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The Spindle Assembly Checkpoint- A Predictor of Anthracycline Sensitivity in Breast Cancer Patients.
by
Meryam Al-waadh
A thesis submitted in conformity with the requirements for the degree of Master of Science
Department of Laboratory Medicine and Pathobiology University of Toronto
© Copyright by Meryam Al-waadh, 2016.
ii
The Spindle Assembly Checkpoint- A predictor of
anthracycline sensitivity in breast cancer patients.
Meryam Al-waadh
Master of Science
Laboratory Medicine and Pathobiology
University of Toronto
2016
Abstract
Identifying drug response remains a major challenge in breast cancer patients treated with adjuvant chemotherapy,
as some patients relapse following treatment. Breast cancer consists of differences in tumour biology, which
potentially correlates to differential treatment response patterns in patients. Resistance to commonly used
chemotherapeutics such as anthracyclines poses an obstacle within the clinical setting, and no clinically validated
biomarker exists to identify patients who will respond to treatment. Through analysis of the BR9601 trial we have
been able to identify the spindle assembly checkpoint (SAC) as a potential predictor of anthracycline sensitivity,
allowing us to further analyze this mechanism at the molecular level using anthracycline resistant breast cancer cells.
To further elucidate the mechanism, we interrogated the SAC signal within our resistant cell lines and found that the
SAC signal was significantly downregulated in the resistant cells. Investigation into SAC dysregulation could
represent a mechanism for identifying anthracycline response.
iii
Acknowledgments
This work was completed under the supervision of Dr. John Bartlett and Dr. Melanie Spears of
the Transformative Pathology lab at the Ontario Institute for Cancer Research (OICR).
I would like to thank my supervisor Dr. John Bartlett for his guidance and support throughout
this project. I would like to sincerely thank Dr. Melanie Spears for her ongoing input, guidance
and leadership in designing this project. Thank you to my committee members, Dr. Paul Hamel
(Department of Laboratory Medicine and Pathobiology), Dr. Irene Andrulis (Department of
Laboratory Medicine and Pathobiology) and Dr. Rima Al-Awar (Department of Pharmacology
and Toxicology) for their support throughout this project. I would like to sincerely thank all the
members of the Transformative Pathology lab for their tutelage, patience in my training and
support for the duration of my project. I would like to specifically thank Linda Liao and Nicola
Lyttle for their assistance with developing and maintaining the resistant cell lines, Dr. Marsela
Braunstein for her assistance with the flow cytometry analysis and Nazleen Lobo for her
expertise and assistance with the siRNA knockdown design.
iv
Table of Contents
Acknowledgments .......................................................................................................................... iii
List of Abbreviations ..................................................................................................................... vi
List of Tables ............................................................................................................................... viii
List of Figures ................................................................................................................................ ix
Chapter 1 ......................................................................................................................................... 1
Introduction and Background ..................................................................................................... 1 1
1.1 Breast Cancer Epidemiology and Etiology. ........................................................................ 1
1.2 Breast cancer pathology ...................................................................................................... 3
1.3 Predictive and Prognostic biomarkers ................................................................................ 4
1.3.1 Breast Cancer Staging- Nottingham Prognostic Index (NPI) ................................. 4
1.3.2 Estrogen and Progesterone receptors ...................................................................... 5
1.3.3 Human epidermal receptor-2 (HER2) ..................................................................... 6
1.4 Breast Cancer molecular subtypes ...................................................................................... 7
1.5 Treatment ............................................................................................................................ 8
1.5.1 Surgery .................................................................................................................... 8
1.5.2 Adjuvant treatment- Chemotherapy ........................................................................ 8
1.6 Predictive markers of anthracycline benefit ..................................................................... 10
1.7 Ch17CEP duplication ........................................................................................................ 11
1.7.1 Chromosomal Instability ....................................................................................... 12
1.7.2 Spindle Assembly Checkpoint .............................................................................. 13
1.7.3 BubR1 ................................................................................................................... 15
1.7.4 Mad2 ..................................................................................................................... 15
1.7.5 Securin .................................................................................................................. 16
1.7.6 Breast cancer specific gene-1: Synuclein gamma ................................................. 16
1.7.7 In-vitro chemotherapy resistant cell lines ............................................................. 17
1.8 The BR9601clinical trial ................................................................................................... 18
1.8.1 Preliminary Results ............................................................................................... 20
1.9 Hypothesis and Aims ........................................................................................................ 23
v
Chapter 2 ....................................................................................................................................... 24
Materials and Methods ............................................................................................................. 24 2
2.1 Breast cancer cell line culture ........................................................................................... 24
2.2 CCK8 assays ..................................................................................................................... 24
2.3 Flow Cytometry ................................................................................................................ 25
2.3.1 Double Thymidine block ...................................................................................... 25
2.4 Western Blots .................................................................................................................... 25
2.4.1 Antibodies ............................................................................................................. 26
2.4.2 Densitometry ......................................................................................................... 27
2.5 qPCR ................................................................................................................................. 27
2.5.1 ΔΔCT .................................................................................................................... 28
2.6 siRNA knockdowns .......................................................................................................... 29
Chapter 3 ....................................................................................................................................... 30
Results ...................................................................................................................................... 30 3
3.1 Isogenic EpiR cell lines, MDA-MB-231 and ZR-75-1, display 8-11X more resistance
to epirubicin relative to native cells. ................................................................................. 30
3.2 Cell Cycle Analysis: G2M phase time points 10hrs & 12hrs. .......................................... 31
3.2.1 Double thymidine block synchronization confirmed through cyclin B and E. ..... 32
3.3 Analysis of BubR1 and Mad2 at the G2M phase time points. .......................................... 34
3.4 Increased SNCG gene expression observed within EpiR cell lines. ................................. 38
3.5 Time course (24-96hrs) knockdown of BubR1 in native breast cancer cell lines. ........... 40
3.6 Significant increase in epirubicin resistance observed following knockdown of BubR1
in native cell lines. ............................................................................................................ 42
Chapter 4 ....................................................................................................................................... 44
Discussion ................................................................................................................................ 44 4
Chapter 5 ....................................................................................................................................... 51
Conclusion................................................................................................................................ 51 5
References ..................................................................................................................................... 53
vi
List of Abbreviations
AI Aromatase inhibitors
APC/C Anaphase-promoting complex/cyclosome
ASCO American Society of Clinical Oncology
BCA Bicinchoninic acid assay
BCSG1 Breast cancer specific gene 1
BubR1 Budding-uninhibited-by-benzimidazoles-related-1
Bub1B Budding-uninhibited-by-benzimidazoles-1-homolog-beta
CCK-8 Cell-Counting-Kit-8
Cdc20 Cell division cycle 20
CH17CEP Chromosome 17 centromere enumeration probe
CMF Cyclophosphamide-methotrexate-fluorouracil
CRISPR Clustered regularly interspaced short palindromic repeats
Ct Threshold cycle value
DCIS Ductal carcinoma in-situ
DMEM Dulbecco modified eagle medium
DMSO Dimethyl sulfoxide
EBCTCG Early Breast Cancer Trialists` Collaborative Group
ELISA Enzyme-linked immunosorbent assay
EpiR Epirubicin resistant
ER Estrogen receptor
ERbB Epidermal growth factor receptor
Epi-CMF Epirubicin-cyclophosphamide- methotrexate-fluorouracil
FACS Fluorescence-activated cell sorting
FBS Fetal bovine serum
FISH Fluorescent in-situ hybridization
HER2 Human epidermal growth factor receptor 2
HR Hazard ratio
HRP Horse radish peroxidase
IC50 Inhibitory concentration at 50%
IBC Invasive breast cancer
vii
IHC Immunohistochemistry
Mad2 Mitotic arrest deficient 2
MCC Mitotic checkpoint complex
MDR Multi-drug resistant genes
MINDACT Microarray in node negative disease may avoid chemotherapy
NEAT National Epirubicin Adjuvant Trial
NPI Nottingham prognostic index
NTC Non-targeting control
OSM cytokine Oncostatin M
OS Overall survival
PI Propidium iodide
PTTG1 Pituitary tumor-transforming 1
PR Progesterone receptor
PVDF Polyvinyl difluoride
RFS Relapse-free survival
RT-PCR Reverse-transcription polymerase chain reaction
SAC Spindle Assembly Checkpoint
SDS Sodium dodecyl sulfate
SNCG Synuclein gamma
TAILORx Trial Assigning IndividuaLized Options for Treatment(Rx)
TBS Tris buffered saline
TNBC Triple-negative breast cancer
TNM tumour, nodes, metastasis
TOPIIA Topoisomerase type II-alpha
viii
List of Tables
Chapter 1
Table 1: Breast cancer molecular subtypes ..................................................................................... 8
Table 2: ATCC breast cancer cell lines and hormone receptor expressions. ................................ 24
Chapter 2
Table 3: Antibodies and respective dilutions routinely used for western blot analysis.. .............. 26
Table 4: Sample densitometry calculation of BubR1 normalized to its actin control .................. 27
Table 5: ΔΔCt sample calculations following qPCR run.............................................................. 28
ix
List of Figures
Chapter 1
Figure 1: Age-standardized mortality rates (ASMR) for breast, lung and colorectal cancer in
Canadian women. ............................................................................................................................ 1
Figure 2: Age-standardized incidence rate (ASIR) for breast, lung and colorectal cancer in
Canadian women. ............................................................................................................................ 1
Figure 3: WHO Global Breast Cancer statistics among male and females .................................... 2
Figure 4: Anatomy of a normal breast.. .......................................................................................... 3
Figure 5: Schematic of the SAC signal within the G2M phase of the cell cycle. ........................ 14
Figure 6: Schematic representation of the BR9601 clinical trial .................................................. 19
Figure 7:Kaplan Meir survival curves of patients displaying low and high BubR1 expression. .. 21
Chapter 3
Figure 8: A. Dose response curve of MDA-MB-231 and ZR-75-1 Native vs EpiR cell lines. Red
curve- native cell lines, blue curve- EpiR cell lines. B. Half-maximal inhibitory survival curve
analysis (IC50) .............................................................................................................................. 30
Figure 9: FlowJo analysis of synchronized cells collected at 0hrs, 8hrs, 10hrs, 12hs and 24hrs. 31
Figure 10: Western blots and densitometry analysis of cyclins B, E normalized against actin.
MDA-MB-231. A. Native, B. EpiR and ZR-75-1, C. Native, D. EpiR. ....................................... 33
Figure 11: Western blots and densitometry analysis of BubR1 and Mad2 normalized against
actin. MDA-MB-231 A. BubR1, B. Mad2 and ZR-75-1, C. BubR1, D. Mad2.. ......................... 35
Figure 12: Gene expression analysis of BubR1 and Mad2 at G2M phase time points MDA-MB-
231 A. BubR1, B. Mad2 and ZR-75-1 C. BubR1, D. Mad2. ....................................................... 37
Figure 13: Gene expression analysis of SNCG, BubR1 and Mad2. MDA-MB-231 A. BubR1, B.
Mad2 C.SNCG. ZR-75-1 D. BubR1, E. Mad2, F.SNCG.. ........................................................... 39
Figure 14: Western blot knockdown of BuBR1 and GAPDH in MDA-MB-231 native cell lines.
....................................................................................................................................................... 41
Figure 15: Western blot knockdown of BuBR1 and GAPDH in ZR-75-1 native cell lines. ........ 41
Figure 16: A. Dose response curve of native siBub1B knockdown MDA-MB-231 and ZR-75-1.
B. Half-maximal inhibitory survival curve analysis(IC50) .......................................................... 43
1
Chapter 1
Introduction and Background 1
1.1 Breast Cancer Epidemiology and Etiology.
According to the Canadian Cancer Society, breast cancer is the most commonly diagnosed
cancer in women with an estimated 25,000 new cases in 2015, it is also the 2nd
leading cause of
death from cancer among women in Canada1. With the increased use of mammography
screening2 and effective therapies administered following primary treatment
3 the age
standardized mortality rate of breast cancer has decreased by 44% since 19861. Relative to lung
cancer (Figure 1), the breast cancer mortality rate in Canada has decreased considerably with
similar statistics observed in the United Kingdom, United States and Australia1. The age-
standardized incidence rate (Figure 2) for breast cancer has largely remained consistent
throughout the years 1986-2015.
