Genetic and Chemical Genetic Approaches to Identifying Liabilities in Malignant Brain Cancer

David Tieu

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Molecular Genetics University of Toronto

David Tieu

Master of Science

Department of Molecular Genetics University of Toronto

2019 Abstract

Glioblastoma (GBM) remains the most common primary malignant brain tumor in adults.

Patients face a dismal prognosis despite efforts to identify novel therapeutic targets. Following conventional therapy, patients relapse with treatment-refractory GBM, which remains largely unexplored. This thesis explores the functional genetic landscape of recurrent GBM (rGBM) using unbiased CRISPR-Cas9 knockout screening. Negative selection screens in two patient- derived rGBM cell models provided evidence for the essential role of EGLN1, PTP4A2 and

TIMELESS in treatment-refractory GBM. Pursuing the idea of identifying targets in rGBM, I explored modulators of BMI1, a master-regulator of brain cancer tumorigenesis. I carried out a chemical genetic screen to identify perturbations that sensitize cells to the effects of PTC-028, a small molecule thought to inhibit BMI1. I validated the loss of WNK1 gene as the strongest positive chemical-genetic interaction with PTC-028. In summary, this thesis describes new rGBM genetic vulnerabilities and sheds light on the mode of action of PTC-028.

Acknowledgments

I would like to express my gratitude to my Principal Investigator Dr. Jason Moffat of the Faculty of Medicine at the University of Toronto, who supported me throughout the course of my degree and provided me with excellent opportunities to conduct my research. I am thankful for his aspiring guidance, invaluably constructive criticism and friendly advice through my research and thesis writing.

In addition, I must also thank my committee members Drs. David Kaplan and Scott Gray-Owen and my advisor Dr. Sheila Singh at McMaster University, who have challenged me and provided me with their insights into my projects. Without their regular support, I would not have experienced the same success.

I would like to especially thank my collaborator Chirayu Chokshi from the Singh lab at McMaster University for his consistent support and scientific discussions to forward the project. We powered together through long days and nights to complete screens and analyses.

I would like to acknowledge my colleagues in the Moffat lab for their thought-provoking discussions and willingness to provide me with protocols and ideas. Every one of you has provided me with immense support.

I am particularly grateful for my family, who have supported me in my pursuit of higher education and allowed me the privilege to follow my dreams. I would also like to thank my partner Lisa Shao, who has been with me on this journey since the very beginning and providing me with unfailing support and continuous encouragement. Finally, I am thankful for my friends, who were of great support in providing a distraction to refresh my mind outside of my research.

iii

Table of Contents

Acknowledgments ...... iii

Table of Contents ...... iv

List of Tables ...... vii

List of Figures ...... viii

List of Appendices ...... x

List of Abbreviations ...... xi

Chapter 1 ...... 1

Introduction ...... 1

Chapter One: Introduction ...... 2

1.1 Introduction to Glioblastoma ...... 2 1.1.1 Glioblastoma (GBM): Burden of the disease ...... 2 1.1.2 Current treatments, biomarkers and actionable targets ...... 3 1.1.3 Major pathways involved in the pathogenesis of primary and secondary GBM ...... 5 1.1.4 Brain tumor initiating cells (BTICs) ...... 6 1.1.5 PTC-028 as a therapeutic molecule targeting the BTIC population ...... 7 1.1.6 Differences between untreated primary and treatment refractory recurrent GBM ...... 9

1.2 Introduction to genome-wide CRISPR-Cas9 functional screens ...... 10 1.2.1 Genome-wide CRISPR-Cas9 functional screens in mammalian cells ...... 10 1.2.2 Fitness and genetic liabilities: Core vs. context-dependent essential genes ...... 11 1.2.3 Chemogenomic profiling using genome-wide CRISPR-Cas9 screens: Identifying sensitizers and suppressors of small molecules ...... 13

1.3 Thesis rationale ...... 14 1.3.1 Goals and objectives ...... 14

Chapter 2 ...... 16

Functional genetic screen in rGBM ...... 16

Chapter Two: Exploring the genetic landscape of rGBM ...... 17

2.1 Introduction ...... 17 iv

2.2 Materials and Methods ...... 17 2.2.1 Cell culture ...... 17 2.2.2 Primary cell line characterizations ...... 18 2.2.3 Pooled genome-wide CRISPR-Cas9 screens using the TKOv3 library ...... 19 2.2.4 Cloning individual sgRNA into lentiCRISPRv2 vector ...... 19 2.2.5 Lentivirus production ...... 20 2.2.6 Screenability and editing efficiency assessment ...... 20 2.2.7 Multiplicity of infection estimation ...... 21 2.2.8 Pooled genome-wide CRISPR dropout screens in GBM cells ...... 21 2.2.9 Bayesian Analysis of Gene EssentiaLity ...... 22 2.2.10 Gene Set Enrichment Analysis ...... 22 2.2.11 Gene knockouts using lentiCRISPRv2 lentiviral vectors ...... 23 2.2.12 PrestoBlue assay ...... 23 2.2.13 Sphere formation assay (in vitro limiting dilution assay) ...... 23

2.3 Results ...... 24 2.3.1 Editing efficiency of CRISPR-Cas9 in GBM cell lines ...... 24 2.3.2 Estimating relative lentiviral infection rates for BT241 and BT972 ...... 25 2.3.3 CRISPR-Cas9 sequencing quality control ...... 26 2.3.4 BAGEL analysis ...... 27 2.3.5 Gene set enrichment analysis (GSEA) ...... 30 2.3.6 Selecting candidate genes for validation ...... 31 2.3.7 Validation experiments targeting PLK1, PSMA5 and PSMD1 ...... 32 2.3.8 Validation experiments targeting EGLN1 ...... 34 2.3.9 Functional validation targeting PTP4A2 ...... 36 2.3.10 Validation experiments targeting TIMELESS ...... 38 2.3.11 Summary of Results ...... 42

2.4 Discussion and Conclusion ...... 43 2.4.1 Genetic essentiality in BT241 and BT972 ...... 43 2.4.2 Functional validation following loss of EGLN1, PTP4A2 and TIMELESS ...... 44 2.4.3 Patient-derived rGBM BTIC model ...... 47 2.4.4 Future Directions ...... 48 2.4.5 Conclusion ...... 49

Chapter 3 ...... 50

PTC-028 as a therapeutic molecule ...... 50

Chapter Three: Identifying novel sensitizers and suppressors of PTC-028 ...... 51

3.1 Introduction ...... 51

3.2 Methods ...... 51 3.2.1 Cell lines ...... 51 3.2.2 Generating a WNK1 knockout in a HAP1 cell line ...... 52 3.2.3 Immunoblot analysis ...... 52 3.2.4 Sanger sequencing ...... 53 3.2.5 Dose-response curves for PTC-028 in cell lines ...... 53 3.2.6 Genome-wide CRISPR screen using PTC-028 in HAP1 cell lines ...... 54 3.2.7 DrugZ analysis ...... 54

3.3 Results ...... 55 3.3.1 Validation of BMI1 knockout cell lines through immunoblot and Sanger sequencing ...... 55 3.3.2 Dose-response of PTC-028 in HAP1 BMI1D cells ...... 56 3.3.3 Sensitizers and suppressors of PTC-028 ...... 57 3.3.4 Validating WNK1D HAP1 cell lines ...... 58 3.3.5 Dose-response using PTC-028 in WNK1D HAP1 cell lines ...... 60 3.3.6 Measuring protein levels of BMI1 and WNK1 following PTC-028 treatment ...... 60 3.3.7 Summary of Results ...... 62

3.4 Discussion and Conclusion ...... 62 3.4.1 Discussion ...... 62 3.4.2 Future experiments ...... 64 3.4.3 Conclusion ...... 65

References ...... 66

Appendix ...... 84

5.1 Appendix A ...... 84

5.2 Appendix B ...... 85

5.3 Appendix C ...... 86

List of Tables

Table 1. WHO Grade classifications for astrocytoma.

Table 2. Characteristics for each of the 3 GBM subtype.

Table 3. Summary of clinical details of the patient-derived cell lines used in this study.

vii

List of Figures

Figure 1. Possible factors influencing the fitness of a cell within a population of cells.

Figure 2. Testing the screenability of BT241 and BT972 patient-derived cell lines.

Figure 3. Infection tests for estimating TKOv3 virus volume required to infect 30% of the cell population.

Figure 4. Quality control metrics for BT241 and BT972 CRISPR-Cas9 screens.

Figure 5. An overview of the CRISPR-Cas9 screen results.

Figure 6. Results of GSEA analyses of essentiality data.

Figure 7. Schematic of how genes were selected for further validation.

Figure 8. Results of functional assays following loss of core essential genes PLK1, PSMA5 and PSMD1.

Figure 9. Results of functional assays following loss of EGLN1.

Figure 10. Results of functional assays following genetic perturbation of PTP4A2 in GBM models.

Figure 11. Functional comparison of pGBM and rGBM following loss of PTP4A2.

Figure 12. Viability and sphere formation assays following loss of TIMELESS in GBM models.

Figure 13. Validation results of BMI1DC1 and BMI1DC2 clonal cell lines by sequencing and immunoblot.

Figure 14: Evaluating PTC-028 (PTC Therapeutics Inc.) dose-responses in BMI1D and WT HAP1 cell lines.

Figure 15: Chemogenomic genome-wide pooled CRISPR-Cas9 screens +/- PTC-028 in human HAP1 (C631) cells.

Figure 16: Validation of WNK1D HAP1 clonal cell lines. viii

Figure 17: Results of dose-response curves for PTC-028 in HAP1 and WNK1D cell lines.

Figure 18: Examination of the levels of WNK1 and BMI1 protein in HAP1 cells.

List of Appendices

Appendix A: A vector map of lentiCRISPRv2.

Appendix B: List of sgRNA used in this thesis including gene name, sequence and target site.

Appendix C: List of primers used in this thesis including the name and primer sequences.

List of Abbreviations

ATCC American type culture collection

BAGEL Bayesian analysis of gene essentiality

BF Bayes factor

BTIC brain tumor initiating cells

Cas9 CRISPR-associated protein-9

CEG2 core essential genes 2.0

CRISPR clustered regularly interspaced short palindromic repeats

DDR DNA damage repair

DMEM Dulbecco’s modified Eagle media

DMSO dimethylsulfoxide

DNA deoxyribonucleic acid

DSB double-strand DNA break

E essential genes

EGF epidermal growth factor

EMT epithelial-mesenchymal transition

ER endoplasmic reticulum

FBS fetal bovine serum

FC fold change

FGF fibroblast growth factor

GBM glioblastoma xi

GS2 gold standard 2

GSEA gene set enrichment analysis

HR homologous recombination

HSC hematopoietic stem cell

KO knockout

LCV2 lentiCRISPR version 2

LFC log2 fold change

LOH loss of heterozygosity

MEK mitogen-activated protein kinase kinase

MGMT methyl guanine methyl transferase

MOI multiplicity of infection

NAMPT nicotinamide phosphoribosyltransferase

NE nonessential genes

NES normalized enrichment score

NHEJ non-homologous end joining

NKCC1 Na-K-2Cl cotransporter 1

NOD/SCID non-obese diabetic/severe combined immunodeficiency

PARP poly (adenosine diphosphate-ribose polymerase)

PCR polymerase chain reaction

PDAC pancreatic ductal adenocarcinoma

xii

PDX patient-derived xenograft pGBM primary glioblastoma

PRC1 Polycomb-repressive complex 1

RFU relative fluorescence units rGBM recurrent glioblastoma

RNA ribonucleic acid

RT radiotherapy

SEM standard error of the mean sgRNA single-guide RNA

SSB single-stranded DNA binding proteins

STR short tandem repeat

TCAG The Centre for Applied Genomics

TCGA The Cancer Genome Atlas Program

TKOv3 Toronto KnockOut Library version 3

TMZ temozolomide

TU transduction units

VHL von Hippel-Lindau

WHO World Health Organization

WT wildtype

xiii

Chapter 1 Introduction

1 2

Chapter One: Introduction 1.1 Introduction to Glioblastoma

1.1.1 Glioblastoma (GBM): Burden of the disease

Glioblastoma (GBM) accounts for greater than 60% of all primary brain tumors in adults (Rock et al., 2012). It has a global incidence of 3.19 per 100,000 people and a median onset of 64 years of age, occurring more frequently in males compared to females (1.6:1) (Ostrom et al., 2013). GBM is designated as a Grade IV astrocytoma by the World Health Organization (WHO), the most malignant grade of cancer (DeAngelis, 2001; Louis et al., 2007). The WHO determines tumor grade using histopathological criteria relating to biological aggressiveness such as necrosis, mitotic figures and vascular endothelial hyperplasia (Louis et al., 2016). Table 1 illustrates the classification of astrocytoma subtypes. GBM is the most aggressive, invasive and undifferentiated brain tumor type and as such, it is listed as a grade IV tumor (Louis et al., 2016).

Relapse in patients treated with standard of care occurs in 92% of patients at approximately 9 months after diagnosis, with an average survival of ~15 months (Hegi et al., 2005; Stupp et al., 2005). GBM is incurable and risk factors consist of radiation and genetic syndromes or predisposition including neurofibromatosis type 1 and 2 and tuberous sclerosis (Crespo et al., 2012). Symptoms of GBM are highly dependent on the tumor site, ranging from persistent headaches to nausea, blurred vision, memory loss and changes in mood (Sizoo et al., 2010). The most frequent location for GBM is the cerebral hemispheres, where 95% of tumors arise in the supratentorial region, while only a few percent of cases are in the cerebellum, brainstem and spinal cord (Larjavaara et al., 2007). Although it was originally thought that GBM was derived from glial cells, a recent study provides an argument that it may actually arise from astrocyte-like neural stem cells within the subventricular zone (Lee et al., 2018).

Two forms of GBM are described in literature, primary or secondary. Primary GBM arises de novo, or without clinical and histological evidences of precursor lesions. On the other hand, secondary GBM arises from the slow progression of pre-existing lower-grade astrocytoma (Ohgaki et al., 2007). These two distinct forms of GBM consists of unique genetic backgrounds with differing survival rates (Table 1).

Table 1. WHO Grade classifications for astrocytoma. These grades are classified based on different subtypes of astrocytoma through their respective molecular signatures (Riemenschneider et al., 2010; Seifert et al., 2015).

Grade WHO grade I WHO grade II WHO grade III WHO grade IV Astrocytoma Pilocytic Low-grade Anaplastic Primary Secondary Subtype astrocytoma astrocytoma astrocytoma Glioblastoma Glioblastoma (85% of all (15% of all GBM patients, GBM patients, Older patients, younger poor patients and prognosis) good prognosis) Molecular -KRAS -EGFR mutated -LOH19q -EGFR -LOH19q Signature mutated -PDGFA/ -RB mutated amplified, -PTEN -BRAF PDGFRa -CDK4 overexpressed, mutated activation overexpressed amplified mutated -PI3K mutated -FGFR1 -OLIG2 -MDM2 -OLIG2 -VEGF mutation expression overexpressed expression overexpressed -NTRK -P16INK4a/ -MDM2 -PDFGRa fusions P14ARF loss amplified amplified -NF1 mutated -LOH 11p -LOH 10q -RB1 mutated -PTPN11 -PTEN -IDH1/2 mutated mutated mutated -PI3K -TP53 mutated mutated/ -ATRX amplified mutated -P16INKa/ p14ARF loss -R3 mutated -VEGF overexpression -TERT promoter mutation

1.1.2 Current treatments, biomarkers and actionable targets

Despite maximum surgical resection using cutting-edge techniques in neuroimaging and neurosurgery, followed by radiotherapy with concurrent chemotherapy (temozolomide or TMZ), prognosis for GBM patients remains grim. Although recent advances in therapy have improved the quality of life for GBM patients, there has been little improvement on extending survival

4 following the diagnosis of this disease since the discovery of TMZ as a treatment in 2005 by Stupp and colleagues (Stupp et al., 2005). The principal mechanism responsible for the cytotoxicity of TMZ is the methylation of deoxyribonucleic acid (DNA) at the N7 and O6 positions on guanine. This leads to the failure of DNA mismatch repair system to find a complementary base for methylated guanine, resulting in DNA nicks and consequently, blocking the cell cycle at the G2-M boundary and triggering apoptosis (Zhang et al., 2012). High methyl guanine methyl transferase (MGMT) activity in tumor cells is associated with poor TMZ response because MGMT is a critical DNA repair protein that protects tumor cells against alkylating chemotherapeutic agents (Cen et al., 2013). Accordingly, one major biomarker in GBM patients is MGMT promoter methylation status. Methylated MGMT significantly improves prognosis after TMZ treatment (Szopa et al., 2017). Treatments of TMZ with radiotherapy include 75 mg/m2/day for 6 weeks and radiotherapy (60Gy in 30 fractions), followed by six maintenance cycles of TMZ 150-200 mg/m2/day for the first 5/28-day cycle (Villa et al., 2014). Current cancer treatments produce undesirable side effects including nausea, fatigue, headache, constipation and myelosuppression and remain largely unsuccessful, attributed to the extensive cellular and genetic intratumoral heterogeneity (Lawrence et al., 2011). Combined with the lack of efficacy of current treatments, there is an urgent need for new molecular targets and therapies.

Novel treatments targeting specific GBM dependencies in clinical trials include anti-angiogenic agents such as anti-VEGF (vascular endothelial growth factor) monoclonal antibodies (Bevacizumab, Vredenburgh et al., 2007), anti-FGF (fibroblast growth factor) antibodies (Loilome et al., 2009; Norden et al., 2015), monoclonal antibodies targeting EGFR (epidermal growth factor receptor) (Erlotinib, Raizer et al., 2010 and Gefitinib, Parker et al., 2013; Uhm et al., 2011), tubulin inhibitors (Phoa et al., 2015; Ciesielski et al., 2018) and various tyrosine kinase inhibitors (Batchelor et al., 2010; Galanis et al., 2005; Kreisl et al., 2009) used in a subset of patients. Despite numerous on-going clinical trials, none have been proven to be effective in improving overall survival.

1.1.3 Major pathways involved in the pathogenesis of primary and secondary GBM

Multiple genetic mutations and signaling pathway aberrations have resulted in dysregulated gene expression that lead to the pathogenesis of GBM. For instance, in de novo or primary GBM, EGFR mutation/amplification, overexpression of MDM2 (mouse double minute 2 homolog), deletion of p16, loss of heterozygosity (LOH) of chromosome 10q (encompassing PTEN or phosphatase and tensin homolog), and TERT (telomerase reverse transcriptase) promoter mutations are the most frequent mutations in patients (Ohgaki et al., 2007). For the slow progression of pre-existing lower-grade astrocytoma or secondary GBM, the overexpression of PDGFA/PDGFRa (platelet derived growth factor subunit A), RB1 (retinoblastoma protein), LOH of 19q and mutations of IDH1/2 (isocitrate dehydrogenase 1/2), TP53 (tumor protein 53) and ATRX are frequent aberrations (Ohgaki et al., 2007). The genetic lesions for GBM affect three main signaling pathways, consisting of RTK/RAS/PI3K (altered in 88% of GBM cases), P53 pathway (altered in 87% of GBM cases) and RB signaling pathway (altered in 78% of GBM cases) (Hanif et al., 2017).

In addition to the two major groups of GBM, four subtypes of GBM have been described based on transcriptomic data (Verhaak et al., 2010). In this study, 206 specimens of GBM tissues were analyzed for genomic characterization. The subtypes include proneural, neural, classical and mesenchymal. However, a more recent study (Wang et al., 2017) concluded that only 3 clear distinct subtypes exist and eliminated the neural subtype. The major distinctions between these three subtypes are summarized in Table 2.

Extensive genetic, epigenetic and microenvironment heterogeneity is a feature within GBM tumors and the main challenge concerning therapeutic failure. Heterogeneity has been reviewed through the single-cell transcriptome (Patel et al., 2014, Sottoriva et al., 2013, Wang et al., 2017). These studies have revealed that multiple cellular subtypes can coexist in different regions of the same tumor and change after therapy and through time. Intriguingly, a group has found that regardless of the composition in cellular state of tumorigenic cells used to initiate the patient-derived xenograft (PDX) model in mice, the resulting tumor consisted of the distribution in cellular state resembling the original patient sample (Neftel et al., 2019). The study proposes that cells from a single cellular state have the flexibility to transition to other states and reconstitute the reference distribution.

Table 2. Characteristics for each of the 3 GBM subtype. These 3 GBM subtypes (proneural, classical and mesenchymal) have been differentiated by their transcriptomic and genomic changes. The median survival is based on the study in Wang et al. (2017).

Subtype Proneural Classical Mesenchymal Tumor -IDH1/2 mutation -EGFR amplification -NF1 loss/mutation Characteristics -PDGFRA amplification and mutation -TP53 loss/mutation -TP53 mutation -INK4A/ARF loss -PTEN loss/mutation -INK4A/ARF loss -Nestin overexpression -MET overexpression -PTEN loss -Notch and SHH -CD44 overexpression -PI3K pathway activation -CHI3L1 overexpression -HIF1α -TNF family and NFKB pathway activation Patient outcome Better Prognosis 14.7 months Worse Prognosis (Median Survival) (17 months) (11.5 months)

1.1.4 Brain tumor initiating cells (BTICs)

A hallmark of GBM is intratumoral heterogeneity at the genetic and cellular levels. This cellular heterogeneity can be explained by the existence of a rare fraction of cells with stem cell-like properties, known as brain tumor initiating cells (BTICs). They may be responsible for treatment failure and tumor recurrence (Singh et al., 2004; Clarke & Fuller, 2006). Tumor initiating cells are a phenotypically distinct subpopulation of tumor cells with the ability to form tumors in immunodeficient (NOD/SCID) mice and can differentiate into bulk tumor cells to reconstitute a heterogenous tumor (Wang and Dick, 2005). Singh and colleagues demonstrated that engrafting as few as 100 BTICs in an immunodeficient NOD/SCID mouse will initiate tumor formation (Singh et al. 2003). Much like stem cells, BTICs are characterized by their ability to proliferate, self-renew and differentiate (Reya et al., 2001; Pardal et al., 2003).

Conventional therapies including chemotherapy and radiation therapy are not equally effective on every cell. These therapies fail to target BTICs and instead, likely enrich this population by preferentially killing differentiated cancer cells with limited tumor-initiating capabilities (Bao et al., 2006). BTICs have been found to exhibit a number of genetic and cellular adaptations that confer resistance to classical therapeutic approaches (Trumpp et al., 2008). These adaptations include dormancy or slow cell cycle kinetics (Patel et al., 2012), efficient DNA repair mechanisms (Bao et al., 2006) and high expression of multidrug-resistance-type membrane transporters (Dean et al., 2009). The potential for BTICs to limitlessly self-renew and regenerate

7 a heterogeneous tumor serves as a potential therapeutic vulnerability (Al-Hajj et al., 2003; Singh et al., 2004). Markers that identify tumor-initiating cells include CD133 (Singh et al., 2004), Bmi1 (Abdouh et al., 2009), L1CAM (Bao et al., 2008), Oct4 (Wang et al., 2015), CD15 (Son et al., 2009), SOX2 (Graham et al., 2003) and nestin (Jin et al., 2013), providing a potential means to directly isolate and target BTICs.

