Evaluating the Impact Reduced FBXO7 Expression Has on Chromosome Instability and Cellular Transformation in Colorectal Cancer Pathogenesis

Evaluating the Impact Reduced FBXO7 Expression has on Chromosome Instability and Cellular Transformation in Colorectal Cancer Pathogenesis

Michaela C. L. Palmer

A Thesis submitted to the Faculty of Graduate Studies of The University of Manitoba in partial fulfillment of the requirements of the degree of

MASTER OF SCIENCE

Department of Biochemistry and Medical Genetics University of Manitoba Winnipeg

i ABSTRACT Colorectal cancer (CRC) is the third most commonly diagnosed and second most lethal cancer in Canada. Therefore, understanding the genes and pathways driving CRC development is crucial to gain mechanistic insight that can be exploited in future therapies and consequently, decrease morbidity and mortality rates. Chromosome instability (CIN), or ongoing changes in chromosome numbers, is a predominant form of genome instability associated with ~85% of CRCs, suggesting it may be a key mechanism driving CRC oncogenesis. Moreover, CIN enables the acquisition of copy number alterations conferring selective growth, proliferation, and survival advantages, therefore CIN is often associated with cellular transformation. Despite these associations, the aberrant genes underlying CIN remain largely unknown. Recent preliminary screens of suspected CIN genes identified FBXO7 as a strong candidate for further study. FBXO7 encodes an F-box protein, a subunit of the SCF complex that normally targets specific protein substrates for proteolytic degradation by the 26S proteasome. Currently, the impact reduced FBXO7 expression has on CIN, cellular transformation and oncogenesis remains unknown. Thus, establishing the effects of diminished FBXO7 expression will shed new light on the underlying mechanisms of CIN. The current study seeks to evaluate the impact diminished FBXO7 expression has in malignant and non-malignant colonic epithelial cell contexts using complementary siRNA and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 approaches coupled with single-cell quantitative imaging microscopy (QuantIM) to assess changes in CIN-associated phenotypes. In doing so, QuantIM approaches determined that reduced FBXO7 expression induced increases in nuclear areas, micronucleus formation and frequencies of spreads with aberrant chromosome numbers. Collectively, this work identifies FBXO7 as a novel CIN gene with clinical implications that may contribute to CRC pathogenesis. Thus, this study is a first step towards gaining a novel understanding of the molecular origins of CIN and cellular transformation in CRC.

ii ACKNOWLEDGEMENTS First, I need to express my greatest appreciation to my supervisor Dr. Kirk McManus. Thank you for seeing my potential, for having confidence in me and teaching me to have confidence in myself. I am so proud of the valuable lessons, accomplishments, and personal growth I have experienced under your guidance. I cannot thank you enough, and as usual, the cakes and cookies are in the mail! Thank you to my committee members, Dr. Yvonne Myal and Dr. Mark Nachtigal. Yvonne, thank you for always taking an interest in my project and offering encouraging words. Mark, thank you for all your support, not just throughout my Master’s degree but through the two years prior, as well. I really appreciated the opportunities to chat and the reminders that everything is a little bit easier if you take it one day at a time. There are not enough baked goods in the world to appropriately thank the members of the McManus laboratory, past and present. Thank you, Dr. Laura Thompson, for your care and patience while training me as a new Co-op student and helping me develop the skills needed to thrive during my Master’s program. To Zelda Lichtenszejn, Lucile Jeusset, Kailee Rutherford, Ally Farrell, Nicole Neudorf, Mirka Sliwowski, Dr. Raghvendra Vishwakarma, Chloe Lepage, Manisha Bungsy, Claire Morden, Cindy Atayan and Tooba Rosi, I am immeasurably grateful for the laughs, friendship, and empathetic ears you have all offered over the years. This project would not have been possible without the generous funding from many organizations including The Natural Sciences and Engineering Research Council and The University of Manitoba. Thank you for your financial support. To Mom, Dad, Kim, Tavia, Kelvin, Curtis, Isaac, Shelley, Chris, and Signy, thank you for being the reprieve I needed from the stress of graduate studies, and reminding me that it is okay to have fun and not take myself so seriously. Lastly, Kristjan, I cannot express how appreciative I am of your strength, patience, and love. Thank you for being (pretending to be?) as excited about my project as I was, and for trying to understand my work. Your steadfast confidence in me encouraged me to keep pushing forward when I had forgotten to have confidence in myself.

iii DEDICATION

To Mom and Dad.

This thesis is dedicated to you, with love. It is a symbol of my undying gratitude and appreciation for the life and opportunities you have given me.

Thank you, and I love you!

iv TABLE OF CONTENTS

ABSTRACT ...... ii ACKNOWLEDGEMENTS ...... iii DEDICATION...... iv TABLE OF CONTENTS ...... v LIST OF TABLES ...... viii LIST OF FIGURES ...... ix LIST OF ABBREVIATIONS ...... x

CHAPTER 1. INTRODUCTION ...... 1 1.1. COLORECTAL CANCER OVERVIEW...... 1 1.1.1. Clinical Features ...... 1 1.1.2. Risk Factors and Screening ...... 2 1.1.3. Diagnosis, Staging and Treatment ...... 3 1.2. MOLECULAR PATHOGENESIS OF COLORECTAL CANCER ...... 5 1.2.1. The Adenoma to Carcinoma Pathway ...... 5 1.2.2. Genome Instability in Colorectal Cancer ...... 7 1.2.3. Chromosome Instability ...... 8 1.3. THE SCF COMPLEX AND F-BOX PROTEINS ...... 12 1.3.1 The SCF Complex is Implicated in Chromosome Instability ...... 14 1.3.2. The F-box Proteins ...... 15 1.3.3. F-box Only Protein 7 ...... 15

CHAPTER 2. RATIONALE, HYPOTHESIS AND RESEARCH AIMS ...... 19 2.1 RATIONALE ...... 19 2.2 HYPOTHESIS AND RESEARCH AIMS...... 19

CHAPTER 3. MATERIALS AND METHODS ...... 20 3.1. BIO-INFORMATICS APPROACHES ...... 20 3.1.1. Genomic Alterations and Survival Analyses ...... 20 3.2. REAGENTS ...... 21 3.3. CELL CULTURE ...... 21 3.3.1. Cell Passaging ...... 22 3.3.2. Cell Counting and Seeding ...... 23 3.3.3. Short Interfering RNA Transfection ...... 24 3.4. WESTERN BLOT ANALYSES ...... 25 3.4.1. Whole Cell Protein Extraction ...... 25 3.4.2. Protein Quantification Using Bicinchoninic Acid Assay ...... 26 3.4.3. Gel Electrophoresis and Western Blot ...... 26

v 3.4.4. Semi-Quantitative Western Blot Analysis ...... 28 3.5. SINGLE-CELL QUANTITATIVE IMAGING MICROSCOPY ...... 28 3.5.1. Cell Fixation and DNA Counterstaining for Nuclear Area and Micronucleus Formation Analyses ...... 29 3.5.2. Image Acquisition and Analysis ...... 29 3.5.3. Mitotic Chromosome Spread Generation and Chromosome Enumeration ...... 30 3.6. CRISPR/CAS9 ...... 31 3.6.1. Escherichia Coli Transformation and Plasmid Preparation ...... 31 3.6.2. Preparation of Lentiviral Particles ...... 32 3.6.3. Lentiviral Transduction of CRISPR Guide RNAs ...... 32 3.6.4. Fluorescence-Activated Cell Sorting ...... 33 3.6.5. Lipid-Mediated Transfection of the Cas9 Expression Plasmid ...... 33 3.6.6. Clonal Expansion and Screening for FBXO7 Gene Edits ...... 34 3.6.7. Genomic DNA Extraction ...... 34 3.6.8. Polymerase Chain Reaction ...... 34 3.6.9. Agarose Gel Electrophoresis ...... 35 3.6.10. DNA Sequencing and Sequence Analyses ...... 36 3.7. CIN TIMECOURSE EXPERIMENTS...... 36 3.8. CELLULAR TRANSFORMATION ASSAYS ...... 37 3.8.1. Nucleus Enumeration Proliferation Assay ...... 37 3.8.2. Clonogenic Assay ...... 37 3.8.3. Soft Agar Colony Formation Assay ...... 37 3.9. STATISTICAL ANALYSES ...... 38 3.9.1. Two-Sample Kolmogorov-Smirnov Tests ...... 38 3.9.2. Mann-Whitney Tests ...... 38 3.9.3. Student’s T-Test ...... 39

CHAPTER 4. RESULTS ...... 40 4.1. Bio-informatic and Reverse Genetic Experiments Reveal Reduced FBXO7 Expression Induces CIN and Cellular Transformation in Colonic Cellular Contexts...... 40 4.2. FBXO7 Copy Number Losses Occur Frequently in Cancer and are Associated with Worse Overall Survival in Colorectal Cancer Patients...... 41 4.3. AIM 1: To Determine the Short-Term Impact Reduced FBXO7 Expression has on CIN. ....43 4.3.1. FBXO7 can be Efficiently Silenced in HCT116, 1CT, RPA and A1309 Cells...... 43 4.3.2. Reduced FBXO7 Expression Induces Increases in CIN Phenotypes in HCT116 Cells. 44 4.3.3. Hypomorphic FBXO7 Expression Drives Increases in CIN Phenotypes in 1CT, RPA and A1309 Cells...... 48 4.3.4. Reduced FBXO7 Expression Synergizes with Altered Expression of Key Colorectal Cancer Driving Genes...... 51 4.4. AIM 2: To Determine the Long-Term Impact Reduced FBXO7 Expression has on CIN and Cellular Transformation...... 55 4.4.1. Generation and Validation of FBXO7+/- and FBXO7-/- Clones A1309 Cells ...... 55

vi 4.4.2. FBXO7+/- and FBXO7-/- Cells Exhibit Dynamic CIN Phenotypes...... 57 4.4.3. Evaluating Cellular Transformation in FBXO7+/- and FBXO7-/- Clones...... 60

CHAPTER 5: SUMMARY, CONCLUSIONS AND DISCUSSION ...... 65 5.1. SUMMARY AND CONCLUSIONS ...... 65 5.1.1. FBXO7 Loss is an Important Etiological Event in Colorectal Cancer Development. ... 69 5.2. FUTURE DIRECTIONS ...... 73 5.2.1. Determining the Mechanisms by which Reduced FBXO7 Expression Induces CIN .... 73 5.2.2. Evaluating the Tumorigenic Potential of FBXO7+/- Knockout Clones ...... 76 5.2.3. Discovery of Novel Therapeutic Strategies ...... 77 5.3. SIGNIFICANCE ...... 79

REFERENCES ...... 80

APPENDIX A: SOLUTIONS ...... 90

APPENDIX B: SUPPLEMENTARY TABLES ...... 102

vii LIST OF TABLES Table 3-1. Properties of Colonic Epithelial Cell Lines Employed in CIN and Cellular Transformation Assays...... 22 Table 3-2. Cell Seeding Densities Employed in Corresponding CIN Assays...... 24 Table 3-3. siRNA Transfection Protocols Employed in Corresponding Assays...... 25 Table 3-4. List of Antibodies Employed for Western Blot Analyses...... 28 Table 3-5. Primers Employed for Polymerase Chain Reaction and DNA Sequencing...... 35 Table 3-6. Reagents and Volumes Used to PCR-Amplify Exons 3 and 4 of FBXO7...... 35 Table 3-7. Thermocycling Conditions for PCR Amplification of Exons 3 and 4 of FBXO7...... 35

Table S1. KS Tests Reveal Significant Increases in Nuclear Area Distributions Following FBXO7 Silencing in HCT116 Cells...... 102 Table S2. MW Tests Identify Significant Increases in Micronucleus Formation Following FBXO7 Silencing in HCT116 Cells...... 102 Table S3. KS Tests Identify Significant Changes in Cumulative Distributions of Chromosome Numbers in FBXO7-Silenced HCT116 Cells...... 102 Table S4. KS Tests Reveal Significant Increases in Nuclear Area Distributions Following FBXO7 Silencing in 1CT, RPA and A1309 Cells...... 103 Table S5. MW Tests Identify Significant Increases in Micronucleus Formation Following FBXO7 Silencing in HCT116 Cells...... 103 Table S6. KS Tests Do Not Identify Significant Changes in Cumulative Chromosome Number Distributions in 1CT, RPA and A1309 Cells...... 104 Table S7. Student’s T-Tests Identify Significant Increases in Frequencies of Chromosome Gains and Losses in Non-Malignant Colonic Epithelial Cells...... 104 Table S8. KS Tests Show Significant Increases in Nuclear Area Distributions in FBXO7- Silenced RPA and A1309 Cells...... 105 Table S9. MW Tests Reveal Significant Increases in Micronucleus Formation in FBXO7- Silenced RPA and A1309 Cells...... 105 Table S10. KS Tests Show Significant Increases in Nuclear Area Distributions in A1309 FBXO7+/- and FBXO7-/- Models Over Time...... 106 Table S11. MW Tests Reveal Significant Increases in Micronucleus Formation in FBXO7- Silenced RPA and A1309 Cells...... 107

viii LIST OF FIGURES Figure 1-1. The Colon and Rectum Anatomy...... 2 Figure 1-2. CIN Drives Intratumoral Genetic and Cell-to-Cell Heterogeneity...... 9 Figure 1-3. The Application of QuantIM to Detect Cell-to-Cell Heterogeneity and CIN...... 11 Figure 1-4. The SCF Complex Targets Proteins for Degradation by the 26S Proteasome...... 14 Figure 1-5. FBXO7 mRNA Structures...... 16 Figure 1-6. Primary FBXO7 Isoform 1 Protein Structure...... 17

Figure 4-1. FBXO7 Alterations are Frequent and Associated with Worse Overall Survival in Colorectal Cancer...... 42 Figure 4-2. FBXO7 is Efficiently Silenced in Colonic Epithelial Cells...... 44 Figure 4-3. FBXO7 Silencing Corresponds with Increases in CIN-Associated Phenotypes in HCT116 Cells...... 46 Figure 4-4. Reduced FBXO7 Expression Induces Significant Differences in Chromosome Number Distributions in HCT116 Cells...... 47 Figure 4-5. Reduced FBXO7 Expression Corresponds with Increases in CIN-Associated Phenotypes in Non-Malignant Colonic Epithelial Cells...... 49 Figure 4-6. Decreased FBXO7 Expression is Induces Increases in Aberrant Chromosome Numbers...... 50 Figure 4-7. FBXO7 Silencing Enhances CIN-Associated Phenotypes in KRAS, TP53, and APC Altered Cells...... 53 Figure 4-8. KRAS, TP53, APC Putative Driver Mutations are Associated with Worse Overall Survival in Colorectal Cancer Patients Harbouring Decreased FBXO7 mRNA Expression...... 54 Figure 4-9. Generation and DNA Sequence Validation of FBXO7+/- and FBXO7-/- Clones in A1309 Cells...... 56 Figure 4-10. CIN-Associated Phenotypes are Dynamic in FBXO7+/- and FBXO7-/- Clones...... 59 Figure 4-11. Reduced FBXO7 Expression Induces Dynamic Changes in Chromosome Numbers...... 60 Figure 4-12. Hypomorphic FBXO7 Expression Variably Effects Proliferation Rates...... 62 Figure 4-13. Reduced FBXO7 Abundance is Associated with Increases in Clonogenic Growth...... 63 Figure 4-14. Reduced FBXO7 Expression Increases Anchorage Independent Growth...... 64

Figure 5-1. FBXO7 was Identified as a Novel CIN Gene in Complementary Short- and Long- Term Assays and Drives Cellular Transformation in Colonic Epithelial Cells...... 66

ix LIST OF ABBREVIATIONS ~ Approximately °C Degrees Celsius > Greater than ≥ Greater than or equal to < Less than μg Microgram(s) μL Microliter(s) μm Micrometer(s) μM Micromolar % Percent ± Plus or minus 2D Two-dimension 3D Three-dimension 5-FU 5-Fluorouracil APC Adenomatous Polyposis Coli BCA Bicinchoninic acid BFP Blue fluorescent protein BFP+ BFP positive BFP+/GFP+ BFP and GFP co-expressing bp Base pair(s) BRAF B-Raf Proto-Oncogene, Serine/Threonine Kinase BSA Bovine serum albumin CCND1 Cyclin D1 CCNE1 Cyclin E1 CCS Cosmic calf serum CDK6 Cyclin dependent kinase 6 CENPA Centromere Protein A cIAP1 Inhibitor of Apoptosis 1 CIMP CpG island methylator phenotype CIN Chromosome instability cm Centimeter(s) c-Myc MYC Proto-Oncogene, bHLH Transcription Factor CO2 Carbon dioxide CPTS Copper phthalocyanine 3,4’,4’’,4’’’-tetrasulfonic acid tetrasodium salt CRC Colorectal cancer CRISPR Clustered Regularly Interspaced Short Palindromic Repeats CUL1 Cullin 1 DAPI 4’,6-diamidino-2-phenylindole DMEM Dulbecco’s Modified Eagle Medium

x E1 Ubiquitin-activating enzyme E2 Ubiquitin-conjugating enzyme E3 Ubiquitin ligase EDTA Ethylenediaminetetraacetic acid FACS Fluorescence activated cell sorting FBS Fetal bovine serum FBXL F-box protein subfamily characterized by leucine rich repeats FBXO F-box protein subfamily containing diverse uncharacterized domains FBXO7 F-box Only Protein 7 FBXO7+/- Heterozygous FBXO7 knockout FBXO7-/- Homozygous FBXO7 knockout FBXW F-box protein subfamily characterized by WD40 amino acid repeats FBXW7 F-box And WD Repeat Domain Containing 7 FOLFIRI Folinic acid, 5-fluorouracil, irinotecan FOLFOX Folinic acid, 5-fluorouracil, oxaliplatin FP FBXO7/Proteasomal-Inhibitor-31 g Gram(s) GFP Green fluorescent protein GSK3β Glycogen Synthase Kinase 3 Beta h Hour(s) HECT Homologous to the E6AP Carboxyl Terminus HRP Horseradish peroxidase HURP Hepatoma Upregulated Protein K Lysine KCl Potassium chloride kDa Kilodalton(s) KM Kaplan-Meier KRAS Kristen Rat Sarcoma Proto-Oncogene KS Kolmogorov-Smirnov LB Luria-Bertani min Minute(s) mL Milliliter(s) MLH1 MutL Homolog 1 mm Millimeter(s) mM Millimolar MN Micronucleus MS Mitotic chromosome spreads MSH2 MutS Homolog 2 MSH6 MutS Homolog 6 MSI Microsatellite instability mTor Mechanistic Target of Rapamycin Kinase

xi MW Mann-Whitney NA Nuclear area N-CIN Numerical chromosome instability ng Nanogram(s) nm Nanometer(s) NRAGE Neurotrophin Receptor-Interacting Melanoma Antigen Gene ntc Nutcracker O2 Oxygen OQL Onco Query Language P Passage PARK15 Parkinson Disease 15 (also known as FBXO7) PBS Phosphate buffered saline PCR Polymerase chain reaction PI Propidium iodide PI31 Proteasomal-Inhibitor-31 PINK1 Phosphatase And Tensin Homolog Induced Kinase 1 PMS2 PMS1 Homolog 2, Mismatch Repair System Component PSMA2 Proteasome 20S Subunit Alpha 2 PVDF Polyvinylidene difluoride QuantIM Quantitative imaging microscopy RBR RING between RING RBX1 RING box protein 1 RING Really interesting new gene RIPA Radioimmunoprecipitation assay RT Room temperature SCF SKP1-CUL1-F-box SCFFBXO7 FBXO7-bound SCF S-CIN Structural chromosome instability SD Standard deviation SDS Sodium dodecyl sulfate sec Second(s) sgRNA Synthetic guide RNA siRNA Short interfering RNA SKP1 S-phase Kinase Associated Protein 1 SKP2 S-phase Kinase Associated Protein 2 SL Synthetic lethal SMAD4 Mothers Against Decapentaplegic Homology Family Member 4 SOC Super Optimal broth with Catabolite repression TAE Tris-acetate ethylenediaminetetraacetic acid TBS Tris buffered saline TBST Tris buffered saline with tween-20

xii TCGA The Cancer Genome Atlas TNM Tumour Node Metastasis TOMM20 Translocase of Outer Mitochondrial Membrane 20 TP53 Tumour Protein P53 UXT-V2 Ubiquitously Expressed Transcript Isoform 2 V Volts vs. Versus WB Western blot WD Tryptophan-aspartic acid

xiii

CHAPTER 1. INTRODUCTION 1.1. COLORECTAL CANCER OVERVIEW In 2020, ~225,800 Canadians were predicted to be newly diagnosed with cancer, while nearly half of all Canadians are expected to be diagnosed with cancer in their lifetime1. To date, cancer remains the leading cause of death in Canada, with ~83,300 deaths in 2020 alone1. Colorectal cancer (CRC) is the third most commonly diagnosed cancer in Canada accounting for ~26,900 new diagnoses each year, and is the second most lethal cancer, accounting for ~11.6% of all cancer related deaths1. While CRC incidence rates have been declining slowly for many decades2, ~50% are still diagnosed at late stages (i.e. III or IV)3 when the 5-year survival is as low as 8%4. Consequently, understanding the genes and pathways driving CRC development is crucial to gain mechanistic insight that can be exploited in future therapies to decrease morbidity and mortality rates associated with CRC. 1.1.1. Clinical Features Traditionally, CRC was proposed to be a homogeneous disease that develops in any segment of the colon or rectum5,6; however, notable differences in molecular mechanism (see Section 1.2, page 5), tumour histology, disease progression and overall survival occur between tumours originating from the proximal colon (i.e. caecum, ascending and transverse colon), the distal colon (i.e. descending and sigmoid colon) and the rectum (Figure 1-1)5-8. Tumours arising in the proximal colon tend to be larger, more advanced, exhibit microsatellite instability (MSI) (detailed further in Section 1.2.2, page 7) and have a flat morphology, while tumours arising from the distal colon frequently exhibit chromosome instability (CIN; detailed in Section 1.2.3, page 8) and have a polypoid (i.e. resembles a polyp) morphology6,8. Proximal and distal CRCs also differ in target site of distant metastases where disease originating in the proximal colon typically metastasizes to the peritoneal cavity while CRCs originating in the distal colon metastasize to the liver and lungs6. Differences between tumours originating from the proximal or distal colon and rectum are likely due to biological factors such as the underlying embryologic origin, morphology, physiology, function, and microenvironment6,9,10. Classically, sporadic, or randomly arising CRCs were thought to develop mainly from adenomatous polyps that followed a predictable stepwise accumulation of mutations, known as the “adenoma to carcinoma pathway” proposed by Fearon and Vogelstein11-13 (see Section 1.2.1, page 5). Recently, more supporting evidence suggests that sporadic CRCs also develop from a second, flat type of polyp known as a sessile serrated polyp14.

Cancers arising from sessile serrated polyps account for ~10-20% of CRCs7, and are more common within the proximal colon15,16. Additionally, the molecular mechanism underlying CRC development from sessile serrated polyps is distinct from cancers arising from adenomatous polyps15,16. Sessile serrated pathway cancers are typically characterized by BRAF (B-raf Proto- Oncogene, Serine/Threonine Kinase) alterations and epigenetic instability (see Section 1.2.2, page 7)15,16.

Figure 1-1. The Colon and Rectum Anatomy. Schematic representing the colon and rectum anatomy. The proximal colon consists of the caecum (purple), ascending colon (blue) and transverse colon (pink), while the distal colon is comprised of the descending colon (green) and sigmoid colon (yellow). The rectum is presented in orange.

