i

Non-mutated Regulators of Cancer Growth in Basal-like Breast Cancer and Transformed

Colon Cells

by

Anwesha Ghosh

Submitted in Partial Fulfillment of the

Requirements for the Degree

Doctor of Philosophy

Supervised by

Dr. Helene R. McMurray, PhD

Department of Biology

Arts, Sciences and Engineering

School of Arts and Sciences

University of Rochester

Rochester, New York

2015

ii

Dedication

This thesis is dedicated to my late grandmother, Mrs. Bharati Sen, who taught me to go out there

and search for the truth.

iii

Biographical Sketch

The author was born in Kolkata, India, on February 16, 1988. She attended Heritage

Institute of Technology, under the West Bengal University of Technology (India), and graduated

with the Bachelor of Technology degree in Biotechnology in 2010. She came to the University of

Rochester, Rochester NY (USA) in the Fall of 2010 and began graduate studies in the Department

of Biology. She received her Master of Science degree in Biology from University of Rochester

in 2012. She has worked as teaching assistant at the Department of Biology from 2010 to 2015.

She pursued her research on the genetics and signaling biology of cancer under the guidance of

Dr. Helene R. McMurray.

iv

Acknowledgements

I owe the deepest gratitude to my advisor, Dr. Helene McMurray, who has been a true mentor

for me in the process of learning how to pursue science. She has been a constant source of

guidance, encouragement and support during the developmental years of my PhD. Alongside

science, she has been a source of inspiration on how to effectively manage time and be a better

communicator.

I am deeply indebted to my committee members: Dr. David Goldfarb, Dr. Matthew Hilton, Dr.

Douglas Portman and Dr. Andrei Seluanov. They have provided constant support and insightful

suggestions that have been invaluable for the progress of my PhD.

I would like to thank Dr. Hartmut “Hucky” Land for his numerous suggestions and invaluable

discussions that I had. I would also like to thank Dr. Craig Jordan for providing crucial support for

the RNA sequencing component of my project. I owe my thanks to Soumyaroop Bhattacharya for

helping me with the analysis of the RNA sequencing results.

A special thanks to Cynthia Landry, Jill van Atta and Daina Bullwinkel for making the grad

school process smoother for me. I also want to thank the past and present members of the lab,

especially Dorothy Heyer, for her constant technical support, Emily Walters, Dr. Pierre Candelaria

and Luca Iorga for their friendship. My special thanks to folks from the neighbouring labs,

especially Aslihan, Brad, Jordan, Mary, Nirmalya, Shweta and Vijaya, for brainstorming sessions,

technical support and personal motivation. I also remember my friends in Rochester and around

the world, who are too numerous to mention and have supported me in their own special ways.

Lastly, I would like to express my eternal gratitude to my parents and my aunt for showering

me with constant love and support and believing in me.

v

Abstract

Cooperation Response Genes (CRGs) are non-mutant regulators of the malignant phenotype

in cancer cells. Distinct sets of CRGs contribute to transformation in different types of cancer, with

accumulating evidence for a set of CRGs regulated by cooperation of mutant p53 and mutant Ras

that are important in multiple epithelial cancers, including transformed colon cells and basal-like

breast cancer (BLBC). Among the CRGs is Notch3, a transmembrane receptor of the Notch family

that regulates cell proliferation and fate specification in multi-cellular organisms. Here, we

demonstrate a cancer-selective role of Notch3 in restricting cell growth of BLBC.

Through genome scale transcriptomic analysis, we discover that there is a cancer-specific

response to activation of Notch3, part of which is required for the cell growth inhibitory role of

Notch3 in BLBC. We specifically identify a novel interaction between Notch and Sfrp2, a known

Wnt pathway antagonist, and show that the genetic interaction is essential for Notch3 to control

BLBC cell growth, concomitant with Sfrp2 dependent changes in expression of cell cycle

regulatory genes.

We also observe that a similar architecture underlies Notch3-mediated growth inhibition in

growth inhibition in the transformed colon cells. Our work elucidates how Notch3 restricts cancer

cell proliferation in transformed colon cells through a genetic interaction between Notch3 and

Sfrp2 that is essential for the cancer-selective growth inhibitory action of Notch3 in transformed

colon cells.

Lastly, we describe a statistical method based on linear regression modeling that refines the

method for identifying CRGs. While 84 of the genes originally identified as CRGs are also

identified by the linear modeling method, twelve new genes are identified as CRGs in the

vi

transformed colon cells. We test the importance of one of the new CRGs, Clca1 and find that

suppressing Clca1 expression causes a significant reduction in tumor formation. Hence the newly

proposed linear modeling method is useful for identifying CRGs critical for tumor formation.

vii

Contributors and Funding Sources

All experiments described in this thesis were performed by the author except for the following:

Chapter II

Figure 2.4: The high throughput RNA sequencing and CuffDiff Analysis was performed by the

UR Functional Genomics Center.

Chapter III

Figure 3.1: Compound perturbations of Sfrp2 knock-down and NIC3 expression in mp53/Ras

cells was performed by Dorothy Heyer and cell count assay was performed by Helene

McMurray.

Chapter IV

Figure 4.3: CRGs were identified by linear modeling and the statistical method was analyzed by

Jesse Llop and Peter Salzman.

Figure 4.4: Comparison of linear modeling with the original synergy score was performed by

Jesse Llop and Peter Salzman.

Funding for the research was provided by the Wilmot Cancer Center and the Breast Cancer

Research Initiative Fund through grants awarded to Dr. Helene R. McMurray.

viii

Table of Contents

Chapter I. General Introduction 1

1.1 Oncogene Cooperation in Cellular Transformation 2

1.2 Approaches to Achieving Cancer Selectivity 8

1.3 Basal-like Breast Cancer 16

1.4 Colorectal Cancer 18

1.5 The Role of Notch Signaling and Wnt Signaling in Cancer 20

1.6 Rationale of the Current Study 24

1.7 Figures 26

Chapter II. Notch3 cancer-selectively controls proliferation via a network of genes

specifically in basal-like breast cancer

28

2.1 Abstract 29

2.2 Introduction 30

2.3 Results 32

2.4 Discussion 39

2.5 Figures 42

Chapter III. Conserved molecular interactions implicated in cancer selective

control of cell growth by Notch3 in mp53/Ras-transformed murine colon cells and

BLBC

56

3.1 Abstract 57

3.2 Introduction 58

ix

3.3 Results 60

3.4 Discussion 63

3.5 Figures 66

Chapter IV. A novel statistical model for identification of synergistically regulated

genes

72

4.1 Abstract 73

4.2 Introduction 74

4.3 Results 77

4.4 Discussion 80

4.5 Figures 82

Chapter V. Discussions 89

5.1 Summary of Findings 90

5.2 Significance of Findings 92

5.3 Future Directions 97

5.4 Conclusions 100

Chapter VI. Materials and Methods 101

6.1 Materials 102

6.1.1 Parental Cell Lines 102

6.1.2 Plasmids 104

6.1.3 shRNA Target Sequences 106

6.1.4 Real-Time PCR Primers 107

6.2 Methods 109

Chapter VII. Appendix 115

x

Bibliography 127

xi

List of Tables

Table 1 Genes responding to Notch3 in Cancer Cells and Their Response in Non-

Cancer Cells According to DESeq Analysis

113

Table 2 Comparison of DESeq, EdgeR and Cuffdiff in fold change and p-value 118

Table 3 Mutation spectrum of BLBC cell lines used in the study 123

xii

List of Figures

Figure 1 Notch3 activation inhibits cell growth in transformed colon cells 26

Figure 2.1 Notch3 restricts proliferation in a cancer-selective manner 42

Figure 2.2 Expression of Notch3 in perturbed BLBC and non-cancerous breast cells 44

Figure 2.3 Induction of Hey/Hes family of genes in response to NIC3 expression in

Cancer Cells and Non-Cancer Cells

46

Figure 2.4 Cancer-specific regulation of gene expression by Notch3 48

Figure 2.5 Comparison of gene expression levels in cancer cells versus non-cancer cells 50

Figure 2.6 Notch3 requires Sfrp2 for cancer-specifically restricting proliferation 52

Figure 2.7 Cancer-specific Notch3 sensitive genes have a hierarchy of interactions 53

Figure 2.8 Notch3 knock-down lowers Sfrp2 expression in BLBC cells 55

Figure 3.1 Notch3 restricts cell growth via induction of Sfrp2 in colon transformed cells 67

Figure 3.2 Notch3 inhibits canonical Wnt pathway gene expression by induction of

Sfrp2

68

Figure 3.3 Sfrp2 induction is insufficient to phenocopy Notch3 activation 70

Figure 4.1 ‘Cooperation Response Genes’, downstream effectors of interaction between

oncogenic mutations

83

Figure 4.2 Examples of synergistic and non-synergistic expression patterns 84

Figure 4.3 Linear modeling synergy coefficient classifies tumor inhibitory genes 85

Figure 4.4 Comparison of linear modeling to the original classifier, synergy score 86

Figure 4.5 Novel CRGs found by linear modeling 87

xiii

Figure 4.6 Knock-down of the newly identified CRG, Clca1, inhibits tumor growth 88

1

Chapter I

General Introduction

2

1.1 Oncogene Cooperation in Cellular Transformation

Cancer cells arise from normal cells as the result of a multistep process involving the

accumulation of multiple oncogenic lesions (Vogelstein and Kinzler, 1993). Such oncogenic

lesions include activating mutations in pro-oncogenic genes and inactivating mutations in tumor

suppressive genes. Examples of pro-oncogenic genes are Ras and Myc. The Ras genes encode for

a family of GTP-binding proteins that act as switches for signaling pathways that regulate cellular

processes like proliferation, differentiation, survival and apoptosis (Giehl, 2005). Oncogenic

mutation of Ras makes the protein independent of GAP binding that typically activates the protein,

so that Ras protein reaches a perpetually “on’ state, causing continuous stimulation of the

downstream signaling (Adjei et al., 2001; Prior et al., 2012). Similarly, the transcription factor,

Myc, lies at the crossroads of multiple growth promoting pathways including Wnt signaling, and

activating mutation in the Myc gene causes the protein to be stabilized and avoid degradation,

causing aberrant signaling (Dang, 2012). Tumor suppressor genes such as p53 typically

accumulate inactivating mutations. P53 is a stress response gene that drives DNA repair, cell cycle

arrest and apoptosis of potentially malignant cells (Ryan et al., 2001). Inactivating mutations in

p53 prevent the protein from binding to DNA and driving these responses (Sigal and Rotter, 2000).

Such mutated oncogenes and tumor suppressors cooperate to transform the cell, leading to

uncontrolled clonal expansion (Pedraza-Farina, 2006).

The evidence that cellular transformation requires cooperation between oncogenic lesions

comes from multiple observations. First of all, cancer is most commonly observed later in life and

the probability of getting cancer increases exponentially with age in both humans and mice

(Armitage and Doll, 1954; Peto et al., 1975; Vogelstein and Kinzler, 1993). Statistical analysis of

epidemiological data from multiple types of cancer suggests that transformation of human cells

3

requires the occurrence of at least 4-6 rate limiting steps (Armitage, 1954). These predictions are

consistent with the various distinct histopathological stages of development of cancers in humans

(Kinzler and Vogelstein, 1996). These rate limiting steps represent accumulation of mutations in

oncogenes and tumor suppressors (Weinberg, 1989; Bishop, 1987). Histological and molecular

analysis of tissues obtained from different stages of transformation show an association between

accumulation of mutations and progression of the malignant disease, with certain mutations

typically associated with a particular stage of the disease (Fearon and Vogelstein, 1990; Kinzler

and Vogelstein, 1996; Vogelstein and Kinzler, 1993; Ingvarsson, 1999; Abate-Shen and Shen,

2000; Bardeesy and DePinho, 2002). For example, in colorectal cancer, mutations in the tumor

suppressor gene APC or proto-oncogene Ras are early events found in premalignant tissues, while

inactivating mutations in the gatekeeper gene p53 are typically found in later stages of malignant

tissues (Fearon and Vogelstein, 1990). More importantly, when premalignant tissues and fully

malignant tissues found within the same tumor are analyzed for mutations, the fully malignant

cells contain the same set of mutations as the premalignant cells, along with a set of novel

mutations additionally acquired (Vogelstein and Kinzler, 1993; Jones et al., 2008).

Moreover, in vitro and in vivo experiments in which combinations of oncogenic mutations

are introduced into different cell types demonstrate that specific combinations of mutations are

sufficient to drive cells into a malignant state. For example, introducing oncogenic Ras causes rat

embryonic fibroblast cells to initially proliferate and then growth arrest in vitro without the ability

to form tumors in immunocompromised mice, whereas activated Myc cooperates with Ras to

transform rat embryonic fibroblast cells, bypassing the growth arrest phenotype (Land et. al, 1983).

Polyoma virus middle-T and Ras transform baby rat kidney cells (Ruley, 1983). Similarly,

combinations of APC and Ras (D’Abaco et al., 1996) and p53 and Ras (Hinds 1989) was sufficient

4

to transform murine colon epithelial cells. Similar observations have been made in rodent models

introducing combinations of oncogenic mutations in various tissues in vivo. For example,

introducing Myc and Ras under breast specific promotors synergistically and dramatically

increases tumor formation in the mouse mammary gland (Sinn et al., 1987), while transgenic

introduction of BRCA1 mutation in the mammary glands of p53 null mice caused basal-like breast

tumor formation (McCarthy et al., 2007). The combination of Myc and Ras produces tumors in a

range of tissues, when introduced mid-gestation in a mouse embryo (Compere et al., 1989).

Similarly, p53 null mice with a heterozygous null Rb mutation show greater tumor formation

versus the presence of either mutation (Williams et al., 1994).

Experiments show that human cells are more complicated and require combinations of

more than two mutations to cause cellular transformation. For example, the combination of SV40

large-T and H-ras is insufficient to transform human embryonic kidney cells and fibroblast cells.

These cells also require constitutive hTERT expression to become tumorigenic (Hahn et al., 1999).

The same combination of oncogenes are sufficient to transform human mammary epithelial cells

as well (Elenbaas et al., 2001). Similarly, human fibroblast cells deficient in p16INK4a are

transformed with the combination of hTERT and either Ras or Myc (Drayton et al., 2003).

It is interesting to note that not all combinations of oncogenes are capable of transforming

cells. This suggests that there may be multiple circuits controlling growth and deregulation of more

than one growth circuits is necessary to trigger uncontrolled growth (Vogelstein and Kinzler,

1993). Mutations in multiple genes may be critical for overcoming cellular defenses to the

introduced oncogene, producing conditions adverse for cellular growth (Hahn and Weinberg,

2002). For example, introduction of exogenous oncogenic Ras often triggers cellular senescence

or apoptosis in both mouse and human cells (Serrano et al., 1997). This premature growth arrest is

5

bypassed by either disrupting p53 alone in mouse cells or disrupting both p53 and Rb pathways in

human cells (Serrano et al., 1997). Disruption of the p53 and Rb pathways prevents the onset of

senescence, allowing the transformed cell to divide indefinitely. Hence, the additional mutations

need to neutralize the antineoplastic responses mounted by the previously introduced oncogene

(Weinberg, 1997).

The malignant cell, transformed by the combinations of oncogenic mutations, is able to

survive indefinitely, proliferate rapidly and uncontrollably independent of growth signals, evade

differentiation, invade to surrounding tissue and migrate to distant locations within the body,

sustain angiogenesis and alter metabolic requirements, along with other novel characteristics

(Hanahan and Weinberg, 2000, 2011). These altered characteristics of the transformed cell are

referred to as the hallmarks of cancer (Hanahan and Weinberg, 2000, 2011). Some of these

hallmarks stem from the deregulation in functions of the oncogenic lesions on being mutated. For

example, neoplastic growth of cells is triggered by activating mutations in the Ras signaling

pathways (Downward, 2003). Activating mutations in the phosphatidylinositol 3- kinase – Akt

pathway allow survival of the cancer cell in multiple cell types (Vivanco and Sawyers, 2002).

Similarly, inactivating mutations in p53 prevents triggering of cell cycle arrest, apoptosis and

senescence phenotypes controlled by the gene (Lowe et al., 1993; Schmitt et al., 2002; Livingstone

et al., 1992).

Hallmarks of the malignant cell are also triggered by the synergistic effect of the oncogenic

combination. The oncogenic combination decides the context dependent role of one of the

oncogenes. Bcl-2 overexpression licenses the pro-proliferative role of Myc in transformed cells,

as mentioned earlier (Vogelstein and Kinzler, 1993). Oncogenic Raf activates MAPK signaling,

triggering proliferation, as well as p53-dependent induction of p21 triggering cell-cycle arrest

6

(Lloyd et al., 1997). Dominant negative p53 prevents cell cycle arrest by blocking induction of

p21, allowing hyper-proliferation to take place in primary rat Schwann cells (Lloyd et al., 1997).

A similar mechanism of for the pro-proliferative role of Raf via p53 deactivation is also seen in

murine keratinocytes (Roper et al., 2001). Oncogenic Ras, in combination with dominant negative

p53, can synergistically regulate cellular motility and increase invasiveness of transformed murine

colon epithelial cells (Xia and Land, 2007). Simultaneous expression of mutant p53 and Ras also

allows the pro-invasive arm of RhoA activity to take place (Xia and Land, 2007). Oncogenic Ras,

by itself, promotes the activity of RhoA, a GTPase essential for cellular migration and invasion by

its membrane localization, while simultaneously suppressing its activity by activating p53 induced

RhoA inhibitor p190RhoGAP (Arthur and Burridge, 2001; Xia and Land, 2007). Expressing

dominant negative p53 with oncogenic Ras downregulates the inhibitory circuit of RhoA

modulation, allowing RhoA activation and driving malignant cell motility and invasiveness (Xia

and Land, 2007). Thus, cooperation between oncogenic lesions drives synergistic changes

occurring further downstream, and further efforts were invested in identifying additional key

synergistic mediators of the transformation process.

Critical mediators of the cancer behavior were identified downstream of cooperating

oncogenes. On introducing dominant negative p53 and oncogenic Ras in murine colon epithelial

cells, 95 annotated genes were found synergistically dysregulated in expression within the

transformed cells, as compared to non-transformed cells (McMurray et al., 2008). These genes,

known as Cooperation Response Genes (CRGs), were enriched in essential regulators of the

transformed state. When their expressions were individually reset back to levels in non-

transformed colon cells, the tumors formed on by many of these perturbations were significantly

smaller than non-perturbed transformed cells. In contrast, much fewer genes that respond to mutant

7

p53 and activated Ras in a non-synergistic fashion had tumor-inhibitory effects (McMurray et al.,

2008, McMurray et al., unpublished data).

CRGs are involved in regulating multiple functions including cellular signaling, autophagy

and metabolism. (McMurray et al., 2008; Kinsey et al., 2014; Smith et al., 2012) For example,

Plac8, a gene synergistically upregulated in expression in response to p53 and Ras, is a critical

regulator of autophagy in cancer cells (McMurray et al., 2008; Kinsey et al., 2014). Cancer cells

depend on Plac8 to enhance and sustain autophagic flux, allowing the maintenance of metabolic

homeostasis within cancer cells (Kinsey et al., 2014). Similarly, Abca1, a gene synergistically

downregulated in expression, is found to mediate cholesterol transfer across the plasma membrane

(McMurray et al., 2008; Smith and Land, 2012). Cancer cells preferentially lower the expression

of Abca1 to maintain elevated levels of cholesterol within the mitochondria, preventing release of

apoptosis-promoting molecules, allowing for survival (Smith and Land, 2012).

Similarly, CRGs were also identified in other types of cancer, including acute myeloid

leukemia, basal cell carcinoma and medulloblastoma, and were found to be essential mediators of

the transformation in such contexts too (Ashton et al., 2012; Eberl et al., 2012; Gotschel et al.,

2013). Importantly, a subset of the original CRGs identified in colorectal cancer downstream of

mutant p53 and activated H-Ras (mp53/Ras) have been found to play critical roles in other

epithelial cancers like pancreatic cancer, androgen-independent prostate cancer and basal-like

breast cancer (BLBC) (Kinsey et al., 2014; Walters et al., unpublished; McMurray et al.,

unpublished).

8

1.2 Approaches to Achieving Cancer Selectivity

An ideal strategy to target cancer would be to identify vulnerabilities present in cancer cells

that do not affect normal cells. Though cancer is actually a collection of many distinct diseases

that differ in tissue of origin, characteristic mutations (The Cancer Genome Atlas et al., 2013; The

Cancer Genome Atlas, 2012), gene expression patterns (Perou et al., 2000) and, only recently

appreciated, cell of origin within a tissue (Molyneux et al., 2010), there appear to be key features

present across cancer types and sub-types, driven by core signaling architectures that appear to be

dysregulated via mutations in different genes across cancers, as discussed earlier. Studies have

found that the transformed cell is dependent on many of these features in a cancer-specific manner,

providing a window of opportunity to identify vulnerabilities present.

One observation made in cancer cells is that often, the transformed cell is dependent on

one or more of its oncogenic proteins or pathways for its survival and continued functioning

(Sharma and Settleman, 2007). Coined by Bernard Weinstein in 2000, this phenomenon is known

as oncogene addiction (Weinstein, 2000; Weinstein 2002; Weinstein and Joe, 2006). This

dependency of the cancer cell has been observed in cell lines, mouse tumor models as well as

human clinical studies using drugs targeting such oncogenic mutations. Myc is an oncogene whose

expression is widely deregulated in multiple types of cancer (Vita and Henriksson, 2006). The first

ever demonstration of oncogene addiction was the inhibition of Myc via antisense RNA in human

tumor-derived promyelocytic leukemia cell lines. Inhibition of Myc caused these cells to stop

proliferating and differentiate (Yokoyama and Imamoto, 1987; Loke et al., 1988). Acute inhibition

of Myc in mouse models of Myc-activated lymphoma, leukemia and skin cancer causes induction

of apoptosis, senescence and regression of angiogenesis, respectively, indicating that the outcome

9

of disruption of oncogene addiction is context dependent (Felsher and Bishop, 1999; Arvanitis and

Felsher, 2006; Pelengaris et al., 1999; Wu et al., 2007; Beer et al., 2004; Boxer et al., 2004).

Similarly, H-Ras, K-Ras and BCR-ABL also demonstrate a similar phenomenon. Inducible

mouse models show that inactivation of H-Ras in melanoma, K-Ras in colorectal cancer and BCR-

ABL in leukemia causes tumor cell apoptosis and abrogates the ability to form tumors in vivo

(Chin et al., 1999; Fisher et al., 2001; Huettner et al., 2000; Shirasawa et al., 1993). Similar

observations have been made for deinduction of these genes in cultured human cell lines too

(Mukhopadhyay et al., 1991; Tokunaga et al., 2000; Kohl et al., 1994; Liu et al., 1998; Golas et

al., 2003).

Alongside classical oncogenes and tumor suppressors, other types of genes have also been

found to drive transformation. Oncogenic microRNAs (also called oncomirs) are also found to be

drivers of the transformation process (Esquela-Kerscher and Slack, 2006). For example, the miR-

17-92 cluster is commonly found to be amplified and overexpressed in B-cell lymphomas and lung

cancers (Ota et al., 2004; He et al., 2005; Hayashita et al., 2005). Constitutive activation of this

polycistronic miRNA cluster cooperates with Myc to accelerate B-cell lymphoma development

(He et al., 2005). It is interesting to find that cancer cells created by oncomir activation are addicted

to the oncomiRs, with inhibition of lung cancer cell growth following inhibition of any of the

miRNAs in the miR-17-92 cluster (Matsubara et al., 2007).

Drugs targeting oncogenes have been utilized in clinical settings with a few clear successes.

Gleevac, an ABL-specific tyrosine kinase inhibitor, has been successfully used in select patients

of chronic myelogenous leukemia whose cancer cells contain BCR-ABL mutations (Kim, 2003).

Antibodies and small molecule inhibitors targeting ErbB2/HER2, a common oncogene amplified

in approximately 30% of breast cancers, cause growth inhibition of cell cultured breast cancer cells

10

as well as xenografts in nude mice, while treatment of patients with HER2+ disease via targeted

antibodies Transtuzumab/Herceptin and Pertuzumab shows clinical success with patients with

high efficacy (Berger et al., 1988; Hudziak et al., 1989; Xia et al., 2002; Rabindran et al., 2004;

Wong et al., 2006; Hudis, 2007; Vogel et al., 2002). Similarly, kinase inhibitors targeting a kinase

domain mutated form of EGFR found in a sub-population of patients with non-small cell lung

cancer show significant clinical responses (Lynch et al., 2004; Pao et al., 2004; Sharma et al.,

2007). Also, vemurafenib, a BRAF kinase inhibitor has been successful in improving the survival

of melanoma and thyroid cancer patients harboring the BRAF V600E mutation (Chapman et al.,

2011; Kim et al., 2013). However it is harder to target non-kinase oncogenes like Ras and Myc

that do not bind to substrates via rapid catalytic processes that can be potentially inhibited (Luo et

al., 2009; Gysin et al., 2011).

In conjunction with activating mutations in oncogenes, cancer cells often possess loss of

function mutations in tumor suppressor genes. Hence, cancer cells show similar addiction to the

inactivation of the tumor suppressor genes, reacting to the reintroduction of wild-type copies of

the tumor suppressors – a phenomenon called tumor suppressor hypersensitivity (Weinstein,

2002). This has been best demonstrated in studies on p53, a tumor suppressor gene that is

inactivated in majority of all human cancers (Soussi, 2007; The Cancer Genome Atlas Database).

Cancer cells undergo apoptosis or senescence on the reintroduction of wild type p53 (Adachi et

al., 1996; Gomez-Manzano et al., 1996; Vater et al., 1996; Martins et al., 2006). However,

translating the concept of tumor suppressor hypersensitivity into clinical applications is harder

because it is difficult to restore or mimic the function of an inactivated or absent gene through

small molecule interventions (Luo et al., 2009).

11

The knowledge that tumors are addicted to mutated oncogenes and tumor suppressor

spurred efforts to identify more of these types of changes and do so across various types of cancer.