Figure14: Age-standardized mortality
rates (ASMR) for breast, lung and
colorectal cancer in Canadian women.
The ASMR rate has decreased by
44%, with 32 deaths out of a
population of 100,000 in 1986, to 17
deaths in 2015. (Canadian Cancer
Society Stats, 2015).
Figure 24: Age-standardized incidence
rate (ASIR) for breast, lung and
colorectal cancer in Canadian women.
The ASIR has stabilized in recent
years but remains quite high in
comparison to lung and colorectal.
(Canadian Cancer Society Stats,
2015).
2
According to the World Health Organization 2012, globally, breast cancer represented the
second (1.67 million cases) most common form of cancer incidence, representing 25% of all
cancers5. Breast cancer incidence increases with age, according to the Surveillance,
Epidemiology and End Results (SEER) program increase in breast cancer incidence was
reported every 10 years from 30-70 years of age6, further supporting the observation that the
majority of breast cancers are found in post-menopausal women. Although incidence is higher
in more developed countries (i.e. North America), survival following detection and subsequent
treatment is higher7, attributed to the increase of screening in comparison to less developed
regions. Additional risk factors implicated in breast cancer incidence include: a prior family
history of breast cancer, genetic factors, obesity following menopause, increased breast density,
increased exposure to hormones (i.e. hormone replacement therapy), contraceptives and
alcohol8. Research from the American College of Physicians has indicated that both increased
breast density and prior familial history of breast cancer were associated with a significant 2-
fold increase of risk of acquiring the disease in women aged 40-49 years9.
Figure 35: WHO Global Breast Cancer statistics among male and females: incidence (blue) and mortality (red).
(GLOBOCAN,2012). Incidence of breast cancer is highest in more developed regions with mortality fairly stable
and lower in both developed and less developed regions.
3
Evidence has suggested that earlier pregnancies coupled with breastfeeding can decrease the risk
of acquiring breast cancer10,11
. Both pregnancy and breastfeeding decrease the overall number of
menstrual lifecycles, decreasing exposure to endogenous hormones which are risk factors of
breast cancer12
. Furthermore, it has been hypothesized that a correlation between lobule
differentiation during pregnancy and a decreased risk of acquiring breast cancer exists13
.
1.2 Breast cancer pathology
The majority of breast cancers are classified as carcinomas14,15
a cancer of the epithelial cells,
specifically as either in-situ (within confined regions of the breast) or invasive carcinomas. In-
situ carcinomas are divided into cancers that are localized within the breast ducts or lobules and
do not invade surrounding tissues (Figure 4).
Figure 416
: Anatomy of a normal breast. Representation of ducts and lobules (For the National Cancer Institute ©
2011 Terese Winslow, U.S. govt. has certain rights).
4
Ductal carcinoma in situ (DCIS) refers to cancer that arises within the breast ducts and remains
within the same location. Identified as a non-lethal cancer, DCIS may transition or become a
precursor for invasive breast cancer (IBC)17
, which becomes an important consideration to take
into account when administering treatment. IBC refers to cancer that has invaded surrounding
tissues within the breast, escaping the duct and/or the lobule regions. IBC is believed to progress
through different stages prior to becoming invasive; one of these stages includes in situ
carcinoma. However, screening mammography has greatly improved the frequency and early
detection of DCIS, currently accounting for 20%-30% of all diagnosed breast cancers in
patients18
. Early detection of DCIS has allowed for the development of effective therapies (i.e.
breast-conserving surgeries)19,20
that allow patients to avoid harsher therapies such as
mastectomies.
1.3 Predictive and Prognostic markers
Predictive and prognostic markers are studied with the intent of determining the appropriate
form of therapy for breast cancer patients taking into account clinical outcomes such as
recurrence or death. Prognostic markers identify the probable course of the cancer in untreated
patients amidst a population, evidence from these markers allows for the identification of
appropriate therapy. Predictive markers identify groups of patients that would be receptive to the
administered therapy from those that would not21
.
1.3.1 Breast Cancer Staging- Nottingham Prognostic Index (NPI)
Staging, a prognostic factor used within the clinical setting, classifies the extent of spread of the
cancer within a patient (i.e. early vs metastatic stage), a common staging system that has been
used by The International Union Against Cancer (among many other organizations) is the
tumour-node-metastasis (TNM) system22
. The classification system relies on analysis of the size
of the tumour (T), the number and location of lymph nodes with cancer cells (N) and if the
cancer has metastasized to other regions (M). Grading classifies breast cancer cells based on
appearance compared to normal cells, observed under a microscope. According to the Canadian
Cancer Society, the grading system observes three features of the tumour: number of cells
actively dividing, change of the size and shape the cells nuclei and percentage of tubular
structure22
. A high grade indicates of rapidly dividing cells, vastly different from normal cells.
5
These factors, such as stage and grade, assist in determining how effective and successful a
specific treatment for a patient will be. Currently there are tools and algorithms that take into
consideration the stage and grade of a tumour used within the clinical setting to determine
treatment decisions for patients. One such tool is the Nottingham Prognostic Index (NPI), which
assists in predicting patient clinical outcome (i.e. survival) and in turn stratifies breast cancer
patients based on their prognosis. The NPI system relies on the examination of three significant
prognostic indicators: a patient’s tumour size, lymph-node status and tumour grading23
. The
advantages of the NPI system include utilizing the mentioned clinical factors in determining
treatment decisions, however its disadvantages lie in its inability to account for breast cancer
heterogeneity and as a result falls short in explaining treatment failure24
. Classification of breast
cancer based on tumour grade, nodes, hormonal receptors and type assist in patient therapy
design25
. The study and observation for the presence/absence of hormonal receptors
estrogen(ER), progesterone(PR) and human epidermal receptor-2(HER2) within breast cancer
patients26
further represents indicators of response to administered therapy. These receptors are
traditional biomarkers, observed/identified within patients as predictors of treatment response27
.
1.3.2 Estrogen and Progesterone receptors
Estrogen receptors (ER) presence or absence is a predictive marker used within the clinical
setting for determining patient sensitivity to endocrine therapy. ER isoforms include: ER-α and
ER-β with ER-α overexpressed in over half of all breast cancers28
. Both ER isoforms are
transcription factors, activated by estrogen, which in turn controls specific genes.
Patients with ER positive (ER+ve
) tumours are treated with endocrine based therapy such as
tamoxifen. The mechanistic action of tamoxifen relies on its ability to function as an ER
antagonist and in turn prevent the transcription and replication of ER activated genes29
. Various
studies have researched the impact of potential predictive biomarkers and treatment impact in
breast cancer within clinical trials, for example The Early Breast Cancer Trialists Collaborative
Group (EBCTCG) combine multiple studies to perform a randomised meta-analysis on early
breast cancer treatment outcomes. The EBCTCG conducted a meta-analysis of approximately
21,000 patients with early breast cancer, to identify ER’s role as a predictive biomarker, they
demonstrated ER+ve
patients treated with tamoxifen had reduced recurrence and breast cancer
mortality, while this benefit was not observed in ER-ve
tumours30
. Aromatase inhibitors (AI)
have also been studied as a potential therapeutic for ER+ve
breast cancer patients, in contrast to
6
tamoxifen, AI inhibits or suppresses aromatase in cancer cells, which is responsible for estrogen
synthesis31
. The meta-analysis conducted by the EBCTCG using patients with ER+ve
tumours
and early breast cancer were administered AI as an initial endocrine therapy as opposed to
tamoxifen, observed over a 5-year period. The patients demonstrated a significant decrease of
30% in recurrence32
within the first year. Tamoxifen, however, remains a prominent therapeutic
option for ER+ve
early breast cancer patients as indicated by the American Society of Clinical
Oncology (ASCO). ASCO has indicated that there is no significance or increase in benefit for
any particular subset of patients of an AI versus tamoxifen treatment option33
.
Similar in function to ER, progesterone receptors (PR) also function as transcriptional factors
that once bound to its receptor allows for transcription and the production of specific proteins to
take place. Expression of ER-α has been observed to regulate the expression of PR, as such the
expression of PR is indicative of a functional ER-α expression within the tumour34,35
serving as
a predictive marker of endocrine based therapy.
1.3.3 Human epidermal receptor-2 (HER2)
The ErbB/ human epidermal receptor (HER) family of proteins consist of four members of
membrane-bound receptor tyrosine kinases: HER1(ErbB1), HER2 (ErbB2), HER3(ErbB3) and
HER4(ErbB4)36
. The ErbB/HER family of receptors are involved in signaling pathways that
include; cellular proliferation, apoptosis, cellular motility and differentiation. However, due to
the receptors prominent role in cell growth, if not tightly regulated will result in uncontrolled
cell growth, leading to tumourigenesis37
. Recombinant proteins have been utilized to
specifically target the HER family receptors and in turn compete with ligands for binding
(extracellular) or preventing receptor dimerization (intracellular). HER2 overexpressed tumours
respond positively to antibody-based therapies most commonly, Trastuzumab (herceptin), a
humanized monoclonal antibody used within the adjuvant treatment setting for invasive breast
cancers over expressing HER2. Its use within the clinical setting has improved both recurrence-
free survival (RFS) and overall survival (OS) in HER2+ve
patients38,39,40
. A study conducted by
Romond et al. 2005 analyzed the results of two clinical trials in which patients with surgically
removed HER2+ve
tumours were administered Trastuzumab. Patients administered Trastuzumab
along with standard of care chemotherapeutics (doxorubicin and cyclophosphamide)
demonstrated an improved outcome; in addition, patients experienced a decrease of
67%(p=0.015) in risk of death relative to those who had not been administered Trastuzumab40
.
7
As such, determination of HER2 status in breast cancer patients is important for determining the
effectiveness and usability of Trastuzumab as a form of therapy. HER2 expression, by
immunohistochemistry (IHC) or fluorescent in-situ hybridization (FISH), within the cells can
lead to the administration of targeted therapy (i.e. Trastuzumab).
Although ER, PR and HER2 expressions have excluded some non-responsive patients from
targeted therapies, they remain incomplete as predictive markers of response lacking in clinical
validity and ineffective in determining alternative treatment for resistant patients.
1.4 Breast Cancer molecular subtypes
Breast cancer is a molecularly heterogeneous disease with biologically distinct subtypes, as a
result molecular classification of the disease has the potential to determine clinical outcome and
therapy design41,42
. Evidence of breast cancer heterogeneity is observed within tumour
pathology, tumour histological factors (i.e. staging, grading) and clinical factors (i.e. hormonal
receptors). Therefore, studying molecular differences in breast cancer supports this trend.
Histologically similar tumours detected through the NPI system may differ in prognosis and
respond to treatment differently, molecular heterogeneity within breast cancer may account for
the differences observed in treatment responses.
Breast cancer is classified into molecular subtypes: luminal (A and B), HER2-type and basal
like Through gene expression profiling43,44
, unique molecular tumour classes (i.e. luminal,
HER2-type and basal-like) were identified and related to the unique molecular biology and
features of the tumour. Each of the subtypes are defined by distinct treatment responses and
prognosis41
. However, obstacles such as resistance to therapy may arise in some patients
regardless of subtype similarity, in which case clinical outcomes are never guaranteed. Patients
with luminal breast cancers, containing positive ER and PR hormones and low grade tumours,
respond positively to hormonal therapies, with the luminal A subtype exhibiting good
prognosis45
. HER2-type, a less common subtype containing an overexpression of the HER2
gene, has been associated with poor outcomes, higher histological grade and aggressive
tumours46
, with patients responding positively to Trastuzumab. Basal-like tumours lack
hormonal receptors (ER-ve
, PR
-ve, HER2
-ve) and are identified with a high histological grade, the
majority of which are classified as triple negative breast cancer (TNBC) subtypes. Patients with
this subtype cannot be treated with hormonal therapies, as a result alternative treatments such as
8
chemotherapies are administered. Table 1 demonstrates the current classification of breast
tumours according to molecular expressions, prognosis and treatment options.