1.1.2. Risk Factors and Screening Risk factors associated with CRC development are numerous and diverse. Factors such as sex, and age (i.e. males and older people) are strongly associated with increased risk for CRC development. More specifically, the incidence rates are 44.4% in males and 34.0% in females1, while the median ages of diagnosis for sporadic colon cancers are 68 and 72 years for males and

females, respectively17-19. Additional factors such as environment, family history, obesity, increased alcohol and red meat consumption, smoking and minimal exercise are also associated with increased risk, though the underlying mechanisms accounting for these associations have yet to be fully elucidated7,14. In contrast with sporadic CRCs, individuals with strong family histories (e.g. those diagnosed with hereditary cancer syndromes) are diagnosed much younger (~40 years old) on average20. While many risk factors are considered modifiable14 (e.g. decreasing red meat consumption or increasing exercise), effective screening programmes are essential for early identification of precancerous lesions/polyps or early stage disease so that treatment strategies are more effective. Most of Canada, including 10 provinces and 1 territory, has implemented systematic CRC screening programmes, though as of 2018 none of the current programmes were reaching the targeted 60% participation21. The Canadian Partnership Against Cancer reported participation in CRC screening programmes varied widely across Canada, ranging from 8.6% in Newfoundland and Labrador to 53% in Saskatchewan22. Screening is recommended for average-risk individuals between the ages of 50-74 who are without familial CRC history, or personal history of CRC or inflammatory bowel disease. Current recommendations by the Canadian Task Force on Preventative Health Care include at-home fecal occult blood tests every two years21 or flexible sigmoidoscopy every 10 years23. Individuals at high-risk for CRC include those with a personal history of colorectal polyps, CRC, or inflammatory bowel disease and/or a familial history of CRC23. High-risk individuals are recommended to be screened earlier and more often, though it has been proposed to schedule screening based on an individual’s family history24. Over 50% of CRCs are suspected to occur in individuals that fall within screening programme parameters3. However, while CRC rates continue to decline, likely due in part to effective screening recommendations, ~50% of CRCs are still diagnosed at late stages (III or IV) when treatment options are limited and prognosis is worse2,3. 1.1.3. Diagnosis, Staging and Treatment Colonoscopy is currently the gold standard for CRC diagnosis as it allows direct visualization of polyps and immediate resection for subsequent histopathological assessment, diagnosis and staging7,14. Staging of resected tissue is essential to determine treatment regimen and prognosis. CRC staging is based on the Tumour Node Metastasis (TNM) classification system that

characterizes tumour size, lymph node involvement and presence of metastases to determine cancer severity and assign the cancer to one of five broad stages (Stage 0 through IV)25,26. Stage 0 disease (carcinoma in situ) is restricted to the intestinal mucosa (i.e. the inner-most layer of the intestinal wall) while Stages I and II are considered early-stage disease and are defined by invasion through additional layers of the intestinal wall and nearby tissues. Stages III and IV are determined based on the extent of lymph node involvement and presence of distant metastases27. Treatment options and efficacy become more limited as disease stage progresses. For many Stage 0 and I CRCs, endoscopic local resection of cancerous lesions/polyps (e.g. polypectomy) is sufficient treatment. However, the gold standard curative treatment for local (i.e. non-metastasized) CRC is surgery and complete resection of the afflicted areas with appropriate margins of seemingly normal tissue (≥ 5 cm) on either side of the affected area7,14,26. Surgery is often paired with neoadjuvant or adjuvant chemotherapy to reduce tumour burden and risk of recurrence, respectively14,26. The typical chemotherapy regimen is a combinatorial approach of standard cytotoxic drugs including folinic acid, 5-fluorouracil (5-FU), and oxaliplatin (FOLFOX) or irinotecan (FOLFIRI)14,28. The mechanism of action of 5-FU is two-fold; 5-FU is metabolized into nucleotide analogs that are erroneously incorporated into nascent DNA and RNA thereby impacting their synthesis, while 5-FU metabolites also bind and inhibit thymidylate synthase, an essential enzyme for nucleotide synthesis29,30. Folinic acid potentiates the effects of 5-FU by stabilizing the interaction between 5-FU metabolites and thymidylate synthase31, while oxaliplatin induces intra-strand crosslinks impacting DNA replication and transcription. Lastly, irinotecan inhibits topoisomerases to prevent re-ligation of DNA during replication fork migration32,33. While systemic chemotherapy is employed for advanced metastatic disease, there have been developments in local treatment options focused on long-term disease control, such as resection or radiotherapy ablation of liver and lung metastases7. Recent research has identified immune checkpoint inhibitors as promising therapeutic strategies for MSI positive (i.e. DNA mismatch repair deficient) CRCs, unfortunately, only ~5% of metastatic CRCs are expected to be susceptible to this treatment34,35. Novel treatment options for advanced disease are crucial as ~90% of patients with metastatic disease ultimately develop drug resistance36,37. However, despite the growing number of therapeutic options, current treatments often induce undesirable side effects (e.g. nausea, diarrhea, nerve damage7,26,28) that negatively affect a patient’s quality of life. As such, a

better understanding of the molecular origins of CRC is essential to ultimately develop more effective treatment options aimed at improving the quality of life of CRC patients. 1.2. MOLECULAR PATHOGENESIS OF COLORECTAL CANCER The heterogeneous nature of CRC can in part be attributed to the underlying molecular mechanisms that differ according to precursor lesion, tumour location (i.e. proximal versus [vs.]. distal; see Section 1.1.1, page 1) and inherited vs. sporadic disease. Hereditary gastrointestinal cancer syndromes, such as Lynch Syndrome and Familial Adenomatous Polyposis, account for ~10-20% of all CRCs7,14,38 and have their own distinct etiological origins, including germline mutations in DNA mismatch repair genes (e.g. MLH1 [mutL homolog 1], and MSH2 [mutS homolog 2]) or APC (Adenomatous Polyposis Coli), respectively13,27. Sporadic CRC accounts for ~80-90% of all cases and typically arises from the classical adenoma to carcinoma pathway (discussed below) over a 15-20-year time frame14,26. 1.2.1. The Adenoma to Carcinoma Pathway The adenoma to carcinoma pathway, originally elucidated by Fearon and Vogelstein11-13, is a well-defined stepwise process driven by the progressive accumulation of mutations in DNA repair genes, tumour suppressor genes and oncogenes11,12. It describes the transition from normal colonic epithelial cells to CRC and is thought to drive the formation of 80-90% of sporadic CRCs, while the remaining 10-20% are derived from the sessile serrated adenoma pathway7,39 described in Section 1.1.1 (page 1). Many of the genes implicated in the adenoma to carcinoma pathway have well defined roles in hereditary gastrointestinal cancer syndromes, including APC in Familial Adenomatous Polyposis40, and other CRC-associated cancer syndromes, including Tumor Protein 53 (TP53) in Li-Fraumeni syndrome40. Studying CRC-associated syndromes has provided insight into the implications these pathogenic genes have in sporadic CRC development41. The progressive accumulation of mutations in the adenoma to carcinoma pathway and subsequent clonal expansion favours oncogenesis by affording cells the requisite selective advantages to survive, proliferate and evolve. It is hypothesized that at least four sequential genetic alterations in APC, KRAS (Kirsten Rat Sarcoma proto-oncogene), TP53 and SMAD4 (Mothers Against Decapentaplegic Homology Family Member 4) are required (detailed below)42,43, and while the alterations typically follow a predictable sequence44, it is more likely the accumulation of mutations that is more important, rather than the specific order in which they occur12,45. Understanding the adenoma to carcinoma pathway is important in selecting representative models for CRC research and in the

context of this study, our cellular models were selected based on the mutational status of critical adenoma to carcinoma pathway genes in order to gain a comprehensive understanding of the role reduced FBXO7 (F-box Only Protein 7) expression has in driving CRC development. Below are brief descriptions of four key genes implicated in CRC pathogenesis, including APC, KRAS, TP53 and SMAD4. 1.2.1.a. APC The canonical APC pathway regulates β-catenin levels by targeting the protein for proteolytic degradation through the ubiquitin-proteasome system. Consequently, APC loss of function causes β-catenin accumulation that upregulates transcription of genes associated with oncogenesis (e.g. CCND1 [Cyclin D1])45,46. Germline mutations of the tumour suppressor gene APC, mapping to 5q21, underlie the development of Familial Adenomatous Polyposis, a hereditary cancer syndrome46, while somatic alterations are proposed to be an initiating event in CRC oncogenesis43,46,47. The majority of APC mutations, > 90% of which are truncating mutations44,48, occur in a mutational hotspot located within the β-catenin binding region46. The occurrence of APC mutations in ~80% of pre-cancerous polyps (i.e. adenomas) strongly supports that APC mutations occur early in oncogenesis47. 1.2.1.b. KRAS KRAS encodes a guanosine 5’-triphosphate-binding protein11,45 involved in the mitogen- activated protein kinase pathway that regulates cell proliferation, senescence and apoptosis13,45. KRAS maps to 12p12 and is the key oncogene within the adenoma to carcinoma pathway. Activating mutations are most commonly found in the regions encoding the guanosine 5’- triphosphate binding domain (codons 12 and 13) and drive constitutive activation45,49. KRAS mutations are less common in small adenomas than APC mutations but are found in 40-50% of large adenomas12,49. As such, KRAS mutations are proposed to be the second key event in CRC oncogenesis. 1.2.1.c. TP53 TP53 encodes a transcription factor colloquially referred to as the “guardian of the genome” for its central roles in inhibiting cellular proliferation in the presence of DNA damage and inducing apoptosis40,45. The TP53 locus maps to 17p13 and germline mutations have pathogenic implications for Li-Fraumeni Syndrome, a condition characterized by early cancer development, including CRC. TP53 is the most frequently altered tumour suppressor gene in human cancers,

including 50-75% of CRCs45,50 and its loss is often associated with development of aneuploid clones and transition to invasive carcinoma40,51. 1.2.1.d. SMAD4 Genetic alterations in the tumour suppressor gene, SMAD4 adversely impact the transforming growth factor β signalling pathway that normally regulates cell growth, differentiation and apoptosis40,45. In fact, germline mutations and SMAD4 (18q21) loss of heterozygosity contribute to the development of Juvenile Polyposis Syndrome, a hereditary gastrointestinal cancer syndrome13. Interestingly however, while SMAD4 loss of function is proposed to contribute to sporadic CRC development, the prevalence of SMAD4 alterations are not as frequent as mutations in other key genes45 as they only occur in ~10-35% of CRCs14,52. Consequently, 18q loss is viewed as a late oncogenic event that is more often associated with disease progression52. 1.2.2. Genome Instability in Colorectal Cancer Genome instability is an enabling hallmark of cancer53,54 and refers to the state of increased genomic alterations, including mutations, copy number alterations and epigenetic changes55,56. Genome instability is a feature of virtually all cancer types and contributes to disease development, progression and the acquisition of drug resistance by altering the expression of key genes implicated in oncogenesis (e.g. oncogenes, tumour suppressors, DNA replication and repair genes, anti-apoptosis genes)54,56. As such, genome instability enables the subsequent acquisition of the classic cancer hallmarks including unlimited replicative potential, self-sufficiency in growth signals and evasion of apoptosis53. In general, genome instability is categorized into three distinct pathways: 1) MSI; 2) CpG island methylator phenotype (CIMP); and 3) CIN56,57. Briefly, MSI arises due to mutations in four DNA mismatch repair genes (MLH1, MSH2, MSH6 [MutS Homolog 6] and PMS2 [PMS1 Homolog 2, Mismatch Repair System Component]) that induce DNA mismatches and expansion/contraction of microsatellites (highly repetitive DNA sequences) 54,56,58. While MSI is diagnostic in Lynch Syndrome patients58, only ~10-20% of sporadic CRCs harbour defects in DNA mismatch repair genes48. In addition to the MSI pathway, ~15% of CRCs exhibit CIMP which are characterized by increases in global DNA methylation (i.e. hypermethylation)40 that drives gene promoter methylation and epigenetic silencing of key regulatory genes such as tumor suppressor, DNA repair and anti-apoptotic genes59. Finally, CIN (detailed further in Section 1.2.3, page 8) is the most prevalent genome instability pathway as it is associated with ~85% of sporadic CRCs55.

CIN drives on-going changes in chromosome complements and is the principal cause of the cell- to-cell karyotypic heterogeneity commonly observed in CRC54,55. Traditionally, MSI and CIN were believed to be mutually exclusive genome instability pathways, while CIMP could occur concurrently with either pathway56,57,60. However, emerging evidence has shown that a small subset of sporadic CRCs (< 12%) exhibit both MSI and CIN61-64. While all three genome instability types occur in CRC, the prevalent pathogenic mechanisms driving oncogenesis may be influenced by the molecular, physiological, and etiological differences (see Section 1.1.1, page 1). For example, proximal CRCs are more likely to exhibit MSI and CIMP, while distal CRCs are more likely to exhibit CIN7. Importantly, ~85% of CRCs exhibit CIN; therefore, gaining a better understanding of the genes and mechanisms inducing CIN is imperative to improve our fundamental knowledge of CRC and identify potential therapeutic strategies. 1.2.3. Chromosome Instability CIN is a common form of genome instability that is defined as an increase in the rate at which whole chromosomes, or large chromosomal fragments are gained or lost, and is a driver of genetic and cell-to-cell heterogeneity 54,55,65. CIN can be further sub-classified into two broad categories: 1) numerical CIN (N-CIN), which involves gains and losses of whole chromosomes55,66-68; and 2) structural CIN (S-CIN), which is characterized by on-going chromosome deletions, amplifications, inversions, and translocations. While N- and S-CIN may occur independently within a given CRC context, both forms can also co-exist and induce increasingly complex karyotypes over time (Figure 1-2)40,56,69. Thus, CIN drives intratumoral heterogeneity that confers selective growth, proliferation and survival advantages40,54,55,70 that are associated with cellular transformation71-73 (see Section 1.2.3.b, page 12), tumour evolution67, the acquisition of drug resistance74 and poor patient outcomes75,76. Hence, CIN is hypothesized to be an early etiologic event driving oncogenesis40,56,67-69.

Figure 1-2. CIN Drives Intratumoral Genetic and Cell-to-Cell Heterogeneity. Schematic illustrating a theoretical example of CIN within a parental cell that for simplicity contains three chromosome pairs. Progressive chromosome gains and losses (CIN) promote formation of a heterogeneous population of genetically distinct daughter cells resulting in intratumoral heterogeneity54. Note that while this example focuses on small-scale gains and losses of whole chromosomes (N-CIN), structural chromosome changes (S-CIN) may occur simultaneously and further contribute to the evolution of chromosome complements.

While CIN is often associated with aggressive disease, the acquisition of drug resistance and poor patient outcomes, recent evidence has shown that tumours exhibiting extreme CIN are associated with improved patient outcomes in some cancer types (e.g. breast, gastric, lung)77,78. It has been suggested that extreme CIN decreases cell fitness and is incompatible with viability, thus cells exceeding a theoretical CIN threshold are lost from the population79,80, while low to intermediate CIN levels may promote tumour evolution. As such, low and intermediate CIN levels are proposed to be the predominant drivers of disease development and progression81-87. Consequently, CIN may be therapeutically exploited to selectively kill cancer cells (reviewed in84-87).

Despite the prevalence of CIN and research efforts to develop therapies exploiting this aberrant phenotype, the underlying molecular determinants of CIN (i.e. genes, proteins and cellular pathways) remain largely unexplored. Established CIN genes, or those whose aberrant expression induces CIN often encode functions within key cellular pathways that are normally essential to maintain chromosome stability, including cell cycle regulation, centromere and mitotic spindle dynamics and DNA repair/replication65,88-91. However, recent data have demonstrated that less intuitive pathways, such as ubiquitin dynamics and protein degradation, are also associated with CIN. Thus, exploring these pathways will be essential to gain unprecedented insight into the molecular determinants of CIN. In this regard, recent work within the McManus laboratory showed that deregulated protein degradation induced by diminished expression of genes encoding the SKP1-CUL1-F-box (SCF) complex corresponds with CIN92-94, suggesting the SCF complex may have pathogenic implications for cancer development. Currently, the impact aberrant SCF complex formation and function have in CIN and cellular transformation remains poorly understood and warrants further investigation to gain novel insight into its potential role(s) in CRC pathogenesis. Thus, it is critical that CIN be assessed using single cell approaches capable of either establishing a rate of CIN or quantifying the cell-to-cell heterogeneity induced by CIN. 1.2.3.a. Methods to Detect Chromosome Instability and Assess Cellular Heterogeneity CIN refers to progressive changes in chromosome numbers that drives cell-to-cell heterogeneity rather than the stable state of aneuploidy, consequently, CIN can only be resolved in approaches assessing single-cells within a population54. Many population-based methods employed to measure gene copy number alterations, such as comparative genomic hybridization, single nucleotide polymorphism arrays and bulk DNA sequencing, are employed erroneously to assess CIN54. As described above, CIN describes a rate of on-going chromosome changes that drives cell-to-cell heterogeneity, while population-based approaches provide a population average, which effectively masks the genetic heterogeneity synonymous with CIN. While CIN can be measured by tracking chromosome dynamics in a single cell and its progeny over time, such approaches are time-consuming, laborious and technically challenging. Alternatively, the progressive nature of CIN and emerging genetic heterogeneity highlight that CIN can be identified through increased cell-to-cell heterogeneity within a population, and effectively measured using single-cell quantitative imaging microscopy (QuantIM) approaches in traditional endpoint analyses (Figure 1-3). This type of analysis has been employed with a variety of sample types

including cell lines and patient samples92-95, and QuantIM approaches are a versatile and rapid way to assess CIN in large cellular populations. In particular, changes in nuclear areas and micronucleus formation have been established as surrogate CIN markers and have been employed to identify novel CIN genes92-94. Conceptually, changes in nuclear areas are associated with large-scale gains and losses of DNA content70,96-98, whereas micronuclei are small DNA-containing bodies found outside the primary nucleus that are associated with a variety of cancer types and are hallmarks of CIN99-102. QuantIM approaches are rapid methods for preliminary identification of CIN genes; however, these approaches do not directly assess chromosome numbers. To complement the QuantIM approaches, mitotic chromosome spreads are generated, and chromosomes are enumerated to identify both gains and losses to uncover novel CIN genes.

Figure 1-3. The Application of QuantIM to Detect Cell-to-Cell Heterogeneity and CIN. Schematic illustrating detection of CIN by measuring cell-to-cell heterogeneity using QuantIM approaches. For illustrative purposes, representative examples of CIN negative (left) and CIN positive (right) populations are compared to identify CIN-associated phenotypes. Assays assessing surrogate markers of CIN (changes in nuclear areas and micronucleus formation) are amenable to high-throughput QuantIM analyses, but do not enable direct visualization of chromosomes and require subsequent validation. An example of a micronucleus is indicated by the arrow.

1.2.3.b. Methods for Evaluating Cellular Transformation Cellular transformation is the crucial transition in which normal cells acquire neoplastic phenotypes characterized by the classic cancer hallmarks presented by Hanahan and Weinberg53,103. CIN is said to be a driver of cellular transformation as the on-going changes in chromosome complements disrupts the balanced expression of key genes associated with oncogenesis, thereby facilitating acquisition of cancer hallmarks. Consequently, transformed cells are better suited to withstand the challenges associated with tumour growth (e.g. hypoxic environments) and treatment (e.g. cell death inhibition)103. Mouse models are commonly utilized to evaluate tumorigenic potential; however, these experiments are laborious and costly. Many in vitro assays are employed to assess cellular transformation and have been developed as cost effective and rapid alternatives104. Cellular transformation assays typically assess key phenotypes associated with early etiological events in oncogenesis including: 1) proliferation93,94,105,106; 2) clonogenic potential107,108; and 3) anchorage independent growth93,94,109,110. Proliferation assays are employed to identify increases in growth rates that are associated with rapidly growing tumors, while clonogenic assays are conducted to determine the ability of a single cell to form progeny, an essential characteristic for tumorigenesis. Soft-agar three-dimensional (3D) colony formation assays assessing anchorage-independent growth are considered the gold standard in vitro assay for investigating cellular transformation. Non-transformed cells require adhesion to a matrix in order to survive and proliferate, otherwise cell death occurs111. The ability to grow unattached is a phenotype not typically observed in non- transformed cells, and thus is an indication of cellular transformation. Collectively, CIN and cellular transformation assays can be used to identify important etiological events in CRC development. 1.3. THE SCF COMPLEX AND F-BOX PROTEINS Ubiquitin is a key post-translational modification that regulates many cellular processes including protein degradation, protein localization, cell-cycle regulation, and DNA repair112,113. Protein ubiquitination is a highly organized process controlled by sequential actions of three enzymes (Figure 1-4): 1) E1 ubiquitin-activating enzyme; 2) E2 ubiquitin-conjugating enzyme; and 3) E3 ubiquitin ligase112,114. Together, the enzymatic cascade catalyzes mono- or polyubiquitination of protein substrates specified by the E3 ubiquitin ligase. The first ubiquitination event involves a covalent bond between the ubiquitin C-terminal glycine and a substrate lysine (K)

residue within the target protein. Ubiquitin chains may be generated through successive ligations of ubiquitin to one of seven K residues (i.e. K6, K11, K27, K29, K33, K48, and K63) within the previously bound ubiquitin molecule113. The fate of the target protein is dictated by the abundance (i.e. mono-, multi-mono- or polyubiquitination) and topology (i.e. branched or linear) of the ubiquitin linkages114-116. While the so-called “ubiquitin code” is becoming increasingly complex, mono- or multi-mono-ubiquitination linkages typically dictate protein localization and chromatin regulation, whereas K63 linked ubiquitin chains (polyubiquitination) are associated with cell signaling and protein recruitment and K48 linked ubiquitin chains induce proteolytic degradation by the 26S proteasome (i.e. the ubiquitin-proteasome system)114-116. The human genome is suspected to encode > 500 E3 ubiquitin ligases115,117 that are classified into three main subgroups: 1) the Really Interesting New Gene (RING)-type, which is the largest E3 family encoded in the human genome and are characterized by a RING subunit responsible for recruiting the E2 enzyme115,118; 2) the Homologous to the E6AP Carboxyl Terminus (HECT)- type115,118; and 3) the RING between RING (RBR)-type115. HECT and RBR-type E3 enzymes do not contain RING subunits and differ based on the mechanisms employed to recruit E2 enzymes. The RING-type ligases consist of several subtypes including the SCF complex E3 ubiquitin ligase115,118. The best-defined role of the SCF complex is the polyubiquitination of protein targets for proteolytic degradation by the ubiquitin-proteasome system; however, the SCF complex also regulates protein localization and activity through monoubiquitin linkages and non-degradative (K63) polyubiquitin chains. The SCF complex consists of three invariable core subunits, namely S-phase kinase associated protein 1 (SKP1), Cullin 1 (CUL1), and RING box protein 1 (RBX1), and a variable fourth subunit known as the F-box protein (detailed in Section 1.3.2, page 15), which confers substrate specificity to the complex (Figure 1-4) 113,119. Section 1.3.1 below describes novel work that identifies the SCF complex as a key regulator of chromosome stability.

Figure 1-4. The SCF Complex Targets Proteins for Degradation by the 26S Proteasome. Schematic illustrating the polyubiquitination (K48) and subsequent degradation of protein targets by the SCF complex. The SCF complex is composed of RBX1, CUL1, SKP1 and an F-box protein, such as FBXO7. E1 = ubiquitin-activating enzyme. E2 = ubiquitin conjugating enzyme.