Recent large-scale projects focused on genomic sequencing of multiple types of cancers have

found that outside common mutations in well studied oncogenes and tumor suppressors such as

p53, Ras, PI3 kinase, PTEN, Rb and p16INK4a, many other mutations are found in cancer cells but

for any individual gene, the frequency of a mutation occurring is much lower than for those

classical oncogenes and tumor suppressors (Cancer Genome Atlas Research Network, 2008; Ding

et al., 2008; Parsons et al., 2009; Sjoblom et al., 2006; Wood et al., 2007). Because of the

heterogeneity of mutations even in cancers of the same tissue, it becomes difficult to distinguish

driver mutations from changes that do not contribute to the malignant phenootype (Luo et al.,

2009). Further analysis is needed to understand whether and which of these low-frequency

mutations contributes to malignancy. Until such functional analysis is performed, we cannot

estimate whether targeting these non-classical mutations will achieve anti-cancer targeting and so

cannot predict whether that will be cancer-specific.

An alternative strategy to identify cancer selective targets is to identify vulnerabilities

present within the cancer cell outside its dependency on mutated lesions. Beyond mutations, cancer

cells are dependent on a wide variety of additional mediators of the transformation process that

may be differentially regulated between cancer cells and normal cells (Solimini et al., 2007). These

genes and pathways may also help the malignant cell cope with additional stresses it faces due to

its transformed state (Luo et al., 2009). This phenomenon is known as non-oncogene addiction

and provides an opportunity to identify additional cancer-selective targets (Solimini et al., 2007;

Luo et al., 2009).

12

One way to accomplish this might be to target non-mutated genes downstream of

oncogenic lesions in the pathway. For example, tumors with inactivating PTEN mutations are often

dependent on PI3K signaling, since PTEN is an inhibitor of PI3K (Cully et al.,2006). Hence, small

molecule PI3K inhibitors would be potentially toxic to the tumor cells addicted to PI3K signaling

(Cully et al., 2006). Similarly, inactivating mutations in Rb, or the cyclin-dependent kinase

inhibitors p16, p21 or p27 (CDKi) exert effects on cell cycle via cyclin-dependent kinase activity

which directly control cell-cycle entry (Aprelikova et al., 1995; Kaye, 2002). Tumors harboring

mutations in these CDKi genes may be particularly sensitive to cyclin-dependent kinase inhibitor

molecules (Luo et al., 2007).

A second way to leverage non-oncogene addiction to specifically target cancer cells is to

identify cellular stresses faced by cancer cells that are not faced by non-cancer cells. Cancer cells

could be targeted by either shutting down pathways required by the cancer cell to cope with such

stresses or by increasing the stress load of the cancer cell to overload the stress-response capability

(Luo et al., 2007). For example, cancer cells often rebalance the levels of expression of various

genes, changing the stoichiometry of protein subunits of different complexes, causing proteolytic

stress on chaperone pathways that regulate the homeostasis of the proteome (Luo et al., 2007).

Heat shock pathways help in promoting protein folding and are often activated in tumors without

being mutated (Whitesell and Lindquist, 2005). HSF1 belongs to this pathway and is induced by

cellular stresses like hypoxia frequently faced by cancer cells, driving the expression of heat shock

proteins that help promote protein refolding, prevent protein aggregation and target misfolded

proteins towards degradation (Dai et al., 2007). Knocking down HSF1 using shRNAs reduces

cancer cell viability, without affecting non-cancer cells such as primary mammary epithelial cells

and human lung fibroblasts (Dai et al., 2007). HSF1 has not been found to be mutated in tumors,

13

and HSF1 overexpression is not sufficient to transform immortalized mouse embryonic fibroblasts

in combination with mutant Ras, whereas inhibiting HSF1 prevents transformation with mutant

p53 and activated Ras or PDGF-B, causing increased cell death. Thus, HSF1induction provides atl

east one mechanism by which cancer cells may depend on a non-mutated protein, in this case to

tolerate proteolytic stress (Dai et al., 2007).

Similarly, cancer cells metabolism is dysregulated, allowing cancer cells to survive in

abnormal stromal environments. Most cancer cells increase glucose uptake and upregulate the

glycolysis pathway to metabolize this glucose (Kim and Dang, 2006). What drives cancer cells

into this highly glycolytic state is not well understood, but this adaptation is believed to be useful

for the cancer cell to divert metabolites towards biosynthesis, reduce ROS production via

mitochondrial oxidative phosphorylation and tolerate varying oxygen levels available in the tumor

microenvironment (DeBerardinis et al., 2008; Kroemer and Pouyssegur, 2008; Vander Heiden et

al., 2009). Cancer cells cannot tolerate inhibition of the glycolytic and metabolite biosynthetic

pathways because of sustained pro-proliferative signals coming from oncogenes (Vander Heiden

et al., 2001; Vander Heiden et al., 2009). Inhibiting either genes involved in glycolysis, including

ATP citrate lyase or lactate dehydrogenase A or biosynthetic genes, such as acetyl CoA

carboxylase or fatty acid synthase causes tumor growth arrest (DeBerardinis et al., 2008; Kroemer

and Pouyssegur, 2008).

Because non-mutated genes critical to the cancer phenotype are a potential source for anti-

cancer targets, efforts have been directed at identifying such genes via RNAi-based screens (Luo

et al., 2009). In these approaches, a pool of shRNA or siRNA molecules are introduced into cancer

cells and shRNAs depleted from this pool under a selective pressure such as cell competition or

growth in limiting conditions are tested further for functions. Distinct RNAi screens compare

14

cancer cells and non-cancer cells (Schlabach et al., 2008), two different kinds of cancer cells (Ngo

et al., 2006) or follow certain phenotypes like proliferation and survival change (Silva et al., 2008).

Other approaches depend on synthetic lethality, the phenomenon due to which a cancer cell is

unable to survive due to change in function of two or more genes simultaneously but not the

individual genes themselves (Ulrich et al., 2011). In the case of RNAi based screens for cancer

regulators, hits are defined as genes that are required only due to the presence of a particular

oncogenic mutation (Luo et al., 2009; Scholl et al., 2009; Barbie et al., 2009; Boettcher et al.,

2014). However RNAi screens can only be used to identify genes whose loss is toxic to the cancer

cell, missing targets that would inhibit cancer cell growth upon activation or increased expression.

CRGs represent non-mutated mediators of transformation that are often found to be

required by malignant cells. Synergistically disregulated downstream of cooperating oncogenes,

CRGs provide an opportunity to identify additional non-oncogenes essential for the transformed

state. Originally identified in a murine model of colon cell transformation based on cooperation

between mutant p53 and activated H-Ras, CRGs have been found to mediate crucial functions of

the cancer cell, as explained above. Plac8, an upregulated CRG, has been demonstrated to be

required by cancer cells to mediate autophagy (Kinsey et al., 2008). Plac8 helps the cancer-cell

cope with proteolytic stress that is unique to the cancerous state and hence Plac8 silencing inhibits

tumor growth in a cancer-selective manner, demonstrating no obvious phenotype in normal tissues.

A subset of CRGs have been found to play essential roles in multiple epithelial cancers including

human colorectal cancer and basal-like breast cancer (McMurray et al., 2008; Walters et al.,

unpublished). Studies in basal-like breast cancer demonstrate that perturbing CRGs have cancer-

specific effects, with perturbed non-cancerous cells showing little or no obvious change in

15

phenotype (Walters et al., unpublished). Hence, there is piling evidence that many CRGs play a

cancer-selective role in regulation of the malignant phenotype.

16

1.3 Basal-like Breast Cancer

Breast cancer is a heterogeneous disease with each type of tumor characterized by distinct

characteristic features and clinical outcomes (Reis-Filho et al., 2005; Simpson et al., 2005; Vargo-

Gogola et al., 2007). Subtypes of breast cancer vary as a result of distinct genetic, epigenetic and

transcriptomic variations (Reis-Filho and Lakhani, 2008; Geyer et al., 2009; Correa-Geyer and

Reis-Filho, 2009; Weigelt et al., 2008; Weigelt and Reis-Filho, 2009). Although the characteristics

of the disease are often directly associated with the patterns of genetic lesions found within the

tumor (Weigelt and Reis-Filho, 2009), it is often found that tumors of the same histological subtype

show contrasting clinical outcomes (Badve et al., 2011).

With the advent of high throughput gene expression measurement technologies, efforts

have been made to classify breast tumors based on molecular features rather than or in addition to

histological features. Microarray analysis of human breast tumors led to division of breast cancer

into at least five distinct subtypes: luminal A, luminal B, normal-like, HER2 positive and basal-

like breast cancers (BLBCs) (Perou et al., 2000; Solie et al., 2001; Sorlie et al., 2003; Hu et al.,

2006; Rakha et al., 2008; Parker et al., 2009; Badve et al., 2011).

Basal-like breast cancer is one of the most aggressive subtypes of breast cancer that

accounts for up to 15 - 25% of all breast cancers and is more likely to affect pre-menopausal

women (Livasy et al., 2006; Haffty et al., 2006; Fulford et al., 2006). The aggressiveness of the

tumors leads to frequent tumor recurrences within the first and third years, with the majority of

patients succumbing to BLBC within the first five years following therapy (Dent et al., 2007;

Tischkowitz et al., 2007). Patients with basal-like breast cancer are found to survive less after the

first occurrence of metastasis compared to patients with non-basal-like tumors (Fulford et al.,

2007). Many BLBCs are triple negative breast cancers characterized by the lack of estrogen

17

receptor and progesterone receptor and lack of HER2 protein overexpression and HER2 gene

amplification (Tomao et al., 2014). The lack of hormone receptor expression makes BLBC

insensitive to modern breast cancer therapies such tamoxifen, which targets estrogen receptor and

herceptin, which targets HER2 protein (Badve et al., 2011).

Established human BLBC cell lines provide an excellent model system to study the disease

in vitro and in vivo (Chavez et al., 2010). Hence, For my studies, I utilized six BLBC lines

representing the two subtypes of basal-like breast cancer, namely Basal A and Basal B. (Table 1)

(Walters et al., unpublished). These BLBC cell lines were compared to two immortalized but non-

tumorigenic mammary epithelial cell lines, MCF10A and MCF12A. (Table 1) (Walters et al.,

unpublished). These cell lines were spontaneously immortalized cells derived from benign breast

tissue of patients of fibrocystic disease (Paine et al., 1992; Soule et al., 1990). These cells are

phenotypically similar to mammary progenitor cells, with the capacity to expnd and, when given

appropriate signals, to differentiate into luminal and basal epithelial cells in vitro to form hollow

acinar structures that recapitulate mammary duct formation (Debnath et al., 2003; Holliday and

Speirs, 2011). Experiments show that conditional deletion of BRCA1 and p53 in mouse mammary

progenitor cells form basal-like breast tumors, suggesting that progenitor cells are the cells of

origin of basal-like breast cancer (Liu et al., 2007; Molyneux et al., 2010; Van Keymeulen et al.,

2011).

18

1.4 Colorectal Cancer

Colorectal cancer is the a leading cause of cancer-related death in the United States, third

only to lung and prostate cancers in men and lung and breast cancers in women (Siegel et al.,

2011). More than 90% of the tumors arise from epithelial cells in the colorectal mucosa

(Hamilton et al., 2010). Though early studies concentrated on familial colorectal cancers to find

common drivers of the disease, only a small proportion (around 5-10%) of the tumors are due to

inherited diseases while the rest appear sporadically (Smith et al., 2002). Alongside common

mutations like APC, p53, K-Ras and Smad4, colorectal tumors also include mutations in Arid1A,

Sox9 and Fam123b (The Cancer Genome Atlas, 2012; Smith et al., 2002), making it particularly

hard to target cancer cells through a specific oncogene addiction. Hence there is need for studies

of common downstream mediators of transformation that are essential to the tumor.

Normal adult colorectal epithelial cells are hard to culture and hence very few in vitro

colorectal cancer transformation models comparing transformed cells to normal cells exist

(D’Abaco et al., 1996; McMurray et al., 2008). We leverage one of the few available models for

normal colon cell function, using murine colorectal epithelial cells derived from a transgenic

mouse (called the Immortomouse) with conditional expression of a temperature-sensitive SV40

large T gene (Jat et al., 1991). Young Adult Mouse Colon (YAMC) cells derived from the

Immortomouse can be cultured indefinitely under permissive conditions, (grown at 33°C and in

the presence of IFNγ), where the SV40 T can promote growth and inhibit senescence by binding

to p53 (Colby and Shenk, 1982). After switching cell cultures to a non-permissive temperature,

37°C – 39°C, the conformation of SV40 T is disrupted, while withdrawal of IFNγ halts further

transcription of the T, leading to loss of the immortal phenotype and cellular senescence over a

two week time period (Whitehead and Robinson, 2008). The mutant p53 and activated Ras

19

transformed YAMC cells have been utilized to identify critical mediators of the malignant

phenotype called CRGs, as discussed earlier (McMurray et al.,2008).

20

1.5 The role of Notch signaling and Wnt signaling in cancer

The Notch signaling pathway is a highly conserved pathway in the animal kingdom, with

Notch signaling architecture apparent in organisms ranging from C. elegans to humans (Artavanis-

Tsakonas et al., 1999; Schweisguth, 2000). Notch signaling is activated by the binding of trans-

membrane ligands Delta, Serrate or Jagged to the Notch receptor, which constrains Notch

activation to cells bordering Delta/Jag-expressing cells. Upon ligand binding, the Notch receptor

undergoes two steps of proteolytic cleavage by ADAM-family metalloproteases and γ-secretase,

causing the release of the Notch intracellular domain (NICD). The NICD travels to the nucleus,

where it cooperates with RBP-Jκ (also known as CSL), a DNA-binding protein and the coactivator

Mastermind (Mam) to promote canonical transcription of target genes (Bray, 2006). The NICD

may also promiscuously interact with other binding partners to activate non-canonical Notch

signaling by driving transcription through other pathway machinery (Kim et al., 2012; Watanabe

et al., 2013). The Notch pathway is known to control cell-fate determination, differentiation, cell

survival, proliferation and angiogenesis in various cell types (Artavanis-Tsakonas et al., 1999;

Miele and Osborne, 1999; Phng and Gerhardt, 2009).

Notch signaling was first implicated in cancer in T lymphoblastic leukemia (T-ALL) when

a chromosomal translocation places the 3’ region of NOTCH1 into the TCRβ locus causing Notch1

activation by expression of the NICD (Ellisen et al., 1991). Though the particular translocation

appeared to be rare, occuring in less than 1% of T-ALL cases, a majority of all T-ALL cases have

been found to harbor mutations that activate NOTCH1 (Weng et al., 2004). Notch signaling was

similarly implicated in solid tumors including breast cancer, medulloblastoma, colorectal cancer,

non-small cell lung carcinoma and melanoma (Ranganathan et al., 2011; Reedijk et al., 2005).

21

Activation of the Notch pathway also has been reported to have tumor suppressive

functions. Notch1 loss of function causes the formation of spontaneous basal cell carcinomas in

older mice and sensitizes these animals to chemically induced skin carcinogenesis (Nicolas et al.,

2003). Evidence of tumor suppressive activity of the Notch pathway is also found in breast cancer,

prostate cancer, lung cancer, hepatocarcinoma, glioblastoma and squamous cell carcinoma

(Harrison et al., 2010; O’Neill et al., 2007; Parr et al., 2004; Shou et al., 2001; Whelan et al., 2009;

Wang et al., 2009; Qi et al., 2003; Sun et al., 2003; Duan et al., 2006; Panelos et al., 2008; Proweller

et al., 2006; Sriuranpong et al., 2001). Reports in basal-like breast cancer suggest a complicated

role as well (Dong et al., 2010; Xu et al., 2012; Lee et al., 2008), with Notch3 being reported as

tumor suppressive (Cui et al., 2013) as well as pro-oncogenic (Yamaguchi et al., 2008). This

suggests that the role of Notch signaling in cancer must be context specific and may be determined

by its interaction with other signaling pathways (Radtke and Clevers, 2005).

Another pathway important for cellular proliferation and lineage development is the Wnt

pathway. The canonical Wnt signaling pathway acts through the effector molecule β-catenin

(Tolwinski and Wieschaus, 2004). In the absence of Wnt signaling, β-catenin is sequestered by the

Axin2/GSK/APC complex and degraded via proteolytic cleavage. Wnt signaling is mediated by

Wnt ligands which bind to the Frizzled (FZD) receptors (Yang-Snyder et al., 1996). Dishevelled

(DSH) becomes activated, which mediates the release of β-catenin from the Axin2/GSK/APC

complex. β-catenin becomes stabilized and is able to move to the nucleus and activate transcription

of target genes (Behrens et al., 1996).

Along with proliferation, β-catenin also has a well-defined role in cell-cell adhesion

(Ozawa et al., 1989). There are other branches of Wnt signaling independent of B-catenin,

22

classified under non-canonical Wnt signaling, that are known to mediate diverse processes like

cell polarity, cell migration and cell-cell adhesion (Montcouquiol et al., 2006).

The Wnt signaling pathway was first implicated in cancer when inactivating mutations of

APC were found to cause tumor formation in a majority of sporadic colorectal cancers and familial

adenomatous polyposis (FAP), a hereditary predisposition to the formation of benign adenomas in

the colon–rectum (Fodde et al., 2001). A fraction of sporadic colorectal cancers also have gain-of-

function mutations of CTNNB1, the B-catenin gene (Polakis 2007). Similar gain-of-function

mutations of CTNNB1 were found in ~20% cases of different cancers (colon cancer, melanoma,

hepatocellular carcinoma, medulloblastoma, hepatoblastoma, gastrointestinal tumors, Wilms

tumors and others) (Polakis 2000). Hence, increased Wnt signaling is implicated as a driver of

cancer. In breast cancer, even though few mutations in the Wnt pathway genes have been found in

patient samples (Candidus et al., 1996; Schlosshauer et al., 2000; Jonsson et al., 2000), nuclear or

cytoplasmic staining of B-catenin is an independent marker that correlates with poor prognosis in

breast cancer (Lin et al., 2000).

Interaction between Notch and Wnt signaling was first reported in the context of the

development and patterning of the wing of Drosophila. (Couso and Martinez Arias, 1994; Hing et

al. 1994) During wing development, Wingless, the Wnt1 orthologue in Drosophila, and Notch are

found to synergistically drive the expression of vestigial (vg) (Zecca and Struhl, 2007). Synergistic

interaction between Notch and Wnt pathways to control cell fate has also been reported in

vertebrate cells, such as during the development of skin precursors (Estrach et al., 2006), the

patterning of the rhombomeres (Cheng et al., 2004) and during somitogenesis (Aulehla and

Hermann, 2004). In Drosophila, Wnt signaling is known to drive Notch signaling via positive

feedback control of expression of Notch ligands (de Celis and Bray, 1997) while Notch signaling

23

is known to antagonize Wnt signaling directly (Brennan et al., 1999) via mechanisms like Notch

suppressing the activity of Armadillo, the B-catenin orthologue in Drosophila in a RBP-Jk-

independent manner (Hayward et al., 2006). In vertebrates, Wnt and Notch signaling are often

associated with precursor differentiation into alternative fates in various cell types like intestinal

development and haematopoiesis, often in an antagonistic fashion with Wnt signaling low and

Notch signaling high (Duncan et al., 2005; Han et al., 2002).

Notch and Wnt pathways are known to interact in cancer as well. The two pathways are

known to synergistically control tumorigenesis in breast and colorectal cancer (Ayyanan et al.,

2006; Fre et al., 2009). In cancers like colorectal cancer Wnt signaling drives Notch signaling by

controlling the expression of Notch ligands like Jag1 (Rodilla et al., 2009; Pannequin et al., 2009).

Notch signaling is also found to antagonize Wnt signaling when Notch acts as a tumor suppressor.

The Notch1-/- mouse epidermal cells, which form basal-cell carcinoma like tumors, show a de-

repression of β-catenin signaling (Nicolas et al., 2003). Similarly Notch1 inhibits β-catenin

signaling through the recruitment of epigenetic modifier SETDB1 in the APCmin mouse colorectal

cancer model (Kim et al., 2012). Thus, the Notch-Wnt interaction is very context specific.

24

1.6 Rationale of the current study

Identification of CRGs downstream of cooperating oncogenes defines novel mediators of

the transformation process, non-oncogenes to which the cancer cell is addicted (McMurray et al.,

2008; Ashton et al., 2012; Eberl et al., 2012; Gotschel et al., 2013). Drugs reversing the CRG

expression signature in colorectal cancer and acute myeloid leukemia have successfully been able

to reduce in vivo tumor formation (Sampson et al., 2013; Ashton et al., 2012). In leukemia, drugs

reversing CRG expression had cancer selective effects, with no discernible toxic effects on normal

cells (Ashton et al., 2012). Drugs found to reverse the CRG signature in transformed colon cells

also had cancer-selective effects at the level of CRG gene expression, but were not tested for

biological selectivity (Sampson et al., 2013). Among CRGs induced in a cancer-selective manner

was Notch3, a Notch pathway receptor molecule (Sampson et al., 2013). Transformed colorectal

cancer cells were found to have lower expression of Notch3 as compared to non-transformed colon

epithelial cells (McMurray et al., 2008), and drug effects on transformed cells were found to

depend on the induction of Notch3 for their effect on tumor formation, because on knocking down

Notch3 (Sampson et al., 2013).

Notch3 is activated, like other Notch receptor molecules, via proteolytic cleavage and

release of the intracellular domain that translocates into the nucleus and drives transcription of

target genes (Artavanis-Tsakonas et al., 1999). Hence, Notch3 was activated by expressing the

constitutively active Notch3 intracellular domain in murine colorectal cancer cells expressing

mutant p53 and constitutively active Ras. When Notch3 was activated, the cancer cells stopped

growing, as compared to non-perturbed colorectal cancer cells. (Fig 1: Unpublished Data)

However, since the non-transformed murine colon epithelial cells senesce on growing in vitro, the

25

colorectal cancer model does not provide a good system to study the cancer-selective effects of

Notch3.

Recent work from our laboratory has defined a role for CRGs in control of malignancy in

BLBC, and in particular has begun to use available non-cancerous mammary cells to examine

cancer selective effects of CRG modulation (Walters et al., unpublished). Thus, the model system

provides an opportunity to study the molecular basis of cancer-selective effects of CRGs, using

Notch3 as an example.

My studies define a novel genetic interaction between Notch3 and Sfrp2, another CRG

known to act as Wnt pathway antagonist. Moreover, we find similar interactions and consequences

of these interactions in both BLBC and transformed colon cells. It also reports a novel statistical

method to refine CRG identification using gene expression data, with Clca1 as a tumor-inhibitory

CRG discovered by the novel statistical method.

26

1.1 Figures

A

B

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0 hrs 24 hrs 48 hrs 72 hrs 96 hrs

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pBp3 pBp3 NIC3

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27

Figure 1: Notch3 activation inhibits cell growth in transformed colon cells.

A. Line graph shows effect of expression of Notch3 intracellular domain (NIC3) in mp53/Ras

cells on cell growth, with the continuous line representing unperturbed cells and the dashed

line representing NIC3 expressing cells. Error bars were SDs of 3 independent

experiments. *: P<0.01; Student’s T Test.

B. Histogram measures the expression of Notch3 in NIC3 expressing mp53/Ras cells. Black

bar represents unperturbed cells and dashed bar represents NIC3 expressing cells.

*:P<0.01; Student’s T Test.

28

Chapter II

Notch3 cancer-selectively controls proliferation via a network of genes specifically in basal-

like breast cancer

29

2.1 Abstract

Cooperation Response Genes (CRGs) are non-mutant regulators of the malignant phenotype of

cancer cells. Distinct sets of CRGs contribute to transformation in different types of cancer, with

accumulating evidence for a set of CRGs regulated by cooperation of mutant p53 and mutant Ras

that are important in multiple epithelial cancers, including basal-like breast cancer (BLBC).

Among the CRGs is Notch3, a transmembrane receptor of the Notch family that is found to cancer-

selectively regulate cell growth in a cancer selective manner. Here, we demonstrate how Notch3

plays a cancer-selective role in restricting proliferation of basal-like breast cancer cells.

Through genome-scale transcriptomic analysis, we demonstrate that Notch3 interacts with other

signaling pathways like the Wnt pathway in a distinct way in basal-like breast cancer versus non-

cancerous breast cells. Moreover, this cancer-specific interaction between Notch and Wnt

signaling selectively controls proliferation of cancerous cells. These studies provide a new insight

into the circuitry underlying the transformed state of basal-like breast cancer cells, advancing our

understanding of how basal-like breast cancer-specific vulnerabilities can arise.

30

2.2 Introduction

Key changes in gene expression are found to occur in response to cooperating combinations of

oncogenic mutations. Such synergistically dysregulated genes, or “cooperation response genes”

(CRGs) have been found to essential for the transformed state, mediating key features of the cancer

cell phenotype like uncontrolled proliferation, indefinite survival and altered metabolism

(McMurray et al., 2008; Ashton et al., 2012; Eberl et al.,2012; Kinsey et al., 2014). CRGs have

been identified and found to play critical roles in multiple cancers including colorectal cancer,

acute myeloid leukemia and basal-like breast cancer (McMurray et al., 2008; Ashton et al., 2012;

Eberl et al., 2012). Importantly, a subset of the original CRGs identified in colorectal cancer

downstream of mutant p53 and activated H-Ras (mp53/Ras) have been found to play critical roles

in other epithelial cancers like pancreatic cancer, androgen-independent prostate cancer and basal-

like breast cancer (BLBC) (Kinsey et al., 2014; Walters et al., unpublished; McMurray et al.,

unpublished). Pharmacological compounds antagonizing the CRG gene expression signature have

tumor inhibitory activity, including HDAC inhibitor (HDACi) treatment of mp53/Ras transformed

colon cells and ErbB2 inhibitor, Tyrophostin AG825, treatment of BCR-ABL/NUP98-HOXA9

transformed leukemia cells (Sampson et al., 2013; Ashton et al., 2012). Interestingly, the HDACi

antagonize the CRG gene signature in colorectal cancer cells while impacting CRG expression

within non-transformed colon cells in a non-specific pattern with overall changes of smaller

magnitude, showing cancer-selective effects (Sampson et al., 2013).

