/hr. Chromatographic separations were carried out using a temperature

gradient of 80 oC held for 1min, then increased at a rate of 20oC/min to 250oC final

temperature which was held for another 6 minutes; total run time of 15.50 minutes.

Flow rate was 0.3ml/min, column temperature 35oC and 0.05% formic acid was

present throughout the run (Locke et al. 2010). Peak retention times were compared

to FAME standards for identification (Sigma).

5.2.8 Microarray analysis: LNCaP cells were starved for 48hrs in 5% CSS

medium, and brought to basal hormone/ growth factor state for 24hr in serum free

medium before 10hr treatment with 10nM insulin (Sigma) or with vehicle control.

Array analysis was done on the Agilent 105k array platform by Nadine Thomlinson

and analyzed by Melanie Lehman for importation into Ingenuity (IPA) Software

(Ingenuity Systems, Inc., Redwood City, CA), as described in chapter 2.14.

5.2.9 Statistics: All statistical analysis was carried out using ANOVA with the

Bonferroni’s Multiple Comparison post-test. All experiments were analysed with

the two-tailed student’s t-test assuming equal variance on Graphpad Prism 5

software (significant p-value was set at 0.05). Students T-test is referenced when

values approach significance by ANOVA/ post-test, or where only changes

between two conditions, control and insulin treatment, are measured.

5.3 Results

5.3.1 Insulin upregulates lipid related genes in prostate cancer cells

We have previously shown that SREBP and lipogenesis genes are upregulated in

CaP progression in both murine prostate xenograft models and human prostate

tissue (Ettinger et al. 2004; Locke et al. 2010). The hypothesized pathways of

insulin action are shown in figure 5.1. In summary, insulin activates/ upregulates

SREBP which causes the upregulation of FASN and lipogenesis. As well, insulin

can upregulate HSL, which then frees fatty acid, such as arachidonate from

membranes (or lipid stores) which is then activated by ACSL3 to be imported into

the mitochondria to activate steroidogenesis by activating StAR, which imports

cholesterol into the mitochondria as part of a complex comprised of DBI and other

helper proteins.

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Figure 5.1: Proposed method of insulin action on lipogenesis and lipid-mediated

steroidogenesis. For Lipogenesis, insulin stimulates the insulin receptor (INSR)

which stimulates sterol response element binding protein (SREBP) transcription/

activation, which promotes fatty acid synthase expression and therefore lipogenesis.

Activated INSR also activates hormones sensitive lipase (HSL), which cleaves

cholesterol and arachidonate from the cell membrane. Arachidonic acid is activated

by long-chain acyl-CoA synthetase (ACSL) and imported into the mitochondria by

diazepam-binding Inhibitor (DBI) and sterol acute regulatory protein (StAR). There

arachidonate promotes StAR transcription and activation for steroidogenesis.

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To examine whether insulin contributes to lipogenesis related pathways at the

mRNA and protein levels, LNCaP cells (5, 10, 16 and 48 hrs) and 22RV1 cells

(48hrs) were treated with 10nM insulin. As demonstrated in figure 5.2A, the most

consistent differences in gene expression in LNCaP cells upon insulin treatments

were seen at 16hrs, where upregulation of enzymes related to the arachidonic acid

activation of steroidogenesis was observed. HSL, which activates free fatty acids

from lipid stores and membranes (Mulder et al. 1999) is upregulated 2.5-fold at

5hrs and approximately 1.75-fold at16hrs (p<0.05). ACSL3, which is downstream

in the pathway, would then activate free fatty acids for mitochondrial import and

activation of steroidogenesis (Duarte et al. 2007; Locke et al. 2010), and at 16hr

ACSL3 mRNA is increased approximately 1.25-fold (p<0.05). DBI mRNA was

upregulated approximately 2-fold at 10 and 16hrs (p<0.05). DBI participates in

cholesterol import to the mitochondria for steroidogenesis, as well as playing a part

in lipogenesis (Swinnen et al. 1998; Ettinger et al. 2004). ACC and FASN

synthesize fatty acids for energy and signalling, and the mRNA for both is

enhanced significantly, 2 and 3-fold, respectively (p<0.05). We have previously

shown that insulin action on mRNA and protein in 22RV1 cells occurs more

prominently at 48hrs for enzymes in the steroidogenesis pathway (Lubik et al.

2011). At 48hr, insulin induction of ACSL3, DBI, ACC and FASN in 22RV1 cells

are comparable to LNCaP cells (p<0.05). Surprisingly, insulin appears to decrease

HSL mRNA at this time point in 22RV1 cells (figure 5.2B).

In LNCaP cells, insulin-induced elevation of lipid related enzyme protein levels

(figure 5.3A) were in general accord with the mRNA data (Figure 5.2A), with 2-

fold increases of HSL at 5 and 16hrs (p<0.05), 1.5-fold increase in ACSL3

(p<0.05), at 5hr, 2-fold increase in ACC at 10hrs ( p<0.05), and 2.5-fold increase in

FASN is demonstrated at 10hrs (p<0.05). The upregulation of protein before

mRNA has been previously described by our group (Lubik et al. 2011); In 1986,

Wool et al. demonstrated that insulin influences the translation of ribosomal (r)

RNA and ribosomal modifications, which may account for the increase in protein

before mRNA (Wool et al. 1968).

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Figure 5.2: Insulin regulates expression of key lipogenic enzymes at the mRNA

level. Following (A) 5, 10, 16 hour insulin treatment (10nM), RNA was extracted

from LNCaP cells and used for QRT-PCR analysis of enzymes involved in

lipogenesis and fatty acid metabolism. (B) 22RV1 cells were treated with 10nM

insulin for 48hrs, before mRNA extraction and analysis as for LNCaP cells. Results

are analyzed by the ∆∆Ct method and normalized to rpl32 as a control gene, before

normalization to vehicle-treated samples (no insulin) for the equivalent time point.

Error bars represent SE (*p<0.05, ANOVA, Bonferroni’s Multiple Comparison

post-test). Experiments were done a minimum of three times.

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In 22RV1 cells the pattern of protein expression was similar to LNCaPs (figure

5.3B). An increase in HSL protein was also demonstrated, 2.25-fold, not quite

statistically significantly (p<0.092) despite decreased mRNA expression, which

may suggest HSL protein stabilization, as the degradation of some lipogenesis

genes is inhibited in CaP (Graner et al. 2004). It may also be that HSL mRNA is

degraded by the time protein is upregulated in 22RV1 cells. Upregulation of

ACSL3 and ACC was also apparent, approximately 2 and 1.6-fold (p<0.05),

respectively. Lastly, 22RV1 cells showed induction of FASN with 48hr insulin

treatment, 1.8 fold (p<0.05). Taken together, these findings demonstrate that insulin

mediates upregulation of enzymes related to fatty acid synthesis and fatty acid

activation of steroidogenesis in CaP cells.

5.3.2 Insulin increases cellular lipid/ fatty acid content

To determine the functional consequences of increased mRNA and protein levels of

lipogenic enzymes, accumulation of neutral lipids, including triglycerides and

cholesterol esters, was measured by Oil-Red O staining (Nunnari et al. 1989). It has

long been known that lipogenesis is stimulated by androgen in CaP cells (Swinnen

et al. 1996); however, the effect of insulin has not been examined. After 48hr

treatment with 10nM insulin, a 2.5-fold increase in lipid staining (p<0.05) is clearly

demonstrated in LNCaPs (figure 5. 4A). To determine if these increases are due to

INSR signalling, LNCaP cells were incubated with BMS tyrosine kinase inhibitor

(Carboni et al. 2009; Huang et al. 2010), CP-751,871 IGF1R receptor inhibitor

antibody (Cohen et al. 2005), or both inhibitors in combination. BMS reagent

blocks IGF1R and INSR activation with equal affinity (Carboni et al. 2009),

whereas CP-751,871 (Ab) has not been shown to block the insulin receptor up to

3000µg/ml in human cells, though it does block activation of hybrid receptors,

dimers of IGF1R and INSR-A or B (Cohen et al. 2005).

This data indicates that in LNCaP cells lipid accumulation is inhibited by BMS

(significant difference in insulin vs insulin and inhibitor is indicated by white *)

(figure 5.4A). Treatment with IGF-IR inhibitor did not completely block the

effectof insulin on total lipid levels, but did suggest a decrease in insulin induced

lipid synthesis, compared to control, which might be attributed to a decrease in

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Figure 5.3: Insulin regulates expression of key lipogenic enzymes at the protein

level. Western blot analysis lipogenesis and fatty acid metabolism enzymes in (A)

LNCaP cells following 5, 10, 16hr treatment with 10nM insulin treatment. (B)

Protein levels from 22RV1 cells were treated for 48hrs with insulin compared to

time point control. Quantitation was performed using Odyssey software, version 1.2

and normalized to GAPDH loading control. Error bars represent SE (*p<0.05,

ANOVA, Bonferroni’s Multiple Comparison post hoc analysis). Experiments were

performed a minimum of three times.

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insulin signalling through hybrid receptors. Complete blockage of lipogenesis

and decrease from base lipid levels by combination of both inhibitors suggests

that together they may affect acetate/ fatty acid precursor uptake. Insulin is

known to increase fatty acid uptake in adipocytes (Stahl et al. 2002), and mRNA

for some of those transport proteins has been demonstrated to be upregulated by

insulin in array experiments, described in chapter 5.3.4. It may also suggest small

levels of toxicity by complete receptor blockade.

A similar pattern is shown in 22RV1cells. A 2.2-fold increase in triglyceride/

cholesterol-ester staining (p<0.05) after 48hr insulin treatment, was seen to parallel

LNCaP data (figure 5.4B). In 22RV1 cells, there is a substantial decrease in

staining with insulin and BMS together, compared to insulin alone, though

complete inhibition as is seen in LNCaPs is not evident (difference between insulin

and insulin plus inhibitor is represented by white*); whereas, Ab also seemed to

cause less insulin induction of lipids, though still significantly increased from basal.

These results suggest that BMS is blocking most phosphorylation of INSR and

hybrid receptors, resulting in a drastic decrease in lipid induction; whereas, Ab is

only inhibiting insulin signalling through hybrid receptors, therefore, not having as

severe an effect on lipid decrease. As was seen in LNCaPs, use of both inhibitors in

combination largely inhibits basal and insulin induced triglyceride/ cholesterol-ester

accumulation; however an increase with insulin was still demonstrable, indicating a

cellular response to lipid deprivation, or incomplete inhibition (Brown 2007). These

findings show that insulin induces triglyceride/ cholesterol-ester storage in CaP

cells, predominantly through the insulin receptor or hybrid receptors.

5.3.3 Analysis of de novo fatty acid synthesis and lipid profile after insulin

treatment

To examine the effect of insulin on de novo synthesis of free fatty acids in prostate

cancer, the fatty acid methyl esterification (FAME) technique was employed.

LNCaP cells were incubated with 14C-labelled acetate for 48hrs in the presence and

absence of insulin. The amount of newly synthesized fatty acids was approximately

4-fold higher in insulin treated cells than in the control (figure 5.5A). Increases in

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Figure 5.4: Intracellular lipid levels were measured by staining cells with Oil Red-

O assay. Values are corrected for a control plate stained without cells to decrease

background absorbance, +SE, n=6. In (A) LNCaP cells, insulin increases

intracellular lipid content (black *= statistical difference from control, p<0.05,

ANOVA, Bonferroni’s Multiple Comparison post hoc analysis), and the total

intracellular lipid content was diminished with 10µM BMS-754807 INSR/

IGF1RRTK (BMS). Insulin induced lipid accumulation of lipids was diminished

with BMS; staining with Oil Red O was significantly reduced compared to insulin

alone (white *, p<0.05, ANOVA). Insulin increased lipid accumulation in cells

treated with 25µg/ml CP-751,871 IGF1Rinhibitor antibody (Ab); however, this was

reduced compared to insulin alone. In the presence of both inhibitors, basal levels

of lipid are diminished and there is little insulin induction. (B) Insulin increases

intracellular lipid, in 22RV1 cells and insulin inducted lipid accumulation was

suppressed by BMS. Insulin increased staining in the presence of Ab; however,

levels suggest a lower induction with Ab treatment. In the presence of both

inhibitors, basal levels of lipid were highly diminished; however, insulin increases

lipid content.

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Figure 5.5: Effect of Insulin on de novo lipogenesis and LNCaP lipid profile. (A)

LNCaP cells were treated for 48hrs with 10nM insulin and 14C-acetate, in the

presence and absence of 10µM BMS-754807 INSR/ IGF1R RTK (BMS) or

25µg/ml IGF1R inhibitor antibody CP-751,871 (Ab). De novo fatty acids were

extracted for fatty acid methyl ester (FAME) analysis and 7 distinct fatty acid peaks

(FA 1-7) were quantified. De novo fatty acid synthesis was measured by

radiometric detection and normalized cell to pellet weight. Bars represent mean

induction of fatty acids corresponding to fatty acid retention time peaks, + SE

(*p<0.05, t-test). (B) Unlabeled fatty acid levels were analysed by GC-MS for FA

content in LNCaP cells after 48hr treatment with 10nM insulin. Mean FA levels

were normalized to cell pellet weight. Fatty acids were identified by comparison to

FAME standards (Sigma). Data is shown as fold change from control, + SE (n=3).

147

fatty acids are statistically significant by t-test, but not ANOVA, likely due to a

small number of replicates (n=3). Both the BMS INSR/ IGF1R inhibitor and the

IGF1R inhibitors were able to block the ability of insulin to stimulate fatty acid

synthesis (figure 5.5A). This suggests that insulin increases fatty acid synthesis via

hybridreceptors and pathways common to both receptors. Another possible

explanation is that this experiment was done with different preparations of the

inhibitors, in two different laboratories (neutral lipid levels were measured in the

Nelson Lab in Brisbane Australia, while the acetate-labeled study was done in

Vancouver, Canada); therefore, there might be slight differences in batch-to-batch

concentrations due to different people making stock solutions; therefore, the

inhibitor Ab in Vancouver might be at a more potent concentration which would

inhibit the insulin receptor. However, it seems more likely that CP-751,871 is

inhibiting hybrid receptors.

A caveat of the fatty acid methyl esterification (FAME) technique is that it employs

radio-labelled fatty acids and cannot identify the synthesised fatty acids without

radio-labelled standards that our laboratory and collaborators do not possess.

In order to determine which fatty acids are increased by insulin induction, LNCaPs

were treated as described above, except that non-radiolabelled acetate was

employed. These extracts were then subjected to gas chromatography–mass

spectrometry (GC-MS) and compared to a set of FAME standards. No differences

were seen in the profile of fatty acids detected between treated and untreated

samples; however, the levels of certain fatty acids were increased with insulin

(figure 5.5B). We have previously shown that certain fatty acids increase with

cancer progression (Locke et al. 2010), including, palmitic acid, stearic acid,

linoleic acid, arachidonic acid, and myristic acid. Due to limited availability and

equipment constraints, these experiments were not repeated to the extent necessary

for statistical analysis, however, the data reveals a clear trend. An increase in

palmitic acid and stearic acid, was observed, and this is in agreement with previous

work in an castrate-resistant LNCaP xenograft CaP model (Locke et al. 2010). The

increase in octadecatrienoic acid suggests that insulin-stimulated lipogenesis

influences CaP progression via multiple pathways, as this fatty acid is one of the

main substrates for the cyclooxygenase (COX) pathway in cancer (Laneuville et al.

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1995). Long-chain fatty acids, such as arachidate and tetracosanoic acid, have been

associated in SREBP dependent pathways, involving fatty acid elongase family

member ELOVL-7 (Tamura et al. 2009). This pathway is associated with not only

accumulation of cholesterol esters and triglycerides, but cholesterol utilization for

steroidogenesis. These findings clearly demonstrate that insulin treatment increases

de novo fatty acid synthesis, as well as indicating that insulin induces the synthesis

of fatty acids important to CaP progression.

5.3.4 Insulin effects on fatty acid metabolism, as demonstrated by microarray

analysis

In order to get a broader understanding of the interplay between insulin and gene

expression in CaP, mRNA microarray analysis of the effect of insulin on LNCaP

cells was performed. Ingenuity Pathway Analysis (IPA) program, a data mining

package which constructs networks of interactions and regulatory events and

compares these pathways across treatments, was used to identify key pathways of

significance following insulin treatment, at the transcript level. Some of the

canonical pathways that were most differentially regulated with insulin were

involved in fatty acid metabolism. In this software, upregulation is indicated by red

colour; whereas, down-regulation is represented by green colour. A general

upregulation of the fatty acid biosynthesis pathway was demonstrated, as shown in

figure 5.6A. Molecules upregulated in this pathway were ACC (also ACACA) (2-

fold), biotinidase (BTD) (2-fold), propionyl-CoA (PCCB) (2-fold), and malonyl-

CoA:ACP acyltransferase (MCAT) (4.65-fold). Viewing the USCS genome

browser, a probe specific to FASN was shown to be upregulated approximately 2-

fold with insulin; however, the array probe of interest was excluded from IPA

analysis because it did not entirely overlap with the coding ref-seq region of the

gene, though most of the probe aligned. FASN upregulation by insulin has been

clearly demonstrated (chapter 5.3.1).

Upregulation of the fatty acid metabolism pathway was also demonstrated (figure

5.6B). There is extensive differential regulation of mRNA for enzymes in this

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pathway by insulin suggested by array analysis, and some of the most intriguing

enzymes in this pathway are upregulated, namely, hydroxyacyl-CoA

dehydrogenase (HADHA) (2.11-fold) (figure 5.6B, *1), acyl-CoA dehydrogenase,

C-2 to C-3 short chain (ACADS) (3.27-fold) (figure 5.6B, *2), acyl-CoA oxidase

(ACOX) 1 and 3 (approximately 2-fold each)(figure 5.6B, *3), solute carrier family

27 (SLC27A) 3,4,and 5 (fatty acid transporters) (-1.6, 2.6, 1.5-fold, respectively)

(figure 5.6B, *4), and acyl CoA synthetise long chain family member (ACSL) 4, 5

and 6 (-5, 1.7, 2.6-fold, respectively)(figure 5.6B, *5). Many of the enzymes in this

pathway have also been implicated in fatty acid metabolism contributing to

colorectal cancer (Yeh et al. 1999).

Figure 5.6A: Lipogenesis/ fatty acid metabolism pathways, as created by Ingenuity

software, overlayed with array data from 10hr treatment of LNCaP cells with 10nM

insulin treatment compared to vehicle control. All fold change values are

statistically significant (p<<0.05, student’s t-test). Experiments were done in

triplicate. Fatty acid biosynthesis pathway: (A) significant increases in mRNA are

demonstrated for ACC, FASN, and related enzymes in the presence of insulin by

microarray. These processes are enhanced by insulin treatment.

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Figure 5.6B: Ingenuity fatty acid metabolism pathways from LNCaP 10hr insulin

treatment arrays. Fatty acid metabolism pathway (B) demonstrates significant

increase in gene induction for enzymes involved in β-oxidation, HADHA, ACAA1

and ACADS (*1,2), genes involved in PPAR pathway of peroxisomal oxidation of

branched fatty acids (ACOX1,3) (*3), genes involved in fatty acid uptake,

SCL27A/ FATP family genes (*4), and ACSL genes involved in the activation of

fatty acids for steroidogenesis initiation and inhibition of apoptosis (*5).

