The role of Norrie Disease Pseudoglioma (NDP) signaling in glioblastoma

by

Ahmed Ali Ahmed Ali Elsehemy

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Laboratory Medicine and Pathobiology University of Toronto

© Copyright by Ahmed Ali Ahmed Ali Elsehemy 2018

ii

The role of Norrie Disease Pseudoglioma (NDP) signaling in

glioblastoma

Ahmed Ali Ahmed Ali Elsehemy

Doctor of Philosophy

Department of Laboratory Medicine and Pathobiology

University of Toronto

2018

Abstract

Norrin is a WNT ligand that binds Frizzled-4 (FZD4) and Low-density lipoprotein receptor-

related protein (LRP5/6) receptor complex to activate canonical WNT/ β-Catenin signaling.

Norrin/FZD4 signaling is involved in the regulation of vasculature in several tissues including

retina, inner ear and for blood-brain barrier function. The role of Norrin in cancer is not very

well characterized. Here, we show that NDP is expressed in a wide range of cancer types, with a

particular enrichment in glioblastoma (GBM) and lower grade glioma (LGG). Kaplan-Meier

survival analysis of publicly available datasets revealed a significant correlation between NDP

expression and survival in GBM, LGG and neuroblastoma. To investigate the function of NDP in

GBM, we performed a set of NDP and FZD4 gain and loss of function experiments in patient-

derived GBM stem cell (GNS) lines. Recently ASCL1 expression was shown to stratify GNS

lines into two cohorts with different tumorigenic, proliferation and differentiation dynamics.

Surprisingly, we found that NDP manipulation resulted in opposite effects in ASCL1hi versus

ASCL1lo lines. NDP inhibited proliferation and sphere formation in ASCL1lo lines through

WNT- dependent mechanisms, while it stimulated proliferation and sphere formation in ASCL1hi

lines through WNT-independent mechanisms. Immunocytochemistry staining for proliferation

markers indicated that NDP affects cycle kinetics and cell cycle exit in both cohorts.

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Interestingly, RNA-Seq analysis of NDP knockdown ASCL1hi and ASCL1lo lines revealed a

remarkable effect of NDP knockdown on cell cycle controlling genes. In addition, the library

revealed a significant number of uniquely expressed genes in each cell line, consistent with the

divergence of NDP molecular functions between the two lines. Collectively, our results indicate

that NDP is involved in the regulation of GBM progression, and that NDP function in GBM

stratifies with ASCL1 expression.

iv

Acknowledgments

I would like to thank my supervisor Dr. Valerie Wallace for her limitless support and guidance

during my PhD studies. I am also thankful for my thesis advisory committee members; Dr.

Michael Taylor and Dr. Stephane Angers for their invaluable advices and guidance. My gratitude

extends to all my current and past colleagues in Wallace lab, especially Dr. Arturo Ortin-

Martinez for his precious help in preparing my final figures in the best presentable way. I am

very grateful to Dr. Peter Dirks and his group, especially Dr. Hayden Selvadurai, for their

significant help, collaboration and contribution to this project. Also, I am very thankful to Dr.

Kenneth Aldape and his group, especially Dr. Yasin Mamtjan, for their help and contribution

with the bioinformatics and computational analysis. I am very grateful for my family and friends

in Egypt and Canada for their continuous support and love.

Finally, I would like to thank sushi and Futurama, who always made me smile and laugh even

during stressful times.

v

Table of Contents

Acknowledgments.......................................................................................................................... iv

Table of Contents .............................................................................................................................v

List of Tables ............................................................................................................................... viii

List of Figures ................................................................................................................................ ix

List of Appendices ......................................................................................................................... xi

Chapter 1. Introduction ....................................................................................................................1

1.1 Glioma and Glioblastoma (GBM) .......................................................................................1

1.1.1 Classification of GBM ................................................................................................5

1.1.2 Transcriptional classification ......................................................................................6

1.1.3 Important aberrations and/or targets in GBM ...........................................................10

1.1.4 Treatment regimen ....................................................................................................15

1.2 GBM stem cells and their therapeutic potential .................................................................18

1.2.1 Glioma stem cell markers and culture systems .........................................................19

1.2.2 Glioma stem cell biology ..........................................................................................20

1.2.3 WNT signaling in glioma stem cells .........................................................................21

1.2.4 BMP signaling in glioma stem cells .........................................................................25

1.2.5 ASCL1 ......................................................................................................................28

1.3 Norrie Disease Pseudoglioma (NDP) in the endothelium and cancer cells .......................31

1.4 Rationale and objective ......................................................................................................34

1.5 Hypothesis..........................................................................................................................35

1.6 Specific aims ......................................................................................................................35

Chapter 2. Materials and Methods .................................................................................................36

2.1 Computational and in silico analysis ..................................................................................36

2.2 Primary tumor cell lines and culture ...................................................................................36

2.3 Recombinant DNA, plasmids and cloning ..........................................................................37

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2.4 Preparation of lentiviral particles and infection ..................................................................39

2.5 In vitro cell proliferation assay ...........................................................................................40

2.6 In vitro extreme limiting dilution assay (ELDA) ................................................................40

2.7 Cell and tissue immunostaining and microscopy ................................................................41

2.8 Cell competition assay ........................................................................................................43

2.9 Cell lysis and Western Blotting ..........................................................................................43

2.10 Small molecules and recombinant proteins.......................................................................44

2.11 Antibodies .........................................................................................................................45

2.12 Dual-Luciferase reporter assay system .............................................................................46

2.13 Flow cytometry analysis ...................................................................................................46

2.14 RNA extraction and qRT-PCR..........................................................................................46

2.15 PCR primers ......................................................................................................................47

2.16 RNA-Seq library ...............................................................................................................48

2.17 Animals .............................................................................................................................49

2.18 Orthotopic xenografting ....................................................................................................49

2.19 Quantification and statistical analysis ...............................................................................50

Chapter 3. Results ..........................................................................................................................51

3.1 NDP expression is enriched in GBM and correlates with survival in several

neurological cancers...........................................................................................................51

3.2 NDP/FZD4 pathway components are expressed in GNS and hNSC cells .........................58

3.3 NDP regulates proliferation and sphere formation of hNSC and GNS cells in vitro .........61

3.4 The biological effects of NDP in GBM stratify with ASCL1 expression levels ................70

3.5 Different molecular pathways mediate the biological function of NDP in ASCL1hi

versus ASCL1lo GBM ........................................................................................................81

3.6 The WNT-independent effects of NDP in ASCL1hi GBM are cell autonomous ................87

3.7 NDP knockdown decreases Ki67+ and SOX2+ populations in ASCL1hi, and increases

the Ki67+ population in ASCL1lo GNS lines ....................................................................90

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3.8 NDP affects cell cycle kinetics in both ASCL1hi and ASCL1lo GNS cells ........................95

3.9 NDP knockdown results in significantly variant differential expression profiles of

ASCL1hi versus ASCL1lo GNS lines ...............................................................................100

3.10 NDP affects tumor progression in xenografted GNS cells .............................................103

Chapter 4. Discussion ..................................................................................................................110

4.1 Novel perspectives of Norrin function: Role in Cancer ....................................................110

4.2 Norrin contributes to the progression of GBM and hNSC in vitro ...................................111

4.3 Divergence of Norrin functions in GBM based on ASCL1 subtype in vitro and in vivo .113

4.4 Potential therapeutic applications of Norrrin ....................................................................117

4.5 Summary and significance ................................................................................................119

References ....................................................................................................................................122

Appendices ...................................................................................................................................165

viii

List of Tables

Table 1. Transcriptional classification of GBM. ............................................................................ 9

Table 2. Sequences of shRNA constructs: .................................................................................... 38

Table 3. Sources and concentrations of used reagents: ................................................................. 44

Table 4. Sources, uses and dilutions of used antibodies: .............................................................. 45

Table 5. Sequences of qRT-PCR primers used in this study: ....................................................... 47

Table 6. In vitro effects of manipulating NDP or FZD4 expression on different cell types ........ 80

ix

List of Figures

Figure 1. Schematic illustration of glioma classification and its several subtypes. ........................ 4

Figure 2. Canonical WNT signaling in mammalian cells. ............................................................ 24

Figure 3. BMP signaling in mammalian cells. .............................................................................. 27

Figure 4. The alternative oscillatory regulation mechanism controlling the expression of

proneural factors such as ASCL1. ................................................................................................ 30

Figure 5. Norrin signaling in human cells. ................................................................................... 33

Figure 6. NDP expression is enriched in GBM and LGG tumor samples from TCGA ............... 53

Figure 7. NDP expression is enriched in GBM cell lines from CCLE. ........................................ 55

Figure 8. NDP expression levels correlate with survival in neurological cancers........................ 57

Figure 9. NDP/FZD4 pathway components are expressed in GNS and hNSC lines, and NDP

expression level is correlated with classical GBM gene set ......................................................... 60

Figure 10. NDP and FZD4 stimulate the proliferation of hNSC-1 in vitro. ................................. 63

Figure 11. NDP and FZD4 stimulate the proliferation of hNSC-3 in vitro. ................................. 65

Figure 12. NDP and FZD4 inhibit the proliferation of G411 cells in vitro. ................................. 67

Figure 13. NDP and FZD4 inhibit the proliferation of G564 cells in vitro. ................................. 69

Figure 14. NDP stimulates the proliferation of G523 cells independent of FZD4 in vitro. ......... 72

Figure 15. NDP stimulates the proliferation of G472 cells independent of FZD4 in vitro. ......... 74

Figure 16. GFP+ G523 shNDP cells diminish after 2 weeks in sphere cultures .......................... 76

Figure 17. Ectopic expression of shorthairpin insensitive NDP construct rescues the effects of

NDP knockdown. .......................................................................................................................... 78

x

Figure 18. The effects of NDP are FZD4 and WNT –dependent in ASCL1lo, and -independent in

ASCL1hi GNS lines. ...................................................................................................................... 84

Figure 19. BMP antagonizes the effects of NDP manipulation in ASCL1hi GNS lines. .............. 86

Figure 20. NDP mediates ASCL1hi GNS proliferation through a cell autonomous mechanism. . 89

Figure 21. Knocking down NDP or FZD4 increases Ki67+ cell population in G564 cells. ........ 92

Figure 22. Knocking down NDP or FZD4 decreases Ki67+ and SOX2+ cell population in G472

cells. .............................................................................................................................................. 94

Figure 23. Knocking down NDP increases Ki67+ but decreases Edu+/Ki67+ cell populations in

G411 cells. .................................................................................................................................... 97

Figure 24. Knocking down NDP decreases both Ki67+ and Edu+/Ki67+ cell populations in

G523 cells. .................................................................................................................................... 99

Figure 25. RNA-Seq analysis for G411 (ASCL1lo) and G523 (ASCL1hi) lines ......................... 102

Figure 26. Knocking down NDP affects tumor progression of xenografted GNS lines............. 105

Figure 27. IHC of formed G411 tumors in vivo indicate growth advantage of NDP knockdown

cells ............................................................................................................................................. 107

Figure 28. IHC of formed G523 tumors in vivo indicate growth disadvantage of NDP

knockdown cells.......................................................................................................................... 109

Figure 29. Proposed model of NDP biological functions in GBM ............................................. 121

xi

List of Appendices

Abbreviations ……………………………………………………………………………...…176

1

Chapter 1. Introduction

Glioblastoma (GBM) is the most common type of adult brain cancer (Ohgaki and Kleiheus,

2013; Weller et al., 2015). Due to several features such as genomic instability, high

heterogeneity, aggressiveness and rapid therapy resistance, GBM is currently considered

“incurable”, with very poor treatment outcomes (Stupp et al., 2009; Thakkar et al., 2014; Stupp

et al., 2005). Several classification systems have been advised trying to categorize GBM patients

into more clinically relevant groups for better treatment options, however; the extreme inter- and

intra-tumor heterogeneity lowers the efficiency and accuracy of these classification initiatives

(Cloughesy, Cavenee and Mischel, 2014). The current standard treatment regimen for GBM

consists of maximal-safe surgical resection followed by an adjuvant combination program of

Chemo and Radio therapy. The current first line chemotherapeutic agent for GBM remains

temozolomide (TMZ) (Stupp et al., 2005). TMZ is only effective in a small subset of patients,

and GBM cells frequently develop resistance to it very rapidly. (Cloughesy, Cavenee and

Mischel, 2014; Stupp et al., 2009). Thus, there is a critical need for better understanding of the

molecular mechanisms underlying GBM in order to develop more efficient and targeted

therapeutic options. Here, we characterize a novel role of Norrin, the protein product of NDP

gene, in regulating the progression of GBM. Norrin is an atypical ligand of WNT signaling,

which was also reported to directly interact with BMP signaling (Xu et al., 2004; Junge et al.,

2009; Ye et al., 2009; Ke et al., 2013; Xu et al., 2012; Deng et al., 2013). Both WNT and BMP

signaling pathways were documented to play a central role in regulating the progression of

several cancers including GBM (de Sousa, Melo and Vermeulin, 2016; Rheinbay et al., 2013;

Wu et al., 2017; Piccirillo et al., 2006; Lee et al., 2008; Liu et al., 2010). Therefore, we sought to

study the role of Norrin in GBM. Due to the remarkable heterogeneity of GBM, we will start this

thesis with a brief introduction about the clinical and biological characterization of this disease,

followed by an introduction about NDP, WNT and BMP signaling.

1.1 Glioma and Glioblastoma (GBM)

Glial tumors can be broadly grouped into two categories based on their infiltrative features:

circumscribed localized gliomas, and diffuse gliomas (Sahm et al., 2012). As the name implies,

2

diffuse gliomas are characterized by their infiltrative aggressive behavior that renders them

highly resistant to surgical resection procedures with near 100% probability of recurrence

following treatment (Aldape et al., 2015). According to the World Health Organization (WHO)

classification system, diffuse gliomas are classified into Low grade (II) and High (III, IV) grade.

Grade IV glioma is also called Glioblastoma (GBM) (Jovcevska, Kocevar and Komel, 2013;

Louis et al., 2007) (Figure 1). GBM is the most common and invasive type of malignant brain

tumor, accounting for approximately 60% of brain and 16% of all brain and central nervous

system tumors in adults (Thakkar et al., 2014; Rock et al., 2012). In addition to rapid

invasiveness, GBM is distinguished from low grade glioma by the remarkable necrosis and

microvascular proliferation (Louis et al., 2007). While GBM occurs predominantly in the higher

brain, it can also occur in other parts of the central nervous system, such as spinal cord and

cerebellum (Blissitt 2014). Previously, it was believed that GBM originates exclusively from

glial cells, however; recent research has questioned this hypothesis (Phillips et al., 2006).

Currently, there is evidence that GBM might also originate from neural stem cells that were

supposed to differentiate into glia or even neuron cell types (Davis 2016).

Historically, the diagnosis of GBM and determination of the subtype was mainly

histopathological based on characteristics such as hypercellularity, nuclear atypia, and mitotic

activity. However, the remarkable advances in understanding the biology of this disease led to

the incorporation of molecular subtyping for markers including IDH status (wildtype versus

mutant), EGFR and MDM-2 expression, loss of heterozygosity (LOH) of chromosome 10q,

which carries PTEN and p16 deletion (Sidaway 2017; Ohgaki and Kleiheus, 2007; Agnihotri et

al., 2013; Cloughesy, Cavenee and Mischel, 2014). Due to the very complex and heterogeneous

nature of GBM, there have been several attempts to more accurately stratify this disease into

more molecularly defined subtypes (Aldape et al., 2015; Cloughesy, Cavenee and Mischel,

2014). In the next sections we will highlight some of these proposed classification systems.

3

4

Figure 1. Schematic illustration of glioma classification and its several subtypes.

Glioma can be categorized into diffuse or circumscribed. Diffuse glioma grade I, II together comprise the “Low

grade glioma” subtype. Grade III is called anaplastic astrocytoma, and grade IV is called glioblastoma (GBM).

5

1.1.1 Classification of GBM

As mentioned in the previous section, the remarkable complexity and heterogeneity of GBM led

to the introduction of several classification systems in an effort to categorize the disease into

clinically meaningful subtypes (Weller et al., 2015). One of the most traditional classification

systems is to divide GBM into two groups; primary and secondary GBM (Figure 1). Primary

GBM refers to de novo tumors that arise without previous known pre-malignant precursors, in

contrast to secondary GBM tumors that arise from the advancement of primary or recurrent low-

grade gliomas (Ohgaki and Kleiheus, 2013). On the molecular level, primary GBM are distinct

from secondary GBM in terms of specific markers and/or cancer drivers. Primary GBM is

remarkable for abnormalities in EGFR, PTEN genes and TERT promoter mutations. On the other

hand, secondary GBM is marked by mutant TP53, IDH 1/2 and ATRX mutations (Ohgaki and

Kleiheus, 2013; Wilson, Karajannis and Harter, 2014).

GBM subtype also correlates with age, with the majority of primary GBMs arising in elderly

patients, and secondary GBMs arising in younger patients (Aldape et al., 2015). Generally,

secondary GBM is rare, accounting for only 5-10% of all GBM cases (Ohgaki and Keliheus,

2013). In addition, a recent study suggested the existence of a third GBM subtype that is distinct

from primary and secondary GBM by mutations in H3F3A and occurs primarily in pediatric

gliomas (Schwartzentruber et al., 2012; Sturm et al., 2012). Noteworthy, IDH mutations are

much more common in secondary GBM (about 70-80% of these tumors harbor IDH mutations,

compared with primary GBM (only about 5-10% are IDH mutant) (Parson et al., 2008; Yan et

al., 2009; Nobusawa et al., 2009). The predominant clinical significance of IDH mutations will

be discussed in the upcoming sections.

In addition to this traditional classification system for GBM, there are more precise systems that

have been proposed based on clinical relevance and/or histological features of the disease. The

most well-established and clinically relevant GBM classification systems based on

transcriptional classifications will be highlighted in the next section.

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1.1.2 Transcriptional classification

The emergence of high throughput screening methods, next generation sequencing techniques, in

combination with large scale tumor data, such as The Cancer Genome Atlas (ACGT) and

International Cancer Genome Consortium (ICGC), has allowed for a more precise and clinically

relevant classification of GBM based on transcriptional profiling rather than clinical history

and/or histology. Based on unsupervised hierarchical clustering of GBM transcriptional, genomic

and epigenomic data, two studies (Phillips et al., 2006; Verhaak et al., 2010) categorized GBM

tumors into four major subtypes with unique genomic hallmarks, epigenomic aberrations, and

transcriptional profiles: classical, proneural, mesenchymal, and neural (Maher et al., 2006; Tso et

al., 2006; Freije et al., 2004) (Table 1).

The classical subtype is mainly characterized by EGFR amplification, PTEN loss, and CDKN2A

loss. The mesenchymal subtype is characterized by mutations or loss of TP53, NF1, and

CDKN2A genes. The proneural subtype is more complex, marked by enrichment of PDGFRA,

CDK6, CDK4, and MET alterations and often IDH1 mutations (Phillips et al., 2006; Verhaak et

al., 2010; Brennan et al., 2013; Wang et al., 2015) (Table 1). The proneural subtype itself can be

further subdivided into CpG (G–CIMP)-positive and -negative subgroups, depending on DNA

methylation patterns produced by IDH1/2 mutations. G-CIMP signature refers to a unique

hympermethylation landscape that is tightly linked to IDH mutation and MGMT promoter

methylation, and subsequently better prognosis and treatment outcomes (Bady et al., 2012;

Noushmehr et al., 2010; Turcan et al., 2012). The clinical and prognostic values of IDH

mutations and MGMT promoter methylation will be discussed in the next sections.

The neural subtype lacks any unique genetic signature. In fact, a recent study using a

comprehensive longitudinal analysis suggested that this subtype is not a real GBM cancer but

results from non-tumor cells contamination to the original samples in the initial classification

reports (Wang et al., 2017; Sidaway 2017). Further studies have suggested up to six subtypes

based on more in-depth transcriptional analysis and classification (Schwartzentruber et al., 2012;

Sturm et al., 2012). Moreover, individual primary GBM tumors have been shown to present

multiple subtypes concurrently (Patel et al., 2014; Meyer et al., 2015).

7

In summary, the introduction of next generation sequencing and large-scale screening techniques

allowed for a shift from the traditional classification of GBM into primary and secondary tumors

to a more precise and clinically relevant transcriptional classification. The transcriptional

classification, which categorizes GBM into four to six subtypes is more specific, clinically

relevant, and allowed for better understanding of GBM biology.

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GBM Subtype Transcriptional

Signature

Common Genomic

aberrations

Proneural Olig2/DLL3/SOX2 TP53, PI3K,

PDGFRA

Mesenchymal YKL40/CD44

NF-κB

NF1

Classical EGFR/AKT2

Chr.7 gain

Chr 10 loss

PDGFRA

Neural MBP/MAL None*

9

Table 1. Transcriptional classification of GBM.

The transcriptional signature of each subtype, as well as common genomic aberrations (based on Phillips et al.,

2006; Verhaak et al., 2010 classifications). *Notably, neuronal subtype does not have any common aberrations, and

its validity as a real GBM subtype is currently under question (Wang et al., 2017; Sidaway 2017).