Among the cancer-specific CRG responses underlying HDACi activity is the Notch3 gene, a

Notch pathway receptor gene. Notch receptors are transmembrane proteins that control

fundamental biological processes like stem cell maintenance and proliferation, differentiation and

cell fate determination (Mizutani et al., 2001). One of the four Notch receptors in mammals,

31

Notch3 is activated via a ligand-receptor interaction that ultimately leads to proteolytic cleavage

that releases the intracellular domain into the cytoplasm (Bray, 2006). The dissociated intracellular

domain then translocates to the nucleus and associates with its heterodimeric partner protein

RBPJκ to form a transcriptional activator complex (Bray, 2006; Ohashi et al., 2010). The complex

drives transcription of multiple genes, canonically including the Hes/Hey family of transcriptional

factors (Bray, 2006).

Here, we demonstrate the cancer-selective role of Notch3 in controlling proliferation of BLBC

cells. Using Notch3 as an example of a CRG playing a transcriptional role, we start investigating

the molecular basis of such cancer-selective effects. Utilizing whole genome-scale analysis of the

transcriptional effects of Notch3 activation, we identify downstream targets of Notch3, at least one

of which is necessary for its role in proliferation. Moreover, we observe a hierarchy within the

interactions of the cancer-specific Notch3 sensitive genes. These studies give us an insight on the

underlying circuitry of basal-like breast cancer cells as compared to non-transformed breast cells

cells, demonstrating the importance of genetic interactions among CRGs, helping us advance the

understanding of how basal-like breast cancer-specific vulnerabilities may arise.

32

2.3 Results

2.3.1 Notch3 restricts proliferation in a cancer-selective manner

Previous work showed that Notch3 expression is lower in colorectal cancer cells than non-

transformed colon cells and its activation inhibits cancer cell growth in transformed colon cells

(McMurray et al., 2008; Figure 1). However, normal colon cells rapidly senesce in vitro, making

it difficult to examine biological effects of genetic perturbation in this model system. Interestingly,

recent studies in our laboratory have shown that a subset of the CRGs identified downstream of

cooperation between mutant p53 and activated H-Ras in colon cells play essential roles in other

epithelial cancers, including pancreatic cancer, androgen-independent prostate cancer and BLBC.

(Kinsey et al., 2014; Walters et al., unpublished; McMurray et al., unpublished). Moreover,

utilizing BLBC and non-transformed mammary epithelial cell lines, our lab has been able to

demonstrate that CRGs have cancer-selective effects in BLBC. (Walters et al., unpublished) Thus,

we leveraged this model system to allow comparison of phenotypic effects of Notch3 perturbation

in addition to molecular comparisons that were ultimately done in both breast and colon cells

(Shown here and Chapter III).

To determine the role of Notch3 in BLBC, we compared the baseline expression of Notch3 at

the level of mRNA across a panel of human BLBC cell lines and in two non-transformed human

mammary epithelial cell lines (Neve et al., 2006; Lehmann et al., 2011), finding that Notch3

expression is significantly lower in the BLBC cells as compared to the non-cancer cells (Figure

2.1A).

Since BLBC cells appear to have lower Notch3 expression as compared to non-cancer cells,

we next tested whether this difference contributed to phenotypic differences between the BLBC

33

and non-transformed breast cells. Because Notch3 is activated via cleavage and release of the

intracellular domain, the Notch3 intracellular domain acts as a constitutively active form of

Notch3. Hence, we expressed the Notch3 intracellular domain (NIC3) via retroviral infection in a

number of BLBC cell lines and measured cell accumulation over 72 hours. We observed that in

each of six basal-like breast cancer cell lines, introduction of the NIC3 inhibited growth of the

cancer cells (Figure 2.1B). This suggested that Notch3 activation restricts the growth of basal-like

breast cancer cells. We also performed the corresponding loss-of-function experiments, assessing

cell accumulation after knocking down Notch3 mRNA in two of the BLBC cell lines, MDA-MB-

231 and HCC1954. Reduction in Notch3 levels allowed BLBC cells to accumulate faster than

control cells (Figure 2.1C). Hence gain of function and loss of function experiments support the

idea that Notch3 activity restricts the growth of BLBC cells.

Because other CRGs appear to have cancer specific effects in BLBC cells (Walters et al.,

unpublished), we hypothesized that Notch3 would inhibit cell growth in a cancer selective fashion.

We tested this by introducing activated Notch3 into non-cancerous breast cells and measuring

accumulation of these cells, observing no significant change in growth of the NIC3 expressing

cells as compared to vector control cells (Figure 1D). To control for the possibility that the lack

of response to NIC3 in non-cancerous mammary cells was due to lack of NIC3 expression, we

examined Notch3 expression in both BLBC cells and non-transformed cells expressing NIC3,

finding similar levels of Notch3 in all perturbed cell populations (Figure 2.2). Morover, we

examined the response of the canonical Hey and Hes family of genes to introduction of NIC. In

both BLBC and non-cancerous cells, we found evidence for Hes/Hey induction, suggesting that

NIC3 activation was high enough to engender expected molecular responses (Figure 2.3) Hence

we concluded that Notch3 selectively restricts growth of BLBC cells.

34

2.3.2 Notch3 controls the expression of numerous genes in a cancer specific manner

Since Notch3 is a transcription factor, we hypothesized that the different phenotypic responses

of BLBC cells and non-cancer cells would be reflected by a differential transcriptional response

in each cell type. RNA was isolated from multiple, independently-generated samples of each cell

line with or without NIC3 expression used for high-throughput RNA sequencing. The raw reads

were mapped using SHRiMP 2.2.3 and gene counts were obtained using HTSeq. Gene counts were

normalized using DESeq and EdgeR, grouping all BLBC lines together and grouping all non-

cancerous cell populations together to identify responses that were highly consistent across these

populations. .

Among the basal-like breast cancer cells, 173 genes were identified whose expression was

disregulated more than two-fold up or down in NIC3 expressing cells as compared to unperturbed

cells. (Table 1) The p-values associated with each of these changes is also listed. Further, we

compared the results of three different algorithms for identifying differentially expressed gene

expression, and each of these gave a similarly rank-ordered gene list, with distinct p-values

associated with each of the changes (Table 2).

To assess cancer-specific and non-specific gene expression responses to NIC3, we compared

fold change in expression of genes responsive to NIC3 in BLBC cells with the change in expression

observed upon NIC3 introduction in non-cancer cells (Table 1). Of 173 genes responsive to NIC3

in BLBC cells, only 35 genes responded similarly in non-cancer cells (Figure 2.4A and B). Among

these genes, we found canonical Notch target genes, including Hey1, Hes1 and Hey2. As

35

independently verified by qPCR, NIC3 activation induced the Hes/Hey family gene expression as

compared to vector control in both cancer cells and non-cancer cells. (Figure 2.4C; Figure 2.3)

Interestingly, out of the 173 NIC3 sensitive genes found in BLBC cells, 112 genes did not

appear to change in non-cancer cells expressing NIC3 and 26 genes move in the opposite direction

in NIC3 expressing non-cancer cells, for example, increasing in BLBC cells while decreasing in

non-cancer cells (Figure 2.4A and B, Table 1). Thus, these genes showed a cancer-specific change

in expression in response to NIC3 expression, mirroring the observed cancer-specific biological

responses.

Gene ontology analysis of genes that respond to NIC3 in a cancer-specific manner revealed

that many of these genes are implicated in prominent signaling pathways with putative roles in

cancer, including the Wnt pathway, the TGFβ pathway and the NFκb pathway. (Figure 2.4D, Table

2) For example, Wnt antagonist Sfrp2 (SFRP2) is induced upon NIC3 expression but only in

BLBC cells, while Cyclin D1 (CCND1) and N-Myc (MYCN) are repressed in response to NIC3

expression in BLBC cells but not non-cancerous cells, as independently verified by qPCR

measurement (Figure 2.4E, Figure 2.5).

2.3.3 Notch3 depends on Sfrp2 for restricting proliferation of basal-like breast cancer cells

Among the genes induced in response to NIC3 is another cooperation response gene, Sfrp2,

which is found to be expressed at lower levels in transformed colon cells as compared to normal

colon cells (McMurray et al.,2008), and in basal-like breast cancer cells as compared to non-

transformed mammary epithelial cells. (Figure 2.6A). Moreover, it is apparently highly sensitive

to Notch3 levels, as knock-down of Notch3 expression reduces Sfrp2 expression. (Figure 2.8)

36

Sfrp2 belongs to the family of secreted frizzled proteins that modulate Wnt signaling by

directly binding to and sequestering Wnt ligands and thus preventing them from binding to cell

surface Frizzled receptors and activating downstream signaling (Alfaro et al., 2008). Activation of

Wnt signaling is known to promote cell growth of all cancer cells, including basal-like breast

cancer. (Polakis, 2000; Yang et al., 2011) Because Notch3 restricts cell growth of BLBC cells and

produces a corresponding increase in expression of the Wnt antagonist Sfrp2, we hypothesized

that Notch3 would depend on the induction of Sfrp2 to inhibit BLBC growth.

To test the hypothesis, we activated Notch3 by expressing NIC3 in BLBC cells with or without

shRNA-mediated knockdown of Sfrp2. We, then, examined the accumulation of each cell

population. As observed earlier, expressing NIC3 in BLBC cells inhibited cell growth. However,

loss of Sfrp2 induction significantly rescued cell growth upon introduction of NIC3 (Figure 2.6B).

To control for the possibility that Sfrp2 knocked-down cells did not respond to NIC3 expression

due to lack of sufficient NIC3 expression, we examined Notch3 expression in all the perturbed

BLBC cells, finding similar levels of Notch3 in both unperturbed and Sfrp2 knocked down cells

expressing NIC3 (Figure 2.6C). These results show that growth inhibition of BLBC cells following

activation of Notch3 requires induction of Sfrp2.

2.3.4 Induction of a subset of Notch3-responsive genes requires induction of Sfrp2

Canonical Wnt signaling is reported to drive cell growth through increased expression of

cell cycle regulators, including the Myc family of transcription factors and Cyclin D1 (Herbst et

al., 2014; Loh et al., 2014). Since Sfrp2 is reported to be a canonical Wnt antagonist and we find

that Sfrp2 expression is increased in BLBC cells expressing NIC3, we hypothesized that NIC3

37

would have effects on downstream targets of Wnt signaling such as CCND1 and MYCN. From

the RNA-sequencing analysis of NIC3 expressing BLBC and normal cells, we find that CCND1

and MYCN are selectively suppressed in response to introduction of NIC3 into BLBC cells (Figure

2.7A).

Given that CCND1 and MYCN decrease under conditions where Notch3 activity and Sfrp2

expression increase, we hypothesized a causal relationship between these events, predicting that

CCND1 and MYCN suppression in BLBC cells would depend on Sfrp2 induction. To test this, we

examined gene expression patterns via RT-qPCR from NIC3-expressing cells with or without

knock-down of Sfrp2. We observed that NIC3 expression corresponded with reduced expression

of CCND1 and MYCN expression in basal-like breast cancer cells, consistent with RNA-

sequencing results. However, while knock-down of Sfrp2 alone had little effect on CCND1 and

MYCN expression, introduction of NIC3 into cells with Sfrp2 knock-down produced significantly

less suppression of CCND1 and MYCN expression that seen with NIC3 alone (Figure 2.7B).

Hence, the reduction in CCND1 and MYCN expression in response to NIC3 depends on the

induction of Sfrp2.

This leads to the question of Sfrp2 induction is required for all the changes in gene expression

seen upon NIC3 introduction into BLBC cells. For example, expression of the TGFβ target gene,

Snai2, appears to be sensitive to NIC3 in a cancer-specific manner (Figure 2.7). To test whether

increased expression of Snai2 in response to NIC3 expression depends on Sfrp2 induction, we

measured the levels of Snai2 mRNA expression in BLBC cells with or without NIC3 and Sfrp2

knock-down, finding that Snai2 expression increased in NIC3-expressing cells, and reduction in

Sfrp2 expression had no effect on Snai2 expression, with similar expression levels in both cell

38

populations (Figure 2.7C). Hence, Sfrp2 is not required by Notch3 to induce Snai2 expression in

basal-like breast cancer cells.

Taken together, the results demonstrate the existence of a cancer-specific response to Notch3

activation at the level of gene expression that corresponds to cancer-selective growth inhibition.

Moreover, my work reveals a novel set of relationships between Notch3, Sfrp2 and downstream

targets of the Wnt pathway that is implicated in this phenotype.

39

2.4 Discussion

CRGs are enriched in essential mediators of the transformation process, representing non-

mutated genetic dependencies present within the cancer cell. Perturbing CRGs impacts the

functioning of cancer cells without affecting non-cancer cells, as shown in basal-like breast cancer

(Walters et al., unpublished). We are just starting to mechanistically understand how cancer cell

function is enabled based on altered expression of CRGs. In this study, we focus on Notch3 as an

example of a cancer cell growth regulatory CRG and start exploring the genetic interactions

through which Notch3 regulates cell growth in a cancer-selective manner.

We observe that activating Notch3 produces a differential transcriptional response between

cancer cells and non-cancer cells. Notch3 depends on at least one of the genes modulated in a

cancer-specific manner, SFRP2, in order to regulate growth of BLBC cells. Thus, dependence on

reduced Notch3 expression represents a non-oncogene addiction, a novel vulnerability in BLBC

cells with a unique mechanism underlying this vulnerability (Luo et al., 2009). The cancer cell

requires reduced Notch3 expression in order to maintain activation of at least the Wnt pathway

and possibly other cancer-specific regulatory programs, without which the cancer cell would

ultimately stop growing. This is reminiscent of synthetic lethality, where Notch3 activation is

lethal for the cancer cell due to simultaneous inhibition of the Wnt pathway. Further studies are

needed to assess whether these interactions represent a very unique feature of the cancer cells or

if the relationship between Notch3, Sfrp2, Cyclin D1 and N-Myc represents the top of an iceberg

of cancer-specific transcriptional programs that represent synthetic dependencies on critical non-

oncogenes.

Our current study reports that Notch3 inhibits proliferation of basal-like breast cancer cells in

a cancer-selective manner. This corresponds with previous work reporting Notch3 as a tumor

40

suppressor in BLBC (Cui et al., 2013), though there is some evidence that Notch3 may act as an

oncogene depending on contextual cues (Yamaguchi et al., 2008). Part of this context could be

determined by the mutation load of the tumors themselves. The basal-like breast cancer cell lines

that we chose to study had p53 and Ras pathway mutations. The role of Notch could also be

governed by the dose of Notch signaling of the transformed cell with respect to its dose in the

originating non-transformed cell. Our current work finds that the role of Notch3 in cell growth

regulation is defined by its interaction with genes of other signaling pathways. In cancer cells,

Notch3 is able to modulate Wnt pathway genes, inhibiting cell growth while in non-cancer cells,

we observe this genetic interaction is absent. This corresponds with previous studies that also show

how Notch signaling as a tumor suppressive pathway act by inhibit canonical Wnt signaling in

cancer (Nicolas et al., 2009; Kim et al., 2012).

While gene regulatory network reconstruction has revealed the beginnings of an interactive

network among CRGs, including suggesting the existence of an interaction between Notch3 and

Sfrp2 in transformed murine colon cells (Almudevar et al., 2011), the data shown here provides

evidence for a functional role of this genetic interaction in BLBC cells, suggesting three key points:

that this interaction plays a role in controlling cancer cell biology; and that this interaction is

conserved between colon transformation and BLBC, as well as across mouse and human cells.

Based on these data, we would predict similar functional consequences for modulation of Notch3

and Sfrp2 in transformed murine colon cells (Chapter III). This suggests that at least some of the

CRGs genetically interact to mediate the transformation process, controlling functions essential

for the cancer cell.

In conclusion, the current findings reported here start to address how CRGs control the

survival and growth of transformed cells in a cancer-selective manner. Using Notch3 as a

41

representative example, we find that cancer cells and non-cancer cells differ in transcriptional

response to Notch3 activation, part of which contributes to the cancer-specific cell growth response

to NIC3 expression. We identify a critical genetic interaction between two CRGs, i.e., the

induction of Sfrp2 by Notch3 activation, which is necessary for Notch3 to control cell growth in

cancer cells. This study starts to shed light on how cancer-selective dependencies are built based

on CRGs.

42

2.5 Figures

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Figure 2.1: Notch3 restricts proliferation in a cancer-selective manner

A. Histogram shows endogenous expression levels of Notch3, as measured by qPCR in non-

cancerous mammary cells, MCF10A and MCF12A, and basal-like breast cancer (BLBC) cell

lines, MDA-MB-231, HCC1954, HCC1569, MDA-MB-468, HCC70 and Hs578T. *: P<0.01, **:

P<0.05; Student’s T Test.

B. Line graphs show effect of expression of the Notch3 intracellular domain (NIC3) in BLBC lines

MDA-MB-231, HCC1954, MDA-MB-468, HCC70, Hs578T and HCC1569, with continuous line

showing vector control and dashed line showing NIC3 expressing cells. *: P<0.01, **: P<0.05;

Student’s T Test.

C. Line graphs show effect of knock-down of Notch3 in BLBC cell lines MDA-MB-231 and

HCC1954, with continuous line showing vector control and dashed lines showing NIC3 knocked

down cells. *: P<0.01, **: P<0.05; Student’s T Test.

D. Line graphs show effect of expression of the Notch3 intracellular domain (NIC3) in non-cancerous

MCF10A and MCF12A, with continuous line showing vector control and dashed line showing

NIC3 expressing cells. NS: Not significant p-value

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Figure 2.2: Expression of Notch3 in perturbed BLBC and non-cancerous breast cells

A. Histograms show expression of Notch3 in all six NIC3 expressing BLBC cell. *: P<0.01;

Student’s T Test. B. Histograms show expression of Notch3 in NIC3 expressing non-transformed breast cells. *:

P<0.01; Student’s T Test.

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Figure 2.3: Induction of Hey/Hes family of genes in response to NIC3 expression in Cancer Cells

and Non-Cancer Cells

Bar graphs show expression of Hes1, Hes5, Hey1 and Hey2 in BLBC and non-transformed mammary

epithelial cells in response to NIC3 expression. *: P<0.01; Student’s T Test.

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Figure 2.4: Cancer-specific regulation of gene expression by Notch3

A. The scatterplot shows the log2fold change in expression in BLBC cells of genes that were

significantly disregulated (Fold Change > 2 or Fold Change < 0.5) in BLBC cells expressing

NIC3 compared to vector control and their corresponding log2fold change in expression in non-

cancerous cells.

B. The pie-chart represents the number of genes that are positively, negatively and uncorrelated in

BLBC and non-cancerous cells in response to NIC3 expression.

C. Histograms show change in expression of Notch target gene Hey2 in response to expression of

NIC3 as compared to vector control in BLBC cells MDA-MB-231 and HCC1954 and non-

cancerous MCF10A and MCF12A. *: P<0.01; Student’s T Test. D. The graphic shows all the signaling pathways whose genes are found to be cancer-specifically

disregulated in response to NIC3, as found by Ingenuity Pathway Analysis (IPA) along with the

predicted modulation of the signaling pathway in response to NIC3.

E. Histograms show change in expression of Wnt pathway antagonist Sfrp2 in response to

expression of NIC3 as compared to vector control in BLBC cells MDA-MB-231 and HCC1954

and non-cancerous MCF10A and MCF12A. *: P<0.01; Student’s T Test.

50

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Figure 2.5: Comparison of gene expression levels in cancer cells versus non-cancer cells

Histograms show the change in expression of cancer-selective NIC3 responsive genes Ccnd1, MYCN and

Snai2 in BLBC cells MDA-MB-231 and HCC1954 and non-transformed mammary epithelial cells

MCF10A and MCF12A. *: P<0.01; Student’s T Test.

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Figure 2.6: Notch3 requires Sfrp2 for cancer-specifically restricting proliferation

A. Histogram shows endogenous expression levels of Sfrp2, as measured by qPCR in non-cancerous

mammary cells, MCF10A and MCF12A, and basal-like breast cancer (BLBC) cell lines, MDA-

MB-231 and HCC1954. *: P<0.01; Student’s T Test. B. Histogram shows effect of knocking down Sfrp2 and expressing NIC3 on cell proliferation of

BLBC cells MDA-MB-231 and HCC1954. Proliferation is measured by number of cells on the

plate-well 72 hours post seeding 50,000 cells within the well. *: P<0.01; ANOVA Test.

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54

Figure 2.7: Cancer-specific Notch3 sensitive genes have a hierarchy of interactions

A. Histograms show expression change of Wnt target genes Ccnd1 and MYCN in response to

knocking down Sfrp2 and expressing Notch3 in BLBC cell lines MDA-MB-231 and HCC1954.

*: P<0.01; ANOVA Test.

B. Histograms show expression change of TGFb pathway gene Snai2 in response to knocking down

Sfrp2 and expressing Notch3 in BLBC cell lines MDA-Mb-231 and HCC1954.

C. Figure shows hierarchy of gene interactions between Notch3 and its cancer sensitive genes.

55

Figure 2.8:Notch3 knock-down lowers Sfrp2 expression in BLBC cells

Histograms show change in expression of Sfrp2 in response to Notch3 knockdown in BLBC cell

lines MDA-MB-231 and HCC1954. **:P<0.05; Student’s T Test.

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Chapter III

Conserved molecular interactions implicated in cancer selective control of cell growth by

Notch3 in mp53/Ras-transformed murine colon cells and BLBC

57

3.1 Abstract

CRGs are essential mediators of transformation in multiple cancers. Originally identified

in colorectal cancer downstream of mutant p53 and Ras, a subset of the CRGs were also found to

control cancer cell behavior in other epithelial cancers, including basal-like breast cancer, playing

a cancer-selective role. Previous studies investigating the molecular basis of the cancer-selective

role of CRGs like Notch3 in cancer cell growth found a unique cancer-specific genetic interaction

between Notch3 and Sfrp2, another CRG, in BLBC cells. Notch3 induces Sfrp2 to restrict cell

growth in BLBC cells, inhibiting Wnt pathway genes in the process. Our current studies find that

a similar genetic interaction exists between Notch3 and Sfrp2 in the murine mp53/Ras transformed

colon cells. Furthermore, this induction of Sfrp2 is necessary for the cell growth restricting role of

Notch3 but not sufficient to phenocopy the effect in both BLBC and transformed colon cells. Taken

together, these data suggest that common genetic interactions underlie the anti-proliferative effects

of Notch3 in both BLBC and transformed murine colon cells.

58

3.2 Introduction

The current body of cancer literature demonstrates cancer as a complex and heterogenous

collection of diseases (Greaves and Maley, 2012; The Cancer Genome Atlas Research Network et

al., 2013), subdividing and categorizing tumors on the basis of tissue of origin, genetic mutations

and histological markers (Perou et al., 2000; Nguyen et al., 2008; Shen et al., 2010; Lex et al.,

2012). However common themes emerge among the seemingly disparate cancers studied,

including common phenotypic features of the cancer cell, known as the hallmarks of cancer

(Hanahan and Weinberg, 2000; 2011), as well as a dependence on altered genetic circuits in the

form of oncogene cooperation (McMurray et al., 2008; Ashton et al., 2012; Eberl et al., 2012).

Hence, we predict that there may be other common underlying molecular features that appear in

seemingly different cancers. Moreover, accumulating evidence points to the dysregulation of

CRGs as a conserved, common feature in a number of cancer types, in particular between BLBC

and colon (Walters et al., unpublished; Chapter II). Thus, we hypothesize that the genetic

interactions observed downstream of Notch3 in BLBC would also feature in Notch3-mediated

growth arrest of transformed colon cells.

While studying how cancer-specific vulnerabilities may arise in BLBC, we identified a

novel interaction between CRGs of the Notch and Wnt pathway wherein Notch3 induces Wnt

pathway antagonist Sfrp2 to restrict BLBC cell growth in a cancer-selective manner, inhibiting

Wnt pathway genes Ccnd1 and MYCN as a consequence (Chapter II). Because we had evidence

that Notch3 also restricts growth of mp53/Ras transformed murine colon cells (mp53/Ras cells,

hereafter), we wanted to investigate whether the underlying molecular control points were

conserved in this model.

59

In mp53/Ras cells, our results will demonstrate a similar dependence of Notch3 on Sfrp2

for the growth restrictive effects of Notch3 activation. The downstream consequences of Notch3

introduction includes inhibition of canonical Wnt pathway target genes CCND1 and MYC, in a

manner depedent on Sfrp2induction. However, while induction of Sfrp2 is necessary for the anti-

growth effect of Notch3, exogenous expression of Sfrp2 is not sufficient on its own to cause growth

arrest in either BLBC or mp53/Ras cells. These studies begin to establish the existence of

commonalities not only among gene expression patterns but also among molecular interactions in

transformed murine colon cells and BLBC cells downstream of cooperating oncogenic mutations.

60

3.3 Results

3.3.1 Notch3 induces Sfrp2 to restrict proliferation in colorectal cancer

Both Notch3 and Sfrp2 were identified as a cooperation response gene synergistically

down-regulated in response to cooperation between the dominant negative mutant form of p53

(p53175H, abbreviated here as mp53) and oncogenic Ras (RasV12, abbreviated as Ras) within the

murine colorectal cancer model (mp53/Ras cells), in murine colon cells (McMurray et al., 2008).

Consistent with results in BLBC cells, activating Notch3 by expressing NIC3 in mp53/Ras cells

causes growth arrest in vitro and significantly inhibits tumor formation in vivo (Figure 2.1A; Figure

3.1). Experiments testing for dependence of Notch3 on Sfrp2 induction in BLBC revealed a critical

role for Sfrp2 in the context of increased Notch3 activity (Chapter II). Hence, we wanted to test

whether the genetic interaction between Notch3 and Sfrp2 existed in mp53/Ras cells, and if so

whether this interaction was critical in control of mp53/Ras cell growth as in BLBC cells. To begin

comparing mp53/Ras and BLBC circuitry, we tested whether Sfrp2 expression was increased upon

Notch3 activation by expression of the NIC3 in mp53/Ras cells (Figure 3.2A). Comparison of

NIC3-expressing cells with vector control populations shows significant increase in Sfrp2

expression in response to NIC3.

Since Notch3 inhibits growth of mp53/Ras cells, we wanted to further test whether this

effect of Notch3 depends on the induction of Sfrp2. Thus, we introduced NIC3 into mp53/Ras

cells with or without shRNA to knock down Sfrp2 expression and cell growth in vitro (Figure

3.2B). We observed that while cells with NIC3 expression alone show reduced growth, this was

rescued by reduced expression of Sfrp2 with no significant difference in cell accumulation between

vector control cells and cells with NIC3 introduced on a background of Sfrp2 knock-down.