1.3.1 The SCF Complex is Implicated in Chromosome Instability Recently the McManus and Nachtigal laboratories identified SKP1, CUL1, and RBX1 as novel CIN genes, as reduced expression of each gene individually induces CIN in CRC92 and/or ovarian cancer contexts93,94. Transient (short interfering RNA [siRNA]) and stable (Clustered Regularly Interspaced Short Palindromic Repeats [CRISPR]/Cas9) in vitro models mimicking decreased SKP1, CUL1, and RBX1 expression were employed in QuantIM approaches and assessed for CIN. Short-term studies revealed diminished expression of each core SCF complex member induced CIN phenotypes, while long-term studies showed that CIN-phenotypes were dynamic and on- going. Additionally, fluorescent imaging microscopy revealed aberrant Centromere Protein A (CENPA) localization during interphase in SKP1-silenced cells, while western blot analyses revealed SKP1, CUL1, and RBX1 silencing was associated with increased Cyclin E1 (CCNE1) abundance. Interestingly, previous studies by Takada et al.120 revealed CCNE1 overexpression induces aberrant CENPA regulation which drives mitotic errors and CIN phenotypes. To further investigate the effects of increased Cyclin E1, the McManus laboratory performed rescue experiments in which SKP1, CUL1, or RBX1 was co-silenced with CCNE1, and revealed partial

phenotypic rescue of CIN phenotypes. These findings suggest reduced expression of core SCF complex members individually, induces aberrant SCF complex function and Cyclin E1 accumulation that drives CIN. However, the partial phenotypic rescue indicates that the misregulation of other proteins are also likely underlying CIN. These data are strongly supportive of the role the SCF complex has in driving CIN, and therefore warrants further investigation to determine the implications of the fourth SCF complex subunit, the F-box protein, in the development of CIN. 1.3.2. The F-box Proteins The human genome encodes 69 F-box proteins121 that are named for the 40 amino acid F-box motif that binds SKP1122,123. F-box proteins are classified into three subgroups based on the substrate protein targeting motif: 1) FBXL – identified by the presence of leucine-rich repeats; 2) FBXW – defined by tryptophan-aspartic acid (WD) 40 amino acid repeats; and 3) FBXO – containing diverse uncharacterized domains122,124. Many F-box proteins have been implicated in pathways governing various cancer hallmarks125. For example, FBXW7 (F-box and WD Repeat Domain Containing 7) is frequently mutated or heterozygously lost in many cancers, resulting in the misregulation of well-established oncogenic substrates such as MYC Proto-Oncogene, bHLH Transcription Factor (c-Myc), Mechanistic Target of Rapamycin Kinase (mTor), and Cyclin E2126,127. While the aberrant expression and/or function of F-box proteins in cancer development and progression are just beginning to emerge, there is a paucity of information regarding most F- box proteins and their potential implications in CIN and cancer pathogenesis. In this regard, this thesis focuses on FBXO7 (detailed below), a member of the FBXO subfamily, and its putative role in maintaining chromosome stability. Preliminary siRNA-based screens identified FBXO7 as a strong candidate CIN gene, but this has yet to be firmly established. In this regard, determining the impact reduced FBXO7 expression has on CIN will provide novel insight into the potential impact aberrant expression may have in CRC development. 1.3.3. F-box Only Protein 7 FBXO7, also known as Parkinson disease 15 (PARK15) due to its role in juvenile Parkinson’s disease, is largely conserved throughout highly evolved animals (i.e. chordates)128, but does not appear to have a functional ortholog in lower eukaryotes, such as budding yeast. However, a functional ortholog, referred to as Nutcracker (ntc), has been identified in Drosophila melanogaster129. Human FBXO7 maps to chromosome 22q12.3 and is comprised of 9 exons and

8 introns130 (Figure 1-5). It is suggested that three protein coding FBXO7 mRNA splice variants exist, of which the predominant variant is isoform 1130. FBXO7 mRNA isoforms differ at the 5’ end (exons 1 and 2) while exon 3 to exon 9 are consistent between the three isoforms. It is important to note that siRNAs employed in this study (see Section 3.3.3, page 24) recognize all three mRNA isoforms.

Figure 1-5. FBXO7 mRNA Structures. Schematic illustrating three FBXO7 mRNA splice variants. FBXO7 mRNA consists of 9 introns (blue rectangles) and 8 exons (v-shaped lines). Note, the three isoforms differ at the 5’ end (exons 1 and 2)130 and siRNAs employed here within (Aim 1, Section 4.3, page 43) recognize all three isoforms. SiRNA binding locations are indicated by coloured rectangles below each isoform.

FBXO7 is present in a wide variety of tissues, including whole blood, brain, ovary, and skeletal128, and localizes to the nucleus and cytoplasm131,132. Human FBXO7 isoform 1 mRNA produces a 58.5 kilodalton (kDa) protein consisting of 522 amino acids, while isoform 2 produces a 49.4 kDa protein (442 amino acids) and isoform 3 produces a 45.8 kDa protein (408 amino acids)133. To date, expression and protein function of isoforms 2 and 3 remain poorly characterized, though isoform 2 has been shown to interact with known substrates of isoform 1 including inhibitor of apoptosis 1 (cIAP1)134 and Phosphatase And Tensin Homolog Induced Kinase 1(PINK1)135. The isoform 1 protein product has five main interaction domains including a ubiquitin-like domain at the amino terminus, a cyclin dependent kinase domain, an FBXO7/proteasomal-inhibitor-31 (PI31) interaction (FP) domain, an F-box domain, and an unstructured proline-rich region130,133 (Figure 1-6). Interestingly, each FBXO7 domain, except the cyclin dependent kinase domain, has been implicated in some aspect of the ubiquitin-proteasome system, including 26S proteasome

regulation, and SCF complex formation and regulation. Recent work by Vingill et al136. demonstrated the ubiquitin-like domain binds to and ubiquitinates proteasomal subunit α2 (PSMA2), a core member of the 26S proteasome. Additionally, Merzetti and colleagues129 showed FBXO7 functional ortholog, ntc, interacts with PI31 at the FP domain to regulate proteasome activity, though this has yet to be confirmed in human cells. The F-box and proline-rich region, relate directly to the SCF complex as the F-box domain binds SKP1, while the proline-rich region is essential for dictating the target protein specificity to the complex137. Indeed, FBXO7 appears to be a critical component of the ubiquitin-proteasome system, as such, it is conceivable that reduced FBXO7 expression may induce CIN through aberrant regulation of the SCF complex and the ubiquitin-proteasome system. Moreover, while the predominant role of the SCF complex involves protein degradation following polyubiquitination with K48 linkages, the FBXO7-bound SCF (SCFFBXO7) complex also regulates protein localization and function through monoubiquitin linkages and K63 polyubiquitination130. Hepatoma Up-Regulated Protein (HURP) was the first SCFFBXO7 complex substrate identified and determined to be targeted by K48 linkages for degradation138. Recent large-scale proteome-wide screens identified Glycogen Synthase Kinase 3 Beta (GSK3β) and Translocase Of Outer Mitochondrial Membrane 20 (TOMM20) as novel SCFFBXO7 complex targets regulated by K63 and multi-monoubiquitin linkages, respectively132. The diverse interaction partners and ubiquitin linkage-types suggest FBXO7 may have wide reaching involvement in CRC pathogenesis.

Figure 1-6. Primary FBXO7 Isoform 1 Protein Structure. Schematic detailing the primary structure of FBXO7 isoform 1. FBXO7 is a 59 kDa protein with 5 functional domains including a ubiquitin-like domain (brown), a cyclin dependent kinase domain (green), an FBXO7/proteasomal-inhibitor-31 (PI31) interaction (FP) domain (blue), an F-box domain (purple) and a proline-rich region (yellow). FBXO7 has known roles in mitophagy, cell cycle regulation, proteasome regulation and binding to the SCF complex.

To date, most of our knowledge of FBXO7 stems from studies investigating its role in a rare form of juvenile Parkinson’s disease132,133,139,140. It has been suggested that mutant FBXO7, typically through single amino acid substitutions, underlies aberrant mitophagy and proteasome activity, which may have pathogenic implications for the neurodegenerative disorder133,135,136,141. In vivo mouse models assessing the impact reduced FBXO7 expression has in Parkinson’s disease noted that heterozygous FBXO7 knockout (FBXO7+/-) mice were similar to their wild-type counterparts with respect to size and motor skills, while homozygous FBXO7 knockout (FBXO7-/-) mice were smaller, showed early-onset motor deficits and died at the beginning of their fourth week of life136, which is equivalent to ~14 years of age in humans. Unsurprisingly, no reports of CRC in FBXO7-/- mice were reported, as the mice were likely too young to develop CRC. The critical role FBXO7 has in Parkinson’s disease pathogenesis indicates FBXO7 is essential for normal cell processes and physiology. However, Parkinson’s disease affects non- cycling neuronal cells, thus, there is a general lack of critical information pertaining to the impact reduced FBXO7 expression and function have in cycling cells and thus CRC pathogenesis.

CHAPTER 2. RATIONALE, HYPOTHESIS AND RESEARCH AIMS 2.1 RATIONALE CRC remains a significant burden on the Canadian healthcare system as it is associated with some of the highest morbidity and mortality rates among all cancer types. Thus, it is essential to gain a better understanding of the aberrant genes and mechanisms driving CRC development to develop improved therapeutic strategies. However, the aberrant genes and mechanisms driving CIN remain largely unknown. Preliminary large-scale siRNA-based screens of suspected CIN genes revealed FBXO7 as a strong candidate and suggest reduced FBXO7 expression may have important implications in CRC pathogenesis. Currently, the impact reduced FBXO7 expression has on CIN, cellular transformation and oncogenesis remain unknown. Accordingly, establishing the effects of diminished FBXO7 expression in appropriate cellular models will shed new light on the mechanisms underlying CIN, which is essential for developing innovative strategies that better combat CRC. In this regard, my project seeks to gain novel insight into the molecular origins of CIN and CRC by determining the impact diminished FBXO7 expression has on CIN and cellular transformation in non-malignant and malignant colonic epithelial cell lines. 2.2 HYPOTHESIS AND RESEARCH AIMS I hypothesize that decreased FBXO7 expression induces CIN that promotes cellular transformation and contributes to CRC development. I will address this hypothesis through two research aims: Aim 1: To determine the short-term impact reduced FBXO7 expression has on CIN. Aim 2: To determine the long-term impact reduced FBXO7 expression has on CIN and cellular transformation.

CHAPTER 3. MATERIALS AND METHODS 3.1. BIO-INFORMATICS APPROACHES All mutation, copy number alteration and mRNA expression data were acquired from The Cancer Genome Atlas (TCGA; Pan-Cancer Atlas data), while data extraction, visualization and statistical comparisons were performed at cBioPortal (www.cbioportal.org)142,143. 3.1.1. Genomic Alterations and Survival Analyses To determine the prevalence of FBXO7 alterations in cancer, data from 10 common cancer types (ovarian, lung, esophageal, breast, bladder, glioblastoma, CRC, pancreas, prostate and leukemia) were scrutinized for genomic alterations using user defined Onco Query Language (OQL [HOMDEL; HETLOSS; AMP; GAIN; MUT]) and were visualized in the OncoPrint tab. Putative copy number alterations were generated from patient data using GISTIC144, in which sample specific thresholds were applied to determine the copy number status per patient sample145. Low mRNA expression levels were also determined on an individual sample basis and were based on the minimum median arm-level copy number deletion per sample. To generate Kaplan-Meier (KM) curves and compare patient groups for survival analyses, OQL was employed to generate tracks based on the experimental question. For example, to compare patients harbouring FBXO7 loss vs. diploid patient samples, two tracks are generated. One track contains patient samples labelled as HOMDEL and HETLOSS, while the second contains patient samples labelled as AMP, GAIN and MUT. To isolate patients with diploid FBXO7 status, defined patient groups of interest were generated in the Overlap tab within the Comparison/Survival option in cBioPortal, and KM curves were subsequently visualized and statistically compared (Log-rank test) in the Survival tab within the Comparison/Survival option with a p-value < 0.05 considered statistically significant. A similar KM curve was generated to assess the impact of reduced mRNA expression on overall survival. OQL terms EXP < -1.5 and EXP > 1.5 were employed to isolate low and high FBXO7 mRNA expression, respectively, while samples exhibiting normal mRNA expression (those that fall between low and high expression levels) were identified in the Overlap tab within the Comparison/Survival option. Similar survival analyses were also conducted for patients harbouring putative driver KRAS, TP53 and APC alterations. To identify samples harbouring putative driver alterations, OQL commands were employed to query patient mutation and copy number status (HOMDEL; HETLOSS; AMP; GAIN; MUT), while patient groups were further separated in the Comparison/Survival tab as

described above. The patient sample alteration status was based on the data from the original TCGA PanCancer Atlas publications where the samples were first described. Data were imported to Prism to customize graphs, while figures were assembled in Photoshop CS6 (Adobe).

3.2. REAGENTS See Appendix A (page 90) for a complete list of solutions and reagents employed in this study.

3.3. CELL CULTURE Four colonic epithelial cells lines were employed in CIN and cellular transformation assays in this study, the characteristics of which are summarized in Table 3-1, while HEK 293T cells (Takara Bio) were employed to generate lentivirus for the CRISPR/Cas9 protocol (see Section 3.6, page 31). HCT116 cells were purchased from American Type Culture Collection (Rockville, MD), while 1CT and its derivative cell lines, RPA and A1309, were generated and graciously provided by Dr. Jerry Shay (University of Texas Southwestern Medical Center). HCT116 cells were cultured in McCoy’s 5A medium (HyClone) supplemented with 10 percent (%) fetal bovine serum (FBS) (Sigma-Aldrich) (Appendix A), while 1CT, RPA and A1309 were cultured in X-medium (Dulbecco’s Modified Eagle Medium [DMEM] with High Glucose/Medium 199) (HyClone) supplemented with 2% cosmic calf serum (CCS) (HyClone) (Appendix A). HEK 293T lentiviral packaging cells were cultured in DMEM high glucose medium containing 10% tetracycline-free FBS (Appendix A). HCT116 cells were cultured in standard 10-centimeter (cm) tissue culture plates (Starstedt), while 1CT, RPA, and A1309 cells were cultured on surface treated Primaria tissue culture plates (Corning) and HEK293T cells were cultured on collagen coated BioCoat tissue culture plates (Corning). HCT116 and HEK 293T cells were maintained in an incubator at

37 degrees Celsius (°C) with 5% carbon dioxide (CO2), while 1CT, RPA and A1309 cells were maintained in low-oxygen (O2) chambers infused with 2% O2, 7% CO2, and 91% nitrogen which were subsequently placed in a 37 °C incubator. A dish with Milli-Q water and cupric sulfate pentahydrate (Appendix A) was placed in the bottom of the incubator to maintain humidity and prevent microbial and fungal growth.

Table 3-1. Properties of Human Colonic Epithelial Cell Lines Employed in CIN and Cellular Transformation Assays. Cell Line HCT116 1CT RPA A1309 Immortalized Immortalized Immortalized Transformed or Transformed (telomerase (telomerase and (telomerase and Immortalized and CDK4) CDK4) CDK4) Culture Adherent Adherent Adherent Adherent Properties Sex Male Male Male Male Culture McCoy’s 5A + X-media + X-media + X-media + Medium 10% FBS 2% CCS 2% CCS 2% CCS Doubling Time ~22 hours ~22 hours < 22 hours < 22 hours Near diploid, Diploid, Diploid, Diploid, Karyotype 45 X, Stable 46 XY, Stable 46 XY, Stable 46 XY, Stable American Type Dr. J. Shay Dr. J. Shay Dr. J. Shay Culture Source (University of (University of (University of Collection Texas) Texas) Texas) (Rockville, MD) Protein Alterations KRAS G13DA Wild-typeB G12VA G12VA Wild-typeB, Wild-typeB, ~50% P53 Wild-typeB Wild-typeB ~50% knockdownC knockdownC Expression of Wild-typeB, truncated proteinD, APC Wild-typeB Wild-typeB ~50% ~70% knockdownC knockdownC AAmino acids are denoted by their single letter code. BRefers to expression of wild-type protein. CExtent of knockdown is with respect to endogenous 1CT levels. DAPC truncated at codon 1309.

3.3.1. Cell Passaging To maintain actively growing subconfluent cultures, cells were passaged in a biological safety cabinet every 2-3 days. Specifically, medium was aspirated from tissue culture plates and adhered cells were washed once with sterile 1× phosphate buffered saline (PBS) (Appendix A). To detach cells from tissue culture plates, 1 millilitre (mL) of 0.05% trypsin containing ethylenediaminetetraacetic acid (EDTA) (Gibco; Life Technologies) was added and plates were incubated for 5 minutes (min) (HCT116 and 1CT) or 2 min (RPA and A1309) at room temperature

(RT). Cells were monitored using an inverted ID03 microscope (Zeiss) equipped with a 10× objective. Once cells were detached, trypsin was neutralized with the addition of 3 mL of 1× PBS and 1 mL of complete McCoy’s medium for HCT116 cells, or 1 mL of trypsin neutralizer (Gibco; Life Technologies) and 2 mL of 1× PBS for 1CT, RPA and A1309 cells. Cells were washed from the bottom of the plate, collected in 15 mL conical tubes (Starstedt) and centrifuged at 140 × g at 21 °C for 5 min in a Legend XFR centrifuge (Thermo Scientific). Once cells were pelleted, supernatant was aspirated from the tubes, and cells were resuspended with 7 mL of 1× PBS for HCT116 cells and 5 mL of 1× PBS for 1CT and derivative cell lines. Approximately 1 mL of cell suspension was added back to the tissue culture plates with fresh complete medium. 1CT, RPA and A1309 cells were placed in a low O2 chamber, which was subsequently filled with the gas mixture described above. Plates and chambers were returned to the incubator. HEK 293T cells were passaged similarly to HCT116 cells, however, cells were only in culture long enough to generate lentiviral particles (~1.5 weeks; see Section 3.6.2, page 32). 3.3.2. Cell Counting and Seeding Cells were processed as described in Section 3.3.1 (page 22); however, following centrifugation and resuspension, HCT116 cells were passed through a 40 micrometer (μm) filter (Falcon) into a 50 mL conical tube (Starstedt) to eliminate cell aggregates. This step was unnecessary for HEK293T, 1CT and derivative cell lines as they readily form single cell suspensions. For all cell lines, a 40 microliter (μL) aliquot of cell suspension was added to 40 μL of 0.2% trypan blue dye (Gibco) in a 0.5 mL microcentrifuge tube. Next, 20 μL aliquots of cell suspension/trypan blue mixture were dispensed in duplicate into a cell counter slide (Cedex Smart Slide, Roche). The average concentration of viable cells per mL was automatically determined based on trypan blue exclusion using a Cedex XS cell counter and Cedex XS software (Roche). This value was used to calculate the appropriate volume of cell suspension to be added to complete medium such that the plates for subsequent experiments (i.e. 6-well and 96-well optical-bottom plates) were seeded with the appropriate number of cells (Table 3-2).

Table 3-2. Cell Seeding Densities Employed in Corresponding CIN Assays. Cell Line AssaysA Plate Format # Cells Volume Medium in Well WB 6-well 40 × 103 2,000 μL HCT116 NA/MN 96-well 200 200 μL MS 6-well 10 × 103 2,000 μL WB 6-well 40 × 103 2,000 μL 1CT NA/MN 96-well 1000 200 μL MS 6-well 12.5 × 103 2,000 μL WB 6-well 40 × 103 2,000 μL RPA, NA/MN 96-well 700 200 μL A1309 MS 6-well 10 × 103 2,000 μL AWB, western blot; NA, nuclear area; MN, micronucleus; MS, mitotic chromosome spread.

3.3.3. Short Interfering RNA Transfection Four ON-TARGETplus siRNA duplexes (siFBXO7-1, -2, -3, -4) targeting unique regions within the FBXO7 coding sequence and a non-targeting siRNA duplex (siControl) were purchased from Dharmacon and resuspended in 1× siRNA buffer (Appendix A) to a stock concentration of 20 micromolar (μM) and a working concentration of 10 μM. A pool (siFBXO7-P) comprised of the four individual FBXO7-targeting siRNAs was generated by combining equal volumes of each 10 μM siRNA duplex. All siRNAs were aliquoted into small volumes and stored at -80 °C and were subjected to a maximum of 5 freeze-thaw cycles. To perform the siRNA-based silencing experiments, cells were seeded as per Table 3-2 ~24 hours (h) prior to siRNA transfection and incubated at 37 °C to allow cells to adhere to the plates. The volumes of transfection reagents, including complete medium, RNAiMAX transfection reagent (Invitrogen) and siRNAs were adjusted based on the plate format (i.e. 6 well plate vs. 96- well plate) and cell density. Each siRNA and RNAiMAX were diluted in separate microcentrifuge tubes containing the appropriate volume of complete medium as indicated in Table 3-3. Each siRNA solution was mixed in a 1:1 ratio with the RNAiMAX solution, inverted gently and incubated at RT for 20 min. Following incubation, transfection mixtures were dispensed dropwise into the appropriate plates, rocked gently, and returned to the incubator for ~4 days.

Table 3-3. siRNA Transfection Protocols Employed in Corresponding Assays. Volume Volume Volume siRNA Total Cell Line AssayA RNAiMAX in Medium in Tube 1B Volume Tube 2B in Well 1 μL in 250 μL 6 μL in 250 μL WB 2,000 μL 2,500 μL medium medium 0.2 μL in 20 μL 0.4 μL in 20 μL HCT116 NA/MN 200 μL 240 μL medium medium 0.25 μL in 250 μL 1.5 μL in 250 μL MS 2,000 μL 2,500 μL medium medium 1 μL in 250 μL 6 μL in 250 μL WB 2,000 μL 2,500 μL medium medium 0.2 μL in 20 μL 0.4 μL in 20 μL 1CT NA/MN 200 μL 240 μL medium medium 0.25 μL in 250 μL 1.5 μL in 250 μL MS 2,000 μL 2,500 μL medium medium 1 μL in 250 μL 6 μL in 250 μL WB 2,000 μL 2,500 μL medium medium 0.2 μL in 20 μL 0.4 μL in 20 μL RPA, A1309 NA/MN 200 μL 240 μL medium medium 0.25 μL in 250 μL 1.5 μL in 250 μL MS 2,000 μL 2,500 μL medium medium AWB, western blot; NA, nuclear area; MN, micronucleus; MS, mitotic chromosome spread. BsiRNAs and RNAiMAX were diluted in complete medium.

