Insights Into MYC Biology Through Investigation of Synthetic Lethal Interactions with MYC Deregulation

Insights into MYC biology through investigation of synthetic lethal interactions with MYC deregulation

Mai Sato

Submitted in partial fulfillment of the requirements

for the degree of Doctor of Philosophy

under the Executive Committee of the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

2014

Mai Sato

ABSTRACT

Insights into MYC biology through investigation of synthetic lethal interactions

with MYC deregulation

Mai Sato

MYC (or c-myc) is a bona fide “cancer driver” oncogene that is deregulated in up to 70% of human tumors. In addition to its well-characterized role as a transcription factor that can directly promote tumorigenic growth and proliferation, MYC has transcription-independent functions in vital cellular processes including DNA replication and protein synthesis, contributing to its complex biology. MYC expression, activity, and stability are highly regulated through multiple mechanisms. MYC deregulation triggers genome instability and oncogene-induced DNA replication stress, which are thought to be critical in promoting cancer via mechanisms that are still unclear.

Because regulated MYC activity is essential for normal cell viability and MYC is a difficult protein to target pharmacologically, targeting genes or pathways that are essential to survive MYC deregulation offer an attractive alternative as a means to combat tumor cells with MYC deregulation. To this end, we conducted a genome-wide synthetic lethal shRNA screen in MCF10A breast epithelial cells stably expressing an inducible MYCER transgene. We identified and validated FBXW7 as a high-confidence synthetic lethal (MYC-SL) candidate gene. FBXW7 is a component of an E3 ubiquitin ligase complex that degrades MYC. FBXW7 knockdown in MCF10A cells selectively induced cell death in MYC-deregulated cells compared to control. As expected, cellular

MYC levels are stabilized when FBXW7 expression is attenuated. Notably, stabilization of MYC is more pronounced compared to other FBXW7 targets. FBXW7 knockdown with

MYC deregulation results in cell cycle defects, as well as CDC45 accumulation on chromatin, suggesting DNA replication stress. Intriguingly, FBXW7 and MYC expression correlate most strongly in the luminal A-subtype of breast cancer associated with low to normal MYC expression. Together, our results suggest that knockdown of FBXW7 increases cellular MYC levels and promotes cell death possibly through accumulation of

MYC-dependent genomic stress, and that FBXW7 inhibition may be selectively synthetic lethal with breast cancers that retain MYC-dependence.

We also identified UVSSA and ERCC8, two genes involved in transcription- coupled repair (TCR), as MYC-SL candidates from our genome-wide screen. TCR is a

DNA damage repair pathway associated with active RNA polymerase II-transcription complexes. We show that both UVSSA and ERCC8 knockdown confer increased lethality selectively in MYC-deregulated cells. This MYC-SL interaction is not exacerbated by exogenous UV irradiation, suggesting that TCR may be required for survival upon MYC deregulation independently of its role in UV damage repair. UVSSA knockdown with MYC deregulation results in cell cycle defects and CHK2 activation, suggesting genomic stress. Intriguingly, we observe that lethality associated with

UVSSA down-regulation in cells expressing MYCER is alleviated by inhibiting transcription. This suggests that transcription-dependent aberrant genomic structures generated during MYC deregulation may require TCR for maintaining survival. Taken together, our results suggest that increased levels of transcription-dependent genomic stress may accumulate with MYC deregulation, and that TCR may have functions outside of repairing UV-induced damage in resolving these lesions or structures.

TABLE OF CONTENTS

List of Figures and Tables vi

Acknowledgements ix

CHAPTER 1 – Introduction 1

1. MYC 2

a. History of MYC 2

b. MYC family 3

c. Functions of MYC 6

i. Transcription 6

1. Transcriptional activator 6

2. Transcriptional repressor 9

3. General amplifier of transcription 9

ii. DNA replication 10

iii. Other proposed functions 13

d. Regulation of MYC 14

i. MYC gene expression 14

ii. Post-translational modifications 16

iii. Degradation 17

1. Significance of MYC turnover 17

2. Ubiquitin-mediated degradation 17

3. Ubiquitin ligases involved in MYC turnover 19

a. FBXW7 21

e. MYC in human cancers 23

i. Hematopoietic cancers 24

i ii. Other non-hematopoietic cancers and breast cancers 24

iii. Current therapies for MYC-driven cancers 26

2. Genome instability 27

a. Genome instability: a hallmark of cancer cells 27

b. MYC and genome instability 28

i. Transcriptional deregulation of cell cycle and genomic processes 28

ii. Oncogene-dependent DNA replication stress 29

iii. Alternative mechanisms 30

c. Crosstalk between transcription, replication, and repair 31

i. Transcription-coupled repair 32

3. Synthetic lethality 37

a. Synthetic lethality in human cancer therapy 38

i. BRCA1/2 and PARPi 39

b. Synthetic lethality and MYC 40

i. Candidate approaches 40

1. DR5 40

2. WRN 41

3. CDKs, ATR, CHK1, and Aurora kinases 42

ii. High-throughput approaches 43

1. Carla Grandori group: CSNK1E 44

2. Thomas Westbrook group: SAE2 45

3. Daniel Murphy group: ARK5 46

4. Running Themes 46

CHAPTER 2 – Genome-wide screen for synthetic lethal genes with

MYC deregulation 48

ii 1. MCF10A-MYCER as a model system 49

a. MCF10A 49

b. MYCER 50

2. Genome-wide shRNA screen using MCF10A-MYCER 51

3. Screen analysis 52

a. Microarray hybridization 53

b. High-throughput sequencing 55

c. Comparison of analysis methods 57

Tables 1-4 60

CHAPTER 3 – MYC is a critical target of FBXW7 74

Abstract 76

Introduction 77

Results 79

MCF10A-MYCER cells: a model for MYC deregulation 79

Screen for synthetic lethal candidates with MYCER activation 81

Loss of FBXW7 together with MYCER activation is lethal 83

FBXW7 knockdown specifically leads to stabilization of active MYCER 85

MYCER stabilization results in accumulation of chromatin-bound

CDC45 and cells in S/G2 phase 86

MYC expression correlates with FBXW7 expression in Luminal A-type

breast cancers 87

Discussion 88

Materials and Methods 93

Figure legends 99

Figures 1-5 102

iii Supplementary Figures 1-9 107

Supplementary Table 1 116

CHAPTER 4 – Transcription-coupled repair is required to survive

MYC-dependent genomic stress 118

Abstract 120

Introduction 120

Results 123

Genome-wide synthetic lethal screen for MYC-SL candidates using

MCF10A-MYCER 123

UVSSA and ERCC8 are synthetic lethal with MYC 124

Synthetic lethal interaction is independent of UV damage 126

UVSSA knockdown with MYC activation results in genomic stress 127

Synthetic lethal effect is dependent on transcription 128

Discussion 129

Materials and Methods 133

Figure legends 136

Figures 1-4 138

Supplementary Figures 1-2 142

CHAPTER 5 – Discussion 144

1. Genome-wide screen: strengths and weaknesses 145

2. FBXW7 knockdown is synthetic lethal with MYC 147

3. Transcription-coupled repair is synthetic lethal with MYC 150

4. Maintenance of MYC deregulation 154

iv REFERENCES 159

APPENDIX 183

APPENXDIX 1 – MCM6 interacts with BUBR1 and AURKA in

Xenopus laevis extracts 184

v LIST OF FIGURES AND TABLES

CHAPTER 1

Figure 1. Domains organization of MYC family proteins. 5

Figure 2. Model for MYC-mediated transcriptional activation

and repression. 8

Figure 3. Direct role of MYC in DNA replication. 12

Figure 4. Multilayered regulation of MYC. 15

Figure 5. The ubiquitin-proteasome system (UPS). 19

Figure 6. The FBXW7-containing E3 ubiquitin ligase complex and

its targets. 22

Figure 7. Mechanisms of MYC-dependent DNA replication stress. 30

Table 1. Clinical, genetic, and biochemical phenotypes associated

with XP, CS, and UVSS. 33

Figure 8. Current model of transcription-coupled repair (TCR). 36

Figure 9. Large-scale screening in mammalian cells. 44

Figure 10. Three functional hubs of MYC synthetic lethal genes. 47

CHAPTER 2

Table 1. List of 169 synthetic lethal candidates from microarray

hybridization analysis of the MCF10A-MYCER screen (p<0.1). 60

Table 2. List of 316 synthetic lethal candidates from HTP sequencing

analysis of the MCF10A-MYCER screen (p<0.01). 64

Table 3. Functional clustering analysis of HTP sequencing results. 72

Table 4. HTP sequencing results for UVSSA (KIAA1530), ERCC8,

and WDR89. 73

CHAPTER 3

Figure 1. MCF10A-MYCER cells: a model for MYC deregulation. 102

Figure 2. Genome-wide screen for synthetic lethal candidates with

MYCER activation. 103

Figure 3. Loss of FBXW7 combined with MYCER activation is lethal. 104

Figure 4. Loss of FBXW7 with MYCER activation results in specific

stabilization of active MYCER protein. 105

Figure 5. MYCER stabilization results in accumulation of cells in

S/G2 phase and chromatin-bound CDC45. 106

Supplementary Figure 1. MYCER protein expressed from the transgene

can enhance transcriptional activity. 107

Supplementary Figure 2. MCF10A-MYCER cells arrest at the

G1/S transition following double thymidine block. 108

Supplementary Figure 3. Schematic of fluorescence-based competition

assay. 109

Supplementary Figure 4. FBXW7 knockdown specifically leads to

stabilization of MYCER. 110

Supplementary Figure 5. FBXW7 knockdown leads to preferential

stabilization of MYCER. 111

Supplementary Figure 6. FBXW7 knockdown with MYCER activation

causes synergistic accumulation of cells in S and G2/M phase. 112

Supplementary Figure 7. Long term FBXW7 knockdown and MYCER

activation do not result in significant levels of DNA damage or apoptosis. 113

Supplementary Figure 8. MYC and FBXW7 expression levels positively

correlate in Luminal A-type breast cancers. 114

vii Supplementary Figure 9. UBE2I knockdown is synthetic lethal with

MYCER activation. 115

Supplementary Table 1. List of top 78 synthetic lethal targets from

MCF10A-MYCER screen. 116

CHAPTER 4

Figure 1. MYC deregulation renders cells sensitive to UVSSA and

ERCC8 knockdown. 138

Figure 2. Synthetic lethal interaction is independent of UV damage. 139

Figure 3. UVSSA knockdown with MYC activation results in accumulation

of cells in S/G2 phase and phosphorylation of CHK2. 140

Figure 4. Synthetic lethal effect is dependent on transcription. 141

Supplementary Figure 1. shRNA clones used for UVSSA knockdown. 142

Supplementary Figure 2. shRNA clones used for ERCC8 knockdown. 143

CHAPTER 5

Figure 1. Model of vulnerabilities during MYC deregulation. 156

APPENDIX 1

Figure 1. MCM3 and MCM6 interact with BUBR1 and AURKA. 185

viii ACKNOWLEDGEMENTS

I would like to thank my parents, brother, and the rest of my extended family for their unconditional support throughout my life and my graduate studies. My parents have always let me do whatever I want to pursue, and I would not be here today without their exceptional parenting and undying love and support. I would also like to thank my grandmother for always wishing the best of me. My greatest thanks also goes to Shinji, my new family and love of my life, for always believing in me and my potential, as well as bringing amazing joy and enrichment to my life.

I would like to greatly thank Dr. Ron Liem and Zaia Sivo from the Pathology &

Cell Biology Graduate Program at the Columbia University Medical Center. Without their support, guidance, and advice, my graduate studies would have not been the same. I give special thanks to Zaia for taking the initiative to tell me what I needed to hear at times of need. Also, great thanks to my fellow Ph.D. friends over the years for sharing experiences together.

I would like to thank my thesis committee members: Dr. Richard Baer, Dr. Jose

M. Silva, and Dr. Bin Zheng, for their valuable advice and guidance through my entire graduate studies. I appreciate greatly that Jose and Bin stayed on my committee despite their recent relocation from Columbia. A special thanks goes to Dr. James Manley for joining the committee for my defense as an additional member.

I would like to give special thanks to Dr. Max Gottesman for guidance, advice, and support throughout the years. Also, my greatest thanks goes to Dr. David

Dominguez-Sola for teaching me all about about MYC as well as countless experimental procedures.

The biggest thanks over my graduate studies goes to the past and present members of the Gautier lab. They have been exceptional mentors, colleagues, friends,

ix and even close to family to me. Special thanks go to Carol Ying, Merav Ben-Yehoyada,

Lane Phillips, Shaun Peterson, Rose Sattler, Kirsten Robertson, Seetha Srinivasan,

Hannah Williams, Tomas Aparicio, Niyo Kato, Krithika Rajagopalan, Gagan Sidhu,

Rebecca Alizzi, and finally, Lily Wang for introducing me to the Gautier lab.

Finally, I would like to thank my mentor, Dr. Jean Gautier. Jean has been everything I wanted my mentor to be – intelligent, practical, reasonable, and fun. He has always been extremely supportive of my scientific learning process that has been slow at times, always providing me with ideas and advice to aid my growth as a scientist. He fosters a unique environment of discussion, sharing, and helping each other in the lab that is a joy to be a part of. I truly feel that the best decision I made in my graduate studies is to join the Gautier lab, and I thank Jean greatly for letting me take part in the lab’s studies, and for being so patient and supportive throughout my project.

CHAPTER 1

Introduction

1. MYC

With more than 25,000 associated publications, MYC is undoubtedly one of the most extensively studied oncogenes today. Since its discovery as a bona fide human oncogene just over 30 years ago, studies on MYC have contributed comprehensively to expanding our knowledge about both human cell biology and cancer biology.

Surprisingly, despite extensive research, many aspects of MYC biology are still not fully understood – from the properties of the MYC gene and protein to the functions of MYC in normal cells and in oncogenesis, the field of MYC biology continues to evolve at an unabated pace. Here we discuss the identification of MYC, the MYC family, the functions and regulation of MYC, and MYC in human cancers.

a. History of MYC

The first MYC gene identified in the late 1970s was from the avian acute oncogenic retrovirus MC29, which caused tumors in chicken (Duesberg et al., 1977).

The viral gene responsible for the chicken myelocytomatosis was identified as v-myc, which was then later matched to the proto-oncogenic c-myc gene in the chicken genome, and then finally identified through homology in the human genome (Sheiness et al., 1980; Vennstrom et al., 1982). Shortly thereafter, groundbreaking studies revealed the human MYC gene was deregulated via chromosomal translocations into the immunoglobulin locus in Burkitt lymphoma (Dalla-Favera et al., 1982; Neel et al., 1982;

Taub et al., 1982). These findings revealed for the first time that endogenous human proto-oncogenes could cause neoplastic transformation in the absence of mutations within the gene, revolutionizing fundamental ideas of tumorigenesis. Today, there is no question that MYC remains one of the most critical proteins in human cancer biology, as

its copy number, expression, stability, and/or activity is altered in up to 70% of all human cancers (Dang, 2012; Santarius et al., 2010).

One of the earliest and most striking phenotypes identified following MYC expression was cellular transformation. MYC expression in embryonic fibroblasts could transform cells, albeit with the activation of additional oncogenes (Land et al., 1983).

These results have been corroborated in transgenic mouse studies, collectively showing that deregulated expression of MYC is sufficient to drive tumorigenesis in various tissues, though additional genetic alterations accelerate tumor formation (Adams et al.,

1985; Chesi et al., 2008; Morton and Sansom, 2013). Multiple studies have also shown that tumor cells can develop an “addiction” to MYC, suggesting that MYC has tumor maintenance properties in addition to initiation (Felsher and Bishop, 1999; Jain et al.,

2002; Marinkovic et al., 2004; Pelengaris et al., 1999). Collectively, these studies have established that MYC is one of relatively few true “cancer driver” genes, and has both causal and maintenance roles in mammalian tumorigenesis.

Importantly, MYC is also an essential gene for growth and normal development.

MYC knockout mice are embryonic lethal at E9.5, implying its critical role during embryogenesis (Davis et al., 1993). This is because MYC controls a variety of cellular processes including proliferation, cell cycle progression, apoptosis, differentiation, angiogenesis, metabolism, DNA replication, and genomic stability (Dang, 2012; Meyer and Penn, 2008). While fascinating, this multifunctional nature of MYC has complicated studies investigating the mechanisms by which MYC causes oncogenesis.

b. MYC family

MYC, or c-myc, is a 439 amino acid protein belonging to a family of proteins also including MYCN (N-Myc), MYCL1 (L-Myc), and a few other less well-characterized

members (Figure 1) (Nau et al., 1985; Slamon et al., 1986). All three members share a basic helix-loop-helix leucine zipper (bHLH-LZ) domain in their C-terminus, which allows for sequence-specific DNA binding upon dimerization with a binding partner, usually

MAX (Blackwood and Eisenman, 1991; Nilsson and Cleveland, 2003). Though it is generally agreed upon that MYC-MAX heterodimerization is required for most MYC functions and oncogenesis (Amati et al., 1993), there is substantial evidence of MAX- independent functions of MYC, initially reported in Drosophila melanogaster, Xenopus laevis early development, and in human cells (Cascon and Robledo, 2012; Gallant and

Steiger, 2009; Lemaitre et al., 1995; Ribon et al., 1994; Steiger et al., 2008). MYC,

MYCN, and MYCL1 also share the highly conserved Myc box I and II domains, which include conserved residues that are posttranslationally modified and/or mediate protein- protein interactions (Figure 1B) (Nilsson and Cleveland, 2003). The N-terminal region spanning Myc boxes I and II in MYC has been extensively characterized as the transactivation domain (TAD), which is required for most of its biological roles including transcription (Figure 1B) (Meyer and Penn, 2008). MYC family members also contain two more conserved Myc boxes, III and IV, which are less well characterized but crucial for induction of transcription and apoptosis (Conacci-Sorrell et al., 2014). Unfortunately, structural information is still not available for MYC outside its HLH-LZ domain.

While MYC expression is ubiquitous and is observed throughout most cell types,

MYCN expression is largely restricted to the brain (Zimmerman and Alt, 1990). Some functions of MYC and MYCN may be redundant since MYCN can substitute for MYC in murine development (Malynn et al., 2000). Like MYC, MYCN deregulation is also seen in human cancers, ranging from brain cancers to breast and small cell lung cancers (Vita and Henriksson, 2006). Particularly well established is the prominence of MYCN amplification in neuroblastomas, which is utilized as a reliable prognostic marker for the disease (Brodeur et al., 1984; Kohl et al., 1984; Schwab et al., 1984). Mouse models

have verified that MYCN is in fact a neuroblastoma driver gene when overexpressed in neuroectodermal cells (Weiss et al., 1997).

Lastly, MYCL1, normally only expressed during development, was identified as an amplified gene in small cell lung cancers (Nau et al., 1985). Although MYCL1 biology is much less understood, MYCL1 amplification has been detected in several other tumor types including ovarian carcinomas, suggesting its non-trivial role in cancer biology (Wu et al., 2003). However, due to the focus of our work, the rest of this document will specifically focus on the biology of MYC (c-myc).

Figure 1. Domains organization of MYC family proteins. (A) Organization of MYC protein. The N-terminal transactivation region (TAD) contains critical domains such as Myc box I and II (MBI and MBII) which mediate protein-protein interactions. The C-terminal BHLH-LZ domain is critical for heterodimerization with MAX or other binding partners and mediates DNA binding. Other functional domains are indicated (Myc Box III and IV, PEST and NLS). (From (Conacci- Sorrell et al., 2014)) (B) Shared domains between MYC family members. MYC, MYCN, and MYCL are encoded by different genes but they all harbor a TAD which contains conserved Myc boxes. The bHLH-LZ is also a common domain, required for DNA binding. (From (Cole and Cowling, 2008))

c. Functions of MYC i. Transcription

MYC’s function as a transcription factor has been the most extensively characterized – in fact, it was thought to be its only molecular function until evidence of transcription-independent functions emerged relatively recently. Nevertheless, MYC’s role as a transcriptional activator and repressor is considered its major molecular function in cells.

1. Transcriptional activator

In general, MYC and its partner MAX dimerize through their respective bHLH-LZ domains, docking onto sequence-specific DNA binding motifs termed E-boxes (5’-

CACGTG-3’) located in the enhancer/promoter regions of target genes (Blackwell et al.,

1990). MYC-MAX dimers have higher affinity for classical E-boxes, though higher cellular concentrations can enhance their occupancy at non-canonical E-boxes as well

(Blackwell et al., 1993). Upon DNA binding, MYC is able to recruit protein complexes with histone acetyltransferase (HAT) activity including TRRAP, TIP60, PCAF, GCN5, p300/CBP, and TBP, through its N-terminal TAD (Figure 2A) (Luscher and Vervoorts,

2012; Meyer and Penn, 2008). These HATs then promote increased acetylation of local histones, resulting in a relaxed, open chromatin state that favors the binding of transcription initiation complexes for efficient transcription of target genes (Figure 2A).

TRRAP plays a key role in directly interacting with MYC through Myc boxes I and II, and subsequently recruiting HATs to target promoters (McMahon et al., 2000). Notably, the

MAD or MXD family of proteins can compete for MAX binding with MYC, antagonizing the transcriptional activity of MYC (Rottmann and Luscher, 2006).

Many genes are key transcriptional targets of MYC. Evidence that MYC activates the expression of cell cycle promoting genes, including cyclin-dependent kinase CDK4 and Cyclin E1, have been confirmed in multiple model systems (Hermeking et al., 2000;

Perez-Roger et al., 1997). Cyclins D1, D2, and A1, as well as CDC25A and the

E2F1/E2F2 transcription factors are thought to be bona fide MYC targets as well (Barrett et al., 1995; Daksis et al., 1994; Jansen-Durr et al., 1993; Meyer and Penn, 2008).

Upregulation of these genes could explain MYC’s transforming activity, however, more comprehensive genome-wide studies are suggesting that MYC biology may be more complicated than once thought.

According to the MYC target gene database, 1697 MYC transcriptional targets have been identified to date, though the list is continuously growing as new targets are identified and validated (www.myc-cancer-gene.org). In contrast to earlier gene-by-gene studies, genome-wide studies utilizing chromatin immunoprecipitation (ChIP) have revealed that MYC binds a surprisingly high number of sites on human DNA when overexpressed: up to 10-15% of the entire genome, including intergenic regions (Cawley et al., 2004; Dang et al., 2006; Fernandez et al., 2003; Li et al., 2003; Orian et al., 2003;

Zeller et al., 2006). Combined with MYC’s very weak transcriptional activation of target genes, this has obscured attempts at defining the “MYC core signature” (MCS). Most studies agree that the MCS generally includes genes involved in cell cycle progression, nucleotide metabolism, ribosome biogenesis, RNA processing, and DNA replication, all of which contribute to a pro-proliferation phenotype (Ji et al., 2011; Margolin et al., 2009;

O'Connell et al., 2003; Perna et al., 2012; Shaffer et al., 2006). However, whether a universal MCS exists at all is still controversial, as more evidence is starting to suggest that MYC functions can be specific to various cell types and contexts.

Of note, MYC is a rather unusual transcription factor since it can stimulate the activity of all three RNA polymerases (I, II, and III). MYC can influence the transcription

of rRNA and tRNA genes by Pol I and III, respectively, in addition to altering mRNA production through Pol II (Gomez-Roman et al., 2003; Grandori et al., 2005). Though less frequently studied, effects of MYC on RNA Pol I and III are likely not trivial, since protein biosynthesis is a critical component of cell proliferation, and these pathways are frequently upregulated in cancer cells (Luscher and Vervoorts, 2012).

Figure 2. Model for MYC-mediated transcriptional activation and repression. (A) MYC-MAX dimers can recruit HATs (P300/CBP, GCN5, TIP60) through interaction with TRRAP or other mediators and increase local histone acetylation. This leads to opening of chromatin for a transcription-permissive state. (From (Cole and Cowling, 2008)) (B) MYC-MAX dimers can repress gene expression by inhibiting Miz1-mediated transcriptional activation at specific genes. When acting as a transcriptional repressor, MYC can recruit histone deacetylases (HDACs) which contribute to promoting a closed local chromatin state. (From (Herkert and Eilers, 2010))

2. Transcriptional repressor

Though less well understood, MYC can also repress the expression of a large number of genes such as anti-proliferative genes involved in cell cycle checkpoints and cyclin-CDK inhibitors, including p21, p27, p15, and GADD45 (Chang et al., 2008; Meyer and Penn, 2008). Again, this additional function sets MYC apart from most canonical transcription factors and adds complexity to MYC biology.

A subset of MYC-mediated repression occurs through interacting with the transcription activators MIZ1 and SP1 (Figure 2B) (Gartel et al., 2001; Schneider et al.,

1997). MYC can displace co-activators of MIZ1 and SP1, repressing their transcriptional activation properties, as illustrated in the case of MYC-dependent transcriptional repression of p15 (Feng et al., 2002). As opposed to HATs, MYC-associated repression complexes can recruit histone deacetylase (HDAC) complexes, leading to the local closing of chromatin structures, favoring transcriptional repression (Figure 2B) (Luscher and Vervoorts, 2012). Interestingly, MYC/MIZ1 complexes can also recruit DNA methyltransferases as a means to repress transcription as well, as shown in the case of p21 (Brenner et al., 2005).

Additionally, MYC is able to activate and/or repress the production of specific microRNAs and long noncoding RNAs, leading to indirect changes in downstream gene expression (Chang et al., 2008; O'Donnell et al., 2005). Studies connecting MYC and small RNAs and their relationship to tumorigenesis are active areas of research.

3. General amplifier of transcription

It was recently proposed that MYC might be a general, instead of sequence- specific, amplifier of transcription. Levens’ group and Young’s group utilized ChIP- sequencing (ChIP-seq) techniques to investigate MYC activity in a genome-wide fashion

and observed that MYC bound already-active promoters (Lin et al., 2012; Nie et al.,

2012). MYC bound preferentially to promoters that were already occupied by RNA Pol II

(RNAPII) and active chromatin marks, and failed to bind to and induce the transactivation of silent genes, regardless of MYC concentration. Even more striking, these patterns were cell-type specific, suggesting that MYC may indiscriminately amplify the existing transcriptional program in the cell without initiating de novo transcription (Lin et al., 2012; Nie et al., 2012).

These results corroborate previous reports showing that MYC is a regulator of transcription pause release. MYC controls transcriptional elongation by inducing release of promoter-proximally paused RNAPII complexes that are poised for transcription (Rahl et al., 2010). RNAPII pausing is a general mechanism that facilitates rapid and efficient transcription upon release by P-TEFb (Adelman and Lis, 2012; Levine, 2011; Seila et al.,

2009). Since pharmacological inhibition of BRD4, which plays a role in P-TEFb recruitment, can lead to regression of MYC-driven tumors in mice, this function of MYC may be relevant to pathogenesis (Delmore et al., 2011; Mertz et al., 2011). It is possible that MYC has dual roles in transcriptional initiation and pause release.

ii. DNA replication

In the late 1980s, it was proposed that MYC may be directly regulating DNA replication, since MYC expression resulted in proliferation and transformation (Gutierrez et al., 1988). During early Xenopus development in which transcription is silent but replication is rapid, MYC is recruited to the nucleus, suggesting a role in DNA replication

(Gusse et al., 1989). However, follow-up studies were not conducted since the transcriptional functions of MYC became uncovered.

In 2007, Dominguez-Sola and colleagues confirmed the original findings that

MYC has a transcription-independent function in DNA replication (Dominguez-Sola et al., 2007). Mass spectrometry identified members of the pre-replicative complex (pre-

RC), which are essential for DNA replication, in complex with MYC. In Xenopus laevis cell-free extracts where DNA replication occurs readily without the presence of active transcription, immuno-depletion of MYC resulted in inhibition of DNA replication, and recovery of replication required full-length recombinant MYC. Overexpression of MYC in extracts increased the initial rate of DNA replication, but did not allow for its completion due to accumulation of replication-dependent DNA damage and S-phase checkpoint activation. These results were corroborated in mouse cells, suggesting that MYC could be directly initiating origin activation, causing aberrant firing of dormant origins leading to harmful replication intermediates that trigger the checkpoint when overexpressed

(Dominguez-Sola et al., 2007). Srinivasan and colleagues recently confirmed this hypothesis by utilizing DNA combing (Figure 3). MYC overexpression decreased inter- origin distances on replicating chromatin and increased aberrant replication intermediates including unidirectional forks and replication-dependent DNA damage

(Figure 3) (Srinivasan et al., 2013). Critically, overexpression of CDC45 phenocopied

MYC overexpression, suggesting an epistatic relationship. MYC phenotypes were dependent on the presence of CDC45, as partial depletion of CDC45 suppressed MYC phenotypes. Taken together, MYC appears to have a substantial role in directly affecting origin firing during DNA replication, possibly by recruiting CDC45 to replicative origins to facilitate origin activation (Figure 3) (Srinivasan et al., 2013). Consistent with these results, other studies have reported similar S-phase phenotypes upon CDC45 overexpression in human cells, as well as its rate-limiting nature in origin firing (Wong et al., 2011) (Frank Grosse, unpublished).

This function of MYC could at least in part explain the widespread association of

MYC with chromatin. In fact, some studies have found E-boxes to coincide with replication origins (Swarnalatha et al., 2012). However, since MYC transcriptionally activates many genes involved in DNA replication including MCMs, CDC6, and CDT1, the relative contribution of MYC towards DNA replication and transcription is still an open question (Eilers and Eisenman, 2008).

DNA combing

Normal Myc, CMG

IOD: inter- Origin distance

Normal IOD, no "asym. or damage !IOD, "asymmetry and DNA damage !IOD, "asymmetry and DNA damage

CMG Myc CMG Myc

Figure 3. Direct role of MYC in DNA replication. Spatio-temporal analysis of DNA replication pattern in Xenopus extract by DNA combing/DNA fiber assay. Overexpression of MYC or CDC45 (CMG complex contains CDC45, MCM2-7, GINS) results in decreased inter-origin distances, increased fork asymmetry, and increased replication-dependent DNA damage. Partial depletion of CDC45 abrogates MYC phenotypes, suggesting that MYC may contribute to origin activation through directly modulating the CMG complex. (From (Srinivasan et al., 2013)

iii. Other proposed functions

MYC plays an integral role in stem cell biology and differentiation. MYC was one of the four original genes required for reprogramming fibroblasts to pluripotency, though later studies showed that it is dispensable in some conditions (Nakagawa et al., 2008;

Takahashi and Yamanaka, 2006). This suggests MYC has a role in self-renewal, however, others have shown that MYC is required to commit to terminal differentiation

(Gandarillas and Watt, 1997). It is possible that MYC plays dual roles at various points of cell differentiation.

Others have suggested that MYC is a global chromatin regulator (Varlakhanova and Knoepfler, 2009). Nuclei in MYCN-null neuronal cells are smaller in size, with condensed chromatin. Global histone acetylation and methylation patterns appear to be markedly altered, suggesting that MYCN could be modifying chromatin in a genome- wide manner (Knoepfler et al., 2006). Similar results were obtained from murine epidermal stem cells, in which MYC activation induced changes in global chromatin acetylation, rather than local changes at select promoters as would be expected from a sequence-specific transcription factor (Frye et al., 2007). Whether these changes are a direct or indirect consequence of MYC expression is unclear.

MYC can also directly promote phosphorylation of the C-terminal tail of RNA polymerase II, regulating mRNA cap methylation and promoting translation (Cowling and

Cole, 2007). Intriguingly, the DNA binding domain of MYC is dispensable for this function, suggesting that not all functions of MYC may require DNA binding or transcription (Cole and Cowling, 2008). Consistent with this, the PC12 murine cell line has no functional MAX protein but is viable, and Drosophila cells without MAX appear to retain substantial MYC activity, such as regulation of Pol III (Hopewell and Ziff, 1995;

Steiger et al., 2008). These studies support the idea that MYC may not require binding with MAX or to DNA for a surprising number of its functions.

d. Regulation of MYC

Since MYC is essential for cycling cells but can drive mitogen-independent growth, MYC levels must be tightly regulated. Thus, cells have evolved an intricate system of MYC regulation in order to closely monitor MYC homeostasis (Figure 4). Here we discuss the regulation of MYC gene expression, protein stability, and degradation.

i. MYC gene expression

In normal cells, MYC expression can be triggered by a variety of signal transduction pathways such as NF-κB, STAT, WNT, Hedgehog, NOTCH, and TGFβ; all of which are under strict control of specific mitogens and proliferative signals (Figure 4)

(Dang, 2012). Transcription of the MYC gene is directly regulated by transcription factors including CNBP, FBP, and TCF, as well as by specific DNA structures and sequences

(Levens, 2013). Several single-nucleotide polymorphisms (SNPs) have been identified in the enhancer region of the MYC gene and are associated with multiple cancers (Dang,

2012).

The MYC mRNA is also under tight regulation. The short-lived MYC mRNA, with an approximate half-life of 20 minutes, can be regulated by several microRNAs including let-7, miR-34, and miR-145, which can fine-tune the levels of MYC transcripts (Cannell et al., 2010; Kim et al., 2009; Sachdeva et al., 2009). Additionally, translation of MYC mRNA is controlled by multiple signaling pathways, adding another layer of regulation

(Soucek and Evan, 2010).