10

1.1.3 Important aberrations and/or targets in GBM

GBM is one of the most complex cancers, with a very large number of genetic variations and

genomic landscapes that result in a high degree of inter- and intra-tumor heterogeneity (Sidaway

2017; Ohgaki and Kleiheus, 2007; Agnihotri et al., 2013; Cloughesy, Cavenee and Mischel,

2014, Patel et al., 2014). Despite this complexity, some of the identified aberrations present high

significance in the classification and/or potential therapeutic strategies in GBM; such aberrations

will be discussed in this section.

1.1.3.1 IDH mutations

One of the most important discoveries that helped understanding the biology of glioma was the

finding that mutations in IDH 1 and 2 genes are quite frequent in low grade gliomas, and quite

rare in advanced GBM (Yan et al., 2009; Nobusawa et al., 2009). Because of the high

significance of IDH mutations, gliomas can be classified based on IDH status into two groups;

IDH wildtype versus IDH mutant tumors (Kloosterhof et al., 2011). In this section, we will

discuss the critical impact of these mutations on tumor progression dynamics and prognosis.

In humans there are three IDH isozymes; IDH 1, 2 and 3 (Kloosterhof et al., 2011). Only IDH 1

and 2 isozymes have been linked to glioma progression and biology (Yan et al., 2009; Nobusawa

et al., 2009). The three IDH isozymes function in the regulation of citric acid (Krebs) cycle on

different levels. IDH 1 and 2 function in the cytosol and mitochondria to reduce nicotinamide

adenine dinucleotide phosphate (NADPH) from NADP+ by catalyzing the oxidative

decarboxylation of isocitrate to α-KG outside of the Krebs cycle, while IDH 3 converts isocitrate

to α-ketoglutarate (α-KG) and NAD+ to NADH (Dang et al., 2009; Figueroa et al., 2010; Ward

et al., 2010; Reutman and Yan 2010). Therefore, the reduced levels of functional wildtype IDH

in combination with reactive oxygen species (ROS) render the cells quite sensitive to oxidative

stress and subsequent damage (Lee et al., 2002; Reitman and Yan 2010). IDH mutations in

glioma seem to be restricted to amino acid residue R132, which is located at the center of the

enzyme active site, and in about 85% of the cases it is a missense heterozygous mutation of

arginine to histidine (R132H) that is utilized for rapid diagnostic purposes by

11

immunohistochemistry or direct (pyro)-sequencing (Watanabe et al., 2009; Capper et al., 2009;

Felsberg et al., 2010; Yan et al., 2009).

IDH mutations are associated with histone methylation, as well as hypermethylation of multiple

CpG islands resulting in a characteristic epigenetic phenotype called the glioma CpG island

methylator phenotype (G-CIMP) (Noushmehr et al., 2010; Turcan et al., 2012; Lu et al., 2012). It

is believed that IDH mutation presents an initial step in the advancement of glioma (Watanabe et

al., 2009; Aldape et al., 2015), however, it is not sufficient on its own (Sasaki et al., 2012). A

second hit must follow IDH mutation to form a glioma, which include TP53, ATRX, TERT

promoter, CIC, and FUBP1 depending on the glioma subtype (Bettegowda et al., 2011; Sahm et

al., 2012; Weber et al., 1996; Ohgaki et al., 2004). IDH mutations in primary GBM are rare

(account for less than 5%) and quite common in secondary GBM and low-grade gliomas

(Nobusawa et al., 2009). In other words, IDH mutations in GBM are strictly linked to either

secondary or proneural tumors (Noushmehr et al., 2010).

From a clinical perspective, several independent studies have reported a significant prognostic

value of IDH mutations in glioma patients, where patients harboring these mutations had

significantly longer overall survival than patients with wildtype IDH (Sanson et al., 2009; Combs

et al., 2011; Weller et al., 2009). This prognostic value of IDH mutation is highly significant in

low grade gliomas (Hartmann et al., 2009). Interestingly, these studies reported that the

prognostic value of IDH mutation status is independent from other prognostic factors such as

age, and disease grade or subtype. Notably, proneural GBM subtype seems to be associated with

a better outcome, however; removing IDH mutant samples from the analysis shows that IDH

wildtype proneural GBM has no prognostic advantage over other GBM subtypes (Cloughesy,

Cavenee and Mischel, 2015; Noushmehr et al., 2010).

It is not clear if IDH mutation status also has a predictive value in response to treatment. A

recent in vitro study suggested that IDH mutation does not affect response to radiotherapy in low

grade glioma (Li et al. 2013). However; a few other studies suggest that IDH mutation might

result in better response to chemotherapy and radiotherapy when applied immediately after

surgery, leading to a better overall survival only in low grade glioma (Juratli et al. 2012;

Houillier et. al, 2010; Hartmann et al., 2011). In conclusion, it is not clear if IDH mutation status

12

has a predictive value in response to treatment in GBM. Nevertheless, the critical biological

contribution of these mutations on the nature of the tumor led to suggestions that IDH mutant

glioma is a completely different disease than IDH wildtype (Young et al., 2015; Aldape et al.,

2015). Subsequently, there have been trials to specifically target IDH mutant GBM utilizing its

unique characteristics, which showed promising preclinical results (Rohle et al., 2013;

Schumacher et al., 2014).

In summary, the discovery of IDH mutation and its consequences on glioma led to significant

enhancements in our understanding of glioma and GBM (where IDH mutation is restricted to

secondary and proneural GBM only) and the differences between the GBM subtypes due to the

substantial molecular effect of this mutation on the tumor biology and genomic/transcriptional

landscape. IDH mutation might represent an initiation event in the formation of glioma,

however; it is not sufficient on its own to drive malignancy and has to be followed by another

hit. Patients with IDH mutant glioma have significantly better prognosis and longer overall

survival compared to those with IDH wildtype glioma. In addition, IDH mutation status is an

important prognostic marker and might present a promising druggable target in GBM.

1.1.3.2 EGFR activation and EGFRvIII

EGFR amplification is one of the most common aberrations seen in GBM, which affects

approximately 40-50% of all GBM patients (Cancer Genome Atlas Res. Netw., 2008; Parson et

al., 2008; Hurtt et al., 1992). About half of these patients express a mutant version of EGFR

called (EGFRvIII). EGFRvIII contains a deletion of exons 2-7, which code for the ligand binding

domain, resulting in a constitutively active receptor tyrosine kinase that activates PI3K signaling,

promotes tumor growth and proliferation, and is correlated with worse clinical outcomes

(Karshunov et al., 2015; Huang et al., 2009; Shinojima et al., 2003; Heimberger et al., 2005).

Notably, there seem to be a functional discrepancy between wildtype EGFR and EGFRvIII, with

the latter being more active, proliferation-inducing and oncogenic (Wang et al., 2009). Similarly,

gain of function mutations in EGFR extracellular domains also lead to constitutive activation and

more proliferation (Lee t. al., 2006). The high frequency of EGFR mutation in GBM led to pre-

clinical research suggesting that targeting EGFR or the mutant version (EGFRvIII) could be an

13

efficient therapeutic strategy (Vivanco et al., 2012; Mukasa et al., 2010). However; clinical trials

using EGFR inhibitors failed to produce the expected success (Kesavabhotla et al., 2012; van den

Bent et al., 2009; Wen et al., 2014). That being said, a single-arm clinical trial suggested a

therapeutic strategy taking advantage of the immunogenicity of the novel, cancer-exclusive

peptide (EGFRvIII) as a target for anti-tumor vaccination. Initial results from this study show

promising outcomes, however; more analysis is required to conclude the efficiency of this novel

strategy (Sampson et al., 2010).

Currently, a lot of GBM research effort is dedicated to decipher the mechanisms through which

EGFR amplified- and EGFRvIII-GBM develop resistance to EGFR inhibitor therapy, with the

loss of PTEN appearing to be central in the acquisition of this resistance (Mellignhoff et al.,

2005; Fenton et al., 2012; Vivanco et al., 2010). Moreover, other tyrosine kinase receptors might

be able to compensate for EGFR inhibition causing tumor insensitivity to the treatment

(Stommel et al., 2007; Snuderl et al., 2011; Szerlip et al., 2012).

1.1.3.3 hTERT promoter mutation

In humans, hTERT is the catalytic subunit of the telomerase enzyme that is responsible for

maintaining the ribonucleoprotein compartment of the telomeres to protect chromosomes from

continuous shortening (Autexier and Lue, 2006; Janknecht, 2004; Lewis and Tollefsbol, 2016).

In a vast majority of cancer types, hTERT promoter mutations lead to abnormal activity of the

enzyme, resulting in deregulation of telomere maintenance. Therefore, higher hTERT expression

levels are associated with worse clinical outcomes (Ducrest et al., 2002; Gertler et al., 2004;

Sanders et al., 2004). hTERT promoter mutations are one of the most common aberrations in

GBM, affecting 70-80% of all primary GBM patients (Killela et al., 2013; Koelsche et al., 2013;

Simon et al., 2015). Several studies have suggested that hTERT mutation might hold a

prognostic value for GBM patients, however; the correlation seems to be complicated and

dependent on other factors, such as IDH mutation status (Labussiere et al., 2014; Simon et al.,

2014; Srivastava et al., 2010; Killela et al., 2013).

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1.1.3.4 PDGFRA activation

Another very common aberration in GBM is the PDGFRA mutation. PDGFRA mutation is

reported in about 15% of GBM patients and seems to be strictly linked to the proneural subtype

(Phillips et al., 2006; Verhaak et al., 2010). There are several reported forms of PDGFRA

mutations in GBM (Fleming et al., 1992; Hermanson et al., 1992; Lokker et al., 2002; Clarke and

Dirks, 2003; Ozawa et al., 2010; Cancer Genome Atlas Res. Netw., 2008), all of which

eventually lead to hyper-activation of PDGFR signaling and therefore aggressive proliferation

and tumor growth (Assanah et al., 2006; Assanah et al., 2009).

For example, almost half of PDGFRA amplification cases in GBM result from a deletion of 243

bases from exons 8 and 9, which leads to synthesis of a truncated, constitutively active

extracellular domain (Clarke and Dirks, 2003; Ozawa et al., 2010). Similarly, about one third

GBM tumors hyper-activate PDGFRA by overexpression of its ligands (PDGF A-D) (Lokker et

al., 2002; Smith et al., 2000). In addition, GBM tumors were reported to express forms of fusion

proteins consisting of the extracellular domains of receptors such as VEGFR, and the

intracellular domain of PDGFRA, leading to a similar constitutive activation (Aldape et al.,

2015).

1.1.3.5 PI3K/AKT Signaling

PI3K/AKT pathway in GBM can be activated as a result of aberrations in multiple common hits

including, EGFR or alternative receptor tyrosine kinases, PIK3CA itself, PTEN loss; and

abnormal AKT phosphorylation (Riemennschneider et al., 2006; Choe et al., 2003; Jiang et al.,

2006). This in turn makes PI3K/AKT one of the most commonly disrupted pathways in GBM,

with about 90% of patients harboring abnormal activity (Cancer Genome Atlas Res. Netw.,

2008). Due to this central role of PI3K/AKT pathway in GBM, there have been several trials to

target it for therapy, however; its interaction with other targets/pathways, namely EGFR

signaling in specific, led to a limited primary success (Cloughesy, Cavenee and Mischel, 2014).

15

1.1.3.6 NF1 and mTOR

NF1 gene is genetically lost, inactivated, or degraded at the protein level in 15% of glioma

tumors (Parsons et al., 2008; McGilly cuddy et al., 2009; Cancer Genome Atlas Res. Netw.,

2008). NF1 mutations are mainly linked with GBM mesenchymal subtype (Verhaak et al., 2010).

NF1 is a tumor suppressor in GBM, which negatively regulates multiple oncogenic and/or

growth promoting pathways including Ras signaling, STAT3, and Mammalian target of

rapamycin (mTOR) (Sandsmark et al., 2007; Banerjee et al., 2010).

mTOR signaling is highly activated in GBM (Network, T. C.., 2013), and its abnormal activity

promotes cell proliferation, protein translation, tumor growth and progression (Lablante and

Sabatini, 2012; Sonenberg and Pause 2006). In addition, mTOR plays a central role in linking

growth factors to the proliferative phenotype through controlling cell metabolism, amino acid

availability and energy production (ATP) (Yecies and Manning 2011; Gwinn et al., 2008).

Recently, mTOR has also been shown to be significantly involved in regulating autophagy,

shedding more light on the role of this pathway in response to stress and proliferation cues (Egan

et al., 2011). mTOR signaling was also reported to be mediating the effect of EGFRvIII and

PTEN loss (Tanaka et al., 2011; Wang et al., 2006). Based on the central role of mTOR in

mediating cancer cell proliferation and tumor growth, there have been clinical trials to target this

pathway in GBM, however; these trials failed to produce favorable results (Cloughesy et al.,

2008)

1.1.4 Treatment regimen

Generally, GBM has a very poor prognosis, with progression-free survival of only 7-8 months,

median overall survival of 14-15 months and a 5-year overall survival of about 10%, even under

aggressive combination therapy programs. Therefore, GBM is considered one of the most

aggressive and lethal cancers (Stupp et al., 2009; Thakkar et al., 2014; Stupp et al., 2005). The

current standard therapeutic regimen for GBM involves major surgical resection, adjuvant

radiation therapy in combination with temozolomide (TMZ) treatment (Stupp et al., 2009).

Despite this aggressive treatment program, the success rate is very low and the disease is

generally considered “surgically incurable” due to its infiltrative and diffused proliferation, as

16

well as the ability to rapidly invade surrounding brain tissues. (Aldape et al., 2015; Cloughesy,

Cavenee and Mischel, 2014; Stupp et al., 2009). One of the main factors leading to this

inefficacy of GBM treatment is the very high incidence of development of resistance against

both radio- and cytotoxic chemotherapy (Masui, Cloughesy and Mischel, 2012; Dunn et al.,

2012). In addition, the poor efficiency of current chemotherapeutic agents to pass the blood-brain

barrier presents an important challenge in the treatment of GBM (Aldape et al., 2015; Sarkaria et

al., 2018). Due to the very poor chemotherapy outcomes in GBM, it is critical to identify the

patient groups that are most likely to benefit from the treatment regimen. In the next section, we

will discuss one of the most important and reliable TMZ predictive factors in GBM⎯MGMT

promoter methylation.

1.1.4.1 MGMT promoter methylation

As mentioned in the previous section, temozolomide (TMZ) is the current standard

chemotherapeutic agent in the treatment of GBM (Stupp et al., 2009). One of the most clinically

important predictive factors for TMZ responsiveness is MGMT promoter methylation (Hegi et

al., 2005; Wick et al., 2012), which is hypermethylated in about 40% of GBM patients. Promoter

hypermethylation of MGMT, which encodes an essential DNA repair enzyme that specifically

repairs DNA damage induced by alkylating chemotherapy agents, such as TMZ, leads to gene

silencing (Hegi et al., 2005; Wick et al., 2012). Subsequently, MGMT promoter

hypermethylation is significantly associated with higher sensitivity to alkylating agents, as well

as better progression-free and overall survival in patients who were treated with alkylating agent,

leading to the proposal of MGMT silencing as a sensitizing strategy for GBM patients treated

with TMZ (Esteller et al., 2000; Hegi et al., 2004; Hegi et al., 2005; Herrlinger et al., 2006;

Weller et al., 2009). This significant predictive value of MGMT promoter methylation led many

GBM treatment centers to adopt a program of TMZ plus radiotherapy followed by TMZ alone

versus radiotherapy alone based on MGMT promoter methylation status (Wick et al., 2013;

Weller et al., 2015; Malmstrom 2012). It is also important to notice that the predictive value of

MGMT promoter methylation is specifically confirmed in elderly GBM patients (Hegi et al.,

17

2005; Malmstrom 2012; Olson, Brastiano and Palma, 2011; Wick et al.2012), however; its

reliability in younger patients is not very clear (Aldape et al., 2015).

In conclusion, the traditional treatment regimen in GBM has a very poor efficacy. This is likely

due to the very complex nature of the disease, as well as its very high level of genetic instability

and phenotypic plasticity that allows for rapid formation of treatment resistance (Johnson et al.,

2014). Therefore, research efforts are currently focused on precision medicine, which will be

discussed in the next section.

1.1.4.2 Novel therapeutic strategies and Precision medicine

As demonstrated in the previous sections, GBM is one of the most complex cancer types with a

very high level of inter- and intra-tumoral heterogeneity as well as genomic instability (Frattini et

al., 2013; Vivanco et al., 2012; Sottoriva et al., 2013). Thus, GBM is currently considered

incurable with a very poor average survival, highlighting the critical need of novel targeted

therapies, including precision or personalized medicine.

Targeted therapy against specific central biological tumorigenic processes of particular interest

in GBM is an example of novel strategies in combination with the traditional TMZ treatment

regimen (Prados et al., 2015). For example, the autophagy inhibitor Chloroquine was shown to

sensitize GBM cells to TMZ treatment in xenograft models (Golden et al., 2014), and enhance

clinical outcomes of adjuvant TMZ and radiotherapy (Briceno et al., 2007). However; clinical

trials of Chloroquine related compounds failed due to toxicity and inefficiency in blocking

autophagy (Rosenfeld et al., 2014).

Immunotherapy presents another active area of GBM treatment research (Hickey et al., 2010).

Several immunologic strategies are currently in clinical trials in GBM. For example, T-Cell

therapy was shown to be a potential beneficial treatment for recurrent GBM (Schuessler et al.,

2014). In addition, the unique, cancer-specific peptide sequence of EGFRvIII was utilized to

produce a specific anti-GBM vaccine that gave promising results in initial trails (Sampson et al.,

2010). More research is required to assess the efficacy of an adjuvant therapy composed of

immunotherapy approaches in combination with TMZ or radiotherapy in GBM (Patel et al.,

18

2014). Finally, precision medicine remains an important goal in the field of GBM treatment.

Despite having a lot of potential, precision medicine is still faced with several challenges and is

still currently under development (Prados et al., 2015).

1.2 GBM stem cells and their therapeutic potential

Cancer stem cells (CSCs) refer to a cell population that are thought to be responsible for tumor

initiation, and exclusively harbor the self-renewal and regeneration attributes of cancer (Dick

2008; Battle and Clevers, 2017; Plaks, Kong and Werb, 2015). This hypothesis of CSCs has

resulted in considerable research efforts to target CSCs for therapy (Podberezin, Wen and Chang,

2013). There are, however, many challenges hindering therapeutic CSC targeting, including the

inherent plasticity of cancer cells. It was reported that cancer cells have the plasticity to shift

from the differentiated to the stem cell phenotype in response to several microenvironmental

stresses, such as chemo- and radio- therapy (Cabrera, Hollingsworth and Hurt, 2015). Thus, it is

important to identify the conditions that control this plasticity in order to propose efficient

therapeutic strategies against CSCs (Doherty et al., 2016). In addition, CSCs isolation and

culture methods almost always depend on the expression of cell surface markers, the ability to

initiate tumors in experimental animals, and/or supplementing cancer cells with mitogens and

developmental factors (Gilbert and Ross, 2009). Subsequently, these experimental approaches

might be marginalizing some CSC populations and selecting for populations with better surface

marker representation, transplantation ability, or cell culture adaptability (Nassar and Blanpain,

2016). Therefore, more research is required to better characterize CSC populations and target

them in different cancer types.

CSCs have been identified in many cancer types (Battle and Clevers, 2017; Plaks, Kong and

Werb, 2015). Similar to developing brain tissues that exhibit cellular hierarchy with a clearly

defined stem cell population (Reya et al., 2001), tumors that arise from the brain, such as lower

grade glioma and GBM, exhibit hierarchal organization with a defined stem cell population that

is thought to be responsible for tumor initiation and harbors the regeneration and self-renewal

ability of the tumor (Ignatova et al., 2002; Galli et al., 2004; Singh et. al, 2004; Tirosh et al.,

2016). In addition to their exclusive self-renewal ability, GBM stem cells have been shown to be

19

the main population responsible for resistance to chemotherapy (Chen et al., 2012) and

radiotherapy (Bao et al., 2006), highlighting their significance for treatment and clinical

outcomes. In the next section, we will briefly introduce the markers and techniques used to

isolate and culture GBM stem cells as a key process in the research for targeted therapy against

them.

1.2.1 Glioma stem cell markers and culture systems

Glioma and GBM stem cells have been shown to express many of the normal neural stem cell

markers including SOX2, NANOG, OLIG2, MYC and Nestin (Hemmati et al., 2003; Ben-Porath

et al., 2008; Suva et al., 2014; Ligon et al., 2007; Kim et al., 2010; Tunici et al., 2004).