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Figure 5.6C: Ingenuity fatty acid metabolism pathways from LNCaP 10hr insulin

treatment arrays. (C) Insulin increases mRNA for enzymes related to arachidonic

acid metabolism. These are enzymes involved in prostaglandin synthesis (PTGS1),

which induce inflammation, angiogenesis, and decrease apoptosis (*1,) and

enzymes involved in metabolism of prostaglandins, such as (PTGDS) which may

be potential biomarkers in CaP (*2). Upregulation of LOX pathway genes (*3) may

indicate increased leukotriene production and metabolism (*4), which may

contribute to proliferation and metastasis in cancer cells. Arachidonic acid

metabolism may also confer apoptosis resistance through EPHX2 (*5), as well as

some drug resistance (CBR1) (*6), and genes involved in these processes are

enhanced by insulin treatment. Cytochrome p450 (CYP) genes which regulate

cholesterol, steroid, and lipid synthesis are also differentially regulated by insulin

(*7).

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Fatty acids are also metabolized through the arachidonic acid pathway (figure 5.6C)

into prostaglandins and leukotrienes. Prostaglandin-endoperoxide synthase (PTGS)

(figure 5.6C, *1) is also known as cyclooxygenase (COX)- 2 (upregulated 1.89-

fold) (Matsuyama et al. 2008), and it transforms arachidonic acid to prostaglandins,

which contribute to carcinogenesis through increased inflammation, angiogenesis

and decreased apoptosis. Prostaglandin D synthase (PTGDS) (figure 5.6C, *2),

which is a potential biomarker for CaP, was also upregulated (3.13-fold) (Bertucci

et al. 2004; Yang et al. 2007). The lipooxygenase (LOX) pathway is also

upregulated (figure 5.6C, *3). This pathway creates leukotrienes and

hydroxyeicosatetraenoic acids (HETEs) from arachidonic acid in a system that is

comprised of LOX isoenzymes: 5-LOX, 12-LOX, and 15-LOX, which are

upregulated 1.599, 2.405, and 2.93-fold respectively, as well as leukotriene

synthases (such as LTC4S) (Sveinbjörnsson et al. 2008). LTC4S mRNA is

increased 4.227-fold in the presence of insulin (figure 5.5C, *4). Human soluble

epoxide hydrolase gene (EPHX2) mRNA, upregulated 1.890-fold by insulin

treatment (figure 5.5C, *5), is part of the same pathway. As well, carbonyl

reductase 1 (CBR1) mRNA, which has been implicated in CaP progression, was

upregulated 1.6-fold (figure 5.5C, *6). The enzyme group 1.14.14.1 is shown in

green (figure 5.6C, *7), which indicates down-regulation. This group is actually

composed of many cytochrome p450 family members (CYP), some of which are

unchanged. In this group, seven family members are decreased between 5-16 fold,

while six family members increase 1.5-5 fold. These enzymes are involved in

cholesterol, steroid, and lipid metabolism, and the modulation of the mRNA

suggests insulin may have a large role in the regulation of these pathways.

This microarray data provides a powerful platform to explain which pathways

insulin may affect for fatty acid biosynthesis and metabolism which may play into

cancer progression; however, these results would need to be validated by QRT and

at the protein levels.

5.4 Discussion

Fatty acid synthesis is dysregulated early in prostate and other cancers (Swinnen et

al. 2002; Baron et al. 2004; Menendez et al. 2006; Flavin et al. 2010). Our group

has previously shown that SREBP, the main “switch” for lipogenesis, is

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dysregulated in CaP (Ettinger et al. 2004), and other groups have shown that

constitutively active Akt signalling contributes to over-expression of FASN and

other lipogenic enzymes, which drives hyperactivity of lipogenic genes (Menendez

et al. 2006); Akt is downstream of INSR signalling. The necessity for fatty acids

and lipids may stem from many cellular needs such as a) membrane building blocks

for cell growth, b) membrane composition changes for altering signalling, c)

synthesis and mobilization of lipids/ fatty acids as autocrine/ paracrine signalling

molecules, (d) energy necessity, and/ or e) to challenge hypoxia by making

oxidizing agents for the respiratory chain (Menendez et al. 2006). Obesity, which is

often accompanied by insulin resistance and hyperinsulinemia, and the metabolic

syndrome that often develops as a result of ADT are associated with poor CaP

prognosis (Horiguchi et al. 2008). FASN over-expression is implicated in the

metabolic syndrome, type 2 diabetes, and cancer (Menendez et al. 2009).

In the present Chapter, it has been shown that insulin upregulates lipogenesis in

LNCaP and 22RV1 CaP cell lines. Swinnen et al. have long since demonstrated

that lipogenesis is upregulated in CaP by SREBP (Swinnen et al. 1997b), which

was upregulated by insulin (Chapter 3). Insulin upregulates HSL, ASCL3, DBI,

ACC, and FASN, all of which contribute to fatty acid synthesis/ fatty acid

activation of steroidogenesis in CaP (Swinnen et al. 1998; Heemers et al. 2006;

Locke et al. 2010). It has been demonstrated that insulin increased cholesterol-

ester/ triglycerides mainly through INSR, and literature suggests regulation could

also occur partly through IGF-IR (Baudry et al. 2001). Lipid pools may contribute

to cancer progression by providing substrates for β-oxidation, increased membrane

growth, and differential lipid production. Increased lipids modify cellular signalling

through change in membrane raft compositions, as well as protein modification;

furthermore, fatty acids may change in intracellular signalling by acting as

signalling modulators themselves (Swinnen et al. 2000; Vanholder et al. 2005;

Lupu et al. 2006; Swinnen et al. 2006; Mashima et al. 2009b). A 4-fold increase in

de novo fatty acid synthesis after insulin treatment has also been demonstrated,

along with an increase in fatty acids known to have roles in CaP progression.

Furthermore, it has been shown herein that insulin stimulates the pathways of fatty

acid biosynthesis and metabolism, and the arachidonic acid pathway, all of which

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may play a role in CaP (Kuhajda 2000; Kuhajda et al. 2000; Liu 2006; Harris

2009).

In this chapter it has been demonstrates that fatty acid biosynthesis, which is

extremely important for CaP progression (Liu 2006), is regulated by insulin, which

agrees with the protein, mRNA and lipid levels shown in this chapter. Furthermore,

genes in this pathway are upregulated by insulin as demonstrated by microarray

analysis. Upregulation of ACC mRNA agrees with QRT-PCR analysis, as does the

upregulation of FASN. Little literature is available for BTD and PCCB, which are

associated with ACC. However, in addition to fatty acid synthesis, they are known

to participate in other processes such as cholesterol synthesis, in human liver

(Suchy et al. 1986; Kuhajda 2000). MCAT, which displayed increase by

microarray, is a coordinate enzyme of both cytosolic and mitochondrial FASN

(Reed et al. 2003; Zhang et al. 2003).

In has also been demonstrated that insulin increase mRNA expression of genes in

the fatty acid metabolism pathways (figure 5.6B). Insulin causes upregulation of

HADHA and ACAA1, which play an important part in β-oxidation of branched

fatty acids, a process which is upregulated in CaP (Zha et al. 2005). In the early

stages of CaP, the expression of stearoyl-CoA desaturase (SCD) is lost, essentially

withdrawing a key rate limiting step to fatty acid synthesis. The disappearance of

SCD occurs at the same time as an increase in FASN and palmitate, the primary

product of FASN activity (Moore et al. 2005). This sequence of events brings

about increased β-oxidation in prostate tumour cells, which is the main energy

source for prostate tumours (Liu 2006). In CaP cells, β-oxidation can occur in both

the mitochondria and peroxisomes. ACAD enzymes, which are also upregulated by

insulin, are involved in mitochondrial β–oxidation (Ensenauer et al. 2005).

Peroxisomal β-oxidation does not contribute to energy levels, but oxidizes those

substrates that cannot be consumed in the mitochondria, such as branched or very

long chain fatty acids. Peroxisomal β-oxidation can occur through the peroxisome

proliferator-activated receptor (PPAR) pathway, or through the non-inducible

pathway which consists of branched-chain ACOX1 and 3, mRNA transcripts for

which were increased in the present array, (figure 5.6B, *3), along with D-

bifunctional protein (DBP) and other cofactors, all of which are over expressed in

CaP (Liu 2006). SLC27A family genes, also known as fatty acid transport proteins

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(FATP) are upregulated by insulin in adipocytes (Stahl et al. 2002). These genes

are mainly upregulated at the mRNA level by insulin in the present array data, with

increases in types 4 and 5 (type 3 is downregulated), suggesting that insulin would

increase fatty acid uptake in CaP cells. This observation of mainly increased fatty

acid uptake molecules is in line with the fact that CaP cells preferentially take up

fatty acids rather than glucose (Liu et al. 2010).

Over-expression of ACSL 3-5 has been noted in various cancers (Mashima et al.

2009a), and our group has previously demonstrated that ACSL3 is important for

converting fatty acids, primarily arachidonic acid, to their acyl-CoA counterparts,

which are then imported into the mitochondria to stimulate steroidogenesis (Locke

et al. 2010). Previously, activated arachidonate has been shown to increase

expression StAR (Cauley et al. 2006). Furthermore, in bovine granulosa cells, fatty

acids (palmitate, stearate, and oleate) are activated by ACSL enzymes, to increase

steroidogenesis (Vanholder et al. 2005). Palmitate and stearate are increased with

insulin treatment in LNCaP cells. In this chapter, it has been demonstrated that

insulin appears to increase ACSL3 in LNCaP cells and 22RV1 cells, though that

increase is not mirrored in our arrays. Array data shows upregulation of ACSL5 and

6, and these enzymes in human glioma cells have been shown to have anti-

apoptotic properties by disruption of apoptosis regulator BAX signalling.

Furthermore, inhibition of ACSL enzymes synergistically enhances the effect of the

chemotherapeutic etoposide in vitro and in vivo (Mashima et al. 2009a).

It was also demonstrated that mRNA of enzymes in the arachadonic acid pathway

were up regulated by insulin, including COX-2 and other prostaglandin/

inflammation promoting genes. Frequent use of COX-2 inhibitors decrease risk of

prostate, breast, lung, and colon cancer (Harris 2009). Of further interest, in rodent

cells, COX-2 upregulation has been shown to upregulate StAR and steroidogenesis

(Calejman et al. 2011). PTGDS has been cited as a potential biomarker, as well as a

therapeutic target for CaP progression due to its association with angiogenesis

(Bertucci et al. 2004; Yang et al. 2007). In this pathway, leukotrienes, like

prostaglandins, are important to the proinflammatory process, and HETEs have

been implicated in inhibition of apoptosis, angiogenesis, the proliferation of cancer

cells, and metastasis (Moreno 2009). 5-LOX and 12-LOX have higher

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immunohistochemical staining in CaP cells than in BPH, but the degree of staining

does not change with grading, where 15-LOX staining increases with grade (Harris

2009). EPHX2 mRNA is upregulated by insulin treatment and has been shown to

mirror AR mRNA expression in CaP, decreased after ADT, but reactivated at

castrate resistance; its inhibition synergized with the effect of anti-androgens

(Vainio et al. 2011). It is also a candidate biomarker. As well, carbonyl reductase 1

(CBR1) mRNA increased and this gene has been demonstrated to confer cytostatic

drug resistance in hepatocarcinoma cells (Huang et al. 2010). Interestingly, a recent

paper has demonstrated CBR1 to be a novel target of epigallocatechin gallate

(EGCG), which is also characterized as a FASN inhibitor (Lupu et al. 2006).

EGCG is also known to decrease tumour growth and mass in breast and prostate

cancer cells (Liao et al. 1995). Findings that insulin may upregulate fatty acid

biosynthesis, metabolism, and arachidonic acid metabolism indicate that these

pathways may provide helpful therapeutic targets in men with metabolic syndrome

after ADT.

Recent studies have shown the importance of lipogenic enzyme dysregulation in

both metabolic syndrome and cancer (Chen et al. 2009), and there has been much

interest in targeting metabolic pathways in both diseases (Flavin et al. 2010). The

overlap between the diseases shows promise for dual treatment. Of interest, Becker

et al. demonstrated that knock down of ACC decreased fatty acid synthesis, and

caused CaP cell death (Becker et al. 2007). Combining metabolic syndrome with

cancer findings Chen, et al. have shown that Polygonum hypoleucum Ohwi , a

traditional remedy of many diseases, including arthritis and rheumatoid arthritis

(inflammatory diseases), which targets ACC, decreased FASN and ACC activity in

HepG2 cells, as well as improving insulin resistance and serum lipid composition in

mice with diet induced metabolic syndrome (Chen et al. 2009). It has also been

proposed that inhibition of ACC may improve cardiovascular outcomes for

metabolic syndrome patients (Harwood et al. 2003). It may be that targeting fatty

acid synthesis would be therapeutic for both CaP and metabolic syndrome, and the

overlap of both diseases.

Though FASN in cancer and metabolic syndrome does not seem to be affected by

classical fatty acid regulation, there has been great interest of late in the omega 3

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and 6 (n3, n6) polyunsaturated fatty acids (PUFA) for aggravation or suppression of

both conditions (Harvei et al. 1997; Esposito et al. 2004; Carpentier et al. 2006;

Harris et al. 2006; Pardini 2006). Intake of higher levels of n3 PUFAs, as compared

to n6, has been demonstrated to decrease incorporation of arachidonic acid into

plasma membranes (Bagga et al. 2003; Larsson et al. 2004). In cancer, arachidonic

acid is the main substrate for synthesis of prostaglandins and leukotrienes, synthesis

of which may be upregulated by insulin, as has been demonstrated above. Insulin

increases fatty acid transport proteins (FATP) which promotes uptake of extracellar

fatty acids. If more n3 PUFAs are available, they will be incorporated into

membranes and be more likely to be used compared to arachidonic acid. Fatty acids

derived from n3 fatty acids have greater affinity for elongases and desaturases than

arachidonic acid does, minimizing conversion of fatty acids to metastasis and

mitogenic factors (Bagga et al. 2003; Muhlhausler et al. 2010). Furthermore, the

decrease in arachidonic acid supply has been demonstrated to suppress oestrogen

metabolism in breast and ovarian cancer cells, and our group has recently

demonstrated that decreased arachidonic acid decreases steroidogenesis in CaP

(Locke et al. 2010).

It has also been demonstrated that n3 PUFA incorporation into non-cancerous cell

membranes can change the fluidity and decrease insulin resistance (de Santa-Olalla

et al. 2009). It has been previously demonstrated that suppression of genes for fatty

acid biosynthesis sensitizes cancer cells to chemotherapeutics, and the same has

been shown for increased intake of n3 PUFAs; furthermore, it has also been

demonstrated that these fatty acids may sensitize tumours to radiation while

minimizing systemic toxicity in breast cancer patients (Bougnoux et al. 2010). Of

particular relevance, a recent study by Muhlausler et al. has demonstrated the

ability for n3 PUFAs to down-regulate lipogenesis enzymes in the liver

(Muhlhausler et al. 2010). These findings demonstrate the wide ranging

involvement of fatty acids in cancer growth but also demonstrate that inhibition of

lipogenic enzymes and interference with their metabolism may be a key step in

cancer/ metabolic syndrome treatment. Dietary intervention may have synergistic

effects with classical medications (Esposito et al. 2004; Itsiopoulos et al. 2009).

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In summary, this chapter demonstrated that 1) insulin upregulates enzymes

involved in lipogenesis and lipid-activated steroidogenesis at both the mRNA and

protein level, 2) insulin increases triglyceride/ cholesterol-ester storage and de novo

fatty acid synthesis through both INSR and IGF1R, 3) fatty acids upregulated by

insulin in this model system are those that have been shown to be involved in

pathways that stimulate cancer growth in other models, and 4) insulin upregulates

lipid/ fatty acid related pathways that promote tumour growth/ inhibit apoptosis.

Array data suggests that insulin increase uptake of exogenous fatty acids by

upregulation of the fatty acid transport proteins, which may contribute to lipid

storage (Wu et al. 2006; Ehehalt et al. 2008) which is then used for β-oxidation or

signalling (steroidogenic, angiogenic, inflammatory, and antiapoptotic), as would

de novo synthesized fatty acids. It is of the utmost importance to understand that

alleviation of the metabolic syndrome in prostate cancer may attenuate cancer

progression; therefore, inhibition of lipogenesis enzymes for cancer therapeutic

purposes may relieve some symptoms of the metabolic syndrome. Because it has

been demonstrated that selective receptor inhibition may not totally quell

lipogenesis/ fatty acid metabolism, it may be that an inhibitor of FASN and lipid

metabolism must specifically target FASN/ ACC, or work at an intersection of

multiple pathways.

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Chapter 6: Drugs Used in Metabolic Syndrome,

Metformin and Simvastatin, Inhibit Fatty Acid

Synthesis and Steroidogenesis in Prostate Cancer

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161

6.1 Introduction

Our group and others have shown the importance of fatty acid synthesis and

steroidogenesis in prostate cancer (CaP) (Swinnen et al. 2002; Ettinger et al. 2004;

Swinnen et al. 2004; Dillard et al. 2008; Locke et al. 2008; Leon et al. 2010).

Furthermore, we have recently shown that insulin acts directly on CaP cells to

induce steroidogenesis (Lubik et al. 2011) (chapters 3 and 4), as well as fatty acid

synthesis (chapter 5) . The pharmacological agents simvastatin and metformin are

used clinically to improve metabolic lipid and insulin profiles. This chapter

investigates their action in CaP cells, in particular in regard to their effect on

important survival pathways: cholesterol synthesis, steroidogenesis, and fatty acid

synthesis.

Metabolic syndrome has become a major public health issue in western countries,

with studies suggesting that 40% of the population of America has some form of

the malady (Santana et al. 2008). Metabolic syndrome is classified in many ways

but in general it is characterized by central obesity, insulin resistance, high serum

glucose levels, systemic arterial hypertension and dyslipidemia (Santana et al.

2008). Other studies indicate that men with metabolic syndrome have higher

prostate cancer (CaP) risk (Laukkanen et al. 2004; Hsing et al. 2007; Pawni et al.

2010). However, recent studies identified an association between serum C-peptide

levels and CaP mortality (Smith et al. 2006; Fowke et al. 2008; Isbarn et al. 2008;

Ma et al. 2008; Cox et al. 2009). Men with C-peptide in the highest quartile were

2.7-4 times more likely to die of CaP (Ma et al. 2008; Cox et al. 2009). Evidence

suggests that both body mass index (BMI) and high serum insulin levels are

independent markers of poor patient prognosis, increased disease aggression and

greater cancer mortality (Ma et al. 2008), and obese patients often have higher

grade cancers and high recurrence rates (Buschemeyer et al. 2007). BMI is

associated with altered insulin levels, as well as levels of other hormones, such as

testosterone, IGFs, and estrogens - elevation of which is linked to CaP (Engeland et

al. 2003; Ma et al. 2008). Obese patients had higher grade cancers (that is higher

Gleason scores), more positive surgical margins and higher recurrence rate (Hsing

et al. 2007; Jayachandran J 2008) and indeed in two studies of over 10 000 men,

those with high BMI were more likely to be diagnosed with high grade, metastatic

or fatal, disease than with local disease (Buschemeyer et al. 2007; Kasper et al.