Importantly, MYC can also auto-regulate its own expression. Early studies showed that exogenous introduction of v-myc or c-myc into cells resulted in down-

regulation of endogenous MYC, demonstrating yet another mechanism by which MYC levels are kept precisely in check (Penn et al., 1990).

Figure 4. Multilayered regulation of MYC. MYC gene expression is triggered by various mitogenic signaling pathways including TGF-b, Wnt, PDGF, and EGF. Transcription of the MYC gene is regulated at the level of DNA structure, transcription initiation, elongation, RNA stability, and transport. At the protein level, MYC is again strictly regulated by posttranslational modifications and degradation pathways that dictate its stability. Finally, MYC protein functions are diverse, and can vary greatly depending on the level of expression and stable MYC proteins available in the cell. (From (Conacci-Sorrell et al., 2014))

ii. Post-translational modifications

The MYC protein is extensively post-translationally modified by phosphorylation, ubiquitination, acetylation, and O-linked glycosylation (Vervoorts et al., 2006). These modifications regulate distinct aspects of the MYC protein.

The best characterized phosphorylation events occur on Serine 62 (S62) followed by Threonine 58 (T58), and are key modifications that regulate MYC’s transcriptional activity and subsequent proteasomal degradation (Thomas and Tansey,

2011). Mutations in these residues result in stabilized mutant proteins and are prevalent in Burkitt lymphoma (Axelson et al., 1995). Several kinases can phosphorylate S62 including ERK, MAPK, JNK, and CDK1, likely in response to different upstream signaling

(Luscher and Vervoorts, 2012). T58 phosphorylation, on the other hand, is generally attributed to GSK3β, which can only phosphorylate T58 when S62 is phosphorylated

(Lutterbach and Hann, 1994). Phosphorylated T58 is then finally recognized as a substrate by ubiquitin ligases, leading to polyubiquitination and degradation of MYC.

Other putative phosphorylation sites have been mapped in MYC, however, the kinases responsible and their functional significance remains unknown.

Interestingly, some of the HAT complexes that MYC recruits to target gene promoters for transcription initiation can acetylate residues on MYC itself. There are at least 8 acetylation sites that have been mapped in the MYC protein, and most appear to be acetylated by p300/CBP, TIP60, and GCN5, which have all been implicated in transcription initiation by MYC (Luscher and Vervoorts, 2012). It has been proposed that acetylation of MYC may compete with ubiquitination to promote stability of the MYC protein, as both modifications occur on lysine residues. This phenomenon has been shown for other transcription factors, however, definitive results still remain to be shown in the case of MYC (Caron et al., 2005). It is also possible that acetylation regulates specific protein-protein interactions.

Finally, ubiquitination is another key process for regulation of MYC stability

(Thomas and Tansey, 2011). MYC is primarily degraded through the ubiquitin- proteasomal system (UPS), in which ubiquitin conjugation serves as a signal for degradation by the 26S proteasome. The next section discusses the various UPS- dependent mechanisms of MYC degradation.

iii. Degradation

1. Significance of MYC turnover

An integral component of MYC regulation is turnover, or degradation. The average half-life of MYC protein is only 20-30 minutes, and in normal proliferating cells,

MYC proteins are constantly kept as low as 1000~6000 molecules per cell (Moore et al.,

1987; Rudolph et al., 1999). This is critical, since even a 2-3 fold increase in this number can cross a threshold amount of MYC and grant tumorigenic potential (Murphy et al.,

2008). To maintain the tumorigenic state, however, MYC levels must be kept below lethal levels, since high levels of MYC expression can activate apoptotic pathways

(Murphy et al., 2008). Thus, sustaining precise levels of MYC is crucial for normal cells for the maintenance of proliferation, and in tumor cells for continued survival.

2. Ubiquitin-mediated degradation

The bulk of MYC destruction in cells occurs through ubiquitin-mediated proteolysis, a highly specific and regulated pathway of protein turnover (Figure 5)

(Thomas and Tansey, 2011). The process is well characterized and occurs through a stepwise program in which proteins are flagged for destruction by the covalent addition of ubiquitin molecules to lysine residues (Figure 5) (Pagan et al., 2013). The canonical

process usually consists of the ubiquitin activating enzyme (E1), the ubiquitin conjugating enzyme (E2), and the ubiquitin ligase (E3), and these components respectively activate, conjugate, then transfer ubiquitin onto the target proteins. The E3 ligase complexes confer substrate specificity, allowing for a widespread but highly regulated process. Importantly, proteins that are targeted to the UPS are polyubiquitinated with linear ubiquitin chains usually consisting of 4 or more molecules linked via Lysine 48 (K48) on the ubiquitin molecule. Monoubiquitination or polyubiquitination of other linkage types such as K63-linkages or branched chains are not targeted to the 26S proteasome, and instead serve as signaling modifications or other functions. The 26S proteasome exclusively recognizes those substrates that are appropriately polyubiquitinated via K48 (Muller and Eilers, 2008).

The interaction between the UPS and target proteins is highly regulated, often by post-translational modifications such as phosphorylation of the substrate. This is the case for MYC, where specific phosphorylation on S62, followed by T58, are required for recognition by different E3 ligase complexes. Notably, ubiquitination can be counteracted by acetylation events as described above, or by deubiquitinating enzymes.

USP28, in particular, has been linked to deubiquitination of MYC (Popov et al., 2007).

Various domains of MYC have been associated with its degradation. A canonical degron region, which is required for its interaction with at least two E3 ligase complexes, resides in its TAD domain and spans Myc boxes I and II (Thomas and Tansey, 2011).

Within these Myc boxes are several key residues, discussed below. Two other domains exist in the central region of the MYC protein, the “D” and “PEST” elements, which also have been linked to MYC stability (Luscher and Vervoorts, 2012).

Figure 5. The ubiquitin-proteasome system (UPS). First, ubiquitin molecules are activated by an ATP-dependent E1 activating enzyme. Ubiquitin is then conjugated onto its target substrate via the E2 conjugating enzyme, followed by the E3 ubiquitin ligase which confers substrate specificity. Linear ubiquitin chains of 4 or more molecules linked through K48 serve as tags for substrate degradation by the 26S proteasome. (From (Pagan et al., 2013))

3. Ubiquitin ligases involved in MYC turnover

More than five different E3 ubiquitin ligase complexes have been implicated in

MYC degradation (Luscher and Vervoorts, 2012; Thomas and Tansey, 2011). The best characterized is the SCF-like E3 ubiquitin ligase complex containing FBXW7 (Welcker et al., 2004). Recent evidence suggests that this FBXW7-containing complex, at least in some cell types, controls the bulk of MYC degradation (King et al., 2013; Takeishi et al.,

2013) (see following section).

The second is SKP2, an oncogene, which is also a component of an SCF-type

E3 ubiquitin ligase complex (Kim et al., 2003). Unlike FBXW7, however, SKP2 interacts with Myc box II of MYC. Interestingly, the in vivo polyubiquitination of MYC by SKP2 promotes MYC transcriptional activity, acting as a cofactor (Kim et al., 2003). Current models suggest that SKP2-mediated degradation of MYC may occur directly on chromatin-bound MYC, where efficient and timely degradation of MYC may enhance its turnover and transcriptional activity (Thomas and Tansey, 2011). Such coupling of

transcriptional activity to degradation has been previously reported for other transcription factors (Holt, 2012). Notably, SKP2 is also a MYC target gene and is upregulated by

MYC, suggesting that MYC may control its own stability (Bretones et al., 2011).

The HECT-domain ubiquitin ligase Huwe1/HectH9 has been implicated in degradation of MYC, as well as p53 (Adhikary et al., 2005). Interestingly, this complex has also been linked to the stress response and DNA repair (Hall et al., 2007; Herold et al., 2008). Huwe1/HectH9 can ubiquitinate MYC through K48- or K63-ubiquitin chains with distinct consequences, as K48-linked polyubiquitination targets MYC for degradation, whereas K63-linkages promote the recruitment of p300, enhancing transcriptional activities of MYC (Adhikary et al., 2005).

The β-TrCP-SCF complex can ubiquitinate the same sites on MYC as those targeted by the FBXW7-containing complex, but instead of linear chains, adds heterotypic polyubiquitin chains that are not recognized by the proteasome (Popov et al.,

2010). This suggests an antagonistic mechanism via competition with FBXW7 (Popov et al., 2010). Since β-TrCP-SCF activity is cell cycle-regulated by kinases such as PLK1, it is possible that this mechanism regulates the abundance of MYC through cell cycle stages (Watanabe et al., 2004).

TRIM32 (Schwamborn et al., 2009), FBX29 (Koch et al., 2007), and the

TRPC4AP/TRUSS complex (Choi et al., 2010) have also been implicated in MYC/MYCN degradation. Little is known about the relative physiological contribution of each degradation pathway, though knockdown of one of these pathways generally only causes modest changes in MYC stability, suggesting that the processes likely cooperate with each other (Thomas and Tansey, 2011). Recent evidence suggests, however, that

FBXW7-mediated degradation of MYC may be particularly significant in tumorigenesis, as discussed below.

a. FBXW7

FBXW7 (F-box and WD repeat containing protein 7, also known as FBW7, AGO,

SEL10, or CDC4) is the substrate-specifying subunit in the SKP1-CUL1-F-box protein

(SCF)-like E3 ubiquitin ligase complex (Figure 6A) (Welcker and Clurman, 2008).

FBXW7 belongs to the F-box family of proteins and is well conserved in eukaryotes. In humans, three isoforms of FBXW7 are expressed which are differentially localized in the cell. Whether these isoforms have specific roles is unclear, but all harbor the conserved

~40 amino acid F-box (interacts with SKP1) and WD repeats (binds phosphorylated substrates) in their C-terminus (Wang et al., 2012).

The FBXW7 ligase complex polyubiquitinates and targets MYC for degradation both in vitro and in vivo (Welcker et al., 2004; Yada et al., 2004). The interaction between FBXW7 and MYC is tightly regulated, as sequential phosphorylation of MYC, first on S62 by one of several kinases, then on T58 by GSK3β, is required to establish the phosphodegron recognized by FBXW7 (Thomas and Tansey, 2011). That T58 is a hotspot for MYC mutations in cancer is not surprising, since it disturbs the interaction of

FBXW7 with MYC and leads to MYC stabilization (Tan et al., 2008). Rendering FBXW7 unable to degrade MYC can increase MYC half-life to up to 120 minutes, throwing off the cellular MYC balance (Salghetti et al., 2000). Additionally, the ubiquitin-specific protease

USP28 appears to be a part of the FBXW7 complex, counteracting the ligase activity of

FBXW7 for further fine-tuning of the process (Popov et al., 2007).

Interestingly, MYC is not the only substrate for the FBXW7 complex – in fact,

FBXW7 can degrade several oncoproteins and master regulators (Figure 6B) (Tan et al.,

2008; Wang et al., 2012). Included in its list of substrates are Cyclin E1, a well- established oncoprotein that drives cell cycle progression, the c-Jun and NOTCH1 oncoproteins, as well as Mcl-1, SREBP, mTOR, KLF5, c-Myb, Aurora A, NF1, NRF1,

HIF-1a, and p100 (Figure 6B) (Wang et al., 2012). The relative cellular contribution of

FBXW7 to the stability of each of its targets and resulting oncogenesis remains elusive, and is an intense area of study.

Figure 6. The FBXW7-containing E3 ubiquitin ligase complex and its targets. (A) FBXW7 (FBW7) is a component of the SCF-like (SKP1-CUL1-F-box) E3 ligase complex. FBXW7 confers substrate specificity to the E3 ligase complex. (B) Known targets of FBXW7. The FBXW7-containing E3 ligase complex targets many major oncoproteins and master regulators for degradation, including MYC, Cyclin E ,and NOTCH1. The repertoire of FBXW7 targets may differ across various cell types depending on specific expression of the oncogenes. (From (Wang et al., 2012))

For these reasons, FBXW7 is thought to be a tumor suppressor, as mutations in

FBXW7 are one of the most frequent genetic aberrations (mutated in >30%) in T-cell acute lymphoblastic leukemia (T-ALL), as well as cholangiocarcinoma, pancreatic, endometrial, and colon cancers (Akhoondi et al., 2007; Thompson et al., 2007; Wang et al., 2012). Interestingly, however, mutations or inactivation of FBXW7 are surprisingly rare in a significant portion of human cancers, including ovarian and breast (<4%)

(Akhoondi et al., 2010; Kwak et al., 2005). This could be due to the complex combinations and interactions of oncoproteins in these cancers, or differential roles of

FBXW7 in specific cell types and contexts.

The relative contribution of FBXW7 to overall MYC stability has been controversial, though it is the most well characterized MYC-degrading complex. Studies in some systems show FBXW7 having minimal effects on MYC protein levels. Deletion of Myc box I did not prolong MYC half-life significantly, suggesting that disruption of the

FBXW7-MYC interaction is not sufficient to promote tumorigenesis (Herbst et al., 2004;

Thomas and Tansey, 2011). In contrast, recent studies in mouse cells demonstrated that

MYC accumulation as a direct consequence of FBXW7 disruption caused leukemogenesis (King et al., 2013; Takeishi et al., 2013). It is clear that at least in some cell types, FBXW7 appears to be a critical regulator of MYC protein levels.

e. MYC in human cancers

Although MYC is generally essential for survival, only low levels of MYC are necessary and sufficient for normal cell viability, and a modest increase in MYC can cause malignant transformation. Up to 70% of all human cancers show aberrant MYC in one of several ways. These include 1) deregulation of expression due to chromosomal translocation of the MYC locus into an active promoter such as immunoglobulin, 2) gene amplification of the MYC locus, 3) virus-mediated insertional mutagenesis, 4) gain of function mutations in the MYC gene, and 5) mutations or inactivation of other genes that regulate MYC stability (Dang, 2012; Vita and Henriksson, 2006). Intriguingly, specific types of MYC deregulation are predominant in certain cancer types, suggesting cell type-specific regulation of MYC. Here we discuss some of the current information about

MYC in various cancer subtypes.

i. Hematopoietic cancers

The first human malignancies linked to MYC were hematopoietic cancers, with the most iconic being Burkitt lymphoma, which is caused by a specific chromosomal translocation between chromosome 8, containing MYC, and chromosome 14,2, or 22, which include immunoglobulin (Ig) genes (Wasylishen and Penn, 2010). The resulting translocation puts the MYC gene under control of the Ig promoter, deregulating its expression. The Eµ-MYC transgenic mouse was established to model this specific translocation, and showed that it effectively induced clonal B-cell lymphoma (Adams et al., 1985). All human cases of Burkitt lymphoma are caused by one of these three possible translocations, illustrating the potency of MYC in this cancer subtype.

MYC rearrangements have also been identified in other hematological malignancies such as diffuse large B-cell lymphoma, multiple myeloma, primary plasma cell leukemia, and acute lymphocytic leukemia (Vita and Henriksson, 2006). Other than chromosomal rearrangements, MYC mutations, as well as deregulation, have also been reported. For example, in T-ALL, activating mutations of NOTCH1, the most frequent genetic alteration found in the disease, cause overexpression of MYC, which contributes significantly to the malignancy (Weng et al., 2006). The prevalence of MYC aberrations in hematopoietic cancers emphasizes the importance of tight MYC regulation in rapidly dividing cells.

ii. Other non-hematopoietic cancers and breast cancers

In solid tumors, MYC is deregulated in various ways depending on the tumor type

(Mazaris and Tsiotras, 2013). Genome-wide studies of amplification and overexpression of MYC family genes have shown significant levels in breast, colorectal, lung, and

prostate cancers, as well as medulloblastoma, neuroblastoma, and rhabdomyosarcoma

(Santarius et al., 2010). Other studies have also implicated MYC/MYCN deregulation in bladder, cervical, gastric, liver, endocrine, and ovarian cancers, as well as melanoma, glioblastoma, and osteosarcoma (Vita and Henriksson, 2006). Whether if MYC is a driver gene in these cancers is largely still an active area of research.

Gene amplification and overexpression of MYC is quite prevalent in breast tumors, though no MYC-associated chromosomal translocations have been identified in breast (Liao and Dickson, 2000; Xu et al., 2010). Meta-analysis studies have reported that MYC amplification (< 2-fold) occurs in about 15% of all breast cancers, while 22-

35% of breast cancers have increased mRNA expression of MYC, and more than 40% have increased MYC protein levels (Deming et al., 2000). The mechanisms of MYC stabilization are unknown, but it likely entails a complex interplay of signaling pathways including TGFβ, WNT/β-catenin, Ras, EGF, Her2/Neu, and ER-α, all of which play critical roles in breast tumorigenesis and can affect MYC expression (Xu et al., 2010).

Breast cancers can be largely classified into five subtypes: normal-like, basal- like, luminal A, luminal B, and Her2/Neu-positive (Sorlie et al., 2001). Particularly, MYC overexpression and amplification have been traditionally associated with the basal-like subtype of breast cancer, often referred to as “triple negative” for being estrogen receptor (ER-α) negative, progesterone receptor negative, and Her2/Neu negative

(O'Toole et al., 2013). Though basal-like breast cancers only account for ~15% of all breast cancers, they are the most challenging to treat, with the worst prognosis, and result in a disproportionate number of deaths (Schneider et al., 2008). Meta-analysis studies have confirmed that MYC, along with E2F, has elevated activity specifically in basal-like breast cancers (Alles et al., 2009).

Increasing evidence also suggests that MYC and BRCA1 may be closely related in breast tumorigenesis. Breast cancers with either germline mutations or spontaneous

inactivation of the breast tumor suppressor gene BRCA1 have been associated with amplification or overexpression of MYC (Grushko et al., 2004; Ren et al., 2013). It has been proposed that BRCA1 directly interacts with MYC and inhibits its transcriptional activity (Wang et al., 1998), suggesting that BRCA1 loss may be an obligate requirement for oncogenic MYC overexpression in the breast (Xu et al., 2010). Moreover, BRCA1 itself has been identified as a MYC transcriptional target, suggesting a complex feedback mechanism (Menssen and Hermeking, 2002).

Interestingly, various breast cancer types may harbor differential sensitivity to

MYC suppression. A fraction of breast cancer cell lines tested with MYC siRNA or shRNA exhibited MYC-independence (Kang et al., 2014; Kessler et al., 2012).

Consistent with this, some MYC-induced mammary tumors in mice were able to escape

MYC-dependence after elimination of MYC induction, and acquired more aggressive phenotypes with genetic signatures similar to that of tumor-initiating cells (Leung et al.,

2012). These findings raise an important question whether traditionally high-MYC breast cancers such as basal-like breast cancers are the best candidates for MYC-based therapies, as it is possible that these cells have already acquired MYC-independence.

iv. Current therapies for MYC-driven cancers

Although MYC is an attractive and obvious target for a wide range of cancer types, successful pharmacological targeting of MYC has been limited. Even as a biomarker, MYC is not very useful other than in specific cases such as neuroblastoma, since it is not always easy to detect MYC deregulation. MYC is not readily “druggable,” due to its nuclear localization and lack of a classical active site. Additionally, since MYC is essential in normal cells, the therapeutic window of direct targeting is slim. Several small molecule inhibitors have been tested to interfere with MYC’s biological activities,

but unfortunately, none have made substantial way into the clinic as of yet (Vita and

Henriksson, 2006).

Notably, a dominant negative MYC mutant, Omomyc, has been tested in mouse models of MYC-driven cancer and has shown potentially promising results (Soucek et al., 2008). Omomyc reversibly inhibits the transcriptional activation function of MYC by blocking the interaction between MYC and MAX. Administration of Omomyc in murine models of non-small cell lung cancer has shown tumor regression with minimal effects on healthy tissue. Also, recent studies have shown promising results with bromodomain inhibitors such as JQ1 in mice (Delmore et al., 2011; Mertz et al., 2011). However, since the toxicity of these drugs in humans is a potential issue, further studies for direct or indirect targeting of MYC-driven tumors remains a crucial area of research.

2. Genome instability a. Genome instability: a hallmark of cancer cells

Historically, genomic instability in human cancer cells has been detected cytologically (Hanahan and Weinberg, 2011). More recently, technology has allowed for high-resolution studies of genomic aberrancies, providing further evidence that genomic instability is a critical hallmark of tumorigenesis and is evident in virtually all cancer types

(Cassidy and Venkitaraman, 2012). Deregulated genomic transactions, such as DNA replication, transcription, and repair, could all contribute to this phenomenon.

Importantly, while genome instability can be a consequence of tumorigenic growth, it may also be a critical prerequisite for tumorigenesis, since a minimum of 6-10 mutations or genetic alterations are typically required for neoplastic transformation (Wade and

Wahl, 2006). An unstable genome is more likely to accumulate these genetic changes more efficiently and quickly.

b. MYC and genome instability

MYC deregulation can directly cause genome instability, as chromosomal abnormalities including translocations, chromosomal missegregation, and aneuploidy can arise from MYC hyperactivation (Karlsson et al., 2003; Prochownik and Li, 2007). In addition, cells with deregulated MYC can display signs of persistent DNA damage and checkpoint activation, as well as increased sensitivity to select DNA damaging agents

(Felsher and Bishop, 1999; Wade and Wahl, 2006). A variety of MYC-dependent processes can directly contribute to genome instability, as discussed below.

i. Transcriptional deregulation of cell cycle and genomic processes

By upregulating genes that promote cell cycle progression and repressing those that inhibit it, deregulated MYC promotes uncontrolled and unscheduled cell division. For example, upregulation of CyclinE/CDK2 and repression of p27 by MYC causes premature entry into S-phase and desensitizes cells to DNA damage-induced cell cycle arrest during replication (Wade and Wahl, 2006). Such a rapid and dysregulated cell cycle is a source of genome instability, since cell cycle checkpoints and DNA repair pathways cannot perform optimally when these processes are uncoupled.

In addition, MYC also induces aberrant gene expression of genes involved in various genomic processes. DNA replication proteins including MCM4, MCM6, MCM7,

CDC6, CDT1, and TOP1 have been identified as upregulated MYC target genes (Eilers and Eisenman, 2008). This could contribute to accelerated DNA replication, DNA replication stress, and subsequent DNA damage. MYC also upregulates genes involved

in nucleotide metabolism, ribosome biogenesis, and RNA processing, which contribute to increased transcription and protein synthesis which in turn yield genomic stress

(Dang, 2012). Additionally, MYC can alter the transcription of mitotic proteins (Li and

Dang, 1999; Menssen et al., 2007). MYC can also induce a DNA damage response

(DDR) in the absence of exogenous DNA damage, inducing the expression of genes such as ATM, Wip1, and TIP60 (Campaner and Amati, 2012). Mechanisms of Myc- induced DDR is not clear, however, evidence for other oncogene-induced DDR have been reported as well (Halazonetis et al., 2008).

ii. Oncogene-dependent DNA replication stress

Aside from transcription, MYC can also induce damage directly through its role in

DNA replication. In both Xenopus cell-free extracts and in human B cells, MYC overexpression gives rise to DNA damage, as well as activation of the S-phase checkpoint (Figure 7) (Dominguez-Sola et al., 2007). This likely is the result of replication stress, as it is dependent on active DNA replication. One of the mechanistic hypotheses was that MYC overexpression during DNA replication gave rise to aberrant replication intermediates, which recruited DDR proteins to stalled or collapsed forks and elicited a checkpoint response. Consistent with this, MYC overexpression led to increased origin firing and asymmetric replication forks (Srinivasan et al., 2013). It is possible that replication-dependent DNA damage is sufficient to drive MYC-dependent genome instability, however, this has not been directly investigated in human cells.

Notably, some transcriptional targets of MYC can also contribute to replication stress, including cyclins D1, D2, and E1 (Bartek and Lukas, 2001). For example, MYC can induce increased Cyclin E1 expression (Perez-Roger et al., 1997). Cyclin E1 overexpression results in increased replication-dependent genomic stress and excessive

origin firing, closely phenocopying MYC overexpression in human cells and Xenopus extracts (Jones et al., 2013). This suggests that oncogene-dependent DNA replication stress induced by MYC can be a combination of direct and indirect pathways (Figure 7).

G1/S+transi(on+ Myc

Pathways%engaged%by%Myc%to%limit%replica9on%stress% Nucleo(de+ Replica(on++ Replica(on+fork+ DNA++ biosynthesis+ Checkpoint+ stability+factors+ Replica(on+ (ATR/CHK1)+ (WRN+)+

Replica(on+stress+ Prolifera(on+ STOP% (DNA+damage)+

Figure 7. Mechanisms of MYC-dependent DNA replication stress. MYC deregulation results in accumulation of replication stress due to elevated transcription, aberrant DNA replication, and deregulated cell cycle progression. To combat these, MYC engages DDR pathways, checkpoints, and other pathways in order to modulate replication stress and DNA damage to maintain an oncogenic and proliferative state, whileCampaner evading lethal and genomic Amati, damage. Figure (From (Campaner 2 and Amati, 2012))

iii. Alternative mechanisms

MYC deregulation can also give rise to gene amplifications, through either re- replication of DNA during a single S-phase, or chromosome breakage. Some studies have attributed MYC-dependent upregulation of CDC6 and CDT1 to the possibility of re- replication (Fernandez et al., 2003). Since re-replication was not observed in Xenopus extracts, this may be a consequence of MYC transcription rather than its direct role in replication (Dominguez-Sola et al., 2007).

Metaphase chromosomal aberrations that can ultimately lead to missegregation or breakage have also been detected in MYC-overexpressing cells (Felsher and Bishop,

1999). Additionally, MYC-dependent alteration of global chromatin structure, as well as p53-dependent checkpoint repression, have been proposed to contribute to genomic instability (Wade and Wahl, 2006). Future studies are crucial to gain insight into the relative significance of these processes.

c. Crosstalk between transcription, replication, and repair

The crosstalk between the various DNA transactions taking place on chromatin is attracting more interest. Transcription, replication, and repair have traditionally been studied as three distinct processes, but increasing evidence suggests that they communicate with each other substantially (Fong et al., 2013). Since tens of thousands of cellular DNA damage occurs per cell per day on average, it only makes sense for these pathways to collaborate in order to eradicate these lesions quickly and efficiently while allowing for faithful and unperturbed replication and transcription (Bernstein et al.,

2013; Lindahl, 1993).

These interactions are especially relevant when studying MYC, since MYC deregulation or overexpression is associated with elevated levels of transcription, replication, and DNA repair. To maintain viability under genomic stress, coordination of these processes may be critical. Consistent with this, MYC overexpression can induce

DDR, suggesting that increased DDR activity may become necessary for survival in conditions of hyperactive replication and transcription (Campaner and Amati, 2012).

Though little has been investigated for the case of MYC specifically, connections between transcription, replication, and repair abound. Transcription and replication have been suggested to communicate through several modes, including chromatin marks and

spatial and temporal regulation (Helmrich et al., 2013). Replication and repair are linked through cell cycle checkpoints that can, at least in part, promote temporal separation of the processes (Bartek and Lukas, 2001). The fork protection complex (FPC) can aid stalled replication forks to prevent collapse during S-phse (Errico and Costanzo, 2012).

Lastly, transcription and repair have also been shown to be coupled in some cases, the most well characterized being the transcription-coupled repair pathway (TCR, or TC-

NER for transcription-coupled nucleotide excision repair) (Vermeulen and Fousteri,

2013). Though existing evidence of TCR is centered specifically on repair of UV-induced

DNA lesions, this pathway is an elegant example of coupling two distinct genomic processes for the preservation of genome integrity.

i. Transcription-coupled repair

Transcription is one of the core processes necessary for survival. Thus, any obstructions in promoters, enhancers, and gene encoding regions of DNA must be thwarted to execute efficient and seamless transcription. To this end, one of the mechanisms that cells have evolved is TCR, which augments the already existing repertoire of DNA repair systems with one specifically coupled to the transcription process (Vermeulen and Fousteri, 2013).

TCR is considered a sub-pathway of the larger nucleotide excision repair (NER) system which repairs bulky adducts on DNA, including cyclobutane pyrimidine dimers

(CPDs) and other UV-induced lesions. At least in experimental conditions, over 90% of

UV lesions appear to be repaired by the global NER pathway, accounting only the remaining <10% for TCR (Limsirichaikul et al., 2009; Nakazawa et al., 2010). However,

TCR is extremely significant since mutations in genes specific to TCR give rise to a distinct set of human diseases including Cockayne syndrome (CS), cerebro-oculo-facio-

skeletal syndrome (COFS), and UV-sensitive syndrome (UVSS) (Table 1). CS and

COFS are characterized by photosensitivity as well as multisystem abnormalities and reduced lifespan, and UVSS, though much more mild, also causes increased photosensitivitySarasin (Table Genome 1) (Lehmann, Medicine 2012, 1982 ;4 Meir:44 a et al., 2000; Spivak, 2005). These Page 2 of 4 http://genomemedicine.com/content/4/5/44 TCR-defective diseases are distinct from xeroderma pigmentosum (XP), which arises from mutations in genes of the global NER pathway.

Table 1. Clinical, genetic and biochemical comparisons of function: a VHS domain (homology with the Vps-27, Hrs XP, CS and UVSS and STAM domain) near the amino terminus and a XP CS UVSS DUF2043 domain (EMBL-EBI IPR018610) near the Sun-sensitivity Yes Yes Yes carboxyl terminus. T e VHS domain has been implicated Skin cancers Yes No No in ubiquitin binding and in interaction with the carboxy- terminal part of RNA Pol II; it has also been suggested Progressive neurological degeneration Yes/noa Yes No that the UVSS-A mutation Cys32Arg might obstruct Developmental abnormalities No Yes No interactions between the VHS domain and ubiquitinated Ageing Yes/noa Yes No proteins. UVSSA truncated mutants lacking either VHS UV-DNA repair def ciency Yes Yes Yes or DUF2043 domains failed to complement the UVSS-A ROS-DNA repair def ciency Yes/noa Yes No defi ciency, indicating that these two domains are Complementation groups 7 2 3 necessary for TC-NER activity. One of the major players in TC-NER is the ten-protein Involved genes XPA to CSA and CSA, CSB XPGb CSB and complex TFIIH, involved in both NER and transcription UVSSA initiation [1]. Nakazawa et al. [7] demonstrated that aSome XP patients exhibit XP and CS (mutated on XPA, XPB, XPD and XPG). bSeven UVSSA interacts with ERCC2, ERCC3, p62 and the CAK genes are involved in XP. CS, Cockayne syndrome; ROS, reactive oxygen species; subcomplex (all part of TFIIH). Furthermore, Zhang et UV, ultraviolet; UVSS, UV-sensitivity syndrome; XP, xeroderma pigmentosum. al. [8] showed that UVSSA interacts with CSA in the absence of UV, and with CSB and RNA Pol II after UV Table 1. UsingClinical, genetic, cell and lines biochemical derived phenotypes from associated two UV withS XP,S-A CS, patients,and UVSS. XP irradiation, and that both UVSSA and CSB are necessary is caused by defects in GG-NER genes, while CS and UVSSA are caused by defects in TCR (TC-NER).Nakazawa XP, CS, and et UVSS al. patients [7] identifiall exhibit UVed sensitivity, homozygous though in UVSS mutations it is relatively for full completion of TC-NER. T us, interaction between mild compared to XP and CS. Though CS patients show congenital abnormalities, UVSS patients(c.367A>T) do not, suggesting leading that the molecular to a premature bases of the diseases stop differ. codon (From (Sarasin, in the UVSSA and the major proteins involved in TC-NER 2012)) KIAA1530 (renamed UVSSA) gene. Interestingly, a suggests that UVSSA may play a pivotal role in this homo zygous missense mutation (p.Cys32Arg) in this gene process. Interestingly,was found unlike in XP anpatients individual who are extremely previously susceptible mis-diagnosed to skin cancer, USP7 has several NER and DNA damage response defects in TCRwith are mild not associated XP. Zhang with skinet alcancer. [8] susceptibility carried out (Table microcell- 1) (Sarasin, (DDR) proteins as substrates. Schwertman et al. [9] mediated chromosome transfer of mouse DNA in order showed that USP7 resided in chromatin-immuno- 2012). TCRto is a complement distinct and critical the pathway, repair defi perhaps ciency with of additional UVSS-A functions human outside precipitated of TC-NER complexes in a UV- and UVSSA- cells, and isolated the mouse homologue of KIAA1530 as dependent manner. In UVSS-A cells, the absence of the gene responsible for UVSS-A. Sequencing of this gene UVSSA correlated with the instability of CSB, probably 33 from several UVSS-A cells also revealed mutations due to the lack of USP7 recruitment in the TC-NER leading to premature termination of the UVSSA protein complex. Indeed, depletion of USP7 by siRNA caused a [8]. Schwertman et al. [9] identifi ed several diff erentially similar RRS defi ciency and decreased levels of CSB. ubiquitinated proteins following UV irradiation of HeLa T ese data indicate that UVSSA and USP7 cooperate to cells. T e most prominent factors were repair proteins protect CSB from UV-induced degradation in TC-NER involved in NER (XPC, DDB2, RNA RNA polymerase via the ubiquitin-proteasome pathway. Because USP7 has (RNA Pol) II and CSB) as well as UVSSA. Using mass multiple roles in the DDR, an important role of UVSSA spectrometry, it was demonstrated that UVSSA interacts might also be to deliver the deubiquitinating enzyme to with the deubiquitinating enzyme ubiquitin carboxyl- the vicinity of TC-NER factors, allowing smooth regu la- terminal hydrolase 7 (also known as ubiquitin-specifi c tion of ubiquitination and deubiquitination. Zhang et al. protease 7, USP7) [8,9]. Transfection of wild-type tagged [8] also showed that this recruitment is CSA-dependent. UVSSA cDNA restored normal RRS in UVSS-A cells, Using local UV damage, it has also been shown that while small interfering RNA (siRNA)-based depletion of tagged-UVSSA accumulated in vivo at UV-induced DNA UVSSA transcripts caused a marked reduction of RRS in lesions (with kinetics similar to CSB), and interacted with normal cells, illustrating that the UVSSA-USP7 complex the elongating form of RNA Pol II (Pol IIo) [9]. Following is crucial for restoration of gene expression following UV UV irradiation, transcription is rapidly inhibited and fast irradiation. Taken together these data indicate that repair of lesions is necessary to ensure survival of UVSSA is the causal gene in UVSS-A, and that the damaged cells. Pol IIo is stalled at UV-induced DNA UVSSA-USP7 complex is involved in TC-NER. lesions and needs fi rst to be displaced by backtracking or degradation to allow access to repair factors. During this UVSSA interactions and the role of the ubiquitin step, Pol IIo is ubiquitinated, and CSA and CSB proteins proteasome pathway are necessary for this process. In UVSS-A cells, the Using three-dimensional structure prediction of UVSSA, ubiquitinated Pol IIo was almost undetectable and the Nakazawa et al. [7] identifi ed two domains of unknown normal Pol IIo form disappeared over a 6-h period

UV-damage repair as not all symptoms of TCR-defective diseases can be explained solely by increased UV sensitivity. Supporting this notion, at least some TCR genes have roles in general transcription and other RNA synthesis pathways, which may explain at least in part why systemic symptoms occur in TCR-defective diseases (Velez-

Cruz and Egly, 2013).