Molecular Subtypes Triple negative HER2-type Luminal B Luminal A
Receptor expression HER2+ve
ER+ve
, PR+ve
Prognosis Poor Good
Therapy Chemotherapy Trastuzumab Endocrine
Table 147
: Breast cancer molecular subtypes (Modified from Wong E, Rebelo J. 2012)
Coloured arrows indicate of the strength of the prognosis within each subtype. Red- poor prognosis and blue- good
prognosis. TNBC subtypes contain poor prognosis (on the higher end of the spectrum) whilst HER2-type is on the
lower end.
1.5 Treatment
1.5.1 Surgery
Surgery represents primary treatment for breast cancer patients, administered to: completely
remove the tumour, observe if the tumour has invaded lymph nodes and treat recurrent
tumours22
. Surgery may involve either breast conserving or mastectomy depending on the extent
and spread of the disease. According to the American Cancer Society, breast conserving
surgery targets only the cancerous tissue leaving the breast, mastectomy involves removal of the
entire breast in addition to lymph nodes if needed48
. According to the National Institutes of
Health, breast-conserving surgery followed by radiation is the preferred primary treatment for
early breast cancer patients as opposed to mastectomy49
, early and routine mammography
screening may allow patients to avoid mastectomy in the event of breast diagnosis and reduce
mortality50
.
1.5.2 Adjuvant treatment- Chemotherapy
According to the American Cancer Society51
and Cancer Research UK52
, chemotherapy maybe
administered to a patient: prior to surgery (neoadjuvant) where the drug is administered to
shrink the tumour prior to excision or following surgery (adjuvant) to ensure of recurrence free
survival as well as to account for micro-tumours that may have evaded the primary treatment.
9
Patients within the advanced stages of the disease or those with the risk of their cancer
metastasizing may also be administered chemotherapy to delay the spread of the cancer. In
most cases, chemotherapy is administered as a cocktail or in combinations of more than one
drug known as chemotherapy regimens. The American Cancer Society indicates of at least five
common chemotherapeutic drugs administered for early breast cancer patients which include the
standard of care chemotherapy regimen cyclophosphamide-methotrexate-fluorouracil (CMF),
taxanes and anthracyclines51
. Chemotherapeutics such as vinorelbine51
, with increased side
effects and direct impact on quality of life, would be administered to late stage breast cancer
patients with the objective of delaying metastasis. The adjuvant treatment process that patients
undergo following primary treatment consists of being administered an anthracycline or taxane-
based regimen as adjuvant therapy, relapse into disease is measured within 6-10 months.
Response rates are initially positive (~30-70%)53
however some of the responses are transient
with patients relapsing or displaying resistance, patients are then administered second-line
therapy drugs (i.e vinblastine).
1.5.2.1 Anthracyclines
Anthracyclines are an effective and widely used chemotherapeutic used to treat breast cancer
patients following primary treatment. Anthracyclines, originally derived from Streptomyces
peucetius, include Daunorubicin, which was the first of the anthracyclines developed displaying
anti-tumour activity in mice54
. Over the years, numerous anthracycline derivatives have been
developed; epirubicin and doxorubicin are used commonly in the clinic55
. Anthracyclines target
tumour cells by intercalating with DNA strands causing damage, formation of free radicals and
binding to the Topoisomerase II-DNA complex, this in turn promotes double-stranded breaks
and degradation of the DNA56
. The benefits of anthracyclines have been demonstrated in a
meta-analysis conducted by the EBCTCG over a period of 10 years, in which breast cancer
patients treated with an anthracycline based regimen observed a lower percentage of tumour
recurrence and breast cancer mortality in comparison to patients treated with CMF. This
represented a 2.6% and 4.1% absolute gain respectively57
. However, anthracyclines have been
implicated in an increased probability of patients acquiring cardiotoxicities due to repeated use
of the drug over time, in addition to the risk of acquiring leukemia58,59
.
Effective chemotherapeutics, such as anthracyclines, are beneficial if administered to the right
patient, the challenge arises in maximizing anthracyclines effectiveness whilst minimizing its
10
toxicity. Predictive biomarkers of anthracycline benefit have been researched 81,82,83,91
, however
no clinically validated marker has been identified, and this may due to an incomplete
understanding of the molecular mechanisms responsible for anthracycline sensitivity within
breast cancer patients.
1.6 Predictive markers of anthracycline benefit
Studies have indicated of HER2 overexpression in approximately 15%-20% of early breast
cancer patients60,61
, overexpression of HER2 is associated with a negative prognosis and
generally increased recurrence of the disease62
. Research has focused on both HER2 and
Topoisomerase IIα (TOPIIA) as potential predictive biomarkers of anthracycline benefit.
Topoisomerases, are responsible for ensuring that the DNA supercoiling is not excessive but
maintained to allow for protein-interactions. Topoisomerases can be split into two functionally
distinct types: type I which are responsible for single stranded breaks of the DNA and type II
which allows for double stranded breaks of DNA through which another double stranded DNA
may pass. Isoforms of type II topoisomerases include topoisomerase IIα and topoisomerase IIβ.
Both isoforms differ in biochemical properties, function and localization, TOPIIA isoform is
linked to cellular proliferation63,64
and as such relevant to DNA replication. Type II
topoisomerase enzymes cleave both DNA strands which in turn serves to manage DNA
supercoils and tangles. Anthracyclines target the TOPII- DNA complex causing breaks and
deregulation, this has led to the initial assumption that TOPIIA overexpression is a potential
target of anthracycline sensitivity65
. Based on previous work completed to date, in vitro studies
have indicated that over expression of the TOPIIA enzyme could result in increased sensitivity
to anthracyclines66,67,68
. Anthracyclines directly target TOPIIA within the cells69
, as such the
predictive impact of TOPIIA overexpression/aberration has been extensively studied with
regards to anthracycline benefit. Studies have indicated TOPIIA aberrations as predictive for
anthracycline benefit in breast cancer patients70,71,72
, however not all results have been
conclusive. Romero et al. 2011 demonstrated the lack of correlation between TOPIIA copy
number aberrations and TOPIIA gene expression in breast cancer, further research and evidence
is required for determining associations between TOPIIA and anthracycline sensitivity73
.
Previous studies identified a potential relationship between HER2 overexpression and
anthracycline sensitivity74
, however evidence supporting the role of HER2 as a predictive
11
marker of anthracycline sensitivity has been inconsistent75,71,79
. A meta-analysis conducted by
Di Leo et al. 2011 consisted of five randomized adjuvant trials that observed HER2 and TOPIIA
as potential markers indicative of anthracycline benefit in breast cancer patients. The results did
not support the significance of either of the markers as exclusive markers of anthracycline
sensitivity in patients that contained either an amplified HER2 signature or amplified/deleted
TOPIIA76
. Both the TOPIIA and HER2 genes are located on the long arm of chromosome 17,
which further adds to the interest of studying these genes in the context of breast cancer therapy.
Genes located on chromosome 17 have been observed to being either
downregulated/overexpressed/abnormal, as in the case of both HER2 and TOPIIA77
.
1.7 Ch17CEP duplication
Human chromosome 17 is implicated in a number of genetic diseases as observed through the
genes that are located on this chromosome. Genes located on the arms of chromosome 17 which
include BRCA1 (early-onset breast cancer marker), p53 (DNA damage), HER2 and TOPIIA
(potential breast cancer predictive markers). Chromosome 17 abnormalities are observed in
breast cancer, these abnormalities include whole chromosome variations, chromosomal
structural changes and gene copy number variations78
, which can be detected through
fluorescence in situ hybridization techniques (FISH). The region of interest is the alpha-satellite
pericentromeric region of chromosome 17 where duplication, within this region is detected by
utilizing a centromere enumeration probe (CEP)79
. CEP17 includes binding to the region of
interest, enumerating the chromosome copy number and identifying the duplicated region within
the chromosome80
. Studies have correlated the duplication of the alpha-pericentromeric region
of chromosome 17 with anthracycline sensitivity in breast cancer patients, resulting in improved
OS and RFS81,82
.
In a recent meta-analysis of five trials completed by Bartlett et al. 2015, FISH was performed
for CEP17, HER2 and TOPIIA to determine their roles as predictive biomarkers. In this study
adjuvant chemotherapy was assessed by comparing patients treated with cyclophosphamide-
methotrexate-fluorouracil against those treated with an anthracycline regimen. The results
demonstrated patients with abnormal gene expression of CEP17 (duplication) and TOPIIA
(aberrations) significantly benefiting from the anthracycline treatment in both OS and RFS,
relative to patients with normal CEP17/TOPIIA gene expression observing no significance in
treatment. HER2 over expression did not display any significance and was not predictive of
12
treatment benefit. Patients exhibiting the CEP17/TOPIIA abnormalities demonstrated a 38%
decrease in risk of relapse when treated with anthracycline based regimens compared to those
patients treated with standard chemotherapeutic CMF83
. However, the alpha-pericentromeric
region of chromosome 17 (Ch17CEP) contains heterochromatin which generally consists of
repetitive and inactive DNA sequences84
. Therefore, no identified or known biological function
exists that would further explain the association of Ch17CEP duplication and anthracycline
benefit in breast cancer patients.
1.7.1 Chromosomal Instability
Ch17CEP describes duplication of a specific region within chromosome 17, chromosomal
instability (CIN) a hallmark of solid tumours, is characterized by either a gain or loss of whole
chromosomes or specific regions of the chromosome. CIN has been correlated to both
sensitivity and resistance to chemotherapeutics85
. Extensive research has been conducted with
regards to CIN’s role in cancer progression, specifically observing the mechanism of CIN
development and subsequent effects86,87
. Causative mechanisms of CIN include two general
types: type I and type II. Type I mechanisms include cell cycle processes responsible for
chromosomal maintenance that include; spindle assembly during mitosis, repair machinery and
G2M/G1S phase checkpoints88
. It is within these processes that CIN may arise if the
mechanism is compromised or dysregulated. For example, within the cell cycle if chromosomes
are misaligned during the metaphase stage of mitosis or if there are misaligned spindle fibers,
chromosomal aneuploidy may occur leading to CIN. Type II mechanisms appear to be involved
in physiological processes rather than genetic processes.
Studies of patients whose tumours contain CIN have been predictive of poor clinical outcomes
in cancer subtypes which included breast cancer89,90
. A study completed by Munro et al. 2012,
demonstrated that tumours with high CIN presence correlated with Ch17CEP duplication in the
BR9601 clinical trial91
. Furthermore, the study observed patients containing a high CIN
experienced a decreased risk of mortality when treated with an anthracycline regimen relative to
the standard of care chemotherapeutic administered (i.e. cyclophosphamide-methotrexate-
fluorouracil). To explain the correlation observed, Ch17CEP is a potential surrogate marker for
CIN. Surrogate markers are used within the clinical setting as tests to assess improved outcomes
to a specific treatment, in addition to indirectly correlating to the clinical outcome92
. A
correlation was found to exist between CIN and the spindle assembly checkpoint (SAC) in
13
breast cancer cells, a study carried out by Yoon et al. 2002 observed breast cancer cell lines with
high levels of CIN also contained defective mitotic spindle checkpoints as demonstrated through
flow cytometry93
. Type I mechanisms of CIN directly impair chromosomal processes that
includes the SAC signal, dysregulation of the SAC signal may provide a potential mechanistic
understanding of Ch17CEP duplication and in turn anthracycline sensitivity.