Reduced expression of Sfrp2 alone had no significant effect on cell growth, suggesting that, as in

61

BLBC cells, restriction of mp53/Ras cell growth by Notch3 activation is dependent on Sfrp2

induction.

3.3.2 Inhibition of Wnt pathway genes in response to Notch3 activation depends on Sfrp2

induction

Our previous work in BLBC cells revealed that along with Sfrp2 induction, Notch3

activation inhibits the expression of Wnt pathway target genes CCND1 and MYCN. Because

NIC3-driven growth arrest appears to depend on Sfrp2 in mp53/Ras cells, we tested whether

additional features of the response to NIC3 expression were similarly dependent on Sfrp2 in

mp53/Ras cells and BLBC cells. First, we simply examined the expression of CCND1 and MYCN

in response to NIC3 introduction into mp53/Ras cells (Figure 3.2A). We observed that while NIC3

expression caused a significant decrease in expression of Ccnd1 in mp53/Ras cells, it did not cause

a strong decrease in expression of MYCN. We also tested the change in expression of MYC, a

gene that is closely related to MYCN, and observed that NIC3 expression strongly and

significantly decreased the expression of MYC in mp53/Ras cells. Hence, in mp53/Ras cells MYC

replaces MYCN in its ability to respond to NIC3 expression.

Further, because in BLBC, we observed that inhibition of Wnt pathway target genes

CCND1 and MYCN in response to NIC3 expression depended upon the induction of Sfrp2, we

asked whether a similar relationship exists between Notch3, Sfrp2, Ccnd1 and Myc in mp53/Ras

cells. To test this, we measured the expression of CCND1 and MYC in NIC3 expressing cells with

or without knock-down of Sfrp2 (Figure 3.2B), finding that while NIC3 expression resulted in

decreased levels of CCND1 and MYC, NIC3 expressing cells in which Sfrp2 was suppressed

62

showed little change in levels of CCND1 and MYC expression, with expression of these genes in

NIC3/Sfrp2 KD cells similar to levels seen in vector control cells. These results show that Sfrp2

induction is necessary for suppression of CCND1 and MYC expression in response to Notch3

activation in mp53/Ras cell, consistent with results from BLBC cells.

3.3.3 Sfrp2 induction is not sufficient to inhibit cancer cell growth

Since Sfrp2 induction is necessary for Notch3 mediated growth arrest in both BLBC cells

and mp53/Ras-transformed colon cells, we wanted to test whether Sfrp2 induction alone is

sufficient to cause growth arrest in cancer cells. To test this, we overexpressed Sfrp2 in the

mp53/Ras colorectal cancer cells as well as two basal-like breast cancer cell lines MDA-MB-231

and HCC1954 and monitored cell growth over time (Figure 3.3A, 3.3B). We observed that in both

the colorectal cancer and basal-like breast cancer cells, there is no significant change in cell growth

in response to Sfrp2 expression. To control for the possibility that the lack of response might relate

to insufficient expression of Sfrp2, we measured the levels of Sfrp2 expression in the perturbed

BLBC and mp53/Ras cells and found that Sfrp2 expression is increased at levels similar to its

induction in response to NIC3 expression (Figure 3.3C). Hence, it appears that while Sfrp2

induction is necessary for the cancer cell response to NIC3, expression of Sfrp2 by itself is not

sufficient to drive growth arrest in cancer cells.

63

3.4 Discussion

Previous studies have demonstrated that CRGs identified downstream of mp53 and Ras in

murine transformed colon cells play a critical role in regulation of other epithelial cancers like

pancreatic cancer and BLBC (Kinsey et al., 2014; Walters et al., unpublished; Chapter II). Our

current studies find that alongside the role of individual CRGs being conserved across multiple

cancer types, we demonstrate that the role of interactions between CRGs are also conserved across

cancer types and between murine and human cells. Notch3 serves as an example of a CRG that

plays similar roles in colorectal cancer and basal-like breast cancer as a critical regulator of cancer

cell growth. Comparing the responses of BLBC cells and their non-transformed counterparts to

perturbation of Notch3 expression revealed a novel mechanism underlying Notch3-mediated arrest

that appears to have shared features in mp53/Ras transformed colon cells.

Our current work finds that similar to BLBC cells, transformed colon cell growth is

regulated by Notch3 through a mechanism involving induction of Sfrp2 that inhibits the expression

of cell cycle-regulator Wnt pathway targets. It is interesting to note that low Notch3 expression in

cancer cells allows high MYCN levels in BLBC, while in transformed colon cells, MYC appears

to a Wnt target. Both c-Myc, encoded by the MYC gene, and n-Myc, encoded by the MYCN gene,

belong to the same family of proteins that controls cellular proliferation, differentiation and

apoptosis (Facchini and Penn, 1998), sharing gene and protein structural features (Henriksson and

Luscher, 1996). In fact, n-myc has been shown to be able to molecularly replace c-myc in

controlling proliferation and differentiation (Malynn et al., 2000). Hence, we consider that altered

action of canonical Wnt signaling on either of these genes observed in response to NIC3 expression

would have similar impact on BLBC and mp53/Ras cells.

64

The induction of Wnt antagonist Sfrp2, along with the inhibition of Wnt pathway genes in

response to NIC3 expression in BLBC and colorectal cancer cells support the hypothesis that

Notch signaling plays a tumor suppressive role by repressing Wnt signaling. This data agrees with

previous observations in colorectal cancer and basal-cell carcinoma of the skin where Notch

behaves in a tumor suppressive role by inhibiting Wnt signaling (Nicolas et al., 2003; Kim et al.,

2012). Notch1 deletion in skin cells leads to transformation, and the basal-cell carcinoma cells

formed are found to possess high levels of nuclear localization of β-catenin, a sign of increased

canonical Wnt signaling (Nicolas et al, 2003). In the APCmin colorectal cancer model, where β-

catenin activity is independent of the APC machinery, Notch1 acts a tumor suppressor by

recruiting the epigenetic modifier SETB1 to repress β-catenin driven transcription (Kim et al.,

2012). Hence, uncontrolled activation of canonical Wnt signaling may be a required mechanism

driving cancer cell proliferation, and down-regulation of Notch family members via oncogene

cooperation may represent a mechanism for cancer cells to allow higher Wnt signaling in the

absence of Wnt pathway mutations.

Thus, in conclusion, we observe that Notch3 restricts cancer cell growth by a similar

mechanism in both BLBC and colorectal cancer cells, showing that not only roles but also

interactions among CRGs are conserved across cancer types. We characterize a genetic interaction

between Notch3 and Sfrp2 that controls cell growth and observe inhibition of Wnt pathway genes

that is dependent on the genetic interaction. We also observe that even though Notch3 activation

depends on Sfrp2 induction to restrict cell growth, induction of Sfrp2 induction by itself is not

sufficient to control cell growth. Together, these results suggest that cancer cells depend on

elevated Wnt signaling to drive cancer cell growth and down-regulation of Notch pathway genes

65

downstream on oncogene transformation is a mechanism by which cancer cells maintain such

elevated levels of Wnt signaling.

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3.5 Figures

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Figure 3.1: Notch3 restricts cell growth via induction of Sfrp2 in colon transformed cells

A. Histogram shows the effect of Notch3 intracellular domain (NIC3) on the expression

levels of Sfrp2 in mp53/Ras cells, as measured by qPCR. *:P<0.01

B. Histogram shows effect of knocking down Sfrp2 and expressing NIC3 on cell

proliferation of mp53/Ras cells. Proliferation is measured by number of cells on the plate-

well 48 hours post seeding 50,000 cells within the well. *:P<0.01

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Figure 3.2: Notch3 inhibits canonical Wnt pathway gene expression by induction of Sfrp2

A. Histograms show the change in expressions of Ccnd1, MYC and MYCN in response

to NIC3 expression in mp53/Ras cells. *: P<0.01; ANOVA Test.

B. Histograms show expression change of Wnt target genes Ccnd1 and MYC in

response to knocking down Sfrp2 and expressing Notch3 in mp53/Ras cells. *:

P<0.01; ANOVA Test

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Figure 3.3: Sfrp2 induction is insufficient to phenocopy Notch3 activation

A. Histograms show Sfrp2 expression upon over-expression in mp53/Ras cells and

BLBC cells MDA-MB-231 and HCC1954. *:P<0.01; Student’s T Test.

B. Line graphs show effect of Sfrp2 overexpression in mp53/Ras cells and BLBC cells

MDA-MB-231 and HCC1954 with continuous line showing vector control cells and

dashed line showing Sfrp2 expressing cells.

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Chapter IV

A novel statistical model for identification of synergistically regulated genes

73

4.1 Abstract

Identifying molecular differences between normal and cancer cells is an important first step

in identifying specific vulnerabilities present in cancer cells. Identified as key gene expression

changes occurring downstream of cooperating oncogenes, CRGs have been found to be essential

mediators of the transformation process, regulating cancerous phenotypes in multiple cancers. Via

transcriptional profiling of a model of malignant transformation in which the contribution of each

oncogenic mutation to the gene expression profile can be assessed, CRGs were initially identified

by an ad hoc ‘synergy score’. Here we propose a simple, rigorous statistic to quantify such synergy.

Our statistic provides a number of attractive features, most important of which is a better

accounting for variability in gene expression data used to search for CRGs. We apply our synergy

statistic to expression profiles of colon cells expressing a dominant negative mutant form of the

tumor suppressor p53 or a constitutively active form of the proto-oncogene Ha-Ras, or the two

together. The new method discovers new genes that may play a role in the malignant phenotype,

with at least one such novel gene identified as promoting tumor formation, such that reducing

expression of this gene by genetic perturbation significantly reduced tumor formation by

mp53/Ras-transformed colon cells. This new method for identification of CRGs will aid in future

searches for tumor regulatory molecules in models of malignant cell transformation.

74

4.2 Introduction

Arising from a normal cell, cancer cells accumulate various key molecular changes

including change in gene expression in order to develop aberrant tumorigenic characteristics

(Zhang et al., 1997; Golub et al., 1999; Ross et al., 2000). The cancer cell becomes dependent on

some of these aberrant gene expression changes, a phenomenon known as non-oncogene addiction

(Solimini et al., 2007; Luo et al., 2009). Interestingly, resetting the expression of such non-mutated

genes critical for the cancer cell is often found to affect cancer cells adversely but produces no

effect on normal cells (Conde et al., 2001; Dewhirst et al.,2008; Dai et al., 2007; Solimini et al.,

2007; Luo et al., 2009). Identification of critical non-mutated genes and pathways could provide

multiplicity of cancer-specific targets.

Malignant transformation of a cell occurs by cooperation between mutated oncogenes and

tumor suppressors (Land et al., 1983; Parada et al., 1984). One way to discover non-mutated genes

with a critical role in the cancer cell is by identifying changes in gene expression that occur

synergistically in response to cooperating oncogenic mutations. Quantifying cooperative

regulation has already proven a promising systems biological method for finding non-oncogenes

(McMurray et al., 2008; Ashton et al., 2012; Eberl et al., 2012). McMurray et. al. described a novel

use of transcriptomics to find gene expression changes essential to the cancer phenotype in a

murine model of colon transformation by identifying genes with synergistically regulated

expression in response to the presence of cooperating oncogenes (Figure 4.1) (McMurray et al.,

2008). By comparing the transcriptome of mp53/Ras transformed murine colon cells with

expression patterns of murine colon cells containing each mutated gene individually, they were

able to quantify the synergistic effects the two oncogenes had on polysomal RNA expression.

Functional testing of synergistically and non-synergistically regulated genes revealed a gene set

75

rich in critical regulators of tumor formation, dubbed as the Cooperation Response Genes (CRGs).

More recent experiments demonstrate that the role of CRGs is conserved in a number of human

cancers and that modulation of CRGs frequently has strong and specific anticancer effects

(McMurray et al., 2008; Kinsey et al., 2014; Walters et al., unpublished; McMurray et al.,

unpublished). CRGs have also been identified and characterized in other types of cancer, including

blast crisis chronic myelogenous leukemia and basal cell carcinoma (Ashton et al. 2012; Eberl et

al., 2012).

Synergy in this context is a non-additive change in gene expression, occurring because of

interaction between the oncogenic mutations that drive transformation; these synergistic effects

exceed the expected additive effects based on the effects of the individual mutations (Perez-Perez

et al., 2009). To date, identification of CRGs has depended on a synergy score that compared the

mean expression of a given gene in cells harboring multiple mutations with the sum of the mean

expressions of that gene in cells with each oncogenic mutation alone. If the change in expression

of that particular gene in response to cooperating mutations is more than the sum of change in

expression of the gene due to the presence of each mutation individually, the gene is classified as

synergistically dysregulated (McMurray et al., 2008). This approach was also used to identify

genes cooperatively dysregulated downstream of a combination of two pharmacological agents

that produced synergistic anticancer effects upon treatment of renal cell carcinoma cells (Han et

al., 2012). However computation of a synergy score based on mean expression values does not

take into account the variation in gene expression of a gene across multiple replicates, causing the

mean to be biased by outliers in the data. (Figure 4.2)

As a proposed solution to better account for variability in gene expression during

identification of cooperative responses, we define synergy as a deviation from additive effect. In

76

particular, gene expression values are modeled as a two-way ANOVA that considers the effect of

each mutation and their combination. Using a proper statistical procedure allows us to model the

noise in the system and properly account for variability by using well understood p-values. We

will show that this novel method has strong detection ability while keeping the false discoveries

to a minimum by looking at the receiver operating characteristic (ROC) curve and discuss selecting

a cut-off point.

Applying the proposed method to data from mp53/Ras-mediated transformation of murine

colon cells, we find that we identify almost the entire original set of CRGs ( 83 out of 95 identified

in McMurray et al., 2008). Using this new statistical procedure, we also identified twelve new

CRGs, and tested the biological role of one of these CRGs, Clca1, by genetic perturbation and

finding reduced tumor formation capacity of mp53/Ras-transformed cells upon knock-down of

this gene. Thus, we report a modified procedure for identification of CRGs in genome scale gene

expression data that robustly identifies genes critical to the malignant phenotype, including a novel

regulator of tumor transformation, Clca1.

77

4.3 Results

In order to identify genes synergistically disregulated downstream of cooperating

oncogenic mutations, we compared gene expression profiles of young adult murine colon (YAMC)

cells expressing either a dominant negative mutant of p53, p53175H (hereafter, mp53), or an

activated form of Ha-Ras, H-Ras12V (hereafter, Ras), both mutant proteins (mp53/Ras) or and

normal parental cells, as described in McMurray et al. (2008). We restricted our investigation to

538 genes differentially expressed between mp53/Ras and YAMC cells, 226 of which are up-

regulated and 312 of which are down-regulated.

We first performed a linear regression analysis on the differentially expressed genes of the

form:

expression ~ bp * p53 + br * Ras + bpr * (p53 ^ Ras)

We ranked the dysregulated genes according to their score. (Figure 4) The first 95 genes ordered

by linear regression were considered CRGs to allow for direct comparison of the outputs of the

linear model with the original synergy score and were further considered for their importance.

In order to assess whether the linear modeling approach identifies a set of genes enriched

for regulators of the malignant phenotype, we considered the 34 genes that have been evaluated

for their effects on tumor growth, as described by McMurray et al. (2008). As with the synergy

score method, it was necessary to separate genes up-regulated in mp53/Ras cells as compared to

YAMC cells from those down-regulated in mp53/Ras cells as compared to YAMC cells, prior to

analyzing the distribution of tumor inhibitory genes over the ranking of synergistic response to

cooperating oncogenes. Among the up-regulated genes, we observe that the ranking procedure

tends to order genes that regulate tumor formation, as shown by red lines, ahead of non-tumor

regulatory genes, as shown by black lines, showing enrichment of genes critical to the cancer

phenotype (Figure 4.3A). For down-regulated genes, however, application of the linear model has

78

a milder effect on the separation of tumor inhibitory genes as compared non-tumor inhibitory

genes, while still producing some separation of this characteristic (Figure 4.3B).

We also drew Receiver Operating Characteristic (ROC) curves, representing the relative

rates of false positives to true positives for our ranking of up-regulated genes and down-regulated

genes, considering genes whose perturbation is tumor inhibitory as true positives and genes whose

perturbation is not tumor regulatory as false positives. Application of the linear model performed

favourably for both up- and down-regulated genes, with up-regulated genes showing the most

consistently high true positive rates with low false positive rates (Figure 4.3C). The ROC curve

indicates that we can keep the false negative rate below 10% for the up-regulated genes and below

20% for the down-regulated genes while obtaining high sensitivity of 70-80%. We also learn from

the ROC curve that selection of a particular p-value cut-off point would not change the false

positive and negative detection rates. This analysis shows that the proposed linear modeling

approach can effectively identify synergistically regulated genes, segregating genes with tumor-

inhibitory potential with a low false positive rate.

Next, we compared the performance of the linear model against the original synergy score.

By plotting the genes according to their score from the linear model as a function of the reported

synergy score (McMurray et al., 2008), we observe that the tumor inhibitory genes, represented by

red circles, are ranked earlier as CRGs by the linear modeling method than by the original synergy

score (Figure 4.4A). Similarly, ROC curves representing the true positive rate versus false positive

rate of linear modeling versus synergy score, we found that the linear model performs better than

the synergy score, considering tumor-inhibitory genes as true positives and non-inhibitory genes

as false positives. Co-plotting ROC curves for both classifiers shows a slight advantage for linear

modeling over the synergy score, in terms of segregating tumor inhibitory genes (Figure 4.4B).

79

On analyzing the actual list of CRGs identified by synergy statistic, we found that 84 of

the genes identified were part of the original set of CRGs, as ranked by synergy score. The linear

model identified twelve new genes that appear to be synergistically regulated in mp53/Ras-

transformed colon cells. Five of these genes were up-regulated in expression, while seven of them

are down-regulated in expression (Figure 4.5A). These genes are known to regulate varied

biological functions (Figure 4.5B).

We start testing the importance of these new CRGs with Clca1, the CRG with the highest

expression. Clca1 expression was reset in mp53/Ras cells with the aim of achieving YAMC cell

levels of expression (Figure 4.6A) , and tumor formation capacity of perturbed cells was compared

to unperturbed control cells. Interestingly, tumors formed by cells with reduced expression of

Clca1 were significantly smaller as compared to vector control cells (Figure 4.6B), showing that

high levels of Clca1 are required for the maintenance of malignant cancer cells.

80

4.4 Discussion

CRGs have been shown to be play a key role in malignancy of various kinds of cancers,

including colorectal cancer, acute myeloid leukemia, pancreatic cancer and breast cancer

(McMurray et al., 2008; Ashton et al., 2012; Kinsey et al., 2014; Walters et al., unpublished).

Identification of synergistically regulated genes provides an effective method for discovery of

critical regulators of cancer cell function. Here, we refine the procedure for identifying CRGs by

accounting for variability in gene expression data as measured by transcriptomic methods. The

approach of applying a linear model and identifying genes whose expression does not fit this model

is a more elegant and rigorous statistical method to find synergistically regulated genes.

Our statistical measure of synergy compares favorably to the original synergy score

described by McMurray et. al. In direct comparison to the synergy score, the linear modeling

approach proposed here ranks differentially expressed genes in a similar order (Figure 4.3A). In

contrast to synergy score, however, our statistic penalizes many genes quite heavily, based on

variation in their expression values across biological replicates in the dataset. In comparing each

of these methods for identifying to known effects of perturbation of genes in this set, we see the

ROC curves (Figure 4.3B) favor our linear synergy model over the original synergy score, both

for up and down regulated genes (Figure 4.3C), providing a quantitative measure of the improved

selection of tumor inhibitory genes by the linear model.

The linear model prioritizes genes biologically relevant to the cancer phenotype, arguing

that the more consistent and strong the cooperative effects of the oncogenic drivers on gene

expression, the more likely a gene is to play a role in the cancer phenotype.. Among genes

differentially expressed between normal and transformed cell lines, our model very strongly favors

genes found through testing via tumor formation assays to be relevant to the cancer phenotype.

81

Application of the linear model to mp53/Ras cooperation-driven transformation of colon

cells data identifies new CRGs including the gene Clca1. Clca1 belongs to the family of Ca2+-

activated chloride channel proteins that help that cell sense its environment, based on osmotic ion

concentration. (Gruber et al., 1998) The role of chloride channel activation is not well understood

in the context of cancer. The activation of other voltage-dependent chloride channels is required

for invasiveness of malignant gliomas and the volume-sensitive chloride channel activation is

found to be required for transformation of cervical epithelium (Chou et al., 1995; Soroceanu et al.,

1999). Interestingly, we find that the expression of Clca1 is synergistically up-regulated in

mp53/Ras cells, and suppression of Clca1 significantly reduces tumor formation in vivo,

demonstrating a novel role for this protein in cancer.

Consequently, the proposed linear modeling approach to identify synergistic responses to

cooperating oncogenic mutations stands validated, both in absolute terms, and relative to the

previous measure of synergy, in its ability to identify genes dysregulated by altered expression

rather than mutation that are essential for transformation. These methods remain valuable tools for

quantifying non-additive regulation.

82

4.5 Figures

Figure 4.1: ‘Cooperation Response Genes’, downstream effectors of interaction between

oncogenic mutations. Scheme illustrating that CRGs are essential mediators of cell

transformation downstream of cooperating oncogenic mutations that control multiple cellular

process required for malignant transformation.

83

Figure 4.2: Examples of synergistic and non-synergistic expression patterns. Expression

levels are shown for each cell line on two genes, one that shows synergistic expression (A), and

one that shows merely additive expression (B). Green lines mark where bpr = 0 in transformed

expression.

84

Figure 4.3: Linear modeling synergy coefficient classifies tumor inhibitory genes. For up-

regulated (A), and down-regulated (B) genes, our classifier ranks tumor inhibitory genes (in red)

favorably. Receiver Operating Characteristic (C) shows the relative rates of False Positive to True

Positive acceptance for our rankings of up-regulated and down-regulated genes, where tumor

inhibitory genes are considered true positives, and non-inhibitory genes false positives. Here we

see favorable performance on both up- and down-regulated genes, with better performance on the

up-regulated.

85

Figure 4.4: Comparison of linear modeling to the original classifier, synergy score. (A) Scatter

plot indicates rank order of significance of interaction in gene expression (p-value, t-test), as

identified by the general linear model (y-axis), compared to the rank order of synergy, as identified

by synergy score (x-axis). Dots are color coded to indicate the effect of perturbation of each gene

on tumor formation capacity of mp53/Ras cells (red, significant reduction in tumor volume; black,

no significant change in tumor volume; gray, not perturbed). Gray lines denote cut-offs in each

ranking. (B) Receiver operating characteristic (ROC) curves show the rate of identification of true

positive genes as compared to false positives for the synergy score (red line) and the general linear

model (blue line). Random chance is indicated by the green diagonal line. The area under each

curve (AOC) is a measure of improvement over random chance. AOC values for linear model and

synergy score are shown below the graph.

86

Figure 4.5: Novel CRGs found by linear modeling. Linear modeling selects 95 CRGs, of which

12 are new. These CRGs span a number of biological processes within the cell (A). The novel

CRGs are shown below (B) on a plot of differential expression between YAMC and mp53/Ras

cells, color coded according to their function.

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Figure 4.6: Knock-down of the newly identified CRG, Clca1, inhibits tumor growth. (A) Bar

graphs show expression of Clca1 in mp53/Ras cells expressing shRNAs targeting Clca1. *: p <

0.01; Student’s T Test. (B) Box plots show tumor volume at 4 weeks post injection, from

mp53/Ras cells stably expressing either of two shRNA constructs targeting the newly identified

CRG, Clca1. ** indicates significantly smaller tumors, as compared to vector control (p < 0.05,

unadjusted, Wilcoxon signed-rank test).

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Chapter V

Discussions

90

5.1 Summary of findings

CRGs represent critical mediators of cancerous transformation present downstream of

cooperating oncogenes. Remarkably, CRGs appear to represent a point of convergence across

distinct types of cancer, playing a similar role in malignancy of human colon, basal-like breast,

pancreatic and prostate cancers (McMurray et al., 2008; Kinsey et al., 2014; Walters et al.,

unpublished; McMurray et al., unpublished). Work done in the basal-like breast cancer model has

shown that resetting CRG expression has cancer-selective effects, inhibiting BLBC growth with

little effect on non-transformed cells (Walters et al., unpublished). My work begins to explore how

such cancer-selective effects of CRGs can arise, with a focus on Notch3 as an example because of

the critical role of this gene in malignant cell growth (Chapter II, Figure 1), the key role of Notch3

in cancer-selective effects of pharmacological targeting of CRGs (Chapter I) and hints at the

potential underlying mechanism by which Notch3 might impinge on cancer cell function

(Almudevar et al., 2011).

Notch3 is a cell surface receptor and transcription factor whose expression is down-

regulated in BLBC cells as compared to non-transformed mammary epithelial cells. In Chapter II

of this thesis, I demonstrate that activation of Notch3 causes a cancer-selective growth inhibition

in BLBC cells, while similar perturbation in non-transformed mammary epithelial cells has no

effect on growth. I discover that there is a cancer-specific transcriptional response to activation of

Notch3, part of which is required for cell growth restriction in response to Notch3 in BLBC.

Specifically, I identify a novel interaction between Notch and Sfrp2, a known Wnt pathway

antagonist, and show that this genetic interaction is essential for Notch3 to control BLBC cell

growth, concomitant with Sfrp2-dependent changes in expression of cell cycle regulatory genes.

Taken together, this work demonstrates a novel genetic interaction present in BLBC but not non-

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cancerous cells involving Notch and Wnt pathway genes that is essential for the cancer-selective

role of Notch3 in restricting BLBC growth.

Since reduced Notch3 expression is also essential for cell growth in transformed murine

colon cells, in Chapter III of the thesis, I test whether similar architecture underlies Notch3-

mediated growth inhibition in mp53/Ras-expressing colon cells. I discovered that just as in BLBC

cells, Notch3 activation drives an induction of Sfrp2 and consequently reduces cell growth while

inhibiting Wnt pathway target genes in transformed colon cells. I further demonstrate that Sfrp2

induction by itself is not sufficient to inhibit cancer cell growth in either BLBC cells or transformed

colon cells. Collectively, Chapter II and Chapter III elucidate how Notch3 restricts cancer cell

proliferation and characterize a genetic interaction between Notch3 and Sfrp2 that is essential for

this cancer-selective growth inhibitory action of Notch3.