3.4. WESTERN BLOT ANALYSES Western blot analyses were employed to determine siRNA-based silencing efficiencies and to screen for CRISPR/Cas9-mediated gene editing events by identifying clones with diminished FBXO7 expression. 3.4.1. Whole Cell Protein Extraction Cells were seeded, transfected with siRNAs, and grown in 6-well tissue culture plates as described in Section 3.3 (page 21). Whole cell protein lysates were harvested 4 days post- transfection. Briefly, cell culture medium was aspirated from the tissue culture plates, cells were washed 3× with 1 mL of chilled (4 °C) 1× PBS and either 200 μL (HCT116) or 100 μL (1CT, RPA, A1309) of lysis buffer consisting of radioimmunoprecipitation assay (RIPA) buffer (Appendix A) and 25× protease inhibitor (Appendix A) was added to each well. Cells were incubated for 5 min at 4 °C following which cell remnants and protein lysates were collected using a cell scraper and transferred to individual 1.5 mL microcentrifuge tubes on ice. Samples were

sonicated 2× for 3 second (sec) pulses with a Sonifer Cell Disrupter (Branson Sonic Power Co.) with a duty cycle of 50% and an output control setting of 6. To remove insoluble debris, samples were pelleted by centrifugation (Biofuge Fresco; Thermo Scientific) at 16,000 × g for 2 min at 4 °C, and the supernatant was carefully removed and transferred to a sterile 1.5 mL microcentrifuge tube. Protein samples were stored at -20 °C for short-term storage (< 2 weeks) or at -80 °C for long-term storage (> 2 weeks). 3.4.2. Protein Quantification Using Bicinchoninic Acid Assay A Pierce Bicinchoninic Acid (BCA) Assay kit (Thermo Scientific) was employed according to the manufacturer’s instructions to quantify protein concentrations from protein samples harvested as described in Section 3.4.1 (page 25). Briefly, Reagent A (containing BCA) and Reagent B (containing 4% cupric sulfate) were mixed in a 1:50 ratio and 200 μL were dispensed into wells of a 96-well plate (Corning). A set of 9 bovine serum albumin (BSA) protein standards with known concentrations ranging from 0 micrograms (μg)/mL to 2000 μg/mL were dispensed in duplicate (25 μL/well), while RIPA (20 μL/well) and protein samples (5 μL/well) with unknown concentrations were dispensed in triplicate. Proteins were incubated at 37 °C for 45 min following which 562 nanometer (nm) absorbance measurements were acquired using the Cytation 3 (BioTek) plate imager. Absorbance values from the BSA standards were utilized to calculate a standard curve, while the 3 absorbance values calculated from each protein sample were averaged and multiplied by 5 to calculate the unknown concentration of protein samples. 3.4.3. Gel Electrophoresis and Western Blot Following protein quantification, 20 μg of protein sample was added to 6× sodium dodecyl sulphate (SDS) Sample Loading Buffer (Appendix A) and enough RIPA to achieve a total loading volume of 20 μL per well. To denature proteins, samples were incubated at 99 °C for 5 min, with 1 min intervals of 700 revolutions per min orbital mixing in a Thermomixer R (Eppendorf), following which samples were placed on ice for at least 1 min. A Miniprotean electrophoresis tank (BioRad) was assembled with a 4% to 20% mini-Protean TGX gel (BioRad) and filled with 1× Running Buffer (Appendix A). A 10 µL volume of Precision Plus Protein Dual Color Standards (BioRad) molecular weight ladder was dispensed in one well of the gel, while the other wells were filled with 20 µL of denatured protein sample. Samples were electrophoresed at 140 volts (V) for 67 min at 4 °C using a PowerPac HC (BioRad) power supply. To prepare for protein transfer, a 0.45 µm polyvinylidene difluoride (PVDF) membrane (Milipore) was activated with methanol,

and rinsed 3× with Milli-Q water. The activated PVDF membrane and extra-thick blotting pads (BioRad) were placed in 1× Transfer Buffer (Appendix A) until it was time to transfer. The gel, membrane and extra-thick blotting pads were assembled in a TransBlot Semi-Dry Transfer Cell (BioRad) with 1× Transfer Buffer, following which proteins were transferred at 14 V for 40 min at RT. To blot for APC (i.e. a high molecular weight protein; ~312 kDa) a few adaptations to electrophoresis and protein transfer protocols were required. First, an 8% mini-Protean TGX gel (BioRad) was employed for electrophoresis followed by a wet transfer. To prepare for wet protein transfer, the gel, membrane and thin blotting pads were assembled in a Miniprotean electrophoresis tank (BioRad) containing an ice pack and filled with 1× Transfer Buffer, following which proteins were transferred at 20 V for 14 h at 4 °C. To assess protein transfer quality, PVDF membranes were stained with 5 mL of copper phthalocyanine 3,4’,4’’,4’’’-tetrasulfonic acid tetrasodium salt (CPTS) (Appendix A) protein stain for 5 min at RT. Membranes were de-stained by washing with Tris-buffered saline (TBS) solution containing 0.1% Tween 20 (TBST) (Appendix A), and blocked with non-fat milk power (5% weight per volume) diluted in 1× TBST (Appendix A) with gentle rocking at RT. Blocked membranes were transferred to 5% non-fat milk containing primary antibodies following the dilutions listed in Table 3-4 and incubated overnight with gentle rocking at 4 °C. The following day, the primary antibody was aspirated, and membranes were washed gently with 1× TBST for 3 × 10 min washes on a Belly Dancer (Stovall Life Science Inc.). Secondary antibodies conjugated to horseradish peroxidase (HRP) were diluted in 5% non-fat milk as per Table 3-4, dispensed onto the membrane and incubated for 1 h with gentle rocking at RT. Secondary antibodies were aspirated and the membranes were washed gently with 1× TBST for 3 × 10 min washes.

Table 3-4. List of Antibodies Employed for Western Blot Analyses.

Primary Antibodies Catalogue Antibody Source Species Dilution Number APC ab40778 Abcam Rabbit 1:1000 TP53 ab7757 Abcam Mouse 1:1000 FBXO7 ab154098 Abcam Rabbit 1:7000 FBXO7 ab167278 Abcam Mouse 1:1000 α-Tubulin ab7291 Abcam Mouse 1:20,000 Cyclophilin B ab16045 Abcam Rabbit 1:150,000 Secondary Antibodies Goat anti-Rabbit 111-035-144 Jackson ImmunoResearch Goat 1:15,000 HRPA Goat anti-Mouse 115-035-146 Jackson ImmunoResearch Goat 1:10,000 HRP AHRP, horseradish peroxidase

3.4.4. Semi-Quantitative Western Blot Analysis To visualize the antibody-labelled proteins of interest, the EZ-ECL kit (FroggaBio) was employed as described by the manufacturer. Briefly, the visualization solution was generated by combining the luminol/enhancer solution with the stable peroxide solution in a 1:1 ratio of which 700 μL was added to the membrane and allowed to incubate for 5 min at RT in the dark. Excess visualization solution was removed, and the membrane was placed in a clear page protector for imaging. A MyECL imager (Thermo Scientific) was employed to acquire standard chemiluminescent images with optimal exposure time empirically determined for each antibody. Images were imported into ImageJ software where semi-quantitative image (densitometry) analysis of protein expression levels was conducted. Band intensities were normalized to their respective loading control (α-tubulin or Cyclophilin B) and expression was presented relative to the control sample (e.g. siControl or NT-Control [negative control CRISPR/Cas9 clones]). Figures were generated in Photoshop.

3.5. SINGLE-CELL QUANTITATIVE IMAGING MICROSCOPY QuantIM approaches were employed in FBXO7 silenced and knockout cells to assess CIN- associated phenotypes including significant changes in cumulative nuclear area frequency

distributions and micronucleus formation. The results of these analyses were subsequently validated using traditional cytogenetic techniques in which mitotic chromosome spreads were prepared from the various experimental and control conditions and chromosomes were enumerated. 3.5.1. Cell Fixation and DNA Counterstaining for Nuclear Area and Micronucleus Formation Analyses Following cell seeding and silencing as described in Sections 3.3.2 (page 23) and 3.3.3 (page 24), respectively, medium was aspirated from the 96-well plates and cells were fixed using freshly prepared 4% paraformaldehyde (Appendix A) for 10 min. The paraformaldehyde was removed, disposed of in designated waste containers, and cells were subsequently washed once with 1× PBS. Nuclei were counterstained with 200 µL/well of 300 nanogram (ng)/mL Hoechst 33342 (Appendix A). To ensure uniform staining, plates were stored at 4 °C in the dark until the following day. 3.5.2. Image Acquisition and Analysis To acquire optimal images, plates were allowed to come to RT prior to imaging. A 3 × 3 matrix of non-overlapping 2-dimensional (2D) images were acquired from each well with a Cytation 3 Cell Imaging Multi-Mode Reader (BioTek) equipped with a 16-bit, gray scale, charge- coupled device camera (Sony) and an Olympus 20× lens (0.45 numerical aperture). The Gen5 software (BioTek) was employed to optimize all image acquisition and analysis settings. Briefly, size and signal intensity thresholds, and edge exclusion filters were applied to remove debris, mitotic and apoptotic bodies and partial nuclei from analysis. Parameters were optimized for each experiment and maintained constant throughout analysis. To determine nuclear areas, the primary mask function of the Gen5 software was employed to automatically detect interphase nuclei included within the pre-defined parameters70. The Gen5 Spot Detection function was employed for detection of micronuclei, defined as being an extra-nuclear Hoechst-stained body that is < 1/3 the size of the primary nucleus with no visible attachment. A secondary mask extending 15 μm from the primary mask was subsequently applied to approximate the cell body boundary. Finally, micronuclei were identified as Hoechst-stained bodies located outside the primary mask, but within the secondary (cell body) mask. The total number of micronuclei per well (condition) was determined, normalized to the total number of nuclei and expressed as the frequency of micronucleus formation146.

To assess whether reduced FBXO7 expression exacerbates nuclear area and micronucleus formation phenotypes in KRAS, TP53 and APC altered cells (RPA and A1309), the FBXO7- silenced conditions from 1CT, RPA and A1309 cells were directly compared. To do so, in each cell line and for each biological replicate, the nuclear areas from all FBXO7-silenced conditions (siFBXO7-2, -4 and -Pool) were combined into a single dataset and were normalized to the mean nuclear area of the siControl condition from the respective biological replicate. Subsequently, the normalized nuclear area dataset from each biological replicate were combined to generate a single dataset per cell line and were compared as described below. Micronucleus formation data was normalized similarly. Nuclear area and micronucleus formation data were imported into Prism v8 (GraphPad) where descriptive statistics, two-sample Kolmogorov-Smirnov (KS) (nuclear areas) and Mann-Whitney (MW) (micronucleus formation) tests were performed (detailed further in Section 3.9, page 38 [Statistical Analyses]). Data were visualized with cumulative distribution frequency graphs (nuclear areas), dot plots (micronucleus formation) and violin plots (micronucleus formation) and figures were prepared in Photoshop. Each experiment was performed a minimum of three times. 3.5.3. Mitotic Chromosome Spread Generation and Chromosome Enumeration To generate mitotic chromosome spreads, cells were seeded (Table 3-2) onto ethanol- sterilized coverslips and silenced as described in Table 3-3. Following four days of growth, cells were treated with KaryoMAX Colcemid (Gibco) at a dilution of 100 ng/mL (Appendix A) in complete medium for 2 h (HCT116) or 4 h (1CT, RPA, A1309). Colcemid and medium were aspirated from each well and 2 mL of 75 millimolar (mM) potassium chloride (KCl) hypotonic solution (Appendix A) was added to each well for 16 min (HCT116) or 20 min (1CT, RPA, A1309). Following the KCl treatment, cells were fixed in 3 × 10 min intervals with a 3:1 mixture of methanol:acetic acid (Appendix A). Fixative was aspirated and coverslips were placed on their side to dry. Coverslips were mounted on glass microscope slides with 4’,6-diamidino-2- phenylindole (DAPI) Mounting Medium (Appendix A) and stored overnight at 4 °C in the dark. Mitotic chromosome spreads were visualized and imaged using an AxioImager Z1 microscope equipped with a 63× (1.4 numerical aperture) oil-immersion, plan apochromat lens and a Zeiss HRm charge-coupled device camera. Image files (16-bit TIFFs) were imported into ImageJ software for visual assessment and manual chromosome enumeration. Chromosome spreads that deviated from the modal karyotype for the cell line employed (see Table 3-1) were classified into

one of three aberrant categories, including: 1) losses (< the modal number); 2) small-scale gains (gain of 1 to 14 chromosomes); and 3) large-scale gains (gain of > 14 chromosomes). Mitotic chromosome spreads were enumerated three times per condition. Data were imported into Prism where cumulative distribution frequencies, bar graphs and dot plots were generated, and descriptive statistics, KS tests and Student’s t-tests were performed. Figures were generated in Photoshop.

3.6. CRISPR/CAS9 CRISPR/Cas9 was employed to generate FBXO7+/- and FBXO7-/- A1309 clones using a two- step approach. First, plasmids constitutively expressing blue fluorescent protein (BFP) and a synthetic guide RNA (sgRNA) strand targeting either FBXO7 exon 3 (5’- TGTATCAATCAGCTGACTG-3’), exon 4 (5’-CTCTGCGAGGGCAGCTCCG-3’) or a non- targeting (NT-Control) sgRNA (Sigma) were packaged into lentivirus (Section 3.6.2, page 32). Lentiviral particles were transduced into recipient cells (Section 3.6.3, page 32), and fluorescence- activated cell sorting (FACS) was employed to isolate BFP positive (BFP+) cells. BFP+ cells were subsequently expanded and transiently transfected with a green fluorescent protein (GFP) and Cas9 expressing plasmid (Sigma). Cells co-expressing BFP and GFP (BFP+/GFP+) were isolated using FACS, expanded, and seeded in a limited dilution to create clonal populations for subsequent validation and experimentation. 3.6.1. Escherichia Coli Transformation and Plasmid Preparation To amplify plasmids employed in CRISPR/Cas9, Stellar Competent Escherichia coli cells (Clonetech) were transformed as per the manufacturers protocol. Briefly, ~5 ng of plasmid was added to 50 µL of competent cells in a 1.5 mL microcentrifuge tube and were placed on ice for ~30 min. Cells were subsequently heat shocked for 45 sec and returned to ice for ~2 min. Warmed (37 °C) Super Optimal broth with Catabolite repression (SOC) medium (Takaro Bio) was added to cells to a final volume of 500 µL and incubated at 37 °C with agitation for 1 h. A 1/100 dilution of transformed cells in SOC medium was prepared, and 100 µL was plated onto petri dishes containing Luria-Bertani (LB) agar and either 60 µg/mL carbenicillin or 50 µg/mL kanamycin (Appendix A) for positive selection of cells containing the sgRNA or Cas9 expression plasmids, respectively, and were incubated overnight at 37 °C.

3.6.2. Preparation of Lentiviral Particles All manipulations to generate lentiviral particles were conducted in a lentiviral-specific biological safety cabinet. To package sgRNA expression plasmids, the Lenti-X Packaging Single Shots system (Takara Bio) was employed as per the manufacturer protocol. Briefly, one 10 cm collagen-coated tissue culture plate (Corning) per sgRNA was seeded with 5.0 × 106 HEK 293T lentiviral packaging cells in 8 mL of appropriate medium (see Section 3.3, page 21) and maintained in an incubator at 37 °C with 5% CO2. Approximately 24 h post seeding, expression plasmids were complexed with Lenti-X Packaging Single Shots and HEK293T cells were transfected with lentiviral particles. Following transfection, cells were incubated for 4 h, at which point 6 mL of medium were added to the cells and were incubated for an additional 48 h. To test for the presence of lentivirus, a small aliquot (~20 μL) of supernatant was applied to a Lenti-X GoStix strip, incubated at room temperature for ~5 min and monitored for the presence of a band, indicating a positive signal, when sufficient lentivirus has been produced (> 5 × 105 infectious units/mL). The remaining supernatant was collected, passed through a 0.45 μm filter, combined in a 3:1 ratio of Lenti-X concentrator (Clontech) and incubated overnight at 4 °C. To create lentiviral stocks, samples were centrifuged at 1,500 × g for 45 min at 4 °C and subsequently resuspended in 1 mL of sterile 1× PBS and stored at -80 °C. 3.6.3. Lentiviral Transduction of CRISPR Guide RNAs Plasmids expressing BFP and FBXO7-targeting or NT-Control sgRNAs were delivered to A1309 cells with lentiviral transduction. Equivalent volumes of each FBXO7-targeting sgRNA lentiviral stock were combined prior to transduction. Cells were seeded (50,000 cells per well) in duplicate per sgRNA into wells of a 24-well tissue culture plate and incubated for 24 h, at which point cells were washed with 1× PBS. To virally transduce cells, FBXO7 and NT-Control lentiviral stocks were diluted in a 1:2 ratio with serum free X-medium of which 200 μL were added to cells/well. Cells and viral particles were incubated at 37 °C for 4 h, following which cells were supplemented with complete X-medium up to a total volume of 500 μL and incubated overnight at 37 °C. Following a 24 h incubation period, medium was removed from the wells, cells were washed 3 times with 1× PBS to ensure remaining lentivirus was removed and 500 μL of fresh complete medium was added. All reagents and plasticware employed during lentiviral manipulations were disposed of in appropriate waster containers filled with bleach. Once confluent, cells were expanded into 35-millimeter (mm) dishes and ultimately expanded to 10 cm

plates using the approaches detailed in Section 3.3.1 (page 22). Actively growing cultures from subconfluent (~80%) 10 cm plates were subsequently employed for FACS. 3.6.4. Fluorescence-Activated Cell Sorting FACS was performed by Monroe Chan in the Regenerative Medicine Flow Cytometry Facility or Christine Zhang in the Faculty of Medicine Flow Cytometry Core Facility at the University of Manitoba on a fee-for-service basis. Isolation of CRISPR/Cas9 edited cells involved FACS at two crucial steps: 1) following lentiviral transduction with FBXO7 or NT-Control sgRNA expression plasmids to isolate BFP+ cells; and 2) following transfection of BFP+ cells with Cas9 expression plasmids to isolate BFP+/GFP+ (i.e. sgRNA+/Cas9+) cells. Control cells for FACS included untransduced cells (i.e. BFP negative and GFP negative), untransduced cells with propidium iodide (PI; cell viability dye) and untransfected cells (i.e. BFP+ and GFP negative) to establish gating parameters for detection of BFP+/GFP+ cells. To prepare samples for FACS, cells were processed and counted as described in Section 3.3.2 (page 23). Following cell counting, the cell suspension was centrifuged again, the supernatant was aspirated, the cell pellet was resuspended in sorting buffer (Appendix A) and PI to a final cell density of ~5 million cells/mL and were transported to the FACS facility on ice. A bulk population of viable cells (PI negative) expressing the appropriate fluorescent proteins (BFP+ or BFP+/GFP+) were separated from remaining cells into a 15 mL conical tube with 2 mL of collection buffer (Appendix A). Cells and collection buffer were transferred to a 35 mm tissue culture plate and placed in a low-O2 chamber in a 37 °C incubator until confluent, at which point cells were expanded by passaging and re-seeding (Section 3.3.1, page 22) into a 10 cm plate. 3.6.5. Lipid-Mediated Transfection of the Cas9 Expression Plasmid Following isolation (FACS; described above) and expansion of BFP+ cells, GFP/Cas9 expression plasmids were delivered to cells by lipid-based transfection using Lipofectamine 2000 (Invitrogen) as per the manufacturer’s instructions. Briefly, BFP+ cells were grown to ~80% confluency, cell culture medium was aspirated and replaced with 7 mL of complete X-medium. To prepare the transfection mixture, 24 μg of plasmid was mixed with 1.5 mL of complete X- medium in a microcentrifuge tube. In a second microcentrifuge tube, 60 μL of Lipofectamine 2000 was added to 1.5 mL of complete X-medium and incubated for 5 min at RT. Next, the diluted DNA was combined with the diluted Lipofectamine 2000, mixed by inverting, and incubated for 20 min at RT. The transfection mixture was added to the cells dropwise and the cells were returned

to the low-O2 chamber and 37 °C incubator for ~24 h, at which point they were prepared for FACS as detailed in Section 3.6.4 (page 33) to isolate BFP+/GFP+ cells. 3.6.6. Clonal Expansion and Screening for FBXO7 Gene Edits To isolate individual CRISPR-edited clones from the BFP+/GFP+ bulk population, cells were seeded into a 96-well plate with conditioned X-medium at a density of one cell per well and plates were monitored to identify wells containing single colonies. Once wells were ~1/3 confluent, cells were trypsinized and transferred to 35 mm dishes containing complete medium with added penicillin and streptomycin antibiotics for downstream protein extraction. Proteins were extracted from each clone, and western blots were performed as detailed in Section 3.4 (page 25). Clones exhibiting decreased FBXO7 levels were selected for subsequent DNA extraction and sequencing. 3.6.7. Genomic DNA Extraction Genomic DNA was extracted using a DNeasy Blood & Tissue Kit (Qiagen). Briefly, confluent cells from a 10 cm dish were trypsinized and centrifuged as described in Section 3.3.1 (page 22). The supernatant was aspirated, and cells were resuspended in 200 μL of 1× PBS. DNA was extracted as per the manufacturer’s protocol, and DNA was eluted with 200 μL UltraPure distilled water (Gibco; Life Technologies). DNA concentrations and purities were determined using a Nano-Drop spectrophotometer (Thermo Scientific) and samples were stored at -20 °C. 3.6.8. Polymerase Chain Reaction Polymerase chain reaction (PCR) was employed to amplify exons 3 and 4 that are targeted by the FBXO7 sgRNAs. Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) was employed to design two pairs of PCR primers (one per exon) flanking each exon (Table 3-5). To ensure high-fidelity amplification, PhusionTM High-Fidelity DNA polymerase (Thermo Scientific) was employed with reactions prepared as per the manufacturer’s instructions using the reagents and volumes indicated in Table 3-6. PCR reaction mixture was dispensed into strip tubes and thermocycling was performed using the parameters indicated in Table 3-7. The resultant PCR products were stored at 4 °C.

Table 3-5. Primers Employed for PCR and DNA Sequencing.

A Exon Primer Sequence Tm Forward 5’-TGTTGGGGAGGTACTGGTGA-3’ 65 °C Exon 3 Reverse 5’-TACGGGCAACTTGAGAGCAG-3’ 65 °C Forward 5’-GATCAATCCCTGTTTCCATGATGC-3’ 65 °C Exon 4 Reverse 5’-TGCTGGCCTAAACAACTCAACA-3’ 66 °C A Tm, melting temperature

Table 3-6. Reagents and Volumes Used to PCR-Amplify Exons 3 and 4 of FBXO7. Volume per 50 μL Final Reagent Reaction Concentration 5x PhusionTM HF Buffer 10 μL 1× 10 nM dNTPs 1 μL 200 μM 10 μM Forward Primer 2.5 μL 0.5 μM 10 μM Reverse Primer 2.5 μL 0.5 μM 2 U/μL PhusionTM High-Fidelity DNA Polymerase 0.5 μL 0.02 U/μL Template DNA 100 ng 5 ng/ μL Nuclease-Free Water to 50 μL

Table 3-7. Thermocycling Conditions for PCR Amplification of Exons 3 and 4 of FBXO7. Step Temperature Time Number of Cycles Initial Denaturation 98˚C 30 sec 1 Denaturation 98˚C 10 sec Annealing 65˚C 30 sec 35 Extension 72˚C 30 sec Final Extension 72˚C 2 min 1 Hold 10˚C

3.6.9. Agarose Gel Electrophoresis Agarose gel electrophoresis was employed to confirm appropriate amplification of FBXO7 exons 3 and 4. A 1.0% agarose gel was prepared by combining 0.5 grams (g) agarose (Invitrogen) and 50 mL of 1× Tris-acetate-EDTA (TAE) buffer (Appendix A) and heating until the agarose was fully dissolved. Once agarose cooled slightly, 5 μL of SYBR Safe DNA Gel Stain (Thermo Scientific) was added and the agarose solution was poured into a cast to solidify. The gel was transferred to an electrophoresis tank containing 1× TAE buffer. Next, PCR products were mixed

in a 5:1 ratio with 6× DNA loading dye (Thermo Scientific) and 5 μL of each sample or a basepair (bp) ladder (O’GeneRuler 1 kb Plus DNA Ladder; Thermo Scientific) was added to each recipient well. Finally, the gel was electrophoresed for ~30 min at 100 V and visualized using a MyECL imager (Thermo Scientific) with ultraviolet light. 3.6.10. DNA Sequencing and Sequence Analyses DNA sequencing was employed to identify clones with CRISPR/Cas9-mediated edits in exons 3 or 4 of FBXO7 and was performed using the Sanger Sequencing service at Génome Québec (Montreal, Canada). PCR products and samples were prepared according to the facility’s submission guidelines. Sequencing was performed in the forward and reverse directions with the primers described in Table 3-5. To predict the size and location of the CRISPR/Cas9-induced mutation, chromatograms were uploaded to CRISP-ID (http://crispid.gbiomed.kuleuven.be), an online program that detects and resolves multiple alleles based on alignment to a reference DNA sequence (i.e. wild-type FBXO7). Clones were determined to be FBXO7+/- if two distinct alleles (i.e. wild-type and mutated) were detected following sequence alignment and the mutated allele was unlikely to produce mutant FBXO7 based on predictions by the bioinformatics tool ExPASy Translate (https://web.expasy.org/translate/). FBXO7-/- clones were identified if two distinct alleles were detected, neither of which were wild-type, nor predicted to produce a mutant protein product.

3.7. CIN TIMECOURSE EXPERIMENTS To assess the temporal dynamics of CIN within A1309 FBXO7+/- and FBXO7-/- clones, the CIN assays described in Section 3.5 (page 28) were performed at regular time intervals (i.e. every 4 passages [p], ~ every two weeks) over 2.5 months. Early passage populations (p0) of two FBXO7+/- clones (FBXO7+/-1 and FBXO7+/-2) and two FBXO7-/- clones (FBXO7-/-A and FBXO7-/- B) and a control (NT-Control) were employed in each experiment. Cells were seeded at 7,000 cells per well and 6 wells per condition in a 96-well plate for nuclear area and micronucleus formation analyses, while cells were seeded onto sterile coverslips at 60,000 cells per well for mitotic chromosome spread analyses. Cells were grown for 72 h and CIN phenotypes were assessed as described in Section 3.5 (page 28). A total of six timepoints were acquired for each clone and control.