The question remains as to whether there is bidirectional plasticity between BTICs and differentiated bulk tumor cells. There has been experimental evidence supporting the bidirectional plasticity theory through the process of reversibly alternating between tumor- initiating neurospheres and differentiated adherent cells (Natsume et al., 2013). Furthermore, it has been established that differentiated cells can undergo a biological process known as epithelial-mesenchymal transition (EMT), which results in multiple epigenetic changes, leading to the dedifferentiation of cells and the acquisition of stem cell features (Mani et al., 2008). In fact, there may be a dynamic equilibrium between BTICs and non-BTICs within a tumor where the cells switch status according to signals. This was presented when CD8+ T cells induced EMT on differentiated breast cancer cells, leading to their conversion into breast cancer stem cells (Santisteban et al., 2009). These studies suggest that in order to prevent tumor recurrence, we need to not only eliminate the BTIC population, but also sustain an environment that maintains a differentiated state.

1.1.5 PTC-028 as a therapeutic molecule targeting the BTIC population

PTC-028 is a small molecule generated through the chemical modification of PTC-209 by PTC Therapeutics Inc. (Bakhshinyan et al., 2019). It has low toxicity, high oral bioavailability and importantly, this molecule preferentially targets the tumor stem cell population in solid tumors (Buechel et al., 2018; Dey et al., 2018; Bakhshinyan et al., 2019). Kreso and colleagues have demonstrated that PTC-028 impedes the self-renewal of colorectal cancer-initiating cells (Kreso et al., 2014), while Bakhshinyan and colleagues showed that PTC-028-treatment reduces stem cell properties of medulloblastoma stem cells in vitro and in vivo (Bakhshinyan et al., 2019). Furthermore, the re-transplantation of PTC-028 treated recurrent medulloblastoma cells into secondary recipient mouse brains demonstrated their diminished ability to initiate tumors

(Bakhshinyan et al., 2019). Finally, Dey and colleagues presented evidence for the molecules’ selectivity to ovarian cancer cell lines with high BMI1 expression (Dey et al., 2018).

BMI1 is a component of the polycomb repressive complex 1 (PRC1). It represses key tumor suppressor genes through the regulation of chromatin structure mediating gene silencing, including CDKN2A and CDKN1A loci (Alkema et al., 1993; Abdouh et al., 2009). BMI1 has been shown to maintain the stem-cell populations in adult tissues, suggesting it may play a critical role in the maintenance of cancer stem cells (Park et al., 2004; Xu et al., 2018). It is frequently overexpressed or dysregulated and promotes cancer cell self-renewal in a number of hematologic and solid malignancies including multiple myeloma (Bolomsky et al., 2016) myelodysplastic syndrome (Mihara et al., 2006), chronic myeloid leukemia (Mohty et al., 2007), acute myeloid leukemia (Chowdhury et al., 2007), ovarian cancer (Bhattacharya et al., 2009), nasopharyngeal carcinoma (Song et al., 2006); medulloblastoma (Wang et al., 2012), Ewing sarcoma family tumors (Cooper et al., 2011) and glioma (Bruggeman et al., 2007; Gargiulo et al., 2013; Abdouh et al., 2009). BMI1 may be an appropriate target in GBM as it is highly enriched in the CD133+ BTIC population and the elimination of BMI1 inhibits the growth and clonogenic potential of BTICs (Abdouh et al., 2009). Cumulatively, these studies illuminate the potential of PTC-028 as a novel therapeutic strategy for targeting GBM BTICs.

1.1.5.1 Mechanism of action of PTC-028

There is preliminary data suggesting that PTC-028 treatment leads to hyperphosphorylation and subsequent depletion of BMI1 at the protein level, resulting in the downregulation of BMI1 signaling (Bakhshinyan et al., 2019; Dey et al., 2018). Furthermore, a number of oncogenic pathways are altered with the treatment of PTC-028 in medulloblastoma cell lines including increased expression in gene sets associated with Toll-like receptors, decreased expression in gene sets associated with ribonucleic acid (RNA) metabolism, cell cycle, translation, and glucose metabolism, the downregulation of MYC signaling, oxidative phosphorylation and glycolysis (Bakhshinyan et al., 2019). Treatment of PTC-028 in cells has been shown to lead to the accumulation of cells in the G2/M phase of the cell cycle and induces caspase-dependent apoptosis (Bolomsky et al., 2017; Dey et al., 2018).

1.1.5.2 PTC-596 in clinical trials

PTC-596 is a clinical analog of PTC-028, with similar profiles and properties (Nishida et al., 2017). PTC-596 has completed phase 1 clinical trials for patients with advanced solid tumors in 2017 according to ClinicalTrials.gov. This study found that PTC-596 is tolerable with manageable gastrointestinal side effects (Infante et al., 2017). Three ongoing phase 1b studies of PTC-596 are in children with newly diagnosed diffuse intrinsic pontine glioma and high-grade glioma, in women with ovarian cancer receiving neoadjuvant chemotherapy and with patients in combination with Dacarbazine in participants with advanced leiomyosarcoma (ClinicalTrials.gov). These preliminary findings signal a favorable safety profile in patients.

1.1.6 Differences between untreated primary and treatment refractory recurrent GBM

Primary GBM (pGBM) patient tumors arise from untreated patients while recurrent GBM (rGBM) patient tumors emerge from patients that have undergone treatment and have relapsed. The biology of treatment refractory rGBM remains severely understudied, as only 10-30% of rGBMs are accessible for re-operation (Barker et al., 1998; Tully et al., 2016; Weller et al., 2013). Consequently, there are few rGBM samples available and little data for this disease. Relapse occurs in almost all patients, often within 2-3cm of the original lesion (Gaspar et al., 1992). Distant recurrences seem to be correlated with low mutation retention rates with an average of 25% compared to 70% of shared mutations in local recurrences when whole-exome sequencing was performed on 15 pairs of local and 7 pairs of distant glioblastoma recurrences (Kim et al., 2015). Korber and colleagues found through studying local rGBM that the majority of rGBM tumors regrew from multiple genetic subclones existing in the pGBM tumors (Korber et al., 2019). Furthermore, Johnson and colleagues reported that treatment of low-grade gliomas with TMZ induced a hypermutated phenotype in a portion of tumors which harbor more than 10- fold higher mutations rates at recurrence compared with the original tumors (Johnson et al., 2014). Finally, Andor and colleagues studied exome sequencing data from 10 paired primary and recurrent tumors and reported that a patient’s pGBM tumor and their paired rGBM tumor in The Cancer Genome Atlas (TCGA) database contain highly divergent driver mutations with subclones appearing and disappearing at recurrence (Andor et al. 2014). These studies support

10 the idea that pGBM and rGBM have different genetic compositions. As current models only utilize treatment-naïve pGBM specimens, there is an urgent need to study rGBM models to identify therapeutic targets. A recent example of a treatment only effective at recurrence is from a study performed by Qazi and colleagues in 2018. They demonstrated that EPHA2 and EPHA3 are coexpressed at higher levels at GBM recurrence and can be used as a cotargeting therapeutic approach in rGBM (Qazi et al., 2018). These studies provide a basis for analyzing the molecular landscape of the evolved molecular landscape at recurrence.

1.2 Introduction to genome-wide CRISPR-Cas9 functional screens

1.2.1 Genome-wide CRISPR-Cas9 functional screens in mammalian cells

The integration of clustered regularly interspaced short palindromic repeats and CRISPR- associated protein 9 (CRISPR-Cas9)-based genome editing in mammalian cells has revolutionized the field of functional genomics (Barrangou and Doudna, 2016; Wright et al., 2016). CRISPR-Cas9 offers a simple approach to introduce targeted mutations in mammalian cells with high specificity (Cong et al., 2013; Sternberg et al., 2014). CRISPR-Cas9 can be used to generate gene knockouts by introducing a single guide RNA (sgRNA) that contains a 20 nucleotide targeting sequence homologous to the region in your gene of interest, directing Cas9 nuclease from S. pyogenes and generating double-stranded breaks (Ran et al., 2013). Error-prone non-homologous end joining (NHEJ) often leads to insertion/deletion mutations and introduces a frameshift mutation, resulting in loss-of-function alleles or gene knockout (Rodgers and McVey, 2016).

Functional genomic screens elucidate genes involved cellular processes associated with pathological disorders (Heynen-Genel et al., 2012) and development (Ihry et al., 2019; Mair et al., 2019). Pooled and lentivirus-packaged genome-wide sgRNA libraries enable researchers to systematically reveal genes required for cellular fitness in an unbiased manner (Hart et al., 2015; Costanzo et al., 2019; Mair et al., 2019). Two genetic screening approaches are commonly used to identify genotype-phenotype relationships in mammalian cells: negative and positive selection screens. A phenotype is a measurable trait (e.g. cell fitness). Negative selection screens identify

11 gene knockouts that exacerbate phenotypes. When assayed under different conditions, negative selection can help identify fitness genes required for specific phenotypes (e.g. context-dependent genes) (Hart et al., 2015). In order to identify genes involved in cell fitness using pooled CRISPR screens, cells are cultured over time and the relative abundance of sgRNAs at endpoint is compared to the initial infected population, whereby sgRNAs that drop out of the population identify essential fitness genes (Hart et al., 2015). Pooled genome-wide CRISPR-Cas9 dropout screens have been conducted in a variety of human cancer cell lines (Wang et al., 2014; Wang et al., 2015; Wang et al., 2019; Hart et al., 2015; Hart et al., 2017; Chen et al., 2015; Steinhart et al., 2017; Shalem et al., 2014; Toledo et al., 2015) and have yielded a rich dataset of background-specific genetic vulnerabilities. An alternative method of functional genomic screening is positive selection. This approach identifies gene-knockouts that promote a certain phenotype (e.g. fitness advantage). Following infection with a pooled CRISPR-Cas9 library, cells that preferentially thrive under different conditions can help determine context-specific resistance mechanisms (Miles et al., 2016). An example of positive selection is drug suppression, whereby sgRNAs targeting genes mediating drug resistance are enriched in the population (Shalem et al., 2014). Cells can be exposed to a drug after mutagenesis with a pooled sgRNA library. Then, the surviving population after treatment is sampled and the sgRNA sequences are analyzed. Enriched sgRNAs relative to the initial pool at infection can be identified as candidate genes for drug resistance, or drug resistance genes (Shalem et al., 2014).

1.2.2 Fitness and genetic liabilities: Core vs. context-dependent essential genes

In evolutionary biology, fitness is the analysis of natural and sexual selection with respect to a genotype or to a phenotype in a given environment (Wade and Kalisz, 1990; Orr, AH., 2009). Cellular fitness shares some resemblance with Darwin’s theory of natural selection. In 1881, Willhelm Roux suggested that Darwin’s theory could be applied to cellular interactions (Moreno and Rhiner, 2014). Cells are constantly subjected to immense pressures, compromising their fitness or ability to proliferate and evade apoptosis (Di Gregorio et al., 2016). The interaction of cells with differential fitness levels leads to the elimination of cells with relatively lower fitness, despite the fact that both cells are viable independently (Bowling et al., 2018). The fitness landscape is a complex, multidimensional function of traits that is determined by a number of

12 parameters, including cell-cycle length, necrosis, anoikis, transcriptional/translational output, epigenetic state, protein synthesis, signaling activity, differentiation, resource uptake, proliferation and metabolic rate (Fig. 1). Fitness is frequently used as a read-out in functional genomic screens. One approach we used to quantify relative fitness is by measuring the relative change in cell numbers over time through investigating metabolic capacity. Then, we can identify deviations to find genes that behave abnormally. The fitness landscape has been studied in a number of models including Escherichia coli (Butland et al., 2008; Typas et al., 2008), Saccharomyces cerevisiae (Costanzo et al., 2016; Dixon et al., 2008; Roguev et al., 2008), mammalian cells (Sancho et al., 2013) and Drosophila melanogaster (Morata and Ripoll, 1975; Marygold et al., 2007; Fischer et al., 2015).

Decreasing cellular fitness can be a viable therapeutic strategy for cancer and other diseases (Hart et al., 2015; Paul et al, 2014). Previous studies have defined essential genetic vulnerabilities that participate in basic cellular processes using genome-wide CRISPR fitness screens in several human cancer cell lines termed core essential genes (Hart et al., 2015; Shalem et al., 2014). Genetic vulnerabilities present under environmental stress or in a unique genetic background allow us to assign functionally relevant genes. This model of gene essentiality was first proposed in 2014 as the Daisy Model (Hart et al., 2014) and more recently represented as a statistical method termed the adaptive Daisy Model, where the authors identified a greater number of pan-cancer core fitness genes (Behan et al., 2019). Context-specific fitness genes are more likely to encode effective therapeutic targets in cancer cells, avoiding the harmful effects related to targeting healthy tissues. For example, ovarian cancer cells with homozygous BRCA1 or BRCA2 loss of function become dependent on PARP (Poly (ADP-ribose) polymerase) activity, resulting in a context-specific dependency which introduces a therapeutic window to apply PARP inhibitors (olaparib) to eliminate the tumorigenic population (Bixel et al., 2015).

Figure 1. Possible factors influencing the fitness of a cell within a population of cells. These factors include, but are not limited to, cell cycle defects, apoptosis, protein synthesis, necrosis, differentiation, metabolic rate, proliferation rate, resource uptake, signaling activity, anoikis and the epigenetic state. This figure was created using images from BioRender.

1.2.3 Chemogenomic profiling using genome-wide CRISPR-Cas9 screens: Identifying sensitizers and suppressors of small molecules

Identifying effective personalized and therapeutic combinations according to specific genetic drivers of drug effectiveness is the foundation for precision oncology (Gonzalez de Castro et al., 2013). The systematic interrogation of multiple genetic backgrounds with small molecules is known as chemogenomic profiling (Giaever et al., 2004). In a chemogenomic screen, the relative abundance of sgRNA in a treated population is compared to the relative abundance of sgRNA in the population of an untreated population at the same timepoint using algorithms such as drugZ (Wang et al., 2017). This allows for the identification of suppressor and synergistic interactions that may provide insight into mechanism(s) of action, genetic vulnerabilities, and resistance mechanisms, all of which may help stratify patient populations for therapeutic interventions.

A number of groups have successfully applied chemogenomic profiling to understand therapeutic molecules. Szlachta and colleagues utilized a CRISPR screening approach to understand interactions that positively or negatively alter the survival of pancreatic ductal adenocarcinoma cells when the MEK signaling pathway is inhibited (Szlachta et al., 2018). Another group identified genetic mediators of cellular resistance and sensitivity to the PARP

14 inhibitor, olaparib (Zimmermann et al., 2018). Liu and colleagues performed CRISPR-Cas9 based genome-wide screens in multiple myeloma cells to identify and delineate mechanisms for immunomodulatory drugs including thalidomide, lenalidomide and pomalidomide sensitivity (Liu et al, 2019). More recently, MacLeod and colleagues have used genome-wide CRISPR- Cas9 screens in glioblastoma cells treated with TMZ to help understand modulators of sensitivity to TMZ (MacLeod et al., 2019). Furthermore, Estoppey and colleagues have used CRISPR-Cas9 system to generate transient homo- and heterozygous deletion libraries resulting in the identification of pathways and targets related to sensitizing cells or suppressing the effects of nicotinamide phosphoribosyltransferase (NAMPT) inhibitor (Estoppey et al., 2017). Wang and colleagues discovered that TOP2A is a suppressor of the topoisomerase inhibitor etoposide using a genome-scale loss-of-function drug-suppressor screen (Wang et al., 2014). Shalem and colleagues identified novel mechanisms of BRAF-inhibitor resistance by using vemurafenib and a loss-of-function screen (Shalem et al., 2014). These studies provide examples of chemogenomic screens used to identify mechanism(s) of action, biomarkers and sensitizers, in search for combinatorial therapy.

1.3 Thesis rationale

1.3.1 Goals and objectives

Pooled genome-wide CRISPR-Cas9 screening provides a means to functionally interrogate genes in an unbiased manner in order to identify genetic vulnerabilities and novel molecular targets of highly fatal cancers. Although some work has been done in identifying genetic liabilities of pGBM cell lines (MacLeod et al., 2019; Hart et al., 2015; Toledo et al., 2015; Li et al., 2014; Sheng et al., 2010), no information is available concerning the genetic liabilities of rGBM cell lines. The goal in Chapter 2 (project 1) is to identify context-specific liabilities in patient-derived rGBM cells. We have obtained two independent low passage patient-derived rGBM BTIC lines (BT241 and BT972) from our collaborator Dr. Sheila Singh at McMaster University. This study aims to reveal a group of novel context-specific genes in rGBM using genome-wide CRISPR- Cas9 screens, which may subsequently be targeted for cancer therapy.

Genome-wide CRISPR-Cas9 screens have allowed researchers to improve on current therapeutic approaches by identifying sensitizers for combinatorial therapy or suppressors for biomarkers. The goal in Chapter 3 (project 2) is to use pooled genome-wide CRISPR-Cas9 screens to identify suppressors and synergistic genetic interactions for the BMI1 modulator PTC-028, a small molecule drug that is being considered for use in multiple cancer types. This study aims to identify genes whose knockout mediates PTC-028 suppression and to better understand the biology behind the effects of this molecule.

1.3.1.1 Project 1 objectives

1. Perform pooled genome-wide CRISPR-Cas9 functional genetic screens using TKOv3 sgRNA library to generate a comprehensive gene essentiality dataset for two patient- derived rGBM BTIC lines. 2. Analyse data using bioinformatic tools and in silico analysis to select target genes for in vitro validation. 3. Characterize and validate the targets in vitro using assays for quantifying self-renewal and metabolic capacity.

1.3.1.2 Project 2 objectives

1. Conduct a pooled genome-wide CRISPR-Cas9 screen in HAP1 cells treated with PTC- 028 throughout the screen for 18 doublings and delineate sets of synergistic and suppressor genes. 2. Characterize and validate the suppressor targets in vitro by generating single cell clones and performing a dose-response curve. 3. Perform an immunoblot to quantify protein expression of the top suppressor gene following PTC-028 treatment.

Chapter 2 Functional genetic screen in rGBM

Acknowledgements:

Patient-derived cell lines (BT241 and BT972) were developed in the Singh lab (McMaster University, Hamilton). Short Tandem Repeat (STR) DNA profiling was performed by Chirayu Chokshi (Singh lab). BT241 genome-wide CRISPR-Cas9 screen was performed at the Donnelly Centre (University of Toronto, Toronto) while BT972 genome-wide CRISPR-Cas9 was performed at the Michael G. DeGroote Centre for Learning and Discovery (McMaster University, Hamilton). TKOv3 library lentivirus was produced by Dr. Katherine Chan (Moffat lab). The logistics of the sequencing and the organization of screening data were performed by Dr. Amy Tong (Moffat lab) and Dr. Katherine Chan. The sequencing libraries for BT972 screen were prepared by Andrea Habsid (Moffat lab). Chirayu Chokshi performed BT972 screenability and MOI determination. BT241 screen, screenability test, MOI determination and the BT972 screen were performed by David Tieu (Moffat lab) and Chirayu Chokshi. Fig. 6a was created in collaboration with Chirayu Chokshi and modified by David Tieu. Cloning of the vectors for screenability testing and validation was done by David Tieu. Sphere formation and PrestoBlue assays were led by Chirayu Chokshi with contributions from David Tieu. Table 3 patient-derived cell line summary was obtained from Dr. Maleeha Qazi (Singh lab). Figure 11 experiments were conducted by Chirayu Chokshi. Dr. Sheila Singh and Dr. Jason Moffat were consulted for data interpretation and experimental designs. Targets were selected in a collaborative effort with Chirayu Chokshi, Dr. Sheila Singh, Dr. Jason Moffat and David Tieu. All figures not mentioned above were created by David Tieu.

Chapter Two: Exploring the genetic landscape of rGBM 2.1 Introduction

There has yet to be an improvement in the overall survival of GBM patients since TMZ was identified as a treatment (Stupp et al., 2005). We suspect that understanding the rGBM genomic landscape is paramount in finding a more effective treatment for GBM patients. By applying a pooled genome-wide CRISPR-Cas9 screening approach, we aim to identify genetic targets in rGBM by exploring dependencies using low passage patient-derived cell lines in collaboration with the Singh lab at McMaster University. Furthermore, we predict that by using a model that highly enriches for BTICs in an established serum-free rGBM patient-derived cell line model (Singh et al., 2003), we can focus our efforts in identifying fitness genes in BTICs. Targeting this population of cells will likely lead to reduced self-renewal, a defining feature for stem cells, in order to prevent patient relapse as discussed in Chapter 1. In this chapter, we performed 2 pooled genome-wide CRISPR-Cas9 screens to elucidate a wealth of information regarding genetic dependencies at recurrence. We explored this dataset and further investigated 3 specific dependencies in rGBM. In addition, we provided evidence that some dependencies identified through the screens are only present at recurrence. In these screens, we define essential genes as genes required for BTICs to maintain metabolic fitness and proliferation.

2.2 Materials and Methods

2.2.1 Cell culture

Patient-derived rGBM BTIC lines (BT24, BT972, BT935 and BT594) were kindly provided by Dr. Sheila Singh (McMaster University, Hamilton). Short Tandem Repeat (STR) DNA profiling was used to authenticate the identity of cell cultures. These cell lines were derived at recurrence from two patients who received standard of care chemotherapy and radiotherapy. BT241 and BT972 cells were cultured as neurospheres in NeuroCult NS-A Basal Medium (Human) (Stemcell Technologies, #05750) supplemented with 50mL Neurocult NS-A Proliferation Supplements (Human) (Stemcell Technologies, #05753), 20ng/mL epidermal growth factor (EGF, Stemcell Technologies, #78006), 10ng/mL fibroblast growth factor (FGF, Stemcell

Technologies, #78003), 2μg/mL heparin (Stemcell Technologies, #07980) and 1% antibiotic- antimycotic solution (Wisent Bioproducts, #450115EL). HEK293T cell line was obtained from American Type Culture Collection (ATCC) (CRL-3216) and propagated adherently in Dulbecco’s modified Eagle’s medium (DMEM, Gibco, Thermo Fisher, #11965084) with 4.5g/L D-glucose and 110mg/L sodium pyruvate, supplemented with 10% fetal bovine serum (FBS, Gibco, Thermo Fisher, #12483012) and 1% penicillin/streptomycin 10,000U/mL (Gibco, Thermo Fisher, #15140122). All cells were maintained in humidified incubators at 37°C and 5%

CO2.

2.2.2 Primary cell line characterizations

Table 3 outlines the known characteristics and mutational profiles of the low-passage serum-free patient-derived rGBM cell lines BT241 (passage 10) and BT972 (passage 9) used in the experiments of this study. These two cell lines were a kind gift of the Singh lab at McMaster University (Hamilton) and derived from two independent patients. BT241 was derived from a female patient, diagnosed at 68 years old, and BT972 was derived from a male patient, diagnosed at 53 years old. Both patients received radiation therapy and temozolomide chemotherapy. BT241 was derived from a patient that exhibited an overall survival of 23 months and recurrence had occurred after 12 months, while BT972 derived from a patient whose GBM recurred after 39 months from diagnosis. The BT241 sample has MGMT promoter methylation while BT972 has a wildtype IDH profile. Both of these samples were determined to be representative of the mesenchymal subtype (Table 3).

Table 3. Summary of clinical details of the patient-derived cell lines used in this study. Cell lines were derived from 2 independent patients. RT: radiotherapy; TMZ: temozolomide; WT: wildtype.