Chapter IV describes a statistical model that refines the method for identifying CRGs. The

statistical method, based on a linear regression model, rank orders genes differentially expressed

in cancer based on the degree non-additivity in their response to the presence of cooperating

oncogenic lesions, while accounting for variance across replicates of gene expression data. We

find that this linear model performs better than the original synergy score in eliminating false

positives, defined as genes with a low probability of inhibiting tumor formation. While 83 of the

genes originally identified as CRGs by the synergy score metric are also identified by the new

linear model, twelve new genes are identified as CRGs. We test the importance of one of the new

CRGs, Clca1 and find that suppressing Clca1 expression in mp53/Ras cells causes a significant

reduction in tumor formation. Hence, the proposed linear modeling approach is a useful method

for identifying CRGs that are critical for tumor formation.

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5.2 Significance of Findings

Arising from normal cells as a result of malignant transformation, tumor cells differ vastly

from their cell of origin at the level of genetic and epigenetic changes to DNA (Feinberg and

Tycko, 2004; Pleasance et al., 2010), at the level of functional output or phenotype (Hanahan and

Weinberg, 2000; 2011) as well as at the level of gene expression (Zhang et al., 1997; Baylin et al.,

2001). Researchers first started to target cancer cells through exploitation of phenotypic

differences between cancer and normal cells, such as ability to grow rapidly, using agents that

target DNA replication or cell division (Esposito et al., 2013; Hurley, 2002). More recently, the

focus has shifted to targeting oncogenic mutations for cancer-selective inhibition of cancer growth

(Weinstein 2002; Weinstein and Joe, 2006; Weinstein et al., 2008). My work and that of others

demonstrate that cancer selective targeting can also be achieved through targeting changes in gene

expression that are essential for the cancer cell to regulate cancer cell behavior (Luo et al., 2009;

Solimini et al., 2007; Conde et al., 2001; Dewhirst et al.,2008; Dai et al., 2007).

While mutations in oncogenes and tumor suppressors drive the transformation process and

cancer cells are often found to be addicted to these mutated genes, it is not simple to target many

genes known to harbor frequent oncogenic mutations. For example, activated oncogenes that lack

kinase activity, such as MYC and other transcription factors, have proven to be difficult to target

pharmacologically (Prochownik, 2004; Luo et al., 2009). Moreover, by virtue of the fact that they

are lost in cancer, tumor suppressor genes have never successfully been reactivated in a clinically

useful fashion (Knudsen and Knudsen, 2008; Hong et al., 2014). Hence, it is essential to identify

other kinds of changes that mediate the transformation process and are essential for the functioning

of cancer cells. CRGs are enriched in such non-mutated regulators of malignant cell transformation

to which the cancer cell is addicted. Understanding how cancer cells are addicted to CRGs reveals

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important mechanisms that underlie cancer-specific action of these genes and provides an

opportunity to identify additional cancer-specific dependencies that can potentially be targeted.

Though CRGs were first identified in the murine colon cells transformed by mutant p53

and activated H-Ras, a subset of CRGs are found to be essential in multiple epithelial cancers

including BLBC. The BLBC model system provides an opportunity to compare the behavior of

transformed cells and non-transformed cells in response to CRG perturbations and previous studies

from our lab had shown that CRGs can play a cancer-specific role in BLBC (Walters et al.,

unpublished). My thesis work begins to tease apart how CRGs can control malignant cell growth

in a cancer-specific manner, focusing on Notch3. The most important observation that can be made

when considering the whole body of my work is the discovery that Notch3 cancer-selectively

inhibits cell growth by eliciting differential transcriptional responses in cancer cells versus non-

cancer cells.

Notch3 belongs to the family of Notch receptor proteins and is found to restrict cancer cell

growth in the BLBC and colorectal cancer model. While characterizing the cancer-specific

response to Notch3 activation in BLBC cells versus non-cancerous breast cells, I observed a

differential transcriptional response between cancer cells and non-cancer cells. This correlates to

previous in silico observations in colorectal cancer where modeling of gene regulatory networks

in cancer cells versus non-cancer cells found that that transcriptional responses of many signaling

pathways changed in cancer cells, with transcription factors like Gli2 and Asxl1 losing most of its

old interactions with other genes and gaining new interactions on transformation (Cordero et al.,

2014). Similarly, another study found that multiple myeloma cells are dependent on IRF4, a

transcription factor, due to differential transcriptional response between cancer cells and normal

B-cells (Shaffer et al., 2008). The differential transcriptional response to Notch3 activation that I

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observe in cancer cells versus non-cancer cells could be a consequence of the dosage of activated

Notch3 in cancer cells and non-cancer cells. Hence, my work contributes to the body of work

addressing how certain signaling pathways play differential roles in cancer cells via regulation of

transcriptional targeting.

From this study, we identify a previously unidentified interaction between Notch3 and

Sfrp2, occurring in BLBC but not non-cancerous cells, which is essential for Notch3 mediated

inhibition of cancer cell growth. Interestingly, Sfrp2, a CRG , down-regulated in transformed

mp53/Ras expressing colon cells and is similarly dysregulated in BLBC. Sfrp2 is known to

antagonize Wnt signaling in various contexts, suggesting that reduced Sfrp2 expression would

promote Wnt signaling and cell growth, which is consistent with my results showing that Sfrp2

induction upon Notch3 activation is a key part of BLBC growth inhibition (Kawano and Kypta,

2003; Esteve et al., 2011). Consistently, we also observe the inhibition of downstream targets of

the Wnt pathway upon Notch3 activation, and these changes are dependent on Sfrp2 induction.

The relationship we observe between Notch and Wnt signaling in BLBC cells is consistent with

other models of cancer in which Notch signaling plays a tumor suppressive role. For example,

Notch1 deletion in skin cells leads to transformation, and the resulting basal-cell carcinoma cells

have increased canonical Wnt signaling as measured increased nuclear localization of β-catenin

(Nicolas et al, 2003). In the APCmin intestinal cancer model, where β-catenin is hyperactivated

through loss of the APC machinery, Notch1 acts a tumor suppressor by recruiting the epigenetic

modifier SETB1 to the promoters of Wnt target genes, which represses β-catenin driven

transcription (Kim et al., 2012). Hence, inhibition of canonical Wnt signaling may be a required

mechanism by which Notch signaling exerts tumor suppressive action.

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While the genetic interaction between Notch3 and Sfrp2 was first characterized in BLBC,

it is interesting to note that the genetic relationship is also found in the murine colon transformation

model, as reported in Chapter III. This piece of evidence suggests that while CRGs are often

similarly dysregulated in different kinds of cancers, the genetic circuitry underlying the

dysregulation of CRGs is also quite similar at least for these relationships in BLBC and

transformed colon cells. While this could be a consequence of similar mutational background of

p53 and Ras/Raf pathway mutations in the colon transformation and BLBC models, driven by the

identity of the mutations themselves, part of this could also be a result of rewiring of genetic

circuitry as part of the transformation process due to the effects of cooperation between the

oncogenic lesions.

Because of the many lines of accumulating evidence that CRGs and interactions between

them contribute significantly to the malignant phenotype, Chapter IV attempts to refine the

technique of CRG identification by applying linear regression modeling. The technique calculates

a measure of synergistic changes in gene expression while taking variance in gene expression into

account. It then rank orders genes according to their synergy measure in order to identify CRGs,

those with the least additive control of their expression. Comparison with another method of CRG

identification, the synergy score, shows that the new mathematical model performs better in

identifying tumor inhibitory CRGs. While most of the CRGs identified by the mathematical model

have been previously described by the original paper, the model also identified twelve new CRGs.

Our current work goes on to demonstrate that at least one of these novel CRGs is critical to the

transformed phenotype of mp53/Ras cells.

Clca1, this newly identified tumor regulatory CRG, belongs to the family of Ca2+-activated

chloride channel proteins that help that cell interact with its environment (Gruber et al., 1998). Our data are

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consistent with previous observations that ion channels often contribute towards tumor formation,

regulating proliferation, differentiation and apoptosis (Lehen’kyi et al., 2011). Chloride channel proteins

like chloride channel 3 (CLC3) have been found to inhibit apoptosis by affecting osmotic pressure of the

cell through ion flow, and are reported to be upregulated in prostate cancer (Shen et al., 2002; Lemonnier

et al., 2004). This correlates with our observation that Clca1 is upregulated in colorectal cancer cells as

compared to non-transformed cells and that knocking down Clca1 expression reduces tumor formation

capacity in vivo. Thus Clca1 represents a proof of concept that CRGs identified by the linear modelling

procedure contain tumor inhibitory genes.

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5.3 Future Directions

The work presented in this thesis starts to address how CRGs can play a cancer-selective

role in BLBC. Notch3 is considered an example of a CRG that controls cell growth in a cancer

specific manner. My work identifying differential transcriptional responses to Notch3 activation

in cancer cells versus non-cancer cells finds numerous genes belonging to various signal

transduction pathways. We focused on Wnt pathway genes because this pathway has been heavily

implicated in driving proliferation of cancer cells, and the observed direction of change in gene

expression of the Wnt pathways genes was consistent with the hypothesis that Notch3 activation

inhibited this signaling. We have, however, not directly demonstrated that Notch3 activation

inhibits the key signaling steps along the Wnt pathway. Canonical Wnt signaling is driven by

nuclear localization of β-catenin. Hence future studies should investigate whether Notch3

activation ultimately leads to reduced nuclear localization of β-catenin and whether activation of

Notch3 is dependent on the inhibition of canonical signaling and nuclear localization of β-catenin.

Our current study indicates that cancer cell growth mediated by Notch3 is dependent on

the induction of Sfrp2. We also observe that downstream of Sfrp2 induction, there is reduced

expression of canonical Wnt target genes MYCN/MYC and Ccnd1. Future studies should also

investigate whether Notch3 mediated growth arrest is dependent on reduction in these genes.

Because there are reports that Myc modulates Ccnd1 expression in multiple cellular contexts

(Philipp et al., 1999; Perez-Roger et al., 1999), I would also test whether there is a hierarchy in

interaction between MYCN/MYC and Ccnd1.

The transcriptomic analysis also identified multiple genes belonging to other signaling

pathways that responded in a cancer-selective manner to Notch3 activation. Hence, future studies

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sould also investigate the interaction of Notch3 with other signaling pathways to drive growth

inhibition. Candidate pathways include the p53 and PTEN pathway that are known to drive cell

growth arrest, apoptosis and senescence in cancer cells (Stambolic et al, 2001; Mayo et al., 2003;

Chen et al., 2005).

While high throughput mRNA sequencing identifies changes in the transcriptional

response to Notch3 activation, we cannot differentiate direct Notch3 targets from indirect changes

in transcriptional regulation. Hence, we will perform chromatin immunoprecipitation, possibly

coupled to high-throughput sequence analysis to determine whether NIC3 directly binds to the

promoter of Sfrp2 and other genes changed in response to Notch3 activation. This will help identify

direct cancer-specific targets of Notch3.

We also could also test whether NIC3 expression restricts transcription via canonical Notch

signaling. Canonical Notch signaling occurs by the nuclear localization of Notch intracellular

domain which then recruits RBPJκ and drives the transcription of Notch target genes. We observe

induction of canonical Notch target genes in response to NIC3 expression in both cancer cells and

non-cancer cells, as reported in Chapter II. Hence, the cancer-specific response of Notch3 may not

stem from canonical Notch signaling. Non-canonical Notch signaling occurs independent of

RBPJκ, when the Notch intracellular domain promiscuously binds to transcriptional machinery of

other pathways to drive transcription of alternative targets (Anderson et al, 2012; Ayaz and

Osborne, 2014). We observe that in the BLBC cells, there is greater expression of genes such as

Axin2 and Runx2 that are capable of promiscuously binding NIC3 and driving non-canonical

Notch signaling. (Data not shown; Kim et al., 2012; Watanabe et al., 2013) Hence, we hypothesize

that in cancer cells, where there is higher expression of these partner molecules, Notch3 activation

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causes greater availability of NIC3 to promiscuously bind with them, driving non-canonical Notch

signaling.

To test whether the cancer-specific role of Notch3 activation occurs through canonical

signaling, we would knock down RBPJκ and assess whether NIC3 expression still drives cancer

cell growth arrest in vitro. We would also individually knock down each of the promiscuous

partner molecules required for non-canonical Notch signaling and test whether NIC3 expression

is dependent on the partner molecules for cancer-selective growth arrest. These experiments could

provide a molecular mechanism by which Notch3 controls cell growth in a cancer-selective

manner.

Finally, the novel statistical method of identifying CRGs provides a useful technique to

improve CRG identification. We would like to utilize this method in other cancer models where

CRGs have been identified and compare the ability of this method to identify CRGs with

previously applied methods. Moreover, the newly discovered CRGs in the colon transformation

model provide additional molecules of interest for further studies, as only one out of twelve of

these CRGs has been tested for its importance in tumor formation. The rest of the CRGs could be

perturbed in the mp53/Ras-expressing colon cells and tested for their requirement in tumor

formation. It will also be interested to see if Clca1 and the other CRGs are essential for BLBC cell

regulation and whether they have a cancer-selective role. Importantly further identification of

interactions among CRGs to control cancer cell behavior will start addressing questions of how

CRGs selectively control cancer cell behavior downstream of oncogenic transformation.

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5.4 Conclusions

Previous work from the lab had demonstrated the importance of CRGs in transformed

colon and BLBC. Importantly CRGs play a cancer-specific role in BLBC. The current work

described in the thesis starts to address how this cancer-selective role of CRGs may be

programmed at the molecular level, using Notch3 as an example. We identify a unique genetic

interaction between Notch3 and Wnt pathway genes that is essential for the cancer cell growth

regulatory role of Notch3, alongside additional differential transcriptional response to Notch3

activation in cancer cells. We go on to establish that a similar genetic interaction exists in

transformed colon cells, also regulating cancer cell growth. Finally, we introduce a more rigorous

method to identify CRGs, which identifies twelve new CRGs. We find that atleast one of these

new CRGs, Clca1, is essential for tumor formation.

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Chapter VI

Materials and Methods

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6.1 Materials

6.1.1 Parental Cell Lines

Name Description Source

YAMC Murine colonic cells derived

from young adult “immorto”

mice (Whitehead et al., 1993)

Burgess, LICR, Melbourne

Phoenix-Eco Ecotropic retroviral

packaging cell line

G. Nolan

Phoenix-GP Amphitropic retroviral

packaging cell line

G. Nolan

HCC1954 Human breast ductal

carcinoma cell line

ATCC

MDA-MB-231 Human breast

adenocarcinoma cell line

ATCC

HCC1569 Human breast metaplastic

carcinoma cell line

ATCC

MDA-MB-468 Human breast

adenocarcinoma cell line

ATCC

HCC70 Human breast primary ductal

carcinoma cell line

ATCC

Hs578T Human breast carcinoma cell

line

ATCC

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MCF10a Human immortalized

mammary epithelial cell line

ATCC

MCF12a Human immortalized

mammary epithelial cell line

ATCC

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6.1.2 Plasmids

Name Description Source

pBabePuro3 Moloney murine leukemia

virus retroviral vector.

Contains puromycin

resistance gene driven by the

SV40 early promoter.

H. Land

pBabePuro3-NIC3 A murine Notch3 intracellular

domain cDNA cloned into the

EcoRI site of pBabePuro3

H.R. McMurray

pBabePuro3-Sfrp2 A murine Sfrp2 cDNA cloned

into the EcoRI site of

pBabePuro3

H.R. McMurray

pSuperRetroPuro A retroviral version of the

pSuper vector designed to

generate shRNA in

mammalian cells, with

expression driven by the HI

RNA promoter. Contains

puromycin resistance gene

driven by a PGL promoter.

H. Land

pSRPuro-hNotch3sh833 A human Notch3 shRNA

targeting sequence inserted

into BglII and HindIII of

pSuperRetroPuro

A. Ghosh

pSRPuro-hNotch3sh1026 A human Notch3 shRNA

targeting sequence inserted

into BglII and HindIII of

pSuperRetroPuro

A. Ghosh

pSRPuro-hNotch3sh1459 A human Notch3 shRNA

targeting sequence inserted

into BglII and HindIII of

pSuperRetroPuro

A. Ghosh

pSuperRetroHygro A retroviral version of the

pSuper vector designed to

generate shRNA in

mammalian cells, with

expression driven by the HI

RNA promoter. Contains

hygromycin resistance gene

driven by a PGK promoter.

OligoEngine

pSRHygro-hSfrp2sh1406 A human Sfrp2 shRNA

targeting sequence inserted

HR McMurray

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into BglII and HindIII of

pSuperRetroHygro

pSRHygro-hSfrp2sh1578 A human Sfrp2 shRNA

targeting sequence inserted

into BglII and HindIII of

pSuperRetroHygro

HR McMurray

pSRHygro-hSfrp2sh1652 A human Sfrp2 shRNA

targeting sequence inserted

into BglII and HindIII of

pSuperRetroHygro

HR McMurray

pSRHygro-Sfrp2sh1274 A murine Sfrp2 shRNA

targeting sequence inserted

into BglII and HindIII of

pSuperRetroHygro

HR McMurray

pSRHygro-Sfrp2sh1476 A murine Sfrp2 shRNA

targeting sequence inserted

into BglII and HindIII of

pSuperRetroHygro

HR McMurray

pLKO A lentiviral vector. Contains

puromycin resistance gene.

Open Biosystems

pLKOshClca1 #1 A murine Clca1 shRNA

targeting sequence inserted

into pLKO

Open Biosystems

pLKOshClca1 #2 A murine Clca1 shRNA

targeting sequence inserted

into pLKO

Open Biosystems

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6.1.3 shRNA Target Sequences

hScr001 – GGTTCACGG

hNotch3sh833 – CCAAAAAGA

hNotch3sh1026 – CTCGCAATA

hNotch3sh1459 – TCATCGATA

hSfrp2sh1406 – TAGCTCACT

hSfrp2sh1578 – CATGCAAAT

hSfrp2sh1652 – GTCTTACAA

Sfrp2sh1274 – TCTCTTGAA

Sfrp2sh1476 – TGGTCAGTC

Clca1 #1 – GCATGGATAAACGGTACAGTA

Clca1 #2 - GCTGAGTTTATAGGTGATTAT

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6.1.4 Real-Time PCR Primers

Murine RhoA Forward – AGCTTGTGGTAAGACATGCTTG

Murine RhoA Reverse – GTGTCCCATAAAGCCAACTCTAC

Human/Murine NIC3 Forward – ATGCCGATGTCAATGCAGTGGATG

Human/Murine NIC3 Reverse – TCTCTTCCTTGCTGTCCTGCATGT

Human/Murine Hey2 Forward – TCTGCCAAGTTAGAAAAGGCTG

Human/Murine Hey2 Reverse – CAAGAGCATGGGCATCAAAGTA

Murine CyclinD1 Forward – AGATGTGGACATCTGAGGG

Murine CyclinD1 Reverse – AGGGGTGATGCAGATTCTATC

Human CyclinD1 Forward – GCTGCGAAGTGGAAACCATC

Human CyclinD1 Reverse – CCTCCTTCTGCACACATTTGAA

Human MYC Forward – GGCTCCTGGCAAAAGGTCA

Human MYC Reverse – CTGCGTAGTTGTGCTGATGT

Murine MYC Forward – CCCTATTTCATCTGCGACGAG

Murine MYC Reverse – GAGAAGGACGTAGCGACCG

Human MYCN Forward – ACCCGGACGAAGATGACTTCT

Human MYCN Reverse – CAGCTCGTTCTCAAGCAGCAT

Murine MYCN Forward – AGGATACCTTGAGCGACTCAGAT

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Murine MYCN Reverse – GGCTCAGGCTCTTCGCTTTTG

Human Snai2 Forward – CGAACTGGACACACATACAGTG

Human Snai2 Reverse – CTGAGGATCTCTGGTTGTGGT

Murine Clca1 Forward – TACGAGGGTGTGGTCATTGCCATT

Murine Clca1 Reverse – TGGCTGGCTTCAAACAGGTAGGTA

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6.2 Methods

6.2.1 Cell Culture

Young adult mouse colon (YAMC) cells (Whitehead 1993), and YAMC cells expressing both

p53175H and HRasV12 (mp53/Ras) cells were maintained as previously described (McMurray

2008; Xia 2007). Briefly, cells were cultured at 33оC and with 5% CO2 in water jacket humidified

incubators. Growth media was RPMI (Gibco) containing 10% (v/v) fetal bovine serum (FBS), 1x

insulin-selenium-transferrin-A (ITS-A) (Gibco), 2.5 μg/mL gentamicin (Gibco), and 25 U/ml

interferon-У (IF) (RandD Systems). Cells were conditionally immortalized when grown at 33оC

and in the presence of IF, due to IF driven expression of a thermosensitive SV40 Large T-antigen,

allowing for expansion of non-mp53/Ras cells in culture. All mp53/Ras cell derivatives were

cultured at 39оC in RPMI media supplemented with 10% (v/v) FBS, 1x ITS-A, and 2.5 μg/mL

gentamicin. YAMC cells and all derivatives were grown on 1 μg/cm2 collagen I coated dishes

(BD Biosciences). DLD-1 and HT-29 human colon cancer cells, as well as amphitropic and

ecotropic phoenix viral producer cells were maintained at 37оC and with 5% CO2 in water jacket

humidified incubators. Growth media was DMEM (Gibco) supplemented with 10% (v/v) FBS, 2

μg/mL gentamicin, and 100 μg/ml kanamycin.

Basal-like breast cancer cell lines were maintained as previously described (Neve et al., 2006;

Walters et al., unpublished). The cells were maintained at 37оC and with 5% CO2 in water jacket

humidified incubators. Growth media was DMEM (Gibco) or RPMI (Gibco) supplemented with

10% (v/v) FBS, 2 μg/mL gentamicin, and 100 μg/ml kanamycin.

Non-transformed mammary epithelial cell lines, MCF10A and MCF12A were maintained as

previously described (Debnath et al., 2003; Neve et al., 2006; Walters et al., unpublished). The

110

cell lines were maintained at 37оC and with 5% CO2 in water jacket humidified incubators. Growth

media was DMEM:F12 (Gibco) supplemented with 5% (v/v) heat activated horse serum, 0.01

mg/ml insulin, 100 ng/ml cholera toxin, 500 ng/ml hydrocortisone, 1X Penicillin/Streptomycin

and 20 ng/ml EGF.

6.2.2 Genetic perturbation of gene expression:

Genetically perturbed cells were derived by retroviral infection with virus containing appropriate

cDNA or shRNA expression constructs. Retrovirus for infection of mp53/Ras cells was produced

by transient transfection of ecotropic phoenix cells. Six hours prior to transfection, ecotropic

phoenix cells were seeded at 2.5x106 cells per 10cm dish in 10 mL of DMEM media. Cells were

then transiently transfected overnight, in standard growth media, with 20 μg of calcium phosphate

precipitated DNA (in 550 μl H2O, 50 μl of 2M CaCl2, and 600 μl 2X HBS (250 mM NaCl, 50

mM HEPES pH 7.1, 1.5 mM Na2HPO4)). DNA was precipitated by adding 2X HBS to

DNA/CaCl2/H2O mixture drop-wise while vortexing and then incubating at room temperature for

20 minutes. Following overnight transfection, media was removed from phoenix cells and replaced

with 4 mL standard growth media. Human infectious retrovirus for infection of BLBC and non-

transformed mammary epithelial cells was produced with amphitropic phoenix cells using the

same procedure described above for ecotropic phoenix cells, except amphitropic phoenix cells

were additionally co-transfected with 3 μg of VSV-G expression vector.

Lentiviral infections were also used to silence the expression of genes. pLKO-shRNA vectors were

identified among the collection at Open Biosystems, and sets of these molecules were tested to

identify appropriate knock-down constructs. For production of lentivirus, pLKO lentiviral

111

constructs were co-transfected with the VSV-G gene and a packaging plasmid containing the gag,

pol, and rev genes into 293TN cells (gift of Dr. C. Proschel).

Infections were performed 48 hours after phoenix producer cell transfections. 24 hours prior to

infection, mp53/Ras cells, BLBC, or non-transformed cells were seeded at 2.5x105 cells

respectively per 10 cm dish. Infections were carried out by filtering virus containing media through

0.45 μm syringe filter (Pall) and adding to target cell dishes, along with polybrene at a final

concentration of 8 μg/ml. Target cell growth media was removed prior to adding virus. Following

90 minutes of infection, virus was removed and fresh virus containing media/polybrene was added.

Three rounds of such infections were carried out at 39оC for mp53/Ras cells and 37оC for BLBC

and non-transformed mammary epithelial cells, after which, 10 mL of fresh media was added. 48

hours post-infection, polyclonal cell populations stably expressing indicated cDNAs and/or

shRNAs were generated by selection in standard growth media containing either 5 μg/mL

puromycin or 250 μg/mL hygromycin. All stable knockdown cell lines were generated were

generated using a pSuper.Retro vector system, while the pBabe vector system was utilized for

stable gene expression.

6.2.3 Quantitation of gene expression:

For collection of RNA, cells were grown without antibiotic for 48 hours, followed by serum

withdrawal for 24 hours prior to harvesting for RNA isolation. Total RNA was extracted from cells

following the standard RNeasy Mini Kit protocol for animal cells, with on-column DNase

digestion (Qiagen). cDNA was generated for qPCR analysis by mixing 5μg RNA with 10 mM

DTT, 400 μM dNTP mixture, 1x SuperScript II reverse transcriptase buffer, and 0.3 ng random

hexamer primer, then denaturing RNA at 90°C for 5 minutes, placing RNA mixture on ice and

112

adding 1μl SuperScript II reverse transcriptase and 1 μl RNaseOUT RNase inhibitor for a total

reaction volume of 50μl (all components from Invitrogen). RT reactions were carried out at 42°C

for 2 hr, followed by heat inactivation of RT enzyme by incubation at 70 degrees Celsius for 10

minutes.