Activation of TCR in normal cells by exogenous UV damage results in preferential repair on the transcribed strand, since repair is directly coupled to actively transcribing RNAPII (Hanawalt and Spivak, 2008). Studies have pointed to at least two important functions of TCR: 1) to remove or displace stalled transcription complexes from the DNA to provide access for TCR proteins and subsequent repair factors, and 2) to remove and repair the lesion for resumption of transcription. TCR is initiated when an engaged RNAPII stalls at a lesion and recruits key factors that are unique to TCR. The best-characterized factors recruited at this stage include Cockayne syndrome A (CSA or

ERCC8) and Cockayne syndrome B (CSB or ERCC6). Mutations in ERCC6 or ERCC8 cause Cockayne syndrome (Vermeulen and Fousteri, 2013). Following recognition and displacement of stalled RNAPII by these proteins, the actual excision of the lesion and gap filling are performed by proteins which perform these functions in global NER, including XPF/ERCC1, XPA, XPG, and TFIIH. After lesion repair, however, TCR requires specific factors including XAB2, HMGN1, UVSSA, and ELL, which all appear to be required for efficient recovery of transcription. Thus, TCR comprehensively orchestrates RNAPII backtracking, recruitment of repair factors, and recovery of proper transcription (Mourgues et al., 2013; Vermeulen and Fousteri, 2013).

Mechanistic details of TCR are not entirely clear. In response to a stalled

RNAPII, CSB is recruited early and is required for recruitment of subsequent TCR factors (Figure 8) (Fousteri et al., 2006; Groisman et al., 2003). Both the DNA-binding domain and ubiquitin-binding domains of CSB are required for its role in TCR, however,

it is unclear if its enzymatic activities (controversial helicase activity and chromatin remodeling activity) are required for TCR in vivo (Vermeulen and Fousteri, 2013).

Notably, CSB has been implicated in a variety of other processes including transcriptional elongation, chromatin maintenance, and mitochondrial DNA stability, which adds complexity (Vermeulen and Fousteri, 2013). Nevertheless, CSB is one of the central players essential for successful TCR.

CSA is an E3 ubiquitin ligase that is also recruited early and is essential for TCR

(Figure 8) (Saijo, 2013). Much interest surrounds the substrates of CSA ligase activity. In vitro, CSA can autoubiquitinate, though this has not been shown in vivo. One of the current models suggests that autoubiquitinated CSA directly interacts with CSB through

CSB’s ubiquitin-binding domain, and this interaction regulates the stability of both proteins at the sites of TCR allowing for their efficient clearance upon completion of repair (Groisman et al., 2006). However, this remains to be demonstrated directly.

UVSSA was recently identified as a novel TCR factor, of which mutations cause

UVSS, a rare photosensitive disorder (Nakazawa et al., 2012; Schwertman et al., 2012;

Zhang et al., 2012). Notably, the mild phenotype of UVSS patients suggests that

UVSSA’s cellular role may only be in TCR, unlike CSA or CSB which mutations cause more profound systemic diseases. UVSSA forms a complex with the deubiquitinating enzyme USP7, which appears to accumulate at stalled RNAPII complexes (Figure 8).

The absence of functional UVSSA impairs TCR and transcription recovery. Collectively, these studies suggest that UVSSA-USP7 stabilizes stalled RNAPII-CSB complexes by counteracting the polyubiquitination of RNAPII and/or CSB, preventing premature degradation to facilitate successful TCR (Sarasin, 2012; Schwertman et al., 2013).

Theses studies revealed that TCR regulation may involve yet unknown mechanisms of extremely intricate balance between ubiquitination and deubiquitination.

Figure 8. Current model of transcription-coupled repair (TCR). (A) When an actively engaged RNAPII encounters a UV-induced lesion, UVSSA-USP7 and CSB are initially recruited to aid in backtracking or displacement of the transcriptional complex. (B) UVSSA-USP7 complex may de- ubiquitinate and stabilize CSB on RNAPII complexes to allow recruitment of other repair factors. (C) CSA is recruited to further stabilize the RNAPII complex and to recruit other TCR factors. The DNA lesion is then able to be repaired by NER factors, and RNAPII can resume transcription, or be recycled for a new round of initiation, following DNA repair. (From (Vermeulen and Fousteri, 2013))

Few other factors have been specifically implicated in TCR. The XPA-binding protein XAB2 appears to be recruited to TCR sites following CSB and CSA, and bridges

TCR with RNA splicing (Kuraoka et al., 2008). XAB2 is required for recovery of transcription. HMGN1 is another factor required for efficient TCR. HMGN1 is a chromatin remodeler, recruited to TCR sites in a CSB- and CSA-dependent manner (Fousteri et al.,

2006). It has been speculated that chromatin remodeling by HMGN1 is required to allow

RNAPII backtracking before repair. Lastly, it was recently reported that ELL, a component of transcriptional elongation complexes, is required for recovery of transcription after TCR (Mourgues et al., 2013). Additional TCR factors may be identified in future studies, as missing links remain in the pathway.

Although TCR has almost exclusively been studied in the context of UV-induced damage repair, TCR may be important under conditions of general transcription stress.

The trigger for the TCR response appears to be stalled RNAPII, which could physiologically arise from situations such as hyperactive transcription or collisions between the transcription and replication machinery. It is possible that such a response becomes critical in cells with persistent genome instability from oncogene activation such as MYC, though no direct evidence exists regarding the relationship between TCR and cancer.

3. Synthetic lethality

The concept of synthetic lethality is based on the idea that different genetic networks in the cell have distinct interactions. The most simplified definition of a pair of synthetic lethal (SL) genes is two genes that confer lethality when lost simultaneously, though neither is individually essential for viability. In other words, while inactivation of one gene can be tolerated, loss of the second gene confers death (Kaelin, 2009).

Synthetic lethality has been well studied in model organisms such as

Saccharomyces cerevisiae and Caenorhabditis elegans (Reinhardt et al., 2009).

Comprehensive screening in these systems have identified thousands of potential classical SL interactions, as well as “synthetic sick” interactions, in which fitness is compromised though not to lethal levels (Dixon et al., 2009). In budding yeast, “synthetic dosage lethality” has been studied as well, exploring genetic vulnerabilities of cells overexpressing certain genes (Tong and Boone, 2006). In most of the literature,

however, “synthetic lethality” is generally loosely used to refer to various genetic interactions that exploit genetic vulnerabilities that arise, not just through the loss but also through point mutations, gain of function, overexpression, or deregulation of specific genes (Kaelin, 2005).

a. Synthetic lethality in human cancer therapy

Efforts to exploit synthetic lethality to combat cancer were pioneered by chemical library screens in yeast cells with defects in cell cycle or DNA repair (Matuo et al., 2012).

This approach was quickly adopted in human cells, as utilizing synthetic lethality for cancer therapy is attractive for several reasons (Hartwell et al., 1997). First, SL interactions that exploit cancer-specific genetic lesions would be selectively lethal to cancer cells, sparing normal cells. This is a critical advantage, since one of the largest ongoing problems of cancer therapy are the detrimental side effects of nonspecific drugs on surrounding normal cells and tissues (Iglehart and Silver, 2009). Secondly, SL interactions could provide alternative druggable targets for commonly deregulated tumor suppressors and oncoproteins that are difficult to directly target pharmacologically. Since many of the known cancer drivers fall into this category, including p53, KRAS, and MYC, utilizing synthetic lethality offers a whole new realm of hope for developing targeted therapy for a larger portion of human cancers (Reinhardt et al., 2009).

Over the last two decades, many potential SL interactions relevant to cancer have been reported. Candidate approaches based on existing hypotheses, as well as genome-wide interrogations for a more unbiased and comprehensive approach, have been widely investigated (Weidle et al., 2011). While many studies have provided promising results, one of the standout discoveries was the SL interaction between

BRCA1/2 and PARP inhibitors (Bryant et al., 2005; Farmer et al., 2005). This pivotal

report has been widely recognized as proof of principle for the real-life relevance of synthetic lethality to cancer therapy, and has triggered increased interest for SL mining in the cancer field.

i. BRCA1/2 and PARPi

BRCA1 and BRCA2 are classic tumor suppressor genes, initially discovered in breast cancers but also with critical tumor suppressor activity in other cancer types

(Foulkes and Shuen, 2013). Sporadic or germline mutations, inactivation, or loss of

BRCA1 and/or BRCA2 account for the majority of hereditary, as well as a proportion of sporadic, breast tumors. The most relevant function of BRCA1 and BRCA2 to tumor suppression is their role in DNA double strand break repair (Liu and West, 2002; Powell and Kachnic, 2003). However, breast cancer cells can still survive with dysfunctional

BRCA1/2, as other DNA repair pathways are able to compensate. This contributes to increased genomic instability, since compensatory pathways can be low fidelity and mutagenic, further contributing to tumorigenesis.

Two groups reported in 2005 that blocking the Poly (ADP-ribose) polymerase

(PARP) enzymes using a chemical inhibitor selectively caused cell death in BRCA1- or

BRCA2-deficient cells (Bryant et al., 2005; Farmer et al., 2005). PARP enzymes are critical in base excision repair (BER), primarily responsible for repairing single-stranded

DNA breaks (Helleday, 2011). Administration of PARP inhibitors in BRCA1/2-deficient cells led to persistent DSBs that caused chromosome instability, cell cycle arrest, and apoptosis, showing that the loss of both BRCA1/2 (HR) and PARP (BER) pathways led to lethal levels of DNA damage. BRCA1/2-deficient cells selectively develop an increased dependence on the PARP pathway, while normal cells are able to survive without it (Bryant et al., 2005; Farmer et al., 2005). Preclinical studies using PARP

inhibitors on cell lines and mice with BRCA-deficiencies have been largely successful

(Bryant et al., 2005; Farmer et al., 2005). Particularly, olaparib has shown promising results in phase I, phase II, and ongoing phase III clinical trials on cancer patients with

BRCA1/2 defects (Fong et al., 2009; Tutt et al., 2010).

b. Synthetic lethality and MYC

A synthetic lethal approach to target oncogenic MYC is attractive, since MYC is a major oncoprotein, and notoriously difficult to inhibit pharmacologically. Several groups have tested panels of small molecule drugs on MYC-overexpressing cancer cells, and have proposed several candidates including chloroquine and topoisomerase inhibitors

(Frenzel et al., 2011; Maclean et al., 2008). However, finding mechanistic explanations for these interactions have proven difficult, as well as replicating results in other cell types. Other groups have taken candidate approaches to test specific genes as SL candidates in MYC-overexpressing cells by predicting vulnerabilities based on known aspects of MYC functions. Some of these studies have brought promising results, including DR5, WRN, and various CDKs, as discussed below. Finally, more recently, large-scale screening for SL interactions have become possible in mammalian cells, and discussed below are three screens that probed for SL interactions with deregulated

MYC.

i. Candidate approaches

1. DR5

A number of studies have reported that cell death pathways may be synthetic lethal with MYC, since MYC can influence apoptosis through transcription, and MYC

overexpression sensitizes cells to apoptosis (Evan et al., 1992; Juin et al., 1999).

Particularly, agonists of the TRAIL-death receptor DR5 are synthetic lethal with MYC

(Wang et al., 2004). Since DR5 is one of MYC’s transcriptional targets, cell surface levels of DR5 are increased in MYC-overexpressing cells, leading to this sensitization.

Although it is unclear whether DR5 is required for MYC-induced apoptosis, this demonstrates the vulnerability of MYC-overexpressing cells to a particular cell death pathway.

The group subsequently performed an siRNA screen targeting the human kinome, and found that GSK3β is synthetic lethal with MYC overexpression in a TRAIL receptor-dependent manner (Rottmann et al., 2005). Knockdown of GSK3β led to stabilization of MYC levels, which recapitulated their previous findings of increased DR5 and induction of apoptosis. This effect was due to reduction of T58 phosphorylation of

MYC, which prevented its interaction with FBXW7, consequently stabilizing the protein

(Rottmann et al., 2005). These early studies pointed to the potential significance of increasing levels of cellular MYC as a means to confer lethality.

2. WRN

Grandori’s group has pioneered studies on the synthetic lethal interaction between MYC overexpression and loss of WRN (Grandori et al., 2004; Grandori et al.,

2003; Moser et al., 2012; Robinson et al., 2009). The WRN gene encodes for one of the

RecQ helicases in humans, and is named for the disease caused by inactivating mutations in this gene. Werner syndrome, at the cellular level, is characterized by accelerated cellular senescence and increased chromosome instability including frequent chromatid breaks (Rossi et al., 2010). Since WRN helicase is involved in DNA repair, these phenotypes are thought to be due to persistence of DNA damage.

The WRN helicase resolves topologically aberrant structures during HR or other

DNA repair pathways to promote successful repair. WRN is also active during DNA replication, resolving structures such as stalled forks to consequently alleviate replication stress (Rossi et al., 2010). In cells derived from patients with Werner syndrome, MYC overexpression leads to excessive accumulation of replication-dependent DNA damage, which results in accelerated senescence (Grandori et al., 2003; Robinson et al., 2009).

This led to activation of the ATR-CHK1 pathway, consistent with accumulation of single- stranded DNA, which colocalized with stalled replication forks. These findings were further confirmed in xenografts and transgenic mouse models, suggesting that the genetic interaction may be relevant to physiological human tumors (Moser et al., 2012).

Interestingly, WRN is a direct transcriptional target of MYC, suggesting a positive feedback loop in which increased activity of WRN creates a more permissive environment for MYC overexpression (Grandori et al., 2003). It is also of note that

Werner syndrome patients do not present MYC-driven cancers, although they are cancer-prone, consistent with the idea that MYC overexpression may confer lethality for cells with WRN loss (Grandori et al., 2003). These studies have introduced the potential of genome instability being a major vulnerability of MYC-overexpressing cells.

3. CDKs, ATR, CHK1, and Aurora kinases

Based on evidence of MYC-dependent cell cycle deregulation and genomic instability, cyclin-dependent kinases have also emerged as attractive SL candidates

(Goga et al., 2007). It has been suggested that CDK2 inhibition causes p53-dependent apoptosis in MYCN-overexpressing neuroblastoma cells, and that MYC overexpression in CDK2-null mouse cells induces senescence (Campaner et al., 2010; Molenaar et al.,

2009). Chemical inhibition of ATR or CHK1 has been shown to trigger a strong DDR

response in MYC-deregulated cells, resulting in apoptosis (Murga et al., 2011).

Furthermore, transgenic mice generated by crossing ATR homozygous hypomorphic mutants with Eµ-MYC have shown that the ATR mutation is able to suppress Eµ-MYC- mediated lymphomagenesis, emphasizing the critical role of ATR (Murga et al., 2011).

CHK1 inhibition has also been shown to effectively target MYCN-overexpressing cells

(Murga et al., 2011). These results are promising, however, whether inhibition of these kinases that are central to cell viability is practical for cancer therapy is a question.

It has also been suggested that Aurora kinases, involved in mitotic regulation, are

SL candidates with MYC overexpression (den Hollander et al., 2010; Yang et al., 2010).

VX-680, an Aurora B inhibitor, showed selective killing of MYC-overexpressing cells

(Yang et al., 2010). However, since VX-680 can additionally inhibit Aurora A and C, as well as ABL, a well-known oncogene, further studies are necessary (Fei et al., 2010).

ii. High-throughput approaches

With the advancement of RNAi technology and bioinformatics, high-throughput screening has become accessible in mammalian cells (Figure 9) (Simpson et al., 2012).

Several large-scale screens for synthetic lethal interactions with high-profile oncogenes have been published in the last several years (Luo et al., 2009; Mills et al., 2013). Three such screens for MYC (MYC-SL screens) have been reported (Kessler et al., 2012; Liu et al., 2012; Toyoshima et al., 2012). Though the complexity of data across the screens emphasize the need for further studies, the individual studies have proposed interesting and novel SL candidates for MYC as discussed below.

A “One gene per well” approach

Wildtype Cancer

B Pooled approach21 Pooled shRNA Screenings: Experimental Approach 355

Fig. 1. Schematic representation of the steps involved in a negative and positive selection screen. The fi rst step in both selection strategies is transducing the target cell line with the lentiviral pool. The infected population that survives after Figure 9.antibiotic Large-scale selection represents screening the starting in mammalian point. In a positive cells. screen, (A) becauseA “one the gene shRNAs per enable well” the approachtarget cell line to utilizes acquirea single a differentiating chemical phenotype or RNAi or allow construct it to survive in aeach selective well. pressure, Cell only death a few shRNAsor other from readouts the original poolcan will be monitoredbe left and to analyze. compared In a negative between screening, different the shRNAs cell that lines are underrepresented (eg. wildtype after vs treatment cancer) are tothe identify analyzed ones. specificTo vulnerabilities. identify these shRNAs, Red it is arrownecessary denotes to analyze a the chemical variation in orthe specificrepresentation siRNA of each/shRNA shRNA after that the confers selective cancer pressurecell-specific in comparison death, to an while untreated tolerated control. by wildtype cells. (From (Kaelin, 2005)) (B) A pooled approach utilizes an siRNA/shRNA library containing individually barcoded clones. In a positive selection screen, cells expressing siRNA/shRNA that confer a growth advantage under selective pressure are enriched in the surviving population. In a negative selection screen, 2.cells Materials expressing siRNA/shRNA that compromise viability under a selective pressure (such as oncogene expression) are depleted from the population and can be analyzed. (From (Rodriguez- 2.1. shRNA LibraryBarrueco et The al., library2013)) pool consists of 58,493 shRNAs integrated into the backbone of miR-30 ( 6 ) and cloned into the pGIPZ lentiviral vector (Open Biosystems GIPZ Lentiviral Human shRNA Library). It is known that 39,458 of these shRNAs target 18,661 human genes, which accounts for about 75% of the human genome. 1. Carla Grandori group: CSNK1ECombining RNA polymerase II promoters with shRNAs in miR- 30 backbones permits ef ficient suppression even with the integra- Toyoshima and colleaguestion of conducteda single copy. Eachan siRNA shRNA screencassette contains using twoisogenic unique human identi fi ers: the shRNA itself and a random 60-nucleotide barcode that was determined for the identi fi cation of a single shRNA foreskin fibroblasts (HFFs) withamong constitutive the human MYC shRNA overexpression library. Overall, the from shRNA a retroviral library vector offers a convenient, fl exible, and effective tool for studying gene function in human cells ( 9 ) .

(Toyoshima et al., 2012). Utilizing a “one gene per well” method, they examined a panel of ~3,000 “druggable” human genes for SL candidates (Figure 9A). They were able to recover 140 high-confidence hits, of which 98% were confirmed by subsequent validation (Grandori, 2013).

They report that CSNK1E expression correlates with MYCN in neuroblastoma samples, and that pharmacological inhibitors of CSNK1E selective kill MYC- overexpressing cells, inducing tumor regression in mouse xenografts (Toyoshima et al.,

2012). These studies demonstrate that such discoveries of novel candidates previously never implicated in MYC biology are one of the greatest advantages of these large-scale screens. Furthermore, since chemical inhibitors of CSNK1E already have been developed and can selectively target MYC-overexpressing cells, at least in experimental conditions, these findings may be translatable to cancer therapy down the line.

2. Thomas Westbrook group: SAE2

Kessler and colleagues used the HMEC mammary epithelial cell line expressing an inducible MYC-ER fusion protein (Kessler et al., 2012). They utilized a pooled human retroviral shRNA library, allowing for simultaneous introduction of 74,905 shRNA constructs into a large population of cells (Figure 9B). Also known as a “dropout screen,” they focused on the shRNAs that were selectively lost from MYC-overexpressing cells compared to control. In contrast to other methods, this process allows for the screening of a large number of genes at once.

They reported 403 high-confidence hits, and focused their studies on SAE2, a component of the human SUMOylation pathway. Intriguingly, SAE2 knockdown in the presence of MYC activation caused mitotic catastrophe, leading to cell death. SAE2 knockdown altered the landscape of MYC-induced gene expression, including genes

involved in mitotic spindle function, which likely led to the phenotype (Kessler et al.,

2012).

3. Daniel Murphy group: ARK5

Liu and colleagues examined ~800 genes as potential MYC-SL candidates (Liu et al., 2012). They employed the “one gene per well” method using siRNA against the human kinome in U2OS cells expressing the inducible MYC-ER transgene (Figure 9A).

For readout, this group utilized PARP cleavage, which signifies apoptosis. Notably, this apoptosis readout could yield different results compared to a dropout-type screen like the two aforementioned.

Identified were two hits, ARK5 and AMPK, which are known regulators of cellular metabolism (Liu et al., 2012). ARK5 (or NUAK1) depletion in MYC-overexpressing conditions resulted in loss of viability and ATP depletion from cells. Since ARK5 and

AMPK are critical in metabolic processes including protein synthesis and mitochondrial respiratory capacity, metabolic stress may be one of MYC’s critical Achilles’ heels (Liu et al., 2012). Especially in the light of increasing evidence linking MYC to metabolic stress and the Warburg effect, it is possible that modulation of metabolic pathways may selectively inhibit the growth of MYC-overexpressing cells (Dang, 2010).

4. Running Themes

Due to the varying methods and tools employed, there are very few MYC-SL candidate overlaps across the three datasets. Of course, there is a possibility that differing modes and levels of MYC deregulation, as well as the cell type in which MYC deregulation occurs, dictates the specific genetic vulnerabilities that manifest, especially if MYC can function as a general amplifer of transcription (Lin et al., 2012; Nie et al.,

2012). However, equally likely is that common underlying MYC-SL networks and pathways, instead of genes, may be uncovered by analyzing the datasets against each other.

Figure 10. Three functional hubs of MYC synthetic lethal genes. Proteomics analysis of three published MYC-SL screens reveal three functional MYC-SL hubs including the MYC-MAX network (I), transcription initiation and elongation (II), and ubiquitination, SUMOylation, checkpoints, and DNA repair (III). Light blue circles represent Toyoshima hits, darker blue represent Kessler hits, and pink represents a set of genes known to functionally interact with MYC (MYC core). Yellow, green, and orange represent gene hits that appear in more than two gene sets. Lines represent genetic interactions. (From (Cermelli et al., 2014))

To this end, Grandori’s group have analyzed the three datasets from the above screens utilizing functional proteomics, and have revealed three functional “hubs” that arise as MYC-SL candidate pathways (Figure 10) (Cermelli et al., 2014). These include:

1) transcriptional initiation and elongation pathways, 2) regulators of the MYC-MAX network, and 3) ubiquitin and sumoylation pathways (Figure 10). Analyses of more diverse datasets should provide further insight into how universal these MYC-SL hubs may be.

CHAPTER 2

Genome-wide screen for synthetic lethal genes with MYC deregulation

1. MCF10A-MYCER as a model system

The consequences of MYC deregulation or overexpression can be studied in multiple experimental settings. Many engineered cell lines or cell lines derived from tumors are available with a wide spectrum of MYC expression and activity. Because cumulative research suggests that MYC functions can be specific and tailored to cell types, it is important to examine MYC effects and regulatory networks in various cellular contexts, especially since MYC can be a more prominent cancer driver in certain cells and tissues compared to others.

Deregulated MYC expression is one of the characteristics of basal-like breast cancers (BBCs) (Alles et al., 2009; Horiuchi et al., 2012; Xu et al., 2010). Furthermore, overexpression of components of the pre-replicative complex (pre-RC) is often associated with the basal phenotype (Alles et al., 2009). Thus, we sought to screen for synthetic lethal candidates with MYC overexpression in a breast epithelial cell line with a basal-like phenotype (Debnath et al., 2003). To this end, we engineered the MCF10A-

MYCER cell line utilizing MCF10A cells and the MYCER (or MYC-ER) transgene.

a. MCF10A

MCF10A (or MCF-10A) is a spontaneously immortalized, non-transformed, human mammary epithelial cell line originally derived from a 36-year-old patient with fibrocystic breast tissue (Soule et al., 1990). MCF10A cells display characteristics of normal breast epithelium including the lack of tumorigenicity when grafted into nude mice, lack of anchorage-independent growth, and dependence on growth factors and hormones for proliferation (Debnath et al., 2003; Soule et al., 1990). MCF10A cells have a relatively stable and near-diploid karyotype. Some have reported MYC gain or

amplification in this cell line, which is speculated to be a consequence of immortalization, however, cells grow in a relatively normal manner (Kadota et al., 2010; Worsham et al.,

2006). MCF10A cells harbor a deletion in the genomic locus including p16, p14ARF, and

CDKN2A, but p53 is wildtype, leaving p53-dependent apoptosis pathways intact (Kadota et al., 2010; Merlo et al., 1995; Worsham et al., 2006).

Importantly, MCF10A cells are negative for estrogen receptor (ER), which contributes to their basal-like phenotype along with the presence of high molecular weight cytokeratins (CK1, 5, 10, 14) and expression of ΔNp63α (DiRenzo et al., 2002).

Because MCF10A cells can form acini in vitro when grown in three-dimentional (3D) cultures, it is often utilized for 3D tumorigenesis assays (Debnath et al., 2003). Such aspects of MCF10A cells make them an appropriate tool to model the genetic networks surrounding basal-like mammary cells.

b. MYCER

The MYCER (or MYC-ER) allele is one of the most widely used methods for allowing inducible MYC expression in cells. The initial MYCER construct was established by fusing the hormone-binding domain of the human estrogen receptor gene to the 3’ end of the human MYC gene (Eilers et al., 1989). The chimeric gene encoded for a protein that bound estrogen with high affinity, in a reversible manner, and was able to be activated by estrogen addition to the cell culture media. Following these initial observations, the MYCER transgene was subsequently modified to its current form,

MYC-ERTAM, in which the human ER domain was replaced with the murine ER domain, and mutations were introduced to render the ER domain to respond exclusively to the synthetic ligand 4-hydroxytamoxifen (4OHT or 4-OHT) (Littlewood et al., 1995). These modifications have allowed bypass of initial problems including the off-target effects of

estrogen towards cell growth, and have allowed the MYCER system to become a powerful tool to study MYC function in vivo (Littlewood et al., 1995).

Other methods of MYC overexpression are also commonly utilized, including 1) transduction of the native MYC transgene, 2) expression of the MYC transgene with protein-stabilizing mutations, and 3) use of cancer cell lines that already overexpress

MYC. Nevertheless, we selected the MYCER system for our genome-wide screen for several reasons. First, the MYCER system allows for readily inducible MYC overexpression using 4OHT. While long-term and constitutive MYC overexpression may alter the genetic programs of cells in complex ways over time, the MYCER system allows for a controlled period of MYC overexpression, minimizing gene expression differences between control and MYC-overexpressing cells. Secondly, a substantial amount of literature suggests that the MYCER protein behaves as normal MYC, at least when assayed for basic MYC functions, suggesting that MYCER can be a physiological model for MYC deregulation or overexpression (Dang, 1999).

2. Genome-wide shRNA screen using MCF10A-MYCER

Using the MCF10A-MYCER system, we conducted a genome-wide shRNA screen utilizing the GIPZ Lentiviral Human shRNA-mir Library (Thermo Scientific Open

Biosystems, Waltham, MA, USA) as described in Rodriguez-Barrueco et al (Rodriguez-

Barrueco et al., 2013). The screen was designed as a negative (or dropout) screen, whereby shRNAs in the library that confer lethality or decreased fitness to cells are depleted from the total surviving population of cells over the course of four weeks following infection. We applied this protocol to compare the shRNAs which expression confers cell lethality between a population of cells treated with vehicle (MYC OFF) or

4OHT (MYC ON). The shRNAs that were selectively depleted from the MYC ON population but not the MYC OFF population were scored as synthetic lethal candidates.

In order to achieve statistically relevant representation of each of the 58,493 shRNA constructs included in the library, 210 million MCF10A-MYCER cells were first infected with the library at an infection efficiency of 30%, optimized for a target of 70 million infected cells at an multiplicity of infection (MOI) of approximately 1. This corresponds to an average of 1000 cells infected with each of the 58,493 shRNA clones, which is sufficient representation based on previous works (Silva et al., 2008). After infection and antibiotic selection using 2 µg/ml puromycin, the infected MCF10A-MYCER cells were passaged twice to allow for 1) population expansion to prepare ten populations of 70 million cells, and 2) shRNA targeting essential genes to be depleted from the initial population in order to reduce background. Following these two passages, cells were divided into ten independent populations of 70 million cells, of which five were treated with vehicle (MYC OFF), and the other five were treated with 200 nM 4OHT

(MYC ON) for the next four weeks. These independent populations served as five technical replicates, since they were later statistically analyzed to generate p-values for reproducibility between populations. However, since each population was independently passaged with vehicle or 4OHT for the next four weeks while constantly maintaining at least 70 million cells per population, they also partially served as biological replicates for our system.

3. Screen analysis

After four weeks, surviving cells from each population were harvested, and genomic DNA was isolated as described previously (Rodriguez-Barrueco et al., 2013).

We used two independent techniques to compare the shRNA representation between

MYC ON and MYC OFF cells. First, microarray hybridization was employed, by taking advantage of the unique shRNA sequences and 60-nucleotide barcodes associated with

each hairpin. Second, high-throughput (HTP) sequencing was employed by PCR-ligating adaptor sequences with marker nucleotides.

a. Microarray hybridization

Genomic DNA was subjected to large scale PCR using primers flanking all shRNA constructs and associated barcodes recovered from the surviving cells. This generated a heterogeneous pooled product of 350 bps on average, which was gel- extracted and purified, labeled with Cy3, then hybridized to customized microarrays harboring complementary probes to the shRNA sequences and barcodes (Agilent

Technologies, Santa Clara, CA, USA). Importantly, on each array, these experimental

PCR products were hybridized against Cy5-labeled reference PCR products generated from the original shRNA library in order to assess the depletion or enrichment of the shRNA compared to the starting material. Data analysis was then performed as previously described (Yu et al., 2013).

2317 shRNA clones were recovered that were depleted at least two-fold in the

MYC ON population compared to MYC OFF. Within these 2317 shRNAs, the fold depletion in MYC ON compared to MYC OFF ranged from 2-fold to 6.63-fold (-

2.73

PubMed. We decided to primarily focus on these annotated genes. Upon functional clustering of these SL candidate genes using the KEGG pathways in the DAVID

Bioinformatics Database, we found enrichment of genes involved in ubiquitin-mediated proteolysis, PPAR (peroxisome proliferator-activated receptor) signaling pathway, fatty acid biosynthesis, fructose and mannose metabolism, and homologous recombination

(Chapter 3, Figure 2). The full list of synthetic lethal candidates (p<0.1) recovered from microarray hybridization is presented in Table 1 in this chapter (Table 1), while the shorter list of 78 high-confidence synthetic lethal targets (p<0.05) is included in the next chapter (Chapter 3, Supplementary Table 1).

We combined this statistical analysis with a candidate approach to select hits for further validation. Since we were initially interested in finding SL candidates that may be involved in maintenance of genome stability during MYC deregulation, we first focused on genes with known roles in genome maintenance. Homologous recombination was one of the identified candidate SL pathways, and TOP3A and TOP3B both ranked within the top 78 SL candidates (Log2 FC<-1, p<0.05). Thus, we first sought to validate TOP3A

(Log2 FC=-1.17, p<0.05), TOP3B (Log2 FC=-1.43, p<0.05), and TOP2B (Log2 FC=-

1.23, p<0.1) as SL candidates with MYC deregulation (Rank 71, 105, 117 in Table 1).