3.8. CELLULAR TRANSFORMATION ASSAYS Cellular transformation assays, including proliferation, colony formation and clonogenic assays, were employed to assess the ability of FBXO7 knockout clones to exhibit phenotypes indicative of cellular transformation. 3.8.1. Nucleus Enumeration Proliferation Assay To assess proliferation rates, FBXO7 knockout clones and NT-Control cell lines were seeded into 96-well optical-bottom plates at a density of 700 cells per well and 6 wells per clone or control. Every day for one week, (i.e. seven timepoints) cells were fixed as described in Section 3.5.1 (page 29). A 4 × 4 matrix of non-overlapping 2D images were acquired from each well with the Cytation 3 microscope described in Section 3.5.2 (page 29). Gen5 software was employed to enumerate nuclei (i.e. cells) within each image and calculate the total number of nuclei imaged. Nuclear counts were normalized to the mean cell number at the first time point to account for discrepancies in seeding densities between conditions. Data were imported into Prism and proliferation curves were generated by graphing the mean of 6 normalized nuclear counts with error bars on a logarithmic scale and as a function of time. Cell doubling time was calculated with the following formula: [duration ∗ log(2)] ÷ [log(final normalized cell number) − log(initial normalized cell number)]:. 3.8.2. Clonogenic Assay To assess capacity for clonogenic growth, clones and controls were seeded sparsely (350 cells per well) into 6-well plates for 2D clonogenic assays. Cells were grown for 14 days, at which point they were fixed (4% paraformaldehyde) and stained with 0.005% crystal violet (Appendix A) for 15 minutes. Next, plates were scanned using a HP Officejet 4620 series scanner and images were imported into ImageJ for processing and analysis. Briefly, individual wells were identified, and colony number and size (≥ 100 µm in diameter) were automatically determined. Data were imported into Prism where graphs (bar graphs and dot plots) were generated. Images and figures were imported into Photoshop and assembled into figure panels. Clonogenic assays were performed twice. 3.8.3. Soft Agar Colony Formation Assay To assess anchorage independent growth, FBXO7 knockout clones were employed in soft agar colony formation assays. To prevent cells from adhering to the bottom of the 6-well plates, a 0.6% agar layer was prepared by combining 2× complete X-medium (Appendix A) in a 1:1 ratio with

sterile 1.2% agarose (Appendix A), of which 2 mL was subsequently dispensed into each well and left at RT to solidify. Cells were passaged (Section 3.3.1, page 22), counted (Section 3.3.2, page 23) and diluted in 2× complete X-medium to a density of 20,000 cells/mL. The cell suspension was added in a 1:1 ratio with 0.8% agarose (final concentration 0.4% agarose) (Appendix A) cooled to ~40 ºC and 2 mL was dispensed into each well containing the first layer of solidified agarose. Plates were cooled to RT and wells were supplemented with 2 mL of complete 1× liquid X-medium. Medium was refreshed once per week for a total of 4 weeks, at which point cells were fixed with 4% paraformaldehyde for 20 min and stained with 0.005% crystal violet (Appendix A) for 45 min. Colonies were quantified using the Cytation3 microscope equipped with a 4× objective lens. Briefly, an 8 × 8 image matrix was acquired per well, however, to image colonies located in multiple focal planes each matrix was collected as a z-stack with 11 optical sections. Gen5 image analysis software was employed to stitch together all 704 images (8 × 8 × 11) to generate a single z-projection from which microscopic colonies ≥ 100 μm in diameter were automatically enumerated.

3.9. STATISTICAL ANALYSES The number of biological replicates (N) and technical replicates (n) are indicated for all experiments presented in this thesis. For all experiments where N > 1, results from a single representative experiment are presented. All statistical analyses were performed using Prism. 3.9.1. Two-Sample Kolmogorov-Smirnov Tests The two sample KS test is a non-parametric test that compares the cumulative frequency distributions of two data sets (i.e. control vs. experimental) and calculates the probability that the two distributions would be as far apart as observed if they had been sampled from the same population (i.e. the p-value). In this study, p-values ≤ 0.05 are considered statistically significant, thereby indicating the distributions are unlikely to be as far apart as observed if they were sampled from the same populations or by random chance. 3.9.2. Mann-Whitney Tests The MW test is a non-parametric test that compares the rank orders of two datasets (i.e. control vs. experimental) and presents a p-value based on the difference of the mean ranks per condition. In this study, p-values ≤ 0.05 are considered statistically significant, indicating that the two mean

ranks are unlikely to be as far apart as observed if they were sampled from the same population or by random chance. 3.9.3. Student’s T-Test The Student’s t-test is a parametric test that compares the means of two unpaired groups (i.e. control vs. experimental), and presents a p-value based on the probability that the difference between the two means occurred by chance. In this study, p-values ≤ 0.05 are deemed to be statistically significant, suggesting the difference in means is unlikely to occur by chance and therefore the differences are statistically significant. Student’s t-tests were performed on data combined from three biological replicates (N = 3).

CHAPTER 4. RESULTS 4.1. Bio-informatic and Reverse Genetic Experiments Reveal Reduced FBXO7 Expression Induces CIN and Cellular Transformation in Colonic Cellular Contexts. CIN is characteristic of virtually all cancer types, is proposed to be an early etiological event driving oncogenesis and tumour evolution, and is associated with poor patient outcomes40,56,67- 69,75,76,147; however, the aberrant genes causing CIN remain largely unknown. This study aims to determine the clinical relevance and impact reduced FBXO7 expression has on CIN and CRC development. As such, bioinformatic approaches were employed to identify: 1) the prevalence of FBXO7 alterations in cancer; 2) the effects of reduced FBXO7 expression on overall survival in CRC patients (Section 4.2, page 41); and 3) the concomitant impacts of FBXO7, KRAS, TP53, and APC alterations in patient samples and cellular models (Section 4.3.4, page 51). To gain a comprehensive understanding of the impacts hypomorphic (i.e. reduced) FBXO7 expression has on CIN, cellular transformation and CRC pathogenesis, both short-term siRNA- based approaches (Sections 4.3.1 - 4.3.4, pages 43 - 54) and long-term CRISPR/Cas9 gene knockout models (Sections 4.4.1 - 4.4.3, pages 55 - 64) were employed. Gene silencing was performed in HCT116, a CRC cell line, and three immortalized, non-malignant colonic epithelial cell lines, namely 1CT, RPA (a 1CT derivative with mutant KRAS, and decreased TP53 and APC expression) and A1309 (an RPA derivative expressing a truncated APC protein) which are detailed in Materials and Methods (Section 3.3, Table 3-1, page 22). While HCT116 cells are a well- established model for investigating CIN70,89,92,148,149, 1CT and derivatives are ideal models in which to assess the impact diminished FBXO7 expression has in early disease development as they incorporate specific gene alterations that model those occuring early in CRC pathogenesis. In general, QuantIM approaches are employed to compare three CIN-associated phenotypes, namely changes in nuclear areas, increases in micronucleus formation, and increases in chromosome gains and/or losses that are statistically compared between experimental and control conditions. In Aim 1, short-term siRNA-based assays identified FBXO7 as a CIN in gene colonic cellular contexts, while in vitro models and patient samples highlighted the exacerbated effects of reduced FBXO7 expression with simultaneous KRAS, TP53, and APC alterations. Further, the on- going and dynamic nature of CIN warrants investigation in long-term studies employing stable FBXO7 knockout models. To determine the long-term effects diminished FBXO7 expression has on CIN and cellular transformation, CRISPR/Cas9 approaches were employed to generate

heterozygous (FBXO7+/-) and homozygous FBXO7 (FBXO7-/-) knockout clones in A1309 cells as they exhibited the greatest impact in CIN in Aim 1 and best represent the genetic background of early CRC. In Aim 2, FBXO7+/- and FBXO7-/- clones were employed in CIN and cellular transformation assays (i.e. proliferation, clonogenicity, anchorage independent growth), and revealed on-going CIN dynamics and increases in cellular transformation phenotypes. Jointly, Aims 1 and 2, and complementary bioinformatics approaches, provide an essential and profound understanding of the implications hypomorphic FBXO7 expression has on CIN in CRC development. 4.2. FBXO7 Copy Number Losses Occur Frequently in Cancer and are Associated with Worse Overall Survival in Colorectal Cancer Patients. To determine the potential clinical impact FBXO7 copy number losses may have in cancer, TCGA data from 10 common cancer types were assessed. As shown in Figure 4-1A, FBXO7 is deleted, amplified or mutated in a variety of cancer types, ranging from < 10% in leukemias to ~80% of ovarian cancers. Interestingly, heterozygous losses (shallow deletions) are commonly observed in 9 of 10 cancers evaluated, including ~30% of CRCs142,143, suggesting FBXO7 loss may be an oncogenic event. Next, to assess whether reduced FBXO7 expression impacts patient outcomes, KM curves comparing CRC patients with FBXO7 heterozygous loss to patients with diploid FBXO7 were generated and assessed for changes in overall survival (Figure 4-1B)142,143. Similar KM curves were generated for patients expressing decreased FBXO7 mRNA vs. normal mRNA expression (Figure 4-1C). Log-rank tests determined heterozygous FBXO7 loss and decreased mRNA expression are associated with significantly worse overall survival compared to their appropriate counterparts. Collectively, these data support the possibility that hypomorphic FBXO7 expression and/or function may be significant drivers of CRC pathogenesis. While these data identify an association between reduced FBXO7 expression and worse outcome, it is now critical to determine the functional and mechanistic impact reduced FBXO7 expression has in disease development, with a particular focus on CIN and cellular transformation.

Figure 4-1. FBXO7 Alterations are Frequent and Associated with Worse Overall Survival in Colorectal Cancer. (A) Bar graph presenting the frequencies of FBXO7 alterations (i.e. homozygous (deep) deletion, heterozygous (shallow) deletion, gene copy number gain, amplification, mutation and multiple alterations) in 10 cancer types142,143. Total numbers of samples assessed for each cancer type are indicated in the x-axis labels. Note ~30% of CRC cases harbour heterozygous FBXO7 loss142,143. (B) KM curve identifies statistically worse overall survival for CRC patients with FBXO7 copy number losses relative to patients with diploid copy numbers142,143. (Log-rank test; p-value ≤ 0.05). (C) KM curve reveals CRC patients with reduced FBXO7 mRNA expression have statistically worse overall survival than patients with normal mRNA expression142,143. (Log-rank test; p-value ≤ 0.05).

4.3. AIM 1: To Determine the Short-Term Impact Reduced FBXO7 Expression has on CIN. 4.3.1. FBXO7 can be Efficiently Silenced in HCT116, 1CT, RPA and A1309 Cells. The silencing efficiencies of four individual siRNAs (siFBXO7-1, -2, -3, -4) and pooled siRNAs (comprised of the four individual siRNAs; siFBXO7-P) targeting unique regions within the FBXO7 coding sequence were determined relative to the non-targeting control (siControl) by western blot. Semi-quantitative analyses were performed by normalizing the signal intensity (i.e. level of expression) of each FBXO7-silenced condition to its respective loading control (α-tubulin or cyclophilin B) and normalized values are presented relative to siControl, which is set to 100%. As shown in Figure 4-2A, siFBXO7-2, -3 and -4 exhibited similar silencing efficiencies, however, observation of siFBXO7-3 silenced cells under the microscope revealed high levels of cell death in HCT116 cells. As such, the two most efficient silencing siRNAs (siFBXO7-2, -4) along with siFBXO7-P (employed to address potential off-target effects) were utilized in all subsequent experiments. Prior to assessing silencing efficiencies in 1CT, RPA and A1309, untransfected proteins from each cell line were blotted to quantify TP53 and APC abundance. Recall, RPA and A1309 harbour altered KRAS, TP53 and APC expression including expression of mutant KRAS and truncated APC proteins. Figure 4-2B confirms RPA and A1309 cells express ~50% of endogenous TP53 compared to 1CT, while full length APC abundance was reduced to ~49% in RPA and ~29% in A1309 and truncated APC protein was only observed in A1309 cells. KRAS abundance was not assessed due to the lack of appropriate antibodies to detect the mutant protein. Following characterization of 1CT and derivative cell lines, silencing efficiencies of siFBXO7-2, -4 and -P were determined, and showed FBXO7 abundance was reduced to ~0%-17% of endogenous levels in the three non-malignant cell lines employed (Figure 4-2C).

Figure 4-2. FBXO7 is Efficiently Silenced in Colonic Epithelial Cells. (A) Semi-quantitative western blot showing decreased FBXO7 expression relative to siControl in HCT116 cells following transfection with four individual (siFBXO7-1, -2, -3, -4) and pooled (siFBXO7-P) siRNAs. FBXO7 expression is normalized to the α-tubulin loading control and is presented relative to siControl, which is set to 100%. SiFBXO7-2 and -4 are the two most efficient individual siRNA duplexes. (N = 2). (B) Semi-quantitative western blots of untransfected proteins showing diminished TP53 (left) and APC (right) expression in RPA and A1309 cells relative to 1CT, which is set to 100%. TP53 and full-length APC expression are normalized to Cyclophilin B. Note that the truncated APC (~145 kDa) is observed only in A1309 cells. (N = 1). (C) Semi- quantitative western blots showing reduced FBXO7 expression following silencing 1CT (left), RPA (middle) and A1309 (left). Cells were treated with siFBXO7-2, -4 and siFBXO7-P siRNA duplexes. FBXO7 expression is normalized to the respective loading control (Cyclophilin B) and presented relative to siControl. (N = 3).

4.3.2. Reduced FBXO7 Expression Induces Increases in CIN Phenotypes in HCT116 Cells. To determine the potential impact reduced FBXO7 expression has on CIN, transient siRNA- based silencing was first performed in HCT116 and changes in CIN-associated phenotypes, including nuclear areas and micronucleus formation were assessed using QuantIM (see Section 3.5, page 28). Initial qualitative assessment of nuclear areas following FBXO7 silencing showed visual increases in nuclear areas and nuclear area heterogeneity (Figure 4-3A) relative to siControl. Subsequent statistical comparisons of cumulative nuclear area frequency distributions indicated significant increases (two-sample KS test; p-value ≤ 0.05; Table S1) in nuclear area distributions (i.e. rightward shift) in the FBXO7 silenced conditions relative to siControl (Figure 4-3B). Additionally, the nuclear area distributions (x-axis) from FBXO7 silenced conditions span a broader range relative to siControl indicating increased heterogeneity. Subsequent micronucleus formation analyses revealed a 2.8- (siFBXO7-2), 7.6- (siFBXO7-4) and 4.7- (siFBXO7-P) fold

increase in micronuclei following silencing relative to siControl that were statistically significant (MW test; p-value ≤ 0.05) (Figure 4-3C and D, Table S2). The nuclear area and micronucleus formation data presented above show reduced FBXO7 expression induces CIN-associated phenotypes that collectively imply FBXO7 is a novel CIN gene in HCT116; however, neither assay directly assesses chromosome numbers. To determine whether reduced FBXO7 expression also adversely impacts chromosome numbers (i.e. induces chromosome gains and/or losses) mitotic chromosome spreads were generated, enumerated, and evaluated for numerical aberrations compared to the modal number (45) for HCT116, including chromosome losses (< 45), small-scale gains (46-59) and large-scale gains (≥ 60) (Figure 4-4). Chromosome enumeration revealed statistically significant differences (two-sample KS test, p-value ≤ 0.05) in cumulative chromosome number distribution frequencies in FBXO7-silenced conditions relative to siControl (Table S3). More specifically, FBXO7 silencing induced a 2.1- to 2.3-fold increase in the frequencies of spreads with aberrant chromosome numbers that were determined to be significant in each FBXO7-silenced condition (Student’s t-test, p-value ≤ 0.05). Collectively, the nuclear area, micronucleus formation and mitotic chromosome spread analyses identify FBXO7 as a novel CIN gene in HCT116 cells, suggesting reduced expression may have pathogenic implications for CRC.

Figure 4-3. FBXO7 Silencing Corresponds with Increases in CIN-Associated Phenotypes in HCT116 Cells. (A) Low-resolution images of Hoechst-stained nuclei showing visual increases in nuclear areas and cell-to-cell heterogeneity following FBXO7 silencing relative to siControl. (B) Graph showing statistically significant increases (rightward shift) in cumulative nuclear area distribution frequencies following FBXO7 silencing relative to siControl (two-sample KS test; na, not applicable; ** p-value < 0.01; **** p-value < 0.0001). (N = 3, n = 6). (C) High-resolution image of Hoechst-stained nucleus and a micronucleus (arrowhead). (D) Dot plot identifying significant increases in micronucleus frequencies following FBXO7 silencing relative to siControl. The median values are indicated by red bars, while the fold increase in the medians are presented below the statistical information. (MW test; ** p-value < 0.01). (N = 3, n = 6).

Figure 4-4. Reduced FBXO7 Expression Induces Significant Differences in Chromosome Number Distributions in HCT116 Cells. (A) Representative high-resolution images of DAPI-stained mitotic chromosome spreads displaying the modal number of 45 chromosomes (siControl), loss (< 45), small-scale gain (46- 59), and large-scale gain (≥ 60) in HCT116 cells. Chromosome numbers (N =) are indicated in the top right. (B) Cumulative chromosome number distribution graph reveals significant changes following FBXO7 silencing relative to siControl (two-sample KS test; na, not applicable; * p-value ≤ 0.05; *** p-value < 0.001; **** p-value < 0.0001). (N = 3, n > 100 spreads/condition). (C) Dot plot presenting the frequencies of chromosome spreads with aberrant chromosome numbers following FBXO7 silencing. The fold increase relative to siControl are indicated below the statistical information and are significant relative to siControl. (Student’s t-test; * p-value ≤ 0.05; ** p-value < 0.01). (N = 3, n > 100 spreads/condition).

4.3.3. Hypomorphic FBXO7 Expression Drives Increases in CIN Phenotypes in 1CT, RPA and A1309 Cells. To begin to determine the potential impact reduced FBXO7 expression may have in early CRC development, similar siRNA-based assays to those performed above were conducted in non- malignant, non-transformed colonic epithelial cells (i.e. 1CT and derivatives). Qualitative assessment of nuclei in siControl and FBXO7 silenced cells showed increases in nuclear areas in 1CT, RPA, and A1309 cells (Figure 4-5 left). Statistical analyses identified trending increases (two-sample KS test) in cumulative nuclear area distributions that are significant for siFBXO7-4 and -P in 1CT cells, while all three FBXO7-silenced conditions are significant relative to controls in RPA and A1309 cells (Figure 4-5 middle, Table S4). Note, that in general, the total nuclear area distributions from FBXO7-silenced conditions were larger than siControl and indicative of increases in nuclear area heterogeneity, which is an expected outcome associated with CIN. Micronucleus formation analyses in 1CT showed trending increases in frequencies that were significant (MW test) within the siFBXO7-P condition (~1.4-fold greater than siControl; Figure 4-5A right, Table S5). RPA cells showed significant increases in micronucleus frequency in siFBXO7-2 and -4 that were ~1.6-fold greater than siControl, while siFBXO7-2 (~1.8-fold increase) and -P (~2.4-fold increase) were significant in A1309 (MW test; Figure 4-5B and C). To assess changes in chromosome numbers, mitotic chromosome spreads were generated, enumerated, and compared to the modal number (46) for 1CT and the derivatives. Significant differences in chromosome number distributions were observed within siFBXO7-P in RPA cells, while distributions were not significantly different in 1CT and A1309 cells (Figure 4-6, Table S6). However, statistical comparison of aberrant spreads from three biological replicates in 1CT revealed a 1.1- to 2.9-fold increase in frequencies of spreads with aberrant chromosome numbers that were significant in siFBXO7-P (Student’s t-test, Table S7). RPA showed a 1.4- to 1.7-fold increase in chromosome gains and/or losses that were statistically significant in siFBXO7-P, while A1309 revealed a 1.3- to 2.6-fold increase that were significant in siFBXO7-4 and -P (Student’s t- test;). Taken together, these findings demonstrate that diminished FBXO7 expression induces CIN phenotypes, and thus, FBXO7 is a CIN gene in non-malignant colonic epithelial cells and may have important pathogenic implications for CRC development.

Figure 4-5. Reduced FBXO7 Expression Corresponds with Increases in CIN-Associated Phenotypes in Non-Malignant Colonic Epithelial Cells. (A) Low-resolution images (left) of Hoechst-stained nuclei from 1CT cells showing visual increases in nuclear areas following siFBXO7-P silencing (bottom) relative to siControl (top). Cumulative distribution frequency graph (middle) and dot plot (right) reveal trending and statistically significant increases in nuclear area distributions (two-sample KS test) and micronucleus formation (MW test) following FBXO7 silencing relative to siControl in 1CT cells. Fold increases in mean micronucleus frequencies are presented below the statistical information. A minimum of 600 nuclei were analyzed per condition and the red bars indicate the medians. Similar data are presented for RPA (B) and A1309 (C) cells. (na, not applicable; ns, not significant p-value > 0.05; * p-value ≤ 0.05; ** p-value < 0.01; **** p-value < 0.0001). (N = 3; n = 6).

Figure 4-6. Decreased FBXO7 Expression is Induces Increases in Aberrant Chromosome Numbers. (A) Cumulative distribution frequency graph (left) comparing chromosome number distributions following FBXO7 silencing relative to siControl in 1CT cells (two-sample KS test). Dot plots (right) presenting the frequency of aberrant chromosome numbers following silencing with Student’s t-tests identify statistically significant differences. Fold increases in aberrant spreads are presented below the statistical information. The red bars indicate the medians. Similar panels are presented for RPA (B) and A1309 (C) cells. (na, not applicable; ns, not significant p-value > 0.05; * p-value ≤ 0.05; **** p-value < 0.0001). (N = 3, n > 100 spreads/condition).

4.3.4. Reduced FBXO7 Expression Synergizes with Altered Expression of Key Colorectal Cancer Driving Genes. Altered KRAS, TP53, and APC expression is associated with early CRC development (detailed previously in Section 1.2.1, page 5). To gain additional insight into the potential role decreased FBXO7 expression has in early disease development, the potential synergistic effects of hypomorphic FBXO7 expression with altered KRAS, TP53, and APC expression with were compared as described in Section 3.5.2 (page 29). Briefly, for each biological replicate, nuclear areas from all FBXO7-silenced conditions (siFBXO7-2, -4, and -P) were combined and normalized to the mean nuclear area of siControl. Next, the normalized values from each biological replicate were combined into a single dataset for the three cell lines (1CT, RPA and A1309) and cumulative nuclear area distribution frequencies from each cell line were generated and statistically compared. The two-sample KS tests revealed significant increases in overall nuclear area distributions for RPA and A1309 relative to 1CT cells (Figure 4-7A, Table S8), and for A1309 relative to RPA. Micronucleus frequencies were normalized similarly to nuclear area data as described above and statistically compared using the MW test. In agreement with the nuclear area findings, normalized micronucleus frequencies exhibited statistically significant increases for RPA and A1309 relative to 1CT (Figure 4-7B, Table S9); however, unlike the nuclear areas, no significant difference was observed between RPA and A1309 cells. Furthermore, highly pronounced increases in the overall micronucleus formation distribution range (i.e. increases in heterogeneity) occurred within A1309, whereas the overall distributions were similar between 1CT and RPA. Collectively, these data indicate that CIN-associated phenotypes become more pronounced following FBXO7 silencing in cells harbouring additional genetic alterations (RPA and A1309) and are particularly enhanced within A1309 cells which harbour truncated APC expression. To determine whether exacerbated phenotypes are clinically relevant, I compared survival of CRC patients exhibiting altered expression of FBXO7 and/or, KRAS, TP53 and APC within TCGA datasets. As described in Section 3.1.1 (page 20), OQL terms were employed to identify patients harbouring decreased FBXO7 mRNA expression, while the remaining genes of interest (KRAS, TP53, and APC) were individually queried with OQL commands to identify patients harbouring putative driver alterations (i.e. mutations or copy number alterations; Figure 4-8A). The patient sample alteration status was based on the data from the original TCGA PanCancer Atlas publications where the samples were first described. For KRAS, TP53, and APC individually, the

Overlap option in the Comparison/Survival tab was employed to identify patients within three distinct groups based on their alteration status including: 1) decreased FBXO7 mRNA with concomitant KRAS (or TP53 or APC) driver alterations (i.e. dual-altered); 2) KRAS (or TP53 or APC) driver alterations; and 3) decreased FBXO7 mRNA. All statistical comparisons (Log-rank test) are made relative to the dual-altered condition. Figure 4-8B shows that patients harbouring decreased FBXO7 mRNA exhibit trends towards decreased overall survival, while KRAS altered patients exhibit trends towards increased overall survival compared to patients with simultaneous FBXO7 and KRAS alterations, but neither reached significance. KM curves comparing TP53 altered patients revealed that patients with TP53 mutations alone exhibit significantly improved overall survival compared to dual-altered patients while patients with decreased FBXO7 mRNA alone did not show significant differences (Figure 4-8C). Survival curves from patients harbouring APC driving alterations showed similar trends to those observed following comparisons of TP53 altered patients (Figure 4-8D). Collectively, these data indicate that decreased FBXO7 mRNA synergizes with KRAS, TP53 and APC alterations and has negative clinical implications in CRC patients, which is in line with in vitro data showing exacerbated CIN phenotypes in RPA and A1309 cells. Consequently, decreased FBXO7 expression may be an important etiological event in CRC pathogenesis that promotes CIN and may drive development of aggressive tumours leading to worse overall survival in CRC patients.