Additionally, multiple glioma stem cell surface markers have been identified to reliably select

and isolate these populations with experimental approaches such as flow cytometry. These cell

surface markers include CD133 (Hemmati et al., 2003; Liu et al., 2006), CD15 (Son et al., 2009),

CD44 (Liu et al., 2006), Integrin α6 (Lathia et al., 2010), and L1CAM (Bao et al., 2008). Despite

being the most commonly used glioma stem cell marker, the validity and efficiency of using

CD133 to isolate these cells is now under question after demonstration of the existence of

CD133-negative neural stem cell populations (Sun et al., 2009; Beier et al., 2007; Beier et al.,

2011). Therefore, the isolation of glioma and GBM stem cell populations is a complex procedure

and often lacks uniformity (Lathia et al., 2015).

Due to this complexity and variability of techniques used to isolate glioma stem cells,

researchers introduced alternative surrogates to be used for studying these populations.

Neurosphere culture, which takes advantage of the clonogenicity of CSCs in serum free cultures,

is one of the most commonly used surrogate techniques to enrich for glioma stem cell

populations (Goodell et al., 1996; Kondo et al., 2004). Notably, this culture system requires the

addition of mitogens, such as EGF and FGF, to the medium to inhibit differentiation and to retain

the self-renewal characteristics. This approach might in turn result in a bias towards CSC

populations that express the corresponding mitogen receptors (Pastrana, Silva-Vargas and

Doetsch, 2011). In fact, the neurosphere culture, also known as the “sphere formation assay”, is a

direct readout of sphere forming ability, rather than self-renewal and stemness characteristics

20

(Pastrana, Silva-Vargas and Doetsch, 2011). The gold standard for functional validation of

glioma stem cells remains the in vivo limited dilution assay to confirm the ability of a cell

population to initiate tumors that recapitulate the complexity of the original patient-derived

tumor (Bradshaw et al., 2016; Singh et al., 2004). Despite showing a clear advantage over

traditional serum-containing culture methods (Lee et al., 2006), neurosphere-cultured GBM stem

cells fail to recapitulate the original tumor heterogeneity (Lathia et al., 2011; Venere et al.,

2011).

Due to the limitations of the neurosphere culture, another surrogate monolayer technique was

introduced by Pollard and colleagues to propagate glioma stem cells (Pollard et al., 2009). In this

technique, which has been widely adopted for culturing glioma stem cells, cells are cultured as

monolayers on laminin-coated surfaces, and subjected to fewer than 20 passages. Since this

technique was shown to overcome the problems of the traditional neurosphere assay (i.e. cells

grown in the monolayer system perfectly recapitulate original tumor heterogeneity, genotype and

phenotype, and maintain the self-renewal ability) (Pollard et al., 2009; Woolard and Fine, 2009),

we have used it as the standard culture system to maintain our GBM cells for in vitro and in vivo

experiments.

1.2.2 Glioma stem cell biology

As mentioned in the previous section, CSCs are thought to be the tumor population responsible

for self-renewal, regeneration, therapy resistance and tumor recurrence. Thus, CSCs have gained

a great attention in the field of glioma and GBM research (Li et al., 2009; Veneree et al., 2011).

Great effort was put in research to understand the biological mechanisms underlying the

regulation of glioma stem cells; however, the incredible complexity and phenotypic plasticity of

these populations led to a remarkable difficulty in direct translation of this knowledge into more

targeted therapies (Altaner et al., 2008; Safa et al., 2016; Lan et al., 2017).

The biology of glioma stem cells can be regulated on several levels including genetic and

epigenetic, micro-environmental signals, as well as metabolic and niche factors (Lathia et al.,

2015). Some of the transcription factors that were identified as primary regulators of CSC

21

biology in glioma include MYC (Wang et al., 2008), TP53 and PTEN (Zheng et al., 2008),

STAT3 (Sherry et al., 2009), SOX2 (Gangemi et al., 2009), and NANOG (Zbinden et al., 2010).

There is a wide range of pathways and factors that are critical in regulating glioma stem cells

including NOTCH (Chu et al., 2013; Fan et al., 2006), BMP (Yan et al., 2014), WNT (Rheinbay

et al., 2013; Zheng et al., 2010), PDGFR (Kim et al., 2012), EGFR (Jun, Bronson and Charest,

2014), NF-κB (Bhat et al., 2013) and ASCL1 (Rheinbay et al., 2013; Park et al., 2017) pathways.

In the next sections, the roles of WNT, BMP and ASCL1 will be discussed in further detail since

these pathways are of a main concern for this study.

1.2.3 WNT signaling in glioma stem cells

WNT signaling is considered one of the central pathways controlling stem cell maintenance and

proliferation and involved in a very wide range of biological processes and diseases, including

most cancers (Clevers and Nusse, 2012; Zhan, Rindtroff and Boutros, 2017). Early observations

indicated that WNT activation leads to tumorigenesis in several organs (Nusse and Varmus,

1982; Tsukamoto et al., 1988) and subsequently, a great deal of effort was put in studying this

pathway in the initiation and maintenance of the vast majority of human cancers. The net result

of this work is the identification of clinical applications for WNT pathway modulation and

clinical trials of WNT modulators (Krishnamurthy and Kurzrock, 2018).

WNT signaling is mediated through canonical and noncanonical pathways (Nusse and Clevers,

2017). In this study we will be focusing on the canonical WNT signaling (Figure 2). Briefly,

canonical WNT signaling is turned off in the absence of WNT ligand. Under this inactivated

state, a multi-protein complex composed of CKI, AXIN, GSK, DVL and APC targets -Catenin

for phosphorylation, which targets it for proteasome degradation (Figure 2A). The binding of

WNT ligand to one of the Frizzled (FZD) family receptors leads to recruitment and of the multi-

protein complex, preventing it from phosphorylating β-Catenin. Unphosphorylated β-Catenin is

stable and localizes to the nucleus where it facilitates the bind of transcription factors, including

the T-cell factor/lymphoid enhancer factor (TCF/LEF), to DNA resulting in the expression of

WNT target genes (Clevers 2012; McDonald, Tamai and He, 200; Cadigan and Waterman, 2012;

Niehrs 2012) (Figure 2B).

22

The role of WNT in cancer has been extensively studied, revealing a central role of this pathway

in controlling many cancer cell biological processes and regulating the progression of many

cancer types (Polakis 2012; Zhan, Rindtroff and Boutros, 2017). Also, WNT signaling was

shown to be central in regulating the biology of stem cell populations in many organs, including

the hematopoietic system, intestine, skin, brain and embryonic stem cells (Nusse 2008; Nusse et

al., 2008; Clevers, Loh and Nusse, 2014). Consequently, WNT was one of the first pathways to

be studied in CSCs after their discovery. In this cell population, WNT was shown to exhibit a

critical role in regulating stemness, self-renewal, differentiation, proliferation, migration and

invasion (de Sousa, Melo and Vermeulin, 2016; Rheinbay et al., 2013; Wu et al., 2017).

Together, these observations led to the proposal of targeting WNT as a general strategy to

eliminate CSC populations (Polakis 2012; Zhan, Rindtroff and Boutros, 2017).

WNT is generally activated in GBM through genomic aberrations, including a deletion in FAT1,

a negative effector of WNT signaling, as seen in 20% of GBM patients (Morris et L., 2013; Lee

et al., 2016). WNT activation in GBM can also be achieved through epigenetic silencing of WNT

negative regulators including soluble Frizzled-related proteins (sFRPs); a group of antagonists

that bind WNT ligands to inhibit WNT signaling (Roth et al., 2000). Epigenetic silencing of

sFRPs is reported in almost 40% of GBM tumors (Roth et al., 2000). Several studies have

reported an important role of WNT in controlling the stem cell populations in glioma and GBM.

For instance, a network of WNT target genes was found to be essential in maintaining GBM

stem cells (Rheinbay et al., 2013). Similarly, PLAGL2 was found to be highly amplified in

glioma and functions to regulate glioma stem cells by suppressing WNT signaling (Zheng et al.,

2010). Recently, Zhang and colleagues showed that FOXM1 promotes GBM stem cells by

directly facilitating the translocation of β-Catenin to the nucleus, leading to expression of WNT

target genes that promote stemness and induce chemoresistance (Zhang et al., 2011). Moreover,

several components of WNT signaling pathway, such as FZD4 and DVL2, have been reported to

be overexpressed in GBM (Holland 2001; Jin et al., 2011).

Additionally, WNT has been reported to be critically involved in regulating GBM invasion and

epithelial-mesenchymal transition (EMT); the process by which cancer cells switch from

epithelial to mesenchymal phenotype in order to migrate and metastasize to a new location.

WNT activation in GBM was reported to result in overexpression of EMT master regulators,

23

such as SNAIL, TWIST, SLUG, and N-cadherin (Han et al., 2011; Mikheva et al., 2010; Yang et

al., 2010; Kemler et al., 2004). Similarly, several WNT components such as WNT5A and WNT2

have been reported to be directly involved in GBM cell migration (Kamino et al., 2011; Pu et al.,

2009). Consistent with the previously described role of CSCs in therapeutic resistance in other

cancers, several studies showed that WNT significantly affects GBM resistance to therapy by

regulating its cancer stem cell population. For example, Zhang and colleagues showed that

inhibiting the WNT activator, FOXM1, sensitizes GBM cells to TMZ (Zhang et al., 2012).

Moreover, a recent study reported that radioresistent GBM tumors in vivo are highly enriched in

WNT components (Kim et al., 2012). Activation of WNT receptors, such as FZD2, was also

reported to be involved in developing resistance to TMZ (Auger et al., 2006).

Therefore, the very well-established role of WNT in regulating normal stem cells as well as its

clear involvement in GBM biology and CSCs make it an interesting target for potential novel

therapies to overcome the current traditional treatment challenges.

24

Figure 2. Canonical WNT signaling in mammalian cells.

A) In the rest inactive state, the multi-protein complex of (GSK3, CKI, APC, Axin and DVL) remains free

in the cytoplasm, allowing it to target β-Catenin for phosphorylation. Upon phosphorylation, β-Catenin gets

primed for ubiquitin mediated degradation by the proteasome leading to degradation and failure to activate

target genes.

B) In the active state, WNT ligand binds specific FZD receptors leading to the recruitment of the multi-

protein complex, preventing it from phosphorylating β-Catenin. Active unphosphorylated β-Catenin then

translocates to the nucleus where it binds co-transcription factors such as TCF/LEF, which in turn bind

DNA and express WNT target genes.

Inactive Active A

B

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1.2.4 BMP signaling in glioma stem cells

Bone Morphogenetic Protein (BMP) signaling represents another major pathway that controls

stem cell and disease in many organ systems (Bragdon et al., 2011; Wang et. al, 2014) (Figure

3). As a member of the Transforming Growth Factor-beta (TGF-β) superfamily (Urist 1997),

BMP signaling initiates when a homodimer or heterodimer BMP ligand (such as BMP 2/4) binds

BMPRI and BMPRII receptors on the cell surface to form a holocomplex leading to the

activation of downstream signaling cascade. This activated holocomplex recruits and

phosphorylates the key signaling mediators in the pathway, receptor-regulated-Smad (R-Smad;

SMAD1/5/8). Activated phosphorylated SMAD1/5/8 recruits the co-SMAD, SMAD4, to the

complex, which then translocates to the nucleus and regulates transcription of downstream target

genes (Figure 3). Another layer of regulation is provided by the presence of inhibitory SMADs

(I-SMADs) such as the SMAD5/7 complex, which inhibits the pathway by priming the

SMAD1/5/8 complex for ubiquitin-mediated protein degradation (Rahman et al., 2015; Wang et

al., 2014; Zhang and Li, 2005; Bragdon et al., 2011) (Figure 3).

In GBM, BMP4 expression is downregulated relative to surrounding normal brain tissues (Wu

and Yao, 2013). Interestingly, a recent report suggested that BMP4 expression level is inversely

correlated with GBM grade, where lower grade gliomas express significantly higher levels of

BMP4 (Bao et al., 2013). Similarly, downregulation of BMPRIB (a subunit of BMP receptor

complex) was reported in higher grade glioma patients (Liu et al., 2009). Activation of BMP

signaling was reported to promote CSC differentiation and inhibition of tumor growth in GBM

(Piccirillo et al., 2006; Lee et al., 2008; Liu et al., 2010). In addition, glioma stem cells were

reported to inhibit endogenous BMP signaling by secretion of the BMP antagonist Gremlin 1 to

protect themselves from BMP-induced differentiation (Yan et al., 2014; Guan et al., 2017).

Notably, these reports show that BMP signaling might induce astrocyte –and not neuronal-

differentiation. This is particularly important for therapeutic applications because astrocytes can

undergo cell cycle re-entry and regeneration of the stem cell population, whereas neuronal

differentiation, which is associated with terminal cell cycle exit, prevents tumor reformation

(Magnusson et al., 2014; Alcantara et al., 2009; Friedmann et al., 2012).

26

On the other hand, expression of BMP4 was shown to reverse the multi-drug resistant (MDR)

phenotype in GBM and result in a significant sensitization to TMZ treatment (Liu et al., 2013).

Similarly, BMP4 was shown to sensitize GBM cells to bevacizumab, a monoclonal antibody

against VEGF-A, leading to reduced tumor size and invasion (Rahman et al., 2013).

Subsequently, several trials have been introduced to use BMP in GBM therapy. For instance,

oncolytic virus overexpressing BMP4 was shown to induce GBM stem cell differentiation and

lead to significantly prolonged survival in mice (Duggal et al., 2013). Similar results were

obtained by systemic delivery of human fat-derived mesenchymal stem cells engineered to

overexpress BMP4 (Li et al., 2014; Mangraviti et. al, 2016). Collectively, BMP signaling seems

to play an important role in GBM stem cell function, and further studies are required to fully

characterize its role, biological functions, and therapeutic potential.

27

Figure 3. BMP signaling in mammalian cells.

BMP ligand proteins can form a wide variety of homo- or heterodimer molecules, which bind BMPRI and BMPRII,

forming a holocomplex structure. This complex recruit and phosphorylates SMAD1/5, which in turn recruits

SMAD4, forming the key complex in this pathway. BMP can be regulated at multiple levels, including competitive

inhibition by BMP ligand antagonists such as Noggin and Chordin, as well as the presence of inhibitory SMADs

such as SMAD6/7, which functions by degrading the active SMAD1/5/4 complex.

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1.2.5 ASCL1

Achaete-Scute Family BHLH Transcription Factor 1 (ASCL1; also called Mash1) is an

evolutionary conserved basic helix-loop-helix (bHLH) transcription factor that plays a critical

role in neural differentiation of neural progenitor and stem cells (Bertrand et al., 2002; Kim et al.,

2011; Wilkinson, Dennis and Schuurmans 2013). ASCL1 expression is tightly controlled in

neural stem cells (NSC) through the activity of NOTCH signaling, which maintains NSCs by

inducing the expression of HES1 protein that in turn inhibits ASCL1 expression, and subsequent

neural differentiation (Hirata et al., 2002; Masamizu et al., 2006; Kobayashi et al., 2006). HES1

is controlled by a feedback inhibition, whereby the HES1 protein represses its own transcription,

resulting in a continuous autonomous oscillation of its expression, which in turn leads to

oscillating expression of its proneural gene targets, such as ASCL1 (Shimojo et al., 2006;

Imayoshi et al., 2013) (Figure 4). This oscillatory regulation mechanism of proneural gene

expression is critical to generate precise responses to environmental differentiation cues, as well

as stresses and signals surrounding progenitor cells, in addition to maintaining the organism

homeostasis and lineage commitment (Mengel et al., 2010; Pina et al., 2012; Sequerra et. al,

2013). Figure 4 illustrates a graphical representation of this alternative oscillatory regulation

mechanism.

ASCL1 is a unique proneural gene in that it not only promotes neuronal differentiation, which is

the canonical function of members of this gene family, but also promotes proliferation in certain

cellular contexts (Wilkinson, Dennis and Schuurmans 2013). In cycling neural stem cells

(NSCs), ASCL1 functions by directly regulating a large number of genes that control cell cycle

progression at different phases of the cycle during neural development, including E2F, EP400

and CDCA7 (Castro et al., 2011; Li et al., 2014). On the other hand, in differentiating neural

progenitors it interacts with the NOTCH pathway and functions as a pioneer factor that binds to

chromatin, recruits additional transcription factors and promotes the transcription of

differentiation genes (Raposo et a., 2015). ASCL1 is believed to control multiple stages of

neurogenesis, including neuronal differentiation, migration, axon guidance and synapse

formation (Vaconcelos and Castro, 2014; Castro et al., 2011). Generally, ASCL1 functions in

differentiation through pushing progenitor cells to exit cell cycle and adopt a full neuronal

specification and differentiation (Berninger et al., 2007; Chanda et al., 2014; Pang et al., 2011).

29

Recently, a published study uncovered a novel critical role of ASCL1 in GBM stem cell

differentiation (Park et al., 2017). In this study, Park and colleagues discovered that ASCL1

expression categorizes GBM tumors into two subgroups; ASCL1 high (ASCL1hi) and ASCL1

low (ASCL1lo) tumors. Through a set of in vitro and in vivo experiments on GBM stem cells, the

authors provide evidence that ASCL1 is required for neuronal differentiation in GBM, and that

NOTCH inhibition therapy is effective only in the ASCL1hi subgroup, which was described as

“differentiation-competent GBM”. In addition, the study shows that ASCL1 binds to closed

chromatin in promoter and enhancer regions of the neuronal target genes to activate the terminal

neuronal differentiation program. Subsequently, the authors showed evidence that ASCL1

expression significantly sensitizes GBM stem cells to terminal differentiation and inhibits tumor

progression and prolonged survival in xenografted mice. These results provide an example of the

value of understanding the biology of GBM stem cells and exploiting GBM molecular

mechanisms in combination therapeutic approaches. Collectively, these results suggest that the

well-established role of ASCL1 as proneural factor controlling neural stem cell differentiation,

extends to GBM stem cells as well.

30

Ex

pre

ssio

n l

evel

Figure 4. The alternative oscillatory regulation mechanism controlling the expression of

proneural factors such as ASCL1.

The NOTCH target HES1 exhibits a feedback loop regulation, which leads to inhibiting its own expression and

resulting in oscillations in the expression level over the periods of 2-3 hours as illustrated here. HES1 promotes

neural stem cells by inhibiting the expression proneural differentiation factors including ASCL1. Subsequently,

ASCL1 exhibits similar but alternative oscillatory expression levels to HES1.

HES1

ASCL1 2-3 hours period

31

1.3 Norrie Disease Pseudoglioma (NDP) in the endothelium and cancer cells

Norrin, encoded by the X-linked Norrie Disease Pseudogioma (NDP) gene, is a secreted cysteine

knot protein that forms homo-dimers and binds the Frizzled 4 (FZD4) receptor, which in the

presence of co-receptors LRP5 and TSPAN12, activates the downstream canonical WNT

signaling pathway in endothelial cells (Xu et al., 2004; Junge et al., 2009; Ye et al., 2009; Ke et

al., 2013; Lai et al., 2017; Ye, Wang and Nathans, 2010) (Figure 5). The Norrin/FZD4/WNT

signaling axis modulates retina angiogenesis, and disruption of this signaling pathway in humans

leads to defects in retina vasculature, sensory-neural deficits and cognitive disorders (Xu et al.,

2004; Ye et al., 2009; Ye, Wang and Nathans, 2010). While FZD4 and LRP5 are required for the

signaling, TSPAN12, while not essential, confers NDP-dependent signal amplification (Junge et

al., 2009; Lai et al., 2017). Interestingly, a recent study reported a surprising mechanism of

Norrin/FZD4 signaling in which Norrin binds FZD4 receptor, then the ligand/receptor complex

undergoes endocytosis and endo-lysosomal trafficking to control retinal angiogenesis and barrier

function (Zhang et al., 2017), indicating the existence of other unconventional mechanism that

mediate NDP/FZD4 signaling. Figure 5 illustrates the initial stages of Norrin/FZD4 signaling.

In addition to FZD4, several studies present evidence in support of FZD4 and endothelial cell-

independent NDP signaling (McNeill et al., 2013; Deng et al., 2013; Xu et al., 2012). For

example, Norrin was shown to bind LGR4 as an alternative mechanism to activate canonical

WNT signaling (Deng et al., 2013). Moreover, Norrin can competitively inhibit BMP signaling

by binding BMP4, preventing it from binding to BMP2 and activating the BMPRI/BMPRII

receptor complex (Xu et al., 2012; Deng et al., 2013) (Figure 5).

The Norrin/FZD4 signaling axis was also shown to function beyond the retina vasculature to

play a role in establishment of blood-brain barrier (BBB) in the cerebellum, as well as regulating

the endothelium in the inner ear (Xu et al., 2004; Chen et al., 2015; Zhou and Nathans, 2014;

Zhang et al., 2017; Cho, Smallwood and Nathans, 2017; Wang et al., 2012). Additional roles of

Norrin/FZD4 signaling in protecting retinal ganglion cells and preventing degradation of optic

nerve were also uncovered (Dailey et al., 2017; Leopold et al., 2017). Interestingly, Norrin was

32

recently shown to regulate stem cell- derived cardiac progenitors, suggesting a potential role of

Norrin in stem cell biology (Yoon et al., 2018).