However, we were not able to validate a SL interaction through: 1) competition survival assays; 2) the use of chemical inhibitors of topoisomerases; or 3) acute siRNA knockdown (data not shown). We have also attempted to validate H2AFV (Log2 FC<-1, p<0.05, Rank 22 in Table 1), as well as FASN and PARP3 (Log2 FC<-1, p<0.1, Rank 6,

120 in Table 1), as SL candidates using competitive survival assays, but have failed to see results suggesting significant synthetic sickness or lethality (data not shown).

Notably, two independent shRNAs targeting PRMT8 scored as SL candidates within an acceptable p-value (p<0.1, Rank 15 and 136 in Table 1). However, we could not detect a significant synthetic lethality in validation assays (data not shown).

We were able to successfully validate FBXW7 and UBE2I, which are discussed in Chapter 3 (Log2 FC<-1, p<0.05, Rank 126 and 139 in Table 1). This brings our raw

validation success rate to 2/6, at least within the high-confidence SL candidate list

(p<0.05). Though this number seems low, it is also important to note that this screen has an approximately 30% false discovery rate (FDR) with this method of analysis

(0.25

b. High-throughput sequencing

The genomic DNA isolated from the surviving cells was also subjected to PCR using primers with adaptor sequences to the Illumina sequencing platform (Illumina, Inc.,

San Diego, CA). By engineering 6-nucleotide codes within these primers to differentiate each of the ten populations of cells (five replicates each of MYC OFF and MYC ON), all of the PCR products could be sequenced in one lane as a pool. This method allows for sequencing of all PCR products generated from shRNAs present in the surviving cell populations, instead of assessing relative enrichment/depletion as in the microarray hybridization. PCR products were purified from each of the 10 surviving populations (5 replicates each of MYC ON and MYC OFF) and 500ng of each were pooled into a final mixture of 5mg DNA in 25µl of water. This sample was sent to the Columbia Next

Generation Sequencing Facility (Columbia University, New York, NY, USA) and was processed (HiSeq 2000, 200m raw 100bp Single Ends reads per lane, multiplex=1).

Resulting data was analyzed by Dr. Jiyang Yu as previously described (Yu et al., 2013),

to generate a comprehensive list of shRNAs with their respective Log2 FC, p-values, and FDRs based on the high throughput sequencing results.

30,685 total shRNA IDs were identified that were depleted at least two-fold in the

MYC ON cells compared to MYC OFF (-45.81

2425, 1319, and 316 shRNAs, respectively. The list of 316 high confidence synthetic lethal candidates (p<0.01) from this analysis method is included in this chapter (Table 2).

Upon functional clustering, we found that the 316 high-confidence SL candidates

(p<0.01) were enriched in genes involved in ubiquitin-mediated proteolysis and DNA replication. Table 3 shows additional biological pathways that are represented in gene sets with higher p-value cutoffs (Table 3). Interestingly, we recovered two hits within the

316 high-confidence SL candidates (p<0.01), GPATCH1 and NOL6, which appear twice, which means two independent shRNA clones targeting these genes scored as high- confidence SL candidates (Table 2, Rank 34, 102, 94, and 230). These candidates are expected to be higher confidence SL candidates, and should be validated in future studies.

We attempted to validate several genes of interest from the high-confidence SL candidates through a candidate approach. CP, APLF, and MSH6 were chosen for validation from the high-confidence list (p<0.01) due to their low p-values and high rank in terms of FC (Table 2, Rank 3, 221, and 52, respectively). WDR89, ERCC8, and

UVSSA (or KIAA1530) were also chosen for validation due to more than one shRNA clone passing the FC and p-value cutoff for each of these genes (Table 4). Additionally,

HERC2 was also selected since it has a projected role in ubiquitin-mediated proteolysis

(Log2FC=-4.78, p<0.05, not shown in a list). Unfortunately, we failed to demonstrate a

significant synthetic sick or lethal phenotype through competitive survival assays for CP,

APLF, MSH6, WDR89, or HERC2 in our validation studies (data not shown). However, we were successful in validating two of the SL candidates, UVSSA and ERCC8 (see

Chapter 4). Importantly, as with the microarray methods, the false discovery rate (FDR) of the sequencing events in this analysis can be approximately 50-60% (0.5

Again, though the numbers appear low, this could be improved with the introduction of secondary and tertiary layers of analysis.

c. Comparison of analysis methods

Microarrays have been a method of choice for analysis of large-scale screens since it is simple and rapid, as well as being cost-effective. However, it has several limitations and data output can be obscured through several steps. One potential problem is the individual variability in affinity between probes on the arrays. Probes with poor binding affinity can lead to misreading, even if the relative amounts of Cy3- and

Cy5-labeled sample stay constant. In order words, if a poor probe for gene X in a given microarray spot (or feature) only captures a total of 100 cDNAs, while another spot containing a better probe to a difference sequence in gene X cDNA can capture 1000 cDNAs, the first spot may contribute a weaker signal, obfuscating results when in fact both spots could be binding the same proportion of Cy3- and Cy5-labeled species.

Secondly, hybridization conditions can differ between individual array plates, since each replicate is analyzed on a different plate. This may increase the technical variability between replicate samples, obscuring the final results and corresponding statistics. For these reasons, while microarray hybridization is efficient and effective, data analysis must be considered carefully.

On the other hand, HTP sequencing utilizes newer technology, and is able to yield more reads by several orders of magnitude compared to microarrays. Notably, since samples from all replicates can be read in the same lane, technical variability is minimized compared to the use of microarrays. Therefore, HTP sequencing can be considered a more detailed method to analyze these types of screens. However, HTP sequencing has limitations, as certain sequences may be less well read than others on the sequencing platform, creating a bias that would be difficult to identify from the output data. Additionally, the nature of this method yields very large datasets that can complicate subsequent analyses if not handled carefully. Thus, while HTP sequencing allows for in-depth analysis, a robust method must be employed to take full advantage of the data.

Interestingly, the two analysis methods we employed gave rise to different set of data when considered gene-by-gene. When the top 78 SL candidates from the arrays

(Log2 FC<-1, p<0.05) are compared to the 316 SL candidates from the HTP sequencing

(Log2 FC<-1, p<0.01), only three genes are common to both lists: APOBEC3A, LRCH3, and PAQR3 (Rank 80, 68, and 154 in Table 1, and Rank 254, 184, and 154 in Table 2).

Though we have not attempted to validate these results as of yet, these genes could be considered high-confidence SL candidates since they were identified by both methods.

Additionally, the number of high-confidence hits recovered is much higher by HTP sequencing compared to microarray hybridization (18.1-fold higher for p<0.05). This can be explained, at least in part, by the nature of the methods as described above.

Nevertheless, functional clustering from both datasets points to similar pathways present in both datasets, including the ubiquitin-mediated proteolysis pathway (Table 3 and

Chapter 3, Figure 2). This gives us confidence that both methods of analysis are valuable, likely in different ways, and that further analysis of these datasets could uncover the existence of additional synthetic lethal candidates.

Genome-wide screens like our present work are a relatively new endeavor in the field and are constantly being optimized in an ongoing manner. These screens generate extremely large datasets that require bioinformatics methods specifically designed for effective and efficient analysis. Different criteria can be applied to score hits as high confidence, including magnitude of fold change, number of degenerate shRNAs that score for each gene, statistical confidence levels such as p-values, and pathway representation, just to name a few that have already been mentioned. We chose mainly a candidate approach to validate our SL candidates, because of our prior interest in genes involved in genome stability and maintenance. In future studies, however, we will validate other candidates we have identified in order to expand our understanding of a wider range of MYC-SL genes.

Table 1

Table 1. List of 169 synthetic lethal candidates from microarray hybridization analysis of the MCF10A-MYCER screen (p<0.1). Results were filtered according to fold change (Log2 FC<-1) of signal abundance between MYC ON and MYC OFF, and individually associated p-values deduced from the reproducibility between five replicates (p<0.1). Only shRNA corresponding to annotated target genes are shown. Candidates are ranked in order of most depleted in the MYC ON population compared to MYC OFF (Log2 FC value).

Rank Name p-value Log2 FC Acc. Number shRNAi ID 1 ARL5B 0.0500 -2.7089 NM_178815 v2HS_118631 2 C2orf44 0.0100 -2.5672 NM_025203 v2HS_137381 3 TTBK1 0.0300 -2.4316 AB058758 v2HS_238991 4 PSG6 0.0400 -2.3989 NM_002782 v2HS_170657 5 NAT9 0.0500 -2.3896 NM_015654 v2HS_263964 6 PARP3 0.0800 -2.3454 NM_005485 v2HS_68320 7 CCT6B 0.0800 -2.3438 NM_006584 v2HS_197906 8 ZNF212 0.0800 -2.2642 NM_012256 v2HS_19557 9 ANP32C 0.0900 -2.2312 NM_012403 v2HS_44365 10 TRPM6 0.0300 -2.1357 NM_017662 v2HS_155076 11 BZW2 0.0600 -2.1075 NM_014038 v2HS_246855 12 MFN2 0.0100 -2.0827 NM_014874 v2HS_95806 13 NEURL2 0.0700 -2.0307 NM_080749 v2HS_64846 14 ANKRD12 0.1000 -2.0028 NM_015208 v2HS_82023 15 PRMT8 0.1000 -2.0026 NM_019854 v2HS_34352 16 PXK 0.1000 -2.0001 NM_017771 v2HS_202094 17 C20orf30 0.0800 -1.9202 NM_014145 v2HS_53618 18 LARP1 0.1000 -1.8835 NM_033551 v2HS_178542 19 C18orf54 0.0100 -1.8569 NM_173529 v2HS_160761 20 DONSON 0.0300 -1.8499 NM_017613 v2HS_154816 21 ZNF614 0.0400 -1.8499 NM_025040 v2HS_235983 22 H2AFV 0.0100 -1.8450 NM_138635 v2HS_200741 23 NUPL1 0.0500 -1.8381 NM_014778 v2HS_233311 24 LRP12 0.0900 -1.7927 NM_013437 v2HS_58514 25 IFT81 0.0800 -1.7813 NM_014055 v2HS_59598 26 LSM10 0.0900 -1.7548 NM_032881 v2HS_178025 27 OR51B2 0.0800 -1.7478 NM_033180 v2HS_160038 28 CLCA2 0.1000 -1.7416 NM_006536 v2HS_197853 29 GHRHR 0.0600 -1.7341 NM_000823 v2HS_130791 30 ZNF691 0.0100 -1.7257 NM_015911 v2HS_236174 31 PSMD2 0.0500 -1.7213 NM_002808 v2HS_219275 32 CDC14B 0.0400 -1.7209 NM_033331 v2HS_203260 33 PQLC2 0.0600 -1.7160 NM_017765 v2HS_173690 34 RXRG 0.0600 -1.6959 NM_006917 v2HS_94832 35 TRIB3 0.0800 -1.6816 NM_021158 v2HS_66315 36 UGT1A1 0.0300 -1.6796 NM_000463 v2HS_93183

37 ZNF43 0.0900 -1.6701 NM_003423 v2HS_191543 38 ZNF781 0.1000 -1.6565 NM_152605 v2HS_44704 39 PHF5A 0.0800 -1.6172 NM_032758 v2HS_177402 40 CDK2AP2 0.1000 -1.6122 NM_005851 v2HS_216445 41 SOX15 0.0500 -1.6110 NM_006942 v2HS_229072 42 SERPINB12 0.0900 -1.6101 NM_080474 v2HS_43826 43 NUPL2 0.0500 -1.6086 NM_007342 v2HS_86275 44 ANKRD27 0.0500 -1.6068 NM_032139 v2HS_117932 45 EPHA7 0.0100 -1.5984 NM_004440 v2HS_17738 46 NPC1 0.0900 -1.5797 NM_000271 v2HS_75887 47 SULT1C4 0.1000 -1.5718 NM_006588 v2HS_212938 48 FA2H 0.1000 -1.5582 NM_024306 v2HS_135795 49 SC5DL 0.1000 -1.5376 NM_006918 v2HS_94838 50 C14orf101 0.0700 -1.5214 NM_017799 v2HS_173856 51 PRKCG 0.0800 -1.5196 NM_002739 v2HS_263025 52 CCDC53 0.0700 -1.5192 NM_016053 v2HS_97322 53 OSBPL10 0.1000 -1.5174 NM_017784 v2HS_219556 54 ST8SIA4 0.0500 -1.5098 NM_005668 v2HS_238967 55 SLC44A2 0.0500 -1.4952 NM_020428 v2HS_72471 56 SH3BP4 0.0300 -1.4934 NM_014521 v2HS_86832 57 KIAA0509 0.0300 -1.4922 AB007978 v2HS_259524 58 DMKN 0.0500 -1.4804 NM_033317 v2HS_160312 59 C6orf165 0.0600 -1.4761 NM_178823 v2HS_118670 60 STK17A 0.0100 -1.4726 NM_004760 v2HS_36021 61 TLR10 0.0600 -1.4720 NM_030956 v2HS_137828 62 PHF21A 0.0100 -1.4564 NM_016621 v2HS_135309 63 EYA2 0.0200 -1.4531 NM_005244 v2HS_43093 64 PGBD2 0.1000 -1.4419 NM_170725 v2HS_100037 65 PRKRIR 0.1000 -1.4416 NM_004705 v2HS_37080 66 C6orf184 0.0100 -1.4413 XM_168053 v2HS_121031 67 ZFYVE9 0.0100 -1.4407 NM_004799 v2HS_35331 68 LRCH3 0.0400 -1.4395 NM_032773 v2HS_222832 69 GALNT11 0.0700 -1.4352 NM_022087 v2HS_253801 70 NR2E1 0.0100 -1.4308 NM_003269 v2HS_261941 71 TOP3B 0.0100 -1.4280 NM_003935 v2HS_47174 72 WDSOF1 0.0300 -1.4122 NM_015420 v2HS_229697 73 PDS5B 0.0700 -1.4061 NM_015032 v2HS_201063 74 SHFM1 0.0900 -1.4013 NM_006304 v2HS_203233 75 SCYL1 0.0900 -1.3948 NM_020680 v2HS_247649 76 CLDN5 0.0500 -1.3596 NM_003277 v2HS_171412 77 CMTM2 0.0400 -1.3528 NM_144673 v2HS_17530 78 IRAK1 0.0300 -1.3447 NM_001569 v2HS_132369 79 PPP1R15B 0.0900 -1.3363 NM_032833 v2HS_223042 80 APOBEC3A 0.0300 -1.3348 NM_145699 v2HS_286556 81 FGD1 0.0800 -1.3306 NM_004463 v2HS_17382 82 MTERFD3 0.1000 -1.3272 NM_025198 v2HS_236692

83 ZNF217 0.0800 -1.3268 NM_006526 v2HS_196547 84 ERGIC1 0.0300 -1.3258 NM_020462 v2HS_71567 85 PARP15 0.0100 -1.3206 NM_152615 v2HS_44475 86 DMBT1 0.1000 -1.3137 NM_007329 v2HS_190086 87 CTTN 0.1000 -1.3105 NM_005231 v2HS_43340 88 RAE1 0.0500 -1.2995 NM_003610 v2HS_27966 89 LEPREL1 0.0500 -1.2897 NM_018192 v2HS_156224 90 ZNF132 0.0300 -1.2802 NM_003433 v2HS_172208 91 TBC1D19 0.1000 -1.2712 NM_018317 v2HS_174988 92 SLC7A1 0.0500 -1.2710 NM_003045 v2HS_153014 93 TCHP 0.1000 -1.2704 NM_032300 v2HS_138395 94 LAMA2 0.0200 -1.2692 NM_000426 v2HS_92966 95 CAMKV 0.0800 -1.2689 NM_024046 v2HS_98766 96 LOC344065 0.0400 -1.2685 XM_292895 v2HS_143532 97 ATP1B1P1 0.0500 -1.2641 NG_001081 v2HS_169198 98 C1orf182 0.0500 -1.2536 NM_144627 v2HS_18680 99 GCLM 0.0800 -1.2479 NM_002061 v2HS_114117 100 KRIT1 0.0400 -1.2463 NM_004912 v2HS_62790 101 ASAH3 0.0400 -1.2436 NM_133492 v2HS_99964 102 NOG 0.0900 -1.2419 NM_005450 v2HS_69206 103 RGS2 0.0900 -1.2349 NM_002923 v2HS_32625 104 HTR1D 0.0900 -1.2313 NM_000864 v2HS_131007 105 TOP2B 0.0600 -1.2300 NM_001068 v2HS_94088 106 TBX3 0.1000 -1.2293 NM_016569 v2HS_135045 107 SCD5 0.0800 -1.2230 NM_024906 v2HS_176767 108 OLFR89 0.0300 -1.2192 AJ132194 v2HS_18007 109 CYP27A1 0.0900 -1.2165 NM_000784 v2HS_227129 110 C5orf22 0.0900 -1.2072 NM_018356 v2HS_219318 111 ENPEP 0.0600 -1.1989 NM_001977 v2HS_151422 112 DSCR4 0.0800 -1.1931 NM_005867 v2HS_198761 113 CLTB 0.0800 -1.1828 NM_001834 v2HS_150651 114 CDCA7L 0.0900 -1.1821 NM_018719 v2HS_26554 115 C1D 0.0900 -1.1781 NM_006333 v2HS_252502 116 CCDC25 0.0300 -1.1758 NM_018246 v2HS_156496 117 TOP3A 0.0100 -1.1740 NM_004618 v2HS_42272 118 THUMPD3 0.0600 -1.1679 NM_015453 v2HS_96582 119 P2RY6 0.0800 -1.1663 NM_004154 v2HS_173243 120 FASN 0.0800 -1.1630 NM_004104 v2HS_173006 121 C11orf72 0.0700 -1.1623 NM_173578 v2HS_285512 122 MATK 0.0200 -1.1567 NM_002378 v2HS_203423 123 ST14 0.0100 -1.1516 NM_021978 v2HS_228778 124 KIAA0574 0.0300 -1.1513 AB011146 v2HS_130193 125 PERP 0.0200 -1.1513 NM_022121 v2HS_216968 126 FBXW7 0.0200 -1.1469 AY033553 v2HS_89326 127 LENG1 0.0800 -1.1410 NM_024316 v2HS_135854 128 APOA2 0.0100 -1.1369 NM_001643 v2HS_132594

129 C5 0.0400 -1.1342 NM_001735 v2HS_150150 130 SLC36A4 0.0500 -1.1260 NM_152313 v2HS_25433 131 PPP3CC 0.0300 -1.1253 NM_005605 v2HS_57300 132 FCER1A 0.0600 -1.1236 NM_002001 v2HS_265910 133 TRIM26 0.0500 -1.1229 NM_003449 v2HS_172289 134 SPCS2 0.0700 -1.1197 NM_014752 v2HS_95186 135 ELL 0.0500 -1.1181 NM_006532 v2HS_198413 136 PRMT8 0.0100 -1.1163 NM_019854 v2HS_34353 137 PRSS21 0.0500 -1.1146 NM_006799 v2HS_83969 138 XK 0.1000 -1.1030 NM_021083 v2HS_72458 139 UBE2I 0.0200 -1.0971 NM_003345 v2HS_171781 140 QRSL1 0.0400 -1.0967 XM_301163 v2HS_49076 141 PGBD3 0.1000 -1.0956 NM_170753 v2HS_100062 142 C11orf48 0.0400 -1.0951 NM_024099 v2HS_116419 143 KIF3B 0.0700 -1.0895 NM_004798 v2HS_35320 144 C8orf45 0.0800 -1.0855 NM_173518 v2HS_160704 145 OGG1 0.1000 -1.0801 NM_002542 v2HS_152449 146 FOSB 0.0800 -1.0746 NM_006732 v2HS_85030 147 TSKU 0.0700 -1.0743 NM_015516 v2HS_96796 148 GABRA6 0.0800 -1.0712 NM_000811 v2HS_130723 149 EPYC 0.0400 -1.0697 NM_004950 v2HS_61963 150 TNFAIP2 0.0100 -1.0598 NM_006291 v2HS_56492 151 CPOX 0.0600 -1.0588 NM_000097 v2HS_259707 152 PHF5A 0.0900 -1.0586 NM_032758 v2HS_219178 153 KLK2 0.0700 -1.0572 NM_005551 v2HS_63117 154 PAQR3 0.0300 -1.0499 NM_177453 v2HS_211154 155 DGKD 0.0600 -1.0495 NM_003648 v2HS_27433 156 PREB 0.0500 -1.0457 NM_013388 v2HS_64102 157 DUSP8 0.0900 -1.0445 NM_004420 v2HS_18240 158 PXMP2 0.0800 -1.0431 NM_018663 v2HS_30524 159 PPTC7 0.0800 -1.0378 NM_139283 v2HS_56653 160 TBRG1 0.1000 -1.0307 NM_032811 v2HS_177650 161 NKIRAS2 0.0600 -1.0270 NM_017595 v2HS_154743 162 CEND1 0.0300 -1.0194 NM_016564 v2HS_135015 163 SLC39A7 0.0700 -1.0116 NM_006979 v2HS_95053 164 BBOX1 0.0300 -1.0107 NM_003986 v2HS_246341 165 CDKL5 0.0500 -1.0097 NM_003159 v2HS_262311 166 CMTM7 0.0100 -1.0049 NM_138410 v2HS_70332 167 CYP2J2 0.0600 -1.0042 NM_000775 v2HS_112459 168 MYO19 0.0600 -1.0034 NM_025109 v2HS_137025 169 GCN1L1 0.0400 -1.0020 XM_045792 v2HS_68924

Table 2

Table 2. List of 316 synthetic lethal candidates from HTP sequencing analysis of the MCF10A-MYCER screen (p<0.01). Results were filtered according to fold change (Log2 FC<-1) of sequencing read abundance between MYC ON and MYC OFF, and individually associated p-values deduced from the reproducibility between five replicates (p<0.01). Only shRNA corresponding to annotated target genes are shown. Candidates are ranked in order of most depleted in the MYC ON population compared to MYC OFF (Log2 FC value).

Rank Gene Symbol p-value Log2 FC shRNA ID 1 GOLIM4 7.48E-03 -18.52 V2LHS_91423 2 NCRNA00158 3.80E-04 -17.48 V2LHS_47715 3 CP 5.60E-04 -13.12 V2LHS_88470 4 STON1 3.03E-04 -11.73 V2LHS_284988 5 MPV17L 4.72E-04 -11.28 V2LHS_179353 6 RIN2 4.96E-03 -10.93 V2LHS_50849 7 STX7 2.42E-03 -9.82 V2LHS_48525 8 C7orf13 4.72E-03 -8.82 V2LHS_159570 9 STAU1 3.96E-04 -8.5 V2LHS_42690 10 AP3M1 9.63E-05 -8.38 V2LHS_28498 11 NXNL2 5.31E-03 -7.85 V2LHS_18715 12 MOGAT3 3.32E-03 -7.71 V2LHS_209424 13 TPM2 2.77E-04 -7.44 V2LHS_223002 14 FLJ36157 9.71E-03 -7.04 V2LHS_119804 15 LIMA1 7.88E-03 -6.99 V2LHS_115557 16 UBAC2 2.21E-03 -6.96 V2LHS_111302 17 FKBP1A 8.96E-03 -6.89 V2LHS_191885 18 DNMT3B 2.48E-04 -6.81 V2LHS_11913 19 KRTAP4-3 7.21E-03 -6.76 V2LHS_53268 20 TBX22 3.10E-03 -6.64 V2LHS_154236 21 PPIL6 4.69E-03 -6.49 V2LHS_179066 22 FAM182A 3.18E-03 -6.48 V2LHS_269002 23 UBAP1 4.47E-03 -6.42 V2LHS_134830 24 LOC286154 4.50E-04 -6.41 V2LHS_184361 25 POU2F1 1.03E-03 -6.29 V2LHS_170237 26 DCUN1D3 4.21E-03 -6.22 V2LHS_160480 27 MPDU1 3.06E-03 -6.02 V2LHS_67726 28 FGD5 7.89E-03 -5.95 V2LHS_50709 29 HIST1H1A 6.89E-04 -5.82 V2LHS_37904 30 PDXDC1 3.60E-03 -5.8 V2LHS_237009 31 P2RX6 7.03E-03 -5.55 V2LHS_69281 32 PHLDA2 1.70E-03 -5.51 V2LHS_171588 33 HERC3 4.42E-03 -5.46 V2LHS_12972 34 GPATCH1 1.88E-03 -5.44 V2LHS_155291 35 LAPTM4A 6.68E-03 -5.42 V2LHS_73845

36 RAB33A 9.23E-04 -5.34 V2LHS_35338 37 SLC25A46 1.99E-03 -5.26 V2LHS_202639 38 HRH1 1.48E-03 -5.25 V2LHS_130994 39 FAR1 2.49E-03 -5.11 V2LHS_138069 40 PRAC 2.38E-03 -5.08 V2LHS_138880 41 MGC15885 7.45E-03 -5.04 V2LHS_185559 42 NR1I2 6.19E-04 -5.03 V2LHS_3719 43 COL23A1 1.29E-03 -5.01 V2LHS_160442 44 CUL4B 5.02E-03 -5 V2LHS_13208 45 NUDT9 9.37E-03 -4.99 V2LHS_98772 46 ADIPOQ 7.98E-04 -4.91 V2LHS_35308 47 LOC151878 3.30E-03 -4.88 V2LHS_119807 48 CEACAM5 5.63E-03 -4.87 V2LHS_14736 49 HIST1H3F 2.91E-03 -4.86 V2LHS_189035 50 KIAA0564 4.16E-03 -4.86 V2LHS_63167 51 APOBEC4 6.87E-03 -4.85 V2LHS_286923 52 MSH6 5.25E-03 -4.85 V2LHS_258239 53 AMIGO2 2.73E-03 -4.84 V2LHS_47942 54 COL1A1 1.14E-03 -4.72 V2LHS_261190 55 KLHDC5 6.77E-03 -4.69 V2LHS_13667 56 VANGL1 1.98E-03 -4.66 V2LHS_206726 57 CKAP5 2.79E-03 -4.65 V2LHS_95211 58 TTLL4 3.38E-03 -4.65 V2LHS_79760 59 BHLHB3 5.83E-03 -4.62 V2LHS_116721 60 GEN1 4.76E-03 -4.58 V2LHS_121634 61 PMCH 4.88E-03 -4.57 V2LHS_170150 62 C1orf127 5.17E-03 -4.56 V2LHS_160635 63 C5orf23 9.73E-03 -4.56 V2LHS_136341 64 CDS1 6.98E-03 -4.55 V2LHS_112932 65 KCNJ2 4.74E-03 -4.53 V2LHS_131152 66 SLC25A20 3.70E-03 -4.47 V2LHS_92736 67 PROM2 7.35E-03 -4.45 V2LHS_49441 68 OVCH1 6.98E-03 -4.44 V2LHS_81556 69 STYX 6.40E-03 -4.43 V2LHS_149736 70 RUNDC2C 1.96E-03 -4.37 V2LHS_194853 71 TMEM178 8.95E-03 -4.37 V2LHS_15796 72 P2RX3 1.21E-03 -4.34 V2LHS_152524 73 LOC286467 8.13E-04 -4.32 V2LHS_264894 74 ELMOD3 7.16E-04 -4.31 V2LHS_138016 75 GFM2 4.68E-03 -4.3 V2LHS_138846 76 FLJ45445, 8.62E-03 -4.26 V2LHS_188722 77 LOC730139 4.23E-03 -4.26 V2LHS_25169 78 YIF1B 7.04E-03 -4.26 V2LHS_178555 79 ITK 2.71E-03 -4.23 V2LHS_63241 80 RREB1 4.21E-03 -4.19 V2LHS_241472 81 YTHDF2 2.52E-03 -4.16 V2LHS_226992

82 SYCP2L 2.96E-03 -4.15 V2LHS_102215 83 CAMKV 1.06E-04 -4.14 V2LHS_98766 84 TTC15 2.91E-03 -4.12 V2LHS_77490 85 GOLT1A 2.91E-03 -4.11 V2LHS_161693 86 ZNF26 9.78E-04 -4.11 V2LHS_52285 87 DKFZp434H1419 8.99E-03 -4.09 V2LHS_231924 88 HELZ 3.88E-03 -4.09 V2LHS_95824 89 TLR10 3.39E-03 -4.08 V2LHS_137828 90 LOC144920 5.10E-03 -4.05 V2LHS_275308 91 TAF15 1.93E-03 -4.04 V2LHS_172494 92 CAMK2D 3.20E-04 -4.02 V2LHS_112696 93 CLCN2 9.77E-03 -4.02 V2LHS_24541 94 NOL6 4.10E-03 -3.94 V2LHS_116373 95 GPR32 7.70E-04 -3.93 V2LHS_132014 96 CEP135 2.11E-03 -3.91 V2LHS_136592 97 TIFA 4.22E-03 -3.91 V2LHS_45605 98 TP53TG5 3.46E-03 -3.91 V2LHS_208302 99 FLJ32955 5.38E-03 -3.9 V2LHS_21803 100 GRHL2 6.03E-03 -3.9 V2LHS_222311 101 MUC19 1.82E-03 -3.9 V2LHS_178719 102 GPATCH1 2.02E-03 -3.89 V2LHS_155287 103 CPVL 5.52E-03 -3.88 V2LHS_176344 104 NCAPD2 2.44E-03 -3.88 V2LHS_95755 105 FLJ32810 6.20E-03 -3.87 V2LHS_20743 106 PLP2 4.65E-03 -3.87 V2LHS_277050 107 MADCAM1 8.51E-03 -3.84 V2LHS_219254 108 MARVELD2 1.07E-03 -3.83 V2LHS_245803 109 CBLN4 3.65E-03 -3.79 V2LHS_72017 110 HTR2A 5.33E-03 -3.78 V2LHS_111751 111 ROR2 8.69E-04 -3.77 V2LHS_47949 112 PLD1 6.47E-03 -3.73 V2LHS_170093 113 SHISA4 1.54E-03 -3.73 V2LHS_101532 114 KIAA1602 8.77E-03 -3.72 V2LHS_161149 115 MFAP5 4.88E-03 -3.7 V2LHS_172458 116 UBE2H 9.21E-03 -3.68 V2LHS_171775 117 CA11 4.51E-03 -3.66 V2LHS_228420 118 CFH, CFHR1 5.50E-03 -3.65 V2LHS_284943 119 HTA 2.47E-03 -3.63 V2LHS_164195 120 CLCN3 5.45E-03 -3.62 V2LHS_150623 121 LOC285771 2.08E-03 -3.62 V2LHS_183765 122 TM9SF2 7.11E-03 -3.62 V2LHS_68959 123 PI4KB 8.37E-03 -3.61 V2LHS_170034 124 TUBA1A 8.44E-03 -3.61 V2LHS_72065 125 VWC2 5.71E-03 -3.61 V2LHS_62238 126 WARS 1.42E-03 -3.61 V2LHS_33836 127 FLJ41423 9.24E-03 -3.58 V2LHS_284656

128 HAL 8.61E-03 -3.58 V2LHS_132999 129 PRICKLE4 3.99E-03 -3.58 V2LHS_63831 130 C1orf57 2.51E-03 -3.56 V2LHS_138532 131 NR2F2 3.97E-03 -3.54 V2LHS_239584 132 TUBGCP5 3.09E-04 -3.53 V2LHS_118096 133 SNRPA1 3.64E-03 -3.52 V2LHS_153261 134 ZNF556 5.54E-03 -3.52 V2LHS_278105 135 NLGN2 5.33E-03 -3.5 V2LHS_120293 136 GPR137C 6.29E-03 -3.46 V2LHS_91622 137 UGT2A3 3.97E-03 -3.46 V2LHS_157561 138 USP8 4.86E-03 -3.45 V2LHS_49251 139 KIAA1712 1.23E-03 -3.44 V2LHS_100848 140 CAPN7 1.75E-03 -3.43 V2LHS_198039 141 LONRF2 4.39E-03 -3.43 V2LHS_247121 142 C14orf101 9.12E-03 -3.41 V2LHS_173856 143 FAM108B1 4.04E-03 -3.39 V2LHS_97116 144 EBP 4.17E-03 -3.38 V2LHS_250591 145 CLYBL 6.62E-03 -3.37 V2LHS_42101 146 COMMD1 6.65E-03 -3.37 V2LHS_51356 147 CYP11A1 6.42E-03 -3.37 V2LHS_224688 148 NEU1 6.55E-03 -3.37 V2LHS_269212 149 LOC642574 9.49E-03 -3.36 V2LHS_221160 150 TOPORS 1.49E-03 -3.36 V2LHS_13406 151 HIST1H3A 3.21E-03 -3.35 V2LHS_33767 152 IGF1R 3.69E-03 -3.34 V2LHS_20148 153 C19orf59 1.78E-03 -3.33 V2LHS_42482 154 PAQR3 8.57E-04 -3.33 V2LHS_211154 155 LOC145474 4.10E-03 -3.32 V2LHS_54845 156 LOC100130348 1.83E-03 -3.31 V2LHS_162592 157 LTB 4.96E-03 -3.31 V2LHS_134087 158 TRBV19 5.02E-03 -3.31 V2LHS_65032 159 ZBTB4 9.16E-03 -3.3 V2LHS_119203 160 C12orf64 7.64E-03 -3.29 V2LHS_178676 161 LCP1 8.16E-03 -3.29 V2LHS_133929 162 PSMA8 3.65E-03 -3.29 V2LHS_17720 163 TRMT6 1.35E-03 -3.29 V2LHS_134426 164 ICAM1 8.70E-03 -3.28 V2LHS_77358 165 MAPKAPK5 4.77E-03 -3.27 V2LHS_27294 166 FCGR2B, FCGR2C 8.03E-03 -3.26 V2LHS_172622 167 SQSTM1 7.88E-04 -3.25 V2LHS_47986 168 DKFZp564N2472 6.86E-03 -3.24 V2LHS_141621 169 FAM65A 7.93E-03 -3.23 V2LHS_136139 170 STARD13 1.72E-03 -3.23 V2LHS_99559 171 ZNF197 4.91E-03 -3.23 V2LHS_230920 172 HOMEZ 7.66E-03 -3.22 V2LHS_212820 173 FLJ45949 8.99E-03 -3.21 V2LHS_285519