Cell lines BT241 BT972 Primary/Recurrent Recurrent/residual GBM Recurrent/residual GBM Age at diagnosis (years) 68 53 Gender (M/F) F M Treatment RT+TMZ RT+TMZ -> TMZ Overall Survival (months) 23 n.d Time to recurrence (months) 12 39 MGMT promoter status Methylated n.d IDH (WT/MUT) n.d WT Subtype Mesenchymal Mesenchymal

2.2.3 Pooled genome-wide CRISPR-Cas9 screens using the TKOv3 library

The TKOv3 pooled library was previously constructed in the Moffat lab (Hart et al., 2017). It is a sequence-optimized pooled genome-wide sgRNA library that consists of 70,948 sgRNA cloned in an all-in-one lentiviral vector (lentiCRISPRv2, Addgene), targeting all 18,053 protein coding genes with 4 sgRNAs per gene. In addition, the library includes 142 control sgRNAs targeting EGFP (enhanced green fluorescent protein), LacZ and luciferase, resulting in a total library size of 71,090 sgRNAs. The TKOv3 library is sequence-optimized in its design, with improvements from the TKOv1 library (Hart et al., 2015) and consisting of the expanded 684 reference core essential genes (Hart et al., 2017).

2.2.4 Cloning individual sgRNA into lentiCRISPRv2 vector

The lentiCRISPRv2 (LCV2) vector (Appendix A) was digested using 10 units of BsmBI (NEB, #R0580) for 1 hour at 55°C and treated with 1 unit of shrimp alkaline phosphatase (rSAP, NEB, #M0371S) for 30 minutes at 37°C. The product was gel purified using PureLink Quick Gel Extraction Kit (Thermo Fisher, #K210012). Forward and reverse oligonucleotide (100μM) were phosphorylated using 5 units of T4 Polynucleotide Kinase (NEB, #M0201). The phosphorylated oligonucleotides were annealed at 37°C for 30 minutes, 95°C for 5 minutes and ramping down to

25°C at a rate of 5°C/minute. The annealed primers were diluted 250-fold in UltraPure DNase/RNase-Free Distilled Water (Invitrogen, Thermo Fisher, #10977015) and cloned into the digested LCV2 vector at a ratio of 1:5 vector-to-insert molar ratio with 400 units of T4 DNA ligase (NEB, #M0202) at 16°C overnight. Cells were transformed into One Shot Stbl3 Chemically Competent E. coli (Thermo Fisher, Invitrogen, #C737303) in accordance with the manufacturer’s protocol and plated on 15cm LB-carbenicillin (100μg/ml) plates.

2.2.5 Lentivirus production

Lentivirus for each construct was produced by cotransfecting psPAX2 (Addgene, #12260) and pMD2.G (Addgene, #12259) plasmids with the pooled TKOv3 lentiviral plasmid library using X-tremeGENE 9 transfection reagent (Roche, Millipore Sigma, #6365779001). HEK293T cells were seeded at a density of 9x106 cells in each 15cm Corning tissue-culture treated culture dish (Sigma-Aldrich, #CLS430166) and incubated overnight before being transfected with a mixture of psPAX2 (4.8mg), pMD2.G (3.2mg), TKOv3 plasmid library (8mg), and X-tremeGENE 9 (48μL), in accordance with the manufacturer’s protocol. 24 hours after transfection, the medium was changed to serum-free, high BSA growth medium (DMEM, 1% BSA, 1% penicillin/streptomycin). Virus-containing medium was harvested 48 hours after transfection, centrifuged at 1500rpm for 5 minutes, and stored at -80°C.

2.2.6 Screenability and editing efficiency assessment sgRNAs targeting AAVS1 (adeno-associated virus integration site 1), PSMD1 (proteasome 26S subunit, non-ATPase 1) and PSMB2 (proteasome subunit beta 2) were individually cloned into the LCV2 vector as described in section 2.2.4 and the lentivirus was produced for each sgRNA according to section 2.2.5. Cells were infected with the lentivirus for 24 hours, followed by puromycin (Gibco, Thermo Fisher, #A1113802) selection at 1.8 μg/mL and 1.3μg/mL for BT241 and BT972, respectively for 48 hours. PrestoBlue assay as described in 2.1.11 was performed to determine the relative metabolic capacity compared with the AAVS1 targeting noncoding control, quantified by relative fluorescence unit (RFU). Each experiment was performed with a technical

21 replicate of n=5. PSMD1 and PSMB2 were expected to generate lethal knockouts as they are core essential genes. All sgRNA sequences used individually are listed in Appendix B.

2.2.7 Multiplicity of infection estimation

Functional titers were determined by infecting cells with varying volumes of TKOv3 lentiviral library. 5x106 cells per 15cm Corning tissue-culture treated culture dish (Sigma-Aldrich, #CLS430166) were plated and infected with varying volumes of TKOv3 virus. Medium was replaced with puromycin containing medium to select for transduced cells 24 hours after infection and incubated for 48 hours. The multiplicity of infection (MOI) of the titrated virus was determined at 72 hours after infection by comparing the percent survival of infected cells to noninfected control cells.

2.2.8 Pooled genome-wide CRISPR dropout screens in GBM cells

A total of ~4x108 patient-derived GBM cells was infected with TKOv3 lentiviral library (71,090 sgRNAs) at a MOI of ~0.3 to achieve >200-fold coverage of the library after selection for 24 hours. Infected cells were selected for the puromycin selection marker for 48 hours and split into three replicates containing ~3x107 cells each, passaged every 5 to 7 days, maintaining >200-fold coverage. ~3x107 cells were collected for genomic DNA extraction at day 0 and ~1.5x107 cells were collected at every passage afterwards. The screen was completed at T35 and T54 for BT241 and BT972, respectively. Genomic DNA was extracted from cell pellets using the Wizard Genomic DNA Purification Kit (Promega, #A1120) in accordance with the manufacturer’s protocol, precipitated using isopropanol and ethanol and resuspended in EB buffer. sgRNA inserts were amplified via 2-step polymerase chain reaction (PCR) using primers harboring Illumina TruSeq adapters with i5 and i7 barcodes, and the resulting libraries were sequenced on an Illumina HiSeq2500. PCR1 required a total of 50μg of genomic DNA where each reaction setup had 3.5μg of DNA with an annealing temperature of 66°C. PCR2 required 5μL of pooled PCR1 template using unique i5 and i7 index primer combinations for each sample with an annealing temperature of 55°C. All primers are listed in Appendix C. PCR2 product electrophoresed through a 2% agarose gel and the 200bp product was gel purified using

PureLink Quick Gel Extraction Kit (Thermo Fisher, #K210012). Each sample was read at 200- fold library coverage, except T0 samples, which were sequenced to 500x coverage.

2.2.9 Bayesian Analysis of Gene EssentiaLity

The Bayesian Analysis of Gene EssentiaLity (BAGEL) algorithm was used to analyze the screen results (Hart and Moffat, 2016). The fold-change (FC) value of each sgRNA was calculated by normalizing each sample and calculating log2FC between experimental/control for each replicate and sample at each timepoint. The control used in each case was the sgRNA count from the genomic DNA collected after infection or at T0.

Standard positive (n=684) and negative (n=927) Core Essential Genes 2.0 (CEG2) from the essential and nonessential training sets as redefined by Hart and colleagues in 2017 (Hart et al., 2017) were grouped and separated. Each gene was assigned one category depending on the likelihood of essentiality as determined by the FC value. A relative Bayes Factor (BF) score for each gene, representing a confidence measure that the gene knockout results in a fitness defect, was generated (Hart and Moffat, 2015).

2.2.10 Gene Set Enrichment Analysis

Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005) was used to determine statistically significant enrichment of genes for a specific biological activity at the bottom and the top ends of a ranked list of genes. The genes were ranked in descending order based on the BF score or the probability of essentiality, where the top of the list are genes with the highest likelihood of being essential. The datasets were run using 1000 permutations by gene set with the Hallmark gene sets (Liberzon et al., 2015). This resulted in an enrichment score for each statistically significant hallmark gene set, reflecting how frequently members of that gene set occur at the top or bottom of the data set. The resulting data with normalized enrichment scores, accounting for the difference in the gene set sizes against the statistically significant Hallmark gene sets, were visualized using ggplot2 in R (Wickham, 2016).

2.2.11 Gene knockouts using lentiCRISPRv2 lentiviral vectors

Lentivirus was made for specific candidate genes by cloning individual sgRNA into lentiCRISPRv2 vectors as described in section 2.2.4. The virus was made according to the lentivirus production protocol as described in section 2.2.5 and a knockout was generated by incubating the cell line with virus for 24 hours, followed by selection with puromycin for 72 hours. Cells were then distributed for downstream applications.

2.2.12 PrestoBlue assay

Cell (e.g. BT972 or BT241) culture containing primary sphere formation was disassociated into single cells using Liberase Blendzyme 3 (Roche, Sigma-Aldrich, #5401020001). 2000 cells/well in 0.2mL Neurocult complete media were plated in Corning 96 Well TC-Treated Microplates (Sigma-Aldrich, #CLS3595) and incubated for 4 days in humidified incubators at 37°C and 5%

CO2. The assay was performed in quadruplicate for each sample. 200μL of PrestoBlue Cell Viability Reagent (Invitrogen, Thermo Fisher, #A13261), a fluorescent cell metabolism indicator, was added to each well and incubated for 4 hours. Fluorescence was measured using FLUOstar Omega Fluorescence 556 Microplate reader (BMG LABTECH) at excitation and emission wavelengths of 560/590 nm, respectively. Readings were analyzed by Omega software. PrestoBlue is a cell metabolic reagent and functions by measuring the metabolic conversion rate of resazurin. Cells depend on mitochondrial activity to reduce the nonfluorescent blue resazurin to the fluorescent pink resorufin. As a result, it measures metabolic capacity and can be used as a proxy for cell number.

2.2.13 Sphere formation assay (in vitro limiting dilution assay)

Cell cultures (BT972 or BT241) containing primary sphere formation were disassociated into single cells using Liberase Blendzyme 3. 2000 cells/well in 0.2mL were plated in Neurocult complete media using Corning 96 Well TC-Treated Microplates (Sigma-Aldrich, #CLS3595).

Cells were incubated for 4 days in humidified incubators at 37°C with 5% CO2. The number of

24 spheres (ranging from 100μm to 200μm in diameter) formed within each well was counted. The assay was performed in quadruplicate for each sample.

2.3 Results

2.3.1 Editing efficiency of CRISPR-Cas9 in GBM cell lines

In order to screen for essential gene profiles in recurrent GBM (rGBM) cells and prior to the initiation of the screens, we first determined our ability to efficiently and effectively measure the effects of generating loss-of-function mutations in rGBM models we were hoping to screen. We observed a significant decrease in relative metabolic capacity as measured by PrestoBlue in populations of cells treated with gRNAs targeting PSMD1 or PSMB2 compared with control gRNAs, in both BT241 (Fig. 2a) and BT972 (Fig. 2b). Relative to AAVS1-targeting sgRNAs, PSMD1-targeting sgRNAs had an RFU of 38% and 33% in BT241 and BT972, respectively and PSMB2-targeting sgRNAs had an RFU of 65% and 42% in BT241 and BT972, respectively. This experiment indicated that sgRNAs targeting essential genes can cause measurable fitness defects in the patient-derived cell lines we hoped to screen with a genome-wide pooled gRNA library.

Figure 2. Testing the screenability of BT241 and BT972 patient-derived cell lines. PSMD1, PSMB2 and AAVS1 sgRNAs were individually cloned into a LCV2 backbone and used to infect BT241 (a) (n=5) and BT972 (b) (n=5) cells. The relative fluorescence unit was measured using PrestoBlue Assay, revealing decreased metabolic capacity after PSMD1 and PSMB2 genetic perturbation in comparison to AAVS1. Error bars: mean ± S.E.M. Statistics: **P<0.01, ****P<0.0001 by two-tailed unpaired student’s t test.

2.3.2 Estimating relative lentiviral infection rates for BT241 and BT972

Preceding the genome-wide CRISPR-Cas9 screens, we determined the volume of lentiviral supernatant needed to infect 30% of all cells. We strived for a relatively low proportion of infected population at 0.3 to maintain the stable integration of a single sgRNA in each individual cell (Joung et al., 2017). The results were plotted as a linear relationship between lentivirus supernatant volume and the fraction of infected cells (Fig. 3) We determined that 516μL and 2.45mL of the TKOv3 virus was needed to reach a multiplicity of infection (MOI) of 0.3 in BT241 and BT972 cell lines, respectively. The effective lentiviral titer for each screen was determined to be equivalent to 2.69x106 TU/mL (transduction units per mL) for BT241 and 1.36x106 TU/mL for BT972. The difference in effective titer is likely due to the variations in batch to batch virus production, as these experiments were performed using two different batches of lentivirus produced at different times.

Figure 3. Infection tests for estimating TKOv3 virus volume required to infect 30% of the cell population. TKOv3 virus was titrated to infect BT241 (a) and BT972 (b) cells and the proportion of infected cells or multiplicity of infection (MOI) was plotted against the volume of viral supernatant. 516μl and 2.45ml of lentivirus containing the TKOv3 library was required to reach a MOI of 0.3 in BT241 and BT972 cell lines, respectively.

2.3.3 CRISPR-Cas9 sequencing quality control

Following the sequencing of sgRNAs recovered from the pooled genome-wide CRISPR-Cas9 screens performed on the patient-derived cell lines BT241 and BT972, we evaluated the screening performance based on key quality control metrics and technical elements of each screen. One approach to do this is to determine whether there was sufficient separation of the essential and nonessential genes. We were able to observe this through the fold-change curve which was plotted using the fold-change of each gene against the density of the respective gene. Another quality control aspect that was closely looked at was the precision-recall graph. False discovery rate (FDR), precision and recall were calculated using the following equations: FDR = false positive/(false positive + true positive), Precision = 1 – FDR, Recall = true positive/(true positive + false negative). The positives and negatives are defined in the reference set CEG2. This was used to determine the proportion of recalled standard core essential and nonessential genes and their precision rate. In both, BT241 (Fig. 4a and 4b) and BT972 (Fig. 4c and 4d) screens, we see an observable separation between essential and nonessential genes and a recall of over 0.9 with a precision rate of over 0.9. If the screen worked, we expected sgRNAs targeting essential genes to drop out as it progressed over time with a log2(FC) <0. As expected, we

27 observed an increasing separation between the essential and nonessential genes and an improved ability to recall essential genes with higher precision as the screens progressed through time.

Figure 4. Quality control metrics for BT241 and BT972 CRISPR-Cas9 screens. The log2(fold-change) for each gene’s sgRNAs from endpoint to reference (T0) timepoint and the number of genes (density) from the BT241 (a) and BT972 (c) genome-wide CRISPR-Cas9 screens were plotted at various timepoints (T6, T27 and T35 for BT241 or T5, T24 and T54 for BT972). The two populations of essential genes (E, solid lines) and nonessential genes (NE, dotted lines) validated the resolution in separation between the two groups. The proportion of gold standard 2 (GS2) reference core essential genes from CEG2 recalled as a function of its precision rate for BT241 (b) and BT972 (d) screens were plotted. This quanitified each screens’ ability to recall at least 0.9 of CEG2 at a precision rate of 0.9 in the last timepoint.

2.3.4 BAGEL analysis

Using the BAGEL algorithm as described in section 2.2.9, BF scores were calculated to determine the likelihood of essentiality of every gene. The majority of genes are non-essential in both cell lines. The cutoff (denoted by dotted lines in Fig. 5a) used to determine essentiality was

28 set at an FDR <0.05. We note common essentialities such as SOX2 (sex determining region Y- box 2), SOX9 (sex determining region Y-box 9), MYC, PLK1 (polo-like kinase 1) and we observe differential essentialities; for example, CDK4 (cyclin dependent kinase 4) is essential in BT241 but not in BT972 cells. CDK4 encodes a key protein required for cell cycle G1 phase progression and has been implicated in a number of cancer types (Sherr et al., 2016). In contrast, EGFR (epidermal growth factor receptor) and OLIG2 (oligodendrocyte transcription factor 2) are only essential in BT972. EGFR encodes a transmembrane glycoprotein that drives tumorigenesis in a number of cancer types (Voldborg et al., 1997). OLIG2 encodes a transcription factor and is expressed in oligodendroglial brain tumors (Tsigelny et al., 2016). Through these two genome- wide CRISPR-Cas9 screens, we identified a total of 2530 fitness genes in BT241 and 2728 fitness genes in BT972 at a 5% false discovery rate (FDR) (Fig. 5b). Despite the heterogeneity between the two patient-derived cells, there is a total of 1670 fitness genes overlapping between the two screens, whereas 860 and 1058 fitness genes were specifically found to affect fitness in BT241 and BT972, respectively. Finally, looking at the distribution of the BF scores (Fig. 5c) and Log2 Fold-Changes (LFCs) (Fig. 5d), we see a consistent distribution across all the replicates in each of the two screens. The majority of the BF distributions fall between -10 and 0 in both screens, while the majority of the distribution for the LFC values fall between -2 and 2. There were no significant differences between the replicates, informing us that the screens performed consistently between different technical replicates.

Figure 5. An overview of the CRISPR-Cas9 screen results. (a) A scatter plot of BT241 BF scores plotted against BT972 BF scores. The dotted lines indicate FDR=0.05 in each screen and each dot represents 1 gene. Specific genes that will be discussed in further detail are labelled in the plot. (b) A Venn diagram representing the number of fitness genes that overlap between BT241 and BT972 screens at an FDR <0.05. (c) A violin distribution of BF scores for each technical replicate in each screen. The dash lines represent the median BF score in each replicate,

30 while the dotted lines are the first and third quartiles. (d) A violin distribution of the Log2 Fold Change for each technical replicate of each screen. The dashed lines represent the median Log2 Fold Change in each replicate, while the dotted lines are the first and third quartiles.

2.3.5 Gene set enrichment analysis (GSEA)

To help us understand the processes governing fitness genes in BT241 and BT972, I looked for pathways that were enriched among the identified fitness genes. Gene set enrichment analysis was performed using GSEA under the Hallmark pathways gene set (Fig. 6a) and BT972 (Fig. 6b) screens. The analysis provided insight into the biological mechanisms of essentiality, suggesting that each cell line is highly dependent on genes regulated by MYC and overall, have other similar core dependencies including cell cycle progression, DNA repair, unfolded protein response, ER stress, protein secretion and mitotic spindle.

Figure 6. Results of GSEA analyses of essentiality data. Genes in each screen were ranked in descending order according to their BF scores and processed through gene set enrichment analysis under the Hallmark Pathways gene set for BT241 (a) and BT972 (b). The position of the dots indicates the normalized enrichment score (NES), calculated by scoring the number of genes falling in each gene set and normalized by the number of total genes in each set. The size of the dots indicates the number of genes that fall in each gene set. Both models enriched for core cellular processes including proliferation processes, DNA damage processes and ER stress. Processes important for GBM survival, including genes regulated by MYC were also significantly enriched.

2.3.6 Selecting candidate genes for validation

To select candidate genes for further downstream validation, we biased our search to four main criteria (Fig. 7). One criterion is a high BF or a high likelihood that a gene is essential according to the genome-wide screen. We chose genes with a BF greater than 20, resembling an FDR of <0.001. By looking at the normalized read counts, we selected genes that had a Log2(fold- change) less than -2 and observed in at least 3 out of 4 sgRNAs. Next, we prioritized genes that have a relatively low transcriptomic expression in tissues observed through expression databases. Since the desired target may play a vital role in different tissues, it is therefore helpful to

32 examine its expression levels across the human body. It is reasonable to assume that the broader the expression across tissues, the higher the risk for adverse effects when it is targeted systemically. Therefore, transcriptomic expression is an additional parameter that allows for the early assessment of adverse events. Finally, novelty in GBM within the literature is the last aspect that we looked for when selecting genes for validation. Through these criteria, we focused our validation efforts on three specific genes: Egl nine homolog 1 (EGLN1), Protein Tyrosine Phosphatase 4A2 (PTP4A2) and Timeless Circadian Regulator (TIMELESS).

Figure 7. Schematic of how genes were selected for further validation. The guidelines used to select genes included a BF>20, Log2(fold-change) of less than -2.0 and the dropout must be seen in 3 out of 4 sgRNA, a relatively low transcriptomic expression in tissues and novelty within the realm of GBM biology.

2.3.7 Validation experiments targeting PLK1, PSMA5 and PSMD1

We functionally validated some known core essential genes to act as positive controls for our assays and determined the effects of genetic perturbations on cell metabolic capacity and self- renewal. Genes that were targeted include PLK1, PSMA5 and PSMD1. Cells were infected with the respective genes in a LCV2 vector and compared to AAVS1 LCV2-induced knockout. We observed that when cells had a genetic knockout in PLK1, PSMA5 or PSMD1, they experienced profoundly reduced metabolic capacity and sphere formation capabilities in both BT241 (Fig. 8a and Fig. 8c) and BT972 (Fig. 8b and Fig. 8d) cells.

Figure 8. Results of functional assays following loss of core essential genes PLK1, PSMA5 and PSMD1. Cells were infected with virus containing their respective sgRNA, and PrestoBlue assays for BT241 (a) (n=6) and BT972 (b) (n=6) were performed. In addition, sphere formation assays for BT241 (c) (n=6) and BT972 (d) (n=6) were performed for various gene knockouts and each perturbation was compared with the loss of AAVS1. This revealed that targeting core essential genes decreased metabolic and self-renewal capacity. Error bars: mean ± S.E.M. Statistics: **P<0.01, ***p<0.001, ****P<0.0001 by two-tailed unpaired student’s t test.

2.3.8 Validation experiments targeting EGLN1

EGLN1 (Egl-9 Family Hypoxia Inducible Factor 1), also referred to as PHD2, encodes an oxygen-sensitive protein. Under normoxic conditions (20% O2), EGLN1 catalyzes the post- translational modification of 4-hydroxyproline in HIF (hypoxia-inducible factor) alpha proteins (Klotzsche-von Ameln et al., 2011). HIF proteins play a central role in mammalian homeostasis, promoting survival in hypoxic environments through regulating the transcription of approximately 200 genes (Majmundar et al., 2010). Tumor hypoxia has been associated with the involvement of tumor blood vessel formation, metastasis and the development of treatment resistance (Muz et al., 2015). The modification by prolyl hydroxylation regulates HIF subunits by proteasomal degradation through the VHL (von Hippel-Lindau) ubiquitylation complex. VHL is a part of the E3 ubiquitin ligase complex that polyubiquitinates and eliminates the heterodimeric HIF protein (Aggarwal et al., 2010). We identified EGLN1 as a common essential gene between BT241 and BT972 (Fig. 5a)

EGLN1 inhibitors have been developed for testing in patients with anemia (Kaelin, WG., 2016). As a result, it may be possible to repurpose the drug into GBM patients. EGLN2, a paralog of EGLN1 appears to play a HIF-independent role in cell proliferation and apoptosis (Erez et al., 2003). EGLN2 is beginning to garner interest as a potential cancer target; however, there is still very little development in EGLN1 as a cancer target. A study in 2009 found that EGLN1 haplo- insufficiency suppressed tumor growth, invasion and metastasis as a result of tumor vessel normalization resulting in improved tumor oxygenation (Mazzone et al., 2009), supporting a preliminary case for EGLN1 as a therapeutic target.