Previously, our group showed that NDP is a downstream target of Sonic Hedgehog (Shh) and

functions to promote neural progenitor proliferation in the developing retina, a function that is

independent of FZD4 and angiogenesis, and seems to be cell autonomous (McNeill et al., 2012;

McNeill et al., 2013). Subsequently, our group also uncovered a novel role of Norrin signaling in

inhibiting the initiation of the Sonic hedgehog subtype of Medulloblastoma (Shh-MB) (Bassett et

al., 2016). In this study, our group showed that NDP is upregulated in human Shh-MB and in

tumors from the MB- prone Ptch+/- mouse, a widely used model for Shh-MB. This study

provided evidence that germline NDP inactivation or endothelial restricted FZD4 knockout

accelerates tumorigenesis in Ptch+/- mice, identifying a protective role for stromal Norrin/FZD4

signaling in tumor initiation in this model. Together, the biological significance of Norrin is

emerging as an atypical WNT ligand that can also inhibit BMP signaling to control several

biological processes. In addition, preliminary studies suggest NDP might be involved in

regulating the progression of some cancer types.

33

Figure 5. Norrin signaling in human cells.

Norrin is an atypical WNT ligand that specifically binds FZD4 leading to the activation of cascade canonical WNT

signaling in many cells. NDP/FZD4 signaling requires the presence of LRP5 co-receptor. TSPAN12 promotes the

strength and specificity of the signal but is not essential for the process. In addition, recent studies have shown that

NDP can bind BMP4, inhibiting the formation of BMP2/4 and suppressing BMP signaling.

34

1.4 Rationale and objective

Despite the interesting recent discoveries about Norrin as an atypical WNT ligand, there has

been very little research aimed at investigating the role of Norrin in cancer. In fact, there are

currently only two studies that investigated Norrin function in cancer: a) Our study in Shh-MB as

highlighted in the previous section (Bassett et al., 2016), and b) An in vitro study in colon cancer

cell lines (Planutis et al., 2007; Planutis, Planutiene and Holcombe 2014). This is likely because

there was a historical association of Norrin with eye disease and endothelial cell functions until

the recent uncovering of the molecular mechanisms mediating its functions.

GBM is extremely aggressive cancer, with very high degrees of heterogeneity and complexity

(Frattini et al., 2013; Vivanco et al., 2012; Sottoriva et al., 2013). Moreover, the current

treatment regimen has a very poor long-term efficiency (Stupp et al., 2009; Thakkar et al., 2014;

Stupp et al., 2005). Therefore, better understanding of the biological and molecular pathways

regulating this disease is in critical demand. Also, there is a significant need for novel therapeutic

targets to overcome the problems of the traditional treatment, such as the very rapid development

of resistance, and the inability of large compounds to pass the blood-brain barrier (Prados et al.,

2015; Hickey et al., 2010). NDP is shown to be expressed in the brain and other parts of the

central nervous system (Bassett et al., 2016; Ye, Wang and Nathans, 2010), and is shown to

activate canonical WNT signaling leading to regulation of cellular processes (Ye et al., 2009; Ke

et al., 2013; Lai et al., 2017).

Since the role of WNT signaling in cancer is very well-established (Clevers and Nusse, 2012;

Zhan, Rindtroff and Boutros, 2017), it is reasonable to propose a role for Norrin, the atypical

WNT ligand, in cancers where it is expressed. Supported by our previous results in MB (Bassett

et al., 2016), the essential role of WNT in regulating GBM stem cells (Rheinbay et al., 2013;

Zheng et al., 2010), as well as preliminary data showing expression of NDP in GBM (discussed

in details in the results section), we sought to investigate the role of Norrin in GBM stem cells.

In this study, we performed a set of in vitro and in vivo experiments, supported by analysis of

primary patient data, to uncover the potential role of Norrin in GBM stem cells. Despite the very

high degree of heterogeneity and complexity, GBM provides an ideal disease model, due to the

availability of large-scale genomic and transcriptomic data resorts, primary tissues, in addition to

35

well-established experimental models and techniques to propagate the cells in vitro. Beside the

extensive amount of research performed in GBM, the properties and experimental models of the

CSC population in this tumor have been very well characterized.

1.5 Hypothesis

Several studies reported a central role of both WNT and BMP signaling in maintaining the

proliferation and progression of CSC populations in several cancer types including GBM (de

Sousa, Melo and Vermeulin, 2016; Rheinbay et al., 2013; Wu et al., 2017; Piccirillo et al., 2006;

Lee et al., 2008; Liu et al., 2010). Norrin, an atypical WNT ligand that is reported to interact with

BMP signaling as well, was shown to be regulating the progression of medulloblastoma in vivo

and colon cancer in vitro (Bassett et al., 2016; Planutis et al., 2007; Planutis, Planutiene and

Holcombe 2014). Since our preliminary bioinformatics analysis shows that Norrin expression is

enriched in the GBM samples (Figure 1), we hypothesize that Norrin plays a role in regulating

the progression of GBM stem cells. To test this hypothesize we planned the next three specific

aims:

1.6 Specific aims

A) To examine the expression patterns of NDP and its receptor FZD4 in different types of

cancers including GBM and investigate if NDP/FZD4 expression correlates with clinical

outcome.

B) To investigate the effect of NDP and/or FZD4 on the growth and progression of GBM in

vitro and in vivo.

C) To identify the cell biological and molecular mechanisms that mediate NDP/FZD

function in GBM.

36

Chapter 2. Materials and Methods

2.1 Computational and in silico analysis

The Cancer Genome Atlas Data (TCGA) data we used in our analysis is publicly available from

the Genomic Data Commons (GDC) data portal (https://gdc.nci.nih.gov/). The gene expression

datasets were measured using the Illumina Hiseq _RNASeqV2 and log2 transformed by the

UCSC Cancer Browser team. NDP gene expression (Boxplot) across cancers was queried using

cBioPortal – a public online database.

Correlation between gene expression and survival was produced by Kaplan-Meier method using

Partek Genomics Suite software (Partek, St. Louis USA) and a log-rank test was performed to

calculate p values (0.05 was considered as the threshold for significance).

For the Gene-set enrichment analysis (GSEA), we compared NDP low vs. high expression (in

IDH wild type GBM) after data was normalized and differential expression was used to perform

pathway analysis. Statistical significance was determined based on the following criteria: FDR <

0.05, Fold change >1.7 or < -1.7.

Computational and in silico analysis was done in collaboration with Dr. Yasin Mamatjan; Aldape

Lab, Princess Margaret Cancer Research Center, Toronto, Canada.

2.2 Primary tumor cell lines and culture

All GBM stem cell (GNS) and human fetal neural stem cell (hNSC) lines used in this study were

obtained under MTA from the laboratory of our collaborator, Dr. Peter Dirks, at Sickkids

Hospital, Toronto, Canada. These cell lines were derived from primary tumors, as previously

described (Pollard et al., 2009), in accordance with the Research Ethics Board at The Hospital

for Sick Children (Toronto, Canada).

37

Primary-derived cell lines were cultured in neural expansion conditions to promote and maintain

the stem cell phenotype (Pollard et al., 2009). Briefly, tissue culture plates and dishes (BD

Falcon) were coated with Poly L-Ornithin (PLO) (Sigma Aldrich) for 20 minutes, followed by

Laminin (Sigma Aldrich) diluted by a factor of 1:200 in PBS for at least 24 hours before being

used for cell culture. Cells were cultured in Neurocult media (StemCell Technologies),

supplemented with BSA solution (Life Technologies) and 5 ml of 200mM L-Glutamine (Wisent)

per 500 ml bottle of media. This base was then supplied with in-house equivalent to N2 hormone

mix, 10 ng/ml recombinant human Epidermal Growth Factor (EGF) (Sigma Aldrich), 10 ng/ml

recombinant human Basic Fibroblast Growth Factor (bFGF) (StemCell Technologies), 2 μg/ml

Heparin (Sigma Aldrich) and 1X B27 Supplement (Life technologies). Cells were passaged for a

maximum of 20 passages, because it was shown that after 20 passages they start to significantly

lose the stem cell phenotype (Pollard et al., 2009). To dissociate and split cells, we used brief

Accutase (Sigma Aldrich) treatment (5 mins, 37 °C).

In this study we used four GNS (G523, G411, G472, G564) and two hNSC lines (hNSC-1,

hNSC-3). G523, G472 belong to the ASCL1hi subgroup, while G411 and G564 are from the

ASCL1lo subgroup.

Additionally, these cell lines were periodically tested for the expression of a panel of stem cell

and differentiation markers, such as SOX2, O4, TU-J, MAP2, GFAP and Nestin, using

immunocytochemistry to ensure the maintenance of the stem cell phenotype.

For lentiviral production, dual-luciferase reporter system assay, and proof of concept

experiments we used a Human Embryonic Kidney cell line (HEK-293T) obtained from (ATCC).

2.3 Recombinant DNA, plasmids and cloning

To knockdown NDP and FZD4 we used short hairpin RNA (shRNA) oligonucleotides (Table 2).

All shRNAs were cloned into pGFP-C-shLenti plasmid by its provider (Origene). qRT-PCR was

used to confirm knockdown of specific genes, and Western blotting was used to confirm

overexpression.

38

Table 2. Sequences of shRNA constructs:

shRNA Sequence Designed by

ShNDP-A GCACCACTATGTGGATTCTAT The RNAi

Consortium (TRC)

shNDP-C GTCACCCATTGTACAAGTGTA The RNAi

Consortium (TRC)

shFZD4-2 CTCAAGTGTGGCTATGATGCTGGCTTATA Origene

shFZD4-4 CATCACTTCAGGCATGTGGATTTGGTCTG Origene

To rescue the effects of short hairpin knockdown of NDP, a degenerate codon NDP transgene

NDP (Mod-NDP) was cloned into a pLV-mCherry plasmid (Addgene) by GeneArt services

(Thermo Fisher Invitrogen). Briefly, we designed a version of NDP coding sequence consisting

of degenerate codon sequences (codons that are not normally used in human cells), which will be

translated to the same amino acid sequence of normal endogenous NDP gene. This modified

version of NDP (Mod-NDP) was cloned into a lentiviral vector that expresses mCherry as a

fluorescence marker. Next, we co-transduced G523 cells with Mod-NDP and either

shScrambled, shNDP-A, or shNDP-C (with GFP marker). This empty mCherry plasmid

backbone was also used to express mCherry as a reporter in the wildtype cells in the cell

competition assay.

The transduction and expression of normal wildtype Norrin from Mod-NDP was confirmed by

western blotting, and its activity was confirmed by the Dual luciferase reporter system assay.

Cells were examined for the presence of both fluorescence markers (GFP for shRNA, and

mCherry for Mod-NDP).

39

Sequence edits of Mod-NDP:

Degenerate codon version (Mod-NDP):

a tgagaaaaca tgtactagct gcatcctttt ctatgctctc cctgctggtg ataatgggag atacagacag

taaaacggac agctcattca taatggactc ggaccctcga cgctgcatga gacatcatta cgtagacagc

atttcacatc cgctatataa atgctcatca aagatggtgc tcctggccag gtgcgagggg cactgcagcc

aggcgtcacg ctccgagcct ttggtgtcgt tcagcactgt cctcaagcaa cccttccgtt cctcctgtca

ctgctgccgg ccccagactt ccaagctgaa ggcactgcgg ctgcgatgct cagggggcat gcgactcact

gccacctacc ggtacatcct ctcctgtcac tgcgaggaat gcaattcctg

Wildtype human NDP sequence:

a tgagaaaaca tgtactagct gcatcctttt ctatgctctc cctgctggtg ataatgggag atacagacag

taaaacggac agctcattca taatggactc ggaccctcga cgctgcatga ggcaccacta tgtggattct

atcagtcacc cattgtacaa gtgtagctca aagatggtgc tcctggccag gtgcgagggg cactgcagcc

aggcgtcacg ctccgagcct ttggtgtcgt tcagcactgt cctcaagcaa cccttccgtt cctcctgtca

ctgctgccgg ccccagactt ccaagctgaa ggcactgcgg ctgcgatgct cagggggcat gcgactcact

gccacctacc ggtacatcct ctcctgtcac tgcgaggaat gcaattcctg

For ectopic NDP and FZD4 overexpression experiments, we used TrueORF cDNA clones from

Origene. pLenti-C-mGFP was used for NDP while pLenti-C-MYC-DDK was used for FZD4.

2.4 Preparation of lentiviral particles and infection

We used a 3rd generation lentiviral transduction system to stably knockdown or overexpress

genes of interest. To produce virus particles, HEK293T cells were cultured in 15 cm dishes (BD

Falcon) and allowed to adhere for 24 hours. The following day, cells were co-transfected with

the lentiviral expression vector in combination with plasmids expressing virus coat and assembly

proteins (REV, RRE, and VSVG) using Lipofectamin 3000 reagent (Lifetechnologies).

40

Conditional media containing virus particles were collected 24 and 48 hours after transfection.

Virus conditioned medium was passed through 0.45 μm low protein binding membranes

(Sarstedt) to capture any contaminating HEK293T cells or debri. Virus was concentrated by

ultracentrifugation of filtered conditioned medium at 22000 g for 2 hours. Virus pellets were

then reconstituted in PBS. To assess virus titer, we infected a series of HEK293T culture vessels

with 1 μL of a dilution series of the reconstituted virus pellet (1X, 1/10X, 1/102X, 1/103X etc.).

48 hours after the infection cells were analyzed using fluorescence microscopy and infectious

particles titer was assess by manual counting of cells expressing the fluorescent protein reporter.

For lentiviral plasmids that do not express fluorescence proteins, we used qRT-PCR using Lenti-

X qRT-PCR kit (Clontech, Takara). Virus solution was then aliquoted into the desired

volume/concentration and stored at -80 °C.

2.5 In vitro cell proliferation assay

For cell proliferation experiments, cells were counted and seeded in coated 24 well plates at a

density of 20,000 cells/well for slower grower lines, and 10,000 cells/well for faster growing

lines. Cells were quantified at two time points (3 and 6 days), or after one time point for more

complex experiments (6 days) during the logarithmic growth phase. To quantify absolute cell

numbers, cells were incubated with Accutase for 5 minutes at 37 °C and observed for full

dissociation using a light microscope. Then, live cells were counted using Hemocytometer slide

after addition of Trypan Blue exclusion dye (Sigma Aldrich). Cell numbers were normalized to

the seeding density of the first day in culture to assess percentage of cell proliferation. At least 3-

4 technical replicate wells were seeded for each sample, and results were statistically analyzed

using standard methods to confirm significance.

2.6 In vitro extreme limiting dilution assay (ELDA)

To assess sphere formation ability, we used the in vitro extreme limiting dilution assay (ELDA),

as previously described (Hu and Smyth 2009). Briefly, cells were carefully dissociated using

Accutase and thorough pipetting to ensure the formation of single cell suspension. After

41

counting, cells were seeded starting from the second lane of an uncoated, suspension culture 96-

well plate (Sarstedt) at a density of 4000 cells/well, with a minimum of 3 or 4 well replicates per

sample. Peripheral rows and columns of the plate were filled with PBS and not included in the

experiment because they harbor different humidity and proliferation kinetics. Cells in the first

column (4000 cells/well) were then serially diluted from one column to the next till cell density

reached 4 cells/well in the last column. Plates were incubated for one and two weeks at 37 °C,

and sphere forming wells were scored at these time points, as per the protocol instructions (Hu

and Smyth, 2009). Scoring results are then analyzed using ELDA software

(http://bioinf.wehi.edu.au/software/elda/) from Walter+Eliza Hall Institute for Medical Research

(WEHI) according to the developer instructions.

Secondary sphere assays were performed as a proof of concept to confirm our results. After

scoring primary sphere plates, spheres of the same samples were collected and dissociated by

incubation with Accutase for 10 minutes at 37 °C and rigorous pipetting. Single cell suspensions

from primary spheres were seeded into new 96 well plates. Secondary sphere plates were then

incubated for two more weeks then scored and analyzed as described above.

2.7 Cell and tissue immunostaining and microscopy

Cells were seeded at least 24 hours prior to the staining procedure on 8-well chamber slides (BD

Falcon) coated with PLO and Laminin, as described above. Once the cells reach appropriate

confluency and/or intended time point, the culture media was removed and cells were washed

briefly with PBS. For fixation, cells were incubated with electron microscopy grade 4%

Paraformaldehyde (PFA) (Bio-Rad) for 10 minutes at room temperature. Subsequently, the cells

were permeabilized in 0.1% Triton-X in PBS (PBST) for 5 minutes at room temperature

followed by incubation with 5% BSA in PBST (blocking solution) at room temperature to block

nonspecific protein binding. Cells were incubated overnight at 4 °C with primary antibodies

diluted in blocking solution. After washing 3 times with PBS (5 minutes each), cells were

incubated for 1 hour at room temperature with secondary antibodies and DAPI (nuclear stain)

diluted in blocking solution, followed by 3 washes with PBS (5 minutes each), mounted in

mounting medium (DAKO) and stored at 4 °C. Cells were imaged at 20X magnification on a

42

Zeiss M2 epifluoresence microscope with apotome unless otherwise is stated in particular

experiments. Zeiss software was then used to analyze, and edit the images, which were then

quantified manually.

To determine the percentage of cycling cells, we quantified EdU+ (5-ethynyl-2'-deoxyuridine)

population using the Click-iT kit (LifeTechnologies), according to manufacturer’s instructions.

Cells were pulsed with 10 μM EdU for 3 hours prior to harvesting. After the incubation, cells

were fixed, permeabilized and EdU+ cells were fluorescence-labeled and detected according to

the protocol of the kit. In some experiments, cells were also stained for the detection of other

markers/antigens such as Ki67 using a different fluorophore.

For immunostaining in tissue sections, animals were perfused transcardially. Briefly, animals

were injected with 10 ml PBS in the heart to clear the circulatory system, followed by 10 ml 4%

PFA injection. Then, the brain was dissected and in 4% PFA overnight at 4°C. Tissues were then

washed in PBS, and cryoprotected in 30% sucrose/PBS overnight at 4°C. After cryoprotection,

tissues were equilibrated in a 50:50 mixture of Optimal cutting temperature compound (OCT

compound):30% sucrose and embedded into plastic molds and snap-frozen in liquid nitrogen.

Brain sections taken at 16 µm in the coronal plane using a Leica CM1850 cyrostat were air dried

(1-2 hr) on Superfrost Plus positively charged slides (Fisher Scientific) and stored with desiccant

at −20°C. Slides were washed in PBS and permeabilized in 0.1% Triton X-100 in PBS. Slides

were blocked with 10% donkey serum (Sigma Aldrich) in PBS for 30 min at room temperature.

Primary antibodies were diluted in 10% donkey serum and incubated with the slides overnight at

4°C. Next day, sections were incubated with secondary antibodies (Molecular Probes) of the

desired fluorescence at 1:1000 for 1 hour at room temperature, and nuclei were stained with

Hoechst and a coverslip was attached with fluorescence mounting medium (Dako S3023).

Fluorescent images of the tumors were captured using an LSM 780 confocal microscope (Zeiss)

at 20X magnification.

43

2.8 Cell competition assay

To address the question of whether NDP functions in a cell autonomous manner, we used a cell

competition assay. Cells were divided into two groups; one was infected with a lentivirus

expressing mCherry only, while the other was infected with a lentivirus expressing GFP in

addition to shRNA oligonucleotide. After both groups expressed the fluorescence dyes,

mCherry+ and GFP+ cells were mixed in 1:1 ratio and part of the resulting cell mixture was

directly analyzed by flow cytometry to determine the seeding density and ratio. Next, mixed cells

were cultured on pre-coated 24 well plates (BD Falcon) and left to grow for 6 days at 37 °C.

After 6 days, cells were dissociated using Accutase and analyzed by flow cytometry to detect the

ratio of mCherry+: GFP+ cells. Additionally, cell mixtures were cultured on pre-coated 8-well

chamber slides (BD Flacon) and analyzed under the fluorescence microscope for visual analysis

and production of representative images. The assay was repeated in two independent biological

replicates.

2.9 Cell lysis and Western Blotting

For preparation of cell lysates, cells were incubated with RIPA buffer (Cell Signaling

Technology) supplemented with protease inhibitor complex (Roche) and phosphatase inhibitor

complex (Cell Signaling Technology) for 5 minutes, followed by sonication and then centrifuged

(13000 RPM, 15 minutes) to remove nucleic acids and cell debris. Bradford assay was used

according to manufacturer instructions to assess protein concentration. Protein concentrations

were measured using a benchtop spectrophotometer (Eppendorf). After addition of Laemmli

loading buffer (Sigma Aldrich), lysates are incubated at 95 °C to ensure complete protein

denaturation. Western blotting was performed according to standard protocols (wet transfer,

PVDF membranes), and images were developed using Odyssey fluorescence scanner system.

BLUeye prestained protein ladder (GeneDireX) was used as a marker to identify the molecular

weights of target proteins. Housekeeping controls such as GAPDH was used as a loading control.