174 C11orf65 5.18E-03 -3.19 V2LHS_49372 175 THOP1 9.02E-03 -3.19 V2LHS_154041 176 UBXN10 1.76E-03 -3.19 V2LHS_16091 177 C6orf165 1.18E-03 -3.17 V2LHS_118670 178 PDE4DIP 2.25E-03 -3.16 V2LHS_183211 179 LENG1 8.88E-03 -3.15 V2LHS_135855 180 DNAJC21 4.32E-03 -3.14 V2LHS_26661 181 LOC644936 9.07E-03 -3.12 V2LHS_56007 182 HN1L 9.22E-03 -3.11 V2LHS_14937 183 GRM5 8.41E-03 -3.1 V2LHS_237235 184 LRCH3 5.57E-03 -3.1 V2LHS_222832 185 OVOS2 5.38E-03 -3.1 V2LHS_160596 186 PI4K2A 2.22E-03 -3.1 V2LHS_175547 187 CLDN17 5.07E-03 -3.09 V2LHS_16386 188 MTTP 2.51E-03 -3.08 V2LHS_76312 189 PLEKHA3 9.09E-03 -3.08 V2LHS_22292 190 SLC6A8 6.79E-03 -3.08 V2LHS_56679 191 ADK 7.60E-03 -3.07 V2LHS_233106 192 LOC100128108 8.43E-03 -3.06 V2LHS_121352 193 PROC 5.92E-03 -3.04 V2LHS_92305 194 DHX9 4.71E-03 -3.03 V2LHS_225385 195 LOC440518 2.16E-03 -3.03 V2LHS_143326 196 SCAMP4 7.72E-03 -3.03 V2LHS_45909 197 VWA5B2 7.51E-03 -3.03 V2LHS_213570 198 C5orf48 3.22E-03 -3.02 V2LHS_140224 199 OR2A5 1.07E-03 -3.02 V2LHS_59524 200 BOLA3 8.64E-03 -3 V2LHS_121652 201 ZNF432 4.53E-03 -3 V2LHS_79484 202 LOC284661 6.08E-03 -2.99 V2LHS_183283 203 SAMD12 7.54E-03 -2.99 V2LHS_124893 204 ANKK1 9.36E-03 -2.98 V2LHS_188466 205 CXorf22 2.01E-03 -2.98 V2LHS_245763 206 PNMA2 3.44E-03 -2.97 V2LHS_196882 207 RUNX1 5.18E-03 -2.97 V2LHS_150257 208 GGPS1 9.38E-03 -2.96 V2LHS_68132 209 TOR1AIP2 8.80E-03 -2.96 V2LHS_29443 210 FANCF 6.41E-03 -2.94 V2LHS_98198 211 PMPCA 8.89E-03 -2.94 V2LHS_87246 212 DPY19L3 6.62E-03 -2.93 V2LHS_40744 213 RIPPLY2 8.21E-03 -2.93 V2LHS_176894 214 GJB5 8.94E-03 -2.92 V2LHS_42563 215 HSPC072 9.00E-03 -2.92 V2LHS_53336 216 CRISP2 4.78E-03 -2.91 V2LHS_171520 217 STYK1 9.49E-04 -2.91 V2LHS_203597 218 IPPK 1.00E-02 -2.9 V2LHS_98358 219 RTF1 8.62E-03 -2.87 V2LHS_257617

220 ZBTB26 3.32E-03 -2.87 V2LHS_201438 221 APLF 8.05E-03 -2.86 V2LHS_160841 222 NOM1 5.25E-03 -2.86 V2LHS_264810 223 PPAPDC1B 1.30E-03 -2.84 V2LHS_158944 224 SLC10A3 3.39E-03 -2.84 V2LHS_34487 225 CTPS2 9.34E-03 -2.83 V2LHS_34295 226 DES, KRT81, VIM 6.54E-03 -2.83 V2LHS_193029 227 DHX38 9.63E-03 -2.82 V2LHS_60541 228 DHRS7B 4.17E-03 -2.79 V2LHS_96766 229 EXT2 4.65E-03 -2.79 V2LHS_229747 230 NOL6 2.89E-03 -2.79 V2LHS_116375 231 ZFAND3 9.99E-03 -2.79 V2LHS_73350 232 SLC35E3 7.88E-03 -2.78 V2LHS_30620 233 SLC7A13 7.14E-03 -2.78 V2LHS_210577 234 ZC3H13 6.02E-03 -2.78 V2LHS_211696 235 CCNL2 1.81E-03 -2.75 V2LHS_137749 236 KRTAP19-1 7.85E-03 -2.73 V2LHS_166581 237 TMEM138 1.66E-03 -2.73 V2LHS_115942 238 GDPD5 6.91E-03 -2.67 V2LHS_116874 239 HDAC9 1.35E-03 -2.66 V2LHS_47877 240 FLJ25694 4.75E-03 -2.64 V2LHS_190755 241 ECOP, LOC729086 7.52E-03 -2.63 V2LHS_225838 242 FANCM 4.74E-03 -2.63 V2LHS_205559 243 FLJ40330 4.27E-03 -2.62 V2LHS_240144 244 UBR1 6.67E-03 -2.61 V2LHS_42473 245 PLD2 6.00E-03 -2.59 V2LHS_222130 246 HIP1R 3.49E-03 -2.58 V2LHS_212627 247 CLUL1 8.83E-03 -2.57 V2LHS_197932 248 KRT27 1.00E-02 -2.55 V2LHS_226224 249 C1orf168 9.69E-03 -2.52 V2LHS_269806 250 WIF1 3.59E-03 -2.52 V2LHS_196291 251 C9orf152 8.51E-03 -2.51 V2LHS_180399 252 DMTF1 5.76E-03 -2.51 V2LHS_66509 253 TPST2 9.57E-03 -2.51 V2LHS_32342 254 APOBEC3A 3.42E-03 -2.48 V2LHS_286556 255 RPL17 8.22E-03 -2.48 V2LHS_131616 256 SMC4 9.69E-04 -2.48 V2LHS_68092 257 HSPA13 6.60E-03 -2.46 V2LHS_94977 258 GCA 9.00E-03 -2.45 V2LHS_15316 259 LIPM 1.48E-03 -2.45 V2LHS_104388 260 EMP3 8.08E-03 -2.44 V2LHS_113721 261 LOC441476 6.57E-03 -2.43 V2LHS_284840 262 FOXF1 5.91E-03 -2.41 V2LHS_131755 263 ORAI3 6.69E-03 -2.41 V2LHS_28967 264 GAS2L1 6.64E-03 -2.4 V2LHS_196343 265 GJC1 5.70E-03 -2.4 V2LHS_68097

266 UGDH 5.51E-03 -2.39 V2LHS_218865 267 CBX5 6.47E-03 -2.36 V2LHS_240022 268 FRRS1 4.96E-03 -2.34 V2LHS_36891 269 CD84 6.30E-03 -2.33 V2LHS_53205 270 SI 9.70E-03 -2.33 V2LHS_93929 271 TAOK2 9.28E-03 -2.33 V2LHS_201841 272 ACSM5 8.84E-03 -2.31 V2LHS_174334 273 LRRC38 9.49E-03 -2.31 V2LHS_161613 274 HPS3 9.39E-03 -2.29 V2LHS_138857 275 TEX13A 1.00E-02 -2.29 V2LHS_99327 276 KCTD11 6.93E-03 -2.27 V2LHS_254663 277 C1orf63 5.30E-03 -2.25 V2LHS_39942 278 TTC8 1.13E-03 -2.24 V2LHS_24261 279 APRT 9.82E-03 -2.22 V2LHS_270759 280 FAM179A 8.08E-03 -2.22 V2LHS_203801 281 FAM46B 9.34E-03 -2.22 V2LHS_118211 282 CUL1 7.84E-03 -2.21 V2LHS_32372 283 ANXA5 9.55E-03 -2.19 V2LHS_94509 284 UBL4B 8.39E-03 -2.19 V2LHS_180983 285 THAP9 2.50E-03 -2.16 V2LHS_157174 286 COPS5 9.97E-03 -2.13 V2LHS_78187 287 SUMO2 1.55E-03 -2.12 V2LHS_94924 288 FBXW4 2.96E-03 -2.09 V2LHS_202633 289 IMPG1 3.08E-03 -2.09 V2LHS_132345 290 MTMR12 5.69E-03 -2.08 V2LHS_176493 291 EMR3 4.65E-03 -2.07 V2LHS_159322 292 C12orf32, IGHA1 6.53E-03 -2.05 V2LHS_213216 293 DYRK4 4.03E-03 -2.04 V2LHS_139500 294 DBR1 1.02E-03 -2.03 V2LHS_98028 295 TBX1 6.47E-03 -2.02 V2LHS_205864 296 CKM 5.06E-03 -2 V2LHS_279623 297 BRCA1 1.94E-03 -1.99 V2LHS_280987 298 LOC646817 5.32E-03 -1.99 V2LHS_200366 299 PSMB6 6.70E-03 -1.95 V2LHS_277575 300 SNORA65 4.83E-04 -1.92 V2LHS_168584 301 C4orf23 3.87E-03 -1.9 V2LHS_176989 302 ZNF646 8.17E-04 -1.82 V2LHS_78609 303 FBXO8 3.84E-03 -1.81 V2LHS_254559 304 PF4V1 4.21E-03 -1.81 V2LHS_285000 305 TMIGD2 7.07E-03 -1.81 V2LHS_19033 306 COL27A1 3.42E-03 -1.8 V2LHS_178068 307 OSBPL2 8.95E-03 -1.77 V2LHS_203142 308 TNK1 9.81E-03 -1.77 V2LHS_46392 309 PRKAA1 9.74E-03 -1.76 V2LHS_57699 310 AOAH 8.45E-03 -1.75 V2LHS_236895 311 ULBP2 3.85E-03 -1.7 V2LHS_137461

312 MYO3B 6.10E-03 -1.69 V2LHS_631 313 S100A14 5.93E-03 -1.66 V2LHS_58064 314 KIAA0484 8.27E-03 -1.63 V2LHS_258065 315 FRMPD1 7.50E-03 -1.62 V2LHS_95991 316 TANC2 4.98E-03 -1.62 V2LHS_265074

Table 3

Table 3. Functional clustering analysis of HTP sequencing results. Results from HTP sequencing were subjected to KEGG pathway analysis using the DAVID Bioinformatics Database. The majority, but not all, of input genes were recognized in the database. Pathways represented in the HTP sequencing datasets according to DAVID are listed below. First list included SL candidates with Log2FC>-1 and p<0.1 (far left), second list is a smaller dataset with Log2FC>-1 and p<0.05 (middle), and last list includes high- confidence SL candidates with Log2FC>-1 and p<0.01 (far left). Pathways listed are in order of number of genes in the pathway appearing in the dataset.

p<0.1 p<0.05 p<0.01 2425 DAVID IDs 1319 DAVID IDs 297 DAVID IDs

Nucleotide excision repair GnRH signaling pathway Ub mediated proteolysis Fc epsilon RI signaling pathway Fc epsilon RI signaling pathway DNA replication Regulation of autophagy T cell receptor signaling pathway

PPAR signaling pathway Neurotrophin signaling pathway

DNA replication Vibrio cholerae infection

RIG-I-like receptor signaling pathway Epithelial cell signaling

Cytosolic DNA-sensing pathway Ether lipid metabolism

Mismatch repair Leukocyte transendothelial migration

Pathways in cancer Adherens juntion

Oocyte meiosis Oocyte meiosis

Neurotrophin signaling pathway Glycerophospholipid metabolism

Purine metabolism Long-term potentiation

Jak-STAT signaling pathway Tight junction

Apidocytokine signaling pathway Fc gamma R-mediated phagocytosis

Vibrio cholerae infection Mismatch repair

Toll-like receptor signaling pathway VEGF signaling pathway

Small cell lung cancer ErbB signaling pathway

GnRH signaling pathway Nucleotide excision repair

Natural killer cell mediated cytotoxicity

Phosphatidylinositol signaling system

Tight junction

Epithelial cell signaling in H. pylori infection

Maturity onset diabetes of the young

T cell receptor signaling pathway

Table 4

Table 4. HTP sequencing results for UVSSA (KIAA1530), ERCC8, and WDR89. Mutiple shRNA clones targeting UVSSA (KIAA1530), ERCC8, and WDR89 were detected through HTP sequencing results. Below show two shRNA targeting UVSSA, two targeting ERCC8, and three targeting WDR89 which scored with a Log2 FC>-1.

Gene Symbol p-value Log2 FC shRNA ID KIAA1530 2.16E-02 -2.39 V2LHS_274544 KIAA1530 9.83E-02 -2.35 V2LHS_139628

ERCC8 3.00E -02 - 3.2 V2LHS_88681 ERCC8 7.15E-01 -1.22 V2LHS_254783

WDR89 3.97E -02 - 7.24 V2LHS_70786 WDR89 4.99E-02 -4.87 V2LHS_70789 WDR89 8.60E-01 -1.08 V2LHS_70787

CHAPTER 3

MYC is a critical target of FBXW7

This manuscript has been submitted to Cancer Research.

Title/Running title

MYC is a critical target of FBXW7

Authors

Mai Sato1,2, Ruth Rodriguez-Barrueco2, Jiyang Yu3, Catherine Do2, Jose M. Silva1,2, and

Jean Gautier2,4

Affiliations

1Department of Pathology and Cell Biology, Columbia University, New York, NY, USA.

2Institute for Cancer Genetics, Columbia University, New York, NY, USA.

3Department of Biomedical Informatics, Columbia University, New York, NY, USA.

4Department of Genetics and Development, Columbia University, New York, NY, USA.

Correspondence to:

Jean Gautier

1130 Saint Nicholas Avenue, ICRC 603A, New York, NY 10032, USA.

Tel: +1 212 851 4564

Fax: +1 212 851 5284

Email: [email protected] (J. Gautier)

Sources of support

This work was supported in part by NCI grants CA092245 and CA167826 to JG.

Abstract

MYC deregulation is a driver of many human cancers. Altering the balance of MYC protein levels at the level of transcription, protein stability, or turnover is sufficient to transform cells to a tumorigenic phenotype. While direct targeting of MYC is difficult, specific genetic vulnerabilities of MYC-deregulated cells could be exploited to selectively inhibit their growth. Using a genome-wide shRNA screen, we identified 78 synthetic lethal candidates with elevated levels of MYC in human mammary epithelial cells.

Among the candidates, we validated and characterized FBXW7, a component of the

SCF-like ubiquitin ligase complex that targets MYC for proteasomal degradation. Down- regulation of FBXW7 leads to synergistic accumulation of cellular and active chromatin- bound MYC, while protein levels of other FBXW7 targets appear unaffected. Over a four- week time course, continuous FBXW7 down-regulation and MYC activation together cause an accumulation of cells in S-phase and G2/M-phase of the cell cycle. Under these conditions, we also observe elevated chromatin-bound levels of CDC45, suggesting increased DNA replication stress. Moreover, a positive correlation was observed between MYC and FBXW7 expression, specifically in the luminal A-type breast cancers. These results establish that FBXW7 down-regulation is synthetic lethal with high MYC levels, and that MYC is a critical target of FBXW7.

Keywords

MYC, FBXW7, synthetic lethality, CDC45, MCF10A

Introduction

Genetic and epigenetic events altering the expression, the stability, or the activity of oncogenes and tumor suppressor genes participate in the initiation and maintenance of human cancers (Shen and Laird, 2013). Among the genes which expression is modified in cancer, only a relatively small subset is recurrently altered and contributes to the tumorigenic phenotype (Vogelstein et al., 2013). Identification of such “cancer driver genes” has facilitated the development of targeted treatment options in the recent years, yet there are still pharmacological limitations that hinder direct targeting of some major cancer drivers including MYC (Dang, 2012).

The MYC proto-oncogene is deregulated in up to 70% of human tumors including hematopoietic cancers, sarcomas, and solid carcinomas (Klapproth and Wirth, 2010).

MYC is essential for normal cell growth. In particular, MYC is required for cell proliferation and is involved in differentiation, metabolism, and apoptosis, most notably through its function as a bHLH-LZ transcriptional activator and repressor (Meyer and

Penn, 2008). In addition, MYC has direct, transcription-independent functions in DNA replication (Dominguez-Sola et al., 2007) and protein synthesis (Cowling and Cole,

2007), further supporting its major role in cellular homeostasis. Therefore, cellular MYC protein levels are tightly controlled through regulated expression (Liu and Levens, 2006), protein stability (Adhikary and Eilers, 2005), and degradation (Thomas and Tansey,

2011). Indeed, alterations in MYC abundance are a critical component of MYC- dependent tumorigenesis (Murphy et al., 2008).

At least 4 different ubiquitin ligase complexes can target MYC for proteasomal degradation (Thomas and Tansey, 2011), including the SCF-like ligase complex containing FBXW7 (Welcker et al., 2004; Welcker et al., 2004; Yada et al., 2004).

FBXW7 (or Fbw7, Sel-10, hCdc4, or hAgo) is an F-box protein that confers substrate

specificity for this complex (Welcker and Clurman, 2008). While MYC is a documented substrate of FBXW7, FBXW7 also targets several additional oncoproteins and master regulator molecules including CyclinE, NOTCH1, mTOR, SREBP, and c-JUN, among others (Welcker and Clurman, 2008) and the respective contributions of FBXW7- dependent regulation of these proteins towards abnormal cell growth is not known.

Moreover, although the role of FBXW7 as a tumor suppressor is well documented in hematopoietic tumors in which FBXW7 mutations are unequivocally linked to tumorigenesis (Thompson et al., 2007), this is not as clear for other cancer types

(Sgambato et al., 2007; Woo Lee et al., 2006). For example, it has been reported that the inactivation of FBXW7 can be associated with favorable prognosis in a subset of breast cancers (Akhoondi et al., 2010), suggesting that the physiological role of FBXW7 and consequences of its loss may be dependent on cell types and contexts.

Exploring synthetic lethality has provided many critical insights into the biology of oncogenes (Kaelin, 2005). In addition, identifying genes that are essential to cope with activated oncogenes might provide alternatives to direct targeting for cancer therapy

(Kaelin, 2009). In the case of MYC, several genes which loss is synthetic lethal with aberrant MYC expression have been identified. These include DR5 in the death receptor pathway (Wang et al., 2004), the WRN helicase (Grandori et al., 2003; Moser et al.,

2012), the Aurora A/B protein kinases (Yang et al., 2010) and CDK (Goga et al., 2007;

Molenaar et al., 2009) and Chk1/2 kinases (Hoglund et al., 2011; Murga et al., 2011).

More recently, genome-wide approaches have helped to identify the SAE1/2-dependent

SUMOylation pathway (Kessler et al., 2012) and CSNK1E kinase (Toyoshima et al.,

2012) as potential synthetic lethal candidates as well. Intriguingly, studies in different model systems appear to yield unique results, possibly due to MYC’s cell-context specific roles and the differing modes of deregulation.

In the present study, we conducted a genome-wide screen to identify genes that are required to survive MYC overexpression in MCF10A breast epithelial cells conditionally expressing a MYCER allele. We document these synthetic lethal interactions and identify 78 potential synthetic lethal targets. Among these, we have validated the F-box protein FBXW7 as a synthetic lethal candidate in MCF10A-MYCER cells overexpressing MYC. We show that shRNA-mediated knockdown of FBXW7 confers increased lethality to cells with activated MYCER. Active chromatin-bound

MYCER is stabilized in cells with down-regulated FBXW7, while protein stability of other

FBXW7 targets is not affected. We also find that knockdown of FBXW7 upon MYCER activation results in accumulation of cells in S/G2 phase with increased chromatin-bound

CDC45. Furthermore, MYC expression correlates positively with FBXW7 expression in the luminal A-subtype of breast cancers. These results suggest that increased cellular

MYC levels as a result of FBXW7 loss yields sufficient cellular stress to confer selective lethality to those cells with deregulated MYC.

Results

MCF10A-MYCER cells: a model for MYC deregulation

First, we sought to generate a cellular model for MYC conditional expression that recapitulates MYC deregulation in human cancer cells. To this goal, we stably integrated an inducible MYCER allele (Littlewood et al., 1995) into MCF10A breast epithelial cells via retroviral transduction. The MYCER allele consists of a full-length MYC cDNA fused to the hormone-binding domain of the murine estrogen receptor. Upon treatment with the synthetic ligand 4-hydroxytamoxifen (4OHT), MYCER protein is rapidly transported to the nucleus, rendering it active. MCF10A are non-transformed breast epithelial cells that

express markers commonly associated with a basal epithelial phenotype, thus modeling breast cancers of basal-origin which are frequently associated with MYC and CDC45 overexpression (Alles et al., 2009).

Following retroviral transduction of MCF10A-MYCER cells, we selected a clone expressing MYCER (Figure 1a) that fulfilled two criteria: 1) normal levels of endogenous

MYC expression were retained, and 2) MYCER was expressed at levels 2-3 fold higher than endogenous MYC, sufficient for modeling MYC deregulation in human cancers without MYC amplification (Nesbit et al., 1999) (Figure 1a). Following 4OHT treatment, this clone showed a 1.54 fold increase in active, chromatin-bound MYCER. This demonstrates the anticipated 4OHT-dependent nuclear import and activation of MYCER

(Figure 1b). Next, we tested the ability of the MYCER transgene to activate transcription.

MCF10A-MYCER cells were transfected with a luciferase reporter construct harboring 6

E-box sequences, the canonical MYC DNA binding site (Supplementary Figure 1). Upon

4OHT treatment, we observed a 2.3 fold increase in reporter activity (p<0.0001), consistent with the increase in chromatin-bound MYCER (Supplementary Figure 1).

Finally, we tested the ability of the transgene to accelerate entry into S-phase, another hallmark of MYC deregulation. Cells were synchronized at the G1/S transition by double thymidine block and labeled with BrdU following induction of MYCER. S-phase entry was then monitored by the appearance of BrdU foci assessed by indirect immunofluorescence. As anticipated, MYCER induction triggered marked acceleration of

S-phase entry (Figure 1c and Supplementary Figure 2). These results establish the validity of our MCF10A-MYCER to model a near physiological degree of MYC deregulation.

Screen for synthetic lethal candidates with MYCER activation

Next, we performed a genome-wide screen to identify genes which loss is synthetic lethal with aberrant MYC expression utilizing the GIPZ Lentiviral Human shRNA-mir Library (Thermo Scientific Open Biosystems, Waltham, MA, USA). The pooled shRNA plasmid library includes 58,493 shRNA constructs and target 18,661 known human genes (2-3 shRNAs per gene covering 75% of entire genome) as described in Rodriguez-Barrueco et al (Rodriguez-Barrueco et al., 2013). We designed the screen based on negative selection, i.e. we monitored loss of shRNAs to identify the corresponding genes that render cells sensitive to MYCER activation by 4OHT when lost

(Figure 2a).

The screen was performed in 5 independent replicates, which were utilized to generate p-values for screening of high-confidence MYC-SL candidates. Cells were initially transduced with the pooled shRNA library at 30% infection efficiency to achieve, on average, single-copy shRNA integration per cell. 200 million cells were infected to obtain at least 1000 cells infected with each shRNA from the library. Infected MCF10A-

MYCER cells were first allowed to grow in the absence of MYCER induction for two generations to eliminate most shRNAs targeting essential genes. Then, the remaining cells were propagated in either the presence or absence of 4OHT for four weeks, maintaining a constant total cell number of at least 70 million per sample to preserve the representation of at least 1000 cells per shRNA construct. The surviving populations were harvested after four weeks, and genomic DNA was isolated (Figure 2a). Stably integrated shRNA constructs were then amplified by large scale PCR, labeled with fluorescent nucleotides and hybridized to custom microarrays harboring complementary oligonucleotide probes to the unique 60-nucleotide barcodes present on each individual shRNA.

The microarray analysis was performed as described previously (Yu et al., 2013).

We identified 78 candidate genes for which the corresponding shRNAs had a Log2FC

(fold change) value of -1 or less, thus, depleted 2-fold or more in the MYC ON population compared to MYC OFF (with a p<0.05 for the five replicates) (Supplementary Table 1).

Functional annotation clustering using the KEGG pathways in the DAVID Bioinformatics

Database revealed the most represented biological pathways within the synthetic lethal candidates to be ubiquitin mediated proteolysis, PPAR signaling pathway, fatty acid biosynthesis, fructose and mannose metabolism, and homologous recombination

(Figure 2c). Notably, among the top hits we identified UBE2I, the E2 conjugating enzyme cooperating with the SAE1/2 E3 ligase in the SUMOylation pathway. This is consistent with recent studies in which SAE1/2 and UBE21 were identified in similar screens

(Kessler et al., 2012; Liu et al., 2012; Toyoshima et al., 2012). Using a set of independent shRNAs, we validated that loss of UBE2I is indeed synthetic lethal with

MYCER activation in MCF10A-MYCER cells, providing a proof of principle for our system (Supplementary Figure 9a and 9b).

Notably, three major MYC-SL screens have been published to date (Kessler et al., 2012; Liu et al., 2012; Toyoshima et al., 2012). Between the datasets from Kessler’s screen and our present study, we observed four high-confidence MYC-SL candidate genes that overlap: FBXW7, LAMA2, PRMT8, and ZNF614 (Supplementary Table 1)

(Kessler et al., 2012). When we compare our dataset with Toyoshima’s screen, we only observed one overlapping MYC-SL candidate, UBE2I, which has been previously validated by Kessler et al. (Supplementary Table 1) (Kessler et al., 2012; Toyoshima et al., 2012). When comparing our dataset with Liu’s screen, we did not find a direct overlap (Liu et al., 2012). However, one of the eleven top MYC-SL kinases in Liu’s dataset was GSK3B, which is required for phosphorylation of MYC to trigger degradation by FBXW7 (Liu et al., 2012; Welcker et al., 2004). These results suggest that FBXW7-

dependent MYC degradation is a significant MYC-SL pathway since at least three independent MYC-SL datasets include FBXW7 and/or GSK3B. Consistent with this, the

MYC ubiquitination (and SUMOylation) network was determined to be one of the three major functional MYC-SL hubs, in a comprehensive meta-analysis of the three aforementioned MYC-SL datasets (Cermelli et al., 2014). Therefore, we selected

FBXW7 for further analysis, which ranked 58th in our high-confidence MYC-SL list with

Log2FC=-1.1469 (p<0.02, Supplementary Table 1).

Loss of FBXW7 together with MYCER activation is lethal

We elected to further validate FBXW7, the substrate recognition subunit of the

SCF-like E3 ubiquitin ligase complex that regulates degradation of MYC and other oncoproteins (Welcker and Clurman, 2008). First, we wanted to confirm that loss of

FBXW7 is lethal when combined with MYCER activation. We generated cell lines stably expressing either constitutively expressed or doxycycline-inducible shRNA clones targeting FBXW7 to experimentally manipulate its abundance. Following lentiviral infection of stable shRNA constructs and antibiotic selection, FBXW7 knockdown levels were assessed by quantitative RT-PCR. Both constitutive and doxycycline-inducible shRNA clones yielded an average of 45% knockdown of FBXW7 mRNA levels (Figure

3a). We were unable to achieve further knockdown of FBXW7 in MCF10A-MYCER cells, suggesting a threshold level of FBXW7 is required for MCF10A-MYCER viability, even when the MYC transgene is off.

We utilized a fluorescence-based cell competition assay to compare the viability of FBXW7 knockdown cells in the absence or presence of 4OHT over a period of approximately four weeks, to recapitulate the experimental conditions of the screen.

Since all shRNA constructs co-express GFP or RFP, cells expressing shRNA are readily

detectable by fluorescence. A mixed population of 50% MCF10A-MYCER cells without shRNA and 50% MCF10A-MYCER cells infected with shFBXW7 or shCONTROL were plated in MYC OFF or MYC ON conditions and continuously treated with vehicle or

4OHT at every passage. Cells were harvested at the indicated time points during the competition assay (Figure 3b and 3c), and subjected to live cell flow cytometry to measure the percentage of fluorescent surviving cells (Supplementary Figure 3).

Clones stably expressing constitutive and inducible FBXW7 shRNAs displayed a significant decrease in viability with 4OHT expressing MYCER (p<0.01). Survival of cells infected with control shRNA was not affected by MYCER induction over a period of 23 days, whereas cells with FBXW7 knockdown showed 11% less GFP signal in the presence of 4OHT by Day 23 (p<0.003) (Figure 3b). When plotted as the MYC ON/MYC

OFF ratio of surviving cells, we determined that the constitutive FBXW7 knockdown caused a 21% decrease in viability, and the inducible FBXW7 knockdown caused an average of 53% decrease in viability compared to control cells (Figure 3c). The inducible clones likely yield a more robust reaction due to continuous induction of shRNA by

1µg/ml doxycycline addition at every passage.

Of note, due to the lack of reliable FBXW7 antibodies, we were unable to assess the effect of knockdown on FBXW7 protein levels. However, all the shRNAs used in our secondary screen target different sequences within the FBXW7 mRNA from the shRNAs included the library, confirming that the observed phenotype is due to FBXW7 down- regulation. Taken together, these results validate the initial finding from the screen and establish that FBXW7 knockdown is synthetic lethal with MYCER activation.

FBXW7 knockdown specifically leads to stabilization of active MYCER

To explore the mechanism by which FBXW7 down-regulation leads to lethality in

MYC expressing cells, we first sought to assess whether down-regulation of FBXW7 resulted in the stabilization of MYCER. To this end, we examined the cellular and chromatin-bound levels of MYCER upon FBXW7 knockdown. Total cellular levels of

MYCER were significantly increased upon down-regulation of FBXW7 with either constitutive or inducible FBXW7 shRNA (3.5 fold and 6.2 fold, respectively, p<0.05)

(Figure 4a). Moreover, the levels of active, chromatin-bound MYCER detected upon cell fractionation (Mendez and Stillman, 2000) were synergistically increased upon FBXW7 knockdown and MYCER activation (Figure 4b). Chromatin-bound MYCER increased 2-5 fold upon FBXW7 down-regulation and further increased up to 10-13.5 fold when

MYCER was induced by 4OHT (p<0.1) (Figure 4b). These results suggest that FBXW7 knockdown significantly stabilizes deregulated MYCER, resulting in elevated levels of active chromatin-bound MYCER.

As previously noted, FBXW7 controls the proteasome-dependent degradation of several cellular oncogenes which could also be stabilized upon FBXW7 knockdown and account for the observed phenotype (Welcker and Clurman, 2008). Therefore, we examined the stability of other targets of FBXW7 by Western blot: c-Jun, NOTCH1,

CyclinE, and mTOR upon FBXW7 knockdown (Supplementary Figure 4). We find stabilization of MYCER, c-Jun, and CyclinE upon FBXW7 down-regulation in the absence of MYCER activation (Figure 4c). Notably, upon MYCER activation by 4OHT, only MYCER is stabilized when compared to the other FBXW7 targets examined (Figure

4c). Similar results were obtained using the inducible FBXW7 knockdown alleles

(Supplementary Figure 5). These data suggest that FBXW7 knockdown in the context of

MYCER activation leads predominantly to stabilization of MYCER. Our results point to

the major role of FBXW7-mediated MYCER degradation for survival of cells with deregulated MYC.

MYCER stabilization results in accumulation of chromatin-bound CDC45 and cells in S/G2 phase

MYC deregulation triggers DNA damage and genomic instability (Dominguez-

Sola et al., 2007; Felsher and Bishop, 1999; Karlsson et al., 2003; Vafa et al., 2002).

Therefore, we assessed the consequence of down-regulating FBXW7 together with

MYCER activation on DNA damage and apoptosis. We monitored checkpoint activation

(phosphorylation of Chk1 by ATR or Chk2 by ATM) and formation of DNA double strand breaks (phosphorylated H2AX), but could not detect a significant synergistic increase in these markers at any time-point during the four week course of the experiments

(Supplementary Figure 7a). Next, we probed for the apoptosis markers cleaved caspase-3, cleaved PARP, and PUMA, but did not detect significant changes by

Western blotting (Supplementary Figure 7b). We then examined changes in cell cycle distribution. After four weeks of treatment with 4OHT, we found that FBXW7 knockdown cells in which MYC was deregulated showed synergistic accumulation of cells in S phase and G2/M phase (p<0.05) (Figure 5a, 5b, and Supplementary Figure 6). These data suggest that aberrant expression of MYC resulted in slower S-phase progression and/or DNA replication stress, and these phenotypes are exacerbated with FBXW7 knockdown.