In our experiment, EGLN1 knockouts were generated by using LCV2 vectors with 2 of the top 4 sgRNAs based on screening data (Fig. 9). PrestoBlue and sphere formation assays were conducted after selecting for the EGLN1 knockout cells. We see profound decreases in metabolic capacity and a near elimination of the cells’ self-renewal potential.

Figure 9. Results of functional assays following loss of EGLN1. EGLN1 genetic perturbation was performed in BT241 (n=5) and BT972 (n=5) models using lentivirus packaging LCV2 containing one of the top two sgRNAs. The PrestoBlue and sphere formation functional assays resulted in a significant decrease in metabolic capacity and self-renewal abilities compared with AAVS1 knockout cells. Error bars: mean ± S.E.M. Statistics: ****P<0.0001 by two-tailed unpaired student’s t test.

2.3.9 Functional validation targeting PTP4A2

Phosphorylation and dephosphorylation of proteins are critical processes that occur across a broad range of cellular activities (Ardito et al., 2017). The protein tyrosine phosphatase (PTP) superfamily plays an important role in the phosphorylation status of proteins through catalyzing the hydrolytic cleavage of phosphate groups from the tyrosine residues (Kobayashi et al., 2014). We identified PTP4A2, a member of the PTP superfamily as an essential gene in BT972 and BT241 cells (Fig. 5a). There is evidence that PTP4A2 has oncogenic activity that promotes a neoplastic phenotype, including survival, proliferation, invasion, angiogenesis, migration and differentiation (Lazo et al., 2018). It has been shown that the oncogenic activity of PTP4A2 is attributed to the regulation of signaling pathways, including ERK1/2, PI3K/AKT, Src and Rho GTPase (Wei et al., 2018).

The product of the PTP4A2 gene is a promising therapeutic target because multiple human cancers express PTP4A2 at elevated levels associated with increased tumor invasiveness and migration including prostate (Wang et al., 2002), pancreatic (Stephens et al., 2008) and breast cancers (Hardy et al., 2010). PTP4A2 is expressed across all tissues and PTP4A2- deficient mice experience placental insufficiency, impaired spermatogenesis and compromised hematopoietic stem cell renewal (Dong et al., 2012; Dong et al., 2014; Bai et al, 2016; Kobayashi et al., 2014). Moreover, mammary tumors are accelerated in PTP4A2 overexpression mice with the rat c-Neu (HER2) oncogene activation in mammary tissue (Hardy et al., 2010).

In this thesis, PTP4A2 knockout cells were generated by infecting the cells with LCV2 virus containing one of the top performing PTP4A2 targeting sgRNA based on the screening data. We used two independent sgRNAs to validate this experiment. PrestoBlue and sphere formation assays were performed after selecting for the PTP4A2 knockout cells. We observed a significant decrease in metabolic capacity and self-renewal abilities relative to AAVS1-targeting sgRNA (Fig. 10). Furthermore, we extended our validation efforts by examining cellular fitness when PTP4A2 is perturbed in pGBM compared with rGBM. pGBM patient-derived cell lines used in this experiment included BT935 and BT594, while the recurrent cell lines included BT972 and BT241. PTP4A2 perturbation had minimal effect on the pGBM cell lines’ fitness, while the rGBM cell lines experienced a much more significant decrease in fitness according to PrestoBlue

37 and sphere formation assays (Fig. 11). This strongly suggests that PTP4A2 is a rGBM specific fitness gene.

Figure 10. Results of functional assays following genetic perturbation of PTP4A2 in GBM models. PTP4A2 was perturbed in BT241 (n=5) and BT972 (n=5) using lentivirus packing LCV2 containing PTP4A2 sgRNAs. PrestoBlue and sphere formation functional assays were performed. This resulted in decreased metabolic capacity and self-renewal ability in both models compared with the AAVS1-targeting control. Error bars: mean ± S.E.M. Statistics: ****P<0.0001 by two-tailed unpaired student’s t test.

Figure 11: Functional comparison of pGBM and rGBM following loss of PTP4A2. Loss of PTP4A2 was quantified in pGBM cell lines (BT935 and BT594) and rGBM cell lines (BT972 and BT241) using PrestoBlue assay (a and b, respectively) and secondary sphere formation (c and d, respectively). PTP4A2 perturbation was found to have a more significant decrease in fitness in rGBM compared with pGBM. Error bars: mean ± S.E.M. Statistics: *P<0.05, **P<0.01, ****P<0.0001 by two-tailed unpaired student’s t test.

2.3.10 Validation experiments targeting TIMELESS

TIMELESS has multiple roles in cells, including circadian rhythm and cell cycle regulator (Mao et al., 2013). It is a core component of the cell cycle checkpoint system and has been shown to be involved in regulating members of the mammalian circadian core including CLOCK (circadian locomotor output cycles protein kaput), PER (period) and CRY (cryptochrome) proteins (Mao et al., 2013). As part of the cell cycle checkpoint system, TIMELESS acts in maintaining replication fork stability during normal DNA replication, activating cell S-phase checkpoints, promoting proper sister chromatid binding and participating in DNA damage repair (Xie et al., 2015). In

39 response to DNA replication fork stalling, single-stranded DNA (ssDNA) regions are coated by the replication protein A (RPA) complex (Chou et al., 2006). TIPIN then binds to the RPA- coated DNA region in a complex with TIMELESS. Subsequent interaction with CLASPIN promotes ATR-mediated phosphorylation and activation of CHK1, resulting in the inhibition of CDK1 and mitotic events. The reduction in TIMELESS protein appears to decrease the recruitment of PARP1 for DNA damage repair (DDR) and ultimately diminished repair in both homologous recombination (HR) and non-homologous end joining (NHEJ) (Young et al., 2015). As a result, its deficiency increases genome instability, facilitates senescence and thus, there is a potential application for TIMELESS inhibitors in cancer therapy.

Interestingly, circadian regulators have recently been gaining traction as a promotor of stemness maintenance and metabolism in BTICs. Dong and colleagues demonstrated that malignant BTICs are dependent on core clock transcription factors including BMAL1 and CLOCK (Dong et al., 2019). The downregulation of these genes was shown to induce cell cycle arrest and reduced expression of the tricarboxylic acid cycle enzymes. This study strongly suggests that malignant BTICs’ fitness is dependent on functional circadian regulators through stemness maintenance and metabolism.

TIMELESS is overexpressed in a number of tumors including lung (Yoshida et al., 2013), breast (Chi et al., 2017), hepatocellular carcinoma (Elgohary et al., 2015), colorectal cancer (Mao et al., 2013) and cervical carcinomas (Zhang et al., 2017), where higher expression is correlated with poor overall survival and more advanced tumor stages. In vitro experiments knocking down TIMELESS in breast cancer cells show decreases in cell proliferation rate (Chi et al., 2017). Multiple research groups have performed genetic and epigenetic association studies of TIMELESS in breast carcinogenesis and found significant association between stages of breast cancers and TIMELESS promoter hypomethylation in peripheral blood lymphocytes (Fu et al., 2012; Mao et al., 2013). The overexpression of TIMELESS was shown to enhance, while the knockdown suppressed self-renewal of cancer stem cells and migration abilities of breast cancer cells in vitro, possibly by promoting the activation of the oncogene MYC (Chi et al., 2017). MYC dependency in rGBM cell lines has also been demonstrated in our GSEA (Fig. 6).

TIMELESS knockout cell lines were generated by using LCV2 virus with 1 or 2 of the top 4 performing sgRNAs based on CRISPR screens (Fig. 5a). PrestoBlue assay and sphere formation

40 assay were performed after selecting for TIMELESS knockout cells and we observed a significant decrease in metabolic capacity and sphere forming capacity (Fig. 12).

Figure 12. Viability and sphere formation assays following loss of TIMELESS in GBM models. TIMELESS was targeted in BT241 (n=5) and BT972 (n=5) using LCV2 lentivirus with one of the two top-performing sgRNA as determined by the CRISPR-Cas9 screens. The functional assays revealed a decrease in metabolic capacity and self-renewal ability in each cell line compared with AAVS1 control. Error bars: mean ± S.E.M. Statistics: ****P<0.0001 by two- tailed unpaired student’s t test.

2.3.11 Summary of Results

In this study, we used two independent patient-derived rGBM BTIC lines, BT241 and BT972 (Table 3), and demonstrated feasibility of performing genome-wide CRISPR-Cas9 screens in these cell lines, as determined by our ability to capture gold standard positive and negative gene sets (Fig. 2). Subsequently, using the TKOv3 sgRNA library, we successfully identified core- and context-specific fitness genes (Fig. 5). We determined that the screens performed well through a series of quality control metrics (Fig. 4) and were able to identify a total of 2530 fitness genes in BT241 and 2728 fitness genes in BT972 with an FDR <0.05, where 1670 of those fitness genes overlapped (Fig. 5). Both screens yielded the expected core essential gene dropouts such as PLK1, PSMA5 and PSMD1, as well as context-dependent fitness genes including CDK4, OLIG2 and EGFR. Common essentialities between the two different patient- derived lines were also identified, including SOX2, SOX9 and MYC (Fig. 5). Enrichment of these genes highlight core cellular processes including genes regulated by MYC, cell cycle progression, DNA repair, unfolded protein response, ER stress, protein secretion and mitotic spindle (Fig. 6). Next, we filtered the top hits using guidelines including genes with a BF >20, genes experiencing log2(fold-change) <-2, observed with at least 3 out of 4 sgRNAs which left approximately 600 genes in each screen. Each gene was looked at individually to confirm that they have relatively low transcriptomic expression in tissues. We selected 3 genes for further validation: EGLN1, TIMELESS and PTP4A2 (Fig. 7). Along with a few positive controls including PSMA5, PSMD1 and PLK1, these targets were validated using their top two sgRNAs according to the fold-change within the screens, and the functional results were reproduced in rGBM using PrestoBlue and sphere formation assays (Fig. 8-11). All positive controls and the 3 chosen targets validated for reduction in sphere formation and metabolic capacity (p<0.005) when compared with an AAVS1 targeting construct. Finally, we found that PTP4A2 acted as a fitness gene only in rGBM, supporting the screens’ ability to identify rGBM specific fitness genes.

2.4 Discussion and Conclusion

2.4.1 Genetic essentiality in BT241 and BT972

GBM remains one of the deadliest human cancers, characterized by its high tumor heterogeneity and a lack of treatment after tumor relapse. Unfortunately, progress in the treatment of GBM is extremely slow due to the limited understanding of rGBM biology. The understanding of genetic vulnerabilities of rGBM holds great promise for therapeutic targeting; however, a deeper functional understanding is still required. A number of studies have identified genetic vulnerabilities in treatment-naïve pGBM (Toledo et al., 2015, MacLeod et al., 2019). To date, no study has comprehensively interrogated the functional genomic landscape in treatment refractory rGBM. We sought to explore the genomic landscape in rGBM in an effort to determine genes involved in BTIC fitness and drivers of tumorigenicity. This was done using a functional genetics approach with genome-wide CRISPR-Cas9 screens on patient-derived rGBM BTIC cell lines.

Roughly ~2,000 total fitness genes were identified in each of the genome-wide screens, falling in line with the total number of fitness genes identified in other cancer cell lines (Hart et al., 2015). From the screens, we observed that the large majority of genes are non-essential in both BT241 and BT972. The unique genetic essentiality between the two cell lines is particularly interesting as it stresses the heterogeneous nature of GBM tumors between patients. These differences include genes that are essential only in BT972 such as OLIG2 and EGFR, and genes that are essential only in the case of BT241 including CDK4. This evidence supports the idea that BT241 and BT972 have different genetics dependencies and heterogeneity plays a critical role in treatment options. The screen also elucidated some common stem cell related genes such as SOX2 and SOX9 in both set of screens, illustrating the ability to capture the stem cell dependencies that fuel the growth of patient derived BTICs. These results highlight the strength of the GBM BTIC enriched model. The screens also revealed other shared dependencies including MYC (Tateishi et al., 2016; Annibali et al., 2014) and CDK6 (Bellail et al., 2014; Xu et al., 2018), both of which have been cited as potential therapeutic targets for GBM.

Gene set enrichment analysis for each screen identified core cellular processes and known processes upon which GBM depends on such as MYC, proliferation processes such as cell cycle

44 progression (E2F targets and G2/M checkpoint), DNA damage processes such as DNA repair, unfolded protein response, mitotic spindle and ER stress. These results provide further evidence that the core fitness genes have dropped out from the screen and therefore provides greater confidence in the context-specific fitness genes that we have validated.

Interestingly, the results suggest that BT972 is dependent on EGFR for tumorigenesis, in that respect, resembling a classical subtype tumor (Hovinga et al., 2019). BT241 is not dependent on EGFR and is characterized by its aggressive nature, resembling the mesenchymal subtype. However, we know that both of these cell lines were characterized as mesenchymal (Table 3). EGFR dependency presumably is not the only determining factor of a classical subtype.

2.4.2 Functional validation following loss of EGLN1, PTP4A2 and TIMELESS

Unexpectedly, we noticed that some cells survived after attempting to generate knockouts in EGLN1 (Fig. 9a, b), PTP4A2 (Fig. 10a, b) and TIMELESS (Fig. 12a, b). Although we attempted to eliminate our targets in the population by using a significant volume of virus, followed with puromycin selection to eliminate the uninfected population, it is likely that a population of cells remained wildtype. This can be observed by immunoblotting for the protein as a possible control experiment. The persistent presence of the protein could be the result of a lack of mutation, in- frame mutation, silent mutation, or an unsuccessful homozygous knockout. In addition, we are also unable to rule out the possibility that these fitness genes are context-specific to certain cells in this heterogenous population. A significant limitation for the PrestoBlue assay is that we are unable to distinguish dead from metabolically decelerating cells. However, we visually noted that there were more dead cells in the sgRNA-targeting condition in each case compared with our control AAVS1-targeting condition. This will need to be quantified by other means, such as Trypan Blue. Interestingly, it seems that secondary sphere formation was nearly eliminated in each case for EGLN1 (Fig. 9c, d), PTP4A2 (Fig. 10c, d) and TIMELESS (Fig. 12c, d), which suggests that these targets play a crucial role for self-renewal.

In our experiments, we noticed a decrease in metabolic capacity and self-renewal properties when EGLN1 is perturbed in rGBM cells. Bearing in mind that these experiments were

performed in vitro in a normoxic environment (20% O2), HIF1α is suspected to be prolyl hydroxylated by the protein encoded by EGLN1, targeting it for proteasomal degradation by VHL (To et al., 2005). The perturbation of EGLN1 may lead to an increase in HIF1α protein levels, mimicking a hypoxic response. A hypoxic response is typically the result of an environmental stressor, resulting in induced apoptosis (Lenihan and Taylor, 2013). Although a hypoxic response may compromise a cell’s ability to survive, it is suspected that the resistant cells have a higher potential to form tumors (Greijer and Wall, 2004). This was demonstrated when Duan and colleagues found that EGLN1 deficient mice contained retinal astrocytes that were in a less mature state by measuring the markers of astrocytic differentiation, GFAP (glial fibrillary acidic protein) and PAX2 (paired box 2) (Duan and Fong, 2019). This may provide a possible explanation into the functional results of EGLN1 deficient GBM cells, suggesting that these cells have become less differentiated and more quiescent. One caveat is that the experiments were not performed in an in vivo environment and cannot accurately determine what role the microenvironment may play (Klotzsche-von Ameln et al., 2011). Although we may be decreasing metabolic capacity and sphere formation in tumors, explained by the in vitro selection against upregulated hypoxic-related genes, counterproductively, we may be increasing the aggressive nature of the tumor. Interestingly, Kozlova and colleagues (Kozlova et al., 2017) demonstrated that EGLN1 is a direct binding partner of EGFR and regulates its stability in breast cancer. This may provide a possible insight into how EGLN1 is required for the survival of BT972 BTICs, since EGFR had been elucidated as a fitness gene. However, this does not explain the EGLN1 dependency in BT241, suggesting that EGLN1’s association with the destabilization of EGFR is not the only mechanism that makes EGLN1 a fitness gene. Additionally, EGLN1 levels are significantly higher at the transcript level in GBM compared with non-tumor brain tissues according to Gliovis (Bowman et al., 2017) using the TCGA_GBM dataset. These experiments indicate that EGLN1 may be a viable target in rGBM and should be further explored.

The relationship between PTP4A2 and cancer has not been extensively explored. PTP4A2 has been linked to hematopoietic stem cell (HSC) self-renewal as shown using serial bone marrow transplantation assays of PTP4A2-deficient HSCs (Kobayashi et al., 2014). This is potentially a viable target as the screens elucidated other stem-cell-like genes such as SOX2 and SOX9. Targeting the self-renewal ability is arguably more important than targeting a cell viability or

46 proliferation gene, as it can potentially eliminate the suspected BTIC population with stem-like properties, thought to be driving tumorigenesis. Furthermore, PTP4A2 has been shown to promote cell migration and invasion through the ERK-dependent signaling pathway (Wang and Lazo, 2012). Importantly, PTP4A2 is selectively essential in other cell lines according to previous functional studies (Hart et al., 2015;), suggesting a potential therapeutic window in rGBM. In published databases, PTP4A2 is non-essential in pGBM cell lines (Toledo et al., 2015) and only essential in 89 of the 563 screens according to the Broad Institute DepMap (Tsherniak et al., 2017). Furthermore, when we tested the essentiality of PTP4A2 in pGBM, we showed that it did not decrease cellular fitness to the same extent as it did for the perturbation of PTP4A2 in rGBM. These results provide support that our screens have the potential to identify novel rGBM specific fitness genes that no previous pGBM screens have done to date. The ability to identify rGBM fitness genes will increase our ability to target GBM at recurrence, validating our rGBM screening model. Although there has not been evidence in literature implicating PTP4A2 in rGBM, our data suggests that targeting PTP4A2 could reduce tumorgenicity in treatment- refractory rGBM cells.

Aside from TIMELESS, no other circadian regulators were identified as fitness genes in our screens, indicating that the decrease in fitness may not be due to its function as a circadian regulator. A number of studies have implicated the overexpression of TIMELESS in the development and progression of various cancers including lung (Yoshida et al., 2013), hepatocellular carcinoma (Elgohary et al., 2015), breast (Chi et al. 2017) and prostate cancers (Chiang et al., 2014). The overexpression of TIMELESS enhances, while the knockdown of TIMELESS suppresses, the self-renewal of breast cancer stem cells as well as cell invasion and migration in an in vitro setting (Chi et al. 2017). It has been shown that ERK activation promotes TIMELESS expression and depleting TIMELESS increases DNA damage, while triggering G2/M arrest (Neilsen et al., 2019). Furthermore, TIMELESS may progress breast cancer through the activation of MYC by upregulating its expression (Chi et al. 2017). TIMELESS possibly acts on BT241 and BT972 in this same manner, through the deactivation of MYC, an essential gene in both cell models. Furthermore, it is known that TIMELESS contributes to the DNA damage response (DDR) function of PARP1, which is the main sensor for single-stranded DNA binding proteins (SSBs) and double-strand DNA breaks (DSBs) in DNA. TIMELESS is recruited to the damage and is dependent on PARP1 binding. The reduction of TIMELESS has been shown to

47 reduce the recruitment of PARP1 for DDR. As such, if this interaction ceases, it will decrease the efficiency of HR (Young et al., 2015). Similar to PARP1 inhibition, loss of TIMELESS in cells increased radiosensitivity, genomic instability and senescence when a genotoxic agent is applied and therefore may make a good combinatorial target for existing therapies (Young et al., 2015). TIMELESS has yet to be explored in GBM and it may be worth pursuing, since according to bulk transcriptomic data in Gliovis (Bowman et al., 2017) using the TCGA_GBM dataset, there is a significant increase in expression levels of TIMELESS in GBM compared with non-tumor brain tissue.

2.4.3 Patient-derived rGBM BTIC model

In this study, we conducted the first reported genome-wide CRISPR-Cas9 screen in a rGBM model. Previous studies surveying the functional genomic landscape in GBM to date have been conducted on untreated pGBM models (Hart et al., 2015; Toledo et al., 2015; MacLeod et al., 2019). The largest pGBM study was performed by MacLeod and colleagues in 2019. They screened 10 pGBM BTIC lines, grown adherently on poly-L-ornithine and laminin coated cell culture dishes. This is in contrast to the screens performed in this study, which were conducted as non-adherent neurospheres. The addition of substrates to culture may alter the dependencies of cells. This was previously demonstrated in human pluripotent stem cells (Mair et al., 2019). MacLeod identified and validated regulators of stemness as fitness genes including SOX2, SOX9, DOT1L (disruptor of telomeric silencing 1-like histone H3K79 methyltransferase) and SOCS3 (suppressor of cytokine signaling 3) in their pGBM BTIC models. Overall, we see these dependencies in our screens, with the exception of DOT1L, which was not identified as a fitness gene in both of our screens. Intriguingly, when we validated the functional effects of PTP4A2 perturbation in pGBM cell lines, we showed that it does not affect its fitness, according to PrestoBlue and sphere formation assays. This gives us confidence that we are able to uncover rGBM-specific fitness genes and supports our study’s ability to identify unique rGBM dependencies that have never been previously studied.

2.4.4 Future Directions

In order to verify the functional role of these targets, a rescue experiment can be performed to eliminate the possibility of off-target effects. Further studies could be conducted on additional rGBM cell lines to improve the understanding of shared context-specific genetic vulnerabilities and improve the resolution of the genetic overlap. There have been very few rGBM models that can be grown in the lab. Consequently, we cannot broadly address the heterogeneity inherent in GBM. To the best of our ability, we are able to study the few available rGBM models grown in the lab and validate our findings through other existing models.

Additional validation experiments will consist of perturbing the respective genes in various control cell lines including treatment naïve primary cell lines, normal human astrocytes and neural stem cells to determine if they also share the same genetic vulnerabilities. The ideal cell type control would be a patient matched astrocyte-like neural stem cells within the subventricular zone of a patient (Lee et al., 2018). However, this is not readily obtainable, and the experiments may not be feasible. It would also be very important to screen paired match primary and recurrent tumor samples from the same patient to see whether the same genetic vulnerability existed prior to recurrence.

To better understand how the loss of EGLN1, PTP4A2 and TIMELESS lead to a decrease in metabolic capacity and self-renewal, we could apply genetic or chemical perturbation and survey the transcriptomic landscape in addition to stem-cell markers. We can further test these perturbations with standard-of-care therapies to test for synergistic interactions, especially in the case of TIMELESS. Given that PTP4A2 is a phosphatase, phosphoproteomics could be conducted in PTP4A2 null cells to identify its mechanistic targets. Considering that EGLN1 is important for oxygen sensing, it will be important to test these perturbed cells in a physiological normoxic tumor niche (3-7% O2) or a hypoxic chamber to observe how it behaves in a more relevant environment.

Finally, further experiments can be done to realize their therapeutic potential in pre-clinical models such as in a xenograft mouse model. For in vivo work, we can recapitulate a xenograft model and administer a chemical inhibitory compound followed by determining tumor size, overall survival and toxicity.

2.4.5 Conclusion

Collectively, the goal of this study was to develop an understanding of rGBM fitness genes. Understanding rGBM is arguably more important than its primary counterpart and this study has provided a foundation, paving the way for further understanding. We completed the first two genome-wide CRISPR-Cas9 screens in patient-derived rGBM BTICs. Following the screens, we validated 3 genes, including EGLN1, PTP4A2 and TIMELESS and compared the fitness of pGBM and rGBM after PTP4A2 perturbation. We found that targeting PTP4A2 only decreased fitness in rGBM, providing support that the screens have elucidated rGBM-specific fitness genes. Overall, our results provide a rich dataset of fitness genes in rGBM that can be further explored to identify drivers of tumor recurrence.