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2008). Part of this issue may be the difficulty of detecting CaP early in obese men,

due to the difficulty of the digital rectal exam (DRE) and relatively lower PSA of

obese men (Buschemeyer et al. 2007).

Obesity is associated with excess nutrient intake combined with a sedentary

lifestyle (Buschemeyer et al. 2007). In mice, diet induced hyperinsulinemia may

lead to increased activation of cancer proliferation pathways and aggressiveness of

growth; furthermore, murine studies show that high carbohydrate diets contribute to

elevated serum insulin and are associated with a 45% increase in prostate tumour

size (Venkateswaran et al. 2007). Lifestyle intervention, such as diet and exercise,

for reduction of insulin levels has beneficial effects on CaP patients (Tymchuk et

al. 2001; Venkateswaran et al. 2007; Carmody et al. 2008; Cox et al. 2009;

Aronson et al. 2010). The above studies demonstrate that obesity and

hyperinsulinemia, which are key features of metabolic syndrome, are highly

correlated with poor CaP prognosis.

Androgen deprivation therapy (ADT), the hallmark treatment for advanced CaP, is

associated with a plethora of complications, one of which is the development of

several key features of the metabolic syndrome in 55% of patients, with

concomitant hyperinsulinemia, which develops rapidly (Flanagan et al. 2010;

Gallagher et al. 2010). It has been shown that men who develop the metabolic

syndrome phenotype have increased rates of progression to castrate resistance and

decreased overall survival (Braga-Basaria et al. 2006; Cohen 2009; Flanagan et al.

2010). Furthermore, serum C-peptide levels have been positively correlated to PSA

levels (Nandeesha et al. 2008; Flanagan et al. 2010; Neuhouser et al. 2010). Most

studies support our findings that insulin stimulates CaP survival pathways

(Hammarsten et al. 2005; Gallagher et al. 2010), and we have shown that insulin

stimulates steroidogenesis (Lubik et al. 2011) (chapter 3) and lipogenesis (chapter

5). Clinically, these may indicate potential avenues for therapeutic intervention of

CaP progression.

There has been much interest of late in drugs used to treat metabolic syndrome as

cancer therapies. Metformin has been used to treat polyuria (excessive urination, a

symptom of diabetes) since the middle ages (Pollak 2010b), and is one of the most

widely prescribed treatments for type 2 diabetics in the world (Ben Sahra et al.

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2010b). Further, metformin has a favourable side effect profile, causing indigestion,

headache, and diarrhea, and rare cases of lactic acidosis in patients who have pre-

existing renal conditions (Ben Sahra et al. 2010b). It is very effective at lowering

hyperinsulinemia and hyperglycemia and in the treatment of polycystic ovarian y

syndrome (PCOS) (Cheang et al. 2009; Pollak 2010b). There is also much interest

in metformin as a chemo-sensitizer in breast, prostate and lung cancers (Gallagher

et al. 2010; Patel et al. 2010; Sanli et al. 2010). Metformin is known to decrease

serum levels of insulin and insulin-like growth factor-1 (IGF1) (Anisimov 2003),

both of which are associated with cancer progression (Cardillo et al. 2003).

Diabetic patients on metformin had 40% lower incidence of cancer than patients

treated with sulfonylureas or insulin, both of which are associated with increased

cancer risk (Engeland et al. 2003), suggesting that metformin greatly suppresses

insulin related cancer risk.

While the exact mechanism of metformin action on cancer cells is not known, it is

postulated to function through phosphorylation/ activation of AMP-activated kinase

(AMPK) (Engelman et al. 2010; Jalving et al. 2010). AMPK is a cellular energy

regulating “switch.” When activated by increased intracellular AMP:ATP ratios, it

triggers an increase in ATP producing enzymes, as well as down-regulating those

unnecessary for survival, including the key enzymes for steroid and fatty acid

synthesis, such as sterol regulatory element binding protein (SREBP), fatty acid

synthase (FASN), and acetyl CoA carboxylase (ACC) in normal liver and many

cancer types (Fulgencio et al. 2001; Zhou et al. 2001; Luo et al. 2005). Because

CaP aggressiveness is highly associated with increased lipogenesis, the down-

regulation of these enzymes by metformin may have attenuating effects on CaP

progression (Swinnen et al. 1996; Swinnen et al. 2002). There is also evidence that

AMPK activation down regulates 3-hydroxy-3-methylglutaryl (HMG)-CoA

reductase (HMGR) which is responsible for the formation of cholesterol, the

backbone of androgen synthesis (Fisslthaler et al. 2007). In PCOS, a disorder in

females characterized by increased local androgen production, caused by insulin, as

well as systemic hyperinsulinemia and insulin resistance, metformin is seen to

decrease serum testosterone levels and improve the serum lipid profile

(Banaszewska et al. 2009; Cheang et al. 2009). While the effects of metformin

would be targeting improvement in peripheral insulin sensitivity and decrease in

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circulating insulin levels, some studies report a decrease in ovarian androgens

following metformin treatment, while others do not (Banaszewska et al. 2009).

During the progression of CaP following ADT systemic hyperinsulinemia develops

which exacerbates intraprostatic steroid/ androgen production (Braga-Basaria et al.

2006; Locke et al. 2008; Leon et al. 2010; Lubik et al. 2011). The hyperinsulinemic

and hyperandrogenic similarities between these two conditions suggest that

metformin may attenuate the effects on steroidogenesis in CaP as it appears to in

PCOS.

Statins are also shown to have potential for the prevention/ treatment of hormone

dependent cancers in both women and men (Cauley et al. 2006; Browning et al.

2007; Borgquist et al. 2008; Yu et al. 2009; Brennan et al. 2010; Krane et al.

2010), as well as metabolic syndrome and PCOS (Bulcao et al. 2007; Kodaman et

al. 2008; Banaszewska et al. 2009). Statins are used to lower serum cholesterol

levels by inhibition of HMGR, which prevents cholesterol synthesis. Statins have

been shown to decrease risk of biochemical recurrence after radical prostatectomy

or radiation therapy (Hamilton et al. 2010, Loeb et al. 2009). One study even touts

that wide-spread statin use, above screening programs, is responsible for the

declining CaP mortality in the United States (Colli et al. 2008) with the finding that

men on statins had lower PSA scores and lower cholesterol, despite being older and

more overweight. Statin use is associated with reduction in risk of aggressive,

metastatic or fatal CaP, however the mechanisms behind these epidemiological

studies have not been fully investigated. Statins have been shown to improve

insulin sensitivity and lipid/ cholesterol profiles as well as decreasing adrenal

levels of steroidogenic enzymes (Kodaman et al. 2008). Whether statins lower

serum testosterone levels is under debate (Smals et al. 1991; Barclay 2009;

Stanworth et al. 2009). Of relevance to the hypothesis that statins may be potential

cancer therapeutics, statins may also down regulate SREBP1 (Banaszewska et al.

2009), which is an important enzyme in lipogenesis and steroidogenesis (Ettinger et

al. 2004; Bennett et al. 2008; Banaszewska et al. 2009; Lubik et al. 2011).

Cholesterol is essential for androgen and steroid synthesis, and steroidogenesis is a

key pathway for CaP cell survival; therefore, statin HMGR inhibitors (such as

simvastatin) may inhibit cancer growth by interrupting this pathway.

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Because metformin and simvastatin may down regulate important survival

pathways in CaP, namely steroidogenesis and lipogenesis (figure 6.1), the effects of

these drugs on LNCaP cells has been investigated in our model of

hyperinsulinemia. Importantly, this data shows that metformin may decrease both

pathways at the expression and output levels, in a hyperinsulinemic environment;

whereas, unexpectedly, the suppressive effects of simvastatin appear to be

dependent on insulin status, with inhibitory effects relative to insulin induction of

these pathways, but stimulatory effects in the absence of insulin.

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Figure 6.1: Steroidogenesis and lipogenesis pathway showing insulin action on

both pathways and proposed areas of inhibition by metformin and simvastatin.

Metformin activates AMPK, which inhibits SREBP and downstream signalling;

whereas, simvastatin inhibits HMGR.

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6.2.1 In vitro model: LNCaP cells were cultured in phenol red-free RPMI 1640

(Invitrogen, Melbourne, Australia), and 5% foetal calf serum (FBS; Hyclone,

Sigma, Australia). Cells were plated in FBS and at 60% confluence were changed

to 5% charcoal stripped serum (CSS) medium for 24hr, followed by 24hr starvation

in serum-free medium after which cells were treated with 10nM insulin (Sigma) for

48hr or 72hr in the presence or absence of 5mM Metformin (Sigma) or 5µM

Simvastatin (Sigma) or with vehicle control (DMSO). Insulin was refreshed at

24hr. Concentrations were chosen based on effective non-toxic levels from

previous studies (Zhuang et al. 2005; Hoque et al. 2008; Berstein et al. 2010; Ben

Sahra et al. 2010a). All samples were treated with DMSO and PBS vehicle controls

for direct comparison.



before reverse transcription with Superscript III reverse transcriptase (Invitrogen,



detection (Applied Biosystems). Primers used were: HMGR, HMGS, SREBP,

StAR, CYP17A1, HSD17B3, RDH5, PSA, HSL, ACC, FASN, and rpl32. Primers

were designed by Primer 3 software from coding segments of genes, obtained from

the NCBI data bank and ordered from Sigma Proligo (Castle Hill, NSW, Australia).

For Primer sequences, see Appendix A. Gene expression was normalized to the

housekeeping gene rpl32, then expressed relative to the vehicle control at the same

time point. Cycling conditions are shown in chapter 2.7. Data was analyzed with

SDS 2.3 software by means of the ∆∆Ct method (Livak et al. 2001). Experiments

were repeated a minimum of 5 times.








168


visualized using the Li-Cor Odyssey Imager (Li-Cor Biosciences, Lincoln, USA).

Experiments were repeated at least 3 times. Antibodies used were HMGR

(Millipore), SREBP (Santa Cruz), StAR (Abcam), CYP17A1 (Abcam), HSD17B3

(Abnova), RDH5 (Abnova), HSL (Abcam), ACC (Santa Cruz), and FASN (Santa

Cruz). For antibody information, please see Appendix B.

6.2.4 Intracellular cholesterol assay: kindly performed by Hyeongsun Moon,

student of Michelle Hill, University of Queensland: 25µg of LNCaP protein lysate

was used to determine the amount of cholesterol in cells compared to total protein

by means of the Amplex® Red Cholesterol Assay Kit which was used according to

manufacturer’s instructions (Invitrogen). This Assay measures µg cholesterol per

mg protein from cell lysates as described in chapter 2.8. Experiments were repeated

a minimum of three times.

6.2.5 De novo steroid/ cholesterol analysis using radiometric detection:

LNCaP cells were grown in 6 well plates and treated as described in chapter 6.2.1.

At the time of insulin treatment, 6µCi/ml 14C-acetate (PerkinElmer, Woodbridge,

Ontario) was added to each plate for co-incubation. Steroids from 2ml of medium

were extracted with 75/25 hexane/ ethanoloacetate (water-equilibrated ethyl

acetate), dried down and resuspended into 75ul 50% methanol. Detailed method is

described in chapter 2.12. These samples were analysed on the Waters Alliance

2695 HPLC System and Packard Radiometric Detector 150TR Flow Scintillation

Analyzers. Peaks were identified by comparison of retention times to Mix 10

steroid standard (Sigma).

6.2.6 Oil-Red O lipid stain:

Cells were treated as above, except that medium was supplemented with unlabeled

36µM sodium acetate. Oil Red-O lipid quantitation was conducted as described in

chapter 2.13.

6.2.7 Statistics:

All statistical analysis was carried out using ANOVA with the Bonferroni’s

Multiple Comparison post test. All experiments were analysed with the two-tailed

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student’s t-test assuming equal variance on Graphpad Prism 5 software (significant

p-value was evaluated to be 0.05 of lower). T-test is referenced when values

approach significance by ANOVA.

6.3 Results

6.3.1 Metformin inhibits insulin-induced cholesterol synthesis

In the present study, it has been shown that in LNCaP cells, insulin increased the

mRNA levels of both HMG-CoA synthase (HMGS) and reductase (HMGR) 2-fold

(p<0.05) (figure 6.2A). In the presence of metformin, with and without insulin,

HMGS increased 3-fold (p<0.05). The insulin fuelled increase in HMGR (p<0.05),

is prevented by metformin. At the protein level, insulin increases HMGR 1.75-fold,

though both basal levels and insulin stimulation are heavily suppressed by

metformin (figure 6.2B). The effects of metformin on intracellular levels of

cholesterol were then pursued (figure 6.2C). Insulin caused a non-significant

increase in total cholesterol levels in the LNCaP cell line; however, metformin

trends toward reduction of basal cellular cholesterol, though it does decrease

cholesterol in the presence of insulin (p<0.05). Furthermore to investigate de novo

cholesterol synthesis, the incorporation of 14C-acetate into cholesterol was

investigated (figure 6.2D). Cholesterol synthesis was increased with insulin

treatment, approximately 2.5 fold, and metformin blocked cholesterol synthesis

entirely. Because the extraction method used was not optimized for cholesterol,

these values are more qualitative than quantitative. These findings indicate that

metformin decreases enzymes and proteins which contribute to cholesterol

synthesis, resulting in reduced cellular cholesterol levels in the presence and

absence of insulin.

6.3.2 Metformin inhibits steroidogenesis

We have recently established that insulin increases steroidogenesis in CaP cells

(Lubik et al. 2011) (chapter 3); therefore, the effect of metformin on some of the

key enzymes of the steroidogenesis pathway has been examined. SREBP mRNA is

upregulated by insulin (p<0.05); however, that increase was not seen in the

presence of metformin (p<0.05) (figure 6.3A). Sterol acute regulatory protein

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171

Figure 6.2: Insulin treatment increases cholesterol production in LNCaP cells in

the absence of metformin. (A) Following 48 hour insulin treatment (10nM), +/-

5mM metformin, RNA was extracted from LNCaP cells and used for QRT-PCR

analysis of cholesterol pathway enzymes. Results were analyzed by the ∆∆Ct

method and normalized to rpl32 as a control gene, before normalization to vehicle-

treated (no insulin) for the equivalent time point. Experiments were performed at

least 6 times. (B) Western blot analysis of HMGR following 48hr insulin treatment

+/- 5mM metformin. Mean densitometry values are shown. Experiments were

performed at least 3 times. (C) Intracellular total cholesterol assay shows no change

with insulin or metformin alone, and a decrease when both insulin and metformin

are present. Fold change is represented. Experiments were performed at least 6

times. (D) Medium was collected from LNCaP cells after 72hr incubation with

insulin and 6µCi/ml 14C acetate before HPLC and radiometric detection were used

to quantitate extracellular cholesterol. De novo cholesterol synthesis was

undetectable with metformin. Experiments were done in triplicate.

Error bars represent SE (black*= statistical difference from control;

white*=statistical difference from insulin only, p<0.05, as determined by ANOVA,

with Bonferroni’s Multiple Comparison post hoc test).

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173

Figure 6.3: Metformin decreases LNCaP steroidogenesis. (A) Following 48 hour

insulin treatment (10nM), +/- 5mM metformin, RNA was extracted from LNCaP

cells and used for QRT-PCR analysis of steriodogenesis enzymes. Results are

analyzed by the ΔΔCt method and normalized to rpl32 as a control gene, before

normalization to vehicle-treated (no insulin) for the equivalent time point.

Experiments were performed at least 6 times. (B) Western blot analysis of

steroidogenesis enzymes following 48hr insulin treatment +/- 5mM metformin.

Mean densitometry values are shown. Experiments were performed at least 3 times.

(C) Medium was collected from LNCaP cells after 72hr incubation +/- 10nM

insulin, +/- 5mM metformin and 6µCi/ml 14C acetate before HPLC and radiometric

detection were used to identify and quantitate extracellular steroids. Statistically

significant insulin induction was seen in the medium for testosterone using

ANOVA. Increases in androstendione and pregnan3,20dione were significant by t-

test only (n=3). Insulin induced steroid increase was suppressed with metformin.



Bonferroni’s Multiple Comparison post hoc).

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(StAR), a rate limiting enzyme of steroidogenesis and cholesterol uptake into the

mitochondria, is upregulated by insulin at early time points (Lubik et al. 2011), but

not at 48hrs (figure 6.3A); in presence of metformin alone there was a non-

significant decrease in StAR mRNA, though 75% increase is seen with insulin and

metformin (p<0.05). Cytochrome P450 family member (CYP)17A1, which

catalyses numerous steps in synthesis of testosterone from cholesterol including

rate limiting conversion of pregnanolone to downstream substrates, is upregulated

by insulin 2-fold, an effect which is quelled by metformin (p<0.05). Retinol 11-cis

dehydrogenase (RDH5) is one of the main enzymes of the “backdoor pathway,”

which produces dihydrotestosterone (DHT) without a testosterone intermediate.

RDH5 was upregulated more than 2-fold by insulin. In the presence of metformin,

RDH5 was decreased from basal levels and the insulin induction appeared to be

suppressed, though not statistically significantly. Insulin upregulated 17β-

dehydrogenase-3 (HSD17B3), which converts androstenedione to testosterone, 3-

fold (p<0.05). Metformin upregulates HSD17B3 mRNA to a greater extent than

insulin alone and was synergistic in the presence of insulin (p<0.05). PSA induction

is a surrogate for androgen receptor (AR) activation by steroids, and we have

previously shown that insulin induces steroidogenesis to levels sufficient to activate

AR and increases PSA mRNA levels, more at 16 and 24hrs, compared to 48hrs

(Lubik et al. 2011). At 48hr, PSA was induced by insulin (p<0.05); however,

metformin decreased PSA expression at least 50% from basal levels (p<0.05).

At the protein level (figure 6.3B), insulin increased expression of SREBP (1.77-

fold), CYP17A1 (1.5-fold), and RDH5 (1.37-fold) (p<0.05). In the presence of

metformin the protein levels of all the steroid enzymes, except StAR, were

suppressed irrespective of insulin treatment. The increase in StAR is unexpected;

however, it was recently shown that in macrophages over-expression of StAR may

actually lead to suppression of cholesterol synthesis enzymes and increase in

cholesterol efflux (Taylor et al. 2010). Despite the fact that StAR expression is

increased in CaP cells where cholesterol synthesis is increased (Locke et al. 2008;

Lubik et al. 2011), metformin in the presence of insulin may initiate the metabolic

state necessary for StAR inhibition of cholesterol synthesis.

To examine whether metformin actually decreased insulin induced steroidogenesis,

LNCaP cells were incubated with 14C-acetate for 72hr in the presence of inhibitors

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(figure 6.3C). Insulin increased levels of steroids detected by this extraction

method. Testosterone levels are increased approximately 2.4-fold (p<0.05,

ANOVA, Bonferroni’s Multiple Comparison post hoc), androstenedione 1.5 fold,

and pregnane3,20 dione 1.5 fold (p<0.05, student t-test, not by ANOVA). Increases

in other steroids with insulin would likely have reached significance with increased

number of replicates (n>3). Metformin suppresses insulin effects on steroidogenesis

to basal level or below for all steroids in the presence and absence of insulin.