We have recently demonstrated that MYC-dependent DNA replication stress is directly mediated by CDC45, a component of the replicative DNA helicase that marks active origins of replication (Srinivasan et al., 2013). To assess if the cell cycle phenotype we observed could be attributed to aberrant S-phase progression as a result

of accumulation of active MYCER on chromatin, we monitored the levels of chromatin- bound CDC45 in FBXW7 knockdown cells. We find that FBXW7 knockdown in the context of MYCER activation by 4OHT results in a synergistic increase in the levels of chromatin-bound CDC45 (6.7 fold increase, p<0.1), suggesting increased origin firing in the cells (Figure 5c). Our data suggest that FBXW7 knockdown cells with activated

MYCER experience aberrant levels of origin firing during DNA replication, a sign of replication stress, which may contribute to the synthetic lethal phenotype.

MYC expression correlates with FBXW7 expression in Luminal A-type breast cancers

Since regulation of MYC abundance by FBXW7 is critical for MCF10A cells’ viability, we asked if mRNA expression levels of MYC and FBXW7 are correlated in a large panel of breast cancer samples from The Cancer Genome Atlas (TCGA) database.

Comparative analysis of MYC and FBXW7 expression showed a weak correlation when all types of breast cancers were pooled and analyzed together (rho=0.23, p<0.0001)

(Supplementary Figure 8a). Next, we repeated the correlation analysis on specific data subsets corresponding to distinct breast cancer subtypes (normal, Her2, basal, luminal

A, and luminal B). We found a statistically significant positive correlation between MYC and FBXW7 expression specifically in luminal A-type breast cancers (rho=0.4052, p<0.0001) (Supplementary Figure 8b). Luminal A-type breast cancers are the most prevalent breast cancer subtype (40%), and are generally molecularly classified as hormone receptor (ER/PR)-positive and Her2-negative (Schnitt, 2010). Of note, this breast cancer subtype is typically associated with low to normal MYC expression, suggesting that cells retain physiological regulation of MYC abundance (Xu et al., 2010).

This is in contrast to cells derived from breast cancer types with MYC overexpression,

which are unresponsive to MYC suppression (Kang et al., 2014). This observation supports our data, since normal-MYC breast cancers such as luminal A are likely more sensitive to relatively small fluctuations in MYC protein levels which may arise from altered FBXW7 expression.

Discussion

Genome-wide screens utilizing RNAi have become a powerful tool to assess genetic interactions in human cells. Several screens specifically designed to identify genes required for cell survival upon MYC deregulation have been previously published

(Kessler et al., 2012; Liu et al., 2012; Toyoshima et al., 2012). Intriguingly, when we compare the 78 candidates from our screen (Supplementary Table 1) to published datasets, less than 5% of genes overlap in pairwise comparisons of individual screens.

Several reasons may account for these differences. Different studies use normal or cancer cell lines. The mode of MYC deregulation (overexpression vs. inducible) and differences between siRNA and shRNA could also account for some differences.

Furthermore, MYC regulates multiple cellular processes and its function as well as the impact of its expression depends heavily on the particular cellular context. Finally, it has been proposed that MYC acts as a general amplifier of activated transcription, which could explain in part why MYC may be regulating the expression of different genes in different cell types (Lin et al., 2012; Nie et al., 2012). Thus, while the differences observed between datasets are puzzling, they demonstrate the importance of studying

MYC biology within highly controlled and specific contexts that best mimic physiological systems.

We have identified 78 genes which down-regulation is synthetic lethal with

MYCER activation in MCF10A-MYCER cells. Our system utilizes a stably expressed fusion MYCER protein to model MYC deregulation instead of endogenous MYC.

However, previous studies have established that MYCER does not respond to estrogen, which we have further confirmed (data not shown) (Littlewood et al., 1995). Moreover, findings from one of the previously published MYC-SL screens with MYCER were extended to MYC-dependent breast cancer cell lines, supporting MYCER as a faithful model for MYC deregulation (Kessler et al., 2012). However, since we still cannot definitively exclude the possibility of the ER portion affecting our results, we are currently testing the dependence of several MYC-dependent breast cancer cell lines on FBXW7.

We expect these breast cancer cell lines to mimic our results in MCF10A-MYCER.

We have validated FBXW7 as a gene which knockdown causes significantly decreased viability for cells with activated MYCER. Though FBXW7 is known to target several oncogenes for degradation, our findings indicate that knockdown of the gene in the presence of MYCER activation causes preferential stabilization of MYCER. Our data suggest that stabilization of MYCER is critical to cause increased accumulation of

FBXW7 knockdown cells in S/G2 phase and gradual cellular death as demonstrated through viability assays. Though we were not able to detect significant levels of DNA damage or apoptosis markers, this may be attributed to the slow and gradual nature of cell death induction in our experiments. We observe at most 50% decrease in viability of

FBXW7 knockdown cells compared to control cells over the course of four weeks. We believe that any given time-point only a small fraction of cells is expressing markers of

DNA damage or apoptosis, which is in contrast to what is seen following acute MYC overexpression. Nevertheless, the possibility of senescence or alternative cell death pathway activation cannot be excluded, and will have to be examined.

FBXW7 is one of only four MYC-SL candidate genes that were identified from both our screen and Kessler’s screen, emphasizing its potential significance as a major

MYC-SL pathway (Supplementary Table 1) (Kessler et al., 2012). The two other MYC-

SL datasets did not include FBXW7, however, the smaller RNAi libraries utilized in these

screens may not have included FBXW7 (Liu et al., 2012; Toyoshima et al., 2012).

Notably, Liu’s screen that interrogated the human kinome identified GSK3B, the kinase responsible for MYC phosphorylation and required for recognition by FBXW7 (Liu et al.,

2012). These findings suggest that the FBXW7-dependent MYC degradation pathway may be a common MYC-SL pathway across a variety of cell types (Liu et al., 2012).

Though this is not entirely surprising given the already documented major role of FBXW7 in MYC degradation, the identification of common MYC-SL genes and pathways across the datasets has not been an easy task (Cermelli et al., 2014).

It is well established that FBXW7 is a crucial component of the SCF-like ubiquitin ligase complex, which targets ubiquitinated proteins for proteasomal degradation. Since many targets of FBXW7 are oncogenes such as MYC, CyclinE, NOTCH1, and c-JUN,

FBXW7 is generally considered a tumor suppressor gene. In fact, in human T-cell acute lymphoblastic leukemia (T-ALL), FBXW7 is one of the most frequently mutated genes

(approximately 30% of cases (Akhoondi et al., 2007)) and mouse models with tissue specific knockout of FBXW7 in various settings develop both hematopoietic and solid tumors (Wang et al., 2012), corroborating the tumor suppressing functions of FBXW7 in these contexts.

We propose that FBXW7 can also act as a tumor maintenance gene in the context of MYC deregulation. The presence of FBXW7 enables continued proliferation and survival of cells expressing MYC at aberrant levels. Consistent with our results, increased accumulation of MYC is responsible for the loss of leukemic stemness in leukemia-initiating cells lacking FBXW7 (King et al., 2013; Reavie et al., 2013; Takeishi et al., 2013). In a T-ALL mouse model, MYC protein levels in leukemia-initiating cells were found to rely heavily on FBXW7 activity, demonstrating that MYC is the major contributor to the FBXW7 phenotype (King et al., 2013). Indeed, our studies suggest that accumulation of active MYC protein is the major contributor to the synthetic lethal

phenotype seen upon FBXW7 knockdown and MYCER activation. However, we have not tested all documented targets of FBXW7 such as SREBP, p100, NRF1, NF1, Mcl-1,

KLF5, c-Myb, and Aurora A (Wang et al., 2012).

While FBXW7 mutations are found in several diverse human cancer types such as T-ALL, cholangiocarcinoma, stomach, colon, pancreas, and endometrium, they are rare in other cancers (Cheng and Li, 2012). In breast and ovarian cancer, FBXW7 mutations appear to be infrequent (Akhoondi et al., 2010). This could be explained in part by our results showing that MYCER activation combined with loss of FBXW7 in breast epithelial cells results in loss of viability. We hypothesize that inactivating mutations of FBXW7 may not be able to coexist with MYC overexpression in the breast context. Interestingly, our analysis suggests that MYC and FBXW7 expression have a positive correlation, specifically in the luminal A-subtype of breast cancers that tend to have lower levels of MYC. The synthetic lethal effect between MYC deregulation and

FBXW7 knockdown might be most relevant for human breast cancers with lower levels of MYC, since these cells may retain MYC-dependence and thus affected more robustly by alterations in MYC protein levels due to knockdown of FBXW7. To test this hypothesis, more detailed analysis of FBXW7 expression in individual breast cancer subtypes would be necessary.

Pharmacological inhibitors of FBXW7 are still under development and not yet available to test. We hypothesize that treatment of MCF10A-MYCER cells with FBXW7 inhibitor would recapitulate the shRNA-mediated knockdown results. Testing FBXW7 inhibitors in a panel of breast cell lines as well as breast cancer cell lines overexpressing

MYC would be crucial in order to test the relevance of the synthetic lethal interaction we have seen in our system. Nevertheless, our results demonstrating the identification of

FBXW7 as a synthetic lethal target in the context of MCF10A-MYCER adds insight to the still enigmatic role of the interaction between FBXW7 and MYC in human cancer

initiation, progression, and maintenance. Ultimately, we hope that these results could contribute to the development of more diverse targeted clinical treatment options for specific breast cancer subtypes.

Materials and Methods

Cell culture

MCF10A cells were cultured in DMEM/F12 (Invitrogen, Carlsbad, CA, USA) supplemented with 5% horse serum (Invitrogen), 20ng/ml EGF (Peprotech, Rocky Hill,

NJ, USA), 0.5цg/ml hydrocortisone (Sigma-Aldrich Corporation, St. Louis, MO, USA),

100ng/ml cholera toxin (Sigma), 10цg/ml insulin (Sigma), and antibiotics. 293T cells were cultured in DMEM (Invitrogen) supplemented with 10% FBS (Invitrogen) and antibiotics. All cells were incubated at 37ºC and 5% CO2.

Isolation of MYCER cells

MYCER was introduced retrovirally using pBabe-hygro-MYCER into MCF10A cells and transduced clones were selected by the addition of 100цg/ml Hygromycin B (Roche

Holding AG, Basel, Switzerland). Clones were isolated by serial dilution.

Cell lysis and cellular fractionation

Cells were lysed in RIPA buffer (150mM NaCl, 1% NP40, 0.5% DOC, 0.1% SDS, 50mM

Tris pH 8.0), supplemented with protease and phosphatase inhibitors (Sigma), for 15 min on ice. Samples were sonicated (2 x 5 min) and centrifuged for 10 min at 4ºC.

Cellular fractionation was performed as published by Mendez and Stillman (Mendez and

Stillman, 2000).

Production of lentivirus and shRNA

Glycerol stocks of lentiviral shRNA constructs (pGIPZ, pTRIPZ) were obtained from

Thermo Scientific Open Biosystems (Waltham, Massachusetts, USA) and grown in LB medium with 100цg/ml carbenicillin (Sigma) and 25цg/ml Zeocin (InvivoGen, San Diego,

CA, USA). Clones used were FBXW7-7 (V2LHS_202932), FBXW7-8 (V2THS_89328),

FBXW7-10 (V2THS_203045), UBE2I-3 (V2LHS_171776), and UBE2I-6

(V3LHS_376933). Plasmids were isolated using the E.Z.N.A. Plasmid Miniprep kit

(Omega Bio-Tek, Inc., Norcross, GA, USA) and packaged into lentivirus by transfecting

(jetPEI by Polyplus-transfection S.A., Illkirch, France) into 293T cells with pMD.G and pCMVR8.91. Viruses were collected in MCF10A media, filtered, and infected into

MCF10A in the presence of 8цg/ml polybrene (Sigma) and spin infected for 1 hr at

1000rpm at RT. After 24 hr of incubation, infected cells were selected by the addition of

2цg/ml puromycin (Sigma).

shRNA screen

The pooled shRNA screen was carried out using the Thermo Scientific Open Biosystems

GIPZ Lentiviral Human shRNA-mir Library as described in Rodriguez-Barrueco et al

(Rodriguez-Barrueco et al., 2013). The library was packaged into lentivirus as described above, and MCF10A-MYCER cells were infected at 30% efficiency. Infected cells were selected with 2цg/ml puromycin for two passages and divided into 10 independent populations. Five populations were treated with 200nM 4OHT (every 48 hr) and five were treated with vehicle. After 30 days, genomic DNA of surviving cells was isolated by lysing cells with DNA lysis buffer (1% SDS, 100mM EDTA pH8.0, 50mM Tris-HCl pH8,

100nM NaCl), treating with RNAse A (Qiagen N.V., Venlo, Netherlands), and purifying

DNA with phenol chloroform extraction and isopropanol precipitation. Resulting DNA was subjected to PCR to recover the shRNA and associated barcodes, generating a heterogeneous pooled product of approximately 350 bps. The PCR product was gel- extracted and purified, labeled with Cy3 and Cy5 (Roche), and hybridized to customized microarrays (Agilent Technologies, Inc., Santa Clara, CA, USA) harboring complementary probes to the shRNA barcodes. Data analysis was then performed as

described in Yu et al (Yu et al., 2013).

Competition assays

250 000 MCF10A-MYCER cells expressing stably integrated shRNA and 250 000

MCF10A-MYCER cells without shRNA were plated together. At the first passage, fluorescent cells were counted by live cell flow cytometry, and replated with either vehicle or 4OHT. At each subsequent passage, remaining percentage of fluorescent cells was analyzed using flow cytometry. All cell sorting was performed with the BD

FACSCalibur (BD Biosciences, San Jose, CA, USA).

mRNA isolation and RT-PCR

RNA was isolated using the Nucleospin RNA II kit (Clontech Laboratories, Inc., Mountain

View, CA, USA). 1цg of RNA was subjected to reverse transcription using the High

Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Life Technologies,

Carlsbad, CA, USA). cDNA was used as template for quantitative RT-PCR using

ABsolute Blue QPCR SYBR Green Low ROX Mix (Thermo Scientific) and Applied

Biosystems 7500 Fast (Life Technologies, Carlsbad, CA, USA).

Luciferase assay

MCF10A-MYCER cells were plated in 12-well plates. After 24 hr, at 90% confluence, they were transfected with MYC reporter and Renilla plasmid and incubated for 48 hr with or without 4OHT. Using the Dual-Luciferase Reporter Assay System (Promega

Corporation, Fitchburg, WI, USA), relative luciferase activity was measured with the

GloMax Luminometer (Promega).

Double thymidine block and indirect immunofluorescence

MCF10A-MYCER cells were plated on sterilized coverslips. After 24 hr, cells were incubated in 2.5mM thymidine for 17 hr. After a 7 hr recovery, the second block was administered at 2.5mM for 14 hr. Vehicle or 4OHT was added 3 hr prior to release. After

1 hr recovery, cells were treated with an 8 min pulse of 33цM BrdU. Immediately, cells were washed and fixed with 4% paraformaldehyde for 15 min at RT, washed, then permeabilized with 0.2% Triton-X in PBS for 5 min and washed. DNA was denatured in

2N HCl for 20 min at RT, following neutralization with 0.1M sodium tetraborate pH8.5.

Primary antibody incubation with anti-BrdU (#555647, BD Pharmingen, San Jose, CA,

USA) was performed in PBS-0.5%Tween for 30 min at RT, followed by washing, and secondary antibody incubation with goat-anti-mouse-FITC (Jackson ImmunoResearch

Laboratories, Inc., West Grove, PA, USA) was performed in PBS-0.5%Tween for 30 min.

Cells were washed and stained for 1 min with propidium iodide, airdried, and mounted on slides for confocal microscopy. The Zeiss LSM 510 Meta Inverted (Carl Zeiss AG,

Oberkochen, Germany) was used for all confocal imaging.

Western blotting and quantification

Cell lysates were prepared as described, added to 5X Laemmli buffer and heated at

95ºC for 5 min. Samples were run on Tris-Glycine or NuPAGE Tris-Acetate gels

(Invitrogen) and transferred to PVDF membranes at 35V for 2 hr. Membranes were blocked in PBS-0.5%Tween-5%milk for 1 hr and stained with primary antibodies (1:250-

1:2000) for 1 hr or overnight, followed by staining with secondary antibodies (1:10,000) conjugated with HRP (Jackson) for 1 hr. After incubation with ECL (Pierce, Thermo

Fisher Scientific), blots were exposed to film. ImageQuant 5.2 (Molecular Dynamics) was used for quantification.

Cell cycle analysis

At the time of harvest, MCF10A-MYCER cells were vortexed in 0.5 ml PBS, and 5 ml of

70% ethanol was added dropwise for fixing at -20ºC overnight. The next day, cells were incubated in 1 ml PBS for 1 hr on ice and centrifuged. To the resulting pellet, RNAse A was added at 1/400, and propidium iodide stock (10mg/ml, Sigma) was added at 1/1000 for flow cytometry. All analysis was performed with the BD FACSCalibur (BD

Biosciences).

Antibodies

The following antibodies were used for immunostaining: MYC (9E10, Santa Cruz

Biotechnology, Inc., Dallas, TX, USA), c-Jun (sc-44, Santa Cruz), cleaved Notch1 (4147,

Cell Signaling, ), cyclin E (sc-481, Santa Cruz Biotech), mTOR (2972, Cell Signaling

Technology, Inc., Danvers, MA, USA), βactin (A2228, Sigma), Histone H3 (9715, Cell

Signaling), Cdc45 (EPR5759, Epitomics, Burlingame, CA, USA), Phospho-Chk2 (2661,

Cell Signaling), Phospho-H2AX (JBW301, EMD Millipore, Billerica, MA, USA), Cleaved

Caspase-3 (9664, Cell Signaling), PARP-1 (sc-8007, Santa Cruz), PUMA (sc-28226,

Santa Cruz).

Conflict of interest

The authors declare no conflict of interest.

Acknowledgements

We thank Dr. David Dominguez-Sola and Dr. Carol Ying for MYC, CyclinE, and β actin antibodies, as well as experimental guidance and support. c-Jun antibodies were a gift from Dr. Bin Zheng. Notch1 antibodies were shared by Dr. Adolfo Ferrando’s laboratory.

mTOR antibody was shared by Dr. Ramon Parsons’ laboratory. PARP-1 and PUMA antibodies were shared by Dr. Wei Gu’s laboratory. We thank all the mentioned groups for additional assistance and support. This work was supported in part by NCI grants

CA092245 and CA167826 to JG.

Supplementary Information

Supplementary Information accompanies the paper on the website.

Figure legends

Figure 1. MCF10A-MYCER cells: a model for MYC deregulation. (a) MCF10A-MYCER cells express MYCER protein. Whole cell lysates from uninfected or MYCER-infected

MCF10A cells were analyzed by SDS-PAGE. The positions of endogenous MYC and

MYCER are indicated. (b) MYCER protein is activated in response to 4OHT treatment.

Nuclear translocation of MYCER was analyzed by cell fractionation followed by SDS-

PAGE of the chromatin-bound fraction from MCF10A-MYCER cells treated with vehicle or 200nM 4OHT for 48 hr. MYCER increase was normalized to histone H3 levels for quantification. (c) MYCER activation induces accelerated entry into S phase. MCF10A-

MYCER cells were arrested at G1/S with double thymidine. Cells were treated with vehicle (NT=not treated) or 4OHT, then pulse labeled with BrdU 1 hr after release into S phase. BrdU incorporation was assessed using indirect immunofluorescence.

PI=propidium iodide was used to stain genomic DNA.

Figure 2. Genome-wide screen for synthetic lethal candidates with MYCER activation.

(a) Schematic diagram of screen design. See text for details. (b) Identification of synthetic lethal shRNA with MYCER. Heat map shows the relative enrichment or depletion of each shRNA in all experimental replicates. The spectrum between red and blue denote the relative representation of each shRNA barcode in the sample compared to the reference (library at T=0). (c) Five biological pathways most required for the survival of MCF10A-MYCER. Pathway analysis of the top synthetic lethal candidates

(Log2 FC<-1) shows enrichment of genes in five biological pathways. Pathways are listed in order of number of genes appearing from the dataset.

Figure 3. Loss of FBXW7 combined with MYCER activation is lethal. (a) Knockdown of

FBXW7 in MCF10A-MYCER cells. FBXW7 expression was inhibited in MCF10A-

MYCER cells using lentiviral transduction of shRNA. Constitutive (FBXW7-7) and inducible (FBXW7-8 and -10) knockdown levels were analyzed by quantitative RT-PCR.

Results show the mean and SEM of 3 independent experiments. (b) FBXW7 knockdown compromises the viability of cells with MYC ON. Fluorescence-based competition assay over 23 days was performed as described in the text. The GFP-expressing population was measured at the indicated timepoints for untreated (NT) and 4OHT-treated cells.

Cells were treated with vehicle or 4OHT every 72 hr for the duration of the experiments.

Graphs represent the mean of three replicate experiments. (c) Three independent clones of constitutive and inducible FBXW7 shRNA decrease the viability of 4OHT-induced

MCF10A-MYCER cells. Fluorescence-based competition assay data are plotted as ratios of %GFP of MYC ON to MYC OFF at the given timepoints. Graphs represent the mean of three independent experiments.

Figure 4. Loss of FBXW7 with MYCER activation results in specific stabilization of active

MYCER protein. (a) MYCER protein stability is enhanced in FBXW7 knockdown cells.

Whole cell extracts were prepared from MCF10A-MYCER cells with FBXW7 knockdown.

Inducible clones were activated with 1µg/ml doxycycline. Quantified values below each lane are normalized to β actin and averaged for 3 experiments. Error bars represent the

SEM. (b) Active MYCER is stabilized on chromatin in FBXW7 knockdown cells following

4OHT induction. Cells treated with vehicle or 4OHT were fractionated, and fraction P3

(chromatin-bound) was analyzed on SDS-PAGE. Quantifications are normalized to histone H3 and values are averages of 3 experiments. Error bars represent the SEM. (c)

FBXW7 knockdown with MYCER activation results in specific stabilization of MYCER.

Whole cell extracts were prepared from control or FBXW7 knockdown cells and

100

analyzed on SDS-PAGE followed by Western blot for FBXW7 targets (MYCER, c-Jun,

Notch1, Cyclin E, mTOR). Quantifications show the fold enrichment of protein levels in control or FBXW7 knockdown cells normalized to β actin. Results are averages of 3 independent experiments and error bars represent the SEM.

Figure 5. MYCER stabilization results in accumulation of cells in S/G2 phase and chromatin-bound CDC45. (a) Constitutive knockdown of FBXW7 with MYCER activation results in additive accumulation of cells in S and G2/M phase. MCF10A-MYCER cells with control or FBXW7 knockdown were cultured for 7 days in the absence or presence of 4OHT (MYC OFF or MYC ON). Cells were fixed and stained with propidium iodide for cell cycle analysis. Results are averages of 3 independent experiments and error bars represent the SEM. (b) Inducible knockdown of FBXW7 with MYCER activation results in accumulation of cells in S and G2/M phase. Control or FBXW7 knockdown cells were cultured for seven days with 1µg/ml doxycycline and vehicle or 4OHT every 72 hr.

Results are averages of 3 independent experiments and error bars represent the SEM.

(c) Loss of FBXW7 with MYCER activation causes accumulation of CDC45 on chromatin. MCF10A-MYCER cells with control or FBXW7 knockdown were cultured for 7 days with or without 4OHT and fractionated. Fraction P3 was analyzed on SDS-PAGE.

Shown is a representative image of CDC45 blot, and graph shows the average of 3 independent experiments each normalized to histone H3. Error bars represent the SEM.

101

FigureFigure 1 1.

a b c

BrdU PI

-130 -130 MYCER -95 MYCER Vehicle -95 -72 -72 MYC (endg) MYC (endg) -55 -55 1 1.54 -36 -55 HistoneH3 +4OHT

β actin BOUND CHROMATIN

WHOLE CELL LYSATE LYSATE CELL WHOLE -36

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Figure 2 Figure 2.

a Lentiviral infection of b human shRNA library

MCF10A-MYCERTAM

+EtOH +4OHT (MYC OFF) (MYC ON)

Harvest after 4 weeks

DNA extraction PCR Label Hybridization c

Most represented pathways (KEGG) Replica 1 2 3 4 5 1 2 3 4 5 Ubiquitin mediated proteolysis PPAR signaling pathway 4OHT - + Fatty acid biosynthesis Fructose and mannose metabolism -2.2 +2.2 Homologous recombination

103

Figure 3 Figure 3. a b

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Figure 4 Figure 4.

a b WHOLE CELL EXTRACTS CHROMATIN-BOUND FRACTION

4OHT - + - + - + - + - + MYCER MYCER β actin HistoneH3 p<0.03 p=0.04 p=0.05 p<0.1 p<0.05

NS NS MYCER/ACTIN MYCER/ACTIN MYCER/H3 MYCER/H3

105

Figure 5 Figure 5.

a c S phase G2/M phase CHROMATIN-BOUND FRACTION

4OHT - + - +

CDC45 -72

b HistoneH3 S phase G2/M phase CDC45/H3

106 MYC is a critical target of FBXW7

Supplementary Figures 1-7.

Supplementary Figures 1-9

a E-box sequences

1 2 3 4 5 6 HSV-TK Luc+

b Relative MYC reporter activity in Relative MYC reporter activity in uninfected MCF10A cells MYCER-infected MCF10A cells

4 4

3.5 3.5

3 3

2.5 activity 2.5

2 2

1.5 luciferase 1.5

1 1

Relativeluciferase activity 0.5 Relative 0.5

0 0 NT + 4OHT NT + 4OHT

Supplementary Figure 1. MYCER protein expressed from the transgene can enhance transcriptional activity. (a) A reporter plasmid containing 6 E-box sequences (CACGTG) followed by a luciferase reporter was transfected into uninfected parental MCF10A or MCF10A-MYCER cells and luminescence was measured 48 hr after transfection. (b) Luciferase activity from the reporter was normalized to Renilla luciferase activity. Each experiment was done in triplicate and graphs show the average of 3 biological replicates. Error bars represent the SEM.

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Asynchronous cells

After double thymidine block

24h post release from DT block

Supplementary Figure 2. MCF10A-MYCER cells arrest at the G1/S transition following double thymidine block. MCF10A-MYCER cells were subjected to two rounds of 2.5mM thymidine treatment, then fixed and stained with propidium iodide for flow cytometric analysis. Compared to untreated asynchronous cells, thymidine treated cells showed accumulation of cells in M1+M2 (<2N DNA content). 24h after release, the cell cycle distribution returns to normal.

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MCF10A-MYCERTAM MCF10A-MYCERTAM-shRNA

50% GFP or RFP +EtOH +4OHT (MYC OFF) (MYC ON)

FACS analysis

Supplementary Figure 3. Schematic of fluorescence-based competition assay. Uninfected MCF10A-MYCER cells which express no fluorescent proteins are plated on the same plate with MCF10A-MYCER cells infected with a single shRNA clone of choice co-expressing GFP or RFP. At day 1, cells from the plate are subjected to live cell flow cytometry to verify ~50% fluorescence. Subsequently, the cells are split into control or 4OHT treated groups, and passaged every 48-72 hr. At each passage or desired timepoint, remaining fluorescence on the plate is measured by flow cytometry for the duration of the experiment.

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WHOLE CELL EXTRACTS

4OHT - + - + -130

MYCER -95

c-Jun -55 -36

Notch1 -130 -95 -72 Cyclin E -55

mTOR -300 -55 βactin -36

Supplementary Figure 4. FBXW7 knockdown specifically leads to stabilization of MYCER. MCF10A-MYCER cells expressing either control or FBXW7 shRNA were cultured in the presence or absence of 4OHT for 4 weeks. Whole cell lysates (80µg total protein/lane) were run on SDS-PAGE and subjected to Western blot for analysis of major FBXW7 targets. Figure shows a representative image of replicated experiments.

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Supplementary Figure 5. FBXW7 knockdown leads to preferential stabilization of MYCER. MCF10A-MYCER cells stably expressing Dox-inducible shRNA clones of FBXW7 were treated with or without 4OHT (MYC OFF or ON) for 4 weeks, then cell lysates were analyzed by SDS-PAGE/Western blot. Bands were quantified using ImageQuant v5.2 (Molecular Dynamics) and normalized to loading controls. Figure shows averages from 3 independent biological replicates.

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shCONTROL shFBXW7-7

MYC OFF

MYC ON

Supplementary Figure 6. FBXW7 knockdown with MYCER activation causes synergistic accumulation of cells in S and G2/M phase. MCF10A-MYCER cells stably expressing control or FBXW7 shRNA were cultured for 4 weeks in the presence or absence of 4OHT (MYC OFF or ON). Cells were fixed and stained with propidium iodide for flow cytometric analysis. Cells accumulate in the M3+M4 region (<1N DNA content) only in the FBXW7 knockdown and MYC ON condition. Shown is a representative image of 3 independent replicates.

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a b

DNA DAMAGE APOPTOSIS

4OHT - + - + 4OHT - + - + -28 P-Chk2 -72 Cleaved Caspase3 β actin -95 Cleaved PARP1 -28 -28 P-H2AX PUMA

HistoneH3 β actin

Supplementary Figure 7. Long term FBXW7 knockdown and MYCER activation do not result in significant levels of DNA damage or apoptosis. (a) Control or FBXW7 knockdown cells were treated with or without 4OHT for 4 weeks and probed for the presence of phosphorylated Chk2 (in whole cell lysate) or H2AX (in chromatin-bound samples after fractionation) as markers of checkpoint activation/DNA damage. shFBXW7 cells did not show a significant accumulation of either species after long term treatment with 4OHT. (b) Similarly treated cells were probed for the presence of apoptosis markers in whole cell lysates. However, we failed to detect any significant levels of cleaved caspase-3, PARP1, or PUMA in the surviving cells after 4 weeks of treatment. Shown above are representative images of 3 independent replicates.

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Supplementary Figure 8. MYC and FBXW7 expression levels positively correlate in Luminal A-type breast cancers. (a) FBXW7 vs. MYC expression in all breast cancers. (b) FBXW7 vs. MYC expression in luminal A- type breast cancers. RNA-seq (IlluminaHiSeq) data from TCGA breast invasive carcinoma dataset was downloaded from the cancer browser (TCGA_BRCA_exp_HiSeqV2). Overall, 1106 samples were assessed, including 228 LUMINAL A subtypes. Expression of MYC and FBXW7 were estimated in RSEM normalized count and Pearson correlation coefficient (rho) was calculated using STATA (Stata Statistical Software: Release 12. College Station, TX: StataCorp LP).

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Supplementary Figure 9. UBE2I knockdown is synthetic lethal with MYCER activation. (a) MCF10A-MYCER cells stably expressing two independent shRNA clones targeting UBE2I (shUBE2I-3 and shUBE2I-6) were subjected to competitive survival assays. By day 27, MYC ON/MYC OFF survival ratios of shUBE2I cells decreased to 49% (p<0.001), while ratios of shCONTROL cells decreased to 81%(p<0.001) compared to day 0. Graphs represent the average of three replicates. (b) shRNA-mediated knockdown of UBE2I. MCF10A-MYCER cells stably expressing shRNA clones targeting UBE2I (UBE2I-3 and UBE2I-6) were subjected to quantitative RT-PCR. Error bars represent the SEM.

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Supplementary Table 1

Supplementary Table 1. List of top 78 synthetic lethal targets from MCF10A-MYCER screen. Results were filtered according to fold change (Log2 FC<-1) of signal abundance between MYC ON and MYC OFF, and individually associated p-values (p<0.05). Only annotated target genes are shown. Candidates are ranked in order of most depleted in the MYC ON population compared to MYC OFF (Log2 FC value).