Chapter 3 PTC-028 as a therapeutic molecule

Acknowledgements:

BMI1 knockout cell lines in a HAP1 background were obtained from Horizon Discovery. Olga Sizova (Moffat lab) stored and performed Sanger sequencing for the BMI1DC1 and BMI1DC2 cell lines to validate loss of function mutations. TKOv3 library lentivirus was produced by Dr. Katherine Chan (Moffat lab). The logistics of the sequencing and the organization of screening data were performed by Dr. Amy Tong (Moffat lab) and Dr. Katherine Chan. Dr. David Bakhshinyan (Singh lab) was consulted for PTC-028 reagents and advice regarding treatment dosage and experimental design. All experiments not mentioned above were performed at the Donnelly Centre (University of Toronto, Toronto) by David Tieu (Moffat lab). Dr. Sheila Singh and Dr. Jason Moffat were consulted for data interpretation and experimental designs.

50 51

Chapter Three: Identifying novel sensitizers and suppressors of PTC-028 3.1 Introduction

PTC-028 is a small molecule drug that is being considered for use in multiple cancer types. Although it has been shown in multiple studies to be an effective therapeutic agent for tumor initiating cells (Bakhshinyan et al., 2018; Dey et al., 2018), there are still unanswered questions regarding the mechanistic actions of the molecule, biomarkers to predict clinical efficacy, and the potential for combinatorial therapy. One approach to study PTC-028 is through revealing genetic interactions using a chemogenomic screen. We can accomplish this by applying pooled genome-wide CRISPR-Cas9 screen in combination with the small molecule drug. In this chapter, we performed a pooled genome-wide CRISPR-Cas9 screen using a HAP1 cell model along with PTC-028 treatment. We uncovered unexpected sensitizers and suppressors of PTC-028 and investigated the top genetic perturbation that led to decreased PTC-028 efficacy in HAP1 cells by generating clonal knockouts.

3.2 Methods

3.2.1 Cell lines

Commercially available HAP1 cell lines representing primary, therapy-naïve CML cell lines were obtained from Horizon Discovery and propagated in minimal media - Dulbecco’s modified Eagle’s medium (DMEM, Multicell, Thermo Fisher, #319162CL) with 1.982g/L glucose, 0.161g/L L-glutamine, without sodium pyruvate, supplemented with 1% penicillin/streptomycin 10,000U/mL (Gibco, Thermo Fisher, #15140122), and 10% fetal bovine serum (FBS, Gibco, Thermo Fisher, #12483012).

WNK1D cell lines in HAP1 background were generated using HAP1 C631 (obtained from Horizon Discovery) and maintained in rich media - Iscove’s Modified Dulbecco’s Medium (IMDM, Gibco, Thermo Fisher, #12440053) supplemented with 1% penicillin/streptomycin 10,000U/mL (Gibco, Thermo Fisher, #15140122) and 10% fetal bovine serum (FBS, Gibco,

Thermo Fisher, #12483012). All cells were maintained in humidified incubators at 37°C and 5%

CO2.

3.2.2 Generating a WNK1 knockout in a HAP1 cell line

LCV2 plasmid containing a WNK1 sgRNA insert was generated using the approach outlined in section 2.2.4. All sgRNA sequences are listed in Appendix B. 3.0x105 cells were plated in 6 well Corning Costar TC-Treated Multiple Well Plates (Sigma-Aldrich, #CLS3516) and 2μg of LCV2 plasmid containing a WNK1 sgRNA insert was transfected into cells using 48μL X-tremeGENE 9 transfection reagent (Roche, Millipore Sigma, #6365779001) for 24 hours. Cells were selected with puromycin (1μg/mL) (Gibco, Thermo Fisher, #A1113802) for 24 hours and recovered in 20% fetal bovine serum (FBS, Gibco, Thermo Fisher, #12483012) rich media. Cells were counted and resuspended in 1 cell/well in Corning 96 Well TC-Treated Microplates (Sigma- Aldrich, #CLS3595). Cells were expanded and screened for mutations using Sanger sequencing (section 3.2.4).

3.2.3 Immunoblot analysis

Cell pellets were washed with cold phosphate-buffered saline (PBS, Gibco, Thermo Fisher, #14190144) twice, followed by the addition of F lysis buffer (10mM Tris-HCl, pH 7.05, 50mM

NaCl, 30mM Na4P2O7, 50mM NaF, 5μM ZnCl2, 100μM Na3VO4, 1% Triton X-100, 1mM phenylmethylsulphonyl fluoride, 5units/ml α2-macroglobulin, 2.5units/ml pepstatin A, 2.5units/ml leupeptin and 0.15mM benzamidin) and supplemented with Halt Protease and Phosphatase Inhibitor Cocktail (100X) (Thermo Fisher, #78430) to prepare the lysates. Protein levels were quantified using the Pierce BCA protein assay (Thermo Fisher, #23225). 30µg of the protein was prepared with NuPAGE LDS Sample Buffer (4X) (Invitrogen, Thermo Fisher, #NP0007) and 5% 2-Mercaptoethanol (Sigma Aldrich, #M6250). 15µl of sample was loaded on a NuPAGE 4-12% Bis-Tris Protein Gel (Invitrogen, Thermo Fisher, #NP0322BOX) for 10 minutes at 100V and ~1 hour at 140V. The protein was transferred to Hybond-P polyvinylidene difluoride (PVDF) membranes (Sigma Aldrich, #GERPN303F), blocked in 5% nonfat dry milk

53 in TBS with 0.1% TWEEN-20 (TBST) for 1 hour and incubated with primary antibodies in 1% nonfat dry milk TBST including anti-WNK1 (Abcam, ab137687, 1:1000), anti-BMI1 (Millipore Sigma, 05-637, 1:1000), anti-GAPDH (Abcam, ab8245, 1:10000) and anti-β-actin (Abcam, ab8226, 1:10000) overnight at 4°C. Secondary antibodies using HRP-linked anti-rabbit IgG (Cell Signaling, #7074P2, 1:10000) or HRP-linked anti-mouse IgG (Cell Signaling, #7076P2, 1:10000) were incubated on the second day for 60 minutes and the blot was detected using SuperSignal West Pico PLUS Chemiluminescent Substrate (Thermo Fisher, #34580) in accordance with the manufacturer’s protocol. The blots were detected and documented using CL-XPosure Film, 8 x 10 in. (Thermo Fisher, #34091).

3.2.4 Sanger sequencing

Genomic DNA was extracted using Extracta DNA Prep for PCR (Quantabio, #95091) in accordance with the manufacturer’s protocol, and PCR was performed and purified using PureLink PCR Purification Kit (Thermo Fisher, #K310001). All primers are listed in Appendix C. 50ng of PCR product was sent to The Centre for Applied Genomics (TCAG) (Toronto, Canada) for capillary-based fluorescent sequencing on dual ABI 3730XL instruments. SnapGene software (from GSL Biotech; available at snapgene.com) was used to analyze the sequencing results and determine knockout clones.

3.2.5 Dose-response curves for PTC-028 in cell lines

1.5x104 cells were plated in each well of a Corning 96 Well TC-Treated Microplates (Sigma- Aldrich, #CLS3595) in rich or minimal media as specified, 12 hours prior to treatment with 6 replicates. PTC-028 concentrations ranging from 0.15625μM to 0.015643μM were divided evenly between 11 wells, with the DMSO (dimethylsulfoxide) control in the 12th well. Cells were incubated for 72 hours. 19.6μL of PrestoBlue was added to each well and incubated for 3 hours at 37°C, 5% CO2. The plate was read using the Synergy 2 Multi-Mode Microplate Reader (BioTek) at excitation and emission wavelengths of 560/590 nm, respectively. Readings were analyzed by Omega software. A dose-response curve was plotted.

3.2.6 Genome-wide CRISPR screen using PTC-028 in HAP1 cell lines

Genome-wide CRISPR screens using PTC-028 (kindly provided by PTC Therapeutics Inc.) were conducted in minimal media. A total of 50x106 C631 HAP1 cells were infected with TKOv3 lentiviral library (71,090 sgRNAs) with minimal media containing 8μg/mL polybrene infection/transfection reagent (Sigma-Aldrich, #TR10003G) at a MOI of ~0.3 as determined in a similar manner to section 2.2.7 to achieve ~200-fold coverage of the library after selection. At 24 hours after infection, minimal media was replaced with minimal media containing 1μg/mL puromycin. After 48 hours of selection, cells were pooled, counted and split into three replicates containing 15x106 cells in each arm for a total of 3 replicate arms, passaged every 3–4 days, and maintained at 200-fold coverage. At T6, a PTC-028 dose of IC60 was given to the cells and this was replenished when the cells are split every 3-4 days. 30x106 cells was collected for genomic DNA extraction at day 0 (after selection) and 20x106 cells at every passage afterwards, until day 18 after selection or ~10-15 doublings. Genomic DNA was extracted from cell pellets using the Wizard Genomic DNA Purification Kit (Promega, #A1120) in accordance with the manufacturer’s protocol, precipitated using isopropanol and ethanol and resuspended in EB buffer. sgRNA inserts were amplified via PCR using primers harboring Illumina TruSeq adapters i5 and i7 barcodes, and the resulting libraries were sequenced on an Illumina HiSeq2500. All primers are listed in Appendix C. Each sample was read at 200-fold coverage with the exception of T0 samples, which was sequenced at 500-fold coverage.

3.2.7 DrugZ analysis

DrugZ analysis was applied to generate a P-value, false discovery rate (FDR) and normZ score for each gene as described by Wang and colleagues in 2017 (Wang et al., 2017). The log2(fold- change) of each sgRNA in the pool was calculated by normalizing the total read count of each sample at the same timepoint and taking the log ratio, after adding a pseudocount of 5 reads to each sgRNA. Then, the mean and standard deviation of all sgRNA targeting a set of negative control genes – the reference nonessential gene set (Hart et al., 2015) were calculated and these values were used to determine a Z score for each sgRNA. The guide Z score of all sgRNA across all replicates is summed to get a gene-level sumZ score, which is then normalized (by dividing

55 by the square root of the number of summed terms) to the final normZ. A P-value is calculated from the normZ, and false discovery rates are determined using the method of Benjamini and Hochberg (Wang et al., 2017).

3.3 Results

3.3.1 Validation of BMI1 knockout cell lines through immunoblot and Sanger sequencing

PTC-028 is suspected to target cancer cells through the previously described BMI1 mechanism (Bakhshinyan et al., 2019; Dey et al., 2018). I hypothesized that BMI1 knockout (KO) HAP1 isogenic cells (BMI1D) and wildtype (WT) HAP1 cells will respond differently to the effects of PTC-028 as determined by a dose-response curve. More specifically, BMI1D cells should have a decreased response to PTC-028 since the target has been eliminated. As such, the first step of my validation was to obtain BMI1D cells in a HAP1 background. Once the cell lines were generated, I validated the knockout by comparing the BMI1 protein expression levels of BMI1D with WT C631 HAP1 cells through an immunoblot, using GAPDH as a loading control (Fig. 13a). In addition, the region harboring the BMI1 gene was sequenced through Sanger sequencing (Fig. 13b). Both clonal cell lines BMI1DC1 and BMI1DC2 were devoid of the BMI1 protein compared with the high expression in WT cells. The Sanger sequencing of both clonal cell lines depicted deletions leading to a frame shift, with 11bp deletion in BMI1DC1 and 1bp deletion in BMI1DC2. These experiments have validated that the two BMI1D clones have inactivating BMI1 mutations, both at the genetic and protein level.

Figure 13. Validation results of BMI1DC1 and BMI1DC2 clonal cell lines by sequencing and immunoblot. BMI1DC1 and BMI1DC2 were verified for frameshift mutation through Sanger sequencing of the BMI1 gene (b) and the depletion of BMI1 at the protein level by immunoblot analysis (a). BMI1 was detected using anti-BMI1 antibody (Millipore Sigma, 05- 637) and GAPDH (Abcam, ab8245) was used as a loading control.

3.3.2 Dose-response of PTC-028 in HAP1 BMI1D cells

Following the generation of the BMI1D cell lines, dose-response experiments were performed to compare the effects of PTC-028 on the metabolic capacity of BMI1D and WT HAP1 clones. According to PrestoBlue Assays, there were no substantial differences in the PTC-028 dose- response in BMI1DC1, BMI1DC2 and WT cells (Fig. 14). WT HAP1 had an IC50 of 60nM while BMI1DC1 and BMI1DC2 had an IC50 of 59nM and 54nM, respectively.

Figure 14: Evaluating PTC-028 (PTC Therapeutics Inc.) dose-responses in BMI1D and WT HAP1 cell lines. Cells were dosed with PTC-028 at various concentrations ranging from 0.15625μM to 0.015643μM. RFU was determined using PrestoBlue assays and normalized with DMSO treated cells. WT HAP1 had an IC50 of 60nM (n=6), while BMI1DC1 (n=6) and BMI1DC2 (n=6) had an IC50 of 59nM and 54nM, respectively.

3.3.3 Sensitizers and suppressors of PTC-028

As previously described in section 1.2.3, researchers are able to identify synergistic and suppressor gene-drug interactions through chemogenomic profiling. In this thesis, chemogenomic profiling was performed through a genome-wide CRISPR-Cas9 screen by systematically generating a knockout in every protein-coding gene in combination with PTC-028 treatment. Synergistic gene-drug interactions lead to a superior decrease in fitness, evident through lower sgRNA counts at endpoint. Suppressor gene-drug interactions result in increased fitness, apparent through higher sgRNA counts at endpoint. In this thesis, I define a synergistic or suppressor hit as a gene identified with an FDR <0.05 and focused on genes with an FDR <0.01. Since there were no differences in the PTC-028 response between BMI1D and WT HAP1 cells, I next performed a chemogenomic screen in a WT HAP1 model to determine whether there were any significant sensitizers or suppressors for PTC-028. Once the screens were completed, the value of each gene was plotted based on the normal Z value and p-value for the sensitizers (Fig. 15a) and suppressors (Fig. 15b). Some of the top hits for the sensitizing screen included GSTE1 (glutathione s-transferase E1), SPAG5 (sperm associated antigen 5), CMIP (C-Maf-

58 inducing protein), SRPK1 (SRSF protein kinase 1) and USP14 (ubiquitin specific peptidase 14). The top hits for the suppressor side were WNK1 (with-no-lysine 1), CAB39 (calcium binding protein 39), OXSR1 (oxidative stress responsive kinase 1), NRBP1 (nuclear receptor binding protein 1) and TSC22D2 (transforming growth factor-beta-stimulated clone 22 domain 1).

Figure 15: Chemogenomic genome-wide pooled CRISPR-Cas9 screens +/- PTC-028 in human HAP1 (C631) cells. The normZ for each gene was calculated by comparing its fold change at endpoint with the mean and standard deviation of reference negative control genes. The p-value for each gene was computed using the normZ value through the method of Benjamini and Hochberg. normZ and p-value scores were plotted for synergistic and suppressor interactions. (a) Genes that had a synergistic interaction with PTC-028 include GSTE1, SPAG5, CMIP, SRPK1 and USP14. (b) Genes that had a suppressor interaction include WNK1, CAB39, OXSR1, NRBP1 and TSC22D2.

3.3.4 Validating WNK1D HAP1 cell lines

WNK1 was by far the top scoring suppressor from the chemogenomic screen. WNK1 is a serine/threonine kinase involved in multiple roles including proliferation, cell signaling and electrolyte homeostasis by regulating sodium-coupled chloride and potassium-coupled chloride cotransporters (Roy et al., 2015). I generated WNK1D clonal cell lines in a HAP1 background and it is worth noting that WNK1D clonal cell lines grew significantly slower than their wildtype

59 background counterpart. These cell lines were validated for their WNK1 protein expression using immunoblot analysis. The results indicate that the level of WNK1 protein was significantly diminished in WNK1DC1 and WNK1DC2, while WT continued to express high levels of WNK1 protein (Fig. 16a). Although, it should be noted that there is still some expression of WNK1 in each cell line. This may be the result of alternative splicing, which could consist of different exons in mRNA or persistent isoforms that have not been eliminated. β-actin was also detected in each sample as a loading control for the immunoblot. Further validations of the knockout cell lines were done through Sanger sequencing of the WNK1 region and each cell line was determined to have deletions leading to a frame shift, where WNK1DC1 had 1bp deletion and WNK1DC2 had 7bp deletion (Fig. 16b).

Figure 16: Validation of WNK1D HAP1 clonal cell lines. HAP1 cells that had been generated to have WNK1 null alleles were validated. (a) WNK1 protein was detected using anti-WNK1 antibody (Abcam, ab137687) and β-actin acted as a loading control (Abcam, ab8226). (b) Cells were sent for Sanger sequencing at the WNK1 region targeted by the sgRNA. The sgRNA- targeted region targeted contained a 1bp and a 7bp deletion for C1 and C2, respectively.

3.3.5 Dose-response using PTC-028 in WNK1D HAP1 cell lines

Using two isogenic WNK1D and WT HAP1 cells, I generated a dose-response curve (Fig. 17) to validate the effects of PTC-028 on a WNK1D genetic background. I observed that WNK1D cell lines were more resistant to the effects of PTC-028, with an IC50 two-fold greater at 157nM and 175nM for WNK1DC1 and WNK1DC2 compared with WT cells with an IC50 of 70nM.

Figure 17: Results of dose-response curves for PTC-028 in HAP1 and WNK1D cell lines. Sensitivity to PTC-028 in each cell line was determined after cells were treated with PTC-028 at various concentrations ranging from 0.15625μM to 0.015643μM. RFU was determined using PrestoBlue Assay and normalized with DMSO treated cells. WNK1D cells had a two-fold decrease in the effectiveness of PTC-028, from 70nM (n=6) in WT HAP1 to 157nM and 175nM in WNK1DC1 (n=6) and WNK1DC2 (n=6), respectively.

3.3.6 Measuring protein levels of BMI1 and WNK1 following PTC-028 treatment

I next hypothesized that the genetic manipulation of WNK1 will have an effect on the BMI1 protein level. To determine whether knocking out WNK1 plays a role in regulating the endogenous BMI1 protein level and vice versa, I performed an immunoblot to measure BMI1

61 and WNK1 protein levels in WNK1D, BMI1D and WT C631 HAP1 cell lines. Furthermore, I determined the WNK1 and BMI1 expression levels after PTC-028 treatment in the same cell lines to measure the effects this small molecule has on protein expression levels. In Fig. 18, WNK1D cell lines demonstrated a marked decrease in the expression levels of BMI1, while BMI1D cell lines demonstrated reduced expression of WNK1 protein. The addition of PTC-028 treatment decreased both WNK1 and BMI1 protein levels across all cell lines. β-actin was used as a loading control for each sample.

Figure 18: Examination of the levels of WNK1 and BMI1 protein in HAP1 cells. BMI1 and WNK1 protein expression levels in WT, BMI1D and WNK1D cells with and without PTC-028 IC50 treatment were determined using an immunoblot with antibodies detecting for BMI1, WNK1 and β-actin. WNK1D lines had diminished expression of BMI1 and BMI1D lines had reduced expression of WNK1. The addition of PTC-028 decreased both WNK1 and BMI1 protein levels across all cell lines. β-actin loading control remained consistent throughout each lane.

3.3.7 Summary of Results

PTC-028 is thought to act on BMI1 to inhibit the self-renewal of cancer-initiating cells (Bakhshinyan et al., 2019; Dey et al., 2018). I tested the effects of PTC-028 in two BMI1D HAP1 cell lines and expected that there would be a decrease in the efficacy of the drug. Surprisingly, I found that PTC-028 acts similarly on BMI1D cells in comparison with WT HAP1 cells (Fig. 14). As such, I proposed to perform a chemogenomic genome-wide CRISPR-Cas9 screen to identify key sensitizer and suppressor interactions. From this screen, I found a number of genes that when disrupted, sensitize HAP1 cells to the effects of PTC-028 (Fig. 15). The most significant sensitizers include GSTE1, SPAG5, CMIP, SRPK1 and USP14. On the other side of the spectrum, a number of genes were identified that when disrupted, suppress the toxic effects of the drug HAP1 cells, including WNK1, CAB39, OXSR1, NRBP1 and TSC22D2.

This study validated the top suppressor hit WNK1 through the generation of WNK1D HAP1 cell lines, resulting in the reduction in the effectiveness of PTC-028 by over two-fold (from an IC50 of 70nM in WT HAP1 cells to an IC50 of 157nM and 175nM in WNK1DC1 and WNK1DC2 cells, respectively) (Fig. 17). Next, I explored the relationship between WNK1 and BMI1 with and without PTC-028 treatment. The protein levels of WNK1 and BMI1 were noticeably diminished when all the cell lines were treated with PTC-028. In addition, WNK1 protein level was reduced in multiple BMI1D cell lines while BMI1 protein level was reduced in multiple WNK1D cell lines (Fig. 18).

3.4 Discussion and Conclusion

3.4.1 Discussion

PTC-028 is a promising small molecule for the potential treatment of various cancers that are suspected to be initiated through cancer stem cells including medulloblastoma (Bakhshinyan et al., 2019), ovarian cancer (Dey et al., 2018) and colorectal cancer (Kreso et al., 2014). An improved iteration of this molecule, PTC-596 is currently enrolled in a number of early clinical trials. It is important to improve our understanding of how this family of molecules functions and further its potential in the treatment of many other cancers by performing studies to elucidate

63 synergistic and suppressor genetic interactions. It is thought that PTC-028 targets cancer stem cells through a series of pathways including BMI1 (Bakhshinyan et al., 2018; Dey et al., 2018). Since it was found that PTC-028 sensitivity is similar in BMI1D and WT HAP1 cell lines (Fig. 14), it provides an argument that BMI1 is not the molecule’s target.

Next, a chemogenomic analysis was performed using genome-wide CRISPR-Cas9 screen with PTC-028 to identify potential sensitizers (Fig. 15a) and suppressors (Fig. 15b). Strikingly, there was a lack of genes related to PRC1. This indicates that PTC-028 does not have any interaction with members of the PRC1, including BMI1 and validates the dose-response experiment performed on BMI1D cell lines. Although I observed significant synergistic interactions, I placed a particular focus in this study on the suppressors due to limited time. Interestingly, many of the genes producing a suppression phenotype to the drug fall within a pathway with similar roles for ion transport. This suggests that functional ion transporter activity may be required for PTC-028 efficacy. These genes include WNK1, CAB39, OXSR1, NRBP1 and TSC22D2. WNK1 has been shown to have an essential role in the regulation of electrolyte homeostasis through the activation of NKCC1 (Na-K-2Cl cotransporter 1) (Roy et al., 2015). Intriguingly, WNK1 is an upstream regulator of OSXR1 and SPAK (sterile20-related, proline-, alanine-rich kinase) kinases, required for NKCC1 activation (Anselmo et al., 2006). Furthermore, the scaffolding protein CAB39 can facilitate SPAK/OXSR1-independent phosphorylation of NKCC1 activation (Ponce-Coria et al., 2014). Finally, NRBP1 which encodes an adapter protein that is ubiquitously expressed in all cell types has been shown to bind to key transcription factors including TSC22D2, where the loss of NRBP1 results in the accumulation of TSC22D2 (Wilson et al., 2012). TSC22D2 is a transcription factor that has been shown to be induced during osmotic stress (Fiol et al., 2007). Collectively, the most significant genes that have been identified as suppressors of PTC-028 have roles in electrolyte homeostasis and activation of ion transporters (i.e. NKCC1).