SYBR Green-based quantitative PCR was run using cDNA produced as described above for

TLDA, with 1x Bio-Rad iQ SYBR Green master mix, 0.2 µM forward and reverse primer mix,

with gene-specific qPCR primers for each gene tested. Primers were identified using the Primer

Bank database (Wang and Seed, 2003), available at

http://pga.mgh.harvard.edu/primerbank/index.html or designed using the IDT PrimerQuest tool

(https://www.idtdna.com/Scitools/Applications/Primerquest/). Differential gene expression was

calculated by the ∆∆Ct method. Reactions were run on the iCycler (Bio-Rad), as follows: 5 min at

95°C, 45 cycles of 95°C for 30 seconds, 58 to 61°C for 30 seconds, 68 to 72°C for 45 seconds to

amplify products, followed by 40 cycles of 94°C with 1°C step-down for 30 seconds to produce

melt curves.

6.2.4 Cell count assay:

Cells were cultured in 6-well dishes (50,000 cells in 2 ml) in a 37°C incubator with 5% CO2 level

overnight to allow them adhere. Cells were dissociated from the wells with 300 ul trypsin, followed

by 700 ul media to stop trypsinization. Cells were counted using a Bio-Rad® TC-10 Automated

Cell Counter.

6.2.5 High-throughput RNA sequencing:

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Independently isolated RNA from triplicate samples of each condition and cell line was sequenced

at the University of Rochester Genomics Research Center using Illumina HiSeq 2500 at a depth

of 20-25 million mapped reads. The raw reads were mapped using SHRiMP 2.2.3 and gene counts

were obtained using HTSeq. Gene counts were normalized using DESeq, EdgeR and CuffDiff

separately.

6.2.6 Calculation of Synergy Statistic based on Linear Modeling:

Messenger RNA expression profiles of YAMC, mp53, Ras, and mp53/Ras cells were determined

using the following the procedure: Five μg of RNA was reverse transcribed to cRNA and labeled

using a mAMP kit (Ambion). cRNA was then fragmented and hybridization cocktails were made

using the Affymetrix standard protocol for eukaryotic target hybridization. Targets were then

hybridized to Affymetrix Mouse 430 2.0 arrays at 45оC for 16 hours. Following hybridization,

arrays were washed and stained using Affymetrix Fluidics protocol EukGE-WS2v4_450 and then

scanned with the Affymetrix GeneChip Scanner 3000. A total of 10 biological replicates were

measured for each cell line.

For all differentially expressed probes, mean expression for normal and transformed cell lines were

compared. For probes with a lower mean expression in transformed cells, expression values were

adjusted according to the formula:

adjusted expression = 1/(raw expression)

For each mircoarray probe, a linear regression was fitted on the reads for transformed expression

levels and each of the contributing oncogenes:

expression ~ bp * p53 + br * Ras + bpr * (p53 ^ Ras)

Where,

p53 = indicator for presence of the dominant negative p53 mutation

Ras = indicator for presence of the constitutively active H-Ras mutation

bp = fit coefficient for the effect on expression of p53 alone

114

br = fit coefficient for the effect on expression of H-Ras alone

bpr = fit coefficient for the additional contribution of p53 and H-Ras interaction

From this fit model, error was calculated on each observation. This error was assumed to

follow a normal distribution. Consequently, a standard linear modeling was performed, using

coefficient t-test for the significance of the interaction term bpr against the one sided null

hypothesis

H0: bpr ≤ 0

We considered this t-statistic a monotonically increasing function of oncogene

cooperativity, and the corresponding p-value a map to the (0,1) interval. The p-value from this

coefficient hypothesis test then becomes our synergy statistic.