Figure 4-7. FBXO7 Silencing Enhances CIN-Associated Phenotypes in KRAS, TP53, and APC Altered Cells. (A) Cumulative distribution frequency graph showing normalized nuclear areas in 1CT, RPA and A1309. The two-sample KS test determined increases in RPA and A1309 are significant relative to 1CT, and in A1309 relative to RPA. (**** p-value < 0.0001). (N = 3, n = 18). (B) Violin plots reveal significant increases in micronucleus formation in RPA and A1309 cells relative to 1CT. Dashed lines indicate the 25th and 75th percentiles, while the median is indicated by the solid line. (MW test; ns, not significant p-value > 0.05; * p-value ≤ 0.5; ** p-value < 0.001). (N = 3, n = 18).

Figure 4-8. CRC Patient Tumours Harbouring Decreased FBXO7 mRNA Expression with Simultaneous Putative Driver Alterations in Critical CRC Driving Genes are Associated with Worse Overall Survival. (A) Bar graphs identifying putative driving alterations in KRAS, TP53, and APC in CRC patient samples (n = 526). (B) KM curves comparing overall survival between CRC patients harbouring decreased FBXO7 mRNA expression, putative KRAS driving alterations as determined by panel (A) and patients altered for each gene. Log-rank tests do not indicate significant changes in overall survival in patients with FBXO7 or KRAS alterations alone relative to those with simultaneous FBXO7 and KRAS alterations. Similar KM curves are presented for TP53 (C) and APC (D) in which significant decreases in overall survival in patients with simultaneous FBXO7 and TP53 (or APC) were identified compared to patients with TP53 or APC alterations alone. (Log-rank test; p-value ≤ 0.05; na, not applicable) (KRAS n = 309; TP53 n = 398; APC n = 439).

4.4. AIM 2: To Determine the Long-Term Impact Reduced FBXO7 Expression has on CIN and Cellular Transformation. 4.4.1. Generation and Validation of FBXO7+/- and FBXO7-/- Clones A1309 Cells CIN is a dynamic phenotype proposed to drive important etiological events implicated in CRC pathogenesis. To determine the long-term effects diminished FBXO7 expression has on CIN and cellular transformation, CRISPR/Cas9 approaches were employed to generate FBXO7+/- and FBXO7-/- knockout clones in A1309 cells. A1309 cells harbour genetic alterations associated with CRC development while also exhibiting the largest responses in Aim 1, thus are excellent models to investigate early CRC pathogenesis. Moreover, FBXO7+/- knockout models are clinically relevant as ~30% of CRC patients harbour FBXO7 heterozygous loss, while FBXO7-/- clones are ideal genetic models to assess FBXO7 loss of function. Accordingly, FBXO7 knockout models were generated and employed in time course and cellular transformation assays. FBXO7+/-, FBXO7-/- and control (NT-Control) models were generated as described in Materials and Methods (Section 3.6, page 31). Briefly, A1309 cells were transduced with lentiviral vectors containing either sgRNAs targeting FBXO7 exon 3 or 4 or non-targeting (NT-Control) sgRNAs that all co-express BFP. BFP+ cells were subjected to FACS and subsequently transfected with a Cas9 expression plasmid co-expressing GFP. Finally, BFP+/GFP+ cells were isolated by FACS and limited dilutions were used to generate clonal populations. Clones were screened for reduced FBXO7 expression by semi-quantitative western blot analyses and those with reduced expression relative to NT-Control were selected for subsequent DNA sequencing analyses. PCR and DNA gel electrophoresis were employed to amplify and confirm successful amplification of the targeted regions, respectively. PCR products were subjected to bidirectional Sanger sequencing at Génome Québec (Montreal, Canada), with results analysed by CRISP-ID to reveal the size and location of CRISPR/Cas9-mediated edits. Using this approach, two heterozygous (FBXO7+/-1 and FBXO7+/-2) and two homozygous (FBXO7-/-A and FBXO7-/-B) knockout clones were generated. Briefly, FBXO7+/-1 and FBXO7+/-2 harbour a single bp insertion and deletion, respectively, while FBXO7-/-A harbours a 2 bp deletion in allele 1, and a 1 bp deletion in allele two, and FBXO7-/-B possesses a 28 bp in allele 1 and 2 bp deletion in allele 2 (Figure 4-9A). Induced mutations in all clones are suspected to induce premature stop codons (Figure 4-9B) and subsequent mRNA degradation. Western blot analysis revealed FBXO7+/-1 and FBXO7+/-2 retain ~40% expression, while FBXO7-/-A and FBXO7-/-B do not express FBXO7, as expected (Figure 4-9C). Overall, these

data indicate that A1309 FBXO7+/-1, FBXO7+/-2, FBXO7-/-A and FBXO7-/-B are excellent models to investigate the impact heterozygous and homozygous loss have on CIN and cellular transformation.

Figure 4-9. Generation and DNA Sequence Validation of FBXO7+/- and FBXO7-/- Clones in A1309 Cells. (A) DNA sequencing results for FBXO7 exon 3 in CRISPR/Cas9 generated NT-Control cells (Reference; NM_012179) and FBXO7 knockout models. Individual alleles with corresponding edits (yellow highlight) for each FBXO7+/- (FBXO7+/-1; FBXO7+/-2) and FBXO7-/- (FBXO7-/-A; FBXO7-/-B) clone are presented. (B) Protein sequences in the reference (NP_036311) and hypothetical proteins encoded by edited alleles in FBXO7+/- and FBXO7-/- clones. Aberrant amino acids are indicated with yellow highlight, while * identifies a premature stop codon expected to target the corresponding mRNA for nonsense mediated decay and abrogate protein translation. (C) Semi-quantitative western blot presenting reduced FBXO7 levels in FBXO7+/- and FBXO7-/- clones relative to NT-Control. Relative expression for each clone is presented above each lane. Cyclophilin B serves as a loading control.

4.4.2. FBXO7+/- and FBXO7-/- Cells Exhibit Dynamic CIN Phenotypes. To determine the long-term impact heterozygous and homozygous FBXO7 loss has on CIN, nuclear area, micronucleus formation and chromosome enumeration analyses were performed at regular intervals (i.e. every four passages [p], or ~every two weeks) over a 10-week time period and were compared with NT-Control. CIN assays revealed dynamic and heterogeneous phenotypes from p0 to p20. Specifically, FBXO7+/-1 exhibited significant increases in cumulative nuclear area distributions at p0, p4 and p12, but ultimately exhibited smaller (leftward shift) distributions at p16 and p20 compared to NT-Control (Figure 4-10A; Table S10). FBXO7+/-2 showed similar trends to FBXO7+/-1 but exhibited even larger increases in nuclear area distributions from p4 to p16 and only exhibited a smaller distribution at p20. While FBXO7-/-A and FBXO7-/-B initially (p0 to p4) exhibited opposing and significant cumulative nuclear area distributions (i.e. smaller in FBXO7-/-A and larger in FBXO7-/-B relative to NT-Control at p0, vice versa at p4), their distinct evolutionary patterns converged from p8 to p20. Nuclear area distributions in FBXO7-/-A and FBXO7-/-B did not exhibit significant differences relative to NT- Control at p8 but increased at p12 until finally exhibiting significantly smaller distributions from p16 to p20. Ultimately, the FBXO7+/- clones, particularly FBXO7+/-2, exhibited the most dynamic nuclear area distributions from p0 to p20. Subsequent micronucleus formation analysis revealed trending increases in FBXO7+/-1 and FBXO7+/-2 relative to NT-Control at p0 (Figure 4-10B; Table S11). The remaining timepoints, except p12 where significant increases were observed, showed FBXO7+/-1 exhibited decreases in micronucleus frequencies. Contrastingly, FBXO7+/-2 exhibited increases in micronucleus formation at all time points, except p20, that were deemed significant from p4 to p12. FBXO7-/-A and FBXO7-/-B exhibited increases at p0, p8, and p12 that were significant in FBXO7-/-A at p12. Again, FBXO7+/-2 exhibited the greatest increases in micronucleus frequency, while FBXO7+/-1 and the FBXO7-/- clones frequently exhibited similar micronucleus frequencies. The dynamic nuclear area distributions and micronucleus formation are in agreement with what is expected in populations exhibiting CIN. Finally, to specifically assess chromosome number dynamics, a minimum of 100 mitotic chromosome spreads were assessed in each clone from p0 to p20. In agreement with the nuclear area and micronucleus data, chromosome gains and losses were dynamic over the 10-week time course (Figure 4-11). Initially, FBXO7+/-1 and FBXO7+/-2 revealed a 4.5-fold (~95% of spreads) and 3.3-fold (~70% of spreads) increase, respectively, in the frequency of chromosome gains and

losses compared to in NT-Control (~21% of spreads) at p0. While the fold increases in aberrant spreads in the FBXO7+/- clones declined at p4 (3.0-fold in FBXO7+/-1 and 2.9-fold in FBXO7+/-2) and p8 (2.0-fold in FBXO7+/-1 and 1.3-fold in FBXO7+/-2), chromosome gains and losses increased to 3.4 (FBXO7+/-1) and 2.0 (FBXO7+/-2) at p12, before decreasing again at p16 (2.1-fold in FBXO7+/-1 and 2.7-fold in FBXO7+/-2) and p20 (1.8-fold in FBXO7+/-1 and 1.4-fold in FBXO7+/- 2). FBXO7-/-A and FBXO7-/-B exhibited dynamic, but fewer, chromosome number changes from p0 to p20 compared to FBXO7+/- models. For example, FBXO7-/-A and FBXO7-/-B rarely (i.e. 2 out of 6 timepoints) exhibited frequencies > 2.0-fold compared to NT-Control, but exhibited fold increases as large as 2.6 and 2.7 in FBXO7-/-A and FBXO7-/-B, respectively at p12, and as low as 1.4 (FBXO7-/-A) and 1.3 (FBXO7-/-B) at p20 . Interestingly, by p20 both FBXO7+/- and FBXO7-/- clones exhibited similar frequencies of chromosome aberrations that ranged from ~1.3 to 1.8-fold higher than NT-Control. Collectively, these data demonstrate that stable FBXO7 heterozygous and homozygous loss drives dynamic changes in CIN-associated phenotypes that may be an important for early etiological events, such as cellular transformation.

Figure 4-10. CIN-Associated Phenotypes are Dynamic in FBXO7+/- and FBXO7-/- Clones. (A) Cumulative nuclear area distributions reveal dynamic and significant changes in cumulative nuclear area distribution frequencies in FBXO7+/- and FBXO7-/- clones over ~10 weeks (p0 to p20) relative to NT-Control (two-sample KS test; na, not applicable; ns, not significant p-value > 0.05; * p-value ≤ 0.05; *** p-value < 0.001; **** p-value < 0.0001). (n = ≥ 600 cells/condition/nuclei). (B) Dot plots presenting the frequency of micronuclei in FBXO7+/- and FBXO7-/- clones from p0 to p20 relative to NT-Control. MW tests identify significant increases at p4, p8, p12 and p20. (MW test; ns, not significant p-value > 0.05; * p-value ≤ 0.05; ** p-value < 0.01). (N = 1; n = 6).

Figure 4-11. Reduced FBXO7 Expression Induces Dynamic Changes in Chromosome Numbers. Bar graphs presenting the frequencies of chromosome losses (< 46; white), small-scale gains (47- 59; grey) and large-scale gains (≥ 60; black) in FBXO7+/- and FBXO7-/- clones over time. The fold increases in total aberrant chromosome numbers relative to the corresponding NT-Control are presented above each bar. (n = > 100 spreads/condition/timepoint).

4.4.3. Evaluating Cellular Transformation in FBXO7+/- and FBXO7-/- Clones. As CIN has been shown to induce cellular transformation71,72 and FBXO7 is a novel CIN gene, we sought to determine the impact reduced FBXO7 expression has on cellular transformation, FBXO7+/- and FBXO7-/- clones were assessed for the following three parameters: 1) cellular growth/proliferation; 2) clonogenicity; and 3) anchorage independent growth. To assess cellular growth/proliferation (Section 3.8.1, page 37), cells were seeded into 96-well plates and allowed to grow. At set intervals (every 24h for 7 days) cells were fixed, counterstained with Hoechst, imaged and the number of nuclei was quantified for each clone/control from which growth rates were determined. As shown in Figure 4-12, FBXO7+/-and FBXO7-/- clones exhibit variable proliferation rates, where FBXO7+/-1 exhibits a similar doubling time (17.6 h; 0.99-fold change in doubling

time) to NT-Control (17.5 h), while FBXO7+/-2 proliferates slower (20.7 h; 1.2-fold increase). Similarly, FBXO7-/- clones also exhibit variable, but opposing changes in proliferation rates, where FBXO7-/-A exhibits a faster doubling time of 16.8 h (0.96-fold change), while FBXO7-/-B exhibits a slower doubling time of 18.8 h (1.1-fold increase) compared to NT-Control. Next, clonogenic growth capacity was assessed in 2D colony formation assays as described in Materials and Methods (Section 3.8.2, page 37). As shown in Figure 4-13, FBXO7+/- and FBXO7-/- knockout models exhibited increased clonogenic potential relative to NT-Control as determined by increases in colony numbers and sizes. More specifically, FBXO7+/-1 and FBXO7+/-2 exhibited a 4.8- and 14.3-fold increase in colony number relative to NT-Control (4 colonies), respectively, with median colony sizes of 0.16 mm2 (FBXO7+/-1) and 0.13 mm2 (FBXO7+/-2) vs. 0.03 mm2 for NT-Control. Surprisingly, FBXO7-/-A and FBXO7-/-B exhibit milder phenotypes relative to the FBXO7+/- clones with a 4.0- (FBXO7-/-A) and 2.8-fold (FBXO7-/-B) increase in colony numbers and median nuclear areas of 0.05 mm2 and 0.09 mm2, respectively. Finally, soft agar 3D colony formation assays (detailed in Section 3.8.3, page 37) were employed to assess anchorage-independent growth in the FBXO7 knockout and NT-Control clones. Briefly, cells were seeded in a layer of agar to prevent attachment to the plate bottom and were permitted to proliferate for ~4 weeks, at which point cells were fixed, stained (crystal violet), imaged, and analyzed with HCT116 cells serving as a positive control (Figure 4-14). Quantification revealed a 2.0-fold increase in mean number of FBXO7+/-1 colonies (118 colonies) and a median colony size of 0.015 mm2 relative to NT-Control (60.5 colonies, median size = 0.012 mm2), whereas FBXO7+/-2 generated an average of 3.5 colonies and exhibited a smaller median size (0.010 mm2). FBXO7-/-A produced a ~3.5-fold increase in mean colony numbers (~210.5 colonies) with a median size of 0.014 mm2, while FBXO7-/-B produced and average of 21 colonies with a median size of 0.011 mm2. Combined, CIN and cellular transformation assays identify progressive changes in CIN phenotypes in FBXO7+/- and FBXO7-/- knockout cells that ultimately drive cellular transformation and may have important etiological implications in CRC pathogenesis.

Figure 4-12. Hypomorphic FBXO7 Expression Variably Effects Proliferation Rates. Graph presenting the logarithmic growth curves of FBXO7+/- and FBXO7-/- clones over 7 days. Cell numbers were normalized to the mean cell number on Day 1 and data points show the mean cell number  standard deviation (SD). Doubling time was calculated using the following formula: doubling time = [duration ∗ log(2)] ÷ [log(final normalized cell number) − log(initial normalized cell number)]. (N = 2, n = 6).

Figure 4-13. Reduced FBXO7 Abundance is Associated with Increases in Clonogenic Growth. (A) Representative low-resolution images of 2D colony formation two-weeks post-seeding in duplicate wells. Cells were stained with crystal violet for visualization. (B) Bar graph presenting the mean ( SD) number of colonies (≥ 100 µm in diameter), with the fold increase relative to NT- Control presented above each bar. (C) Dot plot showing increases in colony sizes in FBXO7+/- and FBXO7-/- clones compared to the NT-Control. Fold increases in mean size are indicated at the top of each column, and the red bars identify median values. (N = 2, n = 2).

Figure 4-14. Reduced FBXO7 Expression Alters Anchorage Independent Growth. (A) Representative low-resolution images of three-dimensional colony formation four weeks post- seeding in duplicate wells (top and middle rows) in agarose. Cells were fixed, and stained with crystal violet for visualization. Black bounding boxes highlight magnified regions (bottom row). (B) Bar graph presenting the mean ( SD) number of colonies (≥ 100 µm in diameter). The fold increases in mean colony number relative to NT-Control are presented above each bar. (C) Dot plot presenting increases in colony sizes in FBXO7+/- and FBXO7-/- clones compared to NT- Control. Fold increases in mean size are indicated at the top of each column and the red bars identify the median values. (N = 2, n = 2).

CHAPTER 5: SUMMARY, CONCLUSIONS AND DISCUSSION 5.1. SUMMARY AND CONCLUSIONS Within this project I employed two complementary methods to assess the effects reduced FBXO7 expression has on CIN and its potential impacts on CRC pathogenesis. In Aim 1, siRNA- based gene silencing diminished endogenous FBXO7 levels in four colonic epithelial cell lines, whereas in Aim 2, CRISPR/Cas9 was employed to generate stable FBXO7+/- and FBXO7-/- knockout models (Figure 5-1). In each in vitro model, hypomorphic FBXO7 expression was associated with increases in CIN-associated phenotypes, including nuclear areas, micronucleus formation and mitotic chromosome spreads harbouring aberrant chromosome numbers, while FBXO7+/- and FBXO7-/- clones exhibited increases in phenotypes associated with cellular transformation. Though many in vitro models employed within this study exhibited similar FBXO7 expression levels (e.g. ~0% in siFBXO7-2 and siFBXO7-P in RPA, ~40% in FBXO7+/-1 vs. FBXO7+/-2), CIN assays revealed heterogeneous responses between silencing conditions, cell lines, and knockout models. This is expected as CIN drives cell-to-cell genomic heterogeneity and is expected to induce diverse outcomes under different conditions and cell lines as the continuous changes in copy number (gene dosage) likely influence the activity of the various pathways inducing CIN150,151.

Figure 5-1. FBXO7 was Identified as a Novel CIN Gene That Drives Cellular Transformation in Complementary Short- and Long-Term Assays in Colonic Epithelial Cells. Schematic summarizing the complementary methods employed to validate FBXO7 as a novel CIN gene and assess cellular transformation. SiRNA and CRISPR/Cas9 protocols were employed to generate in vitro models of reduced FBXO7 expression in colonic epithelial cells. Subsequent QuantIM CIN assays revealed increases in three CIN-associated phenotypes including nuclear areas, micronucleus formation and chromosome gains and/or losses, while cellular transformation assays revealed variable changes in proliferation rates and increases in clonogenic and anchorage independent growth. KO = knockout.

CIN is defined as an increase in the rate of chromosome gains and losses and as such, is expected that nuclear area and chromosome enumeration analyses would reveal reciprocal increases and decreases in each phenotype compared to the control. Interestingly, silencing experiments unequivocally identified increases in cumulative nuclear area distributions, whereas chromosome enumeration showed higher frequencies of both gains and losses. FBXO7 knockout models exhibited similar discrepancies where trends observed in the nuclear area analyses did not correspond to those observed following chromosome enumeration. This phenomenon may be explained by biological reasons and/or inherent differences between the assays employed. For example, large-scale chromosome gains may be more conducive to cell viability than large-scale chromosome losses. In fact, FBXO7 depleted cells frequently exhibited near tetraploid karyotypes, and as large as near octoploid, whereas karyotypes harbouring fewer than 30 chromosomes (i.e. ~33% loss of the total genome) were never observed following FBXO7 silencing. It has been shown in cervical cancer and glioblastoma cell lines that large-scale chromosome losses are less viable152, therefore, it is likely that the tolerance for chromosome gains is higher than for chromosome losses and cells exhibiting large chromosome losses are lost from the analyses. Furthermore, changes in nuclear areas have been associated with gross DNA content changes70,96-98, while the impact of gains or losses of one or two chromosomes on nuclear areas is unknown. As such, cells exhibiting changes in few chromosomes may go undetected in nuclear area analyses but would be identified during chromosome enumeration. Lastly, the populations analyzed at the experimental end point of nuclear area and chromosome spread analyses differ due to inherent differences between the two assays. Nuclear area analyses are conducted on asynchronous populations where > 90% of cells are in interphase, and cells under-going mitosis are not analysed due to the breakdown of the nuclear membrane, while chromosome spread analyses only evaluate cells capable of entering mitosis. Due to the on-going genomic changes induced by CIN, it cannot be assumed that every cell within a population is equally capable of entering and being arrested in mitosis following Colcemid treatment. Therefore, each assay provides insight into a specific, and likely different, population of cells and highlights the importance of employing complementary approaches to identify CIN genes. Ultimately, the QuantIM approaches employed in this study identified FBXO7 as a novel bona fide CIN gene in precursor and CRC cells.