1.7.2 Spindle Assembly Checkpoint
A study conducted by Hoyt et al. 1991 observed a mutant strain of Saccharomyces cerevisiae
was unable to undergo cell cycle arrest during the mitotic phase due to a loss of microtubule
function. As a result, they were able to identify three genes required for normal cell cycle arrest:
Mad1, Mad2 and Mad3 (BubR1 in humans)94
. Further research led to the understanding that
these genes are conserved in all eukaryotes and function as a mechanism within the
prometaphase/metaphase stage of the cell cycle95
. As mentioned in the previous section,
dysregulation within the SAC signal and its components (kinetochores, mitotic checkpoint
complex proteins, sister chromatid cohesion, centrosome aberrations) will lead to CIN resulting
in aneuploidy within the cells96,97
.
The SAC mechanism ensures that dividing cells receive the correct number of chromosomes by
halting mitosis if chromosomes are misaligned at the metaphase stage of the cell cycle98
, correct
attachment involves spindle fibers attaching to the centromere of each chromosome at either
poles of the cell. In a normal cell, activation of the APC/C by Cdc20 allows the cell to transition
from the metaphase stage into the anaphase stage, this is preceded by the degradation of cyclin
B, securin and the separation of the sister chromatids by separase99,100
. In the event of
checkpoint activation, due to chromosomal misalignment, the cell is blocked from entering the
anaphase stage of the cell cycle. As seen in Figure 5, to rectify the misalignment a cohort of
member proteins within the SAC signal, the mitotic checkpoint proteins; BubR1, Bub3, Mad2
and Mad1 are activated. BubR1 and Mad2 directly bind to Cdc20101
negatively regulating the
APC/C and halt progression into anaphase102
. If components within the SAC mechanism are
defective, cells may enter anaphase prematurely which in turn may give rise to aneuploidy and
CIN103
. In addition to this, cells with a defective SAC are resistant to a range of anticancer drugs
which includes anthracyclines and taxanes104
, this further supports research and interest into
studying the SAC signal as a mechanism indicative of chemotherapeutic benefit within breast
cancer patients.
14
Figure 5: Schematic of the SAC signal within the G2M phase of the cell cycle.
In normal cells the checkpoint is turned off, allowing for the APC/C to activate a cascade of signals which in turn
allows for the cell to begin anaphase. Improper chromosomal alignment results in checkpoint activation, as a result
the cell will arrest during metaphase.
Based on the chromosomal alignment within the cell, the SAC signal may remain turned off
(cell progresses into anaphase) or turned on. In which case if the SAC signal is activated upon
detection of improper chromosomal alignment mitotic arrest will occur. If the SAC signal
remains activated this may result in a mitotic buildup and cell death. However, in the event of a
dysregulated SAC, cells with improper chromosomal alignment may bypass the checkpoint and
proceed with mitosis resulting in potential aneuploidy within the daughter cells.
Direct silencing of the SAC signal has been shown to result in dire consequences, for example
complete absence of SAC member components was observed to result in developmental defects
in mice105,106
. Interestingly research largely supports the possibility of a weakened SAC signal
rather than a compete loss of the signal as a potential precursor for tumour formation within
cells107
. This observation would support the rational that the SAC signal is dysregulated (not
inhibited) in which case cells would progress within the cell cycle resulting in aneuploidy and
chromosomal aberrations such as duplication of the centromeric region of chromosome 17
(Ch17CEP). It remains to be seen whether SAC dysregulation is a precursor of tumour
15
development or if it is a result of it, in either case studying the SAC signal as the mechanistic
explanation for Ch17CEP will assist in further determining the appropriate methodology for
chemotherapy benefit within breast cancer patients.
1.7.3 BubR1
BubR1, a serine/threonine kinase, is a major component of the SAC signal when turned on. As
part of the MCC (mitotic checkpoint complex), it binds to Cdc20 preventing it from binding to
the APC/C108,109
causing the cell to arrest in the presence of chromosomal misalignment. In
addition, BubR1 interacts with CENP-E110
, a kinetochore motor, indicating of BubR1’s
involvement in kinetochore tension and of its versatile roles within the SAC signal. Studies
have shown of the importance of BubR1 within the SAC signal and cell viability. Kops et al.
2004 observed, through the use of small-interfering RNA’s, apoptosis within BubR1 kinase
inhibited cells or cells with reduced BubR1 protein levels 111
. The study demonstrated that low
BubR1 expression within cells resulted in a weakened SAC and as a result cells with misaligned
chromosomes proceeded into anaphase. The study showed the importance of BubR1 activity to
a sustainable SAC signal. Furthermore, it was reported through the study that a 50% reduction
in BubR1 kinase activity was enough to compromise the SAC signal, considered a lethal
consequence for cells including tumour cells. As a result, BubR1 has emerged as a highly
desirable entity to study concerning inhibition of cancer cell growth. In the clinical landscape,
studies have demonstrated that BubR1 overexpression in breast cancer is associated with poor
survival and tumourigenesis112,113
. The gene that codes for BubR1, BUB1B, is overexpressed in
breast cancer at both the transcriptional and translational levels114
with a strong association
observed between BUB1B, other mitotic checkpoint genes and an increased risk for breast
cancer115
.
1.7.4 Mad2
Mad2 functions alongside BubR1 within the MCC when the SAC signal is turned on. Its role,
along with BubR1, focuses on the detection of misaligned chromosomes during the metaphase
stage of the cell cycle. Previous studies have observed that overexpression of Mad2 in breast
cancers (as well as other cancers) results in poor prognosis113, 116
. The importance of Mad2 to
the checkpoints function was assessed in a study conducted by Michel et al.2001, in which
Mad2 was silenced in human cancer cell lines as well as mice primary embryonic fibroblasts.
The cells displayed a defective checkpoint in which premature aneuploidy and CIN were
16
observed; the cells continued to cycle even after exposure to spindle inhibitors were
administered. In addition, the mice with decreased levels of Mad2 developed lung tumours117
.
The study further noted of the possibility that other members of the SAC mechanism might be
implicated in tumour progression through the loss of the checkpoints function, which in turn
could result in both aggressive tumour development and resistance to specific
chemotherapeutics.
1.7.5 Securin
Securin, PTTG1, along with cyclin B are degraded prior to the anaphase stage of the cell cycle.
Prior to its degradation, securin is bound to separase, the protein responsible for cleaving the
cohesion rings of the sister chromatids, which is released as the cell progresses into anaphase118
.
Securin overexpression has been observed in breast cancer patients119, 120
, Solbach et al. 2004,
observed a direct correlation between securin mRNA overexpression and tumour recurrence120
.
High Cdc20 and high securin immunoexpression correlate to patients with a higher risk of
aggressive breast cancer, aneuploidy and poor survival121
.
1.7.6 Breast cancer specific gene-1: Synuclein gamma
SNCG is a member of the synuclein family of neuronal proteins involved in the pathogenesis of
neurodegenerative diseases and largely expressed in brain tissue122
. High SNCG expression has
been observed in aggressive breast cancer tissue relative to normal tissue123,124,125
. Identified as
breast cancer specific gene-1(BCSG1), studies carried out using both in vivo and in vitro models
have found that SNCG expression both increases and promotes breast cancer cell proliferation
and metastasis126,127
. Positive SNCG expression within breast cancer has been significantly
correlated to late stage breast cancer tumours128
. In a study conducted by Wu et al. 2003, 38.8%
of clinical breast cancer samples from a co-hort of 79 patients contained SNCG mRNA
expression. In addition, 79% of late stage breast cancers within the same co-hort were SNCG
positive128
.
Relevant to this project, a yeast-two hybrid screen conducted by Gupta et al.2003 indicated of
an interaction between BubR1 and SNCG129
. Through this study, SNCG expression was
observed to cause chromosomal instability within breast cancer cells during the cell cycle and
affect the mitotic checkpoint. Through the work of Miao et al. 2014, it was observed that the
SNCG-BubR1 interaction (Appendix I) resulted in a structural change directly effecting BubR1
17
kinase activity and binding patterns with Mad2 and Cdc20130
. The paper notes that BubR1 is
not degraded or completely silenced by SNCG, rather it results in a structural change, which in
turn impairs the activity of BubR1 or decreases its overall efficiency. Furthermore, the group
confirmed through a neoadjuvant clinical trial that patients with SNCG positive tumours were in
fact resistant to the effect of chemotherapy (induced-apoptosis). To counter the inhibitory effect
of SNCG on the mitotic checkpoint complex, Inaba et al. 2005 induced overexpression of
BubR1 within breast cancer cell lines that expressed SNCG, as a result the inhibitory effect
decreased131
. SNCG has also been studied as a potential biomarker of microtubule toxin
sensitivity within breast cancer cells. Zhou et al.2006, showed that inhibition of SNCG
expression increased sensitivity to paclitaxel treatment within breast cancer cell lines126
. Due to
its interaction with BubR1 and inhibitory effect on the mitotic complex, upregulation of SNCG
expression within breast cancer cells may be a target for reversing resistance within
anthracycline resistant cell lines.
1.7.7 In-vitro chemotherapy resistant cell lines
The use and development of in-vitro chemotherapy-resistant cancer cell lines have been highly
valuable in the study of molecular mechanisms of resistance and drug sensitivity132,133,134
.
Chemotherapeutic resistant cell lines, have been used over the years to study mechanisms of
drug resistance, dating back to the earliest in vitro model developed in 1970135
. The model
consisted of developing actinomycin D resistant cells from a parental Chinese hamster cell line,
through an incremental dose treatment method. This in turn resulted in a 2500-fold increase in
resistance seen in the resistant cell lines relative to the parental. Our laboratory has successfully
generated epirubicin resistant cell lines representative of the four major breast cancer subtypes
(luminal A and B, TNBC and HER2-type). This work demonstrated that increased resistance
observed in the epirubicin resistant (EpiR) breast cancer cell lines resulted in cells that are able
to proliferate and function with the addition of epirubicin relative to their parental cell lines136
.
The development of an in vitro cell line model allows for a more focused analysis of the
molecular mechanisms responsible for the decreased sensitivity in addition to studying the SAC
signal, specific proteins (i.e. BubR1, Mad2, SNCG) and their direct impact on the cells.
18
1.8 The BR9601clinical trial
The current issue of administering chemotherapy to patients is the lack of benefit that some
patients exhibit following administration. This results in unnecessary side effects and increased
costs137
. The BR9601 clinical trial assessed the effectiveness of anthracyclines as adjuvant
chemotherapy to treat early breast cancer patients. Patients recruited into this trial were pre-and
post-menopausal women with completely excised, histologically confirmed breast cancer, and
confirmed to receive chemotherapy as an adjuvant treatment138
. Patients were randomized into
two treatment arms (Figure 6), those in the control treatment arm (red) received 8 cycles of
CMF (cyclophosphamide 750mg/m2, methotrexate 50mg/m
2, fluorouracil 600mg/m
2)
administered every 21 days. Those in the test treatment arm (blue) received 4 cycles of Epi-
CMF (epirubicin 100mg/m2) every 21 days and 4-cycles of CMF. Patients were monitored over
a 10-12 year period.
The hypothesis developed at the time of the BR9601 trial initiation was that Epi-CMF
(epirubicin in combination with CMF) would result in significant benefit for patients in
recurrence free survival (%) and overall survival (%) relative to the CMF treatment arm. The
results of both the BR9601 and NEAT (a trial designed in parallel with similar objectives)
clinical trials was reported by Poole et al. 2006, in which patients treated with an Epi-CMF
regimen experienced a 7% increase in both OS (HR: 0.67, 95% confidence interval 0.55-0.82,
p<0.001) and RFS (HR:0.69, 95% confidence interval 0.58-0.82, p<0.001) respectively, over a
5-year period relative to patients treated with only CMF139
. The hazard ratios (HR) indicated
that patients treated with an anthracycline regimen demonstrated a decrease in risk of relapse
and death compared to patients treated with only CMF. As such the hypothesis was proven
correct and both trials were instrumental in highlighting the effectiveness and significance of
Epi-CMF as an adjuvant chemotherapeutic for early breast cancer patients.