6.3.3 Metformin decreases lipid content in CaP cells

Fatty acid synthesis is important in CaP progression, shown by its involvement in

multiple pathways associated with survival and tumour aggressiveness, which

promote more rapid tumour growth and spread (Swinnen et al. 1997b; Swinnen et

al. 2002; Swinnen et al. 2004). The enzymes fatty acid synthase (FASN) and acetyl

CoA carboxylase (ACC) are responsible for fatty acid synthesis, which, in contrast

to the glucose dependency of other tumour types, provides the main energy source

for CaP cells (Baron et al. 2004), as well as providing substrates for autocrine/

paracrine signalling and membrane synthesis (Swinnen et al. 2006). Locke et al.

2010 also demonstrated the importance of producing and activating fatty acids for

arachidonic acid synthesis Under castrate conditions in vivo or under hormonal

mediation in vitro, hormone sensitive lipase (HSL) cleaves cholesterol and fatty

acids from the cell membrane (or lipid storage droplets inside the cell) and converts

fatty acids to arachidonic acid, which is activated and imported into the

mitochondria where it works with StAR and diazepam-binding inhibitor/ acyl-CoA-

binding Protein (DBI) to initiate steroidogenesis (Locke et al. 2010)(figure 6.1).

ACC and FASN are upregulated by insulin at the mRNA level and protein levels

(figure 6.4A, B). Metformin inhibits insulin induction of HSL expression and

decreases base levels by 30-70% (p<0.05). Insulin upregulates FASN 3.75-fold

(p<0.05); however, insulin induced FASN levels are decreased to base level or

below with metformin. ACC, which provides the precursors to fatty acid synthesis,

is induced 2.5 fold with insulin (p<0.05), and that induction is suppressed to base

level or below in the presence of metformin. Western blots largely mirror these

findings at the protein level for ACC and FASN, with increases of 1.5 and 1.7

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(p<0.05)


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Figure 6.4: Metformin decreases LNCaP lipogenesis. (A) Following 48hr insulin

treatment (10nM), +/- 5mM metformin, RNA was extracted from LNCaP cells and

used for QRT-PCR analysis of fatty acid metabolism/ synthesis enzymes. Results

are analyzed by the ∆∆Ct method and normalized to rpl32 as a control gene, before

normalization to vehicle-treated (no insulin) for the equivalent time point, n=6. (B)

Western blot analysis of lipogenesis enzymes following 48hr insulin treatment +/-

5mM metformin. Mean densitometry values are shown, experiments were

performed at least 3 times. (C) Intracellular neutral-lipid (cholesterol-ester,

triglyceride) levels were measured with Oil Red-O assay. Values are corrected for

background staining on culture plates. Insulin increases intracellular lipid, though

the levels are brought to one third of base level with metformin only and insulin

induced lipid levels are suppressed to approximately one third as well. Mean

corrected absorbance values are shown; experiments performed at least 6 times.




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respectively; however, increase in HSL protein is suggested in the presence of

insulin, with suggested decrease in the presence of metformin (figure 6.4B), despite

large decreases in the presence of metformin at the mRNA level (figure 6.4A). HSL

protein may be stabilized by modification, and reduction may be seen at a later time

point. Metformin may also affect the functional level.

At the functional level, insulin increased neutral lipid and triglyceride content in

LNCaP cells by ~ two-fold, as demonstrated by Oil Red-O assay(figure 6.4C).

Metformin itself decreases basal or insulin upregulated lipid content by over 50%

(p<0.05).

6.3.4 Simvastatin inhibits insulin induced cholesterol synthesis

The effect of simvastatin on these pathways, in the presence and absence of insulin,

is very different than those of metformin in CaP cells. Interestingly, the two

inhibitors appear to have similar effects to each other in PCOS (Banaszewska et al.

2009). Very large increases in HMGS and HGMR mRNA were seen with

simvastatin in the presence and absence of insulin (p<0.05) (figure 6.5A). HMGS

mRNA is upregulated 40 and 55-fold with simvastatin in the absence and presence

of insulin, respectively. HMGR mRNA is increased 10-fold by simvastatin with or

without insulin (p<0.05). At the protein level, insulin increases HMGR 1.75-fold

(figure 6.5B). Interestingly, HMGR was increased by simvastatin, but suppressed in

the presence of insulin. It has been shown that metformin has different effects on

cellular metabolism depending on the metabolic state of the host and studies

indicate this may be true for simvastatin as well, as, though both diabetic and

nondiabetic patients have similar overall reduction in lipid-mediated coronary

complications, when basal risk is considered, diabetic patients benefit more from

statin treatment than nondiabetics (Costa et al. 2006; Anisimov et al. 2011).

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Figure 6.5: Insulin treatment increases cholesterol production in LNCaP cells in

the absence of simvastatin. (A) Following 48 hour insulin treatment (10nM), +/-

5µM simvastatin, RNA was extracted from LNCaP cells and used for QRT-PCR

analysis of cholesterol pathway enzymes. Results were analyzed by the ΔΔCt


treated (no insulin) for the equivalent time point. Experiments were performed at

least 6 times. (B) Western blot analysis of HMGR following 48hr insulin treatment

+/- 5µM simvastatin. Mean densitometry values are shown. Experiments were

performed at least 3 times. (C) Intracellular cholesterol assay shows a decrease in

fold change from base level with simvastatin in the presence and absence of insulin.

Experiments were performed at least 6 times. (D) Medium was collected from

LNCaP cells after 72hr incubation with insulin and 6µCi/ml 14C acetate before

HPLC and radiometric detection were used to quantitate extracellular cholesterol.

De novo cholesterol synthesis was undetectable with simvastatin. Experiments were

done in triplicate.




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Because the increase in HMGR seemed counterintuitive, the intracellular levels of

cholesterol were then measured (figure 6.5C). There was no increase in total

cellular cholesterol content with insulin. Simvastatin decreased intracellular

cholesterol levels in both the presence and absence of insulin (p<0.05). To

investigate the correlation between de novo and total cholesterol, the incorporation

of 14C-acetate into cholesterol was investigated as above (figure 6.5D). Cholesterol

synthesis was increased with insulin treatment, but simvastatin inhibited new

cholesterol synthesis entirely. Because the extraction method used was not

optimized for cholesterol, these values are more qualitative than quantitative. As a

decrease in total cholesterol was evident with simvastatin and complete inhibition

of cholesterol synthesis with simvastatin, the increases in mRNA levels appear to

be compensatory responses to inhibition of HMGR.

6.3.5 Simvastatin inhibits insulin induced steroidogenesis

Reduction of cellular cholesterol levels by simvastatin suggests that there would be

a decrease in precursors for steroidogenesis; therefore, the effect of simvastatin on

this pathway was examined. SREBP mRNA was upregulated by insulin (p<0.05);

however, levels decreased below basal with simvastatin (p<0.05) (figure 6.6A).

Simvastatin decreased basal StAR mRNA by approximately 30-40% whether

insulin was present or not (p<0.05). Simvastatin effectively suppresses insulin

induction of CYP17A1 and RDH5 mRNA (p<0.05). Surprisingly, simvastatin

appeared to upregulate HSD17B3 mRNA to a similar extent as insulin, yet

represses the insulin induction of HSD17B3 mRNA when both were present to a

greater extent than insulin alone and was synergistic in the presence of insulin

(p<0.05). At 48hr, simvastatin suppressed insulin induction of PSA and decreased

PSA expression by at least 50% compared to basal levels in the absence of insulin

(p<0.05).

At the protein level (figure 6.6B), insulin increased expression of SREBP (1.77-

fold), CYP17A1 (1.5-fold), and RDH5 (1.37-fold). Simvastatin suppressed insulin

activation of all of the investigated steroidogenesis enzymes; however, in the

absence of insulin, SREBP was upregulated, which was not expected., but which

may be a consequence of lipid starvation feedback (Brown 2007). To investigate

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the functional output level, LNCaP cells were incubated with 14C-acetate for 72hr

in the presence of simvastatin (figure 6.6C). As explained above, testosterone

(p<0.05, ANOVA), androstenedione, and pregnan3,20dione (p<0.05 t-test) were

significantly increased with insulin. Simvastatin prevents steroid synthesis in the

presence of insulin. Unexpectedly, simvastatin increased testosterone 3.8 fold

(p<0.05, ANOVA), androstenedione 1.7 fold (p<0.05, t-test), and preganolone 1.6

fold (p<0.05, t-test). This result was surprising given the reduced levels of cellular

cholesterol; therefore, further investigation is necessary. However, because SREBP

and HSD17B3 are increased with simvastatin alone at the protein level, it is

possible that the cells are responding to HMGR inhibition by increasing turnover of

any acetate that was converted to cholesterol into steroids for survival. The reason

that there do not appear to be studies showing statins exacerbating CaP progression

may be that the men who are prescribed statins have pre-existing obesity, with

accompanying metabolic side effects, which are offset by statins (El-Serag et al.

2009). Furthermore, statins have also been shown to decrease the protein levels of

AR in CaP cells, which would render the excess steroid ineffective, which might

explain the decrease in PSA mRNA with simvastatin treatment in the presence of

insulin (Yokomizo et al. 2010). Conversely, it may be that the interaction between

insulin and simvastatin overwhelms or confounds the SREBP pathway, as insulin

would upregulate SREBP-1 and simvastatin would upregulate SREBP-2, which

might compete for SRE binding sites or interrupt signalling mechanisms, which are

already dysregulated in CaP, effectively blocking transcription (Scharnagl et al.

2001; Bennett et al. 2008). ELISA analysis of DHT in LNCaP and 22RV1 media

shows a decrease in DHT levels with simvastatin treatment (data not shown);

therefore, the increase in de novo synthesized steroids in figure 6.6C with

simvastatin may be an experimental artefact.

6.3.6 Simvastatin affects lipid content in CaP cells

Because of the importance of lipogenesis in CaP progression, the effect of

simvastatin on this pathway of CaP progression was investigated. At the mRNA

level, simvastatin inhibits insulin induction of HSL and decreased base levels by

30-70% (p<0.05) (figure 6.7A). At the protein level, there is a trend toward HSL

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Figure 6.6: Simvastatin decreases LNCaP steroidogenesis in insulin treated cells.

(A) Following 48 hour insulin treatment (10nM), +/- 5µM simvastatin, RNA was

extracted from LNCaP cells and used for QRT-PCR analysis of steriodogenesis

enzymes. Results are analyzed by the ΔΔCt method and normalized to rpl32 as a

control gene, before normalization to vehicle-treated (no insulin) for the equivalent

time point. Experiments were performed at least 6 times. (B) Western blot analysis

of steroidogenesis enzymes following 48hr insulin treatment +/- 5µM simvastatin.

Mean densitometry values are shown. Experiments were performed at least 3 times.

(C) Media was collected from LNCaP cells after 72hr incubation +/- 10nM insulin,

+/- 5µM simvastatin and 6µCi/ml 14C acetate before HPLC and radiometric

detection were used to identify and quantitate extracellular steroids. Statistically

significantly increased steroid levels were seen in the medium for testosterone

using ANOVA analysis with insulin treatment, and with simvastatin treatment;

whereas, androstendione and pregnan3,20dione were significant with t-test only.

No steroid peaks were discernable in the presence of simvastatin and insulin

together. Experiments were done in triplicate.




186

increase with insulin, which is not seen in the presence of simvastatin (figure 6.7B).

As demonstrated above, insulin increased ACC and FASN expression at the mRNA

and protein level (p<0.05). Simvastatin alone increased FASN mRNA and protein

approximately 2-fold in the presence and absence of insulin. This may be attributed

to cellular activation of SREBP2, in response to low cholesterol levels (Schoonjans

et al. 1999). A similar finding has been demonstrated in HEPG2 cells (Scharnagl et

al. 2001). ACC, which provides the precursors to fatty acid synthesis, is induced

2.5 fold with insulin (p<0.05), and that induction is suppressed to base level in the

presence of simvastatin. Western blots largely mirror these findings at the protein

level (figure 6.7B).

At the functional level, insulin increased neutral lipid (cholesterol-ester and

triglyceride) content in LNCaP cells by approximately two-fold, as demonstrated

by Oil Red-O assay (figure 6.7C), Simvastatin itself appears to have an inhibitory

effect on insulin induced lipid synthesis (p<0.05, t-test), while increasing lipid

levels from basal in the absence of insulin.

6.4 Discussion

Established risk factors for CaP include hyperinsulinemia, hypertension, obesity,

dyslipidemia, hyperuricemia, and high alanine amino transferase, which together

indicate that there is a metabolic syndrome component in CaP; and similarly, type 2

diabetes has a statistical association with lethal CaP (Hammarsten et al. 2005).

Multiple studies demonstrate that approximately 55% of ADT patients develop co-

incident metabolic syndrome after 12 months of treatment, compared to 22-20% of

men of the same age cohort without CaP or CaP patients not receiving ADT

(Braga-Basaria et al. 2006; Flanagan et al. 2010).

We and others have shown local steroidogenesis and lipogenesis to be two key

pathways in CaP progression (Ettinger et al. 2004; Swinnen et al. 2004; Dillard et

al. 2008; Locke et al. 2008; Lubik et al. 2011). Androgen levels increase in human

and xenograft prostate tumours after castration, despite castrate serum androgen

levels (Locke et al. 2008; Montgomery et al. 2008). Furthermore, advanced CaP,

which is most often accompanied by reactivation of androgen regulated pathways,

cannot be halted even by complete exogenous androgen blockade (Bhanalaph et al.

1974). It has been shown that CaP cells can synthesize cholesterol and steroids de

187

novo (Locke et al. 2008; Lubik et al. 2011), or synthesize steroids from cholesterol

(Dillard et al. 2008); furthermore, cholesterol may play an important role in

steroidogenesis at sites of metastasis, as second-site tumours have three times the

androgen levels of locally contained tumours, which may be fuelling those cells and

providing a favourable growth environment (Montgomery et al. 2008);

furthermore, patients on statin cholesterol lowering treatment have less risk of

metastasis (Colli et al. 2008).

FASN expression has been shown to increase with prostatic intraepithelial

neoplasia (PIN) and cancer grade, and FASN mRNA expression increases in patient

biopsy samples during progression to castrate resistance while using neoadjuvant

hormone therapy (NHT),; furthermore, FASN upregulation is demonstrated in

LNCaP and CWR22 xenograft models during increased tumour growth (Swinnen et

al. 1997b; Swinnen et al. 2004; Locke et al. 2010). Also, insulin is known to

contribute to lipogenesis in breast cancer (Monaco et al. 1977). In summary, both

steroidogenesis and lipogenesis contribute to CaP progression; therefore, it would

be prudent to investigate compounds which would hinder them, and

epidemiological evidence suggest metformin and statin drugs would be excellent

candidates.

Much interest has been generated in drugs that counteract ADT-induced metabolic

dysfunction for CaP therapy (Colli et al. 2008; Hamilton et al. 2010; Loeb et al.

2010). Herein we have demonstrated that metformin and simvastatin suppress

insulin-induced cholesterol synthesis, steroidogenesis and lipogenesis. Metformin,

an activator of AMPK (Sanli et al. 2010), suppresses insulin induced expression of

HMGR, one of the first enzymes in the cholesterol biosynthesis pathway, at both

the mRNA and protein levels, cutting off substrate supply for steroidogenesis.

HMG-CoA synthase (HMGS) upregulation at the level of mRNA may be a

compensatory response for a loss of total cellular cholesterol levels, as the cell

attempts to compensate for decreased cholesterol synthesis. In this study,

metformin suppressed intracellular cholesterol levels in the presence of insulin, and

did suppress de novo cholesterol synthesis. Simvastatin appears to increase the

mRNA levels of both HMGS and HMGR; however, its key function is HMGR

inhibition, and we have shown that it decreases intracellular cholesterol content and

completely suppresses de novo synthesis.

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189

Figure 6.7: Simvastatin differentially affects lipogenesis in the presence and

absence of insulin. (A) Following 48hr insulin treatment (10nM), +/- 5µM

simvastatin, RNA was extracted from LNCaP cells and used for QRT-PCR analysis

of fatty acid metabolism/ synthesis enzymes. Results are analyzed by the ΔΔCt


treated (no insulin) for the equivalent time point (n=6). (B) Western blot analysis of

lipogenesis enzymes following 48hr insulin treatment +/- 5µM simvastatin. Mean

densitometry values are shown. Experiments were performed at least 3 times. (C)

Intracellular neutral-lipid (cholesterol-ester, triglyceride) levels were measured with

Oil Red-O assay. Values are corrected for background staining on culture plates.

Insulin increases intracellular lipid, as does simvastatin to a lesser degree.

Simvastatin diminishes insulin induction of neutral lipid (p<0.05, t-test). Mean

corrected absorbance values are shown. Experiments were performed at least 6

times.




190

Significantly, we have demonstrated the upregulation of SREBP, which controls

steroidogenesis/ lipogenesis, CYP17A1, HSD17B3 and RDH5, along with PSA, at

48hrs with 10nM insulin. Metformin decreases the basal protein levels of these

enzymes, except StAR, which in this context may actually be inhibiting cholesterol

synthesis by down-regulation of related genes such as HMGR (Taylor et al. 2010).

Simvastatin suppresses insulin induction of these enzymes at the protein level, and

either suppresses or does not change basal levels. The exception is SREBP, which

is increased by simvastatin; however, simvastatin still suppresses insulin induction

of SREBP. Importantly, in the presence of metformin or simvastatin, irrespective of

insulin status, AR activation, as demonstrated by PSA upregulation, is curbed. This

is in concordance with the finding that both metformin and simvastatin suppress de

novo steroidogenesis in the presence of insulin. Simvastatin decreases PSA mRNA

to a similar extent to metformin; however, in the absence of insulin, appears to

increase steroidogenesis. The lack of PSA induction by the increased testosterone

may be due to down-regulation of AR (Yokomizo et al. 2010). Furthermore, both

inhibitors suppress insulin induction of lipogenesis enzymes and lipid content in

cancer cells, which are important for cancer cell energy and signalling. These

findings indicate that these drugs may be important as interventions for prostate

cancer progression in the context of metabolic syndrome. It is possible that

metformin may help prevent metabolic syndrome caused by ADT, as is currently

being investigated in a clinical trial (Gurney 2010).

In summary, metformin and simvastatin decrease insulin induced cholesterol and

fatty acid production in LNCaP cells and suppress AR activation/ PSA mRNA

expression levels to 50% of basal. While metformin decreased steroidogenesis in

the presence and absence of insulin, the results presented for simvastatin indicate

that it decreases steroidogenesis only in the presence of insulin. Because of the

interplay between hyperinsulinemia and CaP progression, these findings provide

evidence that these inhibitors may be effective in suppressing the steroidogenic and

lipogenic effects of insulin on CaP. Metformin is likely to act by activating AMPK

in CaP cells, and down regulating ACC, FASN, and HMGR (Luo et al. 2005).