44

2.10 Small molecules and recombinant proteins

Table 3. Sources and concentrations of used reagents:

Reagent Source Working

concentration

Description

Anti-FZD4

blocking antibody

Lexicon

pharmaceuticals

10 nM Monoclonal

blocking antibody

Anti-KLH

blocking antibody

Lexicon

pharmaceuticals

10 nM Isotype matched

control for Anti-

FZD4

IWP-2 Sigma-Aldrich 2 nM Porcupine

inhibitor

XAV939 Sigma-Aldrich 5 nM Tankyrase

inhibitor

rhNorrin R&D systems 100 ng/ml Recombinant

human Norrin

rhWNT3a R&D systems 100 ng/ml Recombinant

human WNT3a

rhBMP4 R&D systems 30 ng/ml Recombinant

human BMP4

DMH1 Sigma-Aldrich 100 nM BMP signaling

inhibitor

45

2.11 Antibodies

Table 4. Sources, uses and dilutions of used antibodies:

Antibody Source Catalog

number

Technique Dilution

Anti-GAPDH EMD Millipore CB1001 Western

blotting

1:5000

Anti-NDP R&D AF3014 Western

blotting

1:500

Anti-MYC Abcam ab32 Western

Blotting, IHC

1:1000

Anti-Ki67 BD Biosciences 550609 ICC 1:100

Anti-Sox2 Abcam ab97959 ICC 1:1000

Anti-SMAD1 Cell Signaling Technology 9743S WB 1:1000

Anti-GFAP Dako Z0334 ICC 1:1000

Anti-MAP2 Abcam ab5392 ICC 1:2000

Anti-O4 Abcam ab53041 ICC 1:1000

Anti-

phospho-

SMAD1/5

Cell Signaling Technology 9516S WB 1:1000

Anti-Nestin Abcam ab22035 ICC 1:500

Anti-TUB33 EMD-Millipore MAB1637 ICC 1:200

Anti-HuAg EMD Millipore MAB1281 IHC 1:1000

46

2.12 Dual-Luciferase reporter assay system

To assess WNT activity, we used the Dual-Luciferase reporter assay system (Promega) to detect

the signal produced by Top-flash (with a β-Catenin activated promoter) reporter plasmid.

Briefly, HEK293T cells were transiently transfected with a plasmid mixture containing NDP,

FZD4, LRP5, TSPAN12, TOP-FLASH, and Renilla as a transfection control. We used

recombinant human Norrin (R&D Systems), recombinant human WNT3a (R&D systems),

WNT3a overexpression, NDP overexpression, or small molecule GSK3 inhibitors (CHIR or Bio)

as positive controls to activate canonical WNT signaling in different experiments. Cells were

incubated for 24 or 48 hours and cell lysates were prepared by passive lysis according to the

manufacturer’s instruction. Subsequently, luminescence signals were measured and normalized

to the renilla internal control using a bench top luminometer. Each sample was measured in at

least 3-4 biological replicates and results were analyzed using standard statistical methods to

confirm significance.

2.13 Flow cytometry analysis

Flow cytometry analysis was used to quantify the percentage of mCherry+: GFP+ for the

competition assay. Cells were lifted using Accutase, washed with PBS then fixed using 4% PFA.

Wild type and single fluorescent marker cells were used as controls for each experiment. The

flow cytometry run, analysis and quantification were performed at The SickKids-UHN Flow and

Mass Cytometry Facility with the assistance of Ms. Emily Reddy.

2.14 RNA extraction and qRT-PCR

We used quantitative real time PCR (qRT-PCR) to quantify gene expression. RNA was extracted

using RNeasy mini-prep (Qiagen), according to manufacturer’s instructions. The concentration

and purity of RNA were assessed using a bench top Nanodrop. First strand complementary

cDNA was reverse transcribed using QuantiTect (Qiagen) kit, according to manufacturer’s

47

instructions. All samples included a no-reverse transcriptase (NRT) negative control to ensure

total elimination of genomic DNA. The resulting cDNA was stored in -20 °C. For the qRT-PCR

experiments, we used the iQ SYBR Green Supermix (Bio-Rad), as per manufacturer’s

instructions. Results were statistically analyzed according to the standard protocols to generate

double-delta CT values and comparative fold changes in gene expression relative to the controls.

All qRT-PCR products were confirmed by gel electrophoresis, sequencing, as well as the

existence of only one melting curve peak/gene product.

2.15 PCR primers

qRT-PCR primers were generated using NCBI primer blast tool with the standard parameters,

and specifically designed to span an exon-exon junction in order to avoid amplification of

genomic gDNA. Primers were synthesized by ACGT company, Toronto, Canada.

Table 5. Sequences of qRT-PCR primers used in this study:

NDP-Forward TGCGTTCCCCTAAGCTGTG

NDP-Reverse ACCAGCAGGGAGAGCATAGA

FZD4-Forwards CTGACTGTAGGCCGGGAAAG

FZD4-Reverse TGACCCCATTTGAGTCCTGC

TSPAN12-Forward CTGCAGAAACGAGGGTAGAGG

TSPAN12-Reverse ACGCCACAAGCCAGTTCTAC

LRP5-Forward GTCGTCGGTGACAGAGTTACA

48

LRP5-Reverse AGCAAGCATCACGTCCTCTG

B-Actin-Forward GAGCACAGAGCCTCGCC

B-Actin-Reverse TCATCATCCATGGTGAGCTGG

GAPDH-Forward ATGTTGCAACCGGGAAGGAA

GAPDH-Reverse AGGAAAAGCATCACCCGGAG

hPRT2-Forward CCTGGCGTCGTGATTAGTGA

hPRT2-Reverse CGAGCAAGACGTTCAGTCCT

2.16 RNA-Seq library

Cells were infected with ShScrambled, shNDP-A, or shNDP-C and after 48h GFP reporter

expression was confirmed and RNA was extracted using RNeasy mini kit (Qiagen). A proportion

of each sample was used to synthesize cDNA and confirm NDP knockdown efficiency using

qRT-PCR as explained above. Subsequently, samples were submitted to Genome Quebec center,

where RNA quality was confirmed using Bioanalyzer (Agilent), then RNA-Seq libraries were

run. The screen consisted of two cell lines; G523 and G411. Each cell line had 9 samples; 3

biological replicates of shScrambled controls, 3 biological replicates of shNDP-A, and 3

biological replicates of shNDP-C transduced cells. Sample extracts were enriched for stranded

poly(A)-mRNA and sequenced on Illumina HiSeq 4000 PE100. After library run, paired-ends

sequencing reads were clipped for adapter sequence, trimmed for minimum quality (Q30) in 3'

and filtered for minimum length of 32 bp using Trimmomatic [PMID: 24695404]. Surviving read

49

pairs were aligned to the Ensembl 87 release GRCh38 Homo sapiens assembly using the STAR

[PMID: 23104886] two-passes method.

A gene-level count-based gene quantification against Ensembl annotations was performed using

HT-seq count [PMID: 25260700] in the intersection-nonempty mode. Exploratory analysis was

conducted using various functions and packages from R and the Bioconductor project [PMID:

25633503]. Differential expression was conducted using both edgeR [PMID: 19910308] and

DEseq [PMID: 20979621]. Terms from the Gene Ontology were tested for enrichment with the

GOseq [PMID: 20132535] R package. Transcript-level assembly, quantification and differential

expression analysis was performed using Cufflinks [PMID: 20436464] and Cuffdiff [PMID:

23222703].

We would like to thank Jose Hector Galvez at the Canadian Centre for Computational Genomics

(C3G) for his help with the RNA-seq analysis. The C3G is a Node of the Canadian Genomic

Innovation Network and is supported by the Canadian Government through Genome Canada.

2.17 Animals

For in vivo transplantation experiments we used 5 to 8-week-old NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ

(NSG) female mice. All mice were purchased from Animal Research Center (ARC), UHN,

Toronto, Canada. Experimental groups consisted of at least 5 mice/ group. Randomization was

not performed. Mice were housed in our facility located at the Krembil Discovery tower and all

experimental protocols were approved by the ethics and biosafety board of the Animal Research

Center (ARC), UHN, Toronto, Canada.

2.18 Orthotopic xenografting

Lentiviral infected GNS cells were dissociated using Accutase, then reconstituted to a

concentration of 50000 cells/μl in PBS. Mice were anaesthetized with Ketamine/Medetomidine

and immobilized using a stereotaxic head frame. After shaving the head, an incision was made at

50

the midline, then a bore-hole was drilled in the skull 1 mm lateral and 2 mm posterior to Bregma.

Using a Hamilton syringe with a 27G round bottom needle, cells were uniformly injected with an

automated nano-injector over the period of 3 minutes. After injection, the needle was left in

place for 5 minutes to avoid cell reflux, then removed slowly and uniformly. Finally, the skull

was covered with bone wax, the incision was closed with sutures (size 5.0) followed by reversal

of the anesthetic. Tramadol (Sigma Aldrich) was used for analgesia according to the ethics board

protocols and recommendations for major surgeries. After surgery, animals were observed on a

daily basis until they developed symptomatic tumors. Upon tumor formation, mice were

sacrificed, perfused with PBS and 4% PFA, and brains were collected and fixed according to

protocols for immunohistochemical analysis, as described above. Mice that developed

complications due to surgery were removed from the study.

2.19 Quantification and statistical analysis

All experiments in this study were repeated at least three independent times (3 biological

replicates), each of them included at least three technical replicates to confirm significance of

observations. All groups in each independent experiment were matched in regard to number of

biological and technical replicates. In addition, cells were matched in regard to passage number

and culture conditions, as well as chemicals and reagents stocks.

Quantification of immunohistochemical markers staining was performed manually with the

assistance of Zeiss software by visually detecting and marking positive cells, then manually

counting them. Quantification of qRT-PCR experiments was performed using the standard

double-delta CT (ΔΔCT) method, comparing the expression levels of experimental samples to

internal controls of housekeeping genes then experimental control of untreated or unmodified

cells. Unpaired two tailed Student’s t-test was used to assess significance unless otherwise is

stated in specific experiments. Standard deviation and standard error of the mean values were

calculated for each experimental group, and standard error of the mean values were used in the

graphical representation of error bars in the figures. Significance of in vivo transplantation and

survival experiments was assessed using Log-rank test.

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Chapter 3. Results

3.1 NDP expression is enriched in GBM and correlates with survival in several neurological cancers

We queried the cancer genome atlas (TCGA) database and found that NDP expression is highly

enriched in primary human gliomas, LGG and GBM (Figure 6A). Interestingly, NDP expression

was also abundant in a wide variety of cancer types, some of which revealed variable expression

levels, like breast cancer (Figure 6A). Similarly, analysis of the cancer cell line encyclopedia

(CCLE) (Barretina et al., 2012) revealed a significant enrichment of NDP expression in brain

tumor lines (Figure 7A). The Norrin receptor, FZD4, is also expressed in different cancer types;

however, the expression levels in GBM are comparable with other cancers (Figure 6B, 7B) and

not highly enriched as in the case of NDP.

Next, we examined whether NDP expression levels correlate with survival in neurological

cancers. We found that NDP expression positively correlates with survival outcomes in GBM,

neuroblastoma and brain astrocytoma (LGG) (Figure 8). In contrast, there was no correlation

between FZD4 expression and survival in GBM (Figure 8). Collectively, these results indicate

the NDP is expressed in a wide range of cancer types, with significant enrichment in GBM.

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53

Figure 6. NDP expression is enriched in GBM and LGG tumor samples from TCGA

A) NDP expression levels among different cancer types from TCGA data portal analyzed by firebrowse server

provided by the Broad Institute, USA (Deng et al., 2017). NDP expression was detected in several types of

cancer including breast, prostate, leukemia, low grade glioma, and GBM cancers. Red boxes represent

tumor samples, blue boxes represent normal counterparts, and white boxes represent missing normal

counterparts.

B) FZD4 expression levels among different cancer types from TCGA data portal analyzed by firebrowse

server provided by the Broad Institute, USA (Deng et al., 2017).

54

A

B

55

Figure 7. NDP expression is enriched in GBM cell lines from CCLE.

A) Analysis of NDP expression levels in CCLE from the Broad Institute, (Barretina et al., 2012), which

contains data from more than 1000 cell lines representing a wide variety of cancer types.

B) Analysis of FZD4 expression levels in CCLE from the Broad Institute, (Barretina et al., 2012), which

contains data from more than 1000 cell lines representing a wide variety of cancer types.

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57

Figure 8. NDP expression levels correlate with survival in neurological cancers

A) NDP expression levels positively correlated with patients’ survival outcomes in GBM, neuroblastoma, and

LGG.

B) FZD4 expression levels do not correlate with survival outcomes in GBM patients.

C) NDP expression levels exhibit divergent correlation patterns with survival outcomes in GBM based on

ASCL1 expression levels. In ASCL1lo GBM patients, there is a striking positive correlation of NDP

expression levels with survival outcomes.

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3.2 NDP/FZD4 pathway components are expressed in GNS and hNSC cells

Using previously described Glioblastoma neural stem cell (GNS) culture protocols (Pollard et al.,

2009), we expanded primary GNS and non-cancerous fetal human neural stem cell (hNSC) lines

to assess the expression of Norrin/FZD4 signaling axis components (NDP, FZD4, LRP5 and

TSPAN12). Quantitative real time qRT-PCR revealed the expression of all signaling components

in almost all tested lines (Figure 9A), however; the expression levels varied significantly from

one line to another. In addition, we performed Gene Set Enrichment Analysis (GSEA) on GBM

tumors and found that NDP expression levels significantly correlate with classical GBM (Figure

9B) and aging brain (Figure 9C) gene sets. Together, the survival and GSEA results suggest that

NDP might be functionally important in GBM.

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60

Figure 9. NDP/FZD4 pathway components are expressed in GNS and hNSC lines, and NDP

expression level is correlated with classical GBM gene set

A) Expression of NDP/FZD4 pathway components (NDP, FZD4, LRP5, TSPAN12) in a panel of 9 primary-

derived GNS was determined by qRT-PCR analysis. Expression levels were normalized to a control

housekeeping gene (hPRT2) and individual cell line expression levels were represented as fold change

relative to the summed average expression level of all lines.

B) Similarly, expression of NDP/FZD4 pathway components (NDP, FZD4, LRP5, TSPAN12) was assessed in

3 hNSC lines by qRT-PCR as described above. Generally, hNSC lines expressed significantly higher levels

of NDP than GNS lines (gave quite higher CT values).

C) GSEA analysis revealed a correlation between NDP and classical GBM and aging brain gene sets. NDP

high group is associated with several significantly enriched pathways, where NDP high group were

enriched for molecular pathways like VERHAAK_GLIOBLASTOMA_CLASSICAL pathway.

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3.3 NDP regulates proliferation and sphere formation of hNSC and GNS cells in vitro

To investigate the role of NDP and FZD4 on growth and clonogenicity of nontransformed hNSC

and tumorigenic GNS lines we generated lentiviral constructs to express gene-specific

shorthairpins (2/gene) or full length cDNAs. We confirmed knockdown efficiency by

quantitative real time PCR (qRT-PCR), and ectopic expression by western blotting (Figure 9,

10). NDP and FZD4 knockdown in two hNSC lines significantly inhibited growth, whereas

ectopic expression of these genes had the opposite effect, indicating that activation of the

NDP/FZD4 signaling pathway is growth promoting in nontransformed NSCs (Figure 10, 11).

Next, we tested the effect of NDP or FZD4 gain and loss of function on GNS growth and sphere

formation. NDP or FZD4 knockdown in ASCL1lo GNS line G411 increased growth (Figure 12)

and sphere formation (Figure 12), and ectopic expression was inhibitory in these assays (Figure

12), which is in direct contrast to our findings in hNSC. We tested a second ASCL1lo GNS line

(G564) and obtained similar results (Figure 13), suggesting that NDP/FZD4 signaling is growth

inhibitory in ASCL1lo GNS cells. Consistent with this possibility, when we stratified GBM on

the basis of ASCL1 expression we found that most of the survival advantage of NDP expression

stratified with tumors that had low levels of ASCL1 expression, while tumors with high ASCL1

levels did not show any correlation between NDP expression level and survival outcomes (Fig

7). Taken together, we find that NDP/FZD4 signaling is growth promoting in non-transformed

hNSC, but is growth inhibitory in ASCL1lo GNS, which is consistent with the survival advantage

associated with NDP expression in ASCL1lo GBM.

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63

Figure 10. NDP and FZD4 stimulate the proliferation of hNSC-1 in vitro.

A) hNSC-1 cells were infected with lentiviral construct expressing shScrambled (control), shNDP-A, shNDP-

C (targeting NDP), shFZD4-2 or shFZD4-4 (targeting FZD4), as described previously, to knockdown the

indicated genes. Next, cells were seeded in 24 well plates (20000 cells/well, 4 replicates/sample), and left

to grow for 3 and 6 days. At each of these time points, cells were lifted, quantified by Trypan blue and

compared to the seeding density to quantify the fold change in cell number. Graphs represent the averages

of 3 independent biological replicates (e.g. 3 independent viral infection experiments). p < 0.05 was

considered significant. Error bars represent standard error of the mean values of the corresponding sample

replicates.

B) To confirm our observations, hNSC-1 cells were infected with an empty lentiviral construct (control),

hNDP (expressing ectopic NDP) or hFZD4 (expressing ectopic MYC-tagged FZD4). Next, cells were used

in a growth assay as described above. Graphs represent the averages of 3 independent biological replicates.

p < 0.05 was considered significant. Error bars represent standard error of the mean values of the

corresponding sample replicates.

C) qRT-PCR was used to confirm knockdown efficiency. Results were normalized to a housekeeping gene

control and represented as fold changes compared to the shScrambled control. PCR products were validated

by sequencing.

D) Western blotting was used to confirm overexpression. Due to the lack of efficient FZD4 antibodies,

MYC tag was used to detect ectopic FZD4 expression.

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65

Figure 11. NDP and FZD4 stimulate the proliferation of hNSC-3 in vitro.

A) hNSC-3 cells were infected with lentiviral construct expressing shScrambled (control), shNDP-A, shNDP-

C (targeting NDP), shFZD4-2 or shFZD4-4 (targeting FZD4), as described previously, to knockdown the

indicated genes. Next, cells were seeded in 24 well plates (20000 cells/well, 4 replicates/sample), and left

to grow for 3 and 6 days. At each of these time points, cells were lifted, quantified by Trypan blue and

compared to the seeding density to quantify the fold change in cell number. Graphs represent the averages

of 3 independent biological replicates (e.g. 3 independent viral infection experiments). p < 0.05 was

considered significant. Error bars represent standard error of the mean values of the corresponding sample

replicates.

B) To confirm our observations, hNSC-3 cells were infected with an empty lentiviral construct (control),

hNDP (expressing ectopic NDP) or hFZD4 (expressing ectopic MYC-tagged FZD4). Next, cells were used

in a growth assay as described above. Graphs represent the averages of 3 independent biological replicates.

p < 0.05 was considered significant. Error bars represent standard error of the mean values of the

corresponding sample replicates.

C) qRT-PCR was used to confirm knockdown efficiency. Results were normalized to a housekeeping gene

control and represented as fold changes compared to the shScrambled control. PCR products were validated

by sequencing.

D) Western blotting was used to confirm overexpression. Due to the lack of efficient FZD4 antibodies,

MYC tag was used to detect ectopic FZD4 expression.

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67

Figure 12. NDP and FZD4 inhibit the proliferation of G411 cells in vitro.

A) G411 cells were infected with lentiviral construct expressing shScrambled (control), shNDP-A, shNDP-C

(targeting NDP), shFZD4-2 or shFZD4-4 (targeting FZD4), as described previously. Next, cells were

seeded in a 24 well plates (20000 cells/well, 4 replicates/sample), and left to grow for 3 and 6 days. At each

of these time points, cells were lifted, quantified by Trypan blue and compared to the seeding density to

identify the fold change in cell number. Graphs represent the averages of 3 independent biological

replicates. p < 0.05 was considered significant. Error bars represent standard error of the mean values of the

corresponding sample replicates.

B) Cells were seeded in 96 well plates in a serial dilution series starting with 2000 cells/well, as described in

Chapter 2. After 2 weeks in culture, wells were scored for the presence of spheres and sphere frequency

was quantified as described in the methods. Graphs represent the averages of 3 independent biological

replicates. p < 0.05 was considered significant. Error bars represent standard error of the mean values of the

corresponding sample replicates.

C) Growth assay with NDP or FZD4 overexpression G411 cells. Cells were infected with an empty lentiviral

construct (control), hNDP (expressing ectopic NDP) or hFZD4 (expressing ectopic FZD4 fused to MYC

reporter) and used in the growth assay as described above. Graphs represent the averages of 3 independent

biological replicates. Graphs represent the averages of 3 independent biological replicates. p < 0.05 was

considered significant. Error bars represent standard error of the mean values of the corresponding sample

replicates.

D) Sphere formation with cells with ectopic NDP or FZD4 expression. Graphs represent the averages of 3

independent biological replicates. Graphs represent the averages of 3 independent biological replicates. p <

0.05 was considered significant. Error bars represent standard error of the mean values of the corresponding

sample replicates.

E) qRT-PCR was used to confirm knockdown efficiency of the shorthairpin constructs. Results were

normalized to a housekeeping gene control and represented as fold changes compared to the shScrambled

control. PCR products were validated by sequencing.