Rank Name p-value Log2 FC Acc. Number shRNAi ID 1 ARL5B 0.0500 -2.7089 NM_178815 v2HS_118631 2 C2orf44 0.0100 -2.5672 NM_025203 v2HS_137381 3 TTBK1 0.0300 -2.4316 AB058758 v2HS_238991 4 PSG6 0.0400 -2.3989 NM_002782 v2HS_170657 5 NAT9 0.0500 -2.3896 NM_015654 v2HS_263964 6 TRPM6 0.0300 -2.1357 NM_017662 v2HS_155076 7 MFN2 0.0100 -2.0827 NM_014874 v2HS_95806 8 C18orf54 0.0100 -1.8569 NM_173529 v2HS_160761 9 DONSON 0.0300 -1.8499 NM_017613 v2HS_154816 10 ZNF614 0.0400 -1.8499 NM_025040 v2HS_235983 11 H2AFV 0.0100 -1.8450 NM_138635 v2HS_200741 12 NUPL1 0.0500 -1.8381 NM_014778 v2HS_233311 13 ZNF691 0.0100 -1.7257 NM_015911 v2HS_236174 14 PSMD2 0.0500 -1.7213 NM_002808 v2HS_219275 15 CDC14B 0.0400 -1.7209 NM_033331 v2HS_203260 16 UGT1A1 0.0300 -1.6796 NM_000463 v2HS_93183 17 SOX15 0.0500 -1.6110 NM_006942 v2HS_229072 18 NUPL2 0.0500 -1.6086 NM_007342 v2HS_86275 19 ANKRD27 0.0500 -1.6068 NM_032139 v2HS_117932 20 EPHA7 0.0100 -1.5984 NM_004440 v2HS_17738 21 ST8SIA4 0.0500 -1.5098 NM_005668 v2HS_238967 22 SLC44A2 0.0500 -1.4952 NM_020428 v2HS_72471 23 SH3BP4 0.0300 -1.4934 NM_014521 v2HS_86832 24 KIAA0509 0.0300 -1.4922 AB007978 v2HS_259524 25 DMKN 0.0500 -1.4804 NM_033317 v2HS_160312 26 STK17A 0.0100 -1.4726 NM_004760 v2HS_36021 27 PHF21A 0.0100 -1.4564 NM_016621 v2HS_135309 28 EYA2 0.0200 -1.4531 NM_005244 v2HS_43093 29 C6orf184 0.0100 -1.4413 XM_168053 v2HS_121031 30 ZFYVE9 0.0100 -1.4407 NM_004799 v2HS_35331 31 LRCH3 0.0400 -1.4395 NM_032773 v2HS_222832 32 NR2E1 0.0100 -1.4308 NM_003269 v2HS_261941 33 TOP3B 0.0100 -1.4280 NM_003935 v2HS_47174 34 WDSOF1 0.0300 -1.4122 NM_015420 v2HS_229697 35 CLDN5 0.0500 -1.3596 NM_003277 v2HS_171412 36 CMTM2 0.0400 -1.3528 NM_144673 v2HS_17530 37 IRAK1 0.0300 -1.3447 NM_001569 v2HS_132369

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38 APOBEC3A 0.0300 -1.3348 NM_145699 v2HS_286556 39 ERGIC1 0.0300 -1.3258 NM_020462 v2HS_71567 40 PARP15 0.0100 -1.3206 NM_152615 v2HS_44475 41 RAE1 0.0500 -1.2995 NM_003610 v2HS_27966 42 LEPREL1 0.0500 -1.2897 NM_018192 v2HS_156224 43 ZNF132 0.0300 -1.2802 NM_003433 v2HS_172208 44 SLC7A1 0.0500 -1.2710 NM_003045 v2HS_153014 45 LAMA2 0.0200 -1.2692 NM_000426 v2HS_92966 46 LOC344065 0.0400 -1.2685 XM_292895 v2HS_143532 47 ATP1B1P1 0.0500 -1.2641 NG_001081 v2HS_169198 48 C1orf182 0.0500 -1.2536 NM_144627 v2HS_18680 49 KRIT1 0.0400 -1.2463 NM_004912 v2HS_62790 50 ASAH3 0.0400 -1.2436 NM_133492 v2HS_99964 51 OLFR89 0.0300 -1.2192 AJ132194 v2HS_18007 52 CCDC25 0.0300 -1.1758 NM_018246 v2HS_156496 53 TOP3A 0.0100 -1.1740 NM_004618 v2HS_42272 54 MATK 0.0200 -1.1567 NM_002378 v2HS_203423 55 ST14 0.0100 -1.1516 NM_021978 v2HS_228778 56 KIAA0574 0.0300 -1.1513 AB011146 v2HS_130193 57 PERP 0.0200 -1.1513 NM_022121 v2HS_216968 58 FBXW7 0.0200 -1.1469 AY033553 v2HS_89326 59 APOA2 0.0100 -1.1369 NM_001643 v2HS_132594 60 C5 0.0400 -1.1342 NM_001735 v2HS_150150 61 SLC36A4 0.0500 -1.1260 NM_152313 v2HS_25433 62 PPP3CC 0.0300 -1.1253 NM_005605 v2HS_57300 63 TRIM26 0.0500 -1.1229 NM_003449 v2HS_172289 64 ELL 0.0500 -1.1181 NM_006532 v2HS_198413 65 PRMT8 0.0100 -1.1163 NM_019854 v2HS_34353 66 PRSS21 0.0500 -1.1146 NM_006799 v2HS_83969 67 UBE2I 0.0200 -1.0971 NM_003345 v2HS_171781 68 QRSL1 0.0400 -1.0967 XM_301163 v2HS_49076 69 C11orf48 0.0400 -1.0951 NM_024099 v2HS_116419 70 EPYC 0.0400 -1.0697 NM_004950 v2HS_61963 71 TNFAIP2 0.0100 -1.0598 NM_006291 v2HS_56492 72 PAQR3 0.0300 -1.0499 NM_177453 v2HS_211154 73 PREB 0.0500 -1.0457 NM_013388 v2HS_64102 74 CEND1 0.0300 -1.0194 NM_016564 v2HS_135015 75 BBOX1 0.0300 -1.0107 NM_003986 v2HS_246341 76 CDKL5 0.0500 -1.0097 NM_003159 v2HS_262311 77 CMTM7 0.0100 -1.0049 NM_138410 v2HS_70332 78 GCN1L1 0.0400 -1.0020 XM_045792 v2HS_68924

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CHAPTER 4

Transcription-coupled repair is required to survive MYC-dependent genomic stress

118

This manuscript is unpublished.

Title

Transcription-coupled repair is required to survive MYC-dependent genomic stress

Authors

Mai Sato1,2, Ruth Rodriguez-Barrueco2, Jiyang Yu3, Jose M. Silva1,2, Max Gottesman4, and Jean Gautier2,5

Affiliations

1Department of Pathology and Cell Biology, Columbia University, New York, NY, USA.

2Institute for Cancer Genetics, Columbia University, New York, NY, USA.

3Department of Biomedical Informatics, Columbia University, New York, NY, USA.

4Institute of Cancer Research, Columbia University, New York, NY, USA

5Department of Genetics and Development, Columbia University, New York, NY, USA.

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Abstract

Oncogene-induced genomic stress is detected in many types of cancers. Maintenance of genome stability is critical for cell survival following oncogene activation (Negrini et al.,

2010). To target pathways required in cancer cells with oncogene activation and to identify genes that are synthetic lethal with deregulation of MYC in MCF10A cells, we conducted a genome-wide screen using the human lentiviral shRNA library. We identified UVSSA and ERCC8, two genes involved in transcription-coupled repair (TCR, or TC-NER), as synthetic lethal candidates with MYC deregulation. Both UVSSA and

ERCC8 knockdowns decreased viability of cells when combined with MYCER activation.

MYC-deregulated cells are also sensitized to double knockdown of UVSSA and ERCC8 as well as to chemical inhibitors of USP7. This finding establishes that the

UVSSA/ERCC8/USP7 module of the TCR pathway is required to cope with MYC overexpression. Notably, the role of UVSSA/ERCC8 in maintaining viability of MYC- deregulated cells is independent of UV-induced damage repair, but dependent on RNA polymerase II-dependent transcription. We propose that transcription-coupled repair is required for alleviating transcription-dependent genomic stress, which may be one of the major sources of MYC-induced genomic instability.

Introduction

Increasing evidence suggests that oncogenes induce various types of genomic stress both directly and indirectly. Oncogene-induced genome instability could be an

Achilles’ heel for cancer cell survival (Moeller et al., 2010; Negrini et al., 2010). Cancer cells with oncogene activation such as MYC and RAS show common markers of genomic instability including increased DNA damage, microsatellite instability, and chromosomal aberrations (Felsher and Bishop, 1999; Saavedra et al., 2000; Wade and

Wahl, 2006). Targeting genome maintenance pathways that allow cells to survive

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oncogene-induced genomic stress can be an effective way to halt tumorigenesis induced by these oncogenes. Furthermore, the mechanism(s) resulting in genomic stress upon oncogene activation in cancer cells are still largely elusive.

MYC is one of the most frequently deregulated, overexpressed, or amplified proto-oncogenes in human cancers ranging from hematopoietic to mesenchymal and epithelial tumors (Meyer and Penn, 2008; Morton and Sansom, 2013). MYC activity is normally tightly regulated both at the mRNA and protein levels to stimulate growth and proliferation only in response to specific mitogenic signals. Thus when deregulated, MYC can promote uncontrolled cell division which is one of the driving forces for tumorigenesis (Meyer and Penn, 2008). Genomic stress caused by MYC overexpression has been documented in tumor cells as well as in mouse models overexpressing MYC

(Kuttler and Mai, 2006; Prochownik, 2008). MYC directly promotes increased RNA transcription as well as DNA replication, two intrinsically mutagenic physiological processes (Dang, 2012; Errico and Costanzo, 2012; Kim and Jinks-Robertson, 2012).

Therefore, it is likely that MYC expression yields genomic instability through aberrant

DNA replication, transcription, or a combination of both (Helmrich et al., 2013).

Moreover, MYC can also accelerate the cell cycle and alter the activity of many other cellular processes by transcriptionally upregulating or downregulating a massive number of downstream genes (Meyer and Penn, 2008). Markers of genomic instability, DNA damage, as well as checkpoint activation have been previously identified in MYC- deregulated cells; however, the relative contribution of different types of genomic stress in MYC-deregulated cells is still unclear (Wade and Wahl, 2006).

Transcription-coupled repair (TCR), also called transcription-coupled nucleotide excision repair (TC-NER), is essential for efficient repair of bulky adducts or UV-induced lesions specifically on actively transcribed DNA strands (Hanawalt and Spivak, 2008;

Vermeulen and Fousteri, 2013). TCR is less well characterized than NER, and specific

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genes that participate in TCR include ERCC8/CSA, ERCC6/CSB, XAB2, HMGN1,

UVSSA, USP7, and ELL, all of which are independent from the global genome NER

(GG-NER) pathway (Mourgues et al., 2013; Vermeulen and Fousteri, 2013). Current evidence suggest that TCR plays several roles and that its components coordinate a cascade of events including 1) stalling, backtracking, or removal of the active RNA

Polymerase II (RNAPII) complex when it encounters a lesion, 2) recruiting GG-NER factors to repair the DNA lesion, and 3) supporting the resumption of proper transcription after repair has been ensured (Vermeulen and Fousteri, 2013). The mechanistic bases of these processes are unknown. TCR accounts for less than 10% of genome-wide UV lesion repair, while global genomic NER (GG-NER) accounts for the rest (Vermeulen and Fousteri, 2013). Nevertheless, mutations in TCR genes give rise to diseases in humans that can range from mild photosensitivity (e.g. UV-SS) to severe, congenital defects with a significantly reduced lifespan (e.g. Cockayne syndrome), similar to GG-

NER defective conditions (Saijo, 2013). This suggests that TCR may have significant cellular roles in addition to repair of exogenous UV lesions.

In the present study, we conducted a genome-wide screen using a pooled lentiviral shRNA library to identify synthetic lethal candidates with MYC deregulation using MCF10A breast epithelial cells carrying a MYCER inducible allele. We found

UVSSA and ERCC8 among our high confidence MYC-synthetic lethal (MYC-SL) candidates, both of which have prominent roles in TCR. We validated that loss of either gene by individual knockdown of UVSSA and ERCC8 specifically induced loss of viability in MYC-expressing cells. Our data therefore suggest that the TCR pathway is synthetic lethal with MYC deregulation. UVSSA knockdown in cells with MYC deregulation results in accumulation of phosphorylated CHK2, indicating DNA damage and genomic stress. Notably, reduced viability of MYC-expressing cells upon TCR down- regulation is independent of UV-induced DNA damage but is dependent on RNA

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transcription, suggesting a UV-independent role for UVSSA in maintenance of genome stability during increased transcription.

Results

Genome-wide synthetic lethal screen for MYC-SL candidates using MCF10A-

MYCER

To investigate MYC-SL candidates, we conducted a genome-wide synthetic lethal screen using MCF10A breast epithelial cells stably expressing the MYC-ER allele, which is readily inducible by addition of 4-hydroxytamoxifen (4OHT). These MCF10A-

MYCER cells were infected with the GIPZ Lentiviral Human shRNA-mir library, a pooled shRNA plasmid library including 58,493 shRNA constructs targeting 18,661 known human genes (2-3 shRNAs per gene covering 75% of entire genome) as described in

Rodriguez-Barrueco et al (Rodriguez-Barrueco et al., 2013). Our screen was designed based on negative selection, whereby loss of shRNAs was monitored to identify genes that render cells sensitive to MYCER activation by 4OHT when lost.

First, MCF10A-MYCER cells were infected with the shRNA library at 30% infection efficiency, optimized to achieve single-copy shRNA integration per cell on average, and at a density of 1000 cells infected per shRNA clone. After a brief period of antibiotic selection, infected cells were divided into independent populations and propagated in the presence or absence of 4OHT for the next four weeks. During this time, total cell numbers were constantly maintained to preserve representation of at least

1000 cells per shRNA clone. After four weeks, genomic DNA was isolated from surviving cells, and stably integrated shRNA constructs were amplified by PCR using primers amenable for high-throughput sequencing. Five independent replicate sets of

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populations were differentiated in the analysis using independent 6-nucleotide barcodes embedded in the primers (for further details, see Chapter 2 and 3).

Upon high-throughput sequencing of the screen results, we recovered 2,425 shRNA IDs that were two-fold or more depleted in the MYC ON population compared to

MYC OFF, within an acceptable p-value (p<0.1). Sub-datasets of this list were also generated using more stringent p-value cutoffs revealing 1,319 shRNA IDs at a confidence level of p<0.05, and 316 shRNA IDs at p<0.01 (Chapter 2, Table 2).

Functional clustering analysis of these high-confidence synthetic lethal candidates using the DAVID Bioinformatics Database showed that pathways enriched in our list included, among others, ubiquitin-mediated proteolysis and DNA replication (Chapter 2, Table 3).

These pathways are consistent with previous studies linking the importance of MYC stability to degradation pathways, and demonstrating the significance of MYC-induced replication stress (Dang, 2012; Muller and Eilers, 2008).

UVSSA and ERCC8 are synthetic lethal with MYC

High-throughput sequencing of recovered shRNAs identified UVSSA (or

KIAA1530) as one of our high-confidence hits. Importantly, two independent shRNA clones targeting UVSSA were recovered within an acceptable p-value within the five replicates (Log2 FC= -2.39, p<0.02, and Log2 FC=-2.35, p<0.09) (Chapter 2, Table 4).

To validate this finding and confirm whether UVSSA knockdown is synthetic lethal with

MYC activation, we down-regulated UVSSA expression in MCF10A-MYCER cells using three independent shRNA clones that targeted different sequences than the library shRNAs. We achieved approximately 40-50% knockdown of mRNA levels (Figure 1A).

To compare the fitness of cells expressing shRNAs targeting UVSSA to cells expressing control shRNAs, we used a fluorescence-based competitive survival assay. Cells with

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UVSSA knockdown expressing GFP or RFP from the shRNA lentiviral vector were plated with cells without shRNA in equal numbers, and the fraction of GFP/RFP- expressing cells was followed with time. Cells with UVSSA down-regulation were markedly less viable in the presence of MYC activation over the course of 30 days

(Figure 1B). While the survival ratio of MYC ON/MYC OFF cells remained approximately at 1.0 for control shRNA cells, the ratio of shUVSSA-1 and shUVSSA-3 cells dropped to

0.81 and 0.78 respectively, and shUVSSA-4 dropped to 0.46 (p<0.001) (Figure 1B).

Each of the shRNA clones target different regions of the UVSSA mRNAs and affect all known isoforms, strongly suggesting that the synthetic lethal effect is specifically due to

UVSSA knockdown (Supplemental Figure 1). We therefore conclude that cells with

UVSSA knockdown have markedly decreased viability when MYC is deregulated, validating the synthetic lethal, or sick, phenotype unraveled in the genome-wide screen.

UVSSA physically interacts with ERCC8/CSA to participate in TCR (Fei and

Chen, 2012). Since we also identified ERCC8/CSA as one of our high-confidence synthetic lethal candidates (Log2 FC=-3.2, p<0.03), we sought to validate ERCC8 as a potential additional MYC-SL candidate. Three independent shRNA clones were used to knockdown ERCC8 mRNA levels to approximately 47-68% of control cells (Figure 1C and Supplemental Figure 2). Competition survival assays with these cells showed a 67-

78% decrease in the MYC ON/MYC OFF survival ratio over the course of 18 days compared to control cells, validating ERCC8 as a synthetic lethal, or sick, candidate with

MYC deregulation as well (p<0.001) (Figure 1D).

Since UVSSA and ERCC8 associate within a protein complex, we next ask whether UVSSA knockdown and ERCC8 knockdown had an additive effect on cell survival in MYC ON cells. We generated double knockdown cells using shRNA clones

UVSSA-4 and ERCC8-3, -4, and -6 in combination. The MYC ON/MYC OFF survival ratios over the course of 30 days dropped to 0.45, 0.49, and 0.41 respectively (p<0.001).

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This epistatic relationship is consistent with the effect of the UVSSA-4 clone alone, suggesting that knockdown of UVSSA and ERCC8 generate the synthetic lethal effect with MYC deregulation through the same pathway (Figure 1B and 1E).

UVSSA and ERCC8 also interact with USP7, a de-ubiquitinating enzyme involved in ubiquitin turnover at RNA polymerase complexes (Schwertman et al., 2012;

Zhang et al., 2012). Treatment of MCF10A-MYCER cells with a chemical inhibitor of

USP7 sensitized MYC ON cells approximately 3-fold over MYC OFF cells (Figure 1F).

These results further suggest that UVSSA and ERCC8, and possibly the TCR pathway as a whole, is synthetic lethal with MYC deregulation.

Synthetic lethal interaction is independent of UV damage

UVSSA is mutated in UV-sensitive syndrome (UV-SS), and cells from UV-SS patients are sensitive to UV radiation, consistent with the role of UVSSA in TCR

(Nakazawa et al., 2012; Schwertman et al., 2012; Zhang et al., 2012). Therefore, we next assessed whether the synthetic lethal effect observed upon UVSSA down- regulation in MYC ON cells was dependent on the documented role of the TC-NER pathway in repair of UV damage. We treated shCONTROL or shUVSSA cells with increasing doses of UV radiation in the presence or absence of MYC activation to see if

UV-induced DNA damage exacerbates the synthetic lethal phenotype. MYC ON cells show mildly increased sensitivity to UV both in the shCONTROL and shUVSSA cells, consistent with previous studies suggesting that MYC overexpression confers slight UV sensitivity (Figure 2A and 2B) (Felsher and Bishop, 1999). UVSSA knockdown alone also confers mild UV sensitivity compared to CONTROL, consistent with previous reports in UV-SS patient cells and knockdown cells (Figure 2B) (Nakazawa et al., 2012;

Schwertman et al., 2012; Zhang et al., 2012). Importantly, the induction of MYC did not

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exacerbate the UV sensitivity of shUVSSA cells (Figure 2B). This suggests that the synthetic lethal effect with MYC is independent of the canonical, UV-induced TCR pathway. It further suggests that TCR, or at least UVSSA, have additional roles independent of UV-lesion repair that are required for survival of MYC-deregulated cells.

UVSSA knockdown with MYC activation results in genomic stress

We sought indicators of genomic stress, since UVSSA knockdown may increase genomic instability in MYC-deregulated cells. Specifically, we assessed the presence of markers associated with DNA damage. We monitored the accumulation of phosphorylated CHK2 at T68, an readout of ATM-dependent checkpoint activation. We found that phosphorylated CHK2 was enriched in UVSSA knockdown cells with MYC activation and peaks at ten days (Figure 3C). Notably, P-CHK2 levels were significantly lower than P-CHK2 levels observed following acute treatment with DNA damaging agents such as MMS, suggesting that the level of genomic damage generated with MYC activation and UVSSA knockdown accumulates slowly (data not shown).

We reasoned that accumulation of DNA damage, as suggested by the presence of P-CHK2, might affect cell cycle progression. After 20 days of culturing UVSSA knockdown cells in MYC OFF or MYC ON conditions, we observed a significant accumulation of cells in S and G2/M phase, suggesting retardation of the cell cycle

(Figure 3A and 3B). Knockdown with clones UVSSA-1, -3, and -4 all resulted in an increase in S-phase cells compared to shCONTROL when combined with MYC induction (Figure 3A). In MYC OFF conditions, there were no significant differences in the number of cells in S-phase (Figure 3A). Cells in G2/M-phase were also increased when combined with UVSSA knockdown and MYC induction (Figure 3B). These results indicate that loss of UVSSA contributes to cell cycle defects when MYC is deregulated.

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Synthetic lethal effect is dependent on transcription

MYC deregulation stimulates both RNA transcription and DNA replication.

Aberrant activation of each pathway can lead to genomic instability independently or in combination (Evertts and Coller, 2012; Helmrich et al., 2013). Previous studies from our laboratory have shown that MYC-induced DNA replication triggers DNA damage

(Dominguez-Sola et al., 2007; Srinivasan et al., 2013). However, it is not known whether hyper-activation of MYC-induced transcription can cause increased DNA damage.

Because UVSSA and TCR regulate the normal progression of RNA PolII on damaged

DNA, it is conceivable that they could also counteract MYC-dependent transcription stress. However, it is not known whether TCR is activated by DNA damage other than

UV-induced lesions. To test directly whether the loss of fitness of cells expressing MYC with down-regulated UVSSA is dependent on DNA replication or RNA transcription, we sought to inhibit either process using low doses of chemical inhibitors of each pathway.

Specifically, we used aphidicolin to inhibit DNA polymerase delta and epsilon, and α- amanitin to inhibit RNA Polymerase II. We reasoned that if UVSSA is required for repairing a specific type of genomic stress caused by MYC deregulation, modulating that process might alleviate the synthetic lethal effect.

In a competitive survival assay, cells were either treated with low doses of aphidicolin to inhibit replication, or low doses of α-amanitin to inhibit transcription, in

MYC ON or MYC OFF conditions (Figure 4A and 4B). Remarkably, treatment with

100ng/ml of α-amanitin increased the MYC ON/MYC OFF survival ratio of UVSSA knockdown cells to levels comparable to that of shCONTROL cells over the course of 14 days (Figure 4A, p<0.005). In contrast, after 26 days, the highest sub-lethal dose of aphidicolin (50ng/ml) did not alter the MYC ON/MYC OFF ratio of UVSSA knockdown

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cells (Figure 4B). These experiments suggest that MYC-dependent transcription plays a role in the synthetic lethal effect between MYC activation and UVSSA knockdown. It further suggests that UVSSA or TCR could function outside of UV-induced damage repair in maintenance of genome integrity during hyper-activation of RNA transcription.

Discussion

Genome instability is one of the hallmarks of cancer cells, and increasing evidence suggests that oncogene activation is a significant contributor to genomic damage and stress leading to genomic instability (Negrini et al., 2010; Woo and Poon,

2004). In the case of MYC activation, multiple pathways cooperate, resulting in genomic stress, checkpoint activation and increased chromosomal instability. This makes it difficult to assess which specific genome maintenance pathways are required for cell survival following MYC activation (Wade and Wahl, 2006). Identifying pathways that are specifically required in MYC-deregulated cells but are dispensable for normal cell survival has clear potential therapeutic value (Kaelin, 2005; Weidle et al., 2011).

To identify such vulnerabilities, we looked for genome maintenance genes and pathways that are synthetic lethal with MYC activation, using a genome-wide shRNA screen. Other screening approaches have also been utilized in the case of MYC-SL screens, such as more targeted gene-by-gene methods using RNAi against a specific subset of known human genes such as the kinome or the druggable genome (Liu et al.,

2012; Toyoshima et al., 2012). In the present study, we conducted a large-scale, genome-wide screen for several reasons. First, we reasoned that a genome-wide approach would provide information about the relative contribution of genome maintenance as a synthetic lethal cellular process with MYC compared to other biological pathways. Second, a genome-wide method allows for identification of candidates beyond the scope of prediction, including previously un-annotated genes and

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recently identified genes. Lastly, a genome-wide approach gives us insight into the multiple pathways that MYC-deregulated cells become dependent on for survival. These advantages allowed us to generate a massive dataset of potential synthetic lethal candidates, and among them we were able to discover transcription-coupled repair, a pathway that had not been functionally associated with MYC.

Three other MYC-SL screens have been recently published (Kessler et al., 2012;

Liu et al., 2012; Toyoshima et al., 2012). Since each screen has utilized different tools and cell lines, raw results have varied with relatively low gene-by-gene overlap of top

MYC-SL candidates between screens (>5%). However, recent meta-analysis performed by Cermelli and colleagues of the MYC-SL screen results from these three studies have suggested that the data converge in three main functional MYC-SL hubs (Cermelli et al.,

2014). Namely, these three hubs are 1) regulators of the MYC-MAX network, 2) transcription initiation and elongation complexes, and 3) DNA repair, cell cycle, and checkpoints (Cermelli et al., 2014). Our results showing that transcription-coupled repair is a MYC-SL pathway are consistent with two of these hubs, since TCR is critical for both transcription and DNA repair. Thus, though previous MYC-SL screens have not reported TCR-specific genes in their results, we are confident that the synthetic lethal effect we observe between MYC deregulation and TCR is functionally significant.

Transcription can be a source of DNA damage. Evidence in E. coli and yeast suggests that transcription can induce significant DNA damage in several ways, including transcription-associated mutagenesis (TAM) and transcription-associated recombination (TAR), rates of which are proportional to transcription rate (Kim et al.,

2007; Nickoloff, 1992; Reimers et al., 2004). Further, collisions between transcription and replication machinery have been shown to contribute to genomic stress. This represents, at least in part, accumulation of R-loops during RNA transcription, which can be a source of DNA damage (Kim and Jinks-Robertson, 2012). In contrast, little is known

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about the contribution of transcription to DNA damage in mammalian cells. Our findings suggest that MYC-deregulated cells experience elevated levels of transcription-induced genomic stress, consistent with these previous findings in E. coli and yeast. Moreover, this specific type of DNA damage may be a major source of genomic stress for human cells with MYC deregulation.

Our results suggest that UVSSA, ERCC8, and possibly the TCR pathway have a previously unidentified role in alleviating such transcription-induced genomic stress.

Knockdown of UVSSA conferred a survival defect specifically to cells with MYC activation, albeit to different degrees, depending on the shRNA clone. These differences in survival ratios, despite a similar mRNA knockdown level may reflect possible off-target effects or altered UVSSA protein stability, which we were unable to test due to unavailability of antibodies. Strikingly, however, we find that the synthetic lethal effect between MYC deregulation and UVSSA down-regulation is alleviated in the presence of

α-amanitin, which inhibits RNA polymerases. This suggests that UVSSA plays a critical role in conferring viability to MYC-deregulated cells in response to hyper-activation of transcription. In contrast, retarding genomic DNA replication with aphidicolin did not modulate the interactions between MYC deregulation and loss of UVSSA. This suggests that UVSSA may be specific to cope with MYC-dependent transcription stress.

Furthermore, this synthetic lethal effect is independent of UV-induced DNA damage, suggesting a separate, UV-independent role of UVSSA or the TCR pathway in genome maintenance. Though we were unable to detect significant accumulation of common markers of DNA damage including phosphorylated H2AX and phosphorylated ATM (data not shown), our observation of consistent accumulation of P-CHK2 suggests genomic stress. While the molecular nature of the damage generated following MYC activation is not known, it is tempting to speculate that R-loops generated during transcription might

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accumulate in MYC ON cells and could be the DNA structure from which DNA damage arises.

MYC is a notoriously difficult protein to target directly with pharmacological inhibitors, making synthetic lethality an extremely attractive approach to MYC-dependent cancer therapy (Reinhardt et al., 2009). Several SL candidates with MYC have been identified in the recent years. However, many tend to be essential or functionally important genes in normal cells and might not be suited targets for therapy. Despite its function in TCR, loss of function of UVSSA results in UV-sensitive syndrome (UV-SS), an extremely rare photosensitive disorder presenting mild photosensitivity and no predisposition to cancer (Nakazawa et al., 2012; Schwertman et al., 2012; Zhang et al.,

2012). Indeed, the few UV-SS patients identified to date show virtually no symptoms aside from mild UV sensitivity, unlike other TCR-related disorders such as Cockayne’s syndrome (CS) associated with severe congenital defects (Vermeulen and Fousteri,

2013). These differences likely stem from additional roles of some TCR proteins in processes such as general transcription (Vermeulen and Fousteri, 2013). Therefore, targeting UVSSA to treat MYC-driven tumors is an attractive possibility, since side effects on normal cells may be minimal compared to other chemotherapeutics.

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Materials and Methods

Cell culture

MCF10A cells and 293T cells were cultured as previously described (see Chapter 3).

Production of lentivirus and shRNA

Glycerol stocks of lentiviral shRNA constructs (pGIPZ, pTRIPZ) were obtained from

Thermo Scientific Open Biosystems (Waltham, Massachusetts, USA) as previously described (see Chapter 3). Clones used were UVSSA-1 (V3THS_398946), UVSSA-3

(V3THS_398945), UVSSA-4 (V2THS_139628), ERCC8-3 (V3LHS_332967), ERCC8-4

(V3LHS_332966), and ERCC8-6 (V3LHS_404566). Each plasmid was purified and packaged into lentivirus as previously described to obtain stable knockdown cell lines

(see Chapter 3).

shRNA screen

The pooled shRNA screen was carried out using the Thermo Scientific Open Biosystems

GIPZ Lentiviral Human shRNA-mir Library as previously described (see Chapter 3).

Genomic DNA isolated from surviving populations was subjected to PCR to recover the shRNA using primers with adaptor sequences compatible for the Illumina HTP sequencing platform (Illumina, Inc., San Diego, CA, USA). Forward primer sequence was 5’-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTC

TTCCGATCT-(6-nt marker)-TAGTGAAGCCACAGATGTA-3’ and reverse primer sequence was 5’-

CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGTAATCCAGAGGTTG

ATTGTTCCA-3’. The approximately 450bp PCR product was gel-extracted and purified, pooled, and subjected to Illumina sequencing. Data analysis was then performed as

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described previously (Yu et al., 2013).

Competition assays

Assays were performed as previously described (see Chapter 3). For experiments using

α-amanitin (A2263, Sigma-Aldrich) or aphidicolin (A0781, Sigma-Aldrich), cells were supplemented every 72 hours with the appropriate drug simultaneously with vehicle or

200nM 4OHT during the duration of the experiments.

mRNA isolation and RT-PCR

Isolation and analysis were performed as previously described (see Chapter 3).

MTS assay

MCF10A-MYCER cells were plated in 96-well plates at a density of 3000 cells per well with vehicle or 200nM 4OHT. The next day, media in wells were replaced with fresh media including the appropriate concentration of DMSO or P5091, along with vehicle or

200nM 4OHT. Each condition was done in triplicate in each experiment. 48 hours later, each well was replaced with fresh media for 1 hour, followed by addition of MTS reagent included in the CellTiter 96 AQueous Non-Radioactive Cell Proliferation Assay (Promega

Corporation, Fitchburg, WI, USA). After 2 hours of incubation, plates were read using the uQuant plate reader (BioTek Instruments, Inc., Winooski, VT, USA).

Clonogenic assay for UV sensitivity

MCF10A-MYCER cells stably expressing shCONTROL or shUVSSA-4 were plated at a density of 500 cells per 100mm plate either with vehicle or 200nM 4OHT. The next day, media was replaced with 2ml PBS and plates were irradiated with appropriate doses of

UV-C (254nm) using the Stratalinker UV Crosslinker 2400 (Stratagene California, La

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Jolla, CA, USA). After irradiation, plates were again replaced with media containing either vehicle or 4OHT and incubated for 10 days at 37ºC and 5% CO2. At the time of harvest, cells were washed twice with PBS, fixed with 100% methanol, stained with crystal violet (C3886, Sigma-Aldrich) solution, and visible colonies were counted.

Western blotting and antibodies

Cells were lysed and analyzed by SDS-PAGE as previously described (see Chapter 3).

The following primary antibodies were used for immunostaining: βactin (A2228, Sigma-

Aldrich Corporation, St. Louis, MO, USA), Phospho-Chk2 (2661, Cell Signaling

Technology, Inc., Danvers, MA, USA).

Cell cycle analysis

Analyses were performed as previously described (see Chapter 3).

Conflict of interest

The authors declare no conflict of interest.

Acknowledgements

We thank Dr. David Dominguez-Sola and Dr. Carol Ying for β actin antibodies, as well as experimental guidance and support. We also thank Dr. Bin Zheng for guidance with the MTT assay. We thank Dr. Wei Gu for providing us with P5091. We thank all the mentioned groups for additional assistance and support. This work was supported in part by NCI grants CA092245 and CA167826 to JG.

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Figure legends

Figure 1. MYC deregulation renders cells sensitive to UVSSA and ERCC8 knockdown.

A) Knockdown of UVSSA. Three independent doxycyline-inducible shRNA clones were used to produce stable UVSSA knockdown cell lines. Black bars show UVSSA mRNA levels without shRNA induction, and gray bars show mRNA levels after 72 hours of

1µg/ml doxycycline treatment. Error bars represent the SEM. B) MYC induction sensitizes cells to UVSSA knockdown. shCONTROL or shUVSSA cells were subjected to a competitive survival assay and viability is represented as survival ratios between

MYC ON/MYC OFF conditions. Graph shows average of 3 independent replicates. C)

Knockdown of ERCC8. Three independent shRNA clones were used to produce stable

ERCC8 knockdown cell lines. Error bars represent the SEM. D) MYC induction sensitizes cells to ERCC8 knockdown. shCONTROL or shERCC8 cells were subjected to competitive survival assay as described. Graph shows average of 3 independent replicates. E) UVSSA and ERCC8 show epistatic relationship in synthetic lethality with

MYC. Competitive survival assays following knockdown of UVSSA and ERCC8 were conducted as described. Graph shows average of 3 independent replicates. F) MYC sensitizes cells to USP7 inhibitor P5091. MCF10A-MYCER cells were subjected to MTS assays in MYC OFF or MYC ON conditions using increasing doses of P5091. Error bars represent the SEM.