While the transient silencing experiments provided essential fundamental insight into the impacts diminished FBXO7 expression has on CIN and early CRC development, FBXO7 knockout models are conducive to long-term CIN studies and investigation of cellular transformation. Importantly, the CIN assays were employed synchronously at regular time intervals (i.e. ~every two weeks), and revealed distinct and on-going evolution between the FBXO7+/- and FBXO7-/- clones. Moreover, direct comparison of each knockout model to each other revealed that all clones evolved differently despite initial syngeneic backgrounds. Recall, CIN drives heterogeneous responses; therefore, the development of the distinct evolutionary patterns observed in in vitro models is expected and likely more representative of CRC as disease in two independent patients, or even distinct tumours within the same patient, are not expected to progress identically153. Additionally, CIN and cellular transformation assays revealed more extreme phenotypes in the FBXO7+/- knockout clones, compared to FBXO7-/- models and were lessened over time. The trends observed throughout the CIN assays may be attributed to the residual FBXO7 present in the FBXO7+/- models. Conceptually, in FBXO7+/- clones the SCFFBXO7 complex continues to regulate substrate degradation, though presumably at half the capacity of diploid cells; therefore, the decreased proteolytic rate drives a slow accumulation of substrates that may not be immediately perceptible to the cell. As such, mechanisms necessary for compensation of reduced FBXO7 abundance (i.e. functional compensation by other F-box proteins, suppressor mutations) are not activated, consequently enabling extreme CIN levels to develop until a critical threshold is reached (p0), at which point the cells exhibiting extreme CIN are lost and those exhibiting intermediate CIN continue to proliferate. In the FBXO7-/- models, the SCFFBXO7 complex substrate accumulation likely occurs twice as fast as FBXO7+/- models, as such compensatory mechanisms preventing extreme CIN are likely activated sooner. Reviews summarizing the current understanding of F- box proteins and their substrates identified many common substrates between F-box proteins125,126. As such, one of the remaining 68 F-box proteins may functionally compensation for FBXO7 loss of function by recognizing and regulating SCFFBXO7 complex substrates and effectively minimize CIN levels to maintain cellular viability and drive karyotypic evolution. Additionally, the acquisition of suppressor mutations (i.e. a second mutation that masks the phenotypic effects of FBXO7 loss) may have occurred due to off-target Cas9 endonuclease activity154,155 or as an early CIN-induced mutational event in which a second, more prevalent CIN gene was mutated and effectively obscures phenotypes caused by FBXO7 loss. Furthermore, suppressor mutations may

occur as a response to selective pressures156-158 (i.e. FBXO7 loss). This sort of genetic compensation has been observed in many model systems (i.e. human cell lines, yeast, mice, flies) as a mechanism to compensate for loss of function genotypes which may involve upregulation of redundant genes or downregulation of genes involved in the same pathways157,158. However, this study investigated 16 in vitro models with reduced FBXO7 expression during short- and long-term experiments and revealed similar increases in CIN phenotypes in all models, thereby supporting the hypothesis that decreased FBXO7 expression drives CIN. Lastly, the exacerbated cellular transformation phenotypes (clonogenicity and anchorage independent growth) in FBXO7+/- clones is likely the result of increased CIN levels observed in these clones. On-going CIN drives evolution of karyotypes that afford cells the necessary advantages to become transformed40,54,55,70; therefore, FBXO7+/- clones have a higher likelihood becoming transformed as they exhibit greater CIN. As such, these data suggest reduced FBXO7 loss has important pathogenic implications in driving CRC development which is consistent with the recorded frequency of FBXO7 heterozygous loss in CRC patient samples and worse overall survival for patients with heterozygous loss or decreased mRNA expression142,143 (see Section 4.2, page 41). While CIN assays provide preliminary insight into early etiological events driving CRC development, in vivo approaches that better represent the normal colonic physiology and microenvironment (discussed further in Section 5.2.2, page 76) will be critical to further elucidate the clinical relevance of diminished FBXO7 expression in CRC. In any case, this work provides novel insight into the molecular determinants driving CIN in malignant and non-malignant colonic epithelial cell contexts and establishes a foundation from which future studies will determine the mechanism(s) by which reduced FBXO7 expression induces CIN and cellular transformation. 5.1.1. FBXO7 Loss is an Important Etiological Event in Colorectal Cancer Development. Despite the prevalence of CIN, little is known about the aberrant genes and mechanisms driving CIN. Moreover, current research into the substrates and pathways regulated by FBXO7 is primarily focused on understanding juvenile Parkinson’s disease, which is not relevant to cancer. As such, our understanding of the role FBXO7 has in cancer, and specifically CRC, is lacking. Accordingly, studies focused on identifying and characterizing the molecular origins of CIN, such as this thesis, deepen our understanding of CRC pathogenesis. The limited research investigating FBXO7 in cancer has identified oncogenic and tumour suppressive roles for FBXO7, however, the

data here within identify FBXO7 a tumour suppressor in a CRC context, and consequently, its loss is an important etiological event in CRC development. Section 4.2 (Figure 4-1, page 42) revealed FBXO7 is preferentially lost in CRC, however, these data refute earlier work by Laman et al.159 in which immunohistochemical analysis revealed high FBXO7 abundance in CRC patient samples (n = 40) compared to normal colonic samples (n = 4) and identifies an oncogenic role for FBXO7. However, the authors did not present data validating the FBXO7 antibody specificity, therefore, it cannot be confirmed that FBXO7 is being identified. Additionally, survival analyses (Figure 4-1, page 40) determined FBXO7 loss is associated with reduced survival and thus is likely an important event contributing to CRC initiation. To further support these findings, CIN assays reveal FBXO7 is a novel CIN gene and identifies FBXO7 as a tumour suppressor in CRC. CIN assays in 1CT revealed the weakest phenotypes among the four cell lines employed in this study, while these data demonstrate reduced FBXO7 alone is sufficient to induce CIN and contribute to CRC development, diminished FBXO7 expression is exacerbated upon loss of additional genes associated with CRC development (i.e. KRAS, TP53 and APC). While similar trends were observed in KM curves in Figure 4-8, there are limitations associated with this analysis. While each KM curve focuses on the effects of diminished FBXO7 mRNA with a specific CRC associated gene (i.e. KRAS, TP53, and APC), the patient samples within the KRAS, TP53, and APC altered groups may harbour alterations in more than one CRC associated gene. As such, the survival trends observed may be due to overlapping patient groups (i.e. patients with simultaneous APC and TP53 alterations) that are included in two or more of the survival analyses. The TCGA PanCancer Atlas CRC dataset only contains 594 patient samples, consequently, generation of patient groups in which all FBXO7, KRAS, TP53, and APC alterations are mutually exclusive is problematic as many groups become too small to analyze meaningfully. More patient samples must be analyzed to gain a better understanding of the clinical relevance of the synergy between reduced FBXO7 expression and KRAS, TP53, and APC alterations; regardless, the bioinformatic approaches and CIN assays discussed above highly support diminished FBXO7 expression as a driver of CRC pathogenesis. Temporal analyses involving both heterozygous and homozygous FBXO7 knockout models determined that CIN is an on-going and dynamic process that is conducive to generation of advantageous karyotypes necessary for cellular transformation. Interestingly, proliferation assays

revealed differential changes (i.e. decreases, no change, and increases) in proliferation rates in FBXO7+/- and FBXO7-/- clones. While Meziane et al.160 demonstrated that FBXO7 knockdown by short hairpin RNAs increased proliferation rates in human and mouse B cells, more recent work by Patel et al.161 indicated reduced FBXO7 expression has opposing effects on T lymphocyte proliferation rates dependent on differentiation stage. As such, it has been noted that FBXO7 activity has highly tissue- and cell-specific effects, which may account for discrepancies between some current literature and the proliferation assays herein130,161,162. Alternatively, the protocol described in Section 3.8.1 (page 37) may require further optimization to generate a more extensive dataset from which proliferation rates can be determined. In the McManus laboratory, traditionally, proliferation rates are measured by Real-Time Cell Analysis in which cells are seeded in a microplate and the electrical impedance of the entire well is measured multiple times a day for a set number of days. Consequently, numerous timepoints are generated to determine proliferation rates that are based on the entire cellular population within the microplate. Due to the specific growth (low oxygen) requirements for A1309 cells, proliferation rates could not be assessed using Real-Time Cell Analysis. The protocol described in Section 3.8.1 (page 37), indicates that seven plates of cell are seeded on day 0 following which cells in one plate are fixed and stained per day and a 4 × 4 matrix of non-overlapping images is obtained. Consequently, compared to Real-Time Cell Analysis, there are fewer data points to generate the proliferation curves, and the curves are based on a subset of cells within the well. As such, optimized protocols and instrumentation that enables continuous measurement of live cells may provide better insight into the effects of diminished FBXO7 expression on proliferation rates. It is worth noting that while proliferation assays produced ambiguous results, this study only provides a short snapshot (~10 weeks) into CRC development which typically occurs over 15-20 years14,26, as such, if the FBXO7+/- and FBXO7-/- clones were permitted to grow longer, they may have evolved to acquire faster growth rates or any other phenotypes associated with cellular transformation. Subsequent clonogenic and soft-agar colony formation assays revealed increases in colony number and size in FBXO7+/- and FBXO7-/- clones, suggesting increased survival, replicative potential and anchorage independent growth which are critical phenotypes for oncogenesis. Notably, however, FBXO7+/-2 and FBXO7-/-B revealed discordant results in the soft- agar assays where they did not exhibit increased colony numbers or sizes, though these results may be attributed to the decrease in proliferation rates observed in these two clones. Additionally, recall

that CIN drives development of a heterogeneous cellular population and transformed cells may account for a small proportion of the overall cellular population. Consequently, transformed cells may be inadvertently excluded or seeded at low density during the seeding process, which is particularly relevant in proliferation assays that employ population averaging approaches that may unintentionally mask increased proliferation rates of transformed cells. Despite the limitations noted above, cellular transformation assays identified increases in essential phenotypes underlying CRC development. Relative to other novel CIN genes (e.g. SKP1 and CUL1), FBXO7+/- and FBXO7-/- cells exhibit comparatively low CIN levels. To illustrate, long-term chromosome enumeration assays in heterozygous SKP1 and RBX1 knockout cells revealed sustained high CIN levels (i.e. ~50-93%) over 10-12 weeks93,94, and while ~70-95% of chromosome spreads in FBXO7+/- and FBXO7-/- clones exhibited aberrant chromosome numbers at p0, frequencies of gains and/or losses decrease to ~40% by p20. Arguably, CIN genes exhibiting greater increases in CIN phenotypes are more likely to drive CRC development and aggressive disease; however, many cancer types have been associated with the “CIN paradox”78 in which cancers exhibiting extreme/high CIN are associated with improved patient outcomes, likely due to increased tumour cell death, whereas cancers with intermediate CIN levels are associated with worse outcomes77,163. Moreover, low and intermediate CIN levels are posited to drive disease evolution81-87. Roylance and colleagues163 employed fluorescent in situ hybridization probes (chromosomes 2 and 15) to develop four Modal Centromere Deviation (MCD) groups based on the mean frequency of nuclei deviating from the modal centromere number (MCD1 [0 - 15%], MCD2 [15 - 30%], MCD3 [30 - 45%], MCD4 [> 45%]). This method identified MCD4 tumours as those exhibiting extreme CIN and the greatest clonal diversity. Interestingly, MCD4 tumours were associated with improved patient outcomes relative to lower MCD groups (MCD 1-3). If MCD groups are employed in the context of the current study, chromosome enumeration data (Figure 4-11, page 60) indicate reduced FBXO7 expression underlies development intermediate CIN populations and consequently, may drive disease development and evolution, and account for the worse overall survival (Figure 4-1, page 42) observed in CRC patients. Collectively, the data show reduced FBXO7 expression induces CIN that drives cell-to-cell and genomic heterogeneity, induces cellular transformation, and is associated with worse overall survival in CRC patients. Hence, bioinformatics approaches, and complementary siRNA-based

assays and CRISPR/Cas9 knockout models generated a holistic view of the role reduced FBXO7 has in CIN and shed novel insight into the early etiological events driving CRC pathogenesis. 5.2. FUTURE DIRECTIONS The culmination of this work identifies FBXO7 as a novel CIN gene that induces cellular transformation and may be a pathogenic driver of CRC pathogenesis. While these findings are a step towards expanding our fundamental understanding of the aberrant genes underlying CIN with potential implications in disease development and progression, they stimulate additional experiments that are essential to gain mechanistic insight, firmly establish clinical causality and ultimately improve outcomes for CRC patients through the development of innovative precision medicine therapeutic strategies. 5.2.1. Determining the Mechanisms by which Reduced FBXO7 Expression Induces CIN 5.2.1.a. Identifying Substrates Polyubiquitinated by the SCFFBXO7 Complex FBXO7 is a key protein in many pathways essential for normal cellular processes (see Section 1.3, page 12); however, the role FBXO7 has in the SCF complex is of particular interest as other SCF complex members were recently identified as novel CIN genes that when lost, induced accumulation of SCF complex substrates, thereby highlighting potential mechanisms by which aberrant SCF complex function drives CIN and cellular transformation92-94. Identification of misregulated protein targets following reduced FBXO7 expression will similarly shed light on the underlying pathways driving CIN and may identify actionable targets for novel therapeutic strategies. To date, very few SCFFBXO7 complex protein substrates targeted for degradation have been identified. HURP encodes a cell cycle regulatory protein and is frequently overexpressed in many cancer types including hepatocellular138, breast164 and bladder165 and is involved in M phase progression through control of the spindle assembly and organization. Additionally, HURP has been associated with induction of aneuploidy and genome instability166-168. Hsu et al.169 identified HURP as the first protein polyubiquitinated and targeted for degradation by the SCFFBXO7 complex. Immunoprecipitation assays identified interactions between FBXO7 and HURP, while western blot analysis demonstrated increased HURP expression following FBXO7 silencing, suggesting SCFFBXO7 mediates HURP degradation. Recently, Spagnol et al.170 identified UXT- (Ubiquitously Expressed Transcript) V2 (isoform 2), as a novel target for polyubiquitination and proteolytic degradation by the SCFFBXO7 complex in a proteome-wide screen. UXT has been

implicated in prostate171 and adrenal172 cancers, however, pathogenesis was associated with both UXT loss and overexpression in each cancer type, respectively. UXT is best known as a regulator of NF-кB signalling173, a frequently aberrantly regulated pathway in cancers174. However, the impacts of UXT-V2, specifically, cancer is relatively unknown. Using liquid chromatography tandem mass spectrometry, Lee et al.147 identified the SCFFBXO7 complex as the 5th most abundant SCF complex, out of 69 potential complexes, in cultured HEK293 cells. For comparison, the SKP2 (S-Phase Kinase Associated Protein 2)-bound SCF complex was identified as the most abundant SCF complex147,175 and has ~40 known substrates to date125. In this regard, it is conceivable that the SCFFBXO7 complex may have a similar number of protein targets due to its abundance in human cells. As such and despite recent efforts, it is likely that many protein targets degraded by the SCFFBXO7 complex remain unknown. Identifying FBXO7 binding partners is essential to advance our understanding of CRC pathogenesis and enable the exploitation of aberrantly regulated pathways. Assays employing FBXO7 overexpressing cells or the FBXO7+/- and FBXO7-/- clones to identify polyubiquitinated substrates (i.e. substrates targeted for degradation) are a logical next step to identify targets normally regulated by the SCFFBXO7 complex and is discussed further below. However, identification of polyubiquitinated proteins may pose a challenge due to their rapid degradation or enzymatic deubiquitination. Trypsin-resistant tandem ubiquitin-binding entities (TR-TUBEs) were recently developed to protect and investigate substrate bound ubiquitin chains176-179. TR-TUBEs are composed of a string of 4 to 8 Ubiquitin Associated (UBA) domains, in which the alanine residues are converted to arginine to inhibit trypsin activity, and various selectable markers (e.g. glutathione S-transferase for antibody labelling, or biotin for streptavidin bead pull-downs). As such, ubiquitin-bound UBA domains (TR-TUBEs) protect polyubiquitin chains from deubiquitination and trypsin activity and may be isolated using selectable markers. TR-TUBEs are amenable to many molecular biology techniques and have been employed to identify polyubiquitinated substrates targeted by the FBXO21-bound SCF complex (SCFFBXO21), TR-TUBE expression plasmids were transiently introduced to FBXO21-expressing and FBXO21 mutant (i.e. lacking the F-box domain) HEK293T cells176. Enrichment methods for TR- TUBE/polyubiquitinated substrate complexes were coupled with mass spectrometry and identified SCFFBXO21 complex targets as those exhibiting increased concentration in wild-type FBXO21- expressing cells and decreased in mutant FBXO21-expressing cells. Similar protocols could be

employed in FBXO7 knockout and overexpression models as the two extremes in expression levels should further highlight differences in substrate abundance. While the aberrant regulation of canonical SCF complex function (i.e. polyubiquitinating protein substrates proteolytic degradation) is a promising mechanism to pursue based on novel findings regarding other SCF complex members (SKP1, CUL1, RBX1), it remains possible that the mechanisms inducing CIN do not involve polyubiquitinated substrates. Recall, the SCFFBXO7 complex also regulates substrates through monoubiquitin linkages, and FBXO7 is involved in other cellular pathways outside the SCF complex. 5.2.1.b. Going Beyond the SCF Complex - Identifying Novel FBXO7 Substrates and Underlying Mechanisms Though F-box proteins frequently target substrate polyubiquitination for proteolytic degradation, FBXO7 is unique amongst F-box proteins as many of its established protein targets do not undergo degradation (e.g. CDK6 [Cyclin dependent kinase 6]130,159, NRAGE [Neurotrophin Receptor-Interacting Melanoma Antigen Gene]180, cIAP134). The SCFFBXO7 complex also modulates protein localization and function through various ubiquitin linkage types (see Section 1.3.3, page 15). Additionally, FBXO7 is implicated in many cellular processes beyond the SCF complex and are detailed further in Section 1.3.1 (page 14). Consequently, the mechanisms by which reduced FBXO7 expression induces CIN may lie outside the aberrant degradation of protein targets, or even beyond the SCF complex entirely. While TR-TUBEs are employed to protect and isolate ubiquitinated substrates for downstream analyses, recent work suggests they are inefficient at binding monoubiquitinated substrates178. As such, TR-TUBEs are inadequate for identifying SCFFBXO7 complex substrates modified by monoubiquitin linkages and would not identify FBXO7 binding partners not regulated by ubiquitin. To identify FBXO7 protein interactors, a proteome-wide assay is crucial. Teixeira et al.132 and Spagnol et al.170 employed a human protein microarray with > 9000 full-length non-denatured proteins and identified GSK3β, TOMM20 and UXT-V2 as putative interactors. However, these interactions occurred under highly artificial contexts and may not accurately reflect native interactions. As such, assays performed in endogenous settings are essential to gain a better understanding of the FBXO7 interactome. Proximity labeling can be employed to identify novel FBXO7 interactors by engineering a fusion protein with FBXO7 and a promiscuous biotin ligase (BioID181 or TurboID182) that biotinylates proteins within a few nanometers of the fusion protein.

Biotinylated targets can be isolated with streptavidin beads for downstream analyses (e.g. mass spectrometry, western blot). However, in this context, a limitation of proximity labelling is the identification of polyubiquitin-regulated substrates within the FBXO7 interactome. Consequently, complementary analyses (e.g. western blot) must be conducted to specifically identify substrates not regulated by polyubiquitination. Ultimately, following identification of protein interactors, specific functional assays can be employed to determine the exact mechanisms by which reduced FBXO7 expression induces CIN, and thereby improves our fundamental understanding of CIN and CRC pathogenesis. 5.2.2. Evaluating the Tumorigenic Potential of FBXO7+/- Knockout Clones The current data indicates heterozygous FBXO7 loss is clinically relevant, induces CIN and promotes cellular transformation in colonic epithelial cell lines, which is in agreement with reduced FBXO7 expression being a pathogenic event in CRC development. Additional research must be performed to fully elucidate the consequences of FBXO7 copy number loss in tumorigenesis. The FBXO7 knockout clones generated in Aim 2 harbour mutations associated with the CRC adenoma to carcinoma sequence (Section 1.2.1, page 5) hypothesized to underlie colorectal oncogenesis and therefore are excellent models to investigate early disease development. However, alternative culturing methods to those employed in this study are required to accurately represent the normal colonic tissue structure and microenvironment. The normal colon topology is highly organized and complex with diverse microbiota. The importance of the tumour microenvironment in cancer development and progression is clear53,183 and it is conceivable that certain influences such as inflammation may influence clonal selection and expansion184,185. To gain a more comprehensive understanding of the effects of reduced FBXO7 expression on tumorigenesis, employing FBXO7+/-, FBXO7-/- and NT-Control clones in adequate tissue mimicking models and models promoting proliferation of select advantageous subclones are the next logical steps. Colorectal organoids are ideal models for investigating mechanisms underlying cancer development as they enable incorporation of factors such as tissue topology, and cell signaling that may impact cellular pathways and drive evolution of karyotypes enabling oncogenesis. A few groups have recently developed organoid culturing protocols to research colonic cells in healthy and disease contexts. Briefly, cells (e.g. FBXO7+/-, FBXO7-/- and NT-Control clones) are permitted to self-organize into 3D aggregates resembling the colon structurally, functionally and

molecularly186-188. Organoid cultures are highly modifiable (e.g. cellular genetic background, extra-cellular matrix, culture medium) and therefore are amenable to recapitulation of many in vivo environments and enable unprecedented insight into cancer development, progression and treatment response at the level of the tissue. However, recent evidence has indicated that long-term culturing may change genetic features of organoids and drive genome instability189. Consequently, organoid models may not be suitable for long-term CIN and cellular transformation assays as it would be difficult to discern whether genetic changes are attributed to diminished FBXO7 expression or an effect of long-term culturing procedures. Therefore, 3D organoids are amenable to short time CIN-assays, but further optimization must be conducted prior to their use in long- term studies. While 3D culturing methods may better represent tissue biology, in vivo mouse models incorporate the dynamics of cell signaling and microenvironment at the organismal level, as such, mouse models remain essential experimental models to advance cancer research. Heterotopic mouse models, such as those derived from subcutaneous flank injections are widely employed in cancer research190,191. While heterotopic models are advantageous due to high sample throughput that is attributed to easy accessibility to injection sites and measuring tumour size, heterotopic models are not accurate representations of the normal colonic environment, and often exhibit atypical metastatic behaviour. As such, orthotopic mouse models enable cells to be introduced by various means directly into the caecum or descending colon and were developed to address the limitations of heterotopic models190,191. FBXO7+/- and FBXO7-/- clones may be introduced to immunodeficient heterotopic or orthotopic mouse models to observed and measure tumour formation in whole organisms and specifically the normal colonic environment. However, mouse models are certainly associated with limitations, including morbidity due to invasiveness of procedures, need for specialized facilities and personnel, and negative public perception of live animals for research purposes190,191. Regardless, complementary 3D in vitro and in vivo models are essential next steps to elucidate the impacts heterozygous and homozygous FBXO7 loss has in CRC tumorigenesis. 5.2.3. Discovery of Novel Therapeutic Strategies The association between heterozygous FBXO7 loss or decreased mRNA expression with worse overall survival in CRC patients suggests reduced FBXO7 expression drives CRC development and perhaps has a role in the development of drug resistance. Moreover, the capacity

to exhibit extreme CIN (discussed in Section 5.1.1, page 69) in FBXO7 depleted cells suggest the deficiency may be an actionable target for CIN-inducing therapies. Additionally, CIN genes are excellent targets for synthetic lethal therapeutic approaches to induce cell death. Therefore, FBXO7+/-, FBXO7-/- and NT-Control clones will be instrumental to identify novel therapeutic strategies exploiting reduced FBXO7 expression. As described in Sections 1.2.3 (page 8) and 5.1.1 (page 69) above, tumours exhibiting extreme CIN are associated with improved patient prognosis, and novel research employing CIN-inducing therapies to exploit this paradox is promising, though is only beginning to be explored84-87. Tumours exhibiting FBXO7 loss may be excellent candidates for such therapeutic strategies as while extreme CIN is possible in FBXO7+/- clones as is demonstrated following chromosome enumeration at p0 and p12, extreme CIN does not appear to be favourable as the population repeatedly returns to intermediate CIN levels. Drugs inhibiting proteins involved in mechanisms commonly associated with CIN (e.g. microtubule assembly, disassembly, and kinetochore attachment) have shown increased cell death in drug resistant cancer cell lines192, and primary patient tumours86,193. Clinically relevant FBXO7+/- and NT-Control clones can be employed in large-scale, high-throughput QuantIM drug screens investigating the impacts of CIN-inducing drugs on cell viability where drugs inducing increased cell death in FBXO7+/- clones, but not in NT-Control cells indicated selective drug targeting that may specifically target cancer cells exhibiting reduced FBXO7 expression and warrant further investigation in future in vivo studies. Another therapeutic strategy worth pursuing in FBXO7 depleted cells is a synthetic lethal approach that exploits the aberrant genetic background of cancer cells to induce highly specific killing. Synthetic lethality occurs when two independently viable genetic alterations occur simultaneously in a single cell and induce cell death87. Synthetic lethal strategies have proven to be effective in BRCA (Breast Cancer Gene) deficient breast and ovarian cancers targeted by PARP (poly ADP-ribose polymerase 1) inhibitors. Clinically relevant FBXO7+/- knockout and NT- Control clones can be employed in large-scale synthetic lethal screens to identify potential interactors that may be exploited in novel therapeutic strategies. Synthetic lethal partners are commonly found within the same cellular pathways, as such, it is important to assess the synthetic lethal potential of other members involved in the SCF complex pathway. If a synthetic lethal interactor is identified, it may be exploited as a druggable (i.e. targeted by chemical inhibitors) target in CRCs with heterozygous FBXO7 loss to induce selective killing of cancer cells. The

pursuit to gain better insight into the susceptibilities of FBXO7 depleted cells is essential to improve the morbidity and mortality rates associated with FBXO7 deleted CRCs. Indeed, this project is an exciting first step towards improving our understanding CIN and CRC pathogenesis, and ultimately highlights many promising future research arms.