Lower intracellular lipid levels deprive CaP cells of energy and alter signal

transduction. Simvastatin directly inhibits HMGR, depriving cells of the necessary

building blocks for essential steroidogenesis, and also down-regulating SREBP,

191

which would result in suppressed fatty acid synthesis. This result has been

demonstrated in the liver of mice with a lovastatin supplemented diet (Bennett et al.

2008). The increase in SREBP protein is unexpected and may be due to feedback

after cholesterol deprivation (Brown 2007); however, in the context of insulin,

simvastatin does suppress SREBP protein. Apparent decrease of HSL with both

inhibitors would decrease steroidogenesis by (1) decreasing the mobility of

cholesterol from cell membranes, and (2) decreasing the availability of arachidonic

acid to stimulate cholesterol uptake into the mitochondria for steroidogenesis.

Metformin and simvastatin may enhance the benefits of ADT in an adjuvant setting

or may manage metabolic syndrome side effects in the 55% of ADT patients who

acquire them, and contributing to patient quality of life (Stossel 2008; Berstein

2010; Dragomir et al. 2010; Clements et al. 2011). It has been suggested that statin

drugs in their current formulation may not reach tumours at effective

concentrations; therefore, novel formulations of statins or other cholesterol

reducing drugs may be effective for cancer treatment (Kong et al. 2004; Solomon et

al. 2008). Furthermore, it has been suggested that hormonal manipulation resulting

in elevated insulin and IGF levels may enhance growth of latent tumours and has

long been suggested that dietary and pharmacological regulation of metabolic

dysfunction should be controlled in cancer patients to slow tumour growth and/ or

inhibit recurrence (Miller 2007; Berstein 2010); therefore, management of insulin

effects may not only decrease cancer risk, it many hinder tumour growth at all

levels, from PIN to recurrence.

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193

Chapter 7: Insulin-like Growth Factor 2 Increases de novo Steroidogenesis in Prostate Cancer Cells

194

195

7.1 Introduction

Past research has shown insulin-like growth factors (IGFs) to be integral in the

progression of cancers (Pollak 2008a). The IGF system plays an important role in

cell biology, regulating growth and development, promoting proliferation,

differentiation, and transformation, as well as inhibiting apoptosis by endocrine,

autocrine and paracrine mechanisms (Cardillo et al. 2003). Though IGF1 has been

extensively studied in prostate cancer (CaP) and many other cancers, the role of

IGF2 in local CaP remains poorly understood. IGF2 is often associated with weaker

signalling than IGF1 through the IGF1 receptor (IGF1R); however, it has been

demonstrated that IGF2 may have insulin-like signalling as well, through the

insulin receptor (INSR), isoform-A (Cardillo et al. 2003; Pandini et al. 2004). Both

insulin and IGFI have been linked to CaP growth (Pollak 2008b); therefore,

illumination of the role of IGF2 in CaP is essential. In many cancers, IGF2 has been

postulated to utilize IGF1R, INSR, or hybrid receptors. Increased expression of

these receptors has been implicated in both CaP and breast cancer progression

(Belfiore et al. 2008; Lann et al. 2008; Cox et al. 2009).

In ovarian cancer, elevated tumour expression of IGF2 correlates with poor

differentiation and positive surgical margins, as well as decreased overall survival

(Sayer et al. 2005). Women with high IGF2 levels had higher risk of breast cancer

recurrence after surgery (Kalla et al. 2010) and in choriocarcinoma IGF2 has been

shown to regulate the invasiveness of cancer cells by induction of INSR signalling

and the Akt and ERK pathways which affect cell adhesion and motility (Diaz et al.

2007). IGF2 is also the single most dysregulated gene in colorectal cancer,

partially through loss of imprinting (Cui et al. 2003; Pollak 2008b). This loss of

imprinting is mirrored in many cancers including CaP (Van Roozendaal et al. 1998;

Cui et al. 2003a; Poirier et al. 2003; Sakatani et al. 2005). Loss of imprinting is

more likely to be seen in CaP tissues than benign prostatic hyperplasia (Paradowska

et al. 2009). In CaP, breast, and ovarian cancer, high tissue levels of IGF2 indicate

poor prognosis (Cardillo et al. 2003; Sayer et al. 2005; Kalla Singh et al. 2007;

Pollak 2008a; Huang et al. 2010). Elevated levels of IGF2 have been postulated to

predict whether increased levels of the prostate cancer biomarker, prostate specific

antigen (PSA) are due to CaP versus benign prostatic hyperplasia (Trojan et al.

2006).

196

Recent studies suggest that local production and/ or retention of growth factors and

steroids may differ significantly to levels in the circulation and may contribute to

CaP progression (So et al. 2005; Stanbrough et al. 2006). Androgen deprivation

therapy, which removes testicular androgens necessary for survival of prostate

cells, is the standard treatment for advanced prostate cancer (So et al. 2005). It has

been shown by our group and others that the local prostatic production of steroids

may be instrumental in driving CaP progression towards the terminal castrate

resistant form (CRPC) (Locke et al. 2008). The clinical application of this is

demonstrated by the cytochrome p450 member (CYP)17A1 inhibitor, Abiraterone,

which inhibits the steroidogenic pathway at crucial junctures and improves patient

outcomes (Attard et al. 2008; Reid et al. 2010). At this disease stage, circulating

androgen levels are low, while the prostate tissue levels are high enough to

reactivate the androgen receptor (AR) and drive progression (Stanbrough et al.

2006; Locke et al. 2008). Based on these findings, it has become imperative to

investigate factors which might affect this process.

IGF2 has been well documented to be involved in steroidogenesis in human and

bovine thecal cells, stimulating expression of steroidogenesis enzyme mRNA, as

well as progesterone synthesis (Spicer et al. 2007). In human adrenocortical cells,

which express the IGF1 and 2 receptors, IGF2 increases basal levels of CYP17A1,

and induces the expression of CYP11A1, CYP17A1, and HSD3B steroidogenesis

enzymes, resulting in an increase in aldosterone production. Furthermore, it has

been reported that IGF2 increases adrenocortical dehydroepiandrosterone (DHEA)

production with greater potency than IGF1 (Fottner et al. 1998). In chapter 3 was

demonstrated that insulin upregulates steroidogenesis in CaP cell lines (Lubik et al.

2011), and it has been shown that in 3T3 fibroblast cells, insulin and IGF2 activate

similar gene expression patterns (Pandini et al. 2004). Therefore it is hypothesised

that increased IGF2 levels in prostate cancer tumours may initiate prostatic de novo

steroidogenesis.

In the present study, it was shown for the first time that IGF2 may contribute to

CaP progression through upregulation of steroidogenesis enzymes and enhanced

steroid production in CaP cell lines. Levels of steroidogenic enzyme mRNA and

protein in LNCaP cells were demonstrated to increase after IGF2 treatment, as well

as de novo steroidogenesis in LNCaP and VCaP cell lines, and activation of AR by

197

de novo synthesized androgens was demonstrated. This data also suggests that IGF2

may be signalling through both IGF1R and INSR to enhance steroidogenesis. It is

also demonstrated in this chapter that IGF2 mRNA increases during progression to

castrate resistance. As a metabolic syndrome and hyperinsulinemia develops in

55% of patients after ADT, speeding progress to castrate resistance (Smith et al.

2006, Flanagan et al. 2010), the effect of insulin on IGF2 mRNA expression was

investigated. It has been demonstrated that IGF2 mRNA is increased in LNCaP and

22RV1 CaP cells when treated with 10nM insulin, indicating an interplay between

insulin and IGF2 in cancer progression.


7.2.1 Laser capture microdissection (LCM) and microarray analysis: Prostastic

tissues included 14 primary prostate cancers from patients undergoing radical

prostatectomy with no therapy before surgery, 12 primary prostate cancers after 1-3

months of Neoadjuvant Hormone Therapy (NHT), 5 primary prostate cancers after

5-6 months NHT, 4 primary prostate cancers after 8-9 months NHT, and 3

hormone refractory prostate cancers. Patients were further grouped according to

the following risk factors: High (PSA >20, Gleason >7, Clinical stage T3-T4),

Intermediate (PSA 10-20, Gleason 7, Clinical stage T2) and Low (PSA<10,

Gleason <7, Clinical stage 1).

A different set of tissues obtained at radical prostatectomy or transurethral prostatic

resection (TUPR) was flash frozen in OCT Compound (Tissue-Tek, VWR, Batavia,

IL) in liquid nitrogen and stored at -80°C until processing. Frozen sections (8μm)

were cut on a cryostat at -25°C, mounted on LCM slides (P.A.L.M. Microlaser

Technologies, Germany), transported on dry ice to freezer and stored at -80C.

Sections were briefly thawed (30s) and fixed with cold 95% ethanol at -25°C in the

cryostat (Leica, Richmond Hill, Ontario, Canada). Hematoxylin staining was

followed by washes in DEPC water and then dehydration in 100% ethanol. LCM

was performed on cancer cells using the PALM Microlaser system (P.A.L.M.

Microlaser Technologies, Germany). Samples were catapulted into sterile caps of

0.5ml eppendorf tubes (RNAse-free) containing 40ul extraction buffer and total

RNA was isolated according to manufacturers’ instructions (PicoPure RNA

Isolation Kit, ARCTURUS, Carlesbad, CA) and subjected to DNase I treatment

using Qiagen RNase-Free DNase kit (Qiagen, Inc, Mississauga, Ontario, Canada).

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Total RNA was eluted into a final volume of 10µl and used for RNA amplification.

The RiboAmp HS RNA Amplification kit (ARCTURUS) was used to generate

amplified amino allyl-modified antisense RNA according to manufacturer’s

instructions. Amplified aRNA was quantified by spectrophotometer and the quality

was assessed by running a 1% denaturing agarose gel. Amino allyl-modified

aRNA was labeled with Cy5 using the Amino Allyl Message Amp IIa RNA

Amplification Kit (Ambion, Streetsville, Ont), and the labelled aRNA was

fragmented with RNA Fragmentation Reagents (Ambion) prior to hybridization.

Microarrays of 34,580 (70-mer) human oligos representing 24,650 genes and

37,123 gene transcripts (Human Operon V3.0, Operon Technologies, Huntsville,

Al) printed on aminosilane coated microarray slides (Matrix Technologies, Hudson,

NH) were supplied by the Microarray Facility of the Prostate Centre at Vancouver

General Hospital. Microarrays were competitively hybridized with 2μg amplified

aRNA from the microdissected samples labelled with Cy5 and 2μg of amplified

Universal Human Reference RNA (Stratagene, Santa Clara, CA) labelled with Cy3

fluors (Amersham Bioscience, Piscataway, NJ). Following overnight hybridization

and washing, arrays were scanned on a Scan Array Express Microarray Scanner

(Perkin Elmer, Woodbridge, Ont.). Signal quality and quantity were assessed using

ImaGene 8.0 software (BioDiscovery, San Diego, CA). Feature data extracted by

ImaGene were subjected to background correction, print-tip-lowess within-array

normalization and G quantile between-array normalization using the Limma

package (R/Bioconductor software). Significant differences between treatment

groups were assessed using a linear regression model. Benjamini-Hochberg

multiple test correction was used to estimate the false discovery rate.

7.2.2 Immunohistochemistry: Immunohistochemical (IHC) score staining intensity

was evaluated from 0 to 3 by an independent pathologist.

Immunohistochemical staining was conducted on the sequential sections of Gleason

graded tissue microarray (TMA) using anti-insulin receptor, β subunit, rabbit

immunoaffinity purified IgG (Upstate Cell Signalling Solutions, Lake placid,

USA),and IGF1R beta rabbit polyclonal antibody (Santa Cruz Biotechnology Inc,

Santa Cruz, CA) by Ventana autostainer model Discover XT (Vantana Medical

System, Tuscan, Arizona) with enzyme labelled biotin streptavidin system and

solvent resistant DAB Map kit. Nonspecific reactivity was assessed by omission of

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the primary antibody. The specificity of staining for IR-β was confirmed by using

Placenta as a positive Control.

Scoring Method:

The slide was scanned with BLISS system (Bacus Lab, North Lombard, IL) and

scored by pathologist (Ladan Fazil, Vancouver Prostate Centre).

Immunohistochemical analysis of IR- β shows rather homogenous cytoplasmic

staining in the cancer cells and discontinuous staining in the basal layer cells of the

benign glands. Intensity varied from 0 to 3. Immunoreactivity of IGF1R was mostly

membranous but occasionally was cytoplasmic. It was present in the basal cells and

luminal cells of the benign glands as well as cancer cells.

The biomarkers were scored using a 4 point scale scoring system by which a

pathologist assigns a value to each immunostain. Descriptively, 0 representted no

staining by any tumour cells, 1 represented a faint or focal staining, 2 represented a

stain of moderate intensity in a convincing number of cells, and 3 represented

intense staining by a sufficient number of cells express this antigen.

7.2.3 In vitro model: LNCaP cells were maintained in phenol- red free RPMI 1640

(Invitrogen, Mulgrave, VIC), supplemented with 5% fetal bovine serum (FBS)

(Invitrogen). Cells were plated for treatment in 5% FBS RPMI before medium was

changed to 5% charcoal-stripped serum (CSS; Hyclone, Hudson, NH) medium for

24hr before 24hr starvation in serum free RPMI, followed by 48-72hr treatment

with 85ng/ml IGF2 (Novozymes, Thebarton, SA) in 0.5% Bovine Serum Albumin

(BSA) or BSA control. IGF2 was refreshed at 24hr intervals as necessary. VCaP

cells were maintained in DMEM medium (Hyclone), and 5% FBS. Cells were

plated for treatment in 5% FBS DMEM before medium was changed to 5%

charcoal-stripped serum (CSS; Hyclone) medium for 24hrs before 24hr starvation

in serum free RPMI, followed by 48-72hr treatment with 85ng/ml IGF2

(Novozymes) in 0.5% Bovine Serum Albumin (BSA) or BSA control. IGF2 was

refreshed at 24hr intervals as necessary. Concentration of IGF2 used was selected

by concentration course of steroidogenesis enzyme mRNA induction;

concentrations between 70-100ng/ml gave similar results. Serum levels of IGF2

tend to be twice that of IGF1, which is usually used in literature at 30ng/ml.

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For insulin experiments, LNCaP and 22RV1 cells were treated as LNCaPs as above

for hormone and growth factor deprivation; however, LNCaP cells were treated

with 10nM insulin (Sigma) for 10hr for microarray experiments (detailed methods

in chapter 2.14), and both LNCaP and 22RV1cells with treated with 10nM insulin

for 48hrs for QRT-PCR analysis. Insulin was refreshed after 24hr.








Sigma Proligo. Primers used were: StAR, CYP17A1, HSD3B, AKR1C3,

HSD17B3, RDH5, SRD5A1, IGF2 and rpl32. For Primer sequences, see Appendix

A. Gene expression was normalized to the housekeeping gene (rpl32), then

expressed relative to the vehicle control at the same time point. Data was analyzed

with SDS 2.3 software by means of the ∆∆Ct method (Livak et al. 2001).

Experiments were repeated a minimum of 5 times.

7.2.5 Western blotting: Protein extraction and western blotting were carried out as




for 1hr in Li-Cor blocking buffer and antibodies were added to the blots in a 1:1

solution of Li-Cor blocking buffer and 0.1% Tween-20-PBS and incubated

overnight at 4°C, before washing and application of secondary antibody for 1hr at

room temperature. Blots were visualized using the Li-Cor Odyssey Imager (Li-Cor

Biosciences, Lincoln, USA). Antibodies used were: StAR (kind gift from Dr. B

Hales, University of Chicago), CYP17A1 (kind gift from Dr. B Hales, University of

Chicago), HSD3B (Santa Cruz), AKR1C3 (Abcam), HSD17B3 (Abnova), RDH5

(Abnova), and SRD5A (Novus). Experiments were repeated at least 3 times.

GAPDH (Abcam) was used as a loading control (Abcam, Sapphire Biosciences,

Waterloo NSW, Australia). For antibody information, please see Appendix B.

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7.2.6 Steroid analysis in LNCaP cells: Cells were grown in 15cm plates and treated

with either 85ng/ml IGF2 in 0.5% BSA or 0.5% BSA in serum free medium for

48hr as described above. Steroid extraction is detailed in chapter 2.10. Two plates

of treated cells were washed with PBS and combined to give a single sample.

Steroids were extracted from the pellet with methyl-tert-butyl ether (MTBE)/

Methanol/ Water extraction, which was dried down and resuspended in acetonitrile,

sonicated and dried down and resuspended in 50% methanol, and then sonicated

and spun to remove any particulates. Samples were derivatized in 0.2M

hydroxylamine HCL. Water equilibrated ethylacetate was used instead of MTBE/

Methanol/ Water for extraction of secreted steroids from medium samples. All

samples were run on the Waters Acquity Liquid Chromatography system and the

Waters Quattro Premier LC/MS/MS, and analysed using Quanlynx Software.

Readings were normalized to cell pellet weight.

7.2.7 Radio-labelled acetate analysis of de novo steroidogenesis in LNCaP and

VCaP cells: Cells were grown in 6 well plates and treated as above. Detailed

description of de novo steroid extraction is described in chapter 2.11. At the time of

IGF2 treatment, 6µCi/ml 14C-acetate (PerkinElmer, Ontario) was added to each

plate for 72hr after which time medium was analyzed for steroid content. Equal

volumes of medium and hexane:ethylacetate (70:30) were incubated at room

temperature for 1 hr and the organic phase was extracted twice. Samples were then

dried and resuspended in 75µl 50% methanol. These samples were analysed on the

Waters Alliance 2695 HPLC System and Packard Radiomatic Detector 150TR

Flow Scintillation Analyzers.

7.2.8 Receptor inhibitor treatment: LNCaP cells were grown in 6 well plates for

24hr in FBS supplemented medium before incubation in 5% CSS medium for 24hr,

and a further 24hr incubation in serum free medium. For AR inhibition, cells were

incubated for 2hr with 25µM bicalutamide before 24hr treatment with IGF2 or

10nM DHT. For inhibition of direct receptor activation by IGF2, cells were then

incubated with either 10µM BMS tyrosine kinase (RTK) inhibitor of INSR and

IGF1R receptor tyrosine kinase, or 25µg/ml CP-751,871 IGF1R inhibitor for 2hr

before addition of 85ng/ml IGF2. All treatments were normalized to vehicle

control, and all samples were treated with DMSO and PBS vehicle control (BMS

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and CP-751,871, respectively) for direct comparison purposes. Cells were then

treated as above with 14C-acetate and IGF for de novo steroidogenesis analysis.

7.2.9 Microarray analysis: LNCaP cells were starved for 48hrs in 5%CSS medium,

and brought to basal hormone/ growth factor state for 24hr in serum free medium

before 10hr treatment with 10nM insulin (Sigma) or with vehicle control. Array

analysis was done on the Agilent 105k array platform by Nadine Thomlinson and

analyzed by Melanie Lehman for importation into Ingenuity (IPA) Software

(Ingenuity Systems, Inc., Redwood City, CA), as described in chapter 2.14.

7.2.10 Statistics: All statistical analysis was done using the two-tailed student’s t-

test assuming equal variance on Graphpad Prism 5 software. P-value of 0.05 or

lower was considered significant.