The results of the BR9601 trial highlighted the effectiveness of anthracyclines as adjuvant
chemotherapies for breast cancer patients, the trial also provided an opportunity to further
examine predictive markers of anthracycline benefit. Since treatment benefit was established,
determining a marker that would identify a population of patients that would benefit from
anthracycline regimens was the next logical step. As such, the SAC signal and member proteins
(BubR1, Mad2 and securin) were tested for correlation to anthracycline sensitivity. The results
19
from this work provided the foundation for studying the molecular mechanism of the SAC
signal and member proteins using an in vitro cell line model.
Figure 6
79,81,83,91,136,138,139: Schematic representation of the BR9601 clinical trial patient randomization into
respective treatment arms (4 cycles), CMF (8 cycles).
20
1.8.1 Preliminary Results
Based on the work completed within the BR9601 trial, we set out to further identify patients
within the trial that would benefit from the Epi-CMF treatment. As such, the goal for this
particular aim was to identify a potential correlation between SAC member protein expression
levels in breast cancer patients and anthracycline sensitivity.
The SAC signal was analyzed as a predictive marker in the BR9601 trial by IHC. The work
completed in this section serves as the foundation for the development of this project and
subsequent results. Through IHC, specific proteins of the SAC signal, BubR1, Mad2 and
securin, were analyzed for expression levels and treatment benefit. High expressions of all three
proteins were detected in the tumours analyzed independent of treatment administered. High
BubR1 expression was significantly associated with a reduced OS (HR=1.87, 95% confidence
interval 1.20-2.91, p=0.005) and RFS(HR=1.52, 95% confidence interval 1.00-2.32, p=0.047),
further supporting what has been identified in the literature112
. High securin expression was
significantly associated with increased OS(HR=0.64, 95% confidence interval 0.43-0.95,
p=0.024) and RFS(HR= 0.56, 95% confidence interval 0.38-0.82, p=0.003) and Mad2
demonstrated no significant associations with OS and RFS.
Further analysis included the formulation of a dysregulated SAC signature that would identify
patients that benefitted from the anthracycline treatment versus CMF. The signature was
formulated based on the initial analysis of the individual markers within the samples: high
BubR1, low securin and low Mad2 expressions defined the dysregulated SAC signature. Based
on this signature 296/321 patients contained at least 1 or >1 dysregulated SAC protein with
15.2% of the patients identified as high SAC dysregulation (all 3 proteins dysregulated).
Furthermore, a significant correlation was observed between increased SAC dysregulation and
decreased OS (HR=3.09, 95% confidence interval 1.79-5.33, p<0.0001) and RFS(HR=3.06,
95%confidence interval 1.79-5.22, p<0.0001).
Finally, the effects of the identified markers were assessed on RFS and OS between patients
receiving the Epi-CMF versus the CMF treatment regimen. As demonstrated in Figure 7,
patients containing high BubR1 expression benefited from the Epi-CMF (blue) treatment
relative to the CMF (red), these patients also experienced an increase in RFS and OS. There was
no significant association observed in patients containing low BubR1 expression. Following a
multivariate regression analysis, which accounted for more than one outcome variable at a time
21
as well as adjusting for: age, nodal status, grade, tumour size, HER2 and ER status, the hazard
ratio for the treatment by marker effect in BubR1 was 0.38 for OS (95% confidence interval
0.13-1.05, p=0.064) and 0.39 for RFS (95% confidence interval 0.14-1.04, p=0.061). Hazard
ratios indicated of an approximate 50% decrease in risk of recurrence or death in patients
containing high BubR1 expression and treated with Epi-CMF (Figures 11B and 11D). Patients
with low BubR1 expression demonstrated a hazard ratio of 1 which indicated of no benefit or
difference in treatment administered (Figures 11A and 11C). In addition, high SAC
dysregulation, through observations of BubR1, Mad2 and securin, was associated with increased
OS and RFS in patients treated with Epi-CMF relative to CMF treatment.
Figure 7:Kaplan Meir survival curves of patients displaying low and high BubR1 expression.
%Relapse free survival A. with low BubR1 expression, B. with high BubR1 expression, %Overall Survival C. low
BubR1 expression, D. high BubR1 expression. Blue curve- CMF and red curve- Epi-CMF.
The OS treatment by marker effect of BubR1 was determined with a hazard ratio of 0.38 (~40% decrease in risk),
95% confidence interval 0.13-1.05 and p=0.064. For the RFS treatment by marker effect of BubR1, hazard ratio
0.39, 95% confidence interval 0.14-1.04 and p=0.061.
22
The BR9601 study highlighted the significance of high BubR1 expression as an independent
predictor of anthracycline benefit in breast cancer patients, as well as identifying a signature for
SAC dysregulation to be studied at the molecular level. We believe that the SAC signal
represents a potential predictive marker of anthracycline sensitivity within breast cancer patients
and through this work we would like to highlight the molecular evidence supporting SAC signal
dysregulation and its correlation to anthracycline sensitivity.
23
1.9 Hypothesis and Aims
The aims of this project are to study the SAC as a potential molecular marker of anthracycline
sensitivity through the use of isogenic epirubicin resistant breast cancer cell lines. Using an in
vitro cell line model, we aim to identify SAC dysregulation and potential correlation to
epirubicin resistance within our resistant cell lines. We hypothesize that the SAC is a potential
biomarker for anthracycline sensitivity in breast cancer patients.
Aim 1: To validate the SAC signal and its surrounding pathways as a biomarker using
anthracycline resistant cell lines.
Our goal for this aim was to validate the SAC member protein expression levels at the molecular
level through western blot and qPCR. Based on our previous clinical work with the BR9601
trial, we hypothesize that here will be significant downregulation of the SAC member proteins,
indicative of a dysregulated SAC signal, within the resistant cell lines. We set out to determine
the SAC signal expression and in turn validate the potential correlation between SAC signal
dysregulation and sensitivity to epirubicin.
Aim 2: To identify a key druggable target, which would reverse the resistance and re-sensitize
the resistant cell lines to epirubicin.
Our goal for this aim was to identify a specific target that would reverse the resistance in the
EpiR cell lines. The ultimate goal would be to provide an alternative treatment for patients
unable to receive an anthracycline-based regimen. We hypothesize that the druggable target
would interact with the SAC signal inducing dysregulation and decreased sensitivity to
epirubicin.
24
Chapter 2
Materials and Methods 2
2.1 Breast cancer cell line culture
The breast cancer cell lines MDA-MB-231 and ZR-75-1 were purchased from ATCC and
cultured in DMEM mixed with 10% FBS and 1% L-glutamine (Gibco, Burlington, Canada).
Epirubicin resistant (EpiR) cell lines were generated in-house by exposing native cells to
incremental doses of epirubicin concentrations and culturing the resistant populations136
,
resistance was defined when the inhibitory concentration at 50% (IC50) of the resistant cell lines
exceeded that of the native cell line counterpart. IC50 assays were carried out using the Cell
Counting Kit-8 (Dojindo,Cedarlane Burlington Canada) and calculated using GraphPad Prism 5
(GraphPad Software, La Jolla, CA, USA). As Table 2 demonstrates, a consistent concentration
of epirubicin was added to the resistant cell lines to maintain resistance within the cells (25nM-
MDA-MB-231, 10nM-ZR-75-1).
Breast Cancer Cell
Line
Hormonal Receptor
Expression
Subtype EpiR1000nM Highest
Tolerated Dose
MDA-MB-231 ER-ve
/PR-ve
/HER2-ve
TNBC 25nM
ZR-75-1 ER+ve
/PR-ve/+ve
/HER2+ve
Luminal B 10nM
Table 2: ATCC breast cancer cell lines and hormone receptor expressions.
2.2 CCK8 assays
CCK-8 assays assessed the cell lines proliferation/cell viability by exposing cells to increasing
doses of epirubicin diluted in dimethyl sulfoxide (DMSO): 0nM/DMSO (control), 0.3nM, 3nM,
10nM, 30nM, 100nM, 300nM, 1000nM, 3000nM, 10,000nM. Cells were plated on a 96-well
plate in triplicates and left for 24 hours to adhere. The cells were then exposed to increasing
concentrations of epirubicin, incubated for 72hrs at 37°C, 5%CO2, following which 10µl of the
CCK-8 assay (Dojindo,Cedarlane Burlington Canada) was administered to each well. Plates
were analyzed using the iMark Microplate Absorbance Reader (Bio-rad Laboratories Inc) at
450nM wavelength. The assay functions on a colorimetric reaction, where dehydrogenase
25
within live cells reduce the tetrazolium salt (WST-8), producing a yellow-orange colour change.
Dose response curves, generated through GraphPad Prism 5 (GraphPad Software, La Jolla, CA,
USA), were then used to calculate and illustrate the IC50 values of the cell lines. Fold change
values calculated as a ratio of IC50 EpiR / IC50 native.
2.3 Flow Cytometry
For cell cycle analysis, cell lines were synchronized using a double thymidine block protocol140
,
following which the cells were collected at the following time points: 0hrs, 4hrs, 8hrs, 10hrs,
12hrs, 16hrs, 24hrs, 32hrs, 48hrs. Cells were trypsinized, washed with PBS, centrifuged and the
pellet fixed with 80% ethanol overnight. Following which, cells were incubated for 1hr with
2mg/mL RNase and 0.1mg/mL of propidium iodide (Sigma-Aldrich) prior to analysis. Flow
cytometry analysis, completed with BD FACSCanto II (BD Biosciences, Mississauga Canada),
was utilized for determining cell cycle time points within the respective cell lines, specifically
identifying the G2M phase time point. Analysis was completed using a univariate Watson
pragmatic algorithm141
(FlowJo software, version 10), G1 represents cells within the G0/G1
phase and G2 represents cells within the G2/M phase142
.
2.3.1 Double Thymidine block
A double thymidine block includes a primary block (17-18hrs), releasing cells into fresh media
(7-9hrs) and a secondary block (17-18hrs) allowing for a synchronized S phase entry. A stock
solution of 100nM of thymidine (Sigma-Aldrich) was made up and filter sterilized prior to use,
this was further diluted to 2nM per well (6-well plate) and added to the media. The thymidine
was stored at 4°C and refreshed every month.
2.4 Western Blots
Prior to collection of the lysate, cells were synchronized and then collected according to the
G2M phase time points determined through flow cytometry analysis (10hrs MDA-MB-231 and
12hrs ZR-75-1). Lysate was collected from both the native and resistant cells lines using 1X
RIPA buffer and quantified using the Pierce ThermoFisher Scientific BCA Protein Assay Kit.
Final protein concentrations were calculated using iMark Microplate Absorbance Reader (Bio-
rad Laboratories, Inc) at 550nM wavelength. OD readings of the samples and 9 bovine serum
albumin standards (BSA), were used to calculate lysate concentrations. In addition to the
volume of lysate, 5X loading buffer and 1X RIPA buffer were added to bring the total volume to
26
40µl. Samples were heat-shocked at 97°C for 6mins, centrifuged and loaded into the respective
wells. Following which, gel electrophoresis was completed for 2hrs at 75-82V. The proteins
were transferred overnight (onto a PVDF membrane) within a gel tank at 4°C with a voltage of
20V. Following which the PVDF membrane was blocked for 1hr in 5% skim milk(diluted in 1X
TBS-Tween20) and incubated with a primary antibody overnight at 4°C. The membrane was
washed for three-15min intervals with 1X TBS-Tween20, incubated for 1hr at room temperature
with an HRP-linked secondary antibody and 0.2µl of Precision Protein Streptactin HRP
conjugate (Bio-rad Laboratories, Inc). The blot was washed again for three-15mins intervals,
treated with BM chemiluminescence western blotting substrate (Sigma-Aldrich) for 1min and
visualized using the gel imaging ChemiDoc MP System (Bio-rad Laboratories, Inc).
2.4.1 Antibodies
The antibodies used primarily for this project included BubR1 and Mad2. Table 3 depicts the
proteins used throughout this project and the antibody dilutions administered based on
optimization work completed.