115

Chapter VII: Appendix

116

7.1 Table 1: Genes responding to Notch3 in Cancer Cells and Their Response in Non-

Cancer Cells According to DESeq Analysis

id CancerfoldChange

CancerP-

value NormalfoldChange

NormalP-

value

VIPR2 0.181059 0.020945 1.13269 0.870608

FRMPD2 0.199413 0.012687 0.733149 0.752917

LOC400950 0.226792 0.018325 0.930443 0.896356

LOC100652892 0.2331 0.040792 0.882086 0.819909

FAM90A10 0.233524 0.025718 0.986848 0.98648

CCND1 0.250989 0.035446 0.564673 0.28591

NGFR 0.252927 0.043323 1.200605 0.603531

HSPB7 0.254037 0.043473 0.616417 0.648804

SPATA18 0.272443 0.012947 1.014893 0.950298

SNORA51 0.273406 0.002447 0.905369 0.879628

KCP 0.289992 0.001832 1.068375 0.801264

CT62 0.318729 0.033671 0.955613 0.97594

MMP13 0.323566 0.036503 0.934813 0.928375

LOC100507139 0.330973 0.024549 0.653874 0.439386

MYCN 0.346622 0.045302 0.859702 0.753133

GPR25 0.34933 0.040511 1.256486 0.840876

IZUMO4 0.35192 0.034487 1.370343 0.585666

GNG3 0.358355 0.020719 1.473071 0.68404

QRFPR 0.382918 0.017721 0.59884 0.31748

FBXO40 0.392431 0.015153 1.030058 0.907708

SEMA3C 0.405798 0.002284 0.649512 0.42559

AGPAT9 0.425498 0.003559 1.036986 0.963033

SLC34A3 0.432256 0.042526 0.761623 0.74848

FBXL22 0.435627 0.041257 0.588322 0.355456

TPTE2 0.447451 0.018959 0.606224 0.533752

MAST4-AS1 0.452005 0.037719 1.220293 0.242255

EXTL1 0.469482 0.018222 1.127839 0.816285

PRICKLE1 0.498796 0.017251 0.940037 0.910662

NR3C2 0.499128 0.00216 0.589958 0.394853

TACC2 2.006583 0.037153 1.096789 0.532536

ARHGEF16 2.007449 0.041513 0.783004 0.562646

MYO7B 2.010683 0.04689 0.605439 0.43816

TAGLN2 2.033992 0.004786 1.225839 0.20165

CCDC19 2.041832 0.013703 1.364533 0.63627

CD82 2.063358 0.002778 1.178228 0.677849

SLC1A4 2.064237 0.006151 0.929302 0.890315

PLK2 2.078904 0.031193 1.130537 0.788954

117

TGFBR1 2.08662 0.020479 1.093027 0.344713

JAG1 2.08857 0.00673 0.714686 0.284393

ADORA2B 2.111559 0.046236 1.881838 0.337902

CCDC80 2.120803 0.014837 0.842231 0.899674

TNFSF14 2.149847 0.022527 0.778323 0.751311

LINC00346 2.167245 0.047904 0.81249 0.874619

TNFRSF21 2.24355 0.000866 0.983107 0.974724

ADAM19 2.285263 0.007515 0.7957 0.641443

NUAK2 2.294652 0.016794 1.431036 0.305976

ITGB3 2.312739 0.023963 0.849229 0.768718

SOCS2 2.324458 0.018501 1.307858 0.532056

PTPRE 2.333835 0.0303 0.662824 0.241933

TLL2 2.354181 0.016929 0.571556 0.344381

MCC 2.38291 0.032273 0.753963 0.606752

LOC100506082 2.394511 0.039816 0.57859 0.305301

LOC100652988 2.416968 0.037606 1.624979 0.654934

XRCC4 2.430463 0.008323 0.629282 0.383888

COL4A1 2.44082 0.041729 0.549423 0.413547

BMI1 2.449299 0.024829 1.208495 0.658234

ENC1 2.46053 0.048827 1.02336 0.957824

ROR1 2.469121 0.041966 1.872295 0.118058

EDNRA 2.569591 0.036326 1.513977 0.627323

AFAP1L2 2.586009 0.042078 0.681614 0.476291

FAM182B 2.608463 0.027324 1.023379 0.981508

DGKB 2.648701 0.006683 0.653547 0.377733

TSPAN9 2.759017 0.008647 1.189519 0.410356

SPHK1 2.770955 0.012242 0.708411 0.467362

MAST4-AS1 2.779462 0.033147 1.329628 0.67145

KIF26B 2.781266 0.027247 0.609257 0.32928

THBS1 2.869102 0.047464 0.508104 0.410654

RNF152 2.872086 0.013749 0.88749 0.873027

C2orf27A 2.935612 0.039594 0.631183 0.568529

PRR5L 2.962134 0.032985 1.04041 0.959357

COL15A1 3.034662 0.022512 1.029766 0.960751

PAPPA 3.05249 0.029396 1.52894 0.313847

4-Sep 3.059746 0.003175 0.66591 0.567911

ARHGAP30 3.105959 0.031574 0.564793 0.513834

C6orf136 3.10796 0.044052 0.958013 0.948755

HRCT1 3.125673 0.003485 1.453991 0.671419

GPR15 3.161011 0.028782 1.337744 0.694487

AXIN2 3.183552 0.009525 0.650626 0.447706

HES4 3.228435 0.039419 0.98331 0.984392

SERPINB9 3.240564 0.010292 1.193882 0.365155

118

FOXS1 3.442116 0.043286 1.141228 0.877309

CYP24A1 3.461646 0.003148 1.049765 0.968734

LRRIQ4 3.475687 0.035556 0.541496 0.305868

LOC158696 3.503303 0.0019 0.921261 0.659892

GPR124 3.641576 0.030101 0.850693 0.349979

MX2 3.946378 0.045163 0.661504 0.644213

KAL1 3.996504 0.025365 0.624288 0.371523

PDZD2 4.148129 0.029239 1.575667 0.463028

TMEM171 4.476454 0.041782 1.173907 0.823844

SFRP2 4.747359 0.038065 1.654721 0.658944

SNAI2 4.949469 0.027388 0.718718 0.368297

CD69 4.977345 0.026388 0.786514 0.833209

OPRD1 5.077258 0.033546 1.373977 0.781545

C8orf33 5.164977 0.039824 0.66908 0.747036

UBQLN3 5.628165 0.035535 1.106589 0.946492

IL4I1 5.796641 0.033739 0.991095 0.979233

CALU 5.952869 0.017257 0.955613 0.97594

SLC46A3 6.073329 0.006481 1.44988 0.404944

TBX15 6.665798 0.03867 1.215533 0.779687

LINC00173 6.955827 0.031781 1.337976 0.407251

NGF 7.178988 0.03489 0.660393 0.778436

CREB3L1 8.138478 0.009482 0.962996 0.917853

NID1 9.305301 0.001981 0.635379 0.549625

PTHLH 9.636783 0.023366 0.847221 0.807828

AWAT1 9.66648 0.037107 0.980745 0.988758

CCDC13 9.832618 0.000773 1.283706 0.554471

HTRA3 10.86674 0.009873 0.607808 0.647078

COL5A3 11.567 0.034422 1.010659 0.988336

LOC100506735 17.59229 0.032244 0.650124 0.695282

SYN1 22.33446 0.031777 1.693727 0.651976

FLT1 197.6398 0.027192 1.743946 0.734572

MGAT4C 525.6169 0.047157 0.717307 0.517369

PTGER3 0.340467 0.00993 86.87689 0.424583

DLX3 0.361598 0.003141 7.860805 0.418376

KCNH3 0.430576 0.01403 21.24104 0.423282

GPR39 0.485223 0.021788 7.051346 0.467763

FLJ37035 2.104785 0.047571 0.449741 0.160831

SHCBP1L 2.242087 0.026138 0.218569 0.386312

LRRC4C 2.298844 0.0272 0.477806 0.671028

QRICH2 2.346979 0.034581 0.26267 0.07461

LOC100652903 2.355681 0.041737 0.422862 0.242806

C9orf142 2.395188 0.024616 0.21924 0.163814

F2RL1 3.240514 0.0071 0.32852 0.068623

119

CTGF 3.244717 0.037739 0.452556 0.124806

COL5A1 3.289488 0.037856 0.303933 0.098371

LOC643650 3.65744 0.009434 0.444321 0.433947

LOC158376 3.726465 0.041318 0.260698 0.275398

LOC100652762 3.993391 0.00916 0.4757 0.174432

HSPB8 3.995186 0.008216 0.22865 0.284602

GRIK2 4.009478 0.003048 0.236255 0.133097

PLEKHG4B 4.667554 0.006045 0.415723 0.136526

LOC100131774 4.91251 0.02132 0.370824 0.430101

HEY2 5.131421 0.024639 0.338503 0.365721

LOC100505880 6.537345 0.011436 0.278271 0.151142

SCG5 8.865437 0.019637 0.403751 0.391336

LOC100506624 11.18629 0.025304 0.233664 0.090064

CCDC102B 11.33875 0.046095 0.46971 0.33739

SPARC 19.27651 0.048834 0.393488 0.425696

SFTA1P 2.59051 0.010804 3.420923 0.382087

SOCS1 2.597587 0.034235 2.286812 0.523663

BMP8B 2.617958 0.038561 2.604961 0.49109

ATP6V0D1 2.632429 0.01311 20.83269 0.300802

EMR4P 2.638945 0.03413 2.238816 0.402092

ADAMTS15 2.679544 0.041958 8.076344 0.097758

USP2 2.699535 0.047411 9.78049 0.385494

SERPINB7 3.042256 0.002339 6.632575 0.217401

F2RL2 3.056687 0.045927 52.74914 0.40867

SLC26A5 3.428623 0.006534 3.480997 0.361126

WNT11 3.715786 0.001891 2.195041 0.633743

ENHO 4.140027 0.005139 2.071687 0.530577

TRPC4 4.448749 0.005094 8.09085 0.47377

MGC39372 4.58926 0.012858 2.328995 0.639829

NRARP 4.626738 0.040032 3.932816 0.054061

ODZ2 4.76769 0.028435 4.266974 0.423236

NOTCH3 4.965876 0.016795 3.952471 0.325034

GAB3 5.038856 0.046177 7.647145 0.401594

PDGFRB 5.944996 0.041453 20.63889 0.399685

HEY1 6.343054 0.031968 134.0128 0.379403

THSD7B 9.137828 0.013053 195.2937 0.413048

HEY2 12.00724 0.024313 2.449738 0.313044

SORBS2 24.35517 0.005617 4.065925 0.489518

XKR4 0.153378 0.002051 0.338503 0.365721

POLN 0.188267 0.006839 0.304215 0.313952

PRB3 0.264647 0.029012 0.372589 0.35767

ITPK1-AS1 0.277181 0.008087 0.385774 0.350981

LOC100505872 0.313696 0.017169 0.332918 0.115203

120

TBR1 0.321369 0.023459 0.162054 0.405677

TTTY4C 0.355019 0.027339 0.334981 0.179182

TTTY4 0.355019 0.027339 0.431408 0.19867

RDM1 0.415826 0.010707 0.326208 0.067661

RSPH6A 0.432475 0.045709 0.469569 0.354763

STAC2 0.457781 0.021926 0.44089 0.424434

LOC386597 0.478014 0.047042 0.463937 0.367963

121

7.2 Table 2: Comparison of DESeq, EdgeR and Cuffdiff in fold change and p-value

id

DESeq

CancerfoldChange

DESeq

Cancer

P-value

Cuffdiff

Cancer

inverse

logfold

change

CuffDiff

Cancer

p-value

EdgeR

Cancer

Fold-

Change

EdgeR

Cancer

p-value

VIPR2 0.181059 0.020945 2.68285 0.467862 0.181059 0.520945

FRMPD2 0.199413 0.012687 1.57287 0.723681 0.199413 0.512687

LOC400950 0.226792 0.018325 2.3207 0.534645 0.226792 0.518325

LOC100652892 0.2331 0.040792 0.508363 0.946141 0.2331 0.540792

FAM90A10 0.233524 0.025718 1.96936 0.549015 0.233524 0.525718

CCND1 0.250989 0.035446 0.384226 0.709024 0.250989 0.535446

NGFR 0.252927 0.043323 1.78071 0.497493 0.252927 0.543323

HSPB7 0.254037 0.043473 1.91649 0.387807 0.254037 0.543473

SPATA18 0.272443 0.012947 1.80775 0.45278 0.272443 0.512947

SNORA51 0.273406 0.002447 1.82072 0.968396 0.273406 0.502447

KCP 0.289992 0.001832 2.06191 0.344901 0.289992 0.501832

CT62 0.318729 0.033671 1.66275 0.228298 0.318729 0.533671

MMP13 0.323566 0.036503 1.77192 0.346829 0.323566 0.536503

LOC100507139 0.330973 0.024549 1.62325 0.736752 0.330973 0.524549

MYCN 0.346622 0.045302 -0.1899 0.918685 0.346622 0.545302

GPR25 0.34933 0.040511 1.68741 0.488406 0.34933 0.540511

IZUMO4 0.35192 0.034487 0.886538 0.576469 0.35192 0.534487

GNG3 0.358355 0.020719 1.66554 0.949174 0.358355 0.520719

QRFPR 0.382918 0.017721 1.39538 0.406904 0.382918 0.517721

FBXO40 0.392431 0.015153 1.30545 0.483768 0.392431 0.515153

SEMA3C 0.405798 0.002284 1.33071 0.128665 0.405798 0.502284

AGPAT9 0.425498 0.003559 1.21819 0.174206 0.425498 0.503559

SLC34A3 0.432256 0.042526 1.66792 0.458492 0.432256 0.542526

FBXL22 0.435627 0.041257 1.1532 0.931082 0.435627 0.541257

TPTE2 0.447451 0.018959 0.375733 0.796083 0.447451 0.518959

MAST4-AS1 0.452005 0.037719 1.11278 0.871237 0.452005 0.537719

EXTL1 0.469482 0.018222 1.08661 0.554165 0.469482 0.518222

PRICKLE1 0.498796 0.017251 0.987458 0.429636 0.498796 0.517251

NR3C2 0.499128 0.00216 0.95261 0.312295 0.499128 0.50216

TACC2 2.006583 0.037153 -1.08988 0.210176 2.006583 0.537153

ARHGEF16 2.007449 0.041513 0.864967 0.584164 2.007449 0.541513

MYO7B 2.010683 0.04689 -0.9282 0.307354 2.010683 0.54689

TAGLN2 2.033992 0.004786 -1.05793 0.132001 2.033992 0.504786

CCDC19 2.041832 0.013703 -0.91378 0.298454 2.041832 0.513703

CD82 2.063358 0.002778 -1.04378 0.175576 2.063358 0.502778

SLC1A4 2.064237 0.006151 -1.06484 0.246534 2.064237 0.506151

122

PLK2 2.078904 0.031193 -1.10767 0.138049 2.078904 0.531193

TGFBR1 2.08662 0.020479 -1.12501 0.194068 2.08662 0.520479

JAG1 2.08857 0.00673 -1.06925 0.137225 2.08857 0.50673

ADORA2B 2.111559 0.046236 -1.04687 0.228838 2.111559 0.546236

CCDC80 2.120803 0.014837 -1.14069 0.158074 2.120803 0.514837

TNFSF14 2.149847 0.022527 -1.08568 0.628789 2.149847 0.522527

LINC00346 2.167245 0.047904 -1.15727 0.203689 2.167245 0.547904

TNFRSF21 2.24355 0.000866 -1.20442 0.087718 2.24355 0.500866

ADAM19 2.285263 0.007515 -1.25002 0.090505 2.285263 0.507515

NUAK2 2.294652 0.016794 -1.23216 0.124619 2.294652 0.516794

ITGB3 2.312739 0.023963 -1.31402 0.146637 2.312739 0.523963

SOCS2 2.324458 0.018501 -1.22919 0.17214 2.324458 0.518501

PTPRE 2.333835 0.0303 -1.21378 0.190021 2.333835 0.5303

TLL2 2.354181 0.016929 -1.3267 0.14065 2.354181 0.516929

MCC 2.38291 0.032273 -1.35824 0.766271 2.38291 0.532273

LOC100506082 2.394511 0.039816 -1.33405 0.416695 2.394511 0.539816

LOC100652988 2.416968 0.037606 -1.31952 0.155396 2.416968 0.537606

XRCC4 2.430463 0.008323 -1.38003 0.132957 2.430463 0.508323

COL4A1 2.44082 0.041729 -1.27204 0.131291 2.44082 0.541729

BMI1 2.449299 0.024829 0.07065 0.958965 2.449299 0.524829

ENC1 2.46053 0.048827 -1.31785 0.085438 2.46053 0.548827

ROR1 2.469121 0.041966 -1.32925 0.247747 2.469121 0.541966

EDNRA 2.569591 0.036326 -1.39109 0.221853 2.569591 0.536326

AFAP1L2 2.586009 0.042078 -1.42989 0.195776 2.586009 0.542078

FAM182B 2.608463 0.027324 -0.74367 0.895487 2.608463 0.527324

DGKB 2.648701 0.006683 -1.45265 0.278488 2.648701 0.506683

TSPAN9 2.759017 0.008647 -1.48877 0.15113 2.759017 0.508647

SPHK1 2.770955 0.012242 -1.44134 0.117978 2.770955 0.512242

MAST4-AS1 2.779462 0.033147 1.11278 0.871237 2.779462 0.533147

KIF26B 2.781266 0.027247 -1.24574 0.426515 2.781266 0.527247

THBS1 2.869102 0.047464 -1.57721 0.036644 2.869102 0.547464

RNF152 2.872086 0.013749 -1.61122 0.073136 2.872086 0.513749

C2orf27A 2.935612 0.039594 -0.86242 0.800856 2.935612 0.539594

PRR5L 2.962134 0.032985 -1.6163 0.177384 2.962134 0.532985

COL15A1 3.034662 0.022512 -1.68286 0.090573 3.034662 0.522512

PAPPA 3.05249 0.029396 -1.65682 0.074583 3.05249 0.529396

4-Sep 3.059746 0.003175 -1.54924 0.430743 3.059746 0.503175

ARHGAP30 3.105959 0.031574 0.130636 0.884952 3.105959 0.531574

C6orf136 3.10796 0.044052 0.113783 0.953163 3.10796 0.544052

HRCT1 3.125673 0.003485 -1.66215 0.148174 3.125673 0.503485

GPR15 3.161011 0.028782 -1.68826 0.116777 3.161011 0.528782

AXIN2 3.183552 0.009525 -1.64971 0.063722 3.183552 0.509525

HES4 3.228435 0.039419 -1.75121 0.171476 3.228435 0.539419

123

SERPINB9 3.240564 0.010292 -1.73641 0.034733 3.240564 0.510292

FOXS1 3.442116 0.043286 -1.82495 0.069395 3.442116 0.543286

CYP24A1 3.461646 0.003148 -1.83871 0.040194 3.461646 0.503148

LRRIQ4 3.475687 0.035556 -1.84382 0.452154 3.475687 0.535556

LOC158696 3.503303 0.0019 -1.82196 0.317467 3.503303 0.5019

GPR124 3.641576 0.030101 -1.95704 0.268897 3.641576 0.530101

MX2 3.946378 0.045163 -2.04339 0.038868 3.946378 0.545163

KAL1 3.996504 0.025365 -2.04288 0.027067 3.996504 0.525365

PDZD2 4.148129 0.029239 -2.13729 0.026039 4.148129 0.529239

TMEM171 4.476454 0.041782 0 1 4.476454 0.541782

SFRP2 4.747359 0.038065

-

1.79769e+308 0.243881 4.747359 0.538065

SNAI2 4.949469 0.027388 -2.34292 0.006733 4.949469 0.527388

CD69 4.977345 0.026388 -1.76864 0.690893 4.977345 0.526388

OPRD1 5.077258 0.033546 -2.00815 0.437159 5.077258 0.533546

C8orf33 5.164977 0.039824 -0.05665 0.937812 5.164977 0.539824

UBQLN3 5.628165 0.035535 -2.39033 0.445365 5.628165 0.535535

IL4I1 5.796641 0.033739 -2.34632 0.996979 5.796641 0.533739

CALU 5.952869 0.017257 -0.15159 0.854332 5.952869 0.517257

SLC46A3 6.073329 0.006481 -2.66281 0.001088 6.073329 0.506481

TBX15 6.665798 0.03867 -1.95868 0.289545 6.665798 0.53867

LINC00173 6.955827 0.031781 -2.72518 0.224542 6.955827 0.531781

NGF 7.178988 0.03489 -2.82063 0.006448 7.178988 0.53489

CREB3L1 8.138478 0.009482 -3.02508 0.000269 8.138478 0.509482

NID1 9.305301 0.001981 -3.36048 0.27814 9.305301 0.501981

PTHLH 9.636783 0.023366 -3.30844 0.031186 9.636783 0.523366

AWAT1 9.66648 0.037107 -1.27163 0.619787 9.66648 0.537107

CCDC13 9.832618 0.000773 -2.5315 0.203819 9.832618 0.500773

HTRA3 10.86674 0.009873 -3.36502 0.000757 10.86674 0.509873

COL5A3 11.567 0.034422 -4.24019 0.007865 11.567 0.534422

LOC100506735 17.59229 0.032244 -4.1342 0.000143 17.59229 0.532244

SYN1 22.33446 0.031777 -4.48728 0.812689 22.33446 0.531777

FLT1 197.6398 0.027192 -7.08885 1.42E-05 197.6398 0.527192

MGAT4C 525.6169 0.047157 -9.24041 3.76E-07 525.6169 0.547157

PTGER3 0.340467 0.00993 1.16031 0.96647 0.340467 0.50993

DLX3 0.361598 0.003141 1.45404 0.158348 0.361598 0.503141

KCNH3 0.430576 0.01403 1.22551 0.448683 0.430576 0.51403

GPR39 0.485223 0.021788 1.08458 0.274365 0.485223 0.521788

FLJ37035 2.104785 0.047571 3.3E-05 0.999991 2.104785 0.547571

SHCBP1L 2.242087 0.026138 -1.2262 0.560459 2.242087 0.526138

LRRC4C 2.298844 0.0272 -1.23368 0.44624 2.298844 0.5272

QRICH2 2.346979 0.034581 -1.26064 0.166434 2.346979 0.534581

LOC100652903 2.355681 0.041737 -1.15814 0.281121 2.355681 0.541737

124

C9orf142 2.395188 0.024616 0.055548 0.944709 2.395188 0.524616

F2RL1 3.240514 0.0071 -1.75051 0.014896 3.240514 0.5071

CTGF 3.244717 0.037739 -1.69313 0.050852 3.244717 0.537739

COL5A1 3.289488 0.037856 -1.75004 0.083909 3.289488 0.537856

LOC643650 3.65744 0.009434 -1.8998 0.708339 3.65744 0.509434

LOC158376 3.726465 0.041318 -1.94526 0.244615 3.726465 0.541318

LOC100652762 3.993391 0.00916 -2.05379 0.013868 3.993391 0.50916

HSPB8 3.995186 0.008216 -2.12191 0.020242 3.995186 0.508216

GRIK2 4.009478 0.003048 -1.81698 0.573814 4.009478 0.503048

PLEKHG4B 4.667554 0.006045 -2.43163 0.031411 4.667554 0.506045

LOC100131774 4.91251 0.02132 -1.86556 0.714374 4.91251 0.52132

HEY2 5.131421 0.024639 -2.60305 0.05273 5.131421 0.524639

LOC100505880 6.537345 0.011436 -2.81903 0.995148 6.537345 0.511436

SCG5 8.865437 0.019637 0 1 8.865437 0.519637

LOC100506624 11.18629 0.025304 -3.65394 0.82903 11.18629 0.525304

CCDC102B 11.33875 0.046095 -3.63251 0.015731 11.33875 0.546095

SPARC 19.27651 0.048834 -4.74729 0.003356 19.27651 0.548834

SFTA1P 2.59051 0.010804 -1.44596 0.073659 2.59051 0.510804

SOCS1 2.597587 0.034235 -1.3762 0.158014 2.597587 0.534235

BMP8B 2.617958 0.038561 0.3004 0.893717 2.617958 0.538561

ATP6V0D1 2.632429 0.01311 -0.06605 0.927219 2.632429 0.51311

EMR4P 2.638945 0.03413 -1.48914 0.365885 2.638945 0.53413

ADAMTS15 2.679544 0.041958 -1.45415 0.09657 2.679544 0.541958

USP2 2.699535 0.047411 -1.37721 0.265968 2.699535 0.547411

SERPINB7 3.042256 0.002339 -1.62348 0.094214 3.042256 0.502339

F2RL2 3.056687 0.045927 -1.64368 0.179699 3.056687 0.545927

SLC26A5 3.428623 0.006534 -1.67329 0.941983 3.428623 0.506534

WNT11 3.715786 0.001891 -1.8206 0.255378 3.715786 0.501891

ENHO 4.140027 0.005139 -1.43111 0.488657 4.140027 0.505139

TRPC4 4.448749 0.005094 -2.14491 0.237102 4.448749 0.505094

MGC39372 4.58926 0.012858 -2.34156 0.087135 4.58926 0.512858

NRARP 4.626738 0.040032 -2.17073 0.012725 4.626738 0.540032

ODZ2 4.76769 0.028435 -2.31751 0.018968 4.76769 0.528435

NOTCH3 4.965876 0.016795 -2.34615 0.023644 4.965876 0.516795

GAB3 5.038856 0.046177 -2.47164 0.112357 5.038856 0.546177

PDGFRB 5.944996 0.041453 -2.65841 0.0066 5.944996 0.541453

HEY1 6.343054 0.031968 -2.58177 0.009817 6.343054 0.531968

THSD7B 9.137828 0.013053 -3.94591 0.229895 9.137828 0.513053

HEY2 12.00724 0.024313 -2.60305 0.05273 12.00724 0.524313

SORBS2 24.35517 0.005617 -4.66787 0.001915 24.35517 0.505617

XKR4 0.153378 0.002051 2.63961 0.97112 0.153378 0.502051

POLN 0.188267 0.006839 1.57351 0.949203 0.188267 0.506839

PRB3 0.264647 0.029012 0.715667 0.742098 0.264647 0.529012

125

ITPK1-AS1 0.277181 0.008087 1.8124 0.967806 0.277181 0.508087

LOC100505872 0.313696 0.017169 1.6589 0.575555 0.313696 0.517169

TBR1 0.321369 0.023459 1.60361 0.474657 0.321369 0.523459

TTTY4C 0.355019 0.027339 1.51513 0.533881 0.355019 0.527339

TTTY4 0.355019 0.027339 1.51513 0.533881 0.355019 0.527339

RDM1 0.415826 0.010707 1.11086 0.504447 0.415826 0.510707

RSPH6A 0.432475 0.045709 1.01796 0.459405 0.432475 0.545709

STAC2 0.457781 0.021926 1.12101 0.440987 0.457781 0.521926

LOC386597 0.478014 0.047042 0.975997 0.770469 0.478014 0.547042

126

Table 3: Mutation Spectrum of BLBC cell lines used in the study

Cell line Gene

Cluster

ER

mutation

PR

mutation

HER2

overexpr

ession

TP53

protein

levels

Age of

patient

(years)

Ethnicity

MDA-MB-

231

BaB - - ++

(Mutated)

51 W

HCC1954 BaA - - + +/- 61 El

HCC1569 BaA - - + -

(Mutated)

70 B

MDA-MB-

468

BaA - - + 51 B

HCC70 BaA - - ++

(Mutated)

49 B

Hs578T BaB - - +

(Mutated)

74 W

MCF10A BaB - - +/-

(Wild-

type)

36 W

MCF12A BaB - - + 60 W

Modified from Neve et al., 2006

127

Bibliography

Abate-Shen, C., and M. M. Shen. "Molecular Genetics of Prostate Cancer." Genes &

Development 14.19 (2000): 2410-434. Web.

Adjei, A. A. "Blocking Oncogenic Ras Signaling for Cancer Therapy." JNCI Journal of the

National Cancer Institute 93.14 (2001): 1062-074. Web.

Aguilera, O., M. F. Fraga, E. Ballestar, M. F. Paz, M. Herranz, J. Espada, J. M. García, A.

Muñoz, M. Esteller, and J. M. González-Sancho. "Epigenetic Inactivation of the

Wnt Antagonist DICKKOPF-1 (DKK-1) Gene in Human Colorectal Cancer."

Oncogene 25.29 (2006): 4116-121. Web.

Alfaro, M. P., M. Pagni, A. Vincent, J. Atkinson, M. F. Hill, J. Cates, J. M. Davidson, J.

Rottman, E. Lee, and P. P. Young. "The Wnt Modulator SFRP2 Enhances

Mesenchymal Stem Cell Engraftment, Granulation Tissue Formation and

Myocardial Repair." Proceedings of the National Academy of Sciences 105.47

(2008): 18366-8371. Web.

Almudevar, Anthony, Matthew N. Mccall, Helene Mcmurray, and Hartmut Land. "Fitting

Boolean Networks from Steady State Perturbation Data." Statistical Applications

in Genetics and Molecular Biology 10.1 (2011): n. pag. Web.

Andersen, Peter, Hideki Uosaki, Lincoln T. Shenje, and Chulan Kwon. "Non-canonical

Notch Signaling: Emerging Role and Mechanism." Trends in Cell Biology 22.5

(2012): 257-65. Web.

Armitage, P., and R. Doll. "The Age Distribution of Cancer and a Multi-stage Theory of

Carcinogenesis." British Journal of Cancer 8.1 (1954): 1-12. Web.

128

Artavanis-Tsakonas, S., M. D. Rand, and R. J. Lake. "Notch Signaling: Cell Fate Control and

Signal Integration in Development." Science 284.5415 (1999): 770-76. Web.

Ashton, John M., Marlene Balys, Sarah J. Neering, Duane C. Hassane, Glenn Cowley,

David E. Root, Peter G. Miller, Benjamin L. Ebert, Helene R. McMurray,

Hartmut Land, and Craig T. Jordan. "Gene Sets Identified with Oncogene

Cooperativity Analysis Regulate In Vivo Growth and Survival of Leukemia Stem

Cells." Cell Stem Cell 11.3 (2012): 359-72. Web.

Ayaz, Furkan, and Barbara A. Ozborne. "Non-Canonical Notch Signaling in Cancer and

Immunity." Frontiers in Oncology 4.345 (2014): n. pag. Web.

Bardeesy, Nabeel, and Ronald A. Depinho. "Pancreatic Cancer Biology and Genetics."

Nature Reviews Cancer 2.12 (2002): 897-909. Web.

Baylin, Stephen B., Manel Esteller, Michael R. Rountree, Kurtis E. Bachman, Kornel

Schuebel, and James G. Herman. "Aberrant Patterns of DNA Methylation,

Chromatin Formation and Gene Expression in Cancer." Human Molecular

Genetics 10.7 (2001): 687-92. Web.

Bishop, J. Michael. “The molecular genetics of cancer.” Science. 235(4786) (1987): 305-311.

Chang, Kyle, Chad J. Creighton, Caleb Davis, Lawrence Donehower, Jennifer Drummond,

David Wheeler, Adrian Ally, Miruna Balasundaram, Inanc Birol, Yaron S N

Butterfield, Andy Chu, Eric Chuah, Hye-Jung E. Chun, Noreen Dhalla, Ranabir

Guin, Martin Hirst, Carrie Hirst, Robert A. Holt, Steven J M Jones, Darlene Lee,

Haiyan I. Li, Marco A. Marra, Michael Mayo, Richard A. Moore, Andrew J.

Mungall, A. Gordon Robertson, Jacqueline E. Schein, Payal Sipahimalani, Angela

Tam, Nina Thiessen, Richard J. Varhol, Rameen Beroukhim, Ami S. Bhatt,

129

Angela N. Brooks, Andrew D. Cherniack, Samuel S. Freeman, Stacey B. Gabriel,

Elena Helman, Joonil Jung, Matthew Meyerson, Akinyemi I. Ojesina, Chandra

Sekhar Pedamallu, Gordon Saksena, Steven E. Schumacher, Barbara Tabak,

Travis Zack, Eric S. Lander, Christopher A. Bristow, Angela Hadjipanayis, Psalm

Haseley, Raju Kucherlapati, Semin Lee, Eunjung Lee, Lovelace J. Luquette,

Harshad S. Mahadeshwar, Angeliki Pantazi, Michael Parfenov, Peter J. Park,

Alexei Protopopov, Xiaojia Ren, Netty Santoso, Jonathan Seidman, Sahil Seth,

Xingzhi Song, Jiabin Tang, Ruibin Xi, Andrew W. Xu, Lixing Yang, Dong Zeng,

J. Todd Auman, Saianand Balu, Elizabeth Buda, Cheng Fan, Katherine A.

Hoadley, Corbin D. Jones, Shaowu Meng, Piotr A. Mieczkowski, Joel S. Parker,

Charles M. Perou, Jeffrey Roach, Yan Shi, Grace O. Silva, Donghui Tan,

Umadevi Veluvolu, Scot Waring, Matthew D. Wilkerson, Junyuan Wu, Wei

Zhao, Tom Bodenheimer, D. Neil Hayes, Alan P. Hoyle, Stuart R. Jeffreys, Lisle

E. Mose, Janae V. Simons, Mathew G. Soloway, Stephen B. Baylin, Benjamin P.

Berman, Moiz S. Bootwalla, Ludmila Danilova, James G. Herman, Toshinori

Hinoue, Peter W. Laird, Suhn K. Rhie, Hui Shen, Timothy Triche, Daniel J.

Weisenberger, Scott L. Carter, Kristian Cibulskis, Lynda Chin, Jianhua Zhang,

Gad Getz, Carrie Sougnez, Min Wang, Gordon Saksena, Scott L. Carter, Kristian

Cibulskis, Lynda Chin, Jianhua Zhang, Gad Getz, Huyen Dinh, Harsha Vardhan

Doddapaneni, Richard Gibbs, Preethi Gunaratne, Yi Han, Divya Kalra, Christie

Kovar, Lora Lewis, Margaret Morgan, Donna Morton, Donna Muzny, Jeffrey

Reid, Liu Xi, Juok Cho, Daniel Dicara, Scott Frazer, Nils Gehlenborg, David I.

Heiman, Jaegil Kim, Michael S. Lawrence, Pei Lin, Yingchun Liu, Michael S.

130

Noble, Petar Stojanov, Doug Voet, Hailei Zhang, Lihua Zou, Chip Stewart, Brady

Bernard, Ryan Bressler, Andrea Eakin, Lisa Iype, Theo Knijnenburg, Roger

Kramer, Richard Kreisberg, Kalle Leinonen, Jake Lin, Yuexin Liu, Michael

Miller, Sheila M. Reynolds, Hector Rovira, Ilya Shmulevich, Vesteinn Thorsson,

Da Yang, Wei Zhang, Samirkumar Amin, Chang-Jiun Wu, Chia-Chin Wu, Rehan

Akbani, Kenneth Aldape, Keith A. Baggerly, Bradley Broom, Tod D. Casasent,

James Cleland, Chad Creighton, Deepti Dodda, Mary Edgerton, Leng Han,

Shelley M. Herbrich, Zhenlin Ju, Hoon Kim, Seth Lerner, Jun Li, Han Liang,

Wenbin Liu, Philip L. Lorenzi, Yiling Lu, James Melott, Gordon B. Mills, Lam

Nguyen, Xiaoping Su, Roeland Verhaak, Wenyi Wang, John N. Weinstein,

Andrew Wong, Yang Yang, Jun Yao, Rong Yao, Kosuke Yoshihara, Yuan Yuan,

Alfred K. Yung, Nianxiang Zhang, Siyuan Zheng, Michael Ryan, David W. Kane,

B. Arman Aksoy, Giovanni Ciriello, Gideon Dresdner, Jianjiong Gao, Benjamin

Gross, Anders Jacobsen, Andre Kahles, Marc Ladanyi, William Lee, Kjong-Van

Lehmann, Martin L. Miller, Ricardo Ramirez, Gunnar Rätsch, Boris Reva, Chris

Sander, Nikolaus Schultz, Yasin Senbabaoglu, Ronglai Shen, Rileen Sinha, S.

Onur Sumer, Yichao Sun, Barry S. Taylor, Nils Weinhold, Suzanne Fei, Paul

Spellman, Christopher Benz, Daniel Carlin, Melisssa Cline, Brian Craft, Kyle

Ellrott, Mary Goldman, David Haussler, Singer Ma, Sam Ng, Evan Paull, Amie

Radenbaugh, Sofie Salama, Artem Sokolov, Joshua M. Stuart, Teresa Swatloski,

Vladislav Uzunangelov, Peter Waltman, Christina Yau, Jing Zhu, Stanley R.

Hamilton, Gad Getz, Carrie Sougnez, Scott Abbott, Rachel Abbott, Nathan D.

Dees, Kim Delehaunty, Li Ding, David J. Dooling, Jim M. Eldred, Catrina C.

131

Fronick, Robert Fulton, Lucinda L. Fulton, Joelle Kalicki-Veizer, Krishna-Latha

Kanchi, Cyriac Kandoth, Daniel C. Koboldt, David E. Larson, Timothy J. Ley,

Ling Lin, Charles Lu, Vincent J. Magrini, Elaine R. Mardis, Michael D. Mclellan,

Joshua F. Mcmichael, Christopher A. Miller, Michelle O'laughlin, Craig Pohl,

Heather Schmidt, Scott M. Smith, Jason Walker, John W. Wallis, Michael C.

Wendl, Richard K. Wilson, Todd Wylie, Qunyuan Zhang, Robert Burton, Mark

A. Jensen, Ari Kahn, Todd Pihl, David Pot, Yunhu Wan, Douglas A. Levine,

Aaron D. Black, Jay Bowen, Jessica Frick, Julie M. Gastier-Foster, Hollie A.

Harper, Carmen Helsel, Kristen M. Leraas, Tara M. Lichtenberg, Cynthia

Mcallister, Nilsa C. Ramirez, Samantha Sharpe, Lisa Wise, Erik Zmuda, Stephen

J. Chanock, Tanja Davidsen, John A. Demchok, Greg Eley, Ina Felau, Brad A.

Ozenberger, Margi Sheth, Heidi Sofia, Louis Staudt, Roy Tarnuzzer, Zhining

Wang, Liming Yang, Jiashan Zhang, Larsson Omberg, Adam Margolin, Benjamin

J. Raphael, Fabio Vandin, Hsin-Ta Wu, Mark D M Leiserson, Stephen C. Benz,

Charles J. Vaske, Houtan Noushmehr, Theo Knijnenburg, Denise Wolf, Laura

Van't Veer, Eric A. Collisson, Dimitris Anastassiou, Tai-Hsien Ou Yang, Nuria

Lopez-Bigas, Abel Gonzalez-Perez, David Tamborero, Zheng Xia, Wei Li, Dong-

Yeon Cho, Teresa Przytycka, Mark Hamilton, Sean Mcguire, Sven Nelander,

Patrik Johansson, Rebecka Jörnsten, Teresia Kling, Jose Sanchez, John N.

Weinstein, Eric A. Collisson, Gordon B. Mills, Kenna R Mills Shaw, Brad A.

Ozenberger, Kyle Ellrott, Ilya Shmulevich, Chris Sander, and Joshua M. Stuart.

"The Cancer Genome Atlas Pan-Cancer Analysis Project." Nature Genetics 45.10

(2013): 1113-120. Web.

132

Compere, S. J. "The Ras and Myc Oncogenes Cooperate in Tumor Induction in Many

Tissues When Introduced into Midgestation Mouse Embryos by Retroviral

Vectors." Proceedings of the National Academy of Sciences 86.7 (1989): 2224-

228. Web.

Conde, Carmen, Manual Mark, Javier Oliver, Aline Huber, Gilbert De Murcia, and Josiane

Ménissier‐de Murcia. "Loss of Poly(ADP-ribose) Polymerase-1 Causes Increased

Tumour Latency in P53-deficient Mice." The EMBO Journal 20.13 (2001): 3535-

543. Web.

Cordero, David, Xavier Solé, Marta Crous-Bou, Rebeca Sanz-Pamplona, Laia Paré-Brunet,

Elizabet Guinó, Sebastiano Biondo, and Víctor Moreno. "Large Differences in

Global Transcriptional Regulatory Programs of Normal and Tumor Colon Cells."

BMC Cancer 14.708 (2014): n. pag. Web.

Couso, J., and A. Arias Martinez. "Notch Is Required for Wingless Signaling in the

Epidermis of Drosophila." Cell 79.2 (1994): 259-72. Web.

D'Abaco, G. M., R. H. Whitehead, and A. W. Burgess. "Synergy between Apc Min and an

Activated Ras Mutation Is Sufficient to Induce Colon Carcinomas." Molecular

Cell Biology 16.3 (1996): 884-91. Web.

Dai, Chengkai, Luke Whitesell, Arlin B. Rogers, and Susan Lindquist. "Heat Shock Factor 1

Is a Powerful Multifaceted Modifier of Carcinogenesis." Cell 130.6 (2007): 1005-

018. Web.

Dang, Chi V. "MYC on the Path to Cancer." Cell 149.1 (2012): 22-35. Web.

133

Dewhirst, Mark W., Yiting Cao, and Benjamin Moeller. "Cycling Hypoxia and Free Radicals

Regulate Angiogenesis and Radiotherapy Response." Nature Reviews Cancer 8.8

(2008): 654. Web.

Drayton, Sarah, Janice Rowe, Rebecca Jones, Radost Vatcheva, Darren Cuthbert-Heavens,

John Marshall, Mike Fried, and Gordon Peters. "Tumor Suppressor P16INK4a

Determines Sensitivity of Human Cells to Transformation by Cooperating

Cellular Oncogenes." Cancer Cell 4.4 (2003): 301-10. Web.

Elenbaas, B. "Human Breast Cancer Cells Generated by Oncogenic Transformation of

Primary Mammary Epithelial Cells." Genes & Development 15.1 (2001): 50-65.

Web.

Elenbaas, B. "Human Breast Cancer Cells Generated by Oncogenic Transformation of

Primary Mammary Epithelial Cells." Genes & Development 15.1 (2001): 50-65.

Web.

Esposito, Luca, Paola Indovina, Flora Magnotti, Daniele Conti, and Antonio Giordano.

"Anticancer Therapeutic Strategies Based on CDK Inhibitors." Current

Pharmaceutical Design 19.30 (2013): 5327-332. Web.

Esteve, P., A. Sandonìs, C. Ibañez, A. Shimono, I. Guerrero, and P. Bovolenta. "Secreted

Frizzled-related Proteins Are Required for Wnt/β-catenin Signalling Activation in

the Vertebrate Optic Cup." Development 138.19 (2011): 4179-184. Web.

Facchini, Linda M., and Linda Z. Penn. "The Molecular Role of Myc in Growth and

Transformation: Recent Discoveries Lead to New Insights." The FASEB Journal

12.9 (1998): 633-51. Web.

134

Fearon, Eric R., and Bert Vogelstein. "A Genetic Model for Colorectal Tumorigenesis." Cell

61.5 (1990): 759-67. Web.

Feinberg, Andrew P., and Benjamin Tycko. "The History of Cancer Epigenetics." Nature

Reviews Cancer 4.2 (2004): 143-53. Web.

Felsher, Dean W., and Bishop, J. Michael. “Reversible tumorigenesis by MYC in

hematopoietic lineages.” Mol Cell. 4(2) (1999): 199-207. Web.

Fodde, Riccardo, Ron Smits, and Hans Clevers. "Apc, Signal Transduction And Genetic

Instability In Colorectal Cancer." Nature Reviews Cancer 1.1 (2001): 55-67. Web.

Giehl, Klaudia. "Oncogenic Ras in Tumour Progression and Metastasis." Biological

Chemistry 386.3 (2005): 193-205. Web.

Greaves, Mel, and Carlo C. Maley. "Clonal Evolution in Cancer." Nature 481.7381 (2012):

306-13. Web.

Götschel, Frank, Daniela Berg, Wolfgang Gruber, Christian Bender, Markus Eberl, Myriam

Friedel, Johanna Sonntag, Elena Rüngeler, Hendrik Hache, Christoph Wierling,

Wilfried Nietfeld, Hans Lehrach, Annemarie Frischauf, Reinhard Schwartz-

Albiez, Fritz Aberger, and Ulrike Korf. "Synergism between Hedgehog-GLI and

EGFR Signaling in Hedgehog-Responsive Human Medulloblastoma Cells

Induces Downregulation of Canonical Hedgehog-Target Genes and Stabilized

Expression of GLI1." Ed. Vladimir N. Uversky. PLoS ONE 8.6 (2013): E65403.

Web.

Gysin, S., M. Salt, A. Young, and F. Mccormick. "Therapeutic Strategies for Targeting Ras

Proteins." Genes & Cancer 2.3 (2011): 359-72. Web.

135

Hahn, W. C., C. M. Counter, and R. A. Weinberg. "Creation of Human Tumour Cells with

Defined Genetic Elements." Nature 400.6743 (1999): 464-68. Web.

Hahn, William C., and Robert A. Weinberg. "Modelling the Molecular Circuitry of Cancer."

Nature Reviews Cancer 2.5 (2002): 331-41. Web.

Han, Tiandong, Donghao Shang, Xiuhong Xu, and Ye Tian. "Gene Expression Profiling of

the Synergy of 5-aza-2′-deoxycytidine and Paclitaxel against Renal Cell

Carcinoma." World Journal of Surgical Oncology 10.1 (2012): 183. Web.

Hanahan, Douglas, and Robert A. Weinberg. "The Hallmarks of Cancer." Cell 100.1 (2000):

57-70. Web.

Hanahan, Douglas, and Robert A. Weinberg. "Hallmarks of Cancer: The Next Generation."

Cell 144.5 (2011): 646-74. Web.

Harrison, H., G. Farnie, S. J. Howell, R. E. Rock, S. Stylianou, K. R. Brennan, N. J. Bundred,

and R. B. Clarke. "Regulation of Breast Cancer Stem Cell Activity by Signaling

through the Notch4 Receptor." Cancer Research 70.2 (2010): 709-18. Web.

Henriksson, M., and B. Luscher. "Proteins of the Myc Network: Essential Regulators of Cell

Growth and Differentiation." Advanced Cancer Research 68.109 (1996): 82.

Web.

Herbst, Andreas, Vindi Jurinovic, Stefan Krebs, Susanne E. Thieme, Helmut Blum, Burkhard

Göke, and Frank T. Kolligs. "Comprehensive Analysis of β-catenin Target Genes

in Colorectal Carcinoma Cell Lines with Deregulated Wnt/β-catenin Signaling."

BMC Genomics 15.74 (n.d.): n. pag. Web.

136

Hinds, P., C. Finlay, and A. J. Levine. "Mutation Is Required to Activate the P53 Gene for

Cooperation with the Ras Oncogene and Transformation." Journal of Virology

63.2 (1989): 739-46. Web.

Hong, Bo, A. Heuvel, Varun Prabhu, Shengliang Zhang, and Wafik El-Deiry. "Targeting

Tumor Suppressor P53 for Cancer Therapy: Strategies, Challenges and

Opportunities." Current Drug Targets 15.1 (2014): 80-89. Web.

Hurley, Laurence H. "DNA and Its Associated Processes as Targets for Cancer Therapy."

Nature Reviews Cancer 2.3 (2002): 188-200. Web.

Ingvarsson, Sigurdur. "Molecular Genetics of Breast Cancer Progression." Seminars in

Cancer Biology 9.4 (1999): 277-88. Web.

Jones, S., W.-D. Chen, G. Parmigiani, F. Diehl, N. Beerenwinkel, T. Antal, A. Traulsen, M.

A. Nowak, C. Siegel, V. E. Velculescu, K. W. Kinzler, B. Vogelstein, J. Willis,

and S. D. Markowitz. "Comparative Lesion Sequencing Provides Insights into

Tumor Evolution." Proceedings of the National Academy of Sciences 105.11

(2008): 4283-288. Web.

Kawano, Y., and R. Kypta. "Secreted Antagonists of the Wnt Signalling Pathway." Journal

of Cell Science 116.13 (2003): 2627-634. Web.

Kim, Hyun-A, Bon-Kyoung Koo, Ji-Hoon Cho, Yoon-Young Kim, Jinwoo Seong, Hee Jin

Chang, Young Min Oh, Daniel E. Stange, Jae-Gahb Park, Daehee Hwang, and

Young-Yun Kong. "Notch1 Counteracts WNT/β-catenin Signaling through

Chromatin Modification in Colorectal Cancer." Journal of Clinical Investigation

122.9 (2012): 3248-259. Web.

137

Kim, Julian A. "Targeted Therapies for the Treatment of Cancer." The American Journal of

Surgery 186.3 (2003): 264-68. Web.

Kinzler, Kenneth W., and Bert Vogelstein. "Lessons from Hereditary Colorectal Cancer."

Cell 87.2 (1996): 159-70. Web.

Knudsen, Erik S., and Karen E. Knudsen. "Tailoring to RB: Tumour Suppressor Status and

Therapeutic Response." Nature Reviews Cancer 8.9 (2008): 714-24. Web.

Land, Hartmut, Luis F. Parada, and Robert A. Weinberg. "Tumorigenic Conversion of

Primary Embryo Fibroblasts Requires at Least Two Cooperating Oncogenes."