I narrowed my validating efforts to the strongest suppressor hit of the screen to better understand what causes cells to suppress the effects of PTC-028. I validated WNK1 by generating WNK1D HAP1 cell lines. The WNK1D HAP1 cell line’s drug response curve to PTC-028 quantified using PrestoBlue (Fig. 17) demonstrated a significant resistance phenotype, a greater than 2-fold decrease in its efficacy, supporting the reproducibility of the chemogenomic screen. A notable limitation to PrestoBlue is that it cannot be used to determine whether the increased

64 resistance to PTC-028 in WNK1D cells is a direct result of a rescue in metabolic capacity. However, it was visually noted that there was more cell death in wildtype HAP1 cells when the same concentration of treatment was applied, although this will require confirmation by viable cell counts using Trypan Blue. Nonetheless, the IC50 concentrations for wildtype and WNK1D HAP1 cell lines are within the nanomolar range and is considered very toxic to cells. This suggests the possibility of PTC-028 acting on multiple targets, including WNK1 protein, where the effects upon each target are additive. Alternatively, the incomplete elimination of WNK1 protein seen in WNK1D HAP1 cell lines (Fig. 16) may allow PTC-028 to enact its cytotoxic effects. Finally, there may be some compensation of WNK1 function, such as through CAB39 and TSC22D2 that promotes the activity of PTC-028. This should be further explored by generating multiple genetic perturbations within a cell.

Similar to previous studies (Bolomsky et al., 2016; Buechel et al., 2018; Dey et al., 2018; Bakhshinyan et al., 2019), I found that there was a decrease in BMI1 protein levels with the treatment of PTC-028 in the cell lines (Fig. 18). Furthermore, the effect of WNK1D having diminished BMI1 protein expression level and vice versa (Fig. 18) may indicate that these two genes may not act independently, which has not been previously described. Since the WNK1 protein level appears to modify the BMI1 protein level, it raises a question as to whether PTC- 028 ultimately acts on the BMI1 pathway. My initial experiment (Fig. 14) indicated that BMI1 protein is not a target of PTC-028 in a HAP1 cell model since BMI1D cell lines do not alter PTC- 028 sensitivity. Another approach to provide definitive evidence and genetically assess whether the modification of BMI1 is required for the action of PTC-028 is by rescuing or overexpressing BMI1 protein in WNK1D cell lines and test its sensitivity against PTC-028. Taken together, using the power of a HAP1 model with chemogenomic interaction analysis, this thesis provides support of a probability that PTC-028 acts on the WNK1 and parallel NKCC1 activation pathways.

3.4.2 Future experiments

Although this thesis has elucidated many genes governing the sensitivity and suppression of PTC-028 in HAP1 cells, further experiments are required to determine if these results are

65 context-specific to a HAP1 background and therefore needs to be reproduced in other cell lines. Dose-response curves should be performed on genetic knockout or with the chemical inhibition of WNK1 in cell lines that are normally sensitive to the effects of PTC-028, including ovarian cancer colorectal cancer and medulloblastoma cell lines.

WNK1 was validated to be a strong suppressor of PTC-028 in HAP1 cells and a number of other genes related to ion cotransporter were also identified, including CAB39, OXSR1, NRBP1 and TSC22D2. Although these genes will likely also validate, it would be important to experimentally validate these genes. One possible approach to do this is by generating a mini- pooled library to validate the suppressor and synergistic interactions for a high-throughput approach. In addition, it would be important to generate cell lines with multiple genetic perturbations to determine if it will exacerbate phenotypes due to functional redundancies.

It is critical to identify the mechanism for further applications in cancer and in doing so, biomarkers can also be introduced for clinical applications. Experiments such as transcriptomic analysis after PTC-028 treatment in cell lines, using a pull-down assay with a bait protein and performing proteomics or phosphoproteomics following PTC-028 treatment are all important experiments to consider for further studies into PTC-028.

3.4.3 Conclusion

In summary, the chemogenomic screens on HAP1 cells with PTC-028 using the TKOv3 library have unraveled synergistic and suppressor interactions. The previous notion that PTC-028 targets BMI1 has been weakened by our PTC-028 dose-response experiments in our HAP1 BMI1D models. Importantly, a new link between WNK1 and genetic perturbations related to the activation of NKCC1 has been established as suppressor interactions with PTC-028. Future studies will seek to delineate the specific mode-of-action of PTC-028 and its family of therapeutic molecules by exploring the role of ion transporters in the therapeutic effects of PTC- 028.

References

Abdouh, M., Facchino, S., Chatoo, W., Balasingam, V., Ferreira, J., & Bernier, G. (2009). BMI1 sustains human glioblastoma multiforme stem cell renewal. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 29(28), 8884-8896. doi:10.1523/JNEUROSCI.0968-09.2009 Aggarwal, S., Negi, S., Jha, P., Singh, P. K., Stobdan, T., Pasha, M. A., . . . Mukerji, M. (2010). EGLN1 involvement in high-altitude adaptation revealed through genetic analysis of extreme constitution types defined in ayurveda. Proceedings of the National Academy of Sciences of the United States of America, 107(44), 18961-18966. doi:10.1073/pnas.1006108107 Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J., & Clarke, M. F. (2003). Prospective identification of tumorigenic breast cancer cells. Proceedings of the National Academy of Sciences of the United States of America, 100(7), 3983-3988. doi:10.1073/pnas.0530291100 Alkema, M. J., Wiegant, J., Raap, A. K., Berns, A., & van Lohuizen, M. (1993). Characterization and chromosomal localization of the human proto-oncogene BMI-1. Human Molecular Genetics, 2(10), 1597-1603. doi:10.1093/hmg/2.10.1597 Andor, N., Harness, J. V., Muller, S., Mewes, H. W., & Petritsch, C. (2014). EXPANDS: Expanding ploidy and allele frequency on nested subpopulations. Bioinformatics (Oxford, England), 30(1), 50-60. doi:10.1093/bioinformatics/btt622 Annibali, D., Whitfield, J. R., Favuzzi, E., Jauset, T., Serrano, E., Cuartas, I., . . . Soucek, L. (2014). Myc inhibition is effective against glioma and reveals a role for myc in proficient mitosis. Nature Communications, 5, 4632. doi:10.1038/ncomms5632 Anselmo, A. N., Earnest, S., Chen, W., Juang, Y. C., Kim, S. C., Zhao, Y., & Cobb, M. H. (2006). WNK1 and OSR1 regulate the na+, K+, 2Cl- cotransporter in HeLa cells. Proceedings of the National Academy of Sciences of the United States of America, 103(29), 10883-10888. doi:0604607103 Ardito, F., Giuliani, M., Perrone, D., Troiano, G., & Lo Muzio, L. (2017). The crucial role of protein phosphorylation in cell signaling and its use as targeted therapy (review). International Journal of Molecular Medicine, 40(2), 271-280. doi:10.3892/ijmm.2017.3036 Bai, Y., Zhou, H. M., Zhang, L., Dong, Y., Zeng, Q., Shou, W., & Zhang, Z. Y. (2016). Role of phosphatase of regenerating liver 1 (PRL1) in spermatogenesis. Scientific Reports, 6, 34211. doi:10.1038/srep34211 Bakhshinyan, D., Venugopal, C., Adile, A. A., Garg, N., Manoranjan, B., Hallett, R., . . . Singh, S. K. (2019). BMI1 is a therapeutic target in recurrent medulloblastoma. Oncogene, 38(10), 1702-1716. doi:10.1038/s41388-018-0549-9 Bao, S., Wu, Q., Li, Z., Sathornsumetee, S., Wang, H., McLendon, R. E., . . . Rich, J. N. (2008). Targeting cancer stem cells through L1CAM suppresses glioma growth. Cancer Research, 68(15), 6043-6048. doi:10.1158/0008-5472.CAN-08-1079

Bao, S., Wu, Q., McLendon, R. E., Hao, Y., Shi, Q., Hjelmeland, A. B., . . . Rich, J. N. (2006). Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature, 444(7120), 756-760. doi:nature05236 Barker, F. G., Chang, S. M., Gutin, P. H., Malec, M. K., McDermott, M. W., Prados, M. D., & Wilson, C. B. (1998). Survival and functional status after resection of recurrent glioblastoma multiforme. Neurosurgery, 42(4), 3. doi:10.1097/00006123-199804000-00013 Barrangou, R., & Doudna, J. A. (2016). Applications of CRISPR technologies in research and beyond. Nature Biotechnology, 34(9), 933-941. doi:10.1038/nbt.3659 Batchelor, T. T., Duda, D. G., di Tomaso, E., Ancukiewicz, M., Plotkin, S. R., Gerstner, E., . . . Jain, R. K. (2010). Phase II study of cediranib, an oral pan-vascular endothelial growth factor receptor tyrosine kinase inhibitor, in patients with recurrent glioblastoma. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology, 28(17), 2817-2823. doi:10.1200/JCO.2009.26.3988 Behan, F. M., Iorio, F., Picco, G., Goncalves, E., Beaver, C. M., Migliardi, G., . . . Garnett, M. J. (2019). Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature, 568(7753), 511-516. doi:10.1038/s41586-019-1103-9 Bellail, A. C., Olson, J. J., & Hao, C. (2014). SUMO1 modification stabilizes CDK6 protein and drives the cell cycle and glioblastoma progression. Nature Communications, 5, 4234. doi:10.1038/ncomms5234 Bhattacharya, R., Nicoloso, M., Arvizo, R., Wang, E., Cortez, A., Rossi, S., . . . Mukherjee, P. (2009). MiR-15a and MiR-16 control bmi-1 expression in ovarian cancer. Cancer Research, 69(23), 9090-9095. doi:10.1158/0008-5472.CAN-09-2552 Bilzer, T., Stavrou, D., Wechsler, W., Wohler, B., & Keiditsch, E. (1991). Antigen variation in a human glioblastoma: From the primary tumor to the second recurrence, permanent cell line and xenotransplantation tumors. Anticancer Research, 11(2), 547-553. Bixel, K., & Hays, J. L. (2015). Olaparib in the management of ovarian cancer. Pharmacogenomics and Personalized Medicine, 8, 127-135. doi:10.2147/PGPM.S62809 Bolomsky, A., Schlangen, K., Schreiner, W., Zojer, N., & Ludwig, H. (2016). Targeting of BMI- 1 with PTC-209 shows potent anti-myeloma activity and impairs the tumour microenvironment. Journal of Hematology & Oncology, 9, 4. doi:10.1186/s13045-016- 0247-4 Boutros, M., Kiger, A. A., Armknecht, S., Kerr, K., Hild, M., Koch, B., . . . Heidelberg Fly Array Consortium. (2004). Genome-wide RNAi analysis of growth and viability in drosophila cells. Science (New York, N.Y.), 303(5659), 832-835. doi:10.1126/science.1091266 Bowling, S., Di Gregorio, A., Sancho, M., Pozzi, S., Aarts, M., Signore, M., . . . Rodriguez, T. A. (2018). P53 and mTOR signalling determine fitness selection through cell competition during early mouse embryonic development. Nature Communications, 9(1), y. doi:10.1038/s41467-018-04167-y

Bowman, R. L., Wang, Q., Carro, A., Verhaak, R. G., & Squatrito, M. (2017). GlioVis data portal for visualization and analysis of brain tumor expression datasets. Neuro- Oncology, 19(1), 139-141. doi:10.1093/neuonc/now247 Bruggeman, S. W., Hulsman, D., Tanger, E., Buckle, T., Blom, M., Zevenhoven, J., . . . van Lohuizen, M. (2007). Bmi1 controls tumor development in an Ink4a/arf-independent manner in a mouse model for glioma. Cancer Cell, 12(4), 328-341. doi:S1535- 6108(07)00264-4 Buechel, M., Dey, A., Dwivedi, S. K. D., Crim, A., Ding, K., Zhang, R., . . . Bhattacharya, R. (2018). Inhibition of BMI1, a therapeutic approach in endometrial cancer. Molecular Cancer Therapeutics, 17(10), 2136-2143. doi:10.1158/1535-7163.MCT-17-1192 Butland, G., Babu, M., Diaz-Mejia, J. J., Bohdana, F., Phanse, S., Gold, B., . . . Emili, A. (2008). eSGA: E. coli synthetic genetic array analysis. Nature Methods, 5(9), 789-795. doi:10.1038/nmeth.1239

Calzolari, F., Michel, J., Baumgart, E. V., Theis, F., Gotz, M., & Ninkovic, J. (2015). Fast clonal expansion and limited neural stem cell self-renewal in the adult subependymal zone. Nature Neuroscience, 18(4), 490-492. doi:10.1038/nn.3963 Cen, L., Carlson, B. L., Pokorny, J. L., Mladek, A. C., Grogan, P. T., Schroeder, M. A., . . . Sarkaria, J. N. (2013). Efficacy of protracted temozolomide dosing is limited in MGMT unmethylated GBM xenograft models. Neuro-Oncology, 15(6), 735-746. doi:10.1093/neuonc/not010 Chen, Y. Y. (2015). Efficient gene editing in primary human T cells. Trends in Immunology, 36(11), 667-669. doi:S1471-4906(15)00218-5 Chi, L., Zou, Y., Qin, L., Ma, W., Hao, Y., Tang, Y., . . . Wu, Z. (2017). TIMELESS contributes to the progression of breast cancer through activation of MYC. Breast Cancer Research : BCR, 19(1), 1. doi:10.1186/s13058-017-0838-1 Chiang, Y. T., Gout, P. W., Collins, C. C., & Wang, Y. (2014). Prostate cancer metastasis- driving genes: Hurdles and potential approaches in their identification. Asian Journal of Andrology, 16(4), 545-548. doi:10.4103/1008-682X.122875 Chou, D. M., & Elledge, S. J. (2006). Tipin and timeless form a mutually protective complex required for genotoxic stress resistance and checkpoint function. Proceedings of the National Academy of Sciences of the United States of America, 103(48), 18143-18147. doi:0609251103 Chowdhury, M., Mihara, K., Yasunaga, S., Ohtaki, M., Takihara, Y., & Kimura, A. (2007). Expression of polycomb-group (PcG) protein BMI-1 predicts prognosis in patients with acute myeloid leukemia. Leukemia, 21(5), 1116-1122. doi:2404623 Ciesielski, M. J., Bu, Y., Munich, S. A., Teegarden, P., Smolinski, M. P., Clements, J. L., . . . Fenstermaker, R. A. (2018). KX2-361: A novel orally bioavailable small molecule dual src/tubulin inhibitor that provides long term survival in a murine model of glioblastoma. Journal of Neuro-Oncology, 140(3), 519-527. doi:10.1007/s11060-018-2992- 4

Clarke, M. F., & Fuller, M. (2006). Stem cells and cancer: Two faces of eve. Cell, 124(6), 1111- 1115. doi:S0092-8674(06)00312-6 Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., . . . Zhang, F. (2013). Multiplex genome engineering using CRISPR/cas systems. Science (New York, N.Y.), 339(6121), 819- 823. doi:10.1126/science.1231143 Cooper, A., van Doorninck, J., Ji, L., Russell, D., Ladanyi, M., Shimada, H., . . . Lawlor, E. R. (2011). Ewing tumors that do not overexpress BMI-1 are a distinct molecular subclass with variant biology: A report from the children's oncology group. Clinical Cancer Research : An Official Journal of the American Association for Cancer Research, 17(1), 56-66. doi:10.1158/1078-0432.CCR-10-1417 Costanzo, M., Kuzmin, E., van Leeuwen, J., Mair, B., Moffat, J., Boone, C., & Andrews, B. (2019). Global genetic networks and the genotype-to-phenotype relationship. Cell, 177(1), 85-100. doi:S0092-8674(19)30096-0 Costanzo, M., VanderSluis, B., Koch, E. N., Baryshnikova, A., Pons, C., Tan, G., . . . Boone, C. (2016). A global genetic interaction network maps a wiring diagram of cellular function. Science (New York, N.Y.), 353(6306), 10.1126/science.aaf1420. doi:aaf1420 Crespo, I., Tao, H., Nieto, A. B., Rebelo, O., Domingues, P., Vital, A. L., . . . Tabernero, M. D. (2012). Amplified and homozygously deleted genes in glioblastoma: Impact on gene expression levels. PloS One, 7(9), e46088. doi:10.1371/journal.pone.0046088

Dean, M. (2009). ABC transporters, drug resistance, and cancer stem cells. Journal of Mammary Gland Biology and Neoplasia, 14(1), 3-9. doi:10.1007/s10911-009-9109-9 DeAngelis, L. M. (2001). Brain tumors. The New England Journal of Medicine, 344(2), 114-123. doi:10.1056/NEJM200101113440207 Dey, A., Xiong, X., Crim, A., Dwivedi, S. K. D., Mustafi, S. B., Mukherjee, P., . . . Bhattacharya, R. (2018). Evaluating the mechanism and therapeutic potential of PTC-028, a novel inhibitor of BMI-1 function in ovarian cancer. Molecular Cancer Therapeutics, 17(1), 39-49. doi:10.1158/1535-7163.MCT-17-0574 Di Gregorio, A., Bowling, S., & Rodriguez, T. A. (2016). Cell competition and its role in the regulation of cell fitness from development to cancer. Developmental Cell, 38(6), 621-634. doi:10.1016/j.devcel.2016.08.012 Dixon, S. J., Fedyshyn, Y., Koh, J. L., Prasad, T. S., Chahwan, C., Chua, G., . . . Boone, C. (2008). Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes. Proceedings of the National Academy of Sciences of the United States of America, 105(43), 16653-16658. doi:10.1073/pnas.0806261105 Dong, Y., Zhang, L., Bai, Y., Zhou, H. M., Campbell, A. M., Chen, H., . . . Zhang, Z. Y. (2014). Phosphatase of regenerating liver 2 (PRL2) deficiency impairs kit signaling and spermatogenesis. The Journal of Biological Chemistry, 289(6), 3799-3810. doi:10.1074/jbc.M113.512079 Dong, Y., Zhang, L., Zhang, S., Bai, Y., Chen, H., Sun, X., . . . Zhang, Z. Y. (2012). Phosphatase of regenerating liver 2 (PRL2) is essential for placental development by down-regulating

PTEN (phosphatase and tensin homologue deleted on chromosome 10) and activating akt protein. The Journal of Biological Chemistry, 287(38), 32172-32179. doi:10.1074/jbc.M112.393462 Dong, Z., Zhang, G., Qu, M., Gimple, R. C., Wu, Q., Qiu, Z., … Rich, J. N. (2019) Targeting glioblastoma stem cells through disruption of the circadian clock. Cancer Discovery. doi:10.1158/2159-8290.CD-19-0215

Duan, L. J., & Fong, G. H. (2019). Developmental vascular pruning in neonatal mouse retinas is programmed by the astrocytic oxygen-sensing mechanism. Development (Cambridge, England), 146(8), 10.1242/dev.175117. doi:dev175117 Elgohary, N., Pellegrino, R., Neumann, O., Elzawahry, H. M., Saber, M. M., Zeeneldin, A. A., . . . Longerich, T. (2015). Protumorigenic role of timeless in hepatocellular carcinoma. International Journal of Oncology, 46(2), 597-606. doi:10.3892/ijo.2014.2751 Erez, N., Milyavsky, M., Eilam, R., Shats, I., Goldfinger, N., & Rotter, V. (2003). Expression of prolyl-hydroxylase-1 (PHD1/EGLN2) suppresses hypoxia inducible factor-1alpha activation and inhibits tumor growth. Cancer Research, 63(24), 8777-8783. Estoppey, D., Hewett, J. W., Guy, C. T., Harrington, E., Thomas, J. R., Schirle, M., . . . Hoepfner, D. (2017). Identification of a novel NAMPT inhibitor by CRISPR/Cas9 chemogenomic profiling in mammalian cells. Scientific Reports, 7, 42728. doi:10.1038/srep42728 Evers, B., Jastrzebski, K., Heijmans, J. P., Grernrum, W., Beijersbergen, R. L., & Bernards, R. (2016). CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nature Biotechnology, 34(6), 631-633. doi:10.1038/nbt.3536 Fiol, D. F., Mak, S. K., & Kultz, D. (2007). Specific TSC22 domain transcripts are hypertonically induced and alternatively spliced to protect mouse kidney cells during osmotic stress. The FEBS Journal, 274(1), 109-124. doi:EJB5569 Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E., & Mello, C. C. (1998). Potent and specific genetic interference by double-stranded RNA in caenorhabditis elegans. Nature, 391(6669), 806-811. doi:10.1038/35888 Fischer, B., Sandmann, T., Horn, T., Billmann, M., Chaudhary, V., Huber, W., & Boutros, M. (2015). A map of directional genetic interactions in a metazoan cell. eLife, 4, 10.7554/eLife.05464. doi:10.7554/eLife.05464 Friedmann-Morvinski, D. (2014). Glioblastoma heterogeneity and cancer cell plasticity. Critical Reviews in Oncogenesis, 19(5), 327-336. doi:1f524cb22209e814,3490845833249f99 Fu, A., Leaderer, D., Zheng, T., Hoffman, A. E., Stevens, R. G., & Zhu, Y. (2012). Genetic and epigenetic associations of circadian gene TIMELESS and breast cancer risk. Molecular Carcinogenesis, 51(12), 923-929. doi:10.1002/mc.20862 Galanis, E., Buckner, J. C., Maurer, M. J., Kreisberg, J. I., Ballman, K., Boni, J., . . . North Central Cancer Treatment Group. (2005). Phase II trial of temsirolimus (CCI-779) in recurrent glioblastoma multiforme: A north central cancer treatment group study. Journal of