Antibodies Dilution
BubR1(BD Transduction Lab) 1°- 1:1000, 2°- 1:2000
Mad2 (BD Transduction Lab) 1°- 1:1000, 2°- 1:2000
Cyclin B1 (Cell Signaling) 1°- 1:1000, 2°- 1:2000
Cyclin E1 (Cell Signaling) 1°- 1:1000, 2°- 1:2000
GAPDH (Cell Signaling) 1°- 1:1000, 2°- 1:2000
Actin (Protein tech) 1°- 1:10,000, 2°- 1: 10,000
Table 3: Antibodies and respective dilutions routinely used for western blot analysis.1°-primary and 2° secondary
27
2.4.2 Densitometry
Densitometry calculations were conducted to further provide a quantitative analysis of the
western blots produced. Utilizing Biorad Image Lab (Image Lab software, version 4.0.1) the
adjusted volume intensity of each band was displayed and compared against the adjusted
volume intensity of its actin counterpart (normalization) yielding a ratio of which was used to
determine the level of up-or down regulation (Table 4).
MDA-MB-231
Bands Label Type Adj. Vol. (Int) Type Adj. Vol. (Int)
Test protein/actin
control
1 Native 0hrs
BubR1
2,995,482.81
Actin
2,339,689.41 1.280290791
2 EpiR 0hrs 4,179,795.43 2,212,224.62 1.889408245
3 Native 10hrs 3,761,316.49 4,068,982.55 0.924387472
4 EpiR 10hrs 3,666,893.33 3,987,547.48 0.919586124
Table 4: Sample densitometry calculation of BubR1 normalized to its actin control. The adjusted volume intensity
for BubR1 is normalized against its native counterpart which is then used to compare native and EpiR up or
downregulation of the protein.
2.5 qPCR
RNA was extracted from cell lines using the RNeasy Mini Kit (Qiagen, Toronto Canada). A total
of 20µg of RNA was analyzed for each sample using Taqman Gene Expression Assays. The
samples used for the qPCR runs included cells synchronized at 0hrs, 10hrs, 12hrs, the purpose
of including cells at 0hrs would allow for comparison between cells collected at the G2M phase
against cells collected at the G1 phase. Reactions were run in triplicates using the Applied
Biosystems Viia 7 real-time PCR instrument and Viia 7 software (ThermoFisher Scientific,
Burlington Canada). Transcript levels were determined using the ΔΔCt method. Primers used
included BubR1, Mad2 and SNCG, the endogenous control was RPL37A and the reference
sample (normalization) was the native cell line collected at 0hrs.
28
2.5.1 ΔΔCT
The ΔΔCt method represents a set of calculations that produces a final value representative of
the test gene normalized expression level (requires a reference sample to be used). In this case
the value relays the fold change of the test gene (upregulated or downregulated) relative to the
control used (0hrs Native) represented by a 1. The ΔΔCt values were calculated from the Ct
values that are the qPCR experimental run output, the Ct value indicates of the number of cycles
the machine completed in order to detect a real signal from the sample. A low Ct value indicates
of abundant target nucleic acids (i.e. < 38). For the experimental work completed, Ct values as
well as delta delta Ct values were obtained from each qPCR run through an excel spreadsheet,
the final fold value was calculated. Table 5 represents a sample calculation completed in which
Ct values are obtained for control and test genes, the Ct test values are subtracted from the Ct
control values (i.e. Test 1 0hrs – Control 1 0hrs). An average of these values is obtained and
subtracted (Ct test average – Ct control average). This value is used for fold change calculations.
Control 1 Control 2 Control 3 Test 1 Test 2 Test 3
ZR Native 0hrs 21.522 21.620 21.426 24.341 24.689 25.046
ZR Native 12hrs 21.689 21.650 21.595 23.943 24.031 24.093
Ct test– Ct reference 2.819 3.069 3.620 2.254 2.381 2.498
Average 3.169 2.377
Delta Ct test – Delta Ct
reference
-0.792
Fold Change (2^- ΔΔCt
) 1.73
Therefore the BubR1 in the ZR Native 12hr sample is upregulated by 1.73
fold relative to the reference sample ZR Native 0hrs.
Table 5: ΔΔCt sample calculations following qPCR run. Control: RPL37A. Test: BubR1. Reference Sample: ZR
Native 0hrs
29
2.6 siRNA knockdowns
Knockdowns were completed using a Dharmacon pooled ON-TARGETplus Human BUB1B
siRNA. The sample was diluted to 20,000nM (using a 5X siRNA diluent) and aliquoted into
25µl volumes. The final concentration was determined through a Nanodrop2000 (ThermoFisher
Scientific, Burlington Canada). Knockdown validation was completed through western blot by
collecting cell lysate at four time points (24-96hrs) to ensure of complete protein knockdown,
controls included siGAPDH (+ve control), siNTC(-ve control), lipofectamine-only and media-
only controls. The controls ensured of selective/specific knockdown of BubR1 prior to running
the CCK-8 assays as well as ensuring that the knockdown would occur during the required time
points. The impact of the knockdowns was assessed using CCK-8 assays and completed in both
native cell lines (MDA-MB-231 and ZR-75-1). IC50 survival curves were analyzed using
GraphPad Prism 5 (GraphPad Software, La Jolla, CA, USA).
30
Chapter 3
Results 3
3.1 Isogenic EpiR cell lines, MDA-MB-231 and ZR-75-1, display 8-11X more resistance to epirubicin relative to native cells.
EpiR cell lines generated from native cells MDA-MB-231 and ZR-75-1 demonstrated an
average 8-fold and 11-fold increase in resistance to epirubicin respectively (Figure 8A). EpiR
cell lines demonstrated a significant increase in resistance to epirubicin relative to the native
cells, MDA-MB-231 EpiR cells versus native (p=0.00026, unpaired t-test) and ZR-75-1 EpiR
cells versus native (p=0.0015, unpaired t-test). Results from this work confirmed the utility of
the in vitro cell line model, validating epirubicin resistance in EpiR cells relative to the native
cells.
Figure 8: A. Dose response curve of MDA-MB-231 and ZR-75-1 Native vs EpiR cell lines. Red curve- native cell
lines, blue curve- EpiR cell lines. B. Half-maximal inhibitory survival curve analysis (IC50) for three separate
experiments. IC50 values are in nanomolar concentrations with the fold change representing IC50 resistant/IC50
native.
31
3.2 Cell Cycle Analysis: G2M phase time points 10hrs & 12hrs.
Figure 9 represents plots produced using FlowJo (FlowJo software, version 10), which depict
the percentage of cells in different phases of the cell cycle at specific time points following a
double thymidine block synchronization140
. At 10hrs, the percentage of native MDA-MB-231
cells within the G2 phase was 31.3% relative to 24.4% of cells within the G1 phase and 36.7%
(G2) of cells relative to 29.6% (G1) for the EpiR cell line. At 12 hrs, the percentage of native
ZR-75-1 cells within the G2 phase was 48.3% relative to 31.7%(G1) and 40.7%(G2) relative to
31.6%(G1) for the EpiR cell line. These results indicate a 10-12hr window for transition to the
G2 phase, with the cells completing a full cell cycle by the ~16-24hr time points.
Figure 9: FlowJo analysis of synchronized cells collected at 0hrs, 8hrs, 10hrs, 12hs and 24hrs (N=1). G1- purple
peak and G2- green peak. Cell lines include MDA-MB-231 Native, MDA-MB-231 EpiR, ZR-75-1 Native and ZR-
75-1 EpiR.
32
3.2.1 Double thymidine block synchronization confirmed through cyclin B and E.
Cyclins B and E expression observed through western blot and densitometry (Figure 10) was
analyzed for each of the cell lines following flow cytometry analysis. Figure 10A demonstrates,
MDA-MB-231 native, cyclin E band intensity was highest at the 0-4hrs time points and again at
24hrs (representing the cells re-entry into the cell cycle). Cyclin B band intensity was highest at
the 8-12hr time points representing the cells entry into the G2M phase of the cell cycle. Figure
10B, MDA-MB-231 EpiR, cyclin E band intensity was highest at the 24hr time point and cyclin
B band intensity was highest from the 4-12hr time points.
Figure 10C, ZR-75-1 native, cyclin E band intensity was highest at the 0-8hr time points and
again at 32hrs. Cyclin B band intensity was undetectable at the 0hr time point and highest at the
12hr time point. Figure 10D, ZR-75-1 EpiR, cyclin E band intensity was highest at the 0-4hrs
time points and again at the 24hr time point. Cyclin B band intensity was highest at the 12hr
time point.
33
Figure 10: Western blots and densitometry analysis of cyclins B, E normalized against actin. MDA-MB-231. A.
Native, B. EpiR and ZR-75-1, C. Native, D. EpiR. Purple- cyclin E, orange- cyclin B
34
3.3 Analysis of BubR1 and Mad2 at the G2M phase time points.
Western blotting was performed on the EpiR and native cell line lysates using the conditions
optimized for determination of expression during the G2M phase; 0hrs (cells released following
first block), 10hrs for MDA-MB-231 cell line and 12hrs for ZR-75-1 cell lines. Figure 11
represents western blot analysis of BubR1 and Mad2 for each of the cell lines, the blots show
the down regulation of BubR1 expression at both the 0hr and 12hr time points within the ZR-75-
1 EpiR cells relative to the native cells. For the MDA-MB-231 cells, the bands display similar
intensity within the 10hr time point. Mad2 protein expression (Figure 11C and 11D) was
observed to not change within the native and EpiR cells at the 10hr and 12hr time points for
either cell lines.
Figures 11A and 11B demonstrates both the western blots and densitometry for BubR1(n=3)
and Mad2 (n=2) normalized against actin. Although no significant difference was observed
between the native and EpiR protein expressions (t-test unpaired), for both BubR1 and Mad2 a
decrease in band intensity was observed at 10hrs for the MDA-MB-231 EpiR cells. Figures 11C
and 11D demonstrated no significant difference in band intensity between the cell lines,
however a decrease in BubR1 and Mad2 band intensity was observed at both 0hrs and 12hrs for
the ZR-75-1 EpiR cells relative to the native cells.
35
Figure 11: Western blots and densitometry analysis of BubR1 and Mad2 normalized against actin. MDA-MB-231
A. BubR1, B. Mad2 and ZR-75-1, C. BubR1, D. Mad2. Red- native, blue- EpiR.
36
In addition to western blots, qPCR analysis was performed. The analysis demonstrated BubR1
and Mad2 gene expression downregulation within the EpiR cell lines relative to the native cells.
Figure 12 depicts the average of three separate experiments carried out confirming the
downregulation of both BubR1 and Mad2 within the cell lines using the native 0hrs cell line as
normalization. Figure 12A demonstrates significantly lower BubR1 gene expression in the
MDA-MB-231 EpiR cell lines relative to the native cells with a 37% decrease in BubR1 gene
expression at 0hrs (p=0.017, unpaired t-test) and 41% decrease at 10hrs (p=0.0044, unpaired t-
test). For Mad2 (Figure 12B), the EpiR cell lines demonstrate a 22% decrease in gene
expression at 0hrs (p=0.030, unpaired t-test) and 41% decrease at 10hrs (p=0.011, unpaired t-
test). Increase in gene expression observed within the native MDA-MB-231 cells from 0hrs to
10hrs is significant only for BubR1 (p=0.013, unpaired t-test) and Mad2 (p=0.12, unpaired t-
test).
Figure 12C demonstrates significantly lower gene expression in the ZR-75-1 EpiR cell lines
relative to the native cells with a 30% decrease in BubR1 gene expression at 0hrs (p=0.012,
unpaired t-test) and 38% decrease at 12hrs (p=0.0021, unpaired t-test). For Mad2 (Figure 12D),
the EpiR cell lines demonstrate a 24% decrease in gene expression at 0hrs (p=0.019, unpaired t-
test) and a 25% decrease at 12hrs (p=0.031, unpaired t-test). Increase in gene expression
observed within the native ZR-75-1 cells from 0hrs to 12hrs is significant only for BubR1
(p=0.007, unpaired t-test) and Mad2 (p=0.08, unpaired t-test).