F) Western blotting was used to confirm overexpression. Due to the lack of efficient FZD4 antibodies,

MYC tag was used to detect ectopic FZD4 expression.

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Figure 13. NDP and FZD4 inhibit the proliferation of G564 cells in vitro.

A) G564 cells were infected with lentiviral construct expressing shScrambled (control), shNDP-A, shNDP-C

(targeting NDP), shFZD4-2 or shFZD4-4 (targeting FZD4), as described previously. Next, cells were

seeded in a 24 well plates (20000 cells/well, 4 replicates/sample), and left to grow for 3 and 6 days. At each

of these time points, cells were lifted, quantified by Trypan blue and compared to the seeding density to

identify the fold change in cell number. Graphs represent the averages of 3 independent biological

replicates. p < 0.05 was considered significant. Error bars represent standard error of the mean values of the

corresponding sample replicates.

B) Cells were seeded in 96 well plates in a serial dilution series starting with 2000 cells/well, as described in

Chapter 2. After 2 weeks in culture, wells were scored for the presence of spheres and sphere frequency

was quantified as described in the methods. Graphs represent the averages of 3 independent biological

replicates. p < 0.05 was considered significant. Error bars represent standard error of the mean values of the

corresponding sample replicates.

C) Growth assay with NDP or FZD4 overexpression G564 cells. Cells were infected with an empty lentiviral

construct (control), hNDP (expressing ectopic NDP) or hFZD4 (expressing ectopic FZD4 fused to MYC

reporter) and used in the growth assay as described above. Graphs represent the averages of 3 independent

biological replicates. Graphs represent the averages of 3 independent biological replicates. p < 0.05 was

considered significant. Error bars represent standard error of the mean values of the corresponding sample

replicates.

D) Sphere formation with cells with ectopic NDP or FZD4 expression. Graphs represent the averages of 3

independent biological replicates. Graphs represent the averages of 3 independent biological replicates. p <

0.05 was considered significant. Error bars represent standard error of the mean values of the corresponding

sample replicates.

E) qRT-PCR was used to confirm knockdown efficiency of the shorthairpin constructs. Results were

normalized to a housekeeping gene control and represented as fold changes compared to the shScrambled

control. PCR products were validated by sequencing.

F) Western blotting was used to confirm overexpression. Due to the lack of efficient FZD4 antibodies,

MYC tag was used to detect ectopic FZD4 expression.

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3.4 The biological effects of NDP in GBM stratify with ASCL1 expression levels

These findings led us to examine the function of NDP/FZD4 in GNS lines with high levels of

ASCL1 expression (ASCL1hi). To address this question, we performed a similar gain and loss of

function analysis in two independent ASCL1hi lines (G523, G472). NDP knockdown resulted in

striking inhibition of proliferation and sphere formation in vitro (Figure 14, 15). Notably, cells

with NDP knockdown, marked by the presence of the GFP reporter from the lentivirus, were

significantly diminished after two weeks in the sphere cultures compared to controls, suggesting

that they have a growth disadvantage (Figure 16). Interestingly, manipulating FZD4 in either

ASCL1hi cell line failed to produce a significant phenotype, suggesting the existence of an

alternative mechanism mediating the effect of NDP expression on growth in these lines (Figure

14, 15). Although one of the two shFZD4 constructs (shFZD4-2) seemed to have a slight effect

on the proliferation and sphere formation in G523 cells, this effect was not reproducible with the

other shFZD4 construct (shFZD4-4). Additionally, overexpressing FZD4 in this line failed to

produce a phenotype, indicating that the slight phenotype observed in case of shFZD4-2 likely to

be an off-target effect. Moreover, both shFZD4 constructs failed to produce a phenotype on

proliferation or sphere formation in G472 cells.

To confirm the specificity of the short hairpin RNA constructs, we tried an alternate CRISPR-

based approach for NDP knockdown, however we were unable to grow out clones. Therefore,

we designed a degenerate codon modified NDP lentivirus (MOD-NDP) and confirmed that this

version could rescue the growth inhibitory effect of shorthairpin NDP knockdown (Figure 17).

Table 6 includes a summary of our in vitro cell growth and sphere formation results in all cell

lines. These findings indicate a significant role of NDP in regulating proliferation and sphere

formation attributes of GNS cells. In addition, the function of NDP in ASCL1hi GNS cells might

be FZD4-independent. Finally, these results indicate a surprising difference in the biological

effects of NDP on GBM in vitro that stratify with ASCL1 expression status.

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Figure 14. NDP stimulates the proliferation of G523 cells independent of FZD4 in vitro.

A) G523 cells were infected with lentiviral construct expressing shScrambled (control), shNDP-A, shNDP-C

(targeting NDP), shFZD4-2 or shFZD4-4 (targeting FZD4), as described previously. Next, cells were

seeded in a 24 well plates (20000 cells/well, 4 replicates/sample), and left to grow for 3 and 6 days. At each

of these time points, cells were lifted, quantified by Trypan blue and compared to the seeding density to

identify the fold change in cell number. Graphs represent the averages of 3 independent biological

replicates. p < 0.05 was considered significant. Error bars represent standard error of the mean values of the

corresponding sample replicates.

B) Cells were seeded in 96 well plates in a serial dilution series starting with 2000 cells/well, as described in

Chapter 2. After 2 weeks in culture, wells were scored for the presence of spheres and sphere frequency

was quantified as described in the methods. Graphs represent the averages of 3 independent biological

replicates. p < 0.05 was considered significant. Error bars represent standard error of the mean values of the

corresponding sample replicates.

C) Growth assay with NDP or FZD4 overexpression G523 cells. Cells were infected with an empty lentiviral

construct (control), hNDP (expressing ectopic NDP) or hFZD4 (expressing ectopic FZD4 fused to MYC

reporter) and used in the growth assay as described above. Graphs represent the averages of 3 independent

biological replicates. Graphs represent the averages of 3 independent biological replicates. p < 0.05 was

considered significant. Error bars represent standard error of the mean values of the corresponding sample

replicates.

D) Sphere formation with cells with ectopic NDP or FZD4 expression. Graphs represent the averages of 3

independent biological replicates. Graphs represent the averages of 3 independent biological replicates. p <

0.05 was considered significant. Error bars represent standard error of the mean values of the corresponding

sample replicates.

E) qRT-PCR was used to confirm knockdown efficiency of the shorthairpin constructs. Results were

normalized to a housekeeping gene control and represented as fold changes compared to the shScrambled

control. PCR products were validated by sequencing.

F) Western blotting was used to confirm overexpression. Due to the lack of efficient FZD4 antibodies,

MYC tag was used to detect ectopic FZD4 expression.

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Figure 15. NDP stimulates the proliferation of G472 cells independent of FZD4 in vitro.

A) G472 cells were infected with lentiviral construct expressing shScrambled (control), shNDP-A, shNDP-C

(targeting NDP), shFZD4-2 or shFZD4-4 (targeting FZD4), as described previously. Next, cells were

seeded in a 24 well plates (20000 cells/well, 4 replicates/sample), and left to grow for 3 and 6 days. At each

of these time points, cells were lifted, quantified by Trypan blue and compared to the seeding density to

identify the fold change in cell number. Graphs represent the averages of 3 independent biological

replicates. p < 0.05 was considered significant. Error bars represent standard error of the mean values of the

corresponding sample replicates.

B) Cells were seeded in 96 well plates in a serial dilution series starting with 2000 cells/well, as described in

Chapter 2. After 2 weeks in culture, wells were scored for the presence of spheres and sphere frequency

was quantified as described in the methods. Graphs represent the averages of 3 independent biological

replicates. p < 0.05 was considered significant. Error bars represent standard error of the mean values of the

corresponding sample replicates.

C) Growth assay with NDP or FZD4 overexpression G472 cells. Cells were infected with an empty lentiviral

construct (control), hNDP (expressing ectopic NDP) or hFZD4 (expressing ectopic FZD4 fused to MYC

reporter) and used in the growth assay as described above. Graphs represent the averages of 3 independent

biological replicates. Graphs represent the averages of 3 independent biological replicates. p < 0.05 was

considered significant. Error bars represent standard error of the mean values of the corresponding sample

replicates.

D) Sphere formation with cells with ectopic NDP or FZD4 expression. Graphs represent the averages of 3

independent biological replicates. Graphs represent the averages of 3 independent biological replicates. p <

0.05 was considered significant. Error bars represent standard error of the mean values of the corresponding

sample replicates.

E) qRT-PCR was used to confirm knockdown efficiency of the shorthairpin constructs. Results were

normalized to a housekeeping gene control and represented as fold changes compared to the shScrambled

control. PCR products were validated by sequencing.

F) Western blotting was used to confirm overexpression. Due to the lack of efficient FZD4 antibodies,

MYC tag was used to detect ectopic FZD4 expression.

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Figure 16. GFP+ G523 shNDP cells diminish after 2 weeks in sphere cultures

G523 shScrambled, shNDP or shFZD4 cells were seeded in sphere culture as previously described. After 2 weeks in

culture, cells were examined under fluorescence microscope to assess the maintenance of GFP expression as a

marker of the cells with shRNA constructs in each sample. Shown are representative images taken by a fluorescence

microscope at 20X magnification. Scale bar, 100 μM.

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Figure 17. Ectopic expression of shorthairpin insensitive NDP construct rescues the effects

of NDP knockdown.

A) G523 shScrambled or shNDP cells were prepared as previously described then transduced with a

lentivirus expressing empty mCherry, or mCherry+Mod-NDP (expressing a degenerate codon version

of NDP that is insensitive to shNDP A or C). After 3 days in culture, infection efficiency was roughly

assessed by the expression of GFP (reporter in the knockdown construct) and mCherry, then used in

subsequent functional assay. Cell proliferation was assessed using Trypan blue proliferation assay.

Cells were seeded in a 24 well plates (20000 cells/well, 4 replicates/sample), and left to grow for 3 and

6 days. At each of these time points, cells were quantified and compared to the seeding density to

identify the fold change in cell number. qRT-PCR was used to confirm knockdown efficiency as

previously described. Mod-NDP was able to completely rescue the effect of endogenous NDP

knockdown. Graphs represent the averages of 3 independent biological replicates. p < 0.05 was

considered significant. Error bars represent standard error of the mean values of the corresponding

sample replicates.

B) Western blotting was used to confirm Mod-NDP overexpression in cells infected with the indicated

lentiviruses. GAPDH expression was used as a loading control.

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Table 6. In vitro effects of manipulating NDP or FZD4 expression on different cell types

Summary of observed effects on growth (examined by Trypan blue proliferation assa), or sphere formation

(examined by ELDA assa) after NDP or FZD4 expression manipulation. Sphere formation of hNSC lines after

manipulating NDP or FZD4 was not tested.

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3.5 Different molecular pathways mediate the biological function of NDP in ASCL1hi versus ASCL1lo GBM

NDP was shown to function predominantly through binding the FZD/LRP5 receptor complex in

the presence of TSPAN12 as a signal amplifier to activate canonical WNT pathway (Xu et al.,

2004; Junge et al., 2009; Ke et al., 2013). However, several subsequent studies suggested the

existence of alternative pathways and/or receptors mediating different biological functions of

NDP (Seuitz et al., 2017; McNeill et al., 2013; Deng et al., 2013, Xu et al., 2012). In the previous

section, we show that manipulating FZD4 in ASCL1lo GNS lines phenocopies manipulation of

NDP, while in ASCL1hi lines, manipulation of FZD4 fails to produce a phenotype. Thus, we

hypothesized that NDP function in ASCL1hi lines is FZD4 and canonical WNT pathway

independent.

To test this hypothesis, we overexpressed NDP in G411 (ASCL1lo) and G523 (ASCL1hi) cells,

then treated them with several WNT inhibitors that act at different levels of the pathway

(monoclonal anti-FZD4 blocking antibody that inhibits FZD4 receptor; and Tankyrase inhibitor

(XAV 939) that stimulates β-Catenin degradation by stabilizing its destruction complex), to test

whether inhibiting WNT signaling is sufficient to block the effect of NDP overexpression.

Expectedly, anti-FZD4 antibody as well as WNT inhibitor treatments were sufficient to inhibit

the growth phenotype of ectopic NDP expression in G411 (ASCL1lo) cells, indicating WNT-

functional dependence of NDP in these cells and confirming the effect of WNT inhibition in this

cohort of GBM (Figure 18). On the other hand, blocking FZD4 or inhibiting WNT signaling

failed to affect the proliferation of G523 cells with or without NDP overexpression, indicating

WNT-functional independence of NDP in these cells (Figure 19). The biological activity of all

reagents was confirmed through a luciferase reporter TOP-Flash assay, as previously described

(Liu et al., 2003) (Figure 18).

NDP was previously reported to antagonize BMP signaling in Xenopus by binding BMP4

protein and preventing it from activating the BMP/SMAD4 signaling cascade (Xu et al., 2012).

Interestingly, BMP activation in GBM stem cells was reported to promote differentiation and

inhibit proliferation (Piccirillo et al., 2006; Lee et al., 2008), which is similar to the phenotype of

NDP knockdown in ASCL1hi GNS lines. Therefore, we hypothesized that NDP might promote

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proliferation and sphere formation in ASCL1hi GNS cells by antagonizing BMP/SMAD4

signaling. To test this hypothesis, we treated NDP overexpressing G523 or G472 cells with

recombinant human BMP4 (rhBMP4). rhBMP4 treatment was able to overcome the effect of

NDP overexpression on the proliferation of G523 cells (Figure 19). Another prediction of this

model is that blocking BMP signaling should rescue the growth defect cause by NDP

knockdown. Therefore, we monitored growth of shNDP G523 cells treated with the specific

BMP inhibitor (DMH1). DMH1 treatment produced a striking stimulation of proliferation in

control cells, indicating that there is an endogenous level of BMP signaling that is growth

inhibitory. However, inhibiting BMP signaling failed to rescue proliferation in NDP knockdown

cells (Figure 19). One possibility is that NDP knockdown results in high levels of BMP signaling

activity that cannot be antagonized by the DMH1. In other words, it is possible the NDP

knockdown rescued the effect of BMP inhibition and not the opposite. Collectively, these results

indicate that NDP functions in WNT-dependent and WNT-independent modes depending on

ASCL1 status in GNS.

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Figure 18. The effects of NDP are FZD4 and WNT –dependent in ASCL1lo, and -

independent in ASCL1hi GNS lines.

A) G411 (ASCL1lo) NDP overexpression cells were prepared as previously described then treated with either

DMSO as a vehicle control, XAV939 (5 μM), anti-FZD4 blocking antibody (10 nM), or its isomatched

control anti-KLH antibody (10 nM). Treatments were re-applied every 3 days. Cell density was then

assessed using Trypan blue proliferation assay after 6 days as previously described and represented as fold

change in cell number relative to seeding density. Graphs represent the averages of 3 independent

biological replicates. p-Value <= 0.05 was considered significant. Error bars represent standard error of the

mean values of the corresponding sample replicates. Graphs represent the averages of 3 independent

biological replicates. p < 0.05 was considered significant. Error bars represent standard error of the mean

values of the corresponding sample replicates.

B) Similar to G411 cells, G523 (ASCL1hi) NDP overexpression cells were treated with the same WNT

inhibitors, and cell proliferation was assessed in the same way. Graphs represent the averages of 3

independent biological replicates. p < 0.05 was considered significant. Error bars represent standard error

of the mean values of the corresponding sample replicates.

C) HEK293T cells were transiently transfected with TOP-flash dual luciferase reporter system assay

components (hFZD4, hLRP5, hTSPAN12, Top-Flash, and Renilla plasmids) as previously described. Then,

cells were treated with recombinant human WNT3a (100ng) in combination with either DMSO or XAV939

(5 μM). Graphs represent the averages of 3 independent biological replicates. p < 0.05 was considered

significant. Error bars represent standard error of the mean values of the corresponding sample replicates.

D) Similarly, HEK293T cells were transiently transfected with TOP-flash dual luciferase reporter system assay

components then treated with recombinant human Norrin (100ng) +anti-KLH or anti-FZD4 to test their

biological activities. Graphs represent the averages of 3 independent biological replicates. p < 0.05 was

considered significant. Error bars represent standard error of the mean values of the corresponding sample

replicates.

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Figure 19. BMP antagonizes the effects of NDP manipulation in ASCL1hi GNS lines.

A) G523 (ASCL1hi) NDP knockdown cells were seeded in 24 well plates (20000 cells/ well) and treated with

either DMSO as a vehicle control, or the BMP inhibitor (DMH1) (100 nM, treatment was re-applied every

3 days). In the second graph, G523 NDP overexpression cells were treated with either PBS as a vehicle

control, or rhBMP4 (15 ng/ml, treatment was re-applied every 3 days). Cell density was then assessed using

Trypan blue proliferation assay as previously described and represented as fold change in cell number

relative to seeding density. Knocking down NDP silenced the effect of DMH1 and rendered cells

insensitive to the treatment while rhBMP4 treatment completely compensated the effect of NDP

overexpression. Graphs represent the averages of 3 independent biological replicates. p < 0.05 was

considered significant. Error bars represent standard error of the mean values of the corresponding sample

replicates.

B) Similar to G523 cells, G472 (ASCL1hi) NDP overexpression cells treated with rhBMP4 to observe effects

on proliferation. rhBMP4 treatment completely compensated the effect of NDP overexpression. Graphs

represent the averages of 3 independent biological replicates. p-Value <= 0.05 was considered significant.

Error bars represent standard error of the mean values of the corresponding sample replicates.

C) To confirm the bioactivity of rhBMP4 and DMH1, HEK293T cells were treated with rhBMP4 (15 or 30

ng) alone or in combination with DMH1 (100 nM) for 2 hours. Following the treatment, cell lysates were

collected and phosphor-SMAD1/5 was detected using Western blotting as a readout for BMP signaling

activation. Total SMAD1 was used as a loading control.

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3.6 The WNT-independent effects of NDP in ASCL1hi GBM are cell autonomous

NDP functions as a short-range paracrine signal to activate FZD4 and the canonical WNT

pathway (Xu et al., 2004), however, it is not known whether the FZD4-independent effects of

NDP in ASCL1hi cells are paracrine or autocrine. To address this issue, we performed a

competition assay, where we mixed equivalent numbers of ASCL1hi cells infected with Lenti-

shNDP-GFP with cells infected with Lenti-mCherry and measured the ratio of mCherry+: GFP+

cells over time. If NDP functions as a paracrine signal, then NDP expressed from the mCherry+

cells should rescue the growth of the NDP-deficient GFP+ cells, which would result in a change

to the ratio of mCherry+ : GFP+ cells during the culture period. The equal seeding of the cultures

was confirmed by flow cytometric analysis of the cultures at day 1 (Figure 20) and then re-

assessed after 6 days. Interestingly, the ratio of mCherry+: GFP + cells remained almost equal in

cultures expressing the scrambled shorthairpin control, whereas the ratio shifted dramatically

towards the mCherry+ cohort in the NDP knockdown samples. Given that the two populations

were intermixed this result strongly suggests that NDP is functioning as an autocrine signal in

ASCL1hi cells (Figure 20). This interpretation is particularly important considering the possible

implications of the results of our study in therapeutic initiatives.

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Figure 20. NDP mediates ASCL1hi GNS proliferation through a cell autonomous

mechanism.

A) G523 (ASCL1hi) (GFP+) shScrambled, shNDP-A, shNDP-C, were mixed with wildtype G523 cells

expressing mCherry cells in 1:1: ratio, and seede in 6-well plates (50000 cells/well). A portion of each

cell mixture was analyzed by FACS to confirm equal seeding (Day 1 histograms). At day 6, cells were

collected and analyzed again by FACS to determine the ratio of mCherry+: GFP+ cells. The

experiment was repeated two independent times to confirm reproducibility (2 biological replicates).

Histogram figures represent one replicate. The GFP+ cell population underwent a striking decrease in

shNDP-A or shNDP-C expressing co-cultures cells, while is remained stable in shScrambled co-

cultures.

B) Portions of cell mixtures from different samples were taken at day1 (seeding day) and day 6, and fixed

on imaging slides to examine the GFP+ and mCherry+ cells. Images were taken by a fluorescence

microscope at 20X magnification.

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3.7 NDP knockdown decreases Ki67+ and SOX2+ populations in ASCL1hi, and increases the Ki67+ population in ASCL1lo GNS lines

Since manipulating NDP expression affects growth and sphere formation in vitro, we examined

proliferation and stem cell markers in our lines after NDP knockdown using

immunohistochemistry. Expectedly, the fraction of cycling cells, marked by Ki67 (marker of cell

proliferation), was significantly and oppositely changed in ASCL1hi versus ASCL1lo GNS lines

after knocking down NDP. In G472 (ASCL1hi), NDP knockdown reduced the proportion of

Ki67+ cells, while in G564 (ASCL1lo) NDP knockdown increased the proportion Ki67+ cells

(Figure 21, 22). Interestingly, SOX2+ population was significantly reduced in G472, but not

G564, after NDP knockdown (Figure 21, 22), which is further confirmation of the difference in

NDP function between ASCL1hi versus ASCL1lo GNS. A change in the proportion of cycling

cells (Ki67+ cell population) is indicative of a change in the balance of cell cycle exit versus cell

cycle re-entry in the population. Therefore, the increase in the cycling pool in ASCL1lo cells with

NDP knockdown is consistent with the increase in sphere formation. Conversely, the reduction

in the cycling pool in ASCL1hi cells is consistent with the reduction in sphere formation. Taken

together these observations suggests that NDP signaling affects GNS self-renewal.