Clinical Oncology : Official Journal of the American Society of Clinical Oncology, 23(23), 5294-5304. doi:JCO.2005.23.622 Gargiulo, G., Cesaroni, M., Serresi, M., de Vries, N., Hulsman, D., Bruggeman, S. W., . . . van Lohuizen, M. (2013). In vivo RNAi screen for BMI1 targets identifies TGF-beta/BMP-ER stress pathways as key regulators of neural- and malignant glioma-stem cell homeostasis. Cancer Cell, 23(5), 660-676. doi:10.1016/j.ccr.2013.03.030 Gaspar, L. E., Fisher, B. J., Macdonald, D. R., LeBer, D. V., Halperin, E. C., Schold, S. C., & Cairncross, J. G. (1992). Supratentorial malignant glioma: Patterns of recurrence and implications for external beam local treatment. International Journal of Radiation Oncology, Biology, Physics, 24(1), 55-57. doi:0360-3016(92)91021-E Giaever, G., Flaherty, P., Kumm, J., Proctor, M., Nislow, C., Jaramillo, D. F., . . . Davis, R. W. (2004). Chemogenomic profiling: Identifying the functional interactions of small molecules in yeast. Proceedings of the National Academy of Sciences of the United States of America, 101(3), 793-798. doi:10.1073/pnas.0307490100 Gonzalez de Castro, D., Clarke, P. A., Al-Lazikani, B., & Workman, P. (2013). Personalized cancer medicine: Molecular diagnostics, predictive biomarkers, and drug resistance. Clinical Pharmacology and Therapeutics, 93(3), 252-259. doi:10.1038/clpt.2012.237 Graham, V., Khudyakov, J., Ellis, P., & Pevny, L. (2003). SOX2 functions to maintain neural progenitor identity. Neuron, 39(5), 749-765. doi:S0896627303004975 Greijer, A. E., & van der Wall, E. (2004). The role of hypoxia inducible factor 1 (HIF-1) in hypoxia induced apoptosis. Journal of Clinical Pathology, 57(10), 1009-1014. doi:10.1136/jcp.2003.015032 Hanif, F., Muzaffar, K., Perveen, K., Malhi, S. M., & Simjee, S. (2017). Glioblastoma multiforme: A review of its epidemiology and pathogenesis through clinical presentation and treatment. Asian Pacific Journal of Cancer Prevention : APJCP, 18(1), 3-9. doi:10.22034/APJCP.2017.18.1.3 Hardy, S., Wong, N. N., Muller, W. J., Park, M., & Tremblay, M. L. (2010). Overexpression of the protein tyrosine phosphatase PRL-2 correlates with breast tumor formation and progression. Cancer Research, 70(21), 8959-8967. doi:10.1158/0008-5472.CAN-10-2041 Hart, T., Brown, K. R., Sircoulomb, F., Rottapel, R., & Moffat, J. (2014). Measuring error rates in genomic perturbation screens: Gold standards for human functional genomics. Molecular Systems Biology, 10, 733. doi:10.15252/msb.20145216 Hart, T., Chandrashekhar, M., Aregger, M., Steinhart, Z., Brown, K. R., MacLeod, G., . . . Moffat, J. (2015). High-resolution CRISPR screens reveal fitness genes and genotype- specific cancer liabilities. Cell, 163(6), 1515-1526. doi:10.1016/j.cell.2015.11.015 Hart, T., & Moffat, J. (2016). BAGEL: A computational framework for identifying essential genes from pooled library screens. BMC Bioinformatics, 17, 8. doi:10.1186/s12859-016- 1015-8

Hart, T., Tong, A. H. Y., Chan, K., Van Leeuwen, J., Seetharaman, A., Aregger, M., . . . Moffat, J. (2017). Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 (Bethesda, Md.), 7(8), 2719-2727. doi:10.1534/g3.117.041277 Hegi, M. E., Diserens, A. C., Gorlia, T., Hamou, M. F., de Tribolet, N., Weller, M., . . . Stupp, R. (2005). MGMT gene silencing and benefit from temozolomide in glioblastoma. The New England Journal of Medicine, 352(10), 997-1003. doi:352/10/997 Heynen-Genel, S., Pache, L., Chanda, S. K., & Rosen, J. (2012). Functional genomic and high- content screening for target discovery and deconvolution. Expert Opinion on Drug Discovery, 7(10), 955-968. doi:10.1517/17460441.2012.711311 Hovinga, K. E., McCrea, H. J., Brennan, C., Huse, J., Zheng, J., Esquenazi, Y., . . . Tabar, V. (2019). EGFR amplification and classical subtype are associated with a poor response to bevacizumab in recurrent glioblastoma. Journal of Neuro-Oncology, 142(2), 337-345. doi:10.1007/s11060-019-03102-5 Ihry, R. J., Salick, M. R., Ho, D. J., Sondey, M., Kommineni, S., Paula, S., . . . Kaykas, A. (2019). Genome-scale CRISPR screens identify human pluripotency-specific genes. Cell Reports, 27(2), 630.e6. doi:S2211-1247(19)30359-6 Ilhan-Mutlu, A., Wohrer, A., Berghoff, A. S., Widhalm, G., Marosi, C., Wagner, L., & Preusser, M. (2013). Comparison of microRNA expression levels between initial and recurrent glioblastoma specimens. Journal of Neuro-Oncology, 112(3), 347-354. doi:10.1007/s11060- 013-1078-6 Jin, X., Jin, X., Jung, J. E., Beck, S., & Kim, H. (2013). Cell surface nestin is a biomarker for glioma stem cells. Biochemical and Biophysical Research Communications, 433(4), 496- 501. doi:10.1016/j.bbrc.2013.03.021 Johnson, B. E., Mazor, T., Hong, C., Barnes, M., Aihara, K., McLean, C. Y., . . . Costello, J. F. (2014). Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science (New York, N.Y.), 343(6167), 189-193. doi:10.1126/science.1239947 Joung, J., Konermann, S., Gootenberg, J. S., Abudayyeh, O. O., Platt, R. J., Brigham, M. D., . . . Zhang, F. (2017). Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nature Protocols, 12(4), 828-863. doi:10.1038/nprot.2017.016 Kim, J., Lee, I. H., Cho, H. J., Park, C. K., Jung, Y. S., Kim, Y., . . . Nam, D. H. (2015). Spatiotemporal evolution of the primary glioblastoma genome. Cancer Cell, 28(3), 318-328. doi:10.1016/j.ccell.2015.07.013 Klotzsche-von Ameln, A., Muschter, A., Mamlouk, S., Kalucka, J., Prade, I., Franke, K., . . . Wielockx, B. (2011). Inhibition of HIF prolyl hydroxylase-2 blocks tumor growth in mice through the antiproliferative activity of TGFbeta. Cancer Research, 71(9), 3306-3316. doi:10.1158/0008-5472.CAN-10-3838 Kobayashi, M., Bai, Y., Dong, Y., Yu, H., Chen, S., Gao, R., . . . Liu, Y. (2014). PRL2/PTP4A2 phosphatase is important for hematopoietic stem cell self-renewal. Stem Cells (Dayton, Ohio), 32(7), 1956-1967. doi:10.1002/stem.1672

Korber, V., Yang, J., Barah, P., Wu, Y., Stichel, D., Gu, Z., . . . Lichter, P. (2019). Evolutionary trajectories of IDH(WT) glioblastomas reveal a common path of early tumorigenesis instigated years ahead of initial diagnosis. Cancer Cell, 35(4), 704.e12. doi:S1535- 6108(19)30102-3 Kozlova, N., Wottawa, M., Katschinski, D. M., Kristiansen, G., & Kietzmann, T. (2017). Hypoxia-inducible factor prolyl hydroxylase 2 (PHD2) is a direct regulator of epidermal growth factor receptor (EGFR) signaling in breast cancer.Oncotarget, 8(6), 9885-9898. doi:10.18632/oncotarget.14241 Kreisl, T. N., Kim, L., Moore, K., Duic, P., Kotliarova, S., Walling, J., . . . Fine, H. A. (2009). A phase I trial of enzastaurin in patients with recurrent gliomas. Clinical Cancer Research : An Official Journal of the American Association for Cancer Research, 15(10), 3617-3623. doi:10.1158/1078-0432.CCR-08-3071 Kreso, A., van Galen, P., Pedley, N. M., Lima-Fernandes, E., Frelin, C., Davis, T., . . . O'Brien, C. A. (2014). Self-renewal as a therapeutic target in human colorectal cancer. Nature Medicine, 20(1), 29-36. doi:10.1038/nm.3418 Larjavaara, S., Mantyla, R., Salminen, T., Haapasalo, H., Raitanen, J., Jaaskelainen, J., & Auvinen, A. (2007). Incidence of gliomas by anatomic location. Neuro-Oncology, 9(3), 319- 325. doi:15228517-2007-016 Lawrence, Y. R., Wang, M., Dicker, A. P., Andrews, D., Curran, W. J., Michalski, J. M., . . . Mehta, M. (2011). Early toxicity predicts long-term survival in high-grade glioma. British Journal of Cancer, 104(9), 1365-1371. doi:10.1038/bjc.2011.123 Lazo, J. S., McQueeney, K. E., Burnett, J. C., Wipf, P., & Sharlow, E. R. (2018). Small molecule targeting of PTPs in cancer. The International Journal of Biochemistry & Cell Biology, 96, 171-181. doi:S1357-2725(17)30237-6 Lee, J. H., Lee, J. E., Kahng, J. Y., Kim, S. H., Park, J. S., Yoon, S. J., . . . Lee, J. H. (2018). Human glioblastoma arises from subventricular zone cells with low-level driver mutations. Nature, 560(7717), 243-247. doi:10.1038/s41586-018-0389-3 Lenihan, C. R., & Taylor, C. T. (2013). The impact of hypoxia on cell death pathways. Biochemical Society Transactions, 41(2), 657-663. doi:10.1042/BST20120345 Li, F., Zhou, K., Gao, L., Zhang, B., Li, W., Yan, W., . . . Jiang, Q. (2016). Radiation induces the generation of cancer stem cells: A novel mechanism for cancer radioresistance. Oncology Letters, 12(5), 3059-3065. doi:10.3892/ol.2016.5124 Li, J., Zhu, S., Kozono, D., Ng, K., Futalan, D., Shen, Y., . . . Chen, C. C. (2014). Genome-wide shRNA screen revealed integrated mitogenic signaling between dopamine receptor D2 (DRD2) and epidermal growth factor receptor (EGFR) in glioblastoma. Oncotarget, 5(4), 882-893. doi:1801 Li, L., Borodyansky, L., & Yang, Y. (2009). Genomic instability en route to and from cancer stem cells. Cell Cycle (Georgetown, Tex.), 8(7), 1000-1002. doi:8041

Liberzon, A., Birger, C., Thorvaldsdottir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015). The molecular signatures database (MSigDB) hallmark gene set collection. Cell Systems, 1(6), 417-425. doi:10.1016/j.cels.2015.12.004 Liu, J., Song, T., Zhou, W., Xing, L., Wang, S., Ho, M., . . . Cang, Y. (2019). A genome-scale CRISPR-Cas9 screening in myeloma cells identifies regulators of immunomodulatory drug sensitivity. Leukemia, 33(1), 171-180. doi:10.1038/s41375-018-0205-y Loilome, W., Joshi, A. D., ap Rhys, C. M., Piccirillo, S., Vescovi, A. L., Gallia, G. L., & Riggins, G. J. (2009). Glioblastoma cell growth is suppressed by disruption of fibroblast growth factor pathway signaling. Journal of Neuro-Oncology, 94(3), 359-366. doi:10.1007/s11060-009-9885-5 Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet, A., . . . Kleihues, P. (2007). The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathologica, 114(2), 97-109. doi:10.1007/s00401-007-0243-4 Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W. K., . . . Ellison, D. W. (2016). The 2016 world health organization classification of tumors of the central nervous system: A summary. Acta Neuropathologica, 131(6), 803-820. doi:10.1007/s00401-016-1545-1 MacLeod, G., Bozek, D. A., Rajakulendran, N., Monteiro, V., Ahmadi, M., Steinhart, Z., . . . Angers, S. (2019). Genome-wide CRISPR-Cas9 screens expose genetic vulnerabilities and mechanisms of temozolomide sensitivity in glioblastoma stem cells. Cell Reports, 27(3), 986.e9. doi:S2211-1247(19)30363-8 Mair, B., Moffat, J., Boone, C., & Andrews, B. J. (2019). Genetic interaction networks in cancer cells. Current Opinion in Genetics & Development, 54, 64-72. doi:S0959-437X(18)30097-2 Mair, B., Tomic, J., Masud, S. N., Tonge, P., Weiss, A., Usaj, M., . . . Moffat, J. (2019). Essential gene profiles for human pluripotent stem cells identify uncharacterized genes and substrate dependencies. Cell Reports, 27(2), 615.e12. doi:S2211-1247(19)30212-8 Majmundar, A. J., Wong, W. J., & Simon, M. C. (2010). Hypoxia-inducible factors and the response to hypoxic stress.Molecular Cell, 40(2), 294-309. doi:10.1016/j.molcel.2010.09.022

Mani, S. A., Guo, W., Liao, M. J., Eaton, E. N., Ayyanan, A., Zhou, A. Y., . . . Weinberg, R. A. (2008). The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell, 133(4), 704-715. doi:10.1016/j.cell.2008.03.027 Mao, Y., Fu, A., Leaderer, D., Zheng, T., Chen, K., & Zhu, Y. (2013). Potential cancer-related role of circadian gene TIMELESS suggested by expression profiling and in vitro analyses. BMC Cancer, 13, 498. doi:10.1186/1471-2407-13-498 Marcotte, R., Brown, K. R., Suarez, F., Sayad, A., Karamboulas, K., Krzyzanowski, P. M., . . . Moffat, J. (2012). Essential gene profiles in breast, pancreatic, and ovarian cancer cells. Cancer Discovery, 2(2), 172-189. doi:10.1158/2159-8290.CD-11-0224

Marcotte, R., Sayad, A., Brown, K. R., Sanchez-Garcia, F., Reimand, J., Haider, M., . . . Neel, B. G. (2016). Functional genomic landscape of human breast cancer drivers, vulnerabilities, and resistance. Cell, 164(1-2), 293-309. doi:S0092-8674(15)01624-4 Martinez, R., Rohde, V., & Schackert, G. (2010). Different molecular patterns in glioblastoma multiforme subtypes upon recurrence. Journal of Neuro-Oncology, 96(3), 321-329. doi:10.1007/s11060-009-9967-4 Martinez, R., Setien, F., Voelter, C., Casado, S., Quesada, M. P., Schackert, G., & Esteller, M. (2007). CpG island promoter hypermethylation of the pro-apoptotic gene caspase-8 is a common hallmark of relapsed glioblastoma multiforme. Carcinogenesis, 28(6), 1264-1268. doi:bgm014 Marygold, S. J., Roote, J., Reuter, G., Lambertsson, A., Ashburner, M., Millburn, G. H., . . . Cook, K. R. (2007). The ribosomal protein genes and minute loci of drosophila melanogaster. Genome Biology, 8(10), r216. doi:gb-2007-8-10-r216 Mazzone, M., Dettori, D., de Oliveira, R. L., Loges, S., Schmidt, T., Jonckx, B., . . . Carmeliet, P. (2009). Heterozygous deficiency of PHD2 restores tumor oxygenation and inhibits metastasis via endothelial normalization. Cell, 136(5), 839-851. doi:10.1016/j.cell.2009.01.020 McDonald, E. R., de Weck, A., Schlabach, M. R., Billy, E., Mavrakis, K. J., Hoffman, G. R., . . . Sellers, W. R. (2017). Project DRIVE: A compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening. Cell, 170(3), 592.e10. doi:S0092-8674(17)30812-7 Mihara, K., Chowdhury, M., Nakaju, N., Hidani, S., Ihara, A., Hyodo, H., . . . Kimura, A. (2006). Bmi-1 is useful as a novel molecular marker for predicting progression of myelodysplastic syndrome and patient prognosis. Blood, 107(1), 305-308. doi:2005-06-2393 Miles, L. A., Garippa, R. J., & Poirier, J. T. (2016). Design, execution, and analysis of pooled in vitro CRISPR/Cas9 screens. The FEBS Journal, 283(17), 3170-3180. doi:10.1111/febs.13770 Mohty, M., Yong, A. S., Szydlo, R. M., Apperley, J. F., & Melo, J. V. (2007). The polycomb group BMI1 gene is a molecular marker for predicting prognosis of chronic myeloid leukemia. Blood, 110(1), 380-383. doi:blood-2006-12-065599 Morata, G., & Ripoll, P. (1975). Minutes: Mutants of drosophila autonomously affecting cell division rate. Developmental Biology, 42(2), 211-221. doi:0012-1606(75)90330-9 Moreno, E., & Rhiner, C. (2014). Darwin's multicellularity: From neurotrophic theories and cell competition to fitness fingerprints. Current Opinion in Cell Biology, 31, 16-22. doi:10.1016/j.ceb.2014.06.011 Morgens, D. W., Deans, R. M., Li, A., & Bassik, M. C. (2016). Systematic comparison of CRISPR/Cas9 and RNAi screens for essential genes. Nature Biotechnology, 34(6), 634-636. doi:10.1038/nbt.3567

Muz, B., de la Puente, P., Azab, F., & Azab, A. K. (2015). The role of hypoxia in cancer progression, angiogenesis, metastasis, and resistance to therapy. Hypoxia (Auckland, N.Z.), 3, 83-92. doi:10.2147/HP.S93413

Natsume, A., Ito, M., Katsushima, K., Ohka, F., Hatanaka, A., Shinjo, K., . . . Kondo, Y. (2013). Chromatin regulator PRC2 is a key regulator of epigenetic plasticity in glioblastoma. Cancer Research, 73(14), 4559-4570. doi:10.1158/0008-5472.CAN-13-0109

Neftel, C., Laffy, J., Filbin, M. G., Hara, T., Shore, M. E., Rahme, G. J., . . . Suva, M. L. (2019). An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell, doi:S0092-8674(19)30687-7

Neilsen, B. K., Frodyma, D. E., McCall, J. L., Fisher, K. W., & Lewis, R. E. (2019). ERK- mediated TIMELESS expression suppresses G2/M arrest in colon cancer cells. PloS One, 14(1), e0209224. doi:10.1371/journal.pone.0209224 Neumann, B., Held, M., Liebel, U., Erfle, H., Rogers, P., Pepperkok, R., & Ellenberg, J. (2006). High-throughput RNAi screening by time-lapse imaging of live human cells. Nature Methods, 3(5), 385-390. doi:nmeth876 Nishida, Y., Maeda, A., Kim, M. J., Cao, L., Kubota, Y., Ishizawa, J., . . . Kojima, K. (2017). The novel BMI-1 inhibitor PTC596 downregulates MCL-1 and induces p53-independent mitochondrial apoptosis in acute myeloid leukemia progenitor cells. Blood Cancer Journal, 7(2), e527. doi:10.1038/bcj.2017.8 Norden, A. D., Schiff, D., Ahluwalia, M. S., Lesser, G. J., Nayak, L., Lee, E. Q., . . . Wen, P. Y. (2015). Phase II trial of triple tyrosine kinase receptor inhibitor nintedanib in recurrent high- grade gliomas. Journal of Neuro-Oncology, 121(2), 297-302. doi:10.1007/s11060-014- 1631-y Ohgaki, H., & Kleihues, P. (2007). Genetic pathways to primary and secondary glioblastoma. The American Journal of Pathology, 170(5), 1445-1453. doi:S0002- 9440(10)61358-2 Orr, H. A. (2009). Fitness and its role in evolutionary genetics. Nature Reviews.Genetics, 10(8), 531-539. doi:10.1038/nrg2603 Ostrom, Q. T., Gittleman, H., Farah, P., Ondracek, A., Chen, Y., Wolinsky, Y., . . . Barnholtz- Sloan, J. S. (2013). CBTRUS statistical report: Primary brain and central nervous system tumors diagnosed in the united states in 2006-2010.Neuro-Oncology, 15 Suppl 2, 56. doi:10.1093/neuonc/not151 Pardal, R., Clarke, M. F., & Morrison, S. J. (2003). Applying the principles of stem-cell biology to cancer. Nature Reviews.Cancer, 3(12), 895-902. doi:10.1038/nrc1232 Park, I. K., Morrison, S. J., & Clarke, M. F. (2004). Bmi1, stem cells, and senescence regulation. The Journal of Clinical Investigation, 113(2), 175-179. doi:10.1172/JCI20800 Parker, J. J., Dionne, K. R., Massarwa, R., Klaassen, M., Foreman, N. K., Niswander, L., . . . Waziri, A. (2013). Gefitinib selectively inhibits tumor cell migration in EGFR-amplified human glioblastoma. Neuro-Oncology, 15(8), 1048-1057. doi:10.1093/neuonc/not053

Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., . . . Bernstein, B. E. (2014). Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science (New York, N.Y.), 344(6190), 1396-1401. doi:10.1126/science.1254257 Paul, J. M., Templeton, S. D., Baharani, A., Freywald, A., & Vizeacoumar, F. J. (2014). Building high-resolution synthetic lethal networks: A 'google map' of the cancer cell. Trends in Molecular Medicine, 20(12), 704-715. doi:10.1016/j.molmed.2014.09.009 Penzo-Mendez, A. I., Chen, Y. J., Li, J., Witze, E. S., & Stanger, B. Z. (2015). Spontaneous cell competition in immortalized mammalian cell lines. PloS One, 10(7), e0132437. doi:10.1371/journal.pone.0132437 Phoa, A. F., Browne, S., Gurgis, F. M., Akerfeldt, M. C., Dobber, A., Renn, C., . . . Munoz, L. (2015). Pharmacology of novel small-molecule tubulin inhibitors in glioblastoma cells with enhanced EGFR signalling. Biochemical Pharmacology, 98(4), 587-601. doi:10.1016/j.bcp.2015.10.014 Pistollato, F., Abbadi, S., Rampazzo, E., Persano, L., Della Puppa, A., Frasson, C., . . . Basso, G. (2010). Intratumoral hypoxic gradient drives stem cells distribution and MGMT expression in glioblastoma. Stem Cells (Dayton, Ohio), 28(5), 851-862. doi:10.1002/stem.415 Ponce-Coria, J., Markadieu, N., Austin, T. M., Flammang, L., Rios, K., Welling, P. A., & Delpire, E. (2014). A novel Ste20-related proline/alanine-rich kinase (SPAK)-independent pathway involving calcium-binding protein 39 (Cab39) and serine threonine kinase with no lysine member 4 (WNK4) in the activation of na-K-cl cotransporters. The Journal of Biological Chemistry, 289(25), 17680-17688. doi:10.1074/jbc.M113.540518 Qazi, M. A., Vora, P., Venugopal, C., Adams, J., Singh, M., Hu, A., . . . Singh, S. K. (2018). Cotargeting ephrin receptor tyrosine kinases A2 and A3 in cancer stem cells reduces growth of recurrent glioblastoma. Cancer Research, 78(17), 5023-5037. doi:10.1158/0008- 5472.CAN-18-0267 Raizer, J. J., Abrey, L. E., Lassman, A. B., Chang, S. M., Lamborn, K. R., Kuhn, J. G., . . . North American Brain Tumor Consortium. (2010). A phase II trial of erlotinib in patients with recurrent malignant gliomas and nonprogressive glioblastoma multiforme postradiation therapy. Neuro-Oncology, 12(1), 95-103. doi:10.1093/neuonc/nop015 Ran, F. A., Hsu, P. D., Wright, J., Agarwala, V., Scott, D. A., & Zhang, F. (2013). Genome engineering using the CRISPR-Cas9 system. Nature Protocols, 8(11), 2281-2308. doi:10.1038/nprot.2013.143 Reya, T., Morrison, S. J., Clarke, M. F., & Weissman, I. L. (2001). Stem cells, cancer, and cancer stem cells. Nature, 414(6859), 105-111. doi:10.1038/35102167 Riemenschneider, M. J., Jeuken, J. W., Wesseling, P., & Reifenberger, G. (2010). Molecular diagnostics of gliomas: State of the art. Acta Neuropathologica, 120(5), 567-584. doi:10.1007/s00401-010-0736-4 Rock, K., McArdle, O., Forde, P., Dunne, M., Fitzpatrick, D., O'Neill, B., & Faul, C. (2012). A clinical review of treatment outcomes in glioblastoma multiforme--the validation in a non- trial population of the results of a randomised phase III clinical trial: Has a more radical

approach improved survival? The British Journal of Radiology, 85(1017), 729. doi:10.1259/bjr/83796755 Rodgers, K., & McVey, M. (2016). Error-prone repair of DNA double-strand breaks. Journal of Cellular Physiology, 231(1), 15-24. doi:10.1002/jcp.25053 Roguev, A., Bandyopadhyay, S., Zofall, M., Zhang, K., Fischer, T., Collins, S. R., . . . Krogan, N. J. (2008). Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science (New York, N.Y.), 322(5900), 405-410. doi:10.1126/science.1162609 Roy, A., Goodman, J. H., Begum, G., Donnelly, B. F., Pittman, G., Weinman, E. J., . . . Subramanya, A. R. (2015). Generation of WNK1 knockout cell lines by CRISPR/cas- mediated genome editing. American Journal of Physiology.Renal Physiology, 308(4), 366. doi:10.1152/ajprenal.00612.2014 Saha, S., Bardelli, A., Buckhaults, P., Velculescu, V. E., Rago, C., St Croix, B., . . . Vogelstein, B. (2001). A phosphatase associated with metastasis of colorectal cancer. Science (New York, N.Y.), 294(5545), 1343-1346. doi:10.1126/science.1065817 Sancho, M., Di-Gregorio, A., George, N., Pozzi, S., Sanchez, J. M., Pernaute, B., & Rodriguez, T. A. (2013). Competitive interactions eliminate unfit embryonic stem cells at the onset of differentiation. Developmental Cell, 26(1), 19-30. doi:10.1016/j.devcel.2013.06.012