Nature 304.5927 (1983): 596-602. Web.

Lehmann, Brian D., Joshua A. Bauer, Xi Chen, Melinda E. Sanders, A. Bapsi Chakravarthy,

Yu Shyr, and Jennifer A. Pietenpol. "Identification of Human Triple-negative

Breast Cancer Subtypes and Preclinical Models for Selection of Targeted

Therapies." Journal of Clinical Investigation 121.7 (2011): 2750-767. Web.

Lex, A., M. Streit, H.-J. Schulz, C. Partl, D. Schmalstieg, P.j. Park, and N. Gehlenborg.

"StratomeX: Visual Analysis of Large-Scale Heterogeneous Genomics Data for

Cancer Subtype Characterization." Computer Graphics Forum 31.3pt3 (2012):

1175-184. Web.

Liu, Yajun, Dongchu Sun, and Chong Z. He. "A Hierarchical Conditional Autoregressive

Model for Colorectal Cancer Survival Data." Wiley Interdisciplinary Reviews:

Computational Statistics 6.1 (2014): 37-44. Web.

Livingstone, Laura R., White, Alicia, Sprouse, Jason, Livanos, Elizabeth, Jacks, Tyler, Tlsty,

Thea D. “Altered cell cycle arrest and gene amplification potential accompany

loss of wild-type p53.” Cell 70(6) (1992): 923-935. Web.

138

Lloyd A.C., Obermuller, F., Staddon, S., Barth, C.F., McMahon, M. and Land, H.

“Cooperating oncogenes converge to regulate cyclin/cdk complexes.” Genes

Dev., 11 (1997): 663–677.

Loh, Yan Ni, Ellen L. Hedditch, Laura A. Baker, Eve Jary, Robyn L. Ward, and Caroline E.

Ford. "The Wnt Signalling Pathway Is Upregulated in an in Vitro Model of

Acquired Tamoxifen Resistant Breast Cancer." BMC Cancer 13.1 (2013): 174.

Web.

Loke SL, Stein C, Zhang X, Avigan M, Cohen J, Neckers LM. Delivery of c-myc antisense

phosphorothioate oligodeoxynucleotides to hematopoietic cells in culture by

liposome fusion: specific reduction in c-myc protein expression correlates with

inhibition of cell growth and DNA synthesis. Curr Top Microbiol Immunol.

141(1988): 282-289.

Lowe, Scott W, Ruley, H. Earl, Jacks, Tyler and Housman, David E. “p-53 dependent

apoptosis modulates the cytotoxicity of anticancer agents.” Cell 74(6) (1993):

957-967. Web.

Luo, Ji, Nicole L. Solimini, and Stephen J. Elledge. "Principles of Cancer Therapy:

Oncogene and Non-oncogene Addiction." Cell 138.4 (2009): 807. Web.

Malynn, Barbara A., Ignacio M. De Alboran, Rónán C. O'Hagan, Roderick Bronson, Laurie

Davidson, Ronald A. DePinho, and Frederick W. Alt. "N-myc Can Functionally

Replace C-myc in Murine Development, Cellular Growth, and Differentiation."

Genes and Development 14.11 (2000): 1390-399. Web.

139

Mccarthy, A., K. Savage, A. Gabriel, C. Naceur, Js Reis-Filho, and A. Ashworth. "A Mouse

Model of Basal-like Breast Carcinoma with Metaplastic Elements." The Journal

of Pathology 211.4 (2007): 389-98. Web.

McMurray, Helene R., Erik R. Sampson, George Compitello, Conan Kinsey, Laurel

Newman, Bradley Smith, Shaw-Ree Chen, Lev Klebanov, Peter Salzman, Andrei

Yakovlev, and Hartmut Land. "Synergistic Response to Oncogenic Mutations

Defines Gene Class Critical to Cancer Phenotype." Nature 453.7198 (2008):

1112-116. Web.

Miele, Lucio, and Barbara Osborne. "Arbiter of Differentiation and Death: Notch Signaling

Meets Apoptosis." Journal of Cellular Physiology 181.3 (1999): 393-409. Web.

Mizutani, T. "Conservation of the Biochemical Mechanisms of Signal Transduction among

Mammalian Notch Family Members." Proceedings of the National Academy of

Sciences 98.16 (2001): 9026-031. Web.

Molyneux, Gemma, Felipe C. Geyer, Fiona-Ann Magnay, Afshan Mccarthy, Howard

Kendrick, Rachael Natrajan, Alan Mackay, Anita Grigoriadis, Andrew Tutt, Alan

Ashworth, Jorge S. Reis-Filho, and Matthew J. Smalley. "BRCA1 Basal-like

Breast Cancers Originate from Luminal Epithelial Progenitors and Not from Basal

Stem Cells." Cell Stem Cell 7.3 (2010): 403-17. Web.

Montcouquiol, Mireille, E. Bryan Crenshaw, and Matthew W. Kelley. "Noncanonical Wnt

Signaling And Neural Polarity." Annual Review of Neuroscience 29.1 (2006):

363-86. Web.

Muzny, Donna M., Matthew N. Bainbridge, Kyle Chang, Huyen H. Dinh, Jennifer A.

Drummond, Gerald Fowler, Christie L. Kovar, Lora R. Lewis, Margaret B.

140

Morgan, Irene F. Newsham, Jeffrey G. Reid, Jireh Santibanez, Eve Shinbrot, Lisa

R. Trevino, Yuan-Qing Wu, Min Wang, Preethi Gunaratne, Lawrence A.

Donehower, Chad J. Creighton, David A. Wheeler, Richard A. Gibbs, Michael S.

Lawrence, Douglas Voet, Rui Jing, Kristian Cibulskis, Andrey Sivachenko, Petar

Stojanov, Aaron Mckenna, Eric S. Lander, Stacey Gabriel, Gad Getz, Li Ding,

Robert S. Fulton, Daniel C. Koboldt, Todd Wylie, Jason Walker, David J.

Dooling, Lucinda Fulton, Kim D. Delehaunty, Catrina C. Fronick, Ryan Demeter,

Elaine R. Mardis, Richard K. Wilson, Andy Chu, Hye-Jung E. Chun, Andrew J.

Mungall, Erin Pleasance, A. Gordon Robertson, Dominik Stoll, Miruna

Balasundaram, Inanc Birol, Yaron S. N. Butterfield, Eric Chuah, Robin J. N.

Coope, Noreen Dhalla, Ranabir Guin, Carrie Hirst, Martin Hirst, Robert A. Holt,

Darlene Lee, Haiyan I. Li, Michael Mayo, Richard A. Moore, Jacqueline E.

Schein, Jared R. Slobodan, Angela Tam, Nina Thiessen, Richard Varhol, Thomas

Zeng, Yongjun Zhao, Steven J. M. Jones, Marco A. Marra, Adam J. Bass, Alex H.

Ramos, Gordon Saksena, Andrew D. Cherniack, Stephen E. Schumacher, Barbara

Tabak, Scott L. Carter, Nam H. Pho, Huy Nguyen, Robert C. Onofrio, Andrew

Crenshaw, Kristin Ardlie, Rameen Beroukhim, Wendy Winckler, Gad Getz,

Matthew Meyerson, Alexei Protopopov, Juinhua Zhang, Angela Hadjipanayis,

Eunjung Lee, Ruibin Xi, Lixing Yang, Xiaojia Ren, Hailei Zhang, Narayanan

Sathiamoorthy, Sachet Shukla, Peng-Chieh Chen, Psalm Haseley, Yonghong

Xiao, Semin Lee, Jonathan Seidman, Lynda Chin, Peter J. Park, Raju

Kucherlapati, J. Todd Auman, Katherine A. Hoadley, Ying Du, Matthew D.

Wilkerson, Yan Shi, Christina Liquori, Shaowu Meng, Ling Li, Yidi J. Turman,

141

Michael D. Topal, Donghui Tan, Scot Waring, Elizabeth Buda, Jesse Walsh,

Corbin D. Jones, Piotr A. Mieczkowski, Darshan Singh, Junyuan Wu, Anisha

Gulabani, Peter Dolina, Tom Bodenheimer, Alan P. Hoyle, Janae V. Simons,

Matthew Soloway, Lisle E. Mose, Stuart R. Jefferys, Saianand Balu, Brian D.

O’Connor, Jan F. Prins, Derek Y. Chiang, D. Neil Hayes, Charles M. Perou,

Toshinori Hinoue, Daniel J. Weisenberger, Dennis T. Maglinte, Fei Pan,

Benjamin P. Berman, David J. Van Den Berg, Hui Shen, Timothy Triche Jr,

Stephen B. Baylin, Peter W. Laird, Gad Getz, Michael Noble, Doug Voet, Gordon

Saksena, Nils Gehlenborg, Daniel Dicara, Juinhua Zhang, Hailei Zhang, Chang-

Jiun Wu, Spring Yingchun Liu, Sachet Shukla, Michael S. Lawrence, Lihua

Zhou, Andrey Sivachenko, Pei Lin, Petar Stojanov, Rui Jing, Richard W. Park,

Marc-Danie Nazaire, Jim Robinson, Helga Thorvaldsdottir, Jill Mesirov, Peter J.

Park, Lynda Chin, Vesteinn Thorsson, Sheila M. Reynolds, Brady Bernard,

Richard Kreisberg, Jake Lin, Lisa Iype, Ryan Bressler, Timo Erkkilä, Madhumati

Gundapuneni, Yuexin Liu, Adam Norberg, Tom Robinson, Da Yang, Wei Zhang,

Ilya Shmulevich, Jorma J. De Ronde, Nikolaus Schultz, Ethan Cerami, Giovanni

Ciriello, Arthur P. Goldberg, Benjamin Gross, Anders Jacobsen, Jianjiong Gao,

Bogumil Kaczkowski, Rileen Sinha, B. Arman Aksoy, Yevgeniy Antipin, Boris

Reva, Ronglai Shen, Barry S. Taylor, Timothy A. Chan, Marc Ladanyi, Chris

Sander, Rehan Akbani, Nianxiang Zhang, Bradley M. Broom, Tod Casasent,

Anna Unruh, Chris Wakefield, Stanley R. Hamilton, R. Craig Cason, Keith A.

Baggerly, John N. Weinstein, David Haussler, Christopher C. Benz, Joshua M.

Stuart, Stephen C. Benz, J. Zachary Sanborn, Charles J. Vaske, Jingchun Zhu,

142

Christopher Szeto, Gary K. Scott, Christina Yau, Sam Ng, Ted Goldstein, Kyle

Ellrott, Eric Collisson, Aaron E. Cozen, Daniel Zerbino, Christopher Wilks, Brian

Craft, Paul Spellman, Robert Penny, Troy Shelton, Martha Hatfield, Scott Morris,

Peggy Yena, Candace Shelton, Mark Sherman, Joseph Paulauskis, Julie M.

Gastier-Foster, Jay Bowen, Nilsa C. Ramirez, Aaron Black, Robert Pyatt, Lisa

Wise, Peter White, Monica Bertagnolli, Jen Brown, Timothy A. Chan, Gerald C.

Chu, Christine Czerwinski, Fred Denstman, Rajiv Dhir, Arnulf Dörner, Charles S.

Fuchs, Jose G. Guillem, Mary Iacocca, Hartmut Juhl, Andrew Kaufman, Bernard

Kohl Iii, Xuan Van Le, Maria C. Mariano, Elizabeth N. Medina, Michael Meyers,

Garrett M. Nash, Phillip B. Paty, Nicholas Petrelli, Brenda Rabeno, William G.

Richards, David Solit, Pat Swanson, Larissa Temple, Joel E. Tepper, Richard

Thorp, Efsevia Vakiani, Martin R. Weiser, Joseph E. Willis, Gary Witkin,

Zhaoshi Zeng, Michael J. Zinner, Carsten Zornig, Mark A. Jensen, Robert Sfeir,

Ari B. Kahn, Anna L. Chu, Prachi Kothiyal, Zhining Wang, Eric E. Snyder, Joan

Pontius, Todd D. Pihl, Brenda Ayala, Mark Backus, Jessica Walton, Jon

Whitmore, Julien Baboud, Dominique L. Berton, Matthew C. Nicholls, Deepak

Srinivasan, Rohini Raman, Stanley Girshik, Peter A. Kigonya, Shelley Alonso,

Rashmi N. Sanbhadti, Sean P. Barletta, John M. Greene, David A. Pot, Kenna R.

Mills Shaw, Laura A. L. Dillon, Ken Buetow, Tanja Davidsen, John A. Demchok,

Greg Eley, Martin Ferguson, Peter Fielding, Carl Schaefer, Margi Sheth, Liming

Yang, Mark S. Guyer, Bradley A. Ozenberger, Jacqueline D. Palchik, Jane

Peterson, Heidi J. Sofia, and Elizabeth Thomson. "Comprehensive Molecular

143

Characterization of Human Colon and Rectal Cancer." Nature 487.7407 (2012):

330-37. Web.

Neve, Richard M., Koei Chin, Jane Fridlyand, Jennifer Yeh, Frederick L. Baehner, Tea Fevr,

Laura Clark, Nora Bayani, Jean-Philippe Coppe, Frances Tong, Terry Speed, Paul

T. Spellman, Sandy Devries, Anna Lapuk, Nick J. Wang, Wen-Lin Kuo, Jackie L.

Stilwell, Daniel Pinkel, Donna G. Albertson, Frederic M. Waldman, Frank

Mccormick, Robert B. Dickson, Michael D. Johnson, Marc Lippman, Stephen

Ethier, Adi Gazdar, and Joe W. Gray. "A Collection of Breast Cancer Cell Lines

for the Study of Functionally Distinct Cancer Subtypes." Cancer Cell 10.6 (2006):

515-27. Web.

Nguyen, P. L., A. G. Taghian, M. S. Katz, A. Niemierko, R. F. Abi Raad, W. L. Boon, J. R.

Bellon, J. S. Wong, B. L. Smith, and J. R. Harris. "Breast Cancer Subtype

Approximated by Estrogen Receptor, Progesterone Receptor, and HER-2 Is

Associated With Local and Distant Recurrence After Breast-Conserving

Therapy." Journal of Clinical Oncology 26.14 (2008): 2373-378. Web.

Nicolas, Michael, Anita Wolfer, Kenneth Raj, J. Alain Kummer, Pleasantine Mill, Mascha

Van Noort, Chi-Chung Hui, Hans Clevers, G. Paolo Dotto, and Freddy Radtke.

"Notch1 Functions as a Tumor Suppressor in Mouse Skin." Nature Genetics 33.3

(2003): 416-21. Web.

Ohashi, Shinya, Mitsuteru Natsuizaka, Yumi Yashiro–Ohtani, Ross A. Kalman, Momo

Nakagawa, Lizi Wu, Andres J. Klein–Szanto, Meenhard Herlyn, J. Alan Diehl,

Jonathan P. Katz, Warren S. Pear, John T. Seykora, and Hiroshi Nakagawa.

"NOTCH1 and NOTCH3 Coordinate Esophageal Squamous Differentiation

144

Through a CSL-Dependent Transcriptional Network." Gastroenterology 139.6

(2010): 2113-123. Web.

Panelos, John, Francesca Tarantini, Milena Paglierani, Claudia Di Serio, Vincenza Maio,

Silvia Pellerito, Nicola Pimpinelli, Marco Santucci, and Daniela Massi.

"Photoexposition Discriminates Notch 1 Expression in Human Cutaneous

Squamous Cell Carcinoma." Modern Pathology 21.3 (2008): 316-25. Web.

Perou, Charles M., Therese Sørlie, Michael B. Eisen, Matt Van De Rijn, Stefanie S. Jeffrey,

Christian A. Rees, Jonathan R. Pollack, Douglas T. Ross, Hilde Johnsen, Lars A.

Akslen, Patrick O. Brown, and David Botstein. "Molecular Portraits of Human

Breast Tumours." Nature 406 (2000): 747-52. Print.

Peto, R., F. J. Roe, P. N. Lee, L. Levy, and J. Clack. "Cancer and Ageing in Mice and Men."

Br J Cancer. 32(4) (1975): 411-426. Web.

Phng, L.-K., and Holger Gerhardt. "Angiogenesis: A Team Effort Coordinated by Notch."

Developmental Cell 16.2 (2009): 196-208. Web.

Pleasance, Erin D., R. Keira Cheetham, Philip J. Stephens, David J. Mcbride, Sean J.

Humphray, Chris D. Greenman, Ignacio Varela, Meng-Lay Lin, Gonzalo R.

Ordóñez, Graham R. Bignell, Kai Ye, Julie Alipaz, Markus J. Bauer, David

Beare, Adam Butler, Richard J. Carter, Lina Chen, Anthony J. Cox, Sarah Edkins,

Paula I. Kokko-Gonzales, Niall A. Gormley, Russell J. Grocock, Christian D.

Haudenschild, Matthew M. Hims, Terena James, Mingming Jia, Zoya Kingsbury,

Catherine Leroy, John Marshall, Andrew Menzies, Laura J. Mudie, Zemin Ning,

Tom Royce, Ole B. Schulz-Trieglaff, Anastassia Spiridou, Lucy A. Stebbings,

Lukasz Szajkowski, Jon Teague, David Williamson, Lynda Chin, Mark T. Ross,

145

Peter J. Campbell, David R. Bentley, P. Andrew Futreal, and Michael R. Stratton.

"A Comprehensive Catalogue of Somatic Mutations from a Human Cancer

Genome." Nature 463.7278 (2009): 191-96. Web.

Polakis, Paul. "The Many Ways of Wnt in Cancer." Current Opinion in Genetics &

Development 17.1 (2007): 45-51. Web.

Polakis, Paul. "Wnt Signaling and Cancer." Genes and Development 14.15 (2000): 1837-851.

Web.

Pedraza-Farina. “Mechanisms of Oncogenic Cooperation in Cancer Initiation and

Metastasis.” Yale J Biol Med. 79(3-4) (2006): 95-103. Web.

Pérez-Pérez, José Manuel, Héctor Candela, and José Luis Micol. "Understanding Synergy in

Genetic Interactions." Trends in Genetics 25.8 (2009): 368-76. Web.

Prior, I. A., P. D. Lewis, and C. Mattos. "A Comprehensive Survey of Ras Mutations in

Cancer." Cancer Research 72.10 (2012): 2457-467. Web.

Prochownik, Edward V. "C-Myc as a Therapeutic Target in Cancer." Expert Review of

Anticancer Therapy 4.2 (2004): 289-302. Web.

Proweller, A. "Impaired Notch Signaling Promotes De Novo Squamous Cell Carcinoma

Formation." Cancer Research 66.15 (2006): 7438-444. Web.

Radtke, F. "Self-Renewal and Cancer of the Gut: Two Sides of a Coin." Science 307.5717

(2005): 1904-909. Web.

Ranganathan, P., K. L. Weaver, and A. J. Capobianco. "Notch Signaling in Solid Tumors: A

Little Bit of Everything but Not All the Time." Nature Reviews Cancer 11 (2011):

338-51. Web.

146

Reedijk, M. "High-level Coexpression of JAG1 and NOTCH1 Is Observed in Human Breast

Cancer and Is Associated with Poor Overall Survival." Cancer Research 65.18

(2005): 8530-537. Web.

Roper E, Weinberg W, Watt FM, Land H. "p19ARF-independent induction of p53 and cell

cycle arrest by Raf in murine keratinocytes." EMBO reports. 2(2) (2001):145-50.

Ruley, H. Earl. "Adenovirus Early Region 1A Enables Viral and Cellular Transforming

Genes to Transform Primary Cells in Culture." Nature 304.5927 (1983): 602-06.

Web.

Ryan, Kevin M., Andrew C. Phillips, and Karen H. Vousden. "Regulation and Function of

the P53 Tumor Suppressor Protein." Current Opinion in Cell Biology 13.3 (2001):

332-37. Web.

Sampson, E. R., H. R. McMurray, D. C. Hassane, L. Newman, P. Salzman, C. T. Jordan, and

H. Land. "Gene Signature Critical to Cancer Phenotype as a Paradigm for

Anticancer Drug Discovery." Oncogene 32.33 (2012): 3809-818. Web.

Schlosshauer, P. W. "APC Truncation and Increased Beta-catenin Levels in a Human Breast

Cancer Cell Line." Carcinogenesis 21.7 (2000): 1453-456. Web.

Schmitt, Clemens A., Fridman, Jordan S., Yang, Meng, Baranov, Eugene, Hoffman, Robert

M., Lowe, Scott W. “Dissection p53 tumor suppressor functions in vivo. Cancer

Cell 1(3) (2002): 289-298. Web.

Schweisguth, François. "Regulation of Notch Signaling Activity." Current Biology 14.3

(2004): R129-138. Web.

147

Serrano, Manuel, Athena W. Lin, Mila E. Mccurrach, David Beach, and Scott W. Lowe.

"Oncogenic Ras Provokes Premature Cell Senescence Associated with

Accumulation of P53 and P16INK4a." Cell 88.5 (1997): 593-602. Web.

Shaffer, Arthur L., N. C. Tolga Emre, Laurence Lamy, Vu N. Ngo, George Wright,

Wenming Xiao, John Powell, Sandeep Dave, Xin Yu, Hong Zhao, Yuxin Zeng,

Bangzheng Chen, Joshua Epstein, and Louis M. Staudt. "IRF4 Addiction in

Multiple Myeloma." Nature 454.7201 (2008): 226-31. Web.

Sharma, Sreenath V. and Settleman, Jeffrey. “Oncogene addiction: setting the stage for

molecularly targeted cancer therapy.” Genes Dev. 21(2) (2007): 3214-3231. Web.

Shen, R., A. B. Olshen, and M. Ladanyi. "Integrative Clustering of Multiple Genomic Data

Types Using a Joint Latent Variable Model with Application to Breast and Lung

Cancer Subtype Analysis." Bioinformatics 26.2 (2010): 292-93. Web.

Sigal, A., and V. Rotter. "Oncogenic Mutations of the P53 Tumor Suppressor: The Demons

of the Guardian of the Genome." Cancer Research 60.24 (2000): 6788-793. Web.

Sinn, Eric, William Muller, Paul Pattengale, Isidore Tepler, Racheal Wallace, and Philip

Leder. "Coexpression of MMTV/v-Ha-ras and MMTV/c-myc Genes in

Transgenic Mice: Synergistic Action of Oncogenes in Vivo." Cell 49.4 (1987):

465-75. Web.

Smith, Bradley, and Hartmut Land. "Anticancer Activity of the Cholesterol Exporter ABCA1

Gene." Cell Reports 2.3 (2012): 580-90. Web.

Smith, G. "Mutations in APC, Kirsten-ras, and P53---alternative Genetic Pathways to

Colorectal Cancer." Proceedings of the National Academy of Sciences 99.14

(2002): 9433-438. Web.

148

Solimini, Nicole L., Ji Luo, and Stephen J. Elledge. "Non-Oncogene Addiction and the Stress

Phenotype of Cancer Cells." Cell 130.6 (2007): 986-88. Web.

Vita, Marina and Henriksson, Marie. “The Myc oncoprotein as a therapeutic target for human

cancer.” Semin Cancer Biol. 16(4) (2006): 318-330. Web.

Vivanco, I, and Sawyers, CL. “The phosphatidylinositol 3-Kinase AKT pathway in human

cancer. “ Nat Rev Cancer. 2(7) (2002): 489-501. Web.

Vogelstein, Bert, and Kenneth W. Kinzler. "The Multistep Nature of Cancer." Trends in

Genetics 9.4 (1993): 138-41. Web.

Walters, Emily, Heyer, Dorothy, Candelaria, Pierre V, Wang, Simeng and McMurray,

Helene R. “CRGs regulate BLBC growth in a cancer-selective manner.”

Unpublished.

Weinberg, Robert A. "Oncogenes, Antioncogenes, and the Molecular Basis of Multistep

Carcinogenesis." Cancer Research 55 (1989): 3713-721. Web.

Weinstein, I. B., A. Joe, and D. Felsher. "Oncogene Addiction." Cancer Research 68.9

(2008): 3077-080. Web.

Weinstein, I. B. "CANCER: Enhanced: Addiction to Oncogenes--the Achilles Heal of

Cancer." Science 297.5578 (2002): 63-64. Web.

Weinstein, I. Bernard, and Andrew K. Joe. "Mechanisms of Disease: Oncogene Addiction—

a Rationale for Molecular Targeting in Cancer Therapy." Nature Clinical Practice

Oncology 3.8 (2006): 448-57. Web.

Weng, A. P., A. A. Ferrando, and J. C. Aster. "Activating Mutations of NOTCH1 in Human

T Cell Acute Lymphoblastic Leukemia." Science 306.5694 (2004): 269-71. Web.

149

Whelan, Jarrett T., Anne Kellogg, Brian M. Shewchuk, Karlene Hewan-Lowe, and Fred E.

Bertrand. "Notch-1 Signaling Is Lost in Prostate Adenocarcinoma and Promotes

PTEN Gene Expression." Journal of Cellular Biochemistry 107.5 (2009): 992-

1001. Web.

Williams, Bart O., Lee Remington, Daniel M. Albert, Shizuo Mukai, Roderick T. Bronson,

and Tyler Jacks. "Cooperative Tumorigenic Effects of Germline Mutations in Rb

and P53." Nature Genetics 7.4 (1994): 480-84. Web.

Xia, Mingxuan, and Hartmut Land. "Tumor Suppressor P53 Restricts Ras Stimulation of

RhoA and Cancer Cell Motility." Nature Structural & Molecular Biology 14.3

(2007): 215-23. Web.

Yang, L., X. Wu, Y. Wang, K. Zhang, J. Wu, Y-C Yuan, X. Deng, L. Chen, C. C H Kim, S.

Lau, G. Somlo, and Y. Yen. "FZD7 Has a Critical Role in Cell Proliferation in

Triple Negative Breast Cancer." Oncogene 30.43 (2011): 4437-446. Web.

Yokoyama, K, and Imamoto, F. “Transcriptional control of the endogenous MYC

protooncogene by antisense RNA.” Proc Natl Acad Sci USA. 84(21) (1987):

7363-7367.

Zhang, Lin, Wei Zhou, Victor E. Velculescu, Scott E. Kern, Ralph H. Hruban, Stanley R.

Hamilton, Bert Vogelstein, and Kenneth W. Kinzler. "Gene Expression Profiles in

Normal and Cancer Cells." Science 276.5316 (1997): 1268-272. Web.