7.3 Results

7.3.1 IGF2 mRNA expression in men undergoing neoadujvant hormone therapy

(NHT)

In order to investigate the clinical significance of IGF2 in CaP progression, the

levels of IGF2 mRNA expression in clinical tumour samples over the time of

progression to castrate resistance (CRPC) in men undergoing NHT after radical

prostatectomy were examined. It was found that increased expression of IGF2 first

occurred by 5-6 months. This reached statistical significance after 8-9 months and

was maintained at CRPC (figure 7.1A). As well, mRNA levels of the IGF2 receptor

(IGF2R) remain constant, which suggests increased availability of the increased

IGF2, although this is also likely to bound to IGF binding proteins (IGFBPs)

(figure 7.1B). Immunohistochemical staining of NHT samples show an increase in

IGF1Rand INSR protein level, both of which are subject to activation by IGF2

(figure 7.1 C and D).

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Figure 7.1: Tissue microarray analysis of IGF2 and receptor expression in prostate

cancer. Prostatic tissues included 14 primary prostate cancers from patients

undergoing radical prostatectomy with no therapy before surgery (NHT 0), 12

primary prostate cancers after 1-3 months of Neoadjuvant Hormone Therapy

(NHT) (NHT 1-3m), 5 primary prostate cancers after 5-6 months NHT (NHT 5-

6m), 4 primary prostate cancers after 8-9 months NHT (NHT 8-9m), and 3

hormone refractory/ castrate resistant prostate cancers (CR). (A) IGF2 mRNA

levels from patient samples were analysed to compare between groups, shown as

mean + SE (*p<0.05), (B) IGF2R mRNA levels from patient samples were

analysed to compare between groups, shown as mean + SE (*p<0.05). (C) Intensity

of immunohistochemistry (IHC) scoring for IGF1R, shown as mean + SE

(*p<0.05). (D) IHC score for INSR subunit beta (b), shown as mean + SE

(*p<0.05).

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7.3.2 IGF2 effect on steroidogenesis enzymes

Because IGF2 has been shown to induce expression of enzymes in the

steroidogenesis pathways (figure 7.2) in other tissues, its effect on LNCaP cells

after 48hr was investigated. There was a statistically significant 2-fold increase in

mRNA levels of StAR, CYP17A1, SRD5A1, and RDH5 (p<0.05), as well as non-

significant 2 and 1.5-fold increases in AKR1C3 and HSD17B3, respectively (figure

7.3A) following IGF2 treatment. The importance of increased StAR is a reflection

of its role ferrying cholesterol into the mitochondria for initiation of

steroidogenesis. CYP17A1 is responsible for many reactions in the pathway

including conversion of pregnanolone. AKR1C3 and HSD17B3 can both convert

androstenedione to testosterone; furthermore, HSD17B3 contributes to “backdoor”

steroidogenesis through conversion of androsterone to androstanediol. The

“backdoor” pathway differs from the classical pathway in that it bypasses

testosterone in the formation of DHT. SRD5A1 and RDH5 catalyse the final

reaction in the synthesis of DHT in the classical and “backdoor” pathways,

respectively. DHT is the most potent activator of AR and its pathways, which

contribute to CaP survival (So et al. 2005). We have previously shown that CaP

cells use both pathways for de novo steroidogenesis, adapting to treatments and

inhibitors to synthesize steroids (Locke et al. 2009).

Parallel increases in protein expression of steroidogenesis enzymes were observed

in LNCaP cells following IGF2 treatment. Expression of the cholesterol chaperone

protein StAR was significantly increased along with the rate-limiting enzyme,

CYP17A1 and HSD17B3 (figure 7.3B). Levels of AKR1C3 and SRD5A proteins

increased, reaching close to statistical significance. In prostate epithelial cells,

AKR1C3 is the major aldo-keto reductase (Penning et al. 2006), and the

upregulation of this enzyme would suggest increased testosterone production. In

contrast to increased mRNA levels, protein levels of RDH5 were unchanged at 48

hours.

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Figure 7.2: Steroidogenesis pathway adapted from Locke et al. indicating enzymes

involved in both classical (left/ centre) and backdoor (far right) pathways of DHT

production (Locke et al. 2008).

206

Figure 7.3: IGF2 regulates expression of steroidogenic enzymes at the mRNA and

protein level. (A) Following 48 hour IGF2 treatment (85ng/ml), RNA was

extracted from LNCaP cells and used for QRT-PCR analysis of steroidogenesis

enzymes. Results are analyzed by ΔΔCt method and normalized to RPL32 mRNA as

a control gene, before normalization to vehicle-treated controls for the equivalent

time point. Error bars represent SE, *p<0.05. (B) Western blot analysis of

steroidogenesis enzymes following 48hr 85ng/ml IGF2 treatment, error bars

represent SE, * p<0.05. Experiments were repeated a minimum of 6 times.

207

7.3.3 IGF2 increases intracellular and secreted steroids. High-performance

liquid chromatography on LNCaP cell pellets extracted with MTBE was used to

investigate differences in steroid content between IGF2 and vehicle treated cells.

IGF2 treatment clearly increased intracellular steroid levels in LNCaP cells (figure

7.4A). Intracellular DHEA and 17OH-progesterone increased 2-fold (p<0.05). A

10-fold increase in androsterone (p<0.05), an intermediate in the backdoor

pathway, was apparent. Pregnenolone and progesterone increased 5 and 3-fold,

respectively (p<0.05). Intracellular testosterone increased 4-fold (p<0.05) from

approximately 0.0131 to 0.053 ng/g cells with treatment, which is consistent with

our previous findings (Locke et al. 2008; Lubik et al. 2011). These concentrations

would be adequate to activate the AR, as it has been shown that androgen

concentrations of approximately 2.92 × 10−6 ng/g may activate AR in prostate

cancer cell lines (Gregory et al. 2001); however, no change was observed in

intracellular DHT.

In contrast, dramatic increases (~10-fold, p<0.05) in secreted steroids were

observed for testosterone, DHT, and androsterone (figure 7.4B). Small increases in

DHEA and 17OH-progesterone were demonstrated, as well as 5 and 9-fold

increases in pregnenolone and progesterone, respectively. It is interesting to note

that levels of steroids at the beginning of the steroidogenesis pathway are increased

intracellularly, while the more potent steroids and androgens further down in the

pathway are increased to a larger extent in medium. After IGF2 treatment,

testosterone and DHT concentrations increased to 0.156nM and 0.0674nM. We and

others have demonstrated that concentrations of androgen at these levels are

sufficient to activate the AR (Gregory et al. 2001; Titus et al. 2005; Locke et al.

2008). In order to show AR activation by de novo synthesized androgens, induction

of PSA mRNA was investigated in the presence and absence of bicalutamide, an

AR antagonist, which suppresses AR gene induction by androgens, as exemplified

by DHT (figure 7.4C). PSA mRNA expression, as a surrogate for AR activation by

androgens produced in response to IGF2, increases 2.18-fold (p<0.05) (figure

7.4C). Herein it is demonstrated that this PSA response is mediated through AR by

abrogation of the IGF2 effect by AR inhibition with bicalutamide.

208

Figure 7.4: IGF2 treatment increases steroid production in LNCaP cells. LNCaP

cells were treated with 85ng/ml IGF2 for 48hr (A) LC-MS was used to identify and

quantitate intracellular steroids showing a significant increase in 17-OH

progesterone, testosterone (T), androsterone, pregnanolone, and progesterone

(n=6). (B) Medium was collected and also analyzed by LC-MS to identify and

quantitate extracellular steroids. Statistically significantly increased steroid levels

were also seen in the medium for testosterone (T), androsterone, and pregnanolone.

Steroid levels were normalized to cell pellet weight and deuterated testosterone for

extraction efficiency and compared to the vehicle time point control. Experiments

were performed at least 6 times. Error bars represent SE (*p<0.05). (C) PSA

mRNA induction by steroids induced due to IGF2 treatment was demonstrated after

24hr incubation, as well as PSA induction by 10nM DHT. PSA induction was

diminished by AR antagonist bicalutamide. Error bars represent SE (*p<0.05, n≥3).

209

7.3.4 IGF2 effect on de novo steroidogenesis in LNCaP and VCaP medium.

Treatment of LNCaP or VCaP cells for 72hr with 14C-labeled acetate and

subsequent HPLC and radiometric detection permits measurement of IGF2

induction of de novo steroidogenesis. In VCaPs, we see an increase in

androstenedione, 5-fold, and androsterone, 3-fold, as well as pregnane3,20dione,

2.6-fold ( p<0.05) (figure 7.5A). There is also an increase in de novo cholesterol,

which is the building block of steroid synthesis (Leon et al. 2010). The method of

extraction used in this experiment is more specific to steroids; therefore, cholesterol

levels are more qualitative than quantitative. In LNCaP cells (figure 7.5B), there

was a statistically significant 4.5-fold increase in testosterone, as well as

androstenedione, androsterone, and pregnenolone (approximately 4-fold), as well as

peaks which have retention times corresponding to cholesterol-related sterols (lipid

area, 3-fold), though no change in cholesterol was apparent. De novo DHT

synthesis was not apparent in these experiments. Because LNCaP and VCAP are

cell lines of different origins, they may adapt to different steroidogenic needs, as

our group has shown previously (Locke et al. 2009; Lubik et al. 2011);

accumulation of cholesterol in VCaP cells compared to LNCaPs may indicate more

time is necessary for VCaPs to produce androgens in quantities measurable by this

method. Spectra for 14C-labeled steroids in VCaP and LNCaP cells are shown in

figure 7.6.

210

Figure 7.5: IGF2 increases de novo steroidogenesis in prostate cancer cells.

Medium was collected from VCaP and LNCaP cells after 72hr incubation with

85ng/ml IGF2 and 6µCi/ml 14C acetate before HPLC and radiometric detection

were used to identify and quantitate extracellular steroids. (A) In VCaPs,

statistically significant increased steroid levels were seen in the medium for

pregnane3,20dione, though a trend of increase is evident for androstenedione,

androsterone and cholesterol. Error bars represent SE (*p<0.05, n=3). In LNCaPs

(B), statistically significantly increased steroid levels were seen in the medium for

all steroids. Error bars represent SE (*p<0.05, n=3).

211


212

Figure 7.6: Radiometric spectra from de novo steroidogenesis with IGF2 in LNCaP

and VCaP cells. Representative spectra of steroids isolated from 2mL of medium

following 85ng/ml treatment of VCaP (A,B) or LNCaP cells (C,D). Mix 10

standards and radiolabelled standards (E) (which included DHT, progesterone, and

cholesterol) were used to identify steroid retention times and peaks were quantified

by measuring area under the curve. For VCaP cells, the control (A) was compared

to 85ng/ml IGF2 treated (B) to calculate fold change. The same was done for

LNCaPs, vehicle-treated control cells (C) being used to normalise IGF2 induced

peaks (D). Steroid retention times were comparable in both LNCaP and VCaP cells

and are identified from 1 to 8, with testosterone the first to elute from the column

and cholesterol the last. De novo synthesised testosterone was detected in LNCaPs

only. Cholesterol was detected to increase in VCaPs but not LNCaPs. One

explanation may be that the 14C labelled acetate was rapidly converted to

testosterone in LNCaPs, where time for steroidogenesis may be elongated in

VCaPs.

213

7.3.5 Receptor blockade of IGF2 effects.

Patient NHT arrays demonstrate that increases in the expression of INSR and

IGF1Rcorrelate with CaP progression, both of which are postulated to be used by

IGF2; therefore, whether either was the primary receptor for steroidogenesis

induction was examined by treating LNCaP cells with BMS-754807 INSR/IGF1R

RTK (BMS) (Carboni et al. 2009; Huang et al. 2010), or the CP-751,871 IGF1R

receptor inhibitor antibody (Cohen et al. 2005), or both. BMS reagent blocks

IGF1R and INSR activation with equal affinity (Carboni et al. 2009), whereas CP-

751,871 (Ab) has not been shown to block the insulin receptor at up to 3000µg/ml

in human cells, though it does block activation of hybrid receptor dimers of IGF1R

and INSR-A or B (Cohen et al. 2005). At the concentrations used, testosterone

synthesis stimulation by IGF2 was 100% surpressed with Ab, compared to 50%

with BMS. A similar effect is seen on androsterone and pregnanolone.

Androstenedione appears to only decrease with Ab. These findings suggest that

IGF2 preferentially signals for steroidogenesis though IGF1R. Because BMS

blocks both IGF1R and INSR, it is unclear whether INSR is involved in IGF2

stimulation of steroidogenesis (figure 7.7).

7.3.6 Insulin may increase expression of IGF2 in prostate cancer cells.

In context of the metabolic syndrome and prostate cancer, increased circulating

levels of IGF2 have not been associated with metabolic syndrome; in fact, low

serum levels of IGF2 are more associated with weight gain and obesity (Sandhu et

al. 2003). However, in the metabolic syndrome phenotype post androgen therapy,

the levels of IGFs in peripheral tissue and cancer cells have not been investigated.

In insulin arrays (serum starved LNCaPs treated for 10hr with 10nM insulin), it was

discovered that IGF2 mRNA is increased in LNCaP cells 2-fold (p=0.023). Also,

IGFBP3 is decreased 16-fold (p=0.003), and IGF2R is decreased approximately 2-

fold (p=0.007) (data not shown). These results suggest that insulin not only

increases IGF2 mRNA in LNCaPs but decreases in IGFBP3 and IGF2R mRNA

would suggest increase in free IGF2. These results would need to be confirmed

with QRT analysis and/ or western blots. The increase in IGF2 by insulin treatment

(48hr) has been substantiated by QRT-PCR of 22RV1 and LNCaP cell mRNA;

IGF2 is upregulated 2 and 1.5-fold, in 22RV1 and LNCaP, respectively (figure 7.8).

214

Figure 7.7: IGF2 increases de novo steroidogenesis in prostate cancer cells through

both IGF1R and INSR. Medium was collected from LNCaP cells after 72hr

incubation with 85ng/ml IGF2 and 6µCi/ml 14C acetate before HPLC and

radiometric detection were used to identify and quantitate extracellular steroids. In

LNCaPs, steroidogenesis was obstructed partially by addition of BMS-754807

INSR/ IGF1R RTK (BMS) (10µM) or more by IGF1R inhibitor antibody CP-

751,871 (Ab) (25µg/ml). Experiments were performed in triplicate.

215

Figure 7.8: Insulin regulates expression of IGF2 at the mRNA level. A) Following

48 hour insulin treatment (10nM), RNA was extracted from 22RV1 and LNCaP

cells and used for QRT-PCR analysis of IGF2. Results were analyzed by ΔΔCt

method and normalized to RPL32 as a control gene. Error bars represent SE

(*p<0.05), n=3.

*

*

216

7.4 Discussion

Understanding the mechanisms that regulate de novo steroidogenesis in prostate

cancer is part of ongoing studies in our laboratory aimed at providing new targets

for the development of effective treatment of this disease. In the present study, it

was demonstrated for the first time that IGF2 contributes to steroidogenesis in CaP

cells via upregulation of obligatory enzyme genes, at both the mRNA and protein

level, coupled with increased total content and de novo creation of steroids at

concentrations known to activate the androgen receptor (Gregory et al. 2001; Locke

et al. 2008). Microarray analysis revealed an upregulation of IGF2 mRNA in

advanced prostate cancer. Finally, there is evidence that IGF2 may utilize mainly

IGF1Rfor steroidogenesis initiation. In breast cancer cell lines, it has been shown

that blocking either receptor will result in increased expression of the other and not

hinder IGF2 related tumour progression (Ulanet et al. 2010); therefore, tissue-

specific inhibition of both receptors, or downstream effectors, may be worthwhile

to investigate for therapy for prostate, breast, hepatocellular, pancreatic and ovarian

cancers, all of which are aggravated by IGF2 (Sayer et al. 2005).

IGF2 is a key regulator of foetal growth; however, its role in normal adult cells has

been largely unstudied (Meinbach et al. 2006). It has been linked to CaP bone

metastasis (Saad et al. 2006). IGF2 is also known to function through IGF1R and

INSR, both of which have been linked to cancer progression (Cox et al. 2009).

Furthermore, loss of IGF2 imprinting in prostatic tissue occurs with age, and is

frequently seen in CaP, and correlates to CaP progression, compared to BPH

(Jarrard et al. 1995; Fu et al. 2008). IGF2 loss of imprinting has also been

demonstrated in the progression of breast, intestinal, heptatocellular, and colorectal

cancers (Van Roozendaal et al. 1998; Cui et al. 2003; Poirier et al. 2003; Sakatani

et al. 2005). High serum IGF2 levels have been correlated with poor outcomes for

patients with locally confined breast cancer (Espelund et al. 2008), but that

conclusion has not been seen in CaP patients (Djavan et al. 2001). However, it has

been suggested that IGF2 levels in serum, when combined with elevated PSA, may

give a clearer distinction between CaP and benign prostatic hyperplasia (Trojan et

al. 2006). We have demonstrated in earlier work that local levels of hormones may

be as important to CaP progression as serum levels (Locke et al. 2008; Locke et al.

2009a).

217

After androgen deprivation therapy, serum androgen levels decrease to castrate

levels; however, androgen concentrations sufficient to stimulate the AR can still be

detected in CaP cells and promote cancer growth (So et al. 2005). This fosters the

notion that the contribution of autocrine/ paracrine (local) enzymes and hormones/

growth factors may contribute to progression more than systemic levels during

cancer progression. Past studies from our group and others indicate that during

progression CaP cells can initiate de novo steroidogenesis from cholesterol or other

precursors at concentrations sufficient to activate the AR and promote CaP growth

(Gregory et al. 2001; Locke et al. 2008; Leon et al. 2010). Prostatic IGF2

concentrations, viewed by mRNA and protein analysis, increase as CaP progresses,

and high Gleason scores correlate with IGF2 expression (Cardillo et al. 2003;

Pollak 2008a). It is demonstrated herein that IGF2 mRNA increases during

progression to CRPC in patients receiving NHT therapy (figure 7.1A). Recent

studies have shown that expression of INSR type A, which is generally

preferentially expressed in foetal and cancer cells over INSR type B, which is

mainly expressed in normal adult tissue, may contribute to cancer progression (Diaz

et al. 2007). IGF2 has much higher affinity for INSR-A and INSR-A/ IGF1R

hybrids, than INSR-B (Pandini et al. 2004; Ulanet et al. 2010). IGF-IR and INSR

staining by immunohistochemistry also increases in progression from normal to

PIN to CaP tissue but did not correlate to Gleason score (Cardillo et al. 2003;

Trojan et al. 2006; Trojan et al. 2006). Furthermore, mutations in IGF2R, which

functions to sequester IGF2 rather than as a signalling receptor, occur early in CaP

progression (Hu et al. 2005). These mutations may result in a lower affinity for

IGF2, leaving more free IGF2 in the tumour microenvironment to activate IGF1R,

INSR, and/ or HR signalling in tumour cells (Hu et al. 2005). In the context of

metabolic syndrome, which is prevalent in many prostate cancer patients

undergoing ADT (Smith et al. 2006) insulin may also increase levels of

bioavailable IGF2, as suggested by microarray analysis and confirmed at the

mRNA level by QRT-PCR. The interplay between IGF2 and insulin may be

important in CaP progression.