Figure 2. Synthetic lethal interaction is independent of UV damage. A) MYC induction mildly sensitizes cells to UV treatment. Clonogenic assay using shCONTROL cells were treated with vehicle or 1µg/ml doxycycline, in combination with vehicle or 200nM 4OHT, and subjected to increasing doses of UVC radiation. Percent survival is based on colony numbers after 10 days of incubation. Error bars represent the SEM. B) UVSSA

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knockdown does not further sensitize MYC-deregulated cells to UV treatment.

Clonogenic assay as described using shUVSSA cells. Error bars represent the SEM.

Figure 3. UVSSA knockdown with MYC activation results in accumulation of cells in

S/G2 phase and phosphorylation of CHK2. A) MYC induction combined with UVSSA knockdown leads to increased accumulation of cells in S-phase. Cells in S-phase were measured using FACS after 30 days of culturing cells with 1µg/ml doxycycline, and vehicle or 200nM 4OHT. Error bars represent the SEM. B) MYC induction combined with

UVSSA knockdown leads to accumulation of cells in G2/M phase. Cells in G2/M were measured as described after 30 days of culturing. Error bars represent the SEM. C)

MYC induction combined with UVSSA knockdown results in accumulation of phosphorylated CHK2. shCONTROL or shUVSSA cells were treated with 1µg/ml doxycycline, and vehicle or 200nM 4OHT for 9 days and whole cell lysates were run on

SDS-PAGE for detection of P-CHK2.

Figure 4. Synthetic lethal effect is dependent on transcription. A) Treatment with α- amanitin inhibits the synthetic lethal effect between MYC induction and UVSSA knockdown. shCONTROL and shUVSSA cells were subjected to competitive survival assays in the presence of 1µg/ml doxycycline, vehicle or 200nM 4OHT, and vehicle or varying doses of α-amanitin, administered every 48 hours over the course of 14 days.

Graph represents 3 independent replicates. B) Treatment with aphidicolin does not affect the synthetic lethal effect between MYC induction and UVSSA knockdown. shCONTROL and shUVSSA cells were subjected to competitive survival assays with 1µg/ml doxycycline, vehicle or 200nM 4OHT, and vehicle or varying doses of aphidicolin, administered every 48 hours. Graph represents 3 independent replicates.

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Figure 1

A C

B D

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Figure 2

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Figure 3

A B

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Figure 4

A B

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Supplementary Figure 1

UVSSA shRNAs used for validation

shUVSSA-1

shUVSSA-3

shUVSSA-4

Supplementary Figure 1. shRNA clones used for UVSSA knockdown. The four isoforms of UVSSA mRNA are represented as pX1, pX2, pX3, and pX4 and align as shown, compared to the UVSSA gene sequence shown on the top row. shRNA clones UVSSA- 1, -3, and -4 used in our studies target different sequences on the mRNAs as shown above, and cover all possible mRNAs. Alignment was conduced using CLC sequence viewer (CLC bio, Aarhus, Denmark).

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Supplementary Figure 2

ERCC8 shRNAs used for validation

shERCC8-3

shERCC8-4

shERCC8-6

Supplementary Figure 2. shRNA clones used for ERCC8 knockdown. The two isoforms of ERCC8 mRNA are represented as pX1 and pX2, and align as shown above compared to the ERCC8 gene sequence. shRNA clones ERCC8-3, -4, and -6 used in our studies target different sequences on the mRNAs as shown above, and cover both mRNA isoforms. Alignment was conducted using CLC sequence viewer (CLC bio, Aarhus, Denmark).

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CHAPTER 5

Discussion

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1. Genome-wide screen: strengths and weaknesses

For my thesis project, I engineered the MCF10A-MYCER system and conducted a genome-wide synthetic lethal screen for genes required for survival of cells with deregulated MYC. Smaller scale screens using a well-by-well approach have been performed in the past. However, comprehensive genome-wide screens in mammalian cells have only become achievable in the last decade with the development of pooled

RNAi technologies and powerful analysis methods (Silva et al., 2005; Silva et al., 2008).

The shRNA library utilized in our screen was a second-generation miR-based lentiviral shRNA library including 58,493 shRNA constructs targeting 18,661 known human genes, with an average of 2-3 shRNAs targeting each gene (Rodriguez-Barrueco et al.,

2013). The library covers approximately 75% of the human genome, which is one of the highest coverage libraries available for mammalian RNAi screening today. When compared to the three other published MYC-SL screens, our screen examined the second largest pool of gene targets next to Kessler’s study (Kessler et al., 2012).

Utilizing this powerful screening method led us to discover a large number of synthetic lethal candidates, or shRNAs that selectively decrease the viability of MYC- deregulated cells. These candidate genes fall into a number of biological pathways that can potentially provide novel information about MYC biology (see Chapter 2). In the present study, we did not seek to validate most of the synthetic lethal candidates we discovered, since the aims of our studies concentrated on MYC-induced genome instability. We could have performed a more focused screen by using a custom shRNA library only targeting genes that have roles in genome maintenance. However, we think that a genome-wide screen was more informative for assessing the relative significance of genome maintenance as a SL pathway amidst many other cellular pathways, as well as providing a wide range of synthetic lethal candidates to pursue in the future. Also, since the genome-wide library we used included some shRNAs targeting previously

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uncharacterized genes, we were able to identify UVSSA, which may have been excluded from smaller libraries targeted for specific biological pathways.

We also learned several valuable lessons through the course of conducting and analyzing our genome-wide screen. Firstly, the potency and specificity of some of the shRNA constructs included in the library varied greatly. We noted this through validation attempts of SL candidates from the screen, when some shRNA clones failed to knockdown mRNA expression of the target gene beyond 10-30% of control levels when tested individually (data not shown). This could be a quality control issue of the original library. These libraries have been and are continuously being modified to achieve better and more consistent knockdown to minimize these issues. However, this issue likely participated in increasing the level of noise in our screen, which is reflected by the modest validation success rate (see Chapter 2). Compared to more stringent, smaller scale screens such as the Toyoshima screen, our validation rate was significantly low

(Toyoshima et al., 2012). Notably, the criteria we used to define our high-confidence hits only included: 1) fold-difference of shRNA abundance between MYC ON and MYC OFF

(Log2 FC<-1), and 2) p-values between the five replicates (p<0.1, p<0.05, or p<0.01).

Additional filters such as secondary screens, protein-protein interactions, interactome analysis, or other bioinformatics methods would likely have increased our validation rates, and these methods will be employed for any future screens we pursue.

We employed two methods to analyze the data: microarray hybridization and

HTP sequencing (see Chaper 2). Initially, microarray hybridization was the sole method available to process data from large-scale screens. Later, HTP sequencing methods were developed to allow efficient analysis at a much higher resolution compared to the arrays. Surprisingly, we found that the raw data acquired from the two methods differ significantly. As anticipated, a substantially larger number of gene targets were identified at each p-value cutoff from HTP sequencing. However, only three gene targets

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overlapped between the high-confidence candidate lists generated by the two methods.

We did find similar biological pathways represented in each dataset, as predicted. From these results, we learned that while both analysis methods are valuable and effective,

HTP sequencing analysis undoubtedly gives a more sensitive and high-resolution data output as compared to microarray hybridization, and that this method would be preferred for future screens.

Nevertheless, we were able to validate different pathways as synthetic lethal candidates with MYC deregulation through our genome-wide screen. Identifying FBXW7 and the MYC degradation pathway as a synthetic lethal candidate was reassuring since it strengthened previous findings in other systems. Importantly, our studies suggest a prominent role of FBXW7 for MYC degradation in MCF10A cells, which may be relevant in breast tumors (Akhoondi et al., 2010; King et al., 2013; Rottmann et al., 2005). In contrast, establishing the synthetic lethal relationship between UVSSA and the TCR pathway with MYC deregulation is novel since TCR has not previously been linked with

MYC deregulation. Our discovery of this pathway as a synthetic lethal candidate with

MYC reflects the value of our genome-wide approach, and additionally gives us confidence that further analysis of our existing data could uncover other novel synthetic lethal candidates in the future.

2. FBXW7 knockdown is synthetic lethal with MYC

In the present study, we found FBXW7 as a synthetic lethal candidate with MYC deregulation in the MCF10A-MYCER system. We validated through a sensitive competitive survival assay that multiple shRNA clones targeting FBXW7 indeed sensitized MYC-deregulated cells and impaired their viability compared to normal cells.

Furthermore, we showed that FBXW7 knockdown leads to accumulation of active MYC on chromatin, as well as cell cycle abnormalities and increased binding of CDC45 to

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chromatin, indicative of replication stress. Interestingly, when MYC was deregulated,

FBXW7 knockdown preferentially stabilized MYC over any of its other documented target oncoproteins. This suggests that the FBXW7-dependent MYC degradation pathway is vital for the survival of MYC-deregulated MCF10A cells, despite the existence of many other alternative degradation pathways (see Chapter 1).

That FBXW7 knockdown impairs the growth of MYC-deregulated cells is not entirely a novel finding, since FBXW7 regulates MYC stability and activity (King et al.,

2013; Rottmann et al., 2005; Takeishi et al., 2013). However, we sought to validate

FBXW7 as a synthetic lethal candidate with MYC deregulation since there are still some conflicting results about this relationship. For example, in some cells and tissue types, disrupting the FBXW7-MYC interaction does not seem to alter MYC half-life or stability

(Chang et al., 2000; Gregory and Hann, 2000; Hemann et al., 2005; O'Neil et al., 2007).

This suggests that depending on cell types and microenvironments, MYC protein levels are controlled by a balance of several pathways. Interestingly, while FBXW7 mutations are common in some specific types of cancers including T-ALL, cholangiocarcinoma, stomach, colon, and pancreas, it is rare in other cancers including breast and ovarian

(Akhoondi et al., 2007). Further, promoter methylation of FBXW7 has been correlated with favorable prognosis in breast cancer (Akhoondi et al., 2010; Cheng and Li, 2012).

Since our system utilized the MCF10A breast epithelial cell line, it is possible that the synthetic lethal effect we see between FBXW7 knockdown and MYC activation is specific to this cell line, or to breast cells in general.

In order to address some of these questions, studies are currently ongoing to expand our results to breast cancer cell lines, including T47D, SKBR3, and MCF7, which have been documented to express normal to high levels of MYC and have distinct gene expression signatures. Additionally, we have done an initial gene expression analysis comparing the expression levels of FBXW7 to MYC in the TCGA breast cancer database

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(Chapter 3). In total breast cancer samples, we see a significant but modest correlation between FBXW7 and MYC expression (Chapter 3, rho=0.23, p<0.0001). Interestingly, upon subtype-specific analysis, we found the most robust correlation in the luminal A- subtype of breast cancers (Chapter 3, rho=0.41, p<0.0001). In contrast, we did not detect any significant correlation between FBXW7 and MYC in basal-like breast cancers

(rho=-0.11, p=0.3242, data not shown) or in any other subtype tested (luminal B, normal- like, Her2-positive, data not shown). Intriguingly, this suggests that the synthetic lethal relationship between FBXW7 and MYC may be more relevant in breast cancer types associated with lower levels of MYC. Though it has been traditionally thought that targeting MYC should be an effective method to attack MYC-overexpressing cancers such as basal-like breast cancers, increasing evidence suggests that these cancers may be able to readily achieve MYC-independence (Leung et al., 2012). It is conceivable that cancer cells with low to normal MYC expression are more susceptible to subtle alterations in MYC stability, given our results with luminal A-type breast cancers. This investigation will be extended in the future to other cancer types as well.

FBXW7 was also identified as a synthetic lethal candidate in Kessler’s screen, though it was not selected for further validation (Kessler et al., 2012). Since their screen was conducted in the human mammary epithelial cell (HMEC) line, this also gives us additional confidence that FBXW7, at least in the breast epithelial context, may be a major MYC degradation pathway that is essential for survival of MYC-deregulated cells.

The other published screens likely did not include FBXW7 as a candidate target since they interrogated the kinome or the druggable genome (Liu et al., 2012; Toyoshima et al., 2012). In support of this, Liu’s screen using siRNA against the kinome identified the

GSK3B kinase as a MYC-SL candidate, which is essential for the interaction between

FBXW7 and MYC (Liu et al., 2012; Welcker et al., 2004). FBXW7 was one of the five genes that overlapped between our SL candidate list and the Kessler dataset (FBXW7,

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FASN, LAMA2, PRMT8, ZNF614) suggesting that this finding may apply to breast epithelial cells in general, and may be relevant to MYC-deregulated breast epithelial tumors, though additional studies are required to investigate this further (Kessler et al.,

2012).

Lastly, it must be noted that pharmacological inhibitors of FBXW7 are not yet available, though there are urgent efforts currently underway for development. Because one of the ultimate goals of our screening approach is uncovering novel targets for therapy of MYC-driven tumors, testing the effect of chemical inhibitors will be a high priority in future studies. Our data indicate that in MCF10A-MYCER cells, approximately

50% of FBXW7 knockdown preferentially confers lethality to cells with MYC activation while normal cells are unaffected. Ideally, a chemical inhibitor of FBXW7 with high specificity should exhibit similar results, which could be useful for treating at least a subset of breast tumors. We would like to test whether FBXW7 inhibitors have differential effects on basal-like high-MYC breast cancer cell lines and luminal-like low-

MYC breast cancer cell lines, to further corroborate our current results.

3. Transcription-coupled repair is synthetic lethal with MYC

We showed that knockdown of UVSSA or ERCC8, components of the TCR pathway, have a synthetic lethal effect with MYC activation (Figure 1, Chapter 4). Since we also see increased sensitivity of MYC-deregulated cells to the USP7 inhibitor P5091, and an epistatic relationship between UVSSA and ERCC8 in a double knockdown, this strongly suggests that the TCR pathway is required for survival of cells with MYC activation (Figure 1, Chapter 4). Although we are still in the process of unraveling the mechanisms underlying this synthetic lethal effect, we have detected cell cycle defects and a significant increase in phosphorylated CHK2, which signifies genomic stress

(Figure 3, Chapter 4). Because the viability of MYC-deregulated cells with UVSSA

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knockdown becomes comparable to control cells when treated simultaneously with low doses of α-amanitin, we suspect that cells with MYC deregulation are under significant transcription-dependent genomic stress that may require the TCR pathway to maintain survival.

While UVSSA and ERCC8 were both recovered as synthetic lethal candidates from our HTP sequencing, we did not recover TCR genes in our microarray hybridization results (Table 1, Chapter 2). UVSSA (KIAA1530) was not included in the total list of genes that were at least 2-fold depleted in the MYC ON population compared to MYC

OFF, as assessed from the microarrays. This can be due to several possibilities, including that UVSSA actually did not score as at least 2-fold depleted in MYC ON compared to MYC OFF. This could be due to the limit of sensitivity of the arrays, or a particularly weak probe for UVSSA. Also, it is possible that a UVSSA “spot” was erroneously omitted on the microarrays. On the other hand, ERCC8 actually did score as more than 2-fold depleted in the MYC ON population compared to MYC OFF in the microarray analysis (Log2 FC=-1.30), but was not included in the candidate list for failing to pass the required p-value cutoffs (p>0.1). Thus, this may be a good example of how the multiple analysis methods we took allowed for a more detailed and thorough probing of our results, since the microarray analysis alone was not able to pick up these candidates due to technological limitations. We predict that there are likely additional genes and pathways that were not represented in the microarray dataset but were included in the HTP sequencing results, and would like to further validate these in the future.

Our results suggest a key role for transcription-dependent genomic stress in

MYC-deregulated cells. Transcription-dependent genomic stress is becoming a growing field of research in the recent years, however, there has been very limited research focusing on this type of genomic stress specifically in conditions of MYC deregulation or

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overexpression (Fong et al., 2013; Kim and Jinks-Robertson, 2012; Wolters and

Schumacher, 2013). Transcription, even in normal cells, is a robust and intrinsically mutagenic process that can be the source of many different types of genomic stress including mutagenesis (TAM), recombination (TAR), collisions with replication forks, and accumulation of transcription-associated R-loops and non-B-DNA structures (Kim and

Jinks-Robertson, 2012). Thus, it is reasonable to speculate that under conditions of MYC deregulation, increased transcriptional activity (Lin et al., 2012; Nie et al., 2012) could lead to increased transcription-associated genomic stress. While MYC-induced cells can adapt to these transcription levels, they may become increasingly dependent on specific genome maintenance pathways such as TCR for survival.

Intriguingly, stalled RNAPII is associated with activation of strong apoptotic signals, thus it has been suggested that transcription impediments can be tumor suppressive (Kim and Jinks-Robertson, 2012). This is consistent with our results in the case of MYC deregulation, in which MYC-deregulated cells are selectively sensitive to knockdown of TCR genes, though we were not able to detect significant levels of apoptosis in our initial studies (data not shown). We speculate that this interaction may be specifically crucial for MYC and other oncogenic transcription factors since transcriptional activity in these cells rise significantly (Lin et al., 2012; Nie et al., 2012).

While we are focusing our studies on MYC, we are also interested in future studies to expand our results to other such models.

The question remains whether other known TCR players, including ERCC6/CSB,

XAB2, HMGN1, and ELL also have a similar synthetic lethal relationship with MYC deregulation. Additionally, it is vital to investigate more detailed molecular mechanisms of transcription-induced DNA damage, since the only evidence we have identified so far is the induction of phosphorylated CHK2. We plan to generate knockdown cell lines of the above TCR players in order to strengthen our findings.

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Whether MYC-induced elevation of transcription can directly induce genomic stress is still a question we are eager to address. At the molecular level, we are interested in testing whether MYC induction leads to accumulation of aberrant or abortive transcripts due to increased transcription, leading to increased transcription- associated stress. When TCR is attenuated, these aberrant or abortive transcripts may accumulate to toxic levels. To test this, we are in the process of conducting a genome- wide chromatin immunoprecipitation experiment using antibodies against RNAPII. We hypothesize that if TCR is required for survival of MYC-deregulated cells due to accumulation of transcription-induced stress, we may detect differences in the landscape of chromatin-bound RNAPII on a genome-wide scale. Importantly, since we do not know if all RNAPII transcription is affected or only select transcripts are affected, we have decided to take a genome-wide approach to interrogate this question.

Another burning question is the nature of the pathological aberrant structure generated by MYC that is normally resolved by TCR. R-loops are attractive candidates and we are interested in inquiring whether MYC-deregulated cells accumulate this potentially toxic intermediate of transcription (Kim and Jinks-Robertson, 2012). R-loops, or RNA-DNA hybrids that can occur in active transcription bubbles when a nascent RNA anneals back to the template DNA strand, lead to displacement of the single-stranded non-transcribed strand (NTS) of DNA. This leaves the NTS vulnerable to a variety of single-strand DNA assaults including base modifications, breaks, mutations, and looping, all of which can lead to lethal genomic damage (Kim and Jinks-Robertson,

2012; Li and Manley, 2006). Accumulation of R-loops is normally limited by the protection of naked RNA by co-transcriptional processes, the activity of TOP1, and also by enzymes such as RNase H which degrade RNA-DNA hybrids (Kim and Jinks-

Robertson, 2012). It is possible that deregulation of MYC leads to accumulation of these intermediates that may require TCR activity, or at least the components of TCR involved

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in the stalling, restart, or recycling of RNAPII complexes, to maintain survival. To start testing whether R-loops are significant in our studies, we are in the process of investigating the effect of overexpressing recombinant RNase H in MYC-deregulated cells with TCR knockdown. Additionally, we plan to specifically monitor the level of R- loops in MYC ON and MYC OFF cells using specific antibodies that recognize DNA-RNA hybrids independently of their sequence (Boguslawski et al., 1986; Kitagawa and Stollar,

1982).

Our findings suggest a novel synthetic lethal relationship between MYC deregulation and genes involved in transcription-coupled repair. While the significance of transcription-associated genomic stress is becoming clearer, whether it is a significant component of oncogene-induced genomic stress has not been extensively studied as of yet. From the evidence we have accumulated so far, we propose that in MYC- deregulated cells, transcription-associated genomic stress may be a significant contributor to genome instability. Many questions remain, including the molecular role of

TCR components, or subcomponents, in non-UV-irradiated conditions, since most existing studies on TCR have focused on exogenous UV irradiation. Nevertheless, we are eager to further explore details into the mostly genetic evidence we have established between MYC and UVSSA/ERCC8 in our studies.

4. Maintenance of MYC deregulation

Previous studies from our laboratory showed that MYC directly regulates DNA replication, specifically origin firing, and that increased DNA replication-dependent genomic damage and checkpoint activation are direct consequences of MYC overexpression (Dominguez-Sola et al., 2007; Srinivasan et al., 2013). As a follow up to these studies, we initially performed our MYC-SL screen in pursuit of finding proteins or enzymes involved in alleviating MYC-dependent replication stress. Although we opted to

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screen for MYC-SL candidates in a genome-wide fashion, we were interested in SL candidates that could possibly be involved in DNA damage repair, or modulators of genomic stress such as topoisomerases or nucleases, which would corroborate our prior findings regarding MYC-dependent replication stress.

We learned through this project that MYC biology is extremely complex. In retrospect, screening a smaller gene set targeted specifically to genome maintenance genes might have been a more efficient method to find a synthetic lethal candidate that we “expected” to discover. However, because we employed a genome-wide screen, we found that MYC is truly a master regulator, regulating diverse cellular pathways. For example, one of the strongest synthetic lethal pathways recovered from our microarray data was metabolism and fatty acid synthesis. The connection between oncogenesis and metabolism has been an emerging and growing field of research, and our results are in agreement that metabolic pathways may be a strong genetic vulnerability for

MYC-deregulated cells. Though we did not follow up much with this data, many other interesting SL candidates are represented in our datasets that should be followed up in separate projects in the future.

We have established that at least two independent pathways are synthetic lethal with MYC in the MCF10A-MYCER system (Figure 1). While there are numerous other

MYC-SL pathways as demonstrated by other groups and by our dataset, we focused our studies on two pathways as genetic vulnerabilities of MYC: 1) ubiquitin-mediated MYC degradation by FBXW7, and 2) transcription-coupled repair involving UVSSA and

ERCC8 (Figure 1). We saw that FBXW7 knockdown alone, despite the existence of multiple MYC degradation pathways, was sufficient in our system to significantly impair the survival of MYC-deregulated cells. This is consistent with the threshold model of

MYC-induced oncogenesis, in which MYC protein levels dictate differential fate for cells

(Murphy et al., 2008). We can hypothesize that our MCF10A-MYCER cells have a

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growth-permissive low level of MYC deregulation when MYCER is activated by 4OHT.

Upon FBXW7 knockdown, MYC protein levels were stabilized and surpassed a threshold level, leading to activation of tumor suppressor and cell death pathways that conferred lethality (Figure 1).

Normal Tumorigenic High MYC MYC MYC MYC MYC MYC MYC MYC MYC MYC FBXW7

Genomic Stress

Aberrant DNA replication Transcription Amplification ! Origin Activation ! transcripts synthesis " Inter-Origin Distance ! DNA damage ! Fork asymmetry and DNA damage Cell death

UVSSA/ ERCC8

Replication stress Transcription stress

Genomic Instability, Tumorigenesis

Figure 1. Model of vulnerabilities during MYC deregulation. At deregulated, tumorigenic levels of cellular MYC, both aberrant DNA replication and amplification of transcription contribute to genomic stress. These are alleviated by DNA repair pathways such as TCR to maintain survival in the tumorigenic state. When cellular MYC exceeds tolerable levels, increased genomic stress leads to cell death. Increase in MYC levels can be suppressed by a steady state of FBXW7-mediated MYC degradation. (Adapted from Jean Gautier)

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At the growth-permissive low level of MYC deregulation, however, cells still experience stress (Figure 1). These may include metabolic stress, proliferative stress,

DNA damage, and replication stress among many others (Dang, 2012). We propose that transcription-associated genomic stress may be one type of stress that increases during low levels of MYC deregulation, likely as a result of increased transcriptional activity induced by MYC and other downstream transcription factors. Under these conditions, survival may become increasingly dependent on processes that alleviate transcription stress such as TCR, or specific components of the TCR pathway that may be critical in

RNAPII metabolism such as UVSSA (Figure 1).

Encouragingly, ubiquitination/SUMOylation and transcription initiation/elongation constitute two of the three functional MYC-SL “hubs” that were determined through comprehensive bioinformatics analysis of data from three previously published MYC-SL screens (Cermelli et al., 2014). These two processes, along with a third including regulators of the MYC-MAX network, were the most highly represented and statistically reliable MYC-SL biological functional groups across the three screens (Cermelli et al.,

2014; Kessler et al., 2012; Liu et al., 2012; Toyoshima et al., 2012). Our findings confirming FBXW7 and UVSSA/ERCC8 as MYC-SL genes are consistent with their results, since FBXW7 is involved in ubiquitination and UVSSA/ERCC8 are related to transcription pathways. We hope to further explore other genes and pathways from our data that have been highlighted as MYC-SL hubs in hopes to gain more detailed insight into MYC-SL networks.

Lastly, we are interested in extending our results to mouse models, possibly by testing the loss of UVSSA in transgenic mice that develop MYC-dependent tumors such as Eµ-MYC, or MYC-induced tumors in mammary tissue (Adams et al., 1985; Leung et al., 2012). Further, the end goal of our studies has always been to contribute to the development of novel methods to combat MYC-driven cancers. To this end, we are

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eager to target UVSSA since we know that UVSS patients with loss of UVSSA function survive relatively well with no significant cancer susceptibility, despite mild UV sensitivity

(Schwertman et al., 2013). Since many of the previously identified MYC-SL targets have been essential or high-risk proteins such as ATR, CDKs, and checkpoint proteins, we are interested to see effects with UVSSA inhibitors. To conclude, we have learned through our studies that deciphering mechanisms of MYC oncogenesis and potential therapy are truly a collaborative and ongoing feat, and are excited to further corroborate the results from this project in our laboratory.

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APPENDIX

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APPENDIX 1 – MCM6 interacts with BUBR1 and AURKA in Xenopus laevis extracts

This work has been published (Lefebvre et al., 2010).

Elucidating comprehensive regulatory networks of protein-protein interactions in specific cell types are critical in confirming significant tumorigenic pathways, as well as in predicting novel ones. The human B-cell interactome (HBCI) developed by Lefebvre and colleagues predicted novel protein-protein interactions between genes involved in the pre-replicative complex (pre-RC), DNA replication, and mitosis (Lefebvre et al., 2010).

Although origin recognition complex proteins (ORCs) had previously been detected on mitotic kinetochores, it was unknown whether the pre-RC complex also interacted with mitotic machinery (Sasaki and Gilbert, 2007).

In collaboration with Dr. Andrea Califano’s laboratory, we investigated the predicted protein-protein interactions between MCM6, a member of the pre-RC complex, and mitotic proteins BUBR1 and AURKA. BUBR1 and AURKA (or Aurora A) have prominent roles in regulation of proper mitosis and induction of the mitotic checkpoint

(Fu et al., 2007). We used Xenopus laevis cell-free extracts to investigate the interactions. Low-speed supernatant was prepared from unfertilized Xenopus oocytes.

These extracts contain large stores of maternal proteins and mRNAs necessary for initial cell cycles during early development, and are devoid of genomic DNA (Danilchick et al.,

1991). I performed co-immunoprecipitation (co-IP) using Xenopus MCM6 (XMCM6) antibodies conjugated to Protein A-Sepharose beads. Beads, following incubation in extracts, were washed and subjected to SDS-PAGE. I found that Xenopus BUBR1

(XBUBR1) and AURKA (XAURKA) co-immunoprecipitated with XMCM6 (Figure 1B).

Similar results were established in human B-cells (Ramos) through MCM3 IP (Figure

1A). Taken together, these results confirm the predicted protein-protein interactions between members of the pre-RC (MCM3/MCM6) and mitotic proteins (BUBR1/AURKA).

184

Importantly, our results in Xenopus extracts suggest that this interaction does not require

Master regulators of germinal center proliferationgenomic DNA. C Lefebvre et al

Figure 1. MCM3 and MCM6 interact with BUBR1 and AURKA. (A) MCM3 interacts with BUBR1 and AURKA in human B cells. In Ramos cells, BUBR1 and AURKA co- immunoprecipated with human MCM3 antibody. Ramos cell lysates were used for endogenous protein IP. (B) MCM6 interacts with BUBR1 and AURKA in Xenopus extracts. Xenopus BUBR1 and AURKA co-immunoprecipitated with Xenopus MCM6 antibody. Figures are representative images of replicate experiments. (Published in (Lefebvre et al., 2010))

Antibodies

XMCM6 antibodies were previously described (Ying and Gautier, 2005). XBUBR1

antibodies (Mao et al., 2003) and XAURKA (Satinover et al., 2006) antibodies were

generously gifted by Dr. Mao and Dr. Stukenberg, respectively.

Figure 6 MCM3 and MCM6 interact with BUBR1 and AURKA. (A) MCM3 co-immunoprecipitates with BUBR1 and AURKA. Cell lysates of Ramos cell line were immunoprecipitated with control IgG or anti-MCM3 antibodies and probed with anti-BUBR1 and anti-AURKA antibodies. (B) MCM6 binds to BUBR1 and AURKA in Xenopus cell-free extracts. Interphase extracts from Xenopus cytosolic fraction were immunoprecipitated with anti-XMCM6 or control IgG antibodies and probed with anti-XMCM6, anti-XBUBR1, and anti-XAURKA antibodies. (C) Co-localization of MCM3 185 with BUBR1 and AURKA in Ramos cells shown by confocal dual-color indirect immunoﬂuorescence with (a) anti-MCM3 and anti-BUBR1 antibodies or (b) anti-MCM3 and anti-AURKA antibodies. DNA was stained with DAPI. Data shown are representative of one of three independent experiments.

Lentiviral infection of B-cell lymphoma cell lines with protease inhibitor cocktail. The lysate was incubated overnight at 41C with mouse IgG or anti-MCM3 antibody (M038-3, MBL Intl), Control shRNA (SHC002), FOXM1 shRNA (TRCN0000015546), or followed by incubation in protein-G agarose beads (17-0618, Amer- MYB shRNA (TRCN0000040062) cloned into pLKO.1-puro lentiviral sham) for 2 hours. The beads were washed in buffer (20 mM Tris–HCl vector was purchased from Sigma. To generate lentiviral particles, the pH 8.1, 150 mM NaCl, 10% glycerol, 0.2% NP-40 and 1 mM EDTA) for individual shRNA clones (2.8 mg) were co-transfected with VSVG four times and incubated in SDS-loading buffer at 1001C for 10 min. envelope plasmid (280 ng) and D8.9 packaging vector (2.5 mg) into Similarly, cell-free extract (LSS) was prepared from unfertilized subconfluent 100 mm plate of 293FTcells using Fugene 6 (Roche). The Xenopus eggs as described earlier(Smythe and Newport, 1991). Co- viral particles were collected at 48 and 72 h post-transfection, filtered IPs were performed by incubating extracts with anti-XMCM6 and concentrated by ultracentrifugation in Beckman SW28 rotor at antibodies as described earlier (Ying and Gautier, 2005)) or control 25 000 rpm for 1.5 h 5 106 ST486 cells were transduced with the viral Â antibodies (Rabbit IgG, Sigma) for 30 min at 41C, followed by addition particles in the presence of 8 mg/ml polybrene and centrifuging at 450 g of Protein A Sepharose beads (GE Healthcare) for 1 h at 41C. Beads for 1.5 h. were washed with ELB-0.2%NP-40 and ELB-0.5MNaCl, then boiled to release bound proteins. Proteins were fractionated by SDS–PAGE and analyzed by standard immunoblotting procedures. Immunoblot analysis For immunoblotting, whole-cell lysates were prepared from ST486 cells by using RIPA buffer (Teknova) with protease inhibitor cocktail Indirect immunofluorescence (Roche). Proteins were fractionated by SDS–PAGE and analyzed by standard immunoblotting procedures using the following antibodies: Ramos cells were fixed in 10% formalin for 20 min and post-fixed in anti-FOXM1 (sc-502; Santa Cruz), anti-MYB (sc-517, Santa Cruz), ice-cold 100% methanol for 20 min. Cells were then blocked in 5% BSA anti-MCM3 (559543, BD Pharmingen), anti-BUBR1 (ab4637, Abcam), for 1 h and incubated with anti-MCM3 (559543, BD Pharmingen) and anti-AURKA (gift from Dr PT Stukenberg), anti-XBubR1 (gift from anti-BUBR1 (ab4637, Abcam) or anti-MCM3, and anti-AURKA Dr Y Mao), and anti-GAPDH (sc-32233; Santa Cruz). (ab13824, Abcam) antibody for 2 h at room temperature. The cells were washed three times in PBS with 0.01% Tween-20 followed by incubation with biotinylated goat anti-mouse IgG for 30 min. It was then incubated with FITC-conjugated goat anti-rabbit antibody and Co-immunoprecipitation Cy3-conjugated Streptavidin antibody (Jackson ImmunoResearch) for Whole-cell lysates of 40 106 Ramos cells were prepared by using cell 30 min. Stained cells were acquired at 100 magnification in LSM510 lysis buffer (50 mM Tris–HClÂ pH 8.1, 150 mM NaCl and 0.5% NP-40) Multiphoton confocal microscope (Zeiss).Â

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