Santisteban, M., Reiman, J. M., Asiedu, M. K., Behrens, M. D., Nassar, A., Kalli, K. R., . . . Knutson, K. L. (2009). Immune-induced epithelial to mesenchymal transition in vivo generates breast cancer stem cells. Cancer Research, 69(7), 2887-2895. doi:10.1158/0008- 5472.CAN-08-3343 Sasmita, A. O., Wong, Y. P., & Ling, A. P. K. (2018). Biomarkers and therapeutic advances in glioblastoma multiforme.Asia-Pacific Journal of Clinical Oncology, 14(1), 40-51. doi:10.1111/ajco.12756 Seifert, M., Garbe, M., Friedrich, B., Mittelbronn, M., & Klink, B. (2015). Comparative transcriptomics reveals similarities and differences between astrocytoma grades. BMC Cancer, 15, 9. doi:10.1186/s12885-015-1939-9 Shalem, O., Sanjana, N. E., Hartenian, E., Shi, X., Scott, D. A., Mikkelson, T., . . . Zhang, F. (2014). Genome-scale CRISPR-Cas9 knockout screening in human cells. Science (New York, N.Y.), 343(6166), 84-87. doi:10.1126/science.1247005 Sharma, S., & Petsalaki, E. (2018). Application of CRISPR-Cas9 based genome-wide screening approaches to study cellular signalling mechanisms. International Journal of Molecular Sciences, 19(4), 10.3390/ijms19040933. doi:E933 Sheng, Z., Li, L., Zhu, L. J., Smith, T. W., Demers, A., Ross, A. H., . . . Green, M. R. (2010). A genome-wide RNA interference screen reveals an essential CREB3L2-ATF5-MCL1 survival pathway in malignant glioma with therapeutic implications. Nature Medicine, 16(6), 671-677. doi:10.1038/nm.2158 Sherr, C. J., Beach, D., & Shapiro, G. I. (2016). Targeting CDK4 and CDK6: From discovery to therapy. Cancer Discovery, 6(4), 353-367. doi:10.1158/2159-8290.CD-15-0894

Shinsato, Y., Furukawa, T., Yunoue, S., Yonezawa, H., Minami, K., Nishizawa, Y., . . . Arita, K. (2013). Reduction of MLH1 and PMS2 confers temozolomide resistance and is associated with recurrence of glioblastoma. Oncotarget, 4(12), 2261-2270. doi:1302 Singh, S. K., Clarke, I. D., Terasaki, M., Bonn, V. E., Hawkins, C., Squire, J., & Dirks, P. B. (2003). Identification of a cancer stem cell in human brain tumors. Cancer Research, 63(18), 5821-5828. Singh, S. K., Hawkins, C., Clarke, I. D., Squire, J. A., Bayani, J., Hide, T., . . . Dirks, P. B. (2004). Identification of human brain tumour initiating cells. Nature, 432(7015), 396-401. doi:nature03128 Sizoo, E. M., Braam, L., Postma, T. J., Pasman, H. R., Heimans, J. J., Klein, M., . . . Taphoorn, M. J. (2010). Symptoms and problems in the end-of-life phase of high-grade glioma patients. Neuro-Oncology, 12(11), 1162-1166. doi:10.1093/neuonc/nop045 Smith, I., Greenside, P. G., Natoli, T., Lahr, D. L., Wadden, D., Tirosh, I., . . . Doench, J. G. (2017). Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the connectivity map. PLoS Biology, 15(11), e2003213. doi:10.1371/journal.pbio.2003213 Son, M. J., Woolard, K., Nam, D. H., Lee, J., & Fine, H. A. (2009). SSEA-1 is an enrichment marker for tumor-initiating cells in human glioblastoma. Cell Stem Cell, 4(5), 440-452. doi:10.1016/j.stem.2009.03.003 Song, L. B., Zeng, M. S., Liao, W. T., Zhang, L., Mo, H. Y., Liu, W. L., . . . Zeng, Y. X. (2006). Bmi-1 is a novel molecular marker of nasopharyngeal carcinoma progression and immortalizes primary human nasopharyngeal epithelial cells. Cancer Research, 66(12), 6225-6232. doi:66/12/6225

Sottoriva, A., Spiteri, I., Piccirillo, S. G., Touloumis, A., Collins, V. P., Marioni, J. C., . . . Tavare, S. (2013). Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proceedings of the National Academy of Sciences of the United States of America, 110(10), 4009-4014. doi:10.1073/pnas.1219747110 Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M., & Sorger, P. K. (2009). Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature, 459(7245), 428-432. doi:10.1038/nature08012 Stark, A. M., Doukas, A., Hugo, H. H., & Mehdorn, H. M. (2010). The expression of mismatch repair proteins MLH1, MSH2 and MSH6 correlates with the Ki67 proliferation index and survival in patients with recurrent glioblastoma. Neurological Research, 32(8), 816-820. doi:10.1179/016164110X12645013515052 Stark, A. M., Witzel, P., Strege, R. J., Hugo, H. H., & Mehdorn, H. M. (2003). p53, mdm2, EGFR, and msh2 expression in paired initial and recurrent glioblastoma multiforme. Journal of Neurology, Neurosurgery, and Psychiatry, 74(6), 779-783. doi:10.1136/jnnp.74.6.779 Steinhart, Z., Pavlovic, Z., Chandrashekhar, M., Hart, T., Wang, X., Zhang, X., . . . Angers, S. (2017). Corrigendum: Genome-wide CRISPR screens reveal a wnt-FZD5 signaling circuit

as a druggable vulnerability of RNF43-mutant pancreatic tumors. Nature Medicine, 23(11), 1384d. doi:10.1038/nm1117-1384d Stephens, B., Han, H., Hostetter, G., Demeure, M. J., & Von Hoff, D. D. (2008). Small interfering RNA-mediated knockdown of PRL phosphatases results in altered akt phosphorylation and reduced clonogenicity of pancreatic cancer cells. Molecular Cancer Therapeutics, 7(1), 202-210. doi:10.1158/1535-7163.MCT-07-0542 Sternberg, S. H., Redding, S., Jinek, M., Greene, E. C., & Doudna, J. A. (2014). DNA interrogation by the CRISPR RNA-guided endonuclease Cas9. Nature, 507(7490), 62-67. doi:10.1038/nature13011 Stupp, R., van den Bent, M J, & Hegi, M. E. (2005). Optimal role of temozolomide in the treatment of malignant gliomas. Current Neurology and Neuroscience Reports, 5(3), 198- 206. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., . . . Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15545-15550. doi:0506580102 Szlachta, K., Kuscu, C., Tufan, T., Adair, S. J., Shang, S., Michaels, A. D., . . . Adli, M. (2018). CRISPR knockout screening identifies combinatorial drug targets in pancreatic cancer and models cellular drug response. Nature Communications, 9(1), 2. doi:10.1038/s41467-018- 06676-2 Szopa, W., Burley, T. A., Kramer-Marek, G., & Kaspera, W. (2017). Diagnostic and therapeutic biomarkers in glioblastoma: Current status and future perspectives. BioMed Research International, 2017, 8013575. doi:10.1155/2017/8013575 Tateishi, K., Iafrate, A. J., Ho, Q., Curry, W. T., Batchelor, T. T., Flaherty, K. T., . . . Wakimoto, H. (2016). Myc-driven glycolysis is a therapeutic target in glioblastoma. Clinical Cancer Research : An Official Journal of the American Association for Cancer Research, 22(17), 4452-4465. doi:10.1158/1078-0432.CCR-15-2274 Teng, J., Carla da Hora, C., Kantar, R. S., Nakano, I., Wakimoto, H., Batchelor, T. T., . . . Tannous, B. A. (2017). Dissecting inherent intratumor heterogeneity in patient-derived glioblastoma culture models. Neuro-Oncology, 19(6), 820-832. doi:10.1093/neuonc/now253 To, K. K., & Huang, L. E. (2005). Suppression of hypoxia-inducible factor 1alpha (HIF-1alpha) transcriptional activity by the HIF prolyl hydroxylase EGLN1. The Journal of Biological Chemistry, 280(45), 38102-38107. doi:M504342200 Toledo, C. M., Ding, Y., Hoellerbauer, P., Davis, R. J., Basom, R., Girard, E. J., . . . Paddison, P. J. (2015). Genome-wide CRISPR-Cas9 screens reveal loss of redundancy between PKMYT1 and WEE1 in glioblastoma stem-like cells.Cell Reports, 13(11), 2425-2439. doi:S2211-1247(15)01321-2

Trumpp, A., & Wiestler, O. D. (2008). Mechanisms of disease: Cancer stem cells--targeting the evil twin. Nature Clinical Practice.Oncology, 5(6), 337-347. doi:10.1038/ncponc1110

Tsherniak, A., Vazquez, F., Montgomery, P. G., Weir, B. A., Kryukov, G., Cowley, G. S., . . . Hahn, W. C. (2017). Defining a cancer dependency map. Cell, 170(3), 576.e16. doi:S0092- 8674(17)30651-7 Tsigelny, I. F., Kouznetsova, V. L., Lian, N., & Kesari, S. (2016). Molecular mechanisms of OLIG2 transcription factor in brain cancer. Oncotarget, 7(33), 53074-53101. doi:10.18632/oncotarget.10628 Tully, P. A., Gogos, A. J., Love, C., Liew, D., Drummond, K. J., & Morokoff, A. P. (2016). Reoperation for recurrent glioblastoma and its association with survival benefit. Neurosurgery, 79(5), 678-689. doi:10.1227/NEU.0000000000001338 Typas, A., Nichols, R. J., Siegele, D. A., Shales, M., Collins, S. R., Lim, B., . . . Gross, C. A. (2008). High-throughput, quantitative analyses of genetic interactions in E. coli. Nature Methods, 5(9), 781-787. Uhm, J. H., Ballman, K. V., Wu, W., Giannini, C., Krauss, J. C., Buckner, J. C., . . . Jaeckle, K. A. (2011). Phase II evaluation of gefitinib in patients with newly diagnosed grade 4 astrocytoma: Mayo/north central cancer treatment group study N0074. International Journal of Radiation Oncology, Biology, Physics, 80(2), 347-353. doi:10.1016/j.ijrobp.2010.01.070 Verhaak, R. G., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., . . . Cancer Genome Atlas Research Network. (2010). Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 17(1), 98-110. doi:10.1016/j.ccr.2009.12.020 Villa, S., Balana, C., & Comas, S. (2014). Radiation and concomitant chemotherapy for patients with glioblastoma multiforme. Chinese Journal of Cancer, 33(1), 25-31. doi:10.5732/cjc.013.10216 Voldborg, B. R., Damstrup, L., Spang-Thomsen, M., & Poulsen, H. S. (1997). Epidermal growth factor receptor (EGFR) and EGFR mutations, function and possible role in clinical trials. Annals of Oncology : Official Journal of the European Society for Medical Oncology, 8(12), 1197-1206. Vredenburgh, J. J., Desjardins, A., Herndon, J. E., Dowell, J. M., Reardon, D. A., Quinn, J. A., . . . Friedman, H. S. (2007). Phase II trial of bevacizumab and irinotecan in recurrent malignant glioma. Clinical Cancer Research : An Official Journal of the American Association for Cancer Research, 13(4), 1253-1259. doi:13/4/1253 Vredenburgh, J. J., Desjardins, A., Herndon, J. E., Marcello, J., Reardon, D. A., Quinn, J. A., . . . Friedman, H. S. (2007). Bevacizumab plus irinotecan in recurrent glioblastoma multiforme. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology, 25(30), 4722-4729. doi:25/30/4722 Wade, M. J., & Kalisz, S. (1990). The causes of natural selection. Evolution; International Journal of Organic Evolution, 44(8), 1947-1955. doi:10.1111/j.1558-5646.1990.tb04301.x Wang, C., Wang, G., Feng, X., Shepherd, P., Zhang, J., Tang, M., . . . Chen, J. (2019). Genome- wide CRISPR screens reveal synthetic lethality of RNASEH2 deficiency and ATR inhibition. Oncogene, 38(14), 2451-2463. doi:10.1038/s41388-018-0606-4

Wang, G., Zimmermann, M., Mascall, K., Lenoir, W. F., Moffat, J., Angers, S., Durocher, D., & Hart, T. Identifying drug-gene interactions from CRISPR knockout screens with drugZ. bioRxiv. doi: https://doi.org/10.1101/232736

Wang, J. C., & Dick, J. E. (2005). Cancer stem cells: Lessons from leukemia. Trends in Cell Biology, 15(9), 494-501. doi:S0962-8924(05)00178-9 Wang, Q., Holmes, D. I., Powell, S. M., Lu, Q. L., & Waxman, J. (2002). Analysis of stromal- epithelial interactions in prostate cancer identifies PTPCAAX2 as a potential oncogene. Cancer Letters, 175(1), 63-69. doi:S0304383501007030 Wang, Q., Hu, B., Hu, X., Kim, H., Squatrito, M., Scarpace, L., . . . Verhaak, R. G. W. (2017). Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell, 32(1), 56.e6. doi:S1535- 6108(17)30253-2 Wang, T., Wei, J. J., Sabatini, D. M., & Lander, E. S. (2014). Genetic screens in human cells using the CRISPR-Cas9 system. Science (New York, N.Y.), 343(6166), 80-84. doi:10.1126/science.1246981 Wang, Y. J., & Herlyn, M. (2015). The emerging roles of Oct4 in tumor-initiating cells. American Journal of Physiology.Cell Physiology, 309(11), 709. doi:10.1152/ajpcell.00212.2015 Wang, Y., & Lazo, J. S. (2012). Metastasis-associated phosphatase PRL-2 regulates tumor cell migration and invasion.Oncogene, 31(7), 818-827. doi:10.1038/onc.2011.281 Wang, Y., Zhe, H., Ding, Z., Gao, P., Zhang, N., & Li, G. (2012). Cancer stem cell marker bmi-1 expression is associated with basal-like phenotype and poor survival in breast cancer. World Journal of Surgery, 36(5), 1189-1194. doi:10.1007/s00268-012-1514-3 Wei, M., Korotkov, K. V., & Blackburn, J. S. (2018). Targeting phosphatases of regenerating liver (PRLs) in cancer. Pharmacology & Therapeutics, 190, 128-138. doi:S0163- 7258(18)30094-9 Weller, M., Cloughesy, T., Perry, J. R., & Wick, W. (2013). Standards of care for treatment of recurrent glioblastoma--are we there yet? Neuro-Oncology, 15(1), 4-27. doi:10.1093/neuonc/nos273 Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. Wilson, C. H., Crombie, C., van der Weyden, L., Poulogiannis, G., Rust, A. G., Pardo, M., . . . Adams, D. J. (2012). Nuclear receptor binding protein 1 regulates intestinal progenitor cell homeostasis and tumour formation. The EMBO Journal, 31(11), 2486-2497. doi:10.1038/emboj.2012.91 Wright, A. V., Nunez, J. K., & Doudna, J. A. (2016). Biology and applications of CRISPR systems: Harnessing nature's toolbox for genome engineering. Cell, 164(1-2), 29-44. doi:S0092-8674(15)01699-2

Xie, S., Mortusewicz, O., Ma, H. T., Herr, P., Poon, R. Y., Helleday, T., & Qian, C. (2015). Timeless interacts with PARP-1 to promote homologous recombination repair. Molecular Cell, 60(1), 163-176. doi:10.1016/j.molcel.2015.07.031 Xu, G., & Li, J. Y. (2018). CDK4, CDK6, cyclin D1, p16(INK4a) and EGFR expression in glioblastoma with a primitive neuronal component. Journal of Neuro-Oncology, 136(3), 445-452. doi:10.1007/s11060-017-2674-7

Xu X, Wang Z, Liu N, Zhang P, Liu H, Qi J, Tu Y. (2018) The mechanism of BMI1 in regulating cancer stemness maintenance, metastasis, chemo- and radiation resistance. Cancer Translational Medicine, 4:59-63. doi: 10.4103/ctm.ctm_7_18 Yoshida, K., Sato, M., Hase, T., Elshazley, M., Yamashita, R., Usami, N., . . . Hasegawa, Y. (2013). TIMELESS is overexpressed in lung cancer and its expression correlates with poor patient survival. Cancer Science, 104(2), 171-177. doi:10.1111/cas.12068 Young, L. M., Marzio, A., Perez-Duran, P., Reid, D. A., Meredith, D. N., Roberti, D., . . . Pagano, M. (2015). TIMELESS forms a complex with PARP1 distinct from its complex with TIPIN and plays a role in the DNA damage response.Cell Reports, 13(3), 451-459. doi:S2211-1247(15)01028-1 Zhang, J., Stevens, M. F., & Bradshaw, T. D. (2012). Temozolomide: Mechanisms of action, repair and resistance. Current Molecular Pharmacology, 5(1), 102-114. doi:EPub-Abstract- CMP-61 Zhang, W., He, W., Shi, Y., Zhao, J., Liu, S., Zhang, F., . . . Zhang, Y. (2017). Aberrant TIMELESS expression is associated with poor clinical survival and lymph node metastasis in early-stage cervical carcinoma. International Journal of Oncology, 50(1), 173-184. doi:10.3892/ijo.2016.3784 Zhao, D., Guo, L., Neves, H., Yuen, H. F., Zhang, S. D., McCrudden, C. M., . . . Lin, Y. (2015). The prognostic significance of protein tyrosine phosphatase 4A2 in breast cancer. OncoTargets and Therapy, 8, 1707-1717. doi:10.2147/OTT.S85899 Zimmermann, M., Murina, O., Reijns, M. A. M., Agathanggelou, A., Challis, R., Tarnauskaite, Z., . . . Durocher, D. (2018). CRISPR screens identify genomic ribonucleotides as a source of PARP-trapping lesions. Nature, 559(7713), 285-289. doi:10.1038/s41586-018-0291-z

Appendix 5.1 Appendix A

A vector map of lentiCRISPRv2. This map was adapted from addgene.org (Zhang lab, catalog #52961) and visualized using the SnapGene software (from GSL Biotech; available at snapgene.com).

84 85

5.2 Appendix B

List of sgRNA used in this thesis including gene name, sequence and target site.

Gene sgRNA Sequence Target Site

AAVS1 GGGGCCACTAGGGACAGGAT chr19:0-0

EGLN1-A GCTGTCCGGTCTCCTTGCCG chr1:231556974-231556993

EGLN1-B GCCCGGATAACAAGCAACCA chr1:231509822-231509841

PLK1 ACCGGCGAAAGAGATCCCGG chr16:23690358-23690377

PSMA5 GTAGAGATTGATGCTCACAT chr1:109957861-109957880

PSMB2 TCAGGAAGGCACCATAGCCG chr1:36074880-36074899

PSMD1 AAAGGAAGACAACCTCCTGA chr2:231936959-231936978

PTP4A2-A TACTCTCAACAAGTTCACAG chr1:32384573-32384592

PTP4A2-B CTCATAGGAGATCTCCACAG chr1:32384631-32384650

TIMELESS-A GCTCATACAAGGTTTCACTG chr12:56827176-56827195

TIMELESS-B TTATTTGCAGGCCTACAAAG chr12:56827324-56827343

WNK1 ATTCTACAGGCACAGTCCCA chr12:971351-971370

5.3 Appendix C

List of primers used in this thesis including the name and primer sequences.

Gene PCR Sequence

F: GAGGGCCTATTTCCCATGATTC V2.1 R: GTTGCGAAAAAGAACGTTCACGG PCR 2 Primer sequences – i5 forward primers AATGATACGGCGACCACCGAGATCTACACTATAGCCTACACTCTTTCCC D501-F TACACGACGCT CTTCCGATCTTTGTGGAAAGGACGAAACACCG AATGATACGGCGACCACCGAGATCTACACATAGAGGCACACTCTTTCC D502-F CTACACGACGCT CTTCCGATCTTTGTGGAAAGGACGAAACACCG AATGATACGGCGACCACCGAGATCTACACCCTATCCTACACTCTTTCCC D503-F TACACGACGC TCTTCCGATCTTTGTGGAAAGGACGAAACACCG AATGATACGGCGACCACCGAGATCTACACGGCTCTGAACACTCTTTCCC D504-F TACACGACGC TCTTCCGATCTTTGTGGAAAGGACGAAACACCG AATGATACGGCGACCACCGAGATCTACACAGGCGAAGACACTCTTTCC D505-F CTACACGACGC TCTTCCGATCTTTGTGGAAAGGACGAAACACCG AATGATACGGCGACCACCGAGATCTACACTAATCTTAACACTCTTTCCC D506-F TACACGACGCTC TTCCGATCTTTGTGGAAAGGACGAAACACCG PCR 2 Primer sequences – i7 reverse primers CAAGCAGAAGACGGCATACGAGATCGAGTAATGTGACTGGAGTTCAGA D701-R CGTGTGCTCTT CCGATCTACTTGCTATTTCTAGCTCTAAAAC CAAGCAGAAGACGGCATACGAGATTCTCCGGAGTGACTGGAGTTCAGA D702-R CGTGTGCTC TTCCGATCTACTTGCTATTTCTAGCTCTAAAAC CAAGCAGAAGACGGCATACGAGATGGAATCTCGTGACTGGAGTTCAGA D704-R CGTGTGCTCTT CCGATCTACTTGCTATTTCTAGCTCTAAAAC CAAGCAGAAGACGGCATACGAGATTTCTGAATGTGACTGGAGTTCAGA D705-R CGTGTGCTCTT CCGATCTACTTGCTATTTCTAGCTCTAAAAC CAAGCAGAAGACGGCATACGAGATACGAATTCGTGACTGGAGTTCAGA D706-R CGTGTGCTCTT CCGATCTACTTGCTATTTCTAGCTCTAAAAC CAAGCAGAAGACGGCATACGAGATAGCTTCAGGTGACTGGAGTTCAGA D707-R CGTGTGCTCTTC CGATCTACTTGCTATTTCTAGCTCTAAAAC

F: GAAGGCATTTTCTTCTCTTGCATCT BMI1 R: CTGTGAAGAAATAAAGAGGGTTGCC

F: AATGCCAAGAGTGTAGCTGT WNK1 R: AATGCAAGGTGGGTACAGGA