37
Figure 12: Gene expression analysis of BubR1 and Mad2 at G2M phase time points MDA-MB-231 A. BubR1, B.
Mad2 and ZR-75-1 C. BubR1, D. Mad2. Red- native, blue- EpiR.
38
3.4 Increased SNCG gene expression observed within EpiR cell lines.
Figure 13A shows significant BubR1 downregulation within the MDA-MB-231 EpiR cells
relative to the native cells by 48% at time points 0hrs (p=0.0018, unpaired t-test) and 47% at
10hrs (p=0.00036, unpaired t-test). Mad2 (Figure 13B) shows significant downregulation within
the EpiR cells relative to the native cells by 10% at time points 0hrs (p=0.044, unpaired t-test)
and 35% 10hrs(p=0.0083, unpaired t-test). In contrast, Figure 13C shows significant
upregulation of SNCG within the MDA-MB-231 EpiR cells relative to the native cells at
0hrs(p=0.014, unpaired t-test) and 10hrs(0.0012, unpaired t-test). SNCG within the MDA-MB-
231 EpiR cells increased by 1029% at 0hrs(p=0.014, unpaired t-test) and 819% at
10hrs(p=0.0012, unpaired t-test). The downregulation of BubR1 and the upregulation of SNCG
potential correlation within the MDA-MB-231 EpiR cells was significant at 0hrs(p=0.013,
unpaired t-test) and 10hrs(p=0.00099, unpaired t-test).
Figure 13D shows significant downregulation of BubR1 within the ZR-75-1 EpiR cells relative
to the native cells by 29% at 0hrs(p=0.020, unpaired t-test) and 35% at 12hrs(p=0.0067,
unpaired t-test). Significant downregulation of Mad2 (Figure 13E) within the EpiR cells was
observed by 27% at 0hrs(p=0.047, unpaired t-test) and 25% at 12hrs(p=0.040, unpaired t-test).
In contrast, SNCG(Figure 13F) was significantly upregulated in the EpiR cell lines relative to
the native cells at 0hrs(p=0.024, unpaired t-test) and 12hrs(p=0.0034, unpaired t-test). SNCG
within the ZR-75-1 EpiR cells increased by 76% at 0hrs(p=0.024, unpaired t-test) and 50% at
12hrs(p=0.0034, unpaired t-test). The downregulation of BubR1 and the upregulation of SNCG
potential correlation within the ZR-75-1 EpiR cells was observed to being significant at
0hrs(p=0.014, unpaired t-test) and 10hrs(p=0.030, unpaired t-test).
39
Figure 13: Gene expression analysis of SNCG, BubR1 and Mad2. MDA-MB-231 A. BubR1, B. Mad2 C.SNCG. ZR-75-1 D. BubR1, E. Mad2,
F.SNCG. Red- native, blue- EpiR.
40
3.5 Time course (24-96hrs) knockdown of BubR1 in native breast cancer cell lines.
A time course study of BubR1 knockdown was performed to estimate the duration of
knockdown of the BubR1 protein. Western blot and CCK-8 assays were performed following
downregulation of the Bub1B gene using siRNA. Figures 14 and 15 represent examples of
western blot confirming knockdown for both native cell lines. Controls included siGAPDH
siNTC, lipofectamine-only and media-only. As demonstrated in Figures 14 and 15, the
siGAPDH control was successful in silencing GAPDH and siBub1B was also successful in
silencing BubR1 (target protein) over a 96hr time period.
41
Figure 14: Western blot knockdown of BuBR1 and GAPDH in MDA-MB-231 native cell line.
Figure 15: Western blot knockdown of BuBR1 and GAPDH in ZR-75-1 native cell line.
42
3.6 Significant increase in epirubicin resistance observed following knockdown of BubR1 in native cell lines.
BubR1 knockdown in both MDA-MB-231 and ZR-75-1 resulted in a significant 3-fold (p=0.04,
ANNOVA single factor) and 2-fold(p=0.05, ANNOVA single factor) increase in resistance to
epirubicin respectively (Figure 16) relative to the controls. Figure 16A demonstrates one
example of triplicate dose-response curves for MDA-MB-231, the siBub1B knockdown cell line
demonstrated the highest IC50 of 404.1nM, siNTC 119.9nM, lipofectamine-only 138.6nM and
media-only 173nM. The dose response curve of ZR-75-1(one example from an n=3), siBub1B
knockdown cell line demonstrated an IC50 value of 319.2nM, siNTC 158nM, lipofectamine-
only 117.2nM and media-only 125.3nM. The average IC50 values for each control and test were
used to determine the final IC50 value across all three indiviual runs. As seen in Figure 16B, the
media-only control demonstrated the lowest standard deviation for both cell lines relative to the
other controls used.
43
Figure 16: A. Dose response curve of native siBub1B knockdown MDA-MB-231 and ZR-75-1. Green curve- siBub1B (target), purple curve-
siNTC, orange curve- lipofectamine only and blue curve- media only curve. B. Half-maximal inhibitory survival curve analysis(IC50) for three
separate experiments. IC50 values are in nanomolar concentrations.
44
Chapter 4
Discussion 4
The purpose of this study was to analyze the SAC signal and member proteins within our EpiR
cells as the mechanistic explanation for Ch17CEP duplication and anthracycline sensitivity.
Based on the findings of this study, we believe that a dysregulated SAC signal, demonstrated by
a downregulation of BubR1 and Mad2, is a driver of anthracycline resistance. Preliminary
results from the BR9601 trial indicated that patients containing low BubR1 expression did not
benefit from the Epi-CMF treatment, this was further identified as a causal relationship as
demonstrated by a knockdown of BubR1 within the native cell lines which was followed by a
significant increase in resistance to epirubicin.
MDA-MB-231 and ZR-75-1 were chosen for this project due to the similar molecular subtype of
these cell lines to the patients analyzed within the BR9601 trial. Patients within this trial were
representative of those who would receive chemotherapy as adjuvant treatment. Our
proliferation study demonstrated both the MDA-MB-231 and ZR-75-1 EpiR cell lines had a
significant increase in resistance compared to native cells. This experiment falls in line with the
work completed by Braunstein et al. 2016, in which resistant MDA-MB-231 cell lines expressed
an average of 67-fold increase in resistance and resistant ZR-75-1 cell lines expressed a 7-fold
increase136
. Importantly, concerning these generated EpiR cell lines was the observation that
multi-drug resistant genes (MDR), cells with the reduced ability of accumulating drug143
, were
not upregulated in the cell lines studied for this project. The fact that MDR genes are not
upregulated in the respective cell lines indicates that an alternative independent mechanism is at
work and responsible for the increased resistance within the EpiR cells.
Western blots and qPCR analysis showed significant downregulation of both BubR1 and Mad2
in both cell lines at all time points (0hrs, 10hrs, 12hrs). The downregulation could be due to a
number of reasons some of which being: the drug directly impacts the SAC protein expression
levels, the structure and/or the function of the proteins has been altered by another target
(upstream or downstream of the SAC signal). Our lab has previously observed, through western
blot analysis that the hormonal receptor expression levels (ER, PR, HER2) of the EpiR breast
cancer subtypes had not changed from their native counterparts136
. This observation supports the
fact that epirubicin is not directly influencing the cell lines protein expression levels and we can
45
safely assume that the expression of BubR1 and Mad2 are independent of epirubicin addition to
the cells. SNCG was significantly upregulated in the EpiR cell lines relative to the native cells,
whilst both BubR1 and Mad2 were downregulated. The upregulation of SNCG and
downregulation of BubR1 within the EpiR cells was also significant, further supporting the
interaction of BubR1 and SNCG indicated within the literature130
. We observed a dramatic
upregulation of SNCG within our MDA-MB-231 cell lines (TNBC); supporting previous results
that demonstrated high SNCG expression in aggressive breast cancer subtypes144
.
By mimicking BubR1’s downregulated expression in the EpiR cell line, a knockdown of BubR1
within the native cells correlated to an increase in epirubicin resistance. This finding supports
the work completed in the BR9601 trial where patients with low BubR1 expression did not
benefit from Epi-CMF, demonstrating that decreased BubR1 expression, due to a dysregulated
SAC signal, is responsible for increased epirubicin resistance within our cell lines. Although we
did not study cellular viability in our knockdowns, we did observe the native knockdown cells
(siBub1B) growing at a slower rate relative to the controls during the 96hr collection period.
Interestingly, the control cells (siNTC, siGAPDH, lipofectamine-only, media-only) grew quickly
(cells stacking), observed through the microscope. This observation indicates towards the
importance of BubR1 in cellular proliferation, if so, further work is required to determine how
the EpiR cells are able to continue proliferating with the decreased BubR1 expression and if this
decrease is directly related to the increase in resistance observed.
The results of the BR9601 clinical trial further supports the molecular analysis work that we
have completed thus far concerning the dysregulation of the SAC signal and its correlation to
increased epirubicin resistance. The results of the trial indicated that patients containing low
BubR1 expression (Figures 7A and 7C) did not observe any significant benefit from the Epi-
CMF treatment relative to the patients that contained a high BubR1 expression (Figures 7B and
7D) and significantly benefited from the treatment. The results of the trial indicated of BubR1’s
significance as an independent predictor of anthracycline benefit with increased OS(HR:0.38,
95% confidence interval 0.13-1.05, p=0.064) and RFS (HR:0.39, 95% confidence interval 0.14-
1.04, p=0.061), our molecular work further supports this conclusion. We believe that decreased
BubR1 expression, indicative of a dysregulated SAC signal, is linked to increased resistance to
epirubicin as observed at both the molecular and clinical levels.
46
Analysis of key cell cycle markers through flow cytometry and western blots, cyclin B and
cyclin E, further confirmed the association between the SAC signals expression and the G2M
phase time point. In order to assess the SAC signal within the generated in vitro model it was
important to determine the specific cell cycle time point in which the SAC signal would
normally function within (mitotic phase). The time points observed for the G2M phase in this
experiment falls in line with the work completed by Whitfield et al. 2002140
, where HeLa cells
were synchronized using a double thymidine block, cells progressed into the G1S phase at 0-
4hrs, entered a synchronous G2M phase within 7-8hrs and completed a full cycle within the 14-
16hr timeframe. Furthermore, supporting the G2M phase time points we have identified through
our work, a study published by Harshman et al. 2014145
observed a G2M phase 8-12hr window
for their MDA-MB-231 cells following a double thymidine synchronization.
Figures 10A and 10B, show cyclin B band intensity is highest during the 8-16hrs time points
with cyclin E band intensity highest at the 24hr time point within the MDA-MB-231 cell lines.
Similar trends are seen within the ZR-75-1 cell lines (Figures 10C and 10D). The western blots
and densitometry indicate of lower cyclin B band intensity towards the end of the G2M phase;
however, it is difficult to determine if this is due to a degradation and if in fact the SAC signal is
functional within these cells. It is possible that the SAC signal is turned off or bypassed due to
inhibition or dysregulation within the signal itself, thus enabling the cell to function normally
regardless of potential CIN or aneuploidy. This would explain the presence and absence of
cyclin B at specific time points within the cells.
For cyclin E, there appeared to be two distinct bands as seen in Figure 10. Multiple bands of
cyclin E have been observed in breast cancer cell lines, with some of these bands expressing
lower-molecular isoforms of the protein (34-49kDa)146
. A study conducted by Keyomarsi et al.
1994, supported the presence of an altered cyclin E protein through analysis of breast cancer
tumour tissue against normal breast tissue, the western blots presented a cyclin E of multiple
isoforms (35-50kDa) within the aggressive breast cancer tissue relative to the normal tissue147
.
Cyclin E has been actively researched as a potential prognostic marker of breast cancer, with
some positive preliminary results as presented by Keyomarsi et al. 2002148
. Although the cyclins
represent an excellent model for studying synchronization, it would appear that these proteins,
specifically cyclin E, are impacted and altered in cancer cells and as a result expression cannot
be guaranteed. For the future, a multivariate or multi-dimensional algorithm could be used for
47
flow cytometry in which case cells would be sorted according to each population (i.e. G0, G1,
G2 and M phase) as opposed to the univariate method.