In summary, IGF2 has been identified as a candidate prostate cancer gene target

and provides evidence that IGF2 activates de novo steroidogenesis in prostate

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cancer cell lines, possibly by acting via both the insulin receptor (INSR) and though

most likely through the IGF1 receptor (IGF1R).

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Chapter 8: General Discussion

220

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8.1 Overview

Prostate cancer (CaP) is the most commonly diagnosed cancer in men and second

leading cause of cancer death in men (Swinnen et al. 2004). In men who have local

tumours which are detected early, radiation or surgery can be effectively curative

(Stanbrough et al. 2006); however, in those with advanced or metastatic disease,

the gold standard therapy is ADT, which is initially successful, but in most cases

there will be relapse, treated by palliative approach with an array of hormone and

growth factor modulating therapies (Risbridger et al. 2010). Early cancers require

androgens for growth; however, after ADT, cells undergo a number of molecular

and biochemical changes including the upregulation of de novo steroidogenesis,

abrogating the need for exogenous androgens, and become castrate resistant, the

terminal form of the disease (Dillard et al. 2008; Locke et al. 2008; Locke et al.

2009a; Leon et al. 2010; Locke et al. 2010). ADT affects the quality of life of the

patients due to the side effects of hormone modulation, such as decrease in libido,

hot flashes, fatigue, cognitive dysfunction, and depression. Long term side effects

include anaemia and osteoporosis.

In approximately 55% of cases, ADT results in metabolic syndrome, accompanied

by the key feature of hyperinsulinemia (Smith et al. 2006), which can occur rapidly

and increase the speed of onset to castrate resistance (Flanagan et al. 2010;

Gallagher et al. 2010). As prostate epithelial cells are not subject to development of

insulin resistance (defined by impaired glucose uptake and metabolism) as occurs

in insulin-sensitive metabolic tissue, hyperinsulinemia is expected to increase

insulin signalling in CaP cells (Belfiore et al. 2008) in parallel to the elevated

insulin levels. Understanding the mechanisms of how metabolic syndrome may

exacerbate CaP progression is important in treatment and prevention of progression

as well as increasing quality of life for patients. It has been the work of this project

to demonstrate that, in AR-positive CaP cell models, insulin promotes

steroidogenesis and lipogenesis pathways that have been implicated in cancer

growth and aggressiveness. It has also been demonstrated that insulin-sensitising/

lipid modifying drugs, may suppress these pathways. Furthermore, it has been

demonstrated that a related growth factor, IGF2, which is often upregulated in CaP,

also contributes to de novo steroidogenesis in prostate tumour cells.

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It is of great importance that in this thesis, it has been demonstrated for the first

time that hyperinsulinemia can induce steroidogenesis in an androgen free CaP

system, as represented by the treatment of CaP cells, LNCaP, 22RV1, and VCaP

with 10nM insulin. Steroidogenesis has been shown by our group and multiple

others to play an important role in CaP progression to CRPC (Gregory et al. 2001a;

Mostaghel et al. 2007; Dillard et al. 2008; Locke et al. 2008; Montgomery et al.

2008; Locke et al. 2009a). Herein it has been demonstrated that insulin increases

steroidogenesis enzyme expression at the mRNA and protein levels (chapter 3) in

the above mentioned cell lines, though the amount induction of the enzymes differs

across the cell lines tested. However, insulin consistently upregulates CYP11A1,

CYP17A1, HSD3B2, HSD17B3, and RDH5 at the mRNA level. Increased

production of steroids/ androgens is consistently demonstrated across all cell lines,

as is induction of PSA mRNA expression through AR activation. The relative

differences between the cell lines may result from their different lineages, and we

have previously demonstrated that CaP cells can upregulate and utilize different

steroidogenic enzymes depending on their need and the modulation of their

microenvironment (Locke et al. 2009a). The microenvironment of different

metastasis, from lymph node or bone for example, may present different precursors

for steroidogenesis and different growth factors and other mediators; therefore, the

cells may upregulate or down regulate different steroidogenesis enzymes/ different

steroids, depending on the environment.

Herein it has also been demonstrated that insulin increases lipogenesis in CaP cell

lines LNCaP and 22RV1 through upregulation of HSL, DB1, ACSL3, ACC and

FASN at the mRNA and protein level. After insulin treatment both de novo

lipogenesis and stored neutral lipids (triglycerides and cholesterol esters) are

increased, and increased lipogenesis is associated with fast growing and spreading

cancers, as well as chemoresistance (Rossi et al. 2003; Menendez et al. 2004b; Lu

et al. 2005; Shah et al. 2006; Orita et al. 2008; Hirsch et al. 2009; Migita et al.

2009; Wang et al. 2009). This is important in the context of metabolic syndrome, as

it is suggested that men with coincident metabolic syndrome after ADT have faster

progression to CRPC (Flanagan et al. 2010). Array information gained by treating

LNCaP cells with insulin, as outlined in chapter 5, also illustrates the role of insulin

in upregulation of genes involved in fatty acid synthesis and metabolism pathways,

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as well as the arachidonic acid pathway, which promotes inflammation and

steroidogenesis, as well as preventing apoptosis in CaP cells (Bagga et al. 2003;

Harris 2009; Locke et al. 2010; Venkateswaran et al. 2010; Goodwin 2010a).

These findings demonstrate not only that insulin upregulates steroidogenesis and

lipogenesis which may contribute to CaP progression, but strongly suggest that men

who undergo ADT should (a) be advised to undergo lifestyle changes to avoid

metabolic syndrome, (b) be monitored for changes in their metabolic state, and (c)

be advised that they would be likely to be good candidates for adjuvant intervention

with steroidogenesis inhibitors such as Abiraterone (Attard et al. 2008; Reid et al.

2010), or fatty acid synthase inhibitors (many of which are being investigated in

laboratory or clinical settings) (Flavin et al. 2010), or pharmaceutical agents for

metabolic “rehabilitation”, the most promising of which is metformin (Berstein

2010; Gurney 2010). In chapter 6, it has been demonstrated that simvastatin and

metformin could be used to suppress steroidogenesis and lipogenesis in a

hyperinsulinemic environment, as they down regulate steroidogenesis and

lipogenesis enzymes at the mRNA and protein level, as well as decreasing/

preventing de novo steroidogenesis and lipogenesis. In an insulin free environment,

simvastatin seems to increase steroidogenesis and lipogenesis; whereas, metformin

seems to decrease basal levels of these pathways in the absence of insulin. As this

thesis has demonstrated that metabolic dysregulation by ADT is likely to promote

CaP progression, the importance of metabolic intervention in men with and without

metabolic syndrome is discussed in chapter 8.2. Because they are similar cancers in

some respects and women with metabolic syndrome/ obesity are more likely to get

breast cancer with poor prognosis (Risbridger et al. 2010), the potential role of

insulin in breast cancer is also discussed, section 8.3.

This study has also demonstrated that IGF2, which shares structural similarities

with insulin, also upregulates steroidogenesis at both the mRNA and protein level,

and increases de novo steroidogenesis in both VCaP and LNCaP cells. Inhibitor

studies demonstrated that AR inhibition decreases PSA mRNA induction by IGF2;

therefore, the newly synthesized androgens must be activating AR. These findings

are especially important in light of the fact that IGF2 expression is increased in CaP

progression, as are the receptors it signals through, IGF1R, INSR, and/ or hybrid

receptors. This data demonstrates a novel role for IGF2, which is not widely

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studied in CaP; however, the importance of IGF2 may be further reaching,

contributing to bone metastasis or a more favourable bone environment.

Furthermore, pilot studies show that insulin upregulates IGF2, which may also

partly explain the increased metastasis in men who develop metabolic syndrome

after ADT (Flanagan et al. 2010). These hypotheses will be further discussed in

chapter 8.4.

8.2 Prostate cancer and metabolic syndrome: reducing risk by nutritional and

pharmacological means

In the present work, it has been demonstrated that insulin increases steroidogenesis

and lipogenesis, which may contribute to CaP progression. Development of the

metabolic syndrome and accompanying hyperinsulinemia can be an unfortunate

effect of ADT; however, it is also important to consider that obesity and lifestyle

also contribute to insulin metabolism and CaP risk (Tymchuk et al. 2001; Carmody

et al. 2008). Micro foci of CaP exist in 42-80% of men from of all races aged over

50 at autopsy (Venkateswaran et al. 2010). In nations with high CaP mortality

these tumours are large and multifocal. Nations with a high percentage of these

types of tumours mostly follow Western diets, and the association between diet and

CaP incidence also exists (Venkateswaran et al. 2010) Recommended dietary

intervention most often consists of decreased total fat intake, increased omega 3

(n3) fat intake, increased intake of whole grain and high fibre cereals, and

decreased animal protein intake/ increased soy protein intake (Carmody et al.

2008; Süral et al. 2008; Aronson et al. 2010; Venkateswaran et al. 2010).

Adherence to the Mediterranean diet, which includes many of these factors, has

shown benefits for CaP and other cancers (Simopoulos 2001; Esposito et al. 2004;

Itsiopoulos et al. 2009). Landberg et al. also demonstrated that diets rich in whole

grains decrease PSA levels, urinary C-peptide levels and serum insulin levels

(Landberg et al. 2010), again suggesting the connection between dietary

intervention, insulin levels, and prostate cancer progression.

It has been postulated that dietary intervention may inhibit carcinogenic pathways,

androgen metabolism, cell cycle processes and apoptosis, maintenance of

mitochondrial membrane potential, insulin and insulin like growth factor signalling,

Akt signalling and oxidative stress (Venkateswaran et al. 2010). Dietary fatty acids

have been linked to increased circulating androgens and conversely, lower fatty

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acids with lower serum testosterone. With increased dietary fatty acids there is

reportedly more oxidative stress and DNA damage and increased risk of CaP,

though there are mixed reviews as to whether it is the amount or the quality of fat

that may have more impact on cancer progression (Venkateswaran et al. 2010).

Decreased dietary fat intake could reduce substrate availability for lipogenesis and

steroidogenesis in CaP cells, including arachidonic acid, which may exacerbate

both pathways, as described in chapter 5. Importantly, a low fat diet would aid in

preventing obesity and hyperinsulinemia, which contribute to the above pathways.

Tellingly, similar dietary and lifestyle interventions are recommended for

prevention of metabolic syndrome as for CaP (Feldeisen et al. 2007).

In fact, it has been suggested that lifestyle may have an enormous impact on cancer

development and outcomes (Laukkanen et al. 2004; Goodwin 2008; Ligibel et al.

2008). Tymuchuk et al. demonstrated that exercise and low-fat diet intervention in

men after ADT slowed CaP cell growth when volunteer serum was supplemented

for FBS in LNCaP medium (Tymchuk et al. 2001). Men were told to eat low-fat

diets with protein mainly from fish or plant sources, whole grains, and fruits and

vegetables. After only 11 days, many had lost weight, approximately 2kg, and had

lower serum triglyceride levels and fasting glucose (which may imply improved

insulin sensitivity). At 14-year, long term follow up, men who complied with this

regime lost an average of 20kg and saw major improvements in their serum lipid

profiles. Importantly, lower levels of serum testosterone were demonstrated in both

long and short term compliance groups. When substituted for serum in LNCaP cell

culture, serum from the intervention men reduced cell growth by approximately 30-

45% compared to pre-intervention, which did not differ in proliferation profile from

FBS. Though it was not a parameter of this study, the authors suggested that

decreasing fasting insulin levels may also play a part in intervention. Furthermore, a

similar study using serum from men with low risk, early stage CaP showed a

decrease in serum PSA and cell growth correlating to adherence to lifestyle

intervention and other small studies show similar results for dietary intervention

(Ornish et al. 2005; Aronson et al. 2010).

Prostate cancer most often affects older men, and adherence of this demographic to

dietary interventions is poor, with few getting their recommended portion of fruits

and vegetables (Carmody et al. 2008). Carmody et al. recommend a diet similar to

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those described above to a small sample of patients after ADT (Carmody et al.

2008). In addition to dietary intervention, they also included the idea of

mindfulness, or feelings of control of the situation, as well as partner support.

Importantly, all patients had self reported increase in quality of life after 3 months.

There was also an average of 10.5 pounds weight loss, and a significant decrease in

PSA doubling time, from 21.5 months to 58.8 months.

Of relevance to this project, Venkateswaran et al. recently published an elegant

review of the effects and mechanisms of diet as chemoprevention for CaP

(Venkateswaran et al. 2010). The authors suggest that the long dormancy of CaP

provides a large opportunity for intervention before cancer progresses to the point

where surgery or ADT are necessary treatments, which might reduce incidence of

biologically indolent cancers, which are over detected in PSA screening. There is

evidence that the ratio of omega 3 (n3) fatty acids to n6 may be very important, the

optimum balance being 1:4, but that ratio is very different from the usual ratio in a

typical Western diet (Venkateswaran et al. 2010). This optimal ratio is seen in

Japan, where the incidence of CaP is very low. The incidence of CaP is even lower

than Japan in populations that follow the diet of Crete, a version of the

recommended Mediterranean diet where the n3:n6 ratio is approximately 1:2.

Following a Mediterranean diet is also shown to be protective of the development

of metabolic syndrome (Tortosa et al. 2007). In murine studies, feeding high n3 diet

to mice reduced CaP growth and increased their total survival, and demonstrated

that n3 fatty acid feeding decreased expression of IGF1, Akt activity, and

mitogenicity as well as decreased expression of inflammatory mediators, such as

COX-1 (Kobayashi et al. 2006; Berquin et al. 2007; Kobayashi et al. 2008;

Berstein et al. 2010). Furthermore, increased intake of n3 fatty acids is also

recommended for treatment of metabolic syndrome, as it changes membrane

composition and fluidity, increasing insulin sensitivity, as well as mediation of

other pathways (Carpentier et al. 2006; de Santa-Olalla et al. 2009). To further

connect dietary intervention, insulin levels, and prostate cancer progression,

Landberg et al. also demonstrated that diets rich in whole grain bran and rye not

only decrease PSA levels, but also decrease urinary C-peptide levels and serum

insulin levels (Landberg et al. 2010). Moreover, epigallocatechin gallate (EGCG),

which is extracted from green tea, and cruciferous vegetables and their extracts,

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especially sulforophane, showed efficacy in vivo and in vitro in decreasing both

CaP progression pathways and metabolic syndrome (Bose et al. 2008; Villegas et

al. 2008; Thielecke et al. 2009; Venkateswaran et al. 2010)

When lifestyle and dietary intervention are not working for metabolic syndrome,

cancer or both, or need enhancement, pharmacological intervention is a valid

option, and efficacy has been demonstrated (Ligibel et al. 2008). For cancer

treatment, dietary modulation alone is not recommended. In this project, it has been

demonstrated that both metformin and simvastatin may decrease insulin induction

of pathways associated with CaP progression in particular steroidogenesis and

lipogenesis. The main function of metformin is to activate AMPK, which then

inhibits the activation of lipogenesis, cholesterol synthesis, and protein synthesis

pathways to conserve cellular energy. Simvastatin inhibits HMGR, which inhibits

steroidogenesis by suppressing cholesterol formation, as well as decreasing

expression of SREBP and ACC, which are important mediators of lipogenesis, in

the presence of insulin. This study suggests that simvastatin may be more a potent

inhibitor of steroidogenesis and lipogenesis in a hyperinsulinemic model.

In breast cancer, metformin use was associated with better clinical response, and

was an independent predictor of complete pathological response and disease free

survival (Jiralerspong et al. 2009). Use of metformin decreases CaP incidence by

44% (Clements et al. 2011). In CaP and other cancers, FASN targeting is specific

for cancer cells (Lu et al. 2005; Orita et al. 2007; Orita et al. 2008; Flavin et al.

2010), and it appears the effects of metformin are selective for breast cancer stem

and non-stem cells in four xenograft mouse models (Hirsch et al. 2009).

Furthermore, it has been demonstrated herein that metformin decreases FASN

production and activity (chapter 6), perhaps providing a potential link between the

two. In vitro studies demonstrate that the major effects of metformin include

inhibition of mTOR, activation of AMPK and inhibition of cell cycle progression,

and in vivo studies show reduced insulin and IGF levels in serum. In fact, in breast

cancer and obesity studies, metformin use appears to have similar benefits to

lifestyle intervention (Ligibel et al. 2008; Goodwin et al. 2009; Goodwin et al.

2011). In prostate cancer studies, Nobes et al. demonstrated that the use of

metformin along with lifestyle changes reduced incidence of metabolic syndrome

after ADT compared to lifestyle intervention alone (Nobes et al. 2009; Clements et

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al. 2011). Metformin is currently in several clinical trials investigating its clinical

utility as an adjuvant treatment in ADT-treated prostate cancer including a large

trial at Westmead Cancer Care Centre (MVENT trial), (Gurney 2010). Metformin

is also in phase 3 trials for breast cancer intervention (Goodwin et al. 2009;

Goodwin et al. 2011).

The attenuation of metabolic syndrome side effects may be very important for the

quality of life of CaP patients, as well as the cessation of progression. In this project

it has been demonstrated that high insulin levels increase metabolic pathways in

cancer that contribute to progression and aggressiveness, steroidogenesis and

lipogenesis. It has also been demonstrated that pharmacological intervention such

as metformin may quell that induction. Dietary and lifestyle intervention, reviewed

above, may be integral for abatement of metabolic syndrome effects on CaP

progression, whether the metabolic syndrome is incident after ADT, or due to pre-

existing obesity or lifestyle factors. Monitoring and regulation of the metabolic

condition of patients in regard to the outcome of their treatment has been suggested

since the 1960s; presently, it is evident that this would have an immense impact on

patient prognosis (Goodwin et al. 2009; Berstein 2010; Goodwin et al. 2011).

8.3 The effect of insulin on breast cancer: background and prospects for study/

intervention

8.3.1 The similarities between breast and prostate cancer

There are many similarities between prostate and breast cancer, as elegantly

explained by Risbridger et al. in a recent review (Risbridger et al. 2010). The main

treatment for both is hormone deprivation, either of androgens or estrogens,

respectively. Oophorectomy, removal of the ovaries, which are the main estrogen

source for females, was first suggested for breast cancer metastasis 100 years ago,

and castration was suggested for advanced CaP in 1969 (Risbridger et al. 2010). In

current practice, both are treated by blockade of gonadal, adrenal, peripheral or

intratumoural steroidogenesis, as well as inhibitors or antagonists of growth factor

and hormone receptors. CaP and breast cancer both initially respond to treatment

then are treated with short term hormonal therapies after relapse, as cancers respond

or not (Risbridger et al. 2010). Estrogens and androgens may play important roles

survival and/ or apoptosis in both diseases. In breast cancer, tamoxifen and

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raloxifene are antagonists to alpha estrogen receptor (ER-α) action, blocking many

survival pathways; activation of ER-α in CaP leads to inflammation and

malignancy. Interestingly, ER-β activation might have antiproliferative and

antiapoptotic effects, which may be important in both cancers and there are

preclinical trials underway to address this (Risbridger et al. 2010). AR/ ER-α

antagonists together with ER-β agonists could present new options for therapy. In

breast cancer and CaP, AR loss and/ or AR negative phenotype is associated with

metastatic disease and poor prognosis (Jarrard et al. 1998; Wikström et al. 2009;

Risbridger et al. 2010). Presence of AR can indicate better prognosis and response

to hormone therapy. In response to de novo and exogenous steroids, bicalutamide

and MDV3100 AR antagonists, as well as abiraterone, demonstrate good efficacy;

however, there is no data available on the use of MDV3100 or abiraterone on breast

cancer. There is hope that abiraterone may be beneficial in breast cancer, as there

are currently phase I/ II trials ongoing with ER-α and/ or AR positive advanced or

metastatic tumours (Risbridger et al. 2010). In breast cancer, aromatase converts

testosterone to estradiol. Aromatase inhibition works well in breast cancer, and as

an adjuvant to other therapies, has benefits for recurrent CaP tumours. Interestingly,

androgens can slow breast cancer growth but DHT can be metabolised into

androstenediol which can activate ER-α (Risbridger et al. 2010).