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Figure 21. Knocking down NDP or FZD4 increases Ki67+ cell population in G564 cells.

A) NDP and FZD4 were knocked down in G564 (ASCL1lo) cells as previously described. 3 days post-

infection, cells were cultured on 8-well chamber slides (100000 cells/well) for immunocytochemistry. After

fixation and permeabilization, cells were incubated with antibodies to detect Ki67+ (red) and SOX2+

(green) cell populations. Hoechst was used as a nuclear marker. Images were taken by a fluorescence

microscope at 20X magnification. The figure shows representative images of different samples. Scale bar,

50 µM.

B) Images were analyzed using ZEN software and Ki67+ or SOX2+ cells were quantified manually. Changes

in these cell populations were represented as fold changes relative to shScrambled controls. Graphs

represent the averages of 3 independent biological replicates. p < 0.05 was considered significant. Error

bars represent standard error of the mean values of the corresponding sample replicates.

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Figure 22. Knocking down NDP or FZD4 decreases Ki67+ and SOX2+ cell population in

G472 cells.

A) NDP and FZD4 were knocked down in G472 (ASCL1hi) cells as previously described. 3 days post-

infection, cells were cultured on 8-well chamber slides (100000 cells/well) for immunocytochemistry. After

fixation and permeabilization, cells were incubated with antibodies to detect Ki67+ (red) and SOX2+

(green) cell populations. Hoechst was used as a nuclear marker. Images were taken by a fluorescence

microscope at 20X magnification. The figure shows representative images of different samples. Scale bar,

50 µM.

B) Images were analyzed using ZEN software and Ki67+ or SOX2+ cells were quantified manually. Changes

in these cell populations were represented as fold changes relative to shScrambled controls. Graphs

represent the averages of 3 independent biological replicates. p < 0.05 was considered significant. Error

bars represent standard error of the mean values of the corresponding sample replicates.

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3.8 NDP affects cell cycle kinetics in both ASCL1hi and ASCL1lo GNS cells

To further analyze the cell biological effects of NDP on proliferation we exposed ASCL1hi

(G523) and ASCL1lo (G411) cells to a short (3 hour) pulse of EdU, to mark cells in S-phase, then

stained them for Ki67 and EdU incorporation and quantified the percentage of EdU+ cells

relative to the total cell population and to the Ki67+ cells. Interestingly, the proportion of

EdU+/Ki67+ cells was reduced by NDP knockdown in G523 and G411 cells (Figure 23, 24).

Because the fraction of EdU+ cells amongst the cycling pool is an estimation of the length of S-

phase, this result means that the other phases of the cycle are lengthened indicating slower cell

cycle progression. Interestingly, the percentage of EdU+ cells regardless Ki67 status was

significantly lower in NDP knockdown G523 cells but unchanged in G411 knockdown cells

compared to controls (Figure 23, 24). These results suggest that NDP knockdown in ASCl1hi

cells inhibits cell cycle re-entry and slows the cell cycle. Whereas, knocking down NDP in

ASCL1lo cells increases cell cycle re-entry but slows the cell cycle. This interpretation raises the

possibility that NDP signaling in ASCL1lo affects two competing mechanisms: one slowing cell

cycle kinetics and the other promoting cell cycle re-entry, with the latter having the dominant

effect.

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Figure 23. Knocking down NDP increases Ki67+ but decreases Edu+/Ki67+ cell

populations in G411 cells.

A) NDP and FZD4 were knocked down in G411 (ASCL1lo) cells as previously described. 3 days post

infection, cells were cultured on 8-well chamber slides (100000 cells/well) to be used for

immunocytochemistry. After fixation and permealization, cells were co-stained with Ki67+ (red) and

EdU+ (green) cell populations. Hoecht was used as a nuclear marker. Images were taken by a

fluorescence microscope at 20X magnification.

B) Ki67+, EdU+ and EdU+/Ki67+ cell populations were manually quantified after analyzing images with

ZEN software. Changes in this cell population were represented as fold changes relative to

shScrambled controls. Graphs show the average of 3 independent biological replicates. Graphs

represent the averages of 3 independent biological replicates. p < 0.05 was considered significant.

Error bars represent standard error of the mean values of the corresponding sample replicates.

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Figure 24. Knocking down NDP decreases both Ki67+ and Edu+/Ki67+ cell populations in

G523 cells.

A) NDP and FZD4 were knocked down in G523 (ASCL1hi) cells as previously described. 3 days post

infection, cells were cultured on 8-well chamber slides (100000 cells/well) to be used for

immunocytochemistry. After fixation and permealization, cells were co-stained with Ki67+ (red) and

EdU+ (green) cell populations. Hoecht was used as a nuclear marker. Images were taken by a

fluorescence microscope at 20X magnification.

B) Ki67+, EdU+ and EdU+/Ki67+ cell populations were manually quantified after analyzing images with

ZEN software. Changes in this cell population were represented as fold changes relative to

shScrambled controls. Graphs show the average of 3 independent biological replicates. Graphs

represent the averages of 3 independent biological replicates. p < 0.05 was considered significant.

Error bars represent standard error of the mean values of the corresponding sample replicates.

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3.9 NDP knockdown results in significantly variant differential expression profiles of ASCL1hi versus ASCL1lo GNS lines

To obtain a global view of the changes in expression profile of GNS cells after NDP knockdown,

we performed RNA-Seq analysis on one ASCL1lo line (G411) and one ASCL1hi line (G523),

after knocking down NDP with two different shRNA constructs. NDP knockdown resulted in

differential expression of significantly more genes in ASCL1lo (about 2400 hits after filtration)

than ASCL1hi cells (About 1600 hits after filtration). The overlapping hits that were identified to

differentially expressed after NDP knockdown in both lines were a little more than 800 (about

50% of ASCL1hi, and 35% of ASCL1lo hits) (Figure 25A). Thus, there is a significant amount of

unique hits that were identified in each line, especially in ASCL1lo (About 65% of ASCL1 lo and

50% of ASCl1hi identified hits were unique). The vast majority of the top common hits between

the two lines were related to cell cycle kinetics (Figure 25B). This list of the common identified

hits incuded master regulators of cell cycle such as, Cyclin A2, Cyclin G2, Cyclin E1, Cyclin B1,

and Cyclin B2. This supports our previous results showing that NDP knockdown affects cell

cycle kinetics in both ASCL1hi and ASCL1lo lines (Figure 21-24). The list of unique identified

hits in ASCL1lo cells was related to remarkably different biological processes. Most of the

ASCL1lo unique identified targets were related to processes such as migration, invasion,

metastasis, EMT and Extracellular matrix modulation (Figure 25C). On the other hand, the

unique identified hits in ASCL1hi were mainly related to cell cycle, proliferation, differentiation

and DNA repair (Figure 25D). While ASCL1lo unique hits list still contained cell cycle and

growth regulating genes, they were significantly underrepresented compared to the common hits,

and the ASCL1hi unique hits lists. The activation of these tumor promoting processes might

indicate the existence of competing mechanisms mediating the function of NDP in ASCL1lo and

explain the phenotype we observe after modulating NDP expression in these cells. Moreover, the

exclusive abundance of differentiation genes in ASCL1hi list supports our previous observation

that SOX2+ population is only changed after NDP knockdown in ASCL1hi but not ASCL1lo cells

(Figure 21, 22). Collectively, these results indicate that NDP knockdown affects cell cycle

kinetics and re-entry in both ASCl1hi and ASCL1lo GNS lines. In addition, these results indicate

that NDP knockdown results in a significant amount of divergent differential gene expression in

ASCL1hi versus ASCL1lo lines.

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Figure 25. RNA-Seq analysis for G411 (ASCL1lo) and G523 (ASCL1hi) lines

A) Venn diagram representing the number of identified differentially expressed genes in each line, and the

proportion of overlapping identified hits (the percentage of overlapping hits is 49% in G523 and 34% in

G411). To prepare the library, G411 and G523 cells were transduced with lentiviral constructs shNDP-A

and shNDP-C to knockdown NDP as mentioned previously. 48 hours after infection, cells were lifted and

a portion of each sample was used to confirm knockdown effieicny by qRT-PCR as previously described.

Subsequently, the quality and integrity of RNA samples was assessed using Nanodrop and Bioanalyzer.

RNA Samples were then sent to the sequencing lab were the RNA-Seq library was run (Genome Quebec,

QC, Canada). The analysis included 3 biological replicates of each construct (Scrambled, shNDP-A,

shNDP-C) for each cell line. The quality assurance, statistics and bioinformatics analysis of the samples

was performed by Jose Hector Galvez at the Canadian Centre for Computational Genomics (C3G), QC,

Canada.

B) Heatmap representing selected genes that are differentially expressed in both lines after NDP knockdown.

For simplification of presentation, the heatmap include comparison between Scrambled and shNDP-C

replicates only, but all genes included in the heatmap were confirmed to be differentially expressed in the

other NDP knockdown construct as well (shNDP-A). All of the represented genes are related to regulation

of cell cycle and proliferation.

C) Heatmap representing selected genes that are differentially expressed in G411 but not G523 cells after

NDP knockdown. For simplification of presentation, the heatmap include comparison between Scrambled

and shNDP-C replicates only, but all genes included in the heatmap were confirmed to be differentially

expressed in the other NDP knockdown construct as well (shNDP-A). Gene represented are related to cell

cycle, proliferation, differentiation, DNA repair, and apoptosis.

D) Heatmap representing selected genes that are differentially expressed in G523 but not G411 cells after

NDP knockdown. For simplification of presentation, the heatmap include comparison between Scrambled

and shNDP-C replicates only, but all genes included in the heatmap were confirmed to be differentially

expressed in the other NDP knockdown construct as well (shNDP-A). Genes represented are related to cell

cycle, proliferation, migration, invasion and extracellular matrix.

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3.10 NDP affects tumor progression in xenografted GNS cells

To validate the function of NDP in vivo, we orthotopically xenografted Nod-SCID Gamma

(NSG) mice with G411 (ASCL1lo) or G523 (ASCL1hi) after knocking down or overexpressing

NDP. Consistent with our in vitro studies, overexpression of NDP or FZD4 in ASCL1lo GNS

significantly prolonged survival in xenografted mice (Figure 26). The tumors that did form had a

reduction in GFP+ cells relative to tumors that formed from cells infected with the control

lentivirus (despite being grafted with cells that were over 90% GFP+), suggesting that there was

a selection bias against NDP or FZD4 overexpressing cells during tumor progression (Figure 27).

NDP knockdown in ASCL1lo cells had a subtle but not significant effect on survival, likely

because of the rapid kinetics of tumor formation (3-4 weeks) of this line would make it difficult

to observe faster tumor formation (Figure 26).

On the other hand, knocking down NDP significantly prolonged survival in mice grafted with

ASCL1hi cells (G523) (Figure 26) and overexpressing NDP in this line significantly shortened

survival (Figure 26). Similar to G411 overexpression cells, tumors that formed in mice

xenografted with G523 cells after NDP knockdown had a great loss of GFP+ cells despite

coming from lines that had over 90% GFP+ cells (Figure 28). This observation suggests that

NDP knockdown in G523 cells results in a strong selective disadvantage during tumor

progression. Nevertheless, Human Nuclear antigen staining confirmed the presence of human

tumor cells in the brains of tumor bearing mice (Figure 27, 28). In conclusion, results in this

section confirm that NDP enhances the progression of ASCL1hi, while inhibiting the progression

of ASCL1lo GBM cells.

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Figure 26. Knocking down NDP affects tumor progression of xenografted GNS lines

A) G411 (ASCL1lo) cells were transduced to knockdown or overexpress NDP or FZD4, as previously

described, to produce stable cell lines with respective modifications. After confirming the knockdown

or overexpression, cells were orthotopically transplanted in NSG mice (1 mm lateral and 2 mm

posterior to the bregma). After developing terminal symptoms, mice were sacrificed, survival days

recorded, and brains were collected for IHC analysis. Knocking down NDP or FZD4 in G411 failed to

affect xenografted mouse survival, likely due to the very fast tumor progression dynamics of this cell

line. However, overexpressing NDP or FZD4 prolonged survival.

B) G523 (ASCL1hi) cells were transduced with two different shNDP, or NDP overexpression constructs

as previously described. After confirming the knockdown or overexpression, cells were orthotopically

xenografted in mice and survival experiment was carried on as described above. NDP knockdown

resulted in a significant prolongation of G523 xenografted mice survival. Overexpressing NDP

resulted in faster tumor progression and shorter mice survival. Graphs represent survival days of mice

transplanted with different samples. n=8/ knockdown group and 6/overexpression group. Results were

analyzed using Graph Pad software. Log-rank p-Value<=0.05 was considered significant.

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Figure 27. IHC of formed G411 tumors in vivo indicate growth advantage of NDP

knockdown cells

A) Representative images of tumors formed in brains of mice transplanted with G411 (ASCL1lo) cells after

knocking down NDP or FZD4. Brains were collected, perfused, sectioned and immunostained with

antibody specific for human nuclear antigen (HuAg), and nuclei were labelled with DAPI. Endogenous

GFP expressed by the knockdown constructs was examined as well. GFP+ cells are still abundant in

shNDP and shFZD4 tumors at the endpoint of the survival experiment. Scale bar, 50 µM.

B) Representative images of tumors formed in brains of mice transplanted with G411 cells after

overexpressing NDP or FZD4. GFP+ and MYC+ cells are significantly diminished in hNDP and hFZD4

overexpression tumors compared to the empty vector control tumors. Brains were collected, perfused,

sectioned and immunostained with antibodies specific for human nuclear antigen (HuAg), MYC (to

detect hFZD4), and nuclei were labelled with DAPI. Endogenous GFP expressed by the overexpression

constructs was examined as well. In case of hFZD4, the overexpression construct expressed a MYC tag

marker, which we detected by an anti-MYC antibody and represented as GFP in the image. Scale bar, 50

µM.

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Figure 28. IHC of formed G523 tumors in vivo indicate growth disadvantage of NDP

knockdown cells

A) Representative images of tumors formed in brains of mice transplanted with G523 (ASCL1hi) cells

after knocking down NDP. Brains were collected, perfused, sectioned and preserved as previously

described. For IHC, brain sections were stained with human nuclear antigen (HuAg), and Dapi.

Endogenous GFP expressed by the knockdown constructs was examined as well. GFP+ cells

diminished in shNDP-A and shNDP-C tumors at the endpoint of the survival experiment compared to

shScrambled control. Scale bar, 50 µM.

B) Representative images of tumors formed in brains of mice transplanted with G523 cells after

overexpressing NDP. Brains were collected, perfused, sectioned and preserved as previously

described. For IHC, brain sections were stained with human nuclear antigen (HuAg), and Dapi.

Endogenous GFP expressed by overexpression constructs was examined as well. GFP+ cells are still

abundant in hNDP tumors at the endpoint of the experiment. Scale bar, 50 µM.

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Chapter 4. Discussion

4.1 Novel perspectives of Norrin function: Role in Cancer

Early studies on Norrin were focused primarily on its function in endothelial cell development

and vascularization. However, the identification of its molecular function and uncovering its role

as an atypical WNT ligand led to the suggestion that it may have additional functions in other

cell types, tissues and/or contexts. Our group identified Norrin as a target of the Sonic hedgehog

signaling pathway in retinal progenitor cells (McNeill et al., 2012) and showed that it has a novel

cell autonomous function in promoting proliferation of these neural progenitors through a FZD4-

independent mechanism (McNeill et al., 2013). Additional non-vascular functions for Norrin

include a neuroprotective role in CNS injury (Dailey et al., 2017; Leopold et al., 2017).

More recently, Norrin has also been implicated in cancer. Planutis and colleagues showed in an

in vitro study using an established colon cancer cell line that Norrin regulates the angiogenesis of

colon cancer (Planutis, Planutiene and Holcombe, 2014). However, additional in vivo and human

tumor data on the role of Norrin in colon cancer require further study. Our group showed that

Norrin inhibits tumor initiation and progression of Shh-medulloblastoma in two genetic mouse

models, an effect that is mediated through the vasculature and stromal remodeling (Bassett et al.,

2016). This was the first in vivo study to demonstrate a tumorigenic function for Norrin signaling

in the vasculature. The direct effect of Norrin neural progenitor proliferation (McNeill et al.,

2013), also raised the possibility that Norrin has additional non-vascular functions in

tumorigenesis.

As a preliminary computational analysis, we used publicly available genomic and transcriptomic

tumor data to determine if there was any association between Norrin and tumorigenesis. This

analysis revealed some very interesting aspects of NDP expression in cancer. First, we observed

variable expression of NDP among the vast majority of cancer types in TCGA. Additionally, the

expression of NDP in low grade glioma (LGG) and GBM was strikingly higher than the average

expression level among all cancer types. Because there was no available data from normal tissue

counterparts to LGG or GBM, it is unclear if NDP is specifically overexpressed in these cancer

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types, or a feature of the cell of origin for these tumors. Norrin was reported to have a

widespread expression in astrocytes in the forebrain and midbrain, and Bergman glia in the

cerebellum of mice during development (Ye, Smallwood and Nathans, 2011). However; the

inefficiency of probes used to detect Norrin in earlier microarray screens makes it difficult to

compare NDP expression in GBM and LGG to normal cell counterparts. Our analysis also

revealed a striking degree of variability in NDP expression as well NDP mutations in non-CNS

cancer types such as breast, bladder and ovarian cancers. It will be important to investigate how

variable NDP expression levels are related to disease stage, progression or clinical outcomes for

these tumor types. Interestingly, in a recent transcriptome-based map associating signaling

pathways with clinical outcome in ovarian cancer there was a striking correlation between NDP

and FZD4 expression and favorable outcomes (Reinartz et al., 2016). Based on this association

in ovarian cancer, it would be interesting to perform similar NDP/FZD4 pathway analyses in the

other cancers where NDP shows a high degree of expression variability, such as breast cancer,

and determine if it is associated with a certain molecular subtype, survival outcomes, or a

specific clinical course. In addition, it is important to determine the factors and molecular

pathway that lead to the activation or inhibition of NDP expression in these cancers.

To have an insight about the relevance of NDP expression in CNS cancers, we investigated the

association between NDP expression levels and survival rates in neurological cancers.

Interestingly, NDP expression was significantly correlated with survival in the three neurological

cancer types we tested: GBM, Astrocytoma (LGG) and Neuroblastoma. FZD4 on the other hand

did not seem to exhibit a similar correlation pattern. Collectively, our preliminary computational

analysis, as well as our previous study in MB, were consistent with the possibility that Norrin

expression is functionally relevant in brain tumors.

4.2 Norrin contributes to the progression of GBM and hNSC in vitro

In collaboration with Dr. Peter Dirks (SickKids, Toronto), we used primary-derived GBM stem

cells (GNS) and primary human fetal neural stem cells (hNSC) for in vitro and in vivo analysis

of NDP/FZD4 function. While this culture system is reported to better recapitulate the genetic

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and transcriptional heterogeneity and stem cell phenotype of the original tumor relative to the

traditional sphere culture system (Pollard et al., 2009), there are a few limitations of this model.

First, the culture system depends on the ability of GBM cells to adhere and expand in monolayer

culture, leading to a selection for stem cells with better inherent adherence characteristics.

Second, the culture media depends on the use of mitogens, such as FGF and EGF, which could

result in a selective bias for GBM cells that are responsive to these factors under monolayer

conditions. Despite these limitations, this monolayer culture system remains one of the most

acceptable models in the field when the results obtained in vitro are confirmed in mouse

xenograft models. Therefore, we verified our observations in xenograft mouse models to confirm

its relevance to original tumors.

First, we examined the expression of NDP/FZD4 pathway components (NDP, FZD4, LRP5,

TSPAN12) in a panel of 9 GNS and 3 hNSC lines. Interestingly, we detected significant

expression of all pathway components in almost all lines, which is consistent with our in silico

analysis, which revealed a strikingly high level of NDP expression in GBM with limited

variability. These observations suggest that there could be a conservation of this pathway in

GBM. Then, to model NDP/FZD4 function in a non-transformed counterpart to GNS, we tested

the effects of NDP or FZD4 gain and loss of function in primary human fetal NSC (hNSC).

hNSCs have been reported to be closely related to the stem cell populations of neurological

cancers, based on the high degree of genetic and phenotypic similarity of both cell types. There,

hNSCs present an important experimental model to study the initiation and development of

several neurological cancers including GBM and Neuroblastoma (Zhang et al., 2017; Gage and

Temple, 2013; Ebben et al., 2010). In fact, the use of hNSC model was shown to be very

valuable for cancer gene therapy research by providing an ideal surrogate for cancer drug

screening (Ahmed et al., 2010). Interestingly, NDP or FZD4 loss of function resulted in a

striking inhibition of proliferation in two hNSC lines that express high or low levels of NDP.