Our results thus far support the possibility of a dysregulated SAC signal within the EpiR cell
lines and that this dysregulation may be a driver of anthracycline resistance, further representing
a causal relationship. Two hypotheses arise from this analysis; 1) either BubR1 and Mad2
function is directly inhibited or altered rather than degraded, by a target, resulting in the
subsequent dysregulation of the SAC signal or 2) the SAC signal has been inhibited or
dysregulated by mutations of an upstream component that in turn impacts BubR1 and Mad2
function and as a result resistance is observed.
In support of the first hypothesis, SNCG upregulation within the EpiR cells appears to impact
the SAC signal through its interaction with BubR1. However, the reasoning behind this
upregulation or the specific interaction this protein has with BubR1 remains to be seen. The
dramatic increase of SNCG expression within the EpiR cell lines leads, particularly as seen in
the MDA-MB-231 cells, to the assumption that hormonal receptors presence/absence may play
an indirect role in SNCG expression. Interestingly, SNCG has been observed to stimulate the
ERα signaling pathway149
and protect Hsp90150
, a chaperone protein of HER2, further
promoting cancer cell proliferation. However, Cirak et al. 2015 did not observe any significant
associations between high SNCG expression and hormonal receptors presence or absence within
taxane resistant breast cancer cell lines151
.
Cirak et al. 2015 studied the interaction of BubR1 and SNCG as potential predictive and
prognostic markers within a clinical trial. Their results indicated of a significant correlation with
low BubR1 expression and increased taxane sensitivity, whilst high SNCG expression (62% of
patients) was significantly associated with decreased taxane sensitivity151
. From our results and
those noted within literature, low BubR1 expression coupled with a high SNCG expression
seems to correlate with decreased chemotherapeutic sensitivity (our work has been with
anthracyclines, literature has largely worked with taxanes). As a result, research has observed
the druggability of SNCG with small molecules and potential re-sensitization of resistant cells.
One methodology reported, has been the use of oncostatin-M (OSM)152
, a member of the
interleukin-6 family of cytokines, as a transcriptional suppressor of SNCG within breast cancer
cell lines153
. The study observed a decrease in SNCG mRNA expression as early as 30mins
following administration, by 2 days the level of SNCG mRNA was decreased to 90%.
48
Alternatively, an SNCG knockdown could be completed within the EpiR cell lines. Miao et al.
2014 completed a knockdown of SNCG within SKBR3 (HER2-type breast cancer) and MDA-
MB-231 cell lines, by inhibiting SNCG expression the docetaxel-mediated apoptosis response
significantly increased within the cells relative to the controls, where SKBR3 witnessed a 35%
increase in apoptotic efficiency and MDA-MB-231 cells witnessed a 24% increase130
.
To further explain the decreased BubR1 expression observed in the patients of the BR9601 trial
and our EpiR cell lines we turn to CIN. CIN has also been studied for its potential as a
predictive marker of anthracycline sensitivity, it has been linked to SAC dysregulation and
benefit from chemotherapy regimens containing anthracyclines91
. A recent study conducted by
Spears et al. 2015, identified a 4 gene signature related to CIN (CIN4) as a potential
independent predictor of anthracycline sensitivity154
, the study also indicated that the CIN4
marker contained genes involved in DNA repair (i.e. SAC) concluding that dysregulation within
these mechanisms may also lead to anthracycline sensitivity. Our work supports the presence of
a dysregulated SAC signal within our resistant cell lines, in addition this dysregulation has been
correlated to an increase in resistance. This finding supports the assumption that a dysregulated
SAC signal as observed through BubR1 expression or a CIN4 gene signature may infer
anthracycline benefit in early breast cancer patients.
Further work is required to confirm the impact of BubR1 silencing on the proliferative ability of
cells. The work of Wang et al. 2004, found that a deficiency of BubR1, through a gene trapping
method, resulted in death for early mice embryos155
. Baker et al. 2004, observed mutant mice
with low levels of BubR1, these mice developed aneuploidy, increased senescence and a variety
of features that mimic physiological aging156
. Interestingly, Schnerch et al. 2013 observed
significant downregulation of BubR1 in acute myeloid leukemia cell lines157
, re-introducing
BubR1 to these cell lines through an inducible retrovirus mechanism re-sensitized the cells to
spindle toxins (i.e.taxanes). Since the knockdown experiments conducted were transient, future
experiments should include the development of a stable knockout BubR1 cell line to further
validate the increase in epirubicin resistance (the IC50 values might increase as a result) and
importantly the impact on cellular viability and proliferation. We believe that BubR1 effects the
cells proliferative ability where growth is slower in comparison to the control cell lines,
however it is still unclear if the impact is detrimental to the cells where the knockout of BubR1
could cause cell death. Implementing a permanent knockout (i.e. CRISPR/Cas9) of BubR1
49
within the cell lines would require additional proliferation assay type experiments due to
BubR1’s prominent role in the SAC signal; this in turn might have some adverse effects on the
cells.
In support of the second hypothesis, the SAC signal and member proteins may be dysregulated
by mutations of an upstream component. Maciejczyk et al. 2013 observed a correlation between
BubR1 overexpression and poor survival in early breast cancer patients112
. In addition to breast
cancer, high BubR1 expression has been associated with poor prognosis in bladder, stomach,
kidney, ovarian and large intestine cancers158,159,160,161
. Interestingly Lee et al. 2009161
attributed
the increase in BubR1 expression within ovarian cancer to an increased mitotic index and
increased proliferation of tumour cells. Overexpression of BubR1 within these cells seems to be
correlated to uncontrolled proliferation or cells that are aneuploid, which could indicate of a
dysregulated SAC signal. A dysregulated SAC signal would result in aneuploid cells, bypassing
mitotic arrest, proliferating and in the case of cancer cells, surviving anthracycline induced
apoptosis162
. An increased mitotic index opens the cell to increased mutations which in turn
results in either cell death or cells surviving with genetic instability (i.e. increased BubR1
expression in breast cancer). It is quite possible that the decreased BubR1 and Mad2
expressions within the EpiR cell lines are caused by accumulated mutations from upstream
genes such as p53, BRCA1 and BRCA2. It has been reported that mutations in BRCA2 impacts
BubR1 acetylation processes, producing a weakened SAC signal163
and mice deficient in
BRCA1 were found to contain decreased Mad2 expression, again leading to a weakened SAC
signal164
. The buildup of these mutations may result in a dysregulated SAC, promoting CIN,
increasing heterogeneity of the tumour and as a result an increase in resistance to
chemotherapy161
. This assumption would support the hypothesis that upstream mutations may
be responsible for the dysregulated SAC signal. Tumour suppressor genes such as p53, BRCA1
and BRCA2, located upstream of the SAC signal, are notable candidates that have been studied
extensively in the context of breast cancer prognosis and tumour proliferation.
The findings generated from clinical trials, such as the BR9601, to study biomarkers cannot
succeed without a thorough understanding of the molecular mechanisms at work. This project
has focused on studying the molecular mechanism of the SAC signal and its member proteins
within an in vitro cell line model, our main objective has been to utilize the molecular
information to further inform and support the clinical findings we have demonstrated thus far.
50
The presence of a dysregulated SAC signal and the upregulation of SNCG within our EpiR cell
lines are potential drivers of anthracycline resistance, with SNCG demonstrating promise as a
druggable target for reversing the resistance. Based on our molecular and clinical evidence,
BubR1 expression is predictive of anthracycline benefit.
51
Chapter 5
Conclusion 5
Through our research, we have observed a downregulation of BubR1, Mad2 and an upregulation
of SNCG within epirubicin resistant breast cancer cells, which in turn may be indicative of SAC
dysregulation and an increase in epirubicin resistance. Furthermore, evidence from our clinical
and molecular work supports BubR1 as a promising independent predictive marker of
anthracycline benefit. Determining the appropriate treatment for breast cancer patients remains
important for the improvement of patient’s overall survival and relapse-free survival. The use of
adjuvant chemotherapies have been clinically demonstrated to improve patient survival165
,
which translates to the continued use of chemotherapy in the adjuvant treatment setting for
breast cancer patients. According to a study conducted in 2011 by the American Cancer
Society, 34% of US female patients with late-stage breast cancer received chemotherapy and
radiation as the adjuvant form of treatment following mastectomy166
. Chemotherapy is widely
used within the breast cancer adjuvant treatment space, issues such as recurrence following
treatment influences both the patient and the clinical practice. A study conducted on the cost of
initial cancer treatment in Ontario by de Oliveira et al. 2013 found that chemotherapy use
increased by 8% in those aged 19-44 years and 17.2% in patients 45years and older. The study
found that chemotherapy costs increased by 5-fold in all breast cancer patients, concluding that
the cost of chemotherapy directly influenced Ontario’s health care budget167
. Until an alternative
to chemotherapeutics is developed with fewer side effects, increased benefits and low-costs, it is
here to stay and we are required to further optimize chemotherapy for patients to ensure that the
right treatment is given to the right patient.
The issue of drug resistance within patients and the inability to stratify patients into treatment
arms, which in turn results in unwarranted side effects and a delay in the treatment process,
remains a serious and detrimental downfall of the current chemotherapeutic landscape. As a
result, determining a predictive marker within patients would aim to bypass the side effects and
ensure patients receive effective treatment. The ultimate goal of our work is to support the
introduction of personalized medicine into breast cancer patient’s treatment plan. The benefits
of introducing personalized medicine into the treatment space has been highlighted within
literature and current clinical studies being carried out such as the TAILORx and MINDACT
52
projects. Interestingly, a 21-gene panel168
, utilized as an assay test for ER+ve
and lymph node –ve
breast cancer was reported as determining the likelihood of patients experiencing tumour
recurrence, in addition to determining the benefit, if any, of being administered chemotherapy.
Utilizing personalized medicine and gene expression-profiling tests, such as the example
mentioned, have been predicted to save costs in comparison to the current health care costs
associated with administering chemotherapy to patients 137,167,169
. Through our work we have
shown evidence of patients, with high BubR1 expression, benefiting from anthracycline
regimens, furthermore our work has laid the foundation for analysis into the potential of BubR1
as a predictive marker of anthracycline benefit. From this, we have established epirubicin
resistant breast cancer cell lines in order to study the mechanism behind epirubicin resistance;
this work was prompted by our group’s earlier identification of Ch17CEP as both a consistent
and significant predictor of anthracycline benefit in a number of clinical trials. By utilizing an in
vitro cell line model, we have been able to identify SAC dysregulation within the EpiR cell lines
and in turn identify SNCG as a potential target for reversing the resistance within our cell lines.
Future directions include identifying SNCG expression within the BR9601 clinical trial through
IHC; we believe that similar to the qPCR work carried out, SNCG should be upregulated in
patients with low BubR1 expression and those that did not benefit from the Epi-CMF treatment.
Following this step, we are interested in targeting SNCG through a knockdown experiment
within the EpiR cell lines and observing the resistance levels, in this case we believe that IC50
values will decrease. Furthermore, developing knockout cell lines of BubR1 utilizing techniques
such as CRISPR/Cas9 would allow us to study the impact of BubR1 silencing on cellular
viability, impact on other SAC member proteins and further confirming the increase in
resistance through a complete and stable BubR1 knockout. It is difficult to continue optimizing
chemotherapeutic regimens that would maximize benefit when we have not identified an
effective method of stratifying patients who would benefit from those that would not. The
identification and validation of predictive markers of treatment benefit represent the future of
personalized medicine, which aims to improve therapy design, reduce current side effects and
costs of chemotherapy administration (i.e. resistance, toxicities). Through this work we aim to
identify the mechanisms behind anthracycline resistance at the molecular level which will
further lay the foundation and groundwork for studies aimed at developing biomarkers that
would detect adjuvant treatment resistance and benefit in breast cancer patients.
53
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