In both breast and prostate cancers, following hormone deprivation, when there is

less circulating estrogen or androgen, there is still a substantial concentration of

steroid hormones measurable in the tumours. There is much interest in the

inhibition of many enzymes in the steroidogenesis pathway for breast cancer

therapy, not only of aromatase, but also the HSD17B enzymes (Miyoshi et al. 2001;

Day et al. 2008; Day et al. 2009; Poirier 2010) and AKR1C3 (Rizner et al. 2006),

as well as abiraterone, as described above (Risbridger et al. 2010), and

sulfotransferase, which bypasses aromatase to convert less potent steroids to

estrogens (Woo et al. 2010). As of yet, it has been assumed that breast cancer can

only synthesize estrogens from exogenous steroids and precursors, as it was for

CaP before the ability of tumours to perform de novo steroidogenesis was

demonstrated by our group (Locke et al. 2008). It may be that breast cancer cells

are also able to produce de novo estrogens, and if so, the triggers for this would be

essential to understand.

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8.3.2 Breast cancer and hyperinsulinemia

In breast cancer patients, obesity and hyperinsulinemia are associated with

recurrence and fatality (Goodwin et al. 2002). Also, high serum insulin levels are

associated with risk of developing breast cancer. It has been demonstrated that

insulin can increase lipogenesis in breast cancer models and that high levels of

FASN are associated with poor prognosis (Menendez et al. 2004a; Menendez et al.

2006; Menendez et al. 2007). Insulin has been shown to upregulate many survival

pathways, such as PI3K, Akt, and mTOR (Wysocki et al. 2010). The idea of

insulin as a mediator of breast cancer, or any cancer, is interesting because insulin

levels can be controlled by lifestyle and pharmacological intervention (Goodwin

2008). Insulin receptor A (INSR-A) is almost ubiquitously expressed and

overexpressed in breast cancer, and is not down regulated by exposure to ligand, as

it is in normal tissue (Crettaz et al. 1984; Goodwin 2008). As intervention,

significant weight loss and low dietary intake of fat lowers insulin levels and

improves disease free survival. Lifestyle intervention is not for everyone, as some

older or disabled patients may find it difficult to adhere to a stringent diet and

exercise routine. However, there is an old adage that states that if exercise was a

drug, it would be the most highly prescribed drug in the world, and studies show

that metformin use had reduction in insulin levels similar to exercise (Goodwin

2008). Metformin is in trials for its perceived efficacy in a wide variety of

conditions (Pollak 2010b). Goodwin et al. recommend that practitioners should

look at the baseline metabolic conditions of patients and base therapeutic decisions

on the status of hormones and growth factor levels (Goodwin 2008; Goodwin et al.

2009; Goodwin 2010a; Goodwin et al. 2011). Monitoring of metabolic symptoms

in CaP patients is also highly recommended, as discussed above (Berstein 2010;

Redig et al. 2010b). It has been demonstrated that high insulin levels may actually

decrease the effectiveness of aromatase inhibitors; therefore, the metabolic status of

those patients would indicate treatment that is different from standard is necessary

for those patients.

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8.3.3 Insulin effects on steroidogenesis in breast cancer

Literature has shown that breast cancer cells express most of the necessary enzymes

for steroidogenesis (Miyoshi et al. 2001; Day et al. 2008; Day et al. 2009; Poirier

2010), and insulin aggravates breast cancer growth, as it does for CaP cells (Lubik

et al. 2011); therefore, our group was intrigued to investigate the expression of

steroidogenesis enzyme mRNA and estradiol production in the MCF7 (AR and ER

positive) breast cancer cell line, in the presence and absence of insulin. In chapter

4.3.3, it was shown that insulin not only increases the expression of steroidogenesis

enzyme MRNA in MCF7 cells, but that insulin increases levels of secreted estradiol

at levels that would activated ER (Mattick et al. 1997). Though an increase in

aromatase was not demonstrated at the mRNA level in MCF7 cells, in 10hr

treatment insulin arrays in LNCaP cells (chapter 2.14), aromatase mRNA increases

approximately 2-fold; therefore, more investigation, especially on the protein level,

in both breast and CaP cells is warranted.

This data supports the hypotheses that (a) breast cancer cells may be capable of de

novo steroidogenesis (b) and insulin may increase de novo steroidogenesis in breast

cancer cells, and exacerbate the conversion of exogenous precursors to estradiol.

These pathways should be further explored, in the presence and absence of

metformin, as metformin may be useful in abrogating these pathways, as well as

lipogenesis, as demonstrated in Chapter 6. Furthermore, metformin has been shown

to reduce cell proliferation in tamoxifen naive and resistant MCF7 cells through

activation of AMPK, but also through down-regulation of ER-α, which may

indicate it could favourably influence estrogen signalling (Berstein et al. 2010), and

the combination of metformin with AR inhibition is more effective for delayed

progression of prostate cancer xenografts than AR inhibition alone (Schayowitz et

al. 2010).

8.4 IGF2, steroidogenesis, and bone metastases

8.4.1 Androgens and bone metastases

CaP mortality is mostly due to metastasis and transition to CRPC (Zhu et al. 2010).

CaP is the most commonly diagnosed cancer in men and second leading cause of

cancer death, owing to its effects on haematopoiesis and bone structure. Bone

metastases are a major cause of morbidity due to the replacement of

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haematopoietic tissue with tumour volume and decreased bone stability with

increased susceptibility to infection (Logothetis et al. 2005). Bone overgrowth can

lead to pain, fracture and spinal cord compression which can lead to loss of strength

in one or both sides of the body. Bone metastases are the result of osteoblastic

activation by cancer cells and suppression of osteoclastic action i.e. more bone

formation than degradation (Logothetis et al. 2005). Prostate cancer is very likely

to metastasize to bone (Tantivejkul et al. 2004); in fact, 70% of men who progress

to CRPC will have bone metastasis (Cross et al. 2007); bone is the third most

common site of metastasis for most cancers (Woodhouse et al. 1997). Metastatic

and invasive cancer cells have selective growth, as cells move to many places in the

body but only grow in areas where appropriate growth factors are present or

extracellular matrix conditions are favourable. They may also selectively adhere to

endothelial luminal surfaces and/ or have selective chemotaxis to organs which

produce selective attraction factors. That androgens are produced by osteoblasts

(Quinkler et al. 2008), and that osteoblasts have AR which are activated and

upregulated by androgens may be important to growth of CaP bone metastases

(Wiren et al. 1997; Wiren 2010). Importantly, increased cholesterol and the

expression of the enzymes for its formation are highly upregulated in metabolomic

studies of CaP metastases compared to local CaP (Thysell et al. 2010).

Furthermore, enzymes for androgen production are highly expressed in bone

metastases samples (Mostaghel et al. 2007; Mostaghel et al. 2008). Increased

production of androgens in both CaP and bone cells may contribute to metastasis

(Koeneman et al. 1998; Crnalic et al. 2010).

8.4.2 IGF2 may promote steroidogenesis contributing to growth of bone

metastases

IGFs are the most abundant non-structural proteins in mineralized bone and IGF2 is

nine times more abundant than IGF1 (Kimura et al. 2010; Zhu et al. 2010). IGFs

promote osteoblast proliferation and differentiation (Woodhouse et al. 1997) and

inhibition of both IGFs appears to decrease development of bone metastatic lesions

in xenograft CaP models, though inhibition of IGF1 does not decrease development

as consistently (Kimura et al. 2010). Interestingly, in prostate cancer cell lines,

LNCaP, DU145 and PC3, IGF1 and 2 demonstrate similar ability to promote

proliferation; however, IGF2 is more potent at stimulating chemotaxis (Ritchie et

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al. 1997). CaP cells can secrete factors that directly or indirectly mediate the bone

microenvironment, such as PSA, which is a serine protease which can cleave

parathyroid hormone related protein (PHRP), a hormone that stimulates bone

resorption. Decreasing bone resorption increases the osteoblastic bone proliferation

phenotype. PSA also cleaves IGFBP3, increasing local IGF concentrations in the

bone microenvironment (Logothetis et al. 2005).

Bone marrow stem cells secrete detectable IGF2, but not IGF1, and IGF2 promotes

androgen action in hormone-sensitive prostate cancer cells by synergistically

promoting PSA expression (Cross et al. 2007). As demonstrated in chapter 7, IGF2

mRNA increased in tumour samples after ADT as patients progressed towards

CRPC, and it has been shown that 70% of patients will have bone metastases at

castrate resistance (Buijs et al. 2009). It may be that increased IGF2 expression

results in de novo steroidogenesis, as demonstrated in chapter 7, which may

influence metastasis. Linking IGF2 to bone metastases in a CaP model, when

human bone was implanted in mice then the bone marrow is injected MDA PCa 2b

AR positive cells, with addition of IGF2 increased cancer growth is apparent

(Kimura et al. 2010). Significantly, a monoclonal antibody against human IGF2

inhibited the IGF2-induced increase in total tumour area in bone implants and PSA

secretion decreased, along with levels of ki67 staining, indicating a decrease in AR

activation and decrease in proliferation of injected cells, respectively. Also, there

was an increase in apoptotic signalling via the caspase cascade (Kimura et al.

2010). Furthermore, treatment of MDA PCa 2b cells with IGF2 and the antibody in

vitro showed decreased signalling through the IGF1R and INSR compared to IGF2

treated cells. The above study shows that IGF2 would be a prime candidate for

targeting to suppress growth of bone metastasis, and it may be that IGF2 promotes

growth of metastasised cells by increasing steroidogenesis, as androgens promote

growth of bone metastases (Vanderschueren et al. 2004; Crnalic et al. 2010; Wiren

2010). Furthermore, because IGF2 promotes chemotaxis (Ritchie et al. 1997), it

may be that targeting of IGF2 would suppress bone metastasis before it takes root.

Methods of preventing IGF2 upregulation may be linked to suppression of

metabolic dysfunction, as described in chapter 8.4.3.

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8.4.3 Insulin and IGF2 may promote prostate cancer progression

It has also been demonstrated that men who develop ADT induced metabolic

syndrome have more rapid progression to CRPC and metastatic disease (Flanagan

et al. 2010). Because IGF2, which is very prevalent in bone and bone metastasis,

increases steroidogenesis, and androgens trigger more aggressive metastasis, and

insulin increases IGF2 mRNA levels, it is interesting to speculate that insulin may

be partly contributing to aggressive metastasis by upregulation of IGF2, and

increased free IGF levels. As discussed in chapter 8.2, metabolic syndrome after

ADT could be treated with metabolic intervention, which if the connection holds

upon further study, suggests IGF2 levels could also be attenuated by these means.

As discussed in chapter 7, insulin upregulates IGF2 at the mRNA level in LNCaP

and 22RV1 prostate cancer cells; therefore, insulin may be partly contributing to

aggressive metastasis (Flanagan et al. 2010) by upregulation of IGF2. Microarray

data also suggests that insulin decreases the expression of proteins that would

sequester IGF2, leading to increased levels of free peptide. As discussed in chapter

8.2, metabolic syndrome after ADT could be treated with metabolic intervention,

which if the connection holds upon further study, suggests IGF2 levels could also

be attenuated by these means. Our group has shown that PSA and some

steroidogenesis enzymes are upregulated in osteoblast-LNCaP 3-dimensional co-

culture compared to LNCaPs alone, demonstrating that this model may be useful to

investigate the interactions between osteoblasts and CaP cells which would lead to

promotion of steroidogenesis and invasiveness caused by insulin, IGF2 and IGF2

levels induced by insulin (Sieh et al. 2010).

235

8.5 Final Conclusion and Summary

Prostate cancer is a complex disease for treatment, where risk factors, such as

hyperinsulinemia, may also be initiated by the treatment, ADT. However, the

understanding of how controlling insulin and growth factor levels may decrease

CaP risk or risk of metabolic syndrome after ADT, which has been demonstrated to

activate key pathways for CaP survival and aggressiveness, will be essential to not

only improving patient longevity, but improving quality of life for patients. The

dynamics of insulin action, as well as the action of IGF2, have been in examined in

this thesis, leading to the following key findings:

1) Insulin upregulates steroidogenesis enzymes in CaP cells at both the mRNA

and protein level, and increases total and de novo steroidogenesis.

Steroidogenesis activates AR, which stimulates activation of CaP survival

pathways.

2) Insulin analogues, such as glargine which has been variably associated with

cancer risk, appear not to stimulate steroidogenesis in CaP cells with any

greater potency than insulin.

3) Insulin upregulates lipogenesis, a hallmark of cancer aggressiveness and

chemoresistance. Insulin upregulated SREBP, ACC, FASN at both the

mRNA and protein level and upregulates triglyceride and cholesterol–ester

levels, and de novo fatty acid synthesis. Furthermore, insulin upregulates

multiple lipid mediated growth and survival pathways in CaP.

4) Metformin, and to a lesser extent simvastatin, may suppress insulin

stimulated steroidogenesis and lipogenesis in CaP cells.

5) IGF2 also stimulates steroidogenesis enzymes in CaP cells at both the

mRNA and protein level, and increases total and de novo steroidogenesis.

Taken together, these results demonstrate that high insulin levels, caused by ADT

or lifestyle factors, and prostatic IGF2 levels, contribute to the pathogenesis of CaP

progression though stimulation of steroidogenesis and/ or lipogenesis. The

pathways stimulated by these peptide hormones and control of these hormones

themselves are excellent candidate targets for CaP therapeutics and intervention.

236

237

Appendices

238

239

APPENDIX A: Primers Used

Enzyme Forward Primer Reverse Primer

Signalling

IGF2 ACGTTTGGCCTCCCTGAACG CTGTGCTACCCCCGCCAAGT

IR-B

(E Vickers at

VGH) CGTCCCCAGAAAAACCTCTTC ACGGCCACCGTCACATTC

IR-A

(E Vickers at

VGH) CTGCACAACGTGGTTTTCGT ACGGCCACCGCACATTC

IGF-IR GAAAGTGACGTCCTGCATTTCA CCGGTGCCAGGTTATGATG

IRS-2 primers and probes ordered from Applied Biosystems (*)

SREBP-1 CGCTCCTCCATCAATGACAA

TCGAGAAAGCGAATGTAGTCG

AT

SREBP-2 TCCGCCTGTTCCGATGTAC TGCACATTCAGCCAGGTTCA

AR CTGGACACGACAACAACCAG CAGATCAGGGGCGAAGTAGA

Steroid

synthesis

StAR GCCCATGGAGAGGCTCTATG TTCCACTCCCCCATTGCTT

CYP11A1 AGTTCTCGGGACTTCGTCAGT GGAGCCCGCCTTCTTGA

CYP17A1 * GTGGGCGCTGCATCACA CAAGAAACGCTCAGGCATGA

HSD3B2 CGGGCCCAACTCCTACAAG TTTTCCAGAGGCTCTTCTTCGT

AKR1C3 TGGGAGGCCATGGAGAAG TTTGACACCCCAATGGACTTG

HSD17B3 TGGGACAGTGGGCAGTGA CGAGTACGCTTTCCCAATTCC

SRD5A1 ACGGGCATCGGTGCTTAAT CCAACAGTGGCATAGGCTTTC

RDH5 GCCCGCCAGCAATGC CGCCCAAAGCCTGAGTCA

PSA AGTGCGAGAAGCATTCCCAAC CCAGCAAGATCACGCTTTTGTT

Aromatase

TGTCTCTTTGTTCTTCATGCTA

TTTCTC

TCACCAATAACAGTCTGGATTT

CC

Lipogenesis

HSL GGAAGTGCTATCGTCTCTGG GGCAGTCAGTGGCATCTC

ACSL3 TTTGCCTCTGGCCCATGT GCGGCATCCGTGAGAAAG

DBI TGGCCACTACAAACAAGCAACT TCCCGGGCCGTTCTG

ACC

CTGTAGAAACCCGGACAGTAGA

AC GGTCAGCATACATCTCCATGTG

FASN CGCTCGGCATGGCTATCT CTCGTTGAAGAACGCATCCA

240

Cholesterol

Synthesis

HMGR GGATGACTCGTGGCCCAGT TCGAGCCAGGCTTTCACTTC

HMGS TTCACCATGCCTGGATCACTT ATCTCAAGGGCAACAATTCCC

Control

rpl32 CCCCTTGTGAAGCCCAAGA GACTGGTGCCGGATGAACTT

241

APPENDIX B: Antibodies

Antibody Source Dilution size Raised in Contact/ Catalog #

Reacts with

Steroid synthesis

SREBP-1 Santa Cruz 200 125-68 Rabbit sc-366

Human, mouse

StAR Hales collaborator 500 ~30 Rabbit

[email protected]

Human, mouse

CYP11A1 Abcam 500 60 Mouse ab673335 Human

CYP17 Hales collaborator 1000 50 Rabbit

[email protected]

Human, mouse

3β HSD Santa Cruz 20 42 Rabbit sc-30821

Hman, mouse, rat

AKR1C3 Abcam 1000 35 Goat ab27491 Human

17βHSD3 Abnova 500 34 Mouse H00003293-AO1

Human, mouse

5αReductase1 Novus 500 29.4 Goat NB 100-149

Human,Chip

RDH5 Anova 250 32 Mouse H00005959-AO1 Human

CYP17A1 Abcam 500 50 Rabbit ab64886 Human

StAR Abcam 500 20 Rabbit ab96637

Human, mouse, rat

Control Ab

GAPDH Abcam <1ul/10ml 36 Mouse ab8245 Human

Lipogenesis

HSL Abcam 500

90 and 117 Rabbit ab45422

Human, mouse, rat

ACSL3 Abnova 500 80 Rabbit PAB5224

Human, mouse, cow, pig, rat

ACC Santa Cruz 100 260 Rabbit sc-30212

human, mouse, rat

FASN Santa Cruz 200 180 Rabbit sc-20140 Human, mouse

HMGR Millipore 500 63 Rabbit O7572

Human, mouse, rabbit, rat

Secondary Antibodies

242

Anti-Rabbit (IRDye 680CW) Licor 10 000 N/A Donkey

926-68023 Rabbit

Anti-Goat (IRDye 800CW) Licor 11 000 N/A Donkey

926-32214 Goat

Anti-Mouse (IRDye 800CW) Licor 12 000 N/A Donkey

926-32212 Mouse

243

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