This effect was confirmed to be specific by a complementary gain of function experiment. Our

observations in hNSCs raise the possibility that NDP/FZD4 might be relevant in normal NSC

maintenance. It would be very interesting to expand on these observations and examine their

clinical relevance, for example, in vivo experiments would be required to examine whether

Norrin is involved in brain regeneration after injury and/or neural cell death. Subsequently,

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activation of NDP/FZD4 pathway might provide a potential therapeutic strategy against brain

damage and degenerative diseases. This is particularly promising since WNT signaling has a

well-established role in brain and spinal cord development and regeneration, as well as

modulating stromal changes and immune response after injury (Oliva et al., 2018; Marchetti and

Pluchino, 2013; Herman et al., 2018; Clevers, Noh and Nusse, 2014).

4.3 Divergence of Norrin functions in GBM based on ASCL1 subtype in vitro and in vivo

To investigate the biological role of NDP/FZD4 in GBM, we performed NDP and FZD4 gain

and loss of function experiments in four GNS lines: 2 of the ASCL1hi subtype and 2 of the

ASCL1lo subtype. Surprisingly, our results indicated that NDP might play a completely opposite

role in subtype. Based on our in vitro observations, NDP inhibits proliferation and sphere

formation in ASCL1lo GNS and promotes these processes in ASCL1hi lines. These results were

confirmed in vivo in orthotopic xenografts. This very interesting divergence of NDP functions in

GBM highlights the importance of the transcriptional and genomic background of the tumor and

how it might determine the outcome of modulating signaling pathways such as NDP/FZD4. In

addition, these results also shed more light on the potential significance of stratifying GBM

tumors based on ASCL1 expression levels.

Another observation from these experiments was the different effects of FZD4 function among

the two groups. FZD4 gain and loss of functions in ASCL1lo lines exactly phenocopies NDP gain

and loss of function, while in ASCL1hi GNS lines FZD4 does not affect proliferation or sphere

formation in vitro. These observations suggested that there is a fundamental difference in the

effect of canonical WNT signaling in these GNS subtypes. Consistent with this possibility, we

found that treatment with a WNT inhibitor or anti-FZD4 antibodies blocked the effects of ectopic

NDP expression in ASCL1lo, but not in ASCL1hi lines. Moreover, these treatments promoted

proliferation in control ASCL1lo cells. Our qRT-PCR results confirmed the expression of FZD4,

LRP5 and TSPAN12 in ASCL1hi lines including the ones we used in our experiments (G523 and

G472). In this instance, Norrin might still bind FZD4 and activate the canonical WNT pathway

in ASCL1hi cells, but that it regulates different biological processes. In that case, it would be

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crucial to investigate what determines the different outcomes of NDP/FZD4 signaling in

ASCL1hi versus ASCL1lo lines, and identify biological processes controlled by NDP/FZD4 axis

in ASCL1hi GNS.

On the other hand, there is a possibility that the functional discrepancy of Norrin in the two GNS

subsets results from the inability of Norrin to bind FZD4 and activate downstream WNT

signaling cascade in ASCL1hi GNS cells. If that is the case, it would be interesting to investigate

how Norrin binds specific receptors and what determines this selection process. There might be

other alternative receptors with more affinity to Norrin that compete with FZD4 in this system.

Additionally, the selective binding of Norrin to specific receptors might be regulated on a

different level through receptor antagonists and competitive inhibitors. Nevertheless, these

observations indicate that Norrin effects on proliferation and sphere formation in ASCL1hi cells

are WNT-independent.

Besides its well documented function as an atypical WNT ligand, Norrin was also reported to

antagonize BMP signaling in human and frog cells (Xu et al., 2012; Deng et al., 2013). This

alternative signaling pathway for Norrin is particularly relevant to our study because BMP

signaling is a potent driver of cell cycle exit and differentiation in GBM (Piccirillo et al., 2006;

Lee et al., 2008). Additionally, Gremlin-1 (a BMP antagonist protein), was recently reported to

promote GBM carcinogenesis by protecting GBM stem cells from BMP-induced differentiation

(Yan et al., 2014; Guan et al., 2017). Interestingly, the effect of Gremlin-1 on GBM is very

similar to the effects we observe with ectopic NDP expression in ASCL1hi GBM. Moreover,

ASCL1hi cells seem to be affected by endogenous BMP because their growth is increased in the

presence of a BMP inhibitor. Therefore, we hypothesized that NDP might promote the

proliferation and sphere formation of ASCL1hi cells through antagonizing BMP signaling. One

prediction of this model is that BMP inhibition should rescue growth in the context of NDP

knockdown, which was not the case in this context. Alternatively, NDP knockdown rendered

cells completely resistant to the BMP inhibitor, suggesting that these pathways do interact, but in

a way that is more complex than our simple model. For example, it is possible that NDP

knockdown results in and increase in BMP4 levels that overwhelm the capacity of DMH1

inhibitor. While we were unable to corroborate this observation with a second inhibitor because

of toxicity, we showed that excess BMP4 is antagonistic to the growth promoting effect of NDP

115

overexpression, which is consistent with this interpretation. To confirm the involvement of BMP

signaling in mediating effects of NDP in this context we would need to perform a thorough gain

and loss of function analysis for BMP4 and its receptor, in combination with NDP gain and loss

of function. In conclusion, our chemical inhibitor experiments confirmed that the effects of NDP

are mediated through WNT in ASCL1lo and that there is an interaction between BMP and NDP

in ASCL1hi cells.

Despite differences in growth and signaling of NDP in both types of GBM, there was a

surprising overlap in their cell biological effects. In both instances, NDP affected the proportion

of cycling cells (Ki67+) in the cultures, suggesting that it functions to control the rate of cell

cycle exit and differentiation, which is also consistent with the effects of NDP on sphere

formation. Even more interesting, NDP knockdown in both cell types affected cell cycle kinetics

based on the reduction of the proportion of cells in S-phase amongst the cycling cell pool

(EdU+/Ki67+). This phenotype in ASCL1lo cells is particularly notable because the overall

effect of NDP knockdown is growth promoting and suggests the existence of other growth

promoting effects of NDP knockdown, which are dominant to the cell cycle inhibitory effects.

More experiments would be required in this context to fully characterize the cell biological

effects of NDP signaling. For example, changes in stemness, differentiation and cell growth

often lead to subsequent changes in cell death and apoptosis. Therefore, we are currently

investigating whether NDP knockdown results in a change in apoptosis in both cohorts. In

addition to TUNEL assay that uses DNA damage as an indication for cell death, we plan to query

a panel of apoptotic proteins such as cleaved-Caspase 3 and cleaved-PARP in NDP knockdown

cells. Notably, NDP was reported to protect retina ganglion cells during oxidative stress

(Leopold et al., 2017; Dailey et al., 2017), so it would be interesting to examine if NDP

knockdown in GNS results in forms of cellular stresses such as oxidative stress and reactive

oxygen species (ROS).

In parallel with these observations, our RNA-Seq screening revealed a significantly different

profile of differential gene expression after knocking down NDP in ASCL1hi versus ASCL1lo

cells. Interestingly, the number of the significant identified hits that were differentially expressed

in ASCL1lo cells after NDP knockdown (about 2400 hits) was quite larger than hits identified in

ASCL1hi cells (about 1600 hits). We categorized these hits into 3 groups; common hits

116

(differentially expressed after NDP knockdown in both lines), ASCL1hi unique, and ASCL1lo

unique hits. The vast majority of the common hits were directly related to cell cycle regulation

and DNA replication, consistent with the phenotypic effects of NDP knockdown on cell cycle

kinetics in both GNS subtypes. This supports with our observations that NDP knockdown affects

EdU+/Ki67+ population (indicating changes in cell cycle kinetics) in both ASCL1hi and ASCL1lo

lines. Notably, many of the unique hits in ASCL1hi GNS line were related to cell cycle kinetics

and DNA replication, as well as differentiation and apoptosis. This is also supported by our ICC

experiments revealed that SOX2+ cell population is affected after NDP knockdown only in

ASCL1hi but not ASCL1lo cells. Conversely, the unique hits in ASCL1lo cells were related to

quite different cellular processes, including invasion, migration, metastasis and extracellular

matrix modulation there were also some death genes. The activation of these processes in

ASCL1lo cells after NDP knockdown could explain the enhanced growth phenotype, even in the

context of slower cell cycle.

While most of our experiments focus on the downstream effects of NDP signaling in GNS and

how it varies between ASCL1hi and ASCL1lo cohorts, it would be very interesting to investigate

the upstream regulation and identify the factors controlling this functional discrepancy. First, it is

important to investigate whether ASCL1 expression level is sufficient to direct the NDP mode of

action. To address this question, we designed an in vitro to investigate whether ASCL1 loss of

function leads to reversal of NDP effects in these cells. If ASCL1 is found to be interacting with

NDP, it would be important to identify the mechanism mediating this interaction and how this

interaction controls GNS tumorigenesis and stemness. Notably, ASCL1 levels were shown to be

significantly associated with the proneural subtype in GBM (Park et al., 2017), therefore it will

be interesting to examine if NDP functions segregate with GBM transcriptional subtypes. We

confirmed the reproducibility of our results in two lines of each cohort, however; expanding this

analysis to include more primary lines and tumor samples would be important to further validate

our hypothesis and assess its reproducibility degree and significance. In conclusion, our results

reveal a striking divergence in the functions of NDP between ASCL1hi and ASCL1lo GNS lines

and indicate the existence of at least two alternative mechanisms that mediate different aspects of

NDP effects.

117

ASCL1 expression levels were shown to be important predictive factors for NOTCH inhibition as

a differentiation therapy, and ASCL1hi lines were termed “differentiation-competent cells” (Park

et al., 2017). Similarly, it is would be interesting to examine whether NDP manipulation can

sensitize GNS cells to differentiation therapy and provide a synergistic value when applied in

combination with NOTCH inhibition. In summary, our results indicate a segregation of NDP

biological effects with ASCL1 expression levels, however; more experiments will be required to

test if ASCL1 and NDP mechanistically interact, and the degree to which this interaction

controls GNS progression.

4.4 Potential therapeutic applications of Norrrin

Despite the enormous research efforts applied to GBM over the last decades, it remains one of

the most lethal and aggressive cancer types with very poor clinical outcomes (Jovcevska,

Kocevar and Komel, 2013; Louis et al., 2007). The current first line chemotherapeutic agent for

GBM remains temozolomide, which is only effective in a small subset of patients who

eventually develop resistance and relapse (Stupp et al., 2009). Therefore, research efforts have

been directed towards developing targeted therapies to treat patients harboring specific

genotypes and/or expressing specific markers (Quartararo et al., 2015; Prados et al., 2015; Chen

et al., 2016). For example, in an interesting application of this concept, Lee and colleagues

studied both genomic and expression profiles of 127 multisector or longitudinal specimens

coming from 52 GBM patients to identify the degree of heterogeneity and apply precision

medicine (Lee et al., 2017). Despite the well documented intra-tumor heterogeneity of GBM, the

authors found that cells from the same tumor mass share a specific degree of genomic and

expression signatures that can be exploited to specifically identify targeted therapy. While the

use of large scale screens to identify potential therapeutic targets has led to a significant

advancement in the field, the learned application of biological knowledge and molecular

signaling information remains highly important for therapeutic initiatives. For example, the

knowledge about the role of ASCL1 in controlling terminal neuronal differentiation of NSCs led

to the hypothesis that ASCL1hi GNS cells are more prone to NOTCH inhibition and

differentiation therapy (Park et al., 2017).

118

In this study, we show an interesting example of this concept. Here, we show that NDP, which is

highly expressed in GBM, can function to either promote or inhibit GBM progression, depending

on the signature of the tumor. These observations suggest that silencing NDP can have potential

therapeutic benefits in ASCL1hi cells. However, the same approach in ASCL1lo GBM, could

promote faster tumor progression. This concept becomes more critical when we consider other

components of WNT signaling pathway. In fact, both WNT activation and inhibition were

suggested as potential therapeutic strategies in GBM (Rampazzo et al., 2013; McCord et al.,

2017; De Robertis et al., 2013). While different models, culture systems, and experimental

conditions might explain the discrepancy in these studies, it is clear that we need better

understanding of GBM biology and and tumor stratification to prescribe the accurate therapy. In

this study, we used the expression of ASCL1 to stratify GBM stem cells. We provide indications

that NDP-mediated activation of canonical WNT signaling might be growth inhibiting in

ASCL1lo GBM stem cells. Similarly, other markers and/or important molecules can be used to

further stratify GBM tumors and propose targeted therapies based on specific tumor profiles.

Our observations in ASCL1hi GNS cells suggest that NDP is a novel target for cancer stem cell

therapy. In this study we provide evidence that NDP promotes the proliferation and sphere

formation of GBM stem cells. It is critical to identify the mechanism underlying these effects of

NDP and whether it is mediated through BMP signaling, because BMP activation leads to

astrocyte and not neuronal differentiation. Astrocyte differentiation is not terminal, as

differentiated astrocytes can re-enter cell cycle and regain the cancer stem cell phenotype in

response stress of radio or chemotherapy. Conversely neuronal differentiation was shown to be

terminal and leading to permanent cell cycle exit (Magnusson et al., 2014; Alcantara et al., 2009;

Friedmann et al., 2012). Therefore, the potential therapeutic benefits of NDP in ASCL1hi cells

should be considered with caution.

As a direct clinically relevant application of these observations, it would be valuable to examine

the effects of inhibiting NDP (by either orthotopic adenovirus delivery of short-haipin RNA or

blocking NDP) on the progression of established ASCL1hi GNS tumors in vivo. Here we show

that silencing NDP in ASCL1hi, and overexpressng it in ASCL1lo GBM stem cells prior to

orthotopic xenografting results in a significant increase in survival. Subsequently, more

experiments would be required to test the effects of NDP manipulation in established in vivo

119

tumors to assess the therapeutic potential of targeting NDP. In ASCL1lo cells, our results argue

that activating NDP/FZD mediated canonical WNT signaling might inhibit tumor progression,

therefore, small molecule agonists and/or WNT ligands can be tested in mice with established

ASCL1lo GBM tumors.

Another important aspect of the function of Norrin comes from its clear involvement in the

stroma and cellular microenvironment, as reported by several studies (Bassett et al., 2016;

Reinartz et al., 2016; Planutis K, Planutiene M and Holcombe, 2014; Planutis et al., 2014).

Remodeling and modification of GBM microenvironment was proposed as a potential

therapeutic strategy in several studies (Volak et al., 2018; Achyut et al., 2017; Hovinga et al.,

2010; Sadahiro et al., 2018). In this study we focused on the cell autonomous functions of NDP

in GBM stem cells, however; it would be interesting to examine the role of NDP in mediating

GBM microenvironment and stroma.

4.5 Summary and significance

To the best of our knowledge, this is the first study to report a cell autonomous role of NDP in

cancer cells. Our results provide evidence for a novel role of Norrin in regulating the progression

of GBM stem cells in vitro and in vivo. Interestingly, we show that NDP inhibits the progression

of ASCL1lo GBM, while promoting the progression of ASCL1hi GBM. In addition, we show that

the effects of NDP in both groups are mediated by different mechanisms. An illustration

summary of our proposed model of NDP functions in GBM stem cells is demonstrated in Figure

29.

This study proposes NDP as a novel targetable protein to treat GBM. Particularly in ASCL1lo

cells where Norrin can present an effective approach to activate canonical WNT signaling and is

likely involved in other important stroma-related functions such as endothelial cell remodeling

and inhibiting angiogenesis.

In addition, our initial computational analysis reveals the abundance of Norrin expression in

many cancer types, suggesting it might play role in regulating other cancers as well.

120

121

Figure 29. Proposed model of NDP biological functions in GBM

A) In ASCL1hi cells, NDP affects cell cycle regulation, but also affects differentiation, apoptosis and DNA

repair in a WNT-independent mechanism resulting in pro-tumorigenic effects on progression.

B) In ASCL1lo cells, NDP affects cell cycle regulation, but also affects migration, invasion and EMT in a

WNT-dependent mechanism resulting in changes in cell cycle re-entry leading to anti-tumorigenic effects

on progression.

122

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Appendices

Abbreviations

AKT2 AKT Serine/Threonine Kinase 2

APC Adenomatous Polyposis Coli

ASCL1 Achaete-Scute Family BHLH Transcription Factor 1

ATP Adenosine Triphosphate

ATRX X-linked ATP-dependent helicase

BBB blood-brain barrier

bFGF basic Fibroblast Growth Factor

bHLH basic Helix-Loop-Helix

BMP Bone Morphogenetic Protein

BMPR Bone Morphogenetic Protein Receptor

BSA Bovine Serum Albumin

CCLE Cancer Cell Line Encyclopaedia

CDK Cyclin Dependent Kinase

CDKN2A Cyclin Dependent Kinase Inhibitor 2A

cDNA Complimentary-DNA

CIC Drosophila Homologue of Capicua

CKI Casein kinase 1

CSC Cancer Stem Cells

166

CT value Cycle Threshold value

DDL3 Delta-like 3

DNA Deoxyribonucleic Acid

DVL Dishevelled

EdU 5-ethynyl-2'-deoxyuridine

EGF Epidermal Growth Factor

EGFR Epidermal Growth Factor Receptor

ELDA Extreme Limited Dilution Assay

EMT Epithelial-Mesenchymal Transition

FACS Fluorescence-Activated Cell Sorting

FAT1 FAT Atypical Cadherin 1

FGF Fibroblast Growth Factor

FOXM1 Forkhead Box M1

FUBP1 Far Upstream Element-Binding Protein 1

FZD Frizzled

GAPDH Glyceraldehyde 3-Phosphate Dehydrogenase

GBM Glioblastoma

G-CIMP Glioma CpG Island Methylator Phenotype

GDC Genomic Data Commons

GFAP Glial Fibrillary Acidic Protein

167

GFP Green Fluorescence Protein

GNS Patient-derived glioblastoma stem cells

GSEA Gene Set Enrichment Analysis

GSK Glycogen Synthase Kinase

H3F3 H3 Histone Family Member 3A

HEK-293T Human Embryonic Kidney-293T cells

HES1 Hairy And Enhancer Of Split 1,

hNSCs human fetal Neural Stem Cells

hPRT2 hypoxanthine Phosphoribosyltransferase 2

ICC Immunocytochemistry

IDH Isocitrate dehydrogenase

IgG Immunoglobulin G

L1CAM L1 Cell Adhesion Molecule

LEF Lymphoid Enhancer Factor

LGG Lower Grade Glioma

LOH Loss of Heterozygosity

LRP Low-density lipoprotein receptor-related protein

MAL Myelin And Lymphocyte Protein

MAP2 Microtubule Associated Protein 2

MB Medulloblastoma

168

MBP Myelin Basic Protein

MDM-2 Mouse Double Minute 2 Homolog

MGMT O-6-Methylguanine-DNA Methyltransferase

mTOR Mammalian Target Of Rapamycin

MYC Avian Myelocytomatosis Viral Oncogene Homolog

NADPH Nicotinamide Adenine Dinucleotide Phosphate

NANOG Homeobox Transcription Factor Nanog

NDP Norrie Disease Pseudoglioma

NF1 Neurofibromin 1

NF-κB Nuclear Factor kappa-light-chain-enhancer of activated B cells

NSCs Neural Stem Cells

NSG mice NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ mice

O4 Oligodendrocyte Markers O4

Olig2 Oligodendrocyte Transcription Factor 2

PARP Poly [ADP-ribose] polymerase 1

PBS Phosphatase Buffered Saline

PDGFRA Platelet-Derived Growth Factor Receptor Alpha

PFA Paraformaldehyde

PI3K Phosphatidylinositol-4,5-bisphosphate 3-Kinase

PLAGL2 Pleiomorphic Adenoma Gene-Like 2

169

PLO Poly L-Ornithin

Ptch Patched gene

PTEN Phosphatase and Tensin Homolog

PVDF Polyvinylidene Fluoride membrane

qRT-PCR quantitative Real-Time Polymerase Chain Reaction

RIPA Radioimmunoprecipitation Assay buffer

RNA Ribonucleic Acid

ROS Reactive Oxygen Species

sFRPs soluble Frizzled-Related Proteins

Shh-MB Sonic hedgehog subtype of Medulloblastoma

SOX2 SRY-Box 2

STAT3 Signal Transducer and Activator of Transcription 3

TCF T-Cell Factor

TCGA The Cancer Genome Atlas

TERT Telomerase Reverse Transcriptase

TGF-β Transforming growth factor-beta

TMZ Temozolomide

TP53 Tumor Protein p53

TSPAN12 Tetraspanin 12

TU-J Tubulin Beta 3 Class III

170

TUNEL Terminal deoxynucleotidyl transferase (TdT) dUTP Nick-End Labeling assay

VEGF Vascular Endothelial Growth Factor

WB Western Blotting

WHO World Health Organization

YKL40 Chitinase 3 Like 1

α-KG α-Ketoglutarate

ΔΔCT Double Delta CT