5.3. SIGNIFICANCE In 2020, ~26,900 Canadians were diagnosed with CRC, while ~9,700 succumbed to the disease1. While the association between CIN and CRC has been established for more than 20 years57, there is still much work to be done to better define the aberrant genes and pathways underlying CIN and driving CRC pathogenesis. The work presented in this thesis is an important step in bridging the gap in our fundamental understanding of CIN and the role it plays in cellular transformation and CRC development. I have identified FBXO7 as a novel CIN gene in colonic epithelial cell contexts by demonstrating that reduced FBXO7 expression induces dynamic changes in CIN phenotypes and contributes to cellular transformation. Identifying and characterizing the genes responsible for CIN is important to better understand early CRC development. Discerning the early etiological events promoting CRC development is crucial for developing precision medicine strategies that exploit genes, like FBXO7, to ultimately improve the lives and outcomes of those living with CRC. Finally, as FBXO7 copy number losses are frequent in many cancer types, my findings have potential relevance beyond the CRC contexts studied in this thesis.

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APPENDIX A: SOLUTIONS CELL CULTURE 1× McCoy’s 5A Complete Culture Medium + 10% FBS Name Amount McCoy’s 5A Medium (Hyclone) 450.0 mL FBS (Sigma-Aldrich) 50.0 mL Total Volume 500.0 mL

1× X Medium + 2% Cosmic Calf Serum Name Amount Dulbecco’s Modified Eagle Medium with High Glucose (Hyclone) 800.0 mL Medium 199 (Hyclone) 200.0 mL EGF (PeproTech; 20 ng/mL) 20.0 μL Insulin (Sigma; 10 μg/mL) 5.0 mL Apo-transferrin (Sigma; 2 μg/mL) 40 μL Hydrocortisone (Sigma; 1 μg/mL) 50 μL Sodium selenite (Sigma; 5 nM) 1.0 μL Cosmic Calf Serum (Hyclone) 20.0 mL Total Volume ~1.0 L - Combine first seven ingredients - Remove 20 mL before adding Cosmic Calf Serum

1× DMEM High Glucose Culture Medium + 10% Tetracycline-Free FBS Name Amount DMEM High Glucose Medium (HyClone) 450.0 mL Tetracycline-Free FBS (Clontech) 50.0 mL Total Volume 500.0 mL

Cupric Sulfate Pentahydrate Name Amount Cupric Sulfate Pentahydrate 26.0 g Milli-Q Water Up to 1.0 L Total Volume 1.0 L

10× Phosphate-Buffered Saline (PBS; Stock Solution) Name Amount NaCl 80.0 g KCl 3.0 g Na2HPO4 14.4 g KH2PO4 2.4 g Milli-Q Water Up to 1.0 L Total Volume 1 L - Titrate to pH 7.4

1× PBS Name Amount 10× PBS (Stock Solution) 100.0 mL Milli-Q Water 900.0 mL Total Volume 1.0 L

GENE SILENCING 1× siRNA Buffer Name Amount 5× siRNA Buffer (Dharmacon) 100.0 μL DEPC-treated Water 400.0 μL Total Volume 500.0 μL

WESTERN BLOT Modified Radioimmunoprecipitation Assay (RIPA) Buffer Name Amount 50 mM Tris – pH 8.0 5.0 mL 150 mM NaCl 7.5 mL SDS (0.1% [w/v]) 500 μL Sodium Deoxycholate (0.5% [w/v]) 0.5 g NP40 (1% [w/v]) 1.0 mL Milli-Q Water Up to 100 mL Total Volume 100.0 μL - Protect from light and store at 4˚C

25× Protease Inhibitor Name Amount Protease Inhibitor cOmplete EDTA-free (Roche) 100.0 μL DEPC-treated Water 400.0 μL Total Volume 500.0 μL - Vortex until dissolved - Store at -20˚C in 50 μL aliquots

Lysis Buffer Name Amount Modified RIPA Buffer 955.0 μL 25× Protease Inhibitor 45.0 μL Total Volume 1.0 mL

4× Tris-HCl/SDS, pH 6.8 (0.5M Tris-HCl Containing 0.4% SDS) Name Amount Tris 955.0 μL SDS 45.0 μL Milli-Q Up to 100 mL Total Volume 100.0 mL - Titrate to pH 6.8 with 1N HCl - Store at 4˚C

6× SDS Sample Loading Buffer Name Amount 4× Tris-HCl/SDS 6.5 mL Glycerol 3.0 mL SDS 1.0 mL β-mercaptoethanol 600.0 μL Bromophenol Blue 1.2 mg Total Volume ~10 mL - Store 0.5 mL aliquots at -20˚C; warm to RT before use

10× Running Buffer Name Amount Tris Base 30.0 g Glycine 144.0 g SDS 10.0 g Milli-Q Water Up to 1.0 L Total Volume 1.0 L

1× Running Buffer Name Amount 10× Running Buffer 100.0 mL Milli-Q Water 900.0 mL Total Volume 1.0 L

1× Transfer Buffer Name Amount 10× Running Buffer 50.0 mL Methanol 100.0 mL Milli-Q Water 350.0 mL Total Volume 500.0 mL

Copper Phthalocyanine 3,4’,4’’,4’’’-tetrasulfonic acid Tetrasodium Salt (CPTS) Name Amount CPTS 50.0 mg HCl 1.0 mL Milli-Q Water Up to 1.0 L Total Volume 1.0 L

10× Tris Buffered Saline (TBS) Name Amount NaCl 80.0 mg KCl 2.0 g 1 M Tris – pH 7.5 250 mL Milli-Q Water Up to 1.0 L Total Volume 1.0 L

1× TBS-Tween20 (TBST) Name Amount 10× TBS 100.0 mL Tween-20 1.0 mL Milli-Q Water Up to 1.0 L Total Volume 1.0 L

Non-fat Milk Blocking Solution (5% [w/v]) Name Amount Non-fat Milk Powder (Carnation) 5.0 g TBST Up to 100.0 mL Total Volume 100.0 mL

CHROMOSOME INSTABILITY ASSAYS Paraformaldehyde Fixative (4% [w/v]) Name Amount Paraformaldehyde (VWR Canlab) 0.4 g 1× PBS 10.0 mL Total Volume 10.0 mL - Bring to a slight boil to dissolve paraformaldehyde, cool to RT prior to use

Hoechst 33342 (1mg/mL Stock Solution) Name Amount Hoechst 33342 (Thermo Scientific) 10.0 mg 1× PBS Up to 10.0 mL Total Volume 10.0 mL - Protect from light and store at -20˚C

Hoechst 33342 (300 ng/mL Working Dilution) Name Amount Hoechst 33342 Stock Solution 12.0 μL 1× PBS Up to 40.0 mL Total Volume 40.0 mL

Colcemid (100 ng/mL Working Dilution) Name Amount KaryoMAX Colcemid (Gibco; 10 μg/mL) 10.0 μL Complete Cell Culture Medium 990.0 μL Total Volume 1.0 mL

KCl (1 M Stock Solution) Name Amount KCl 7.5 g Milli-Q Water Up to 100.0 mL Total Volume 100.0 mL

KCl (75 mM Working Dilution) Name Amount KCl (1 M Stock Solution) 7.5 mL Milli-Q Water 92.5 mL Total Volume 100.0 mL

3:1 Methanol:Acetic Acid (Fixative) Name Amount Methanol 12.0 mL Acetic Acid 4.0 mL Total Volume 16.0 mL

4’,6-Diamidino-2-phenylindole (DAPI; 50 μg/mL Stock Solution) Name Amount DAPI (Sigma-Aldrich; 5 mg/mL) 10.0 μL 1× PBS 990.0 μL Total Volume 1.0 mL - Protect from light and store at 4˚C

DAPI mounting media Name Amount DAPI (50 μg/mL Stock Solution) 10.0 μL Vectashield Mounting Medium (Vector Laboratories) 990.0 μL Total Volume 1.0 mL - Protect from light and store at 4˚C

CRISPR/CAS9 Carbenecillin (50 mg/mL) Name Amount Carbenecillin (VWR Canlab) 0.5 g Milli-Q Water 10.0 mL Total Volume 10.0 mL - Store at -20˚C

Luria-Bertani (LB) Agar Plates + 60 μg/mL Carbenicillin Name Amount Tryptone 5.0 g Yeast Extract 2.5 g NaCl 5.0 g LB Agar 7.5 g Milli-Q Water Up to 500 mL Carbenicillin (50 mg/mL) 600 μL Total Volume ~500 mL - Combine first 5 ingredients and pour into bottle(s) for autoclaving - Autoclave to dissolve agar and sterilize - While still warm, add carbenicillin and mix - Pour into 10 cm plates (~20 mL/plate); allow agar to cool and solidify - Store at 4˚C

Kanamycin (50 mg/mL) Name Amount Kanamycin Sulfate (Fisher Scientific) 0.5 g Milli-Q Water 10.0 mL Total Volume 10.0 mL - Store at -20˚C

LB Agar Plates + 50 μg/mL Kanamycin Name Amount Tryptone 5.0 g Yeast Extract 2.5 g NaCl 5.0 g LB Agar 7.5 g Milli-Q Water Up to 500 mL Kanamycin (50 mg/mL) 500 μL Total Volume ~500 mL - Combine first 5 ingredients and pour into bottle(s) for autoclaving - Autoclave to dissolve agar and sterilize - While still warm, add kanamycin and mix - Pour into 10 cm plates (~20 mL/plate); allow agar to cool and solidify - Store at 4˚C

LB Broth + 60 μg/mL Carbenicillin Name Amount Tryptone 5.0 g Yeast Extract 2.5 g NaCl 5.0 g LB 7.5 g Milli-Q Water Up to 500 mL Carbenicillin (50 mg/mL) 600 μL Total Volume ~500 mL - Combine first 5 ingredients - Autoclave to sterilize - Allow to cool, add carbenicillin and mix

LB Broth + 50 μg/mL Kanamycin Name Amount Tryptone 5.0 g Yeast Extract 2.5 g NaCl 5.0 g LB 7.5 g Milli-Q Water Up to 500 mL Kanamycin (50 mg/mL) 500 μL Total Volume ~500 mL - Combine first 5 ingredients - Autoclave to sterilize - Allow to cool, add kanamycin and mix

Sorting Buffer Name Amount 1× PBS 5 mL EDTA (500 mM) 10.0 μL Cosmic Calf Serum 50.1 μL 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (1M) 125 μL Total Volume ~5.2 mL - Combine first two ingredients - Filter to sterilize - Add last two ingredients

Collection Buffer Name Amount Complete X-media 2 mL/sample 1× Penicillin-Streptomycin To final dilution 1:100 Total Volume ~2 mL/sample

50× Tris-Acetate-EDTA (TAE) Buffer Name Amount Tris 242.0 g Acetic Acid 57.1 mL Disodium EDTA 18.61 g Milli-Q Water up to 1.0 L Total Volume 1.0 L

1× TAE Buffer Name Amount 50× TAE Buffer 20.0 mL Milli-Q Water 980.0 mL Total Volume 1.0 L

SOFT AGAIN COLONY FORMATION ASSAYS 2× Dulbecco’s Modified Eagle Medium with High Glucose Name Amount Dulbecco’s Modified Eagle Medium with High Glucose 13.5 g Powder (Hyclone) Milli-Q Water 500.0 mL Total Volume 500.0 mL - Adjust to pH 7.4 - Pass through 0.22 µm filter to sterilize

2× Medium 199 Name Amount Medium 199 Powder (Hyclone) 9.5 g Milli-Q Water 500.0 mL Total Volume 500.0 mL - Adjust to pH 7.4 - Pass through 0.22 µm filter to sterilize

2× X Medium + 4% CCS Name Amount 2× Dulbecco’s Modified Eagle Medium with High Glucose 400.0 mL 2× Medium 199 100.0 mL EGF (PeproTech; 20 ng/mL) 20.0 μL Insulin (Sigma; 10 μg/mL) 5.0 mL Apo-transferrin (Sigma; 2 μg/mL) 40 μL Hydrocortisone (Sigma; 1 μg/mL) 50 μL Sodium selenite (Sigma; 5 nM) 1.0 μL Cosmic Calf Serum (Hyclone) 20.0 mL Total Volume 500.0 mL

2× McCoy’s 5A Cell Culture Medium Name Amount McCoy’s 5A Powder (Sigma-Aldrich) 11.9 g Sodium Bicarbonate 2.2 g Milli-Q Water up to 500.0 mL Total Volume 500.0 mL - Adjust to pH 7.4 - Pass through 0.22 µm filter to sterilize

2× McCoy’s 5A Cell Culture Medium + 20% FBS Name Amount 2× McCoy’s 5A Cell Culture Medium 40.0 mL FBS 10.0 mL Total Volume 50.0 mL

Agarose (1.2% [w/v]) Name Amount Agarose (Invitrogen) 1.2 g Milli-Q Water 100.0 mL Total Volume 1.0 L - Autoclave to dissolve agarose and sterilize - Warm in microwave prior to use

Agarose (0.8% [w/v]) Name Amount Agarose (Invitrogen) 0.8 g Milli-Q Water 100.0 mL Total Volume 100.0 mL - Autoclave to dissolve agarose and sterilize - Warm in microwave prior to use

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Crystal Violet (0.1% [w/v/] Stock Solution) Name Amount Crystal Violet (Sigma-Aldrich) 0.1 g Methanol 10.0 mL Milli-Q Water 90.0 mL Total Volume 100.0 mL

Crystal Violet (0.005% [w/v] Working Dilution) Name Amount Crystal Violet (0.1% Stock Solution) 2.5 mL Milli-Q Water 47.5 mL Total Volume 50.0 mL

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APPENDIX B: SUPPLEMENTARY TABLES

Table S1. KS Tests Reveal Significant Increases in Nuclear Area Distributions Following FBXO7 Silencing in HCT116 Cells. Condition p-valueA SignificanceB D-statisticC siControl - - - siFBXO7-2 < 0.0001 **** 0.1517 siFBXO7-4 < 0.0001 **** 0.1733 siFBXO7-P < 0.0001 **** 0.1874 Ap-values calculated from two-sample KS tests for the listed condition relative to siControl. BSignificance level (**** p-value < 0.0001). CD-statistic (maximum deviation between two distribution curves).

Table S2. MW Tests Identify Significant Increases in Micronucleus Formation Following FBXO7 Silencing in HCT116 Cells. Condition nA p-valueB SignificanceC siControl 6 - - siFBXO7-2 6 0.0043 ** siFBXO7-4 6 0.0022 ** siFBXO7-P 6 0.0043 ** ANumber of wells analyzed. Bp-values calculated from two-sample MW tests for the listed condition relative to siControl. CSignificance level (** p-value < 0.01).

Table S3. KS Tests Identify Significant Changes in Cumulative Distributions of Chromosome Numbers in FBXO7-Silenced HCT116 Cells. Condition p-valueA SignificanceB D-statisticC siControl - - - siFBXO7-2 < 0.0001 **** 0.4039 siFBXO7-4 0.0112 * 0.2089 siFBXO7-P 0.0001 *** 0.2672 Ap-values calculated from two-sample KS tests for the listed condition relative to siControl. BSignificance level (**** p-value < 0.0001). CD-statistic (maximum deviation between two distribution curves).

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Table S4. KS Tests Reveal Significant Increases in Nuclear Area Distributions Following FBXO7 Silencing in 1CT, RPA and A1309 Cells. Condition p-valueA SignificanceB D-statisticC 1CT siControl - - - siFBXO7-2 0.2149 ns 0.0486 siFBXO7-4 < 0.0001 **** 0.1341 siFBXO7-P < 0.0001 **** 0.1727 RPA siControl - - - siFBXO7-2 0.0016 ** 0.0437 siFBXO7-4 < 0.0001 **** 0.0860 siFBXO7-P < 0.0001 **** 0.1364 A1309 siControl - - - siFBXO7-2 < 0.0001 **** 0.1967 siFBXO7-4 < 0.0001 **** 0.2188 siFBXO7-P < 0.0001 **** 0.2207 Ap-values calculated from two-sample KS tests for the listed condition relative to siControl. BSignificance level (ns, not significant, p-value > 0.05; ** p-value < 0.01; **** p-value < 0.0001). CD-statistic (maximum deviation between two distribution curves).

Table S5. MW Tests Identify Significant Increases in Micronucleus Formation Following FBXO7 Silencing in HCT116 Cells. Condition nA p-valueB SignificanceC 1CT siControl 6 - - siFBXO7-2 6 0.9372 ns siFBXO7-4 6 0.0649 ns siFBXO7-P 6 0.0411 * RPA siControl 6 - - siFBXO7-2 6 0.0152 * siFBXO7-4 6 0.0260 * siFBXO7-P 6 0.2403 ns A1309 siControl 6 - - siFBXO7-2 6 0.0065 ** siFBXO7-4 6 0.0649 ** siFBXO7-P 6 0.0043 ** ANumber of wells analyzed. Bp-values calculated from two-sample MW tests for the listed condition relative to siControl. CSignificance level (ns, not significant, p-value > 0.05; * p-value ≤ 0.05; ** p-value < 0.01).

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Table S6. KS Tests Do Not Identify Significant Changes in Cumulative Chromosome Number Distributions in 1CT, RPA and A1309 Cells. Condition p-valueA SignificanceB D-statisticC 1CT siControl - - - siFBXO7-2 0.4879 ns 0.1173 siFBXO7-4 > 0.9999 ns 0.0204 siFBXO7-P > 0.9999 ns 0.0204 RPA siControl - - - siFBXO7-2 > 0.9999 ns 0.0400 siFBXO7-4 0.2646 ns 0.1418 siFBXO7-P 0.0020 ** 0.2618 A1309 siControl - - - siFBXO7-2 0.9947 ns 0.0604 siFBXO7-4 0.1980 ns 0.1514 siFBXO7-P 0.0640 ns 0.1774 Ap-values calculated from two-sample KS tests for the listed condition relative to siControl. BSignificance level (ns, not significant, p-value > 0.05; ** p-value < 0.01; **** p-value < 0.0001). CD-statistic (maximum deviation between two distribution curves).

Table S7. Student’s T-Tests Identify Significant Increases in Frequencies of Chromosome Gains and Losses in Non-Malignant Colonic Epithelial Cells. Condition p-valueA SignificanceB 1CT siControl - - siFBXO7-2 0.0671 ns siFBXO7-4 0.4532 ns siFBXO7-P 0.0487 * RPA siControl - - siFBXO7-2 0.0245 * siFBXO7-4 0.1329 ns siFBXO7-P 0.2461 ns A1309 siControl - - siFBXO7-2 0.1011 ns siFBXO7-4 0.0302 * siFBXO7-P 0.0125 * Ap-values calculated from two-sample KS tests for the listed condition relative to siControl. BSignificance level (ns, not significant, p-value > 0.05; ** p-value < 0.01; **** p-value < 0.0001).

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Table S8. KS Tests Show Significant Increases in Nuclear Area Distributions in FBXO7- Silenced RPA and A1309 Cells. Condition p-valueA SignificanceB D-statisticC

1CT - - - RPA (relative to 1CT) < 0.0001 **** 0.0345 A1309 (relative to 1CT) < 0.0001 **** 0.1254 A1309 (relative to RPA) < 0.0001 **** 0.1029 Ap-values calculated from two-sample KS tests for the listed condition relative to the cell line listed. BSignificance level (**** p-value < 0.0001). CD-statistic (maximum deviation between two distribution curves).

Table S9. MW Tests Reveal Significant Increases in Micronucleus Formation in FBXO7- Silenced RPA and A1309 Cells. Condition nA p-valueB SignificanceC 1CT 18 - - RPA (relative to 1CT) 18 0.0045 ** A1309 (relative to 1CT) 18 0.0346 * A1309 (relative to RPA) 18 0.9380 ns ANumber of wells analyzed. Bp-values calculated from two-sample MW tests for the listed condition relative to the cell line listed. CSignificance level (ns, not significant, p-value > 0.05; * p-value ≤ 0.05; ** p-value < 0.01).

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Table S10. KS Tests Show Significant Increases in Nuclear Area Distributions in A1309 FBXO7+/- and FBXO7-/- Models Over Time

Passage Condition p-valueA Significance B D-statisticC NT-Control - - - FBXO7+/-1 < 0.0001 **** 0.0993 p0 FBXO7+/-2 < 0.0001 **** 0.0860 FBXO7-/-A < 0.0001 **** 0.1386 FBXO7-/-B < 0.0001 **** 0.1414 NT-Control - - - FBXO7+/-1 < 0.0001 **** 0.1084 p4 FBXO7+/-2 < 0.0001 **** 0.3742 FBXO7-/-A 0.0121 * 0.0450 FBXO7-/-B < 0.0001 **** 0.1580 NT-Control - - - FBXO7+/-1 0.1793 ns 0.0877 p8 FBXO7+/-2 < 0.0001 **** 0.2435 FBXO7-/-A 0.2228 ns 0.0849 FBXO7-/-B 0.8588 ns 0.0571 NT-Control - - - FBXO7+/-1 < 0.0001 **** 0.1733 p12 FBXO7+/-2 < 0.0001 **** 0.4269 FBXO7-/-A < 0.0001 **** 0.0866 FBXO7-/-B 0.0024 ** 0.0391 NT-Control - - - FBXO7+/-1 < 0.0001 **** 0.1163 p16 FBXO7+/-2 < 0.0001 **** 0.4237 FBXO7-/-A < 0.0001 **** 0.2528 FBXO7-/-B < 0.0001 **** 0.2689 NT-Control - - - FBXO7+/-1 < 0.0001 **** 0.1405 p20 FBXO7+/-2 0.0003 *** 0.1075 FBXO7-/-A < 0.0001 **** 0.4884 FBXO7-/-B < 0.0001 **** 0.4801 Ap-values calculated from two-sample KS tests for the listed condition relative to NT-Control BSignificance level (ns, not significant, p-value > 0.05; * p-value ≤ 0.05; ** p-value < 0.01; *** p-value < 0.001; **** p-value < 0.0001). CD-statistic (maximum deviation between two distribution curves).

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Table S11. MW Tests Reveal Significant Increases in Micronucleus Formation in FBXO7- Silenced RPA and A1309 Cells. Passage Condition nA p-valueB SignificanceC NT-Control 6 - - FBXO7+/-1 6 0.2403 ns p0 FBXO7+/-2 6 0.0931 ns FBXO7-/-A 6 0.4848 ns FBXO7-/-B 6 0.3095 ns NT-Control 6 - - FBXO7+/-1 6 0.0043 ** p4 FBXO7+/-2 6 0.0022 ** FBXO7-/-A 6 0.3095 ns FBXO7-/-B 6 0.2403 ns NT-Control 5 - - FBXO7+/-1 6 0.2294 ns p8 FBXO7+/-2 6 0.0173 * FBXO7-/-A 6 0.1775 ns FBXO7-/-B 6 0.1775 ns NT-Control 6 - - FBXO7+/-1 6 0.0260 * p12 FBXO7+/-2 6 0.0022 ** FBXO7-/-A 6 0.0043 ** FBXO7-/-B 6 0.1320 ns NT-Control 6 - - FBXO7+/-1 6 0.1320 ns p16 FBXO7+/-2 6 0.2403 ns FBXO7-/-A 6 0.0649 ns FBXO7-/-B 6 0.2403 ns NT-Control 6 - - FBXO7+/-1 6 0.8182 ns p20 FBXO7+/-2 6 0.0152 * FBXO7-/-A 6 0.0152 * FBXO7-/-B 6 0.8182 ns ANumber of wells analyzed. Bp-values calculated from two-sample MW tests for the listed condition relative to the cell line listed. CSignificance level (ns, not significant, p-value > 0.05; * p-value ≤ 0.05; ** p-value < 0.01).

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