Validating WDR12 As a Potential Drug Target in Triple Negative Breast Cancer

Validating WDR12 as a Potential Drug Target in Triple Negative Breast Cancer

Sophie Benhamron

A thesis submitted in conformity with the requirements for the degree of Master of Science Pharmacology and Toxicology University of Toronto

Validation of WDR12 as a Potential Drug Target in Triple Negative Breast Cancer

Sophie Benhamron

Master of Science

Pharmacology and Toxicology University of Toronto

2019

Abstract

Compared to other breast cancers (BC), triple negative breast cancers (TNBCs) confer lower survival and higher disease recurrence. TNBCs lack receptors targeted by current therapies, requiring new therapeutic target identification. Recently, the ribosome biogenesis protein WDR12 was identified as a potential target from RNAi databases. WDR12 belongs to the WD40-repeat family of scaffolding proteins, which have become interesting targets due to their druggable structure. I hypothesized that TNBC cell lines would be more sensitive to WDR12 knockdown (KD) due to their increased rate of ribosome biogenesis. WDR12 depletion led to decreased proliferation in all BC cell lines that could not be attributed to apoptosis or cell cycle arrest. Interestingly, WDR12 KD decreased nucleolar size to a greater extent in TNBC, indicating a potential role for WDR12 in ribosome biogenesis. In conclusion, WDR12 may be a promising therapeutic target in TNBC, although additional confirmatory testing will be required.

Acknowledgments

I’d like to begin by saying that I am eternally grateful to have been given the opportunity to study at the University of Toronto and alongside the incredible scientists at the Ontario Institute of Cancer Research. This venture could not have been possible without the support and mentorship from my supervisor, Dr. Rima Al-Awar, and the members of the Drug Discovery team. This experience has been of the most formative years of my life, both intellectually and emotionally, which I will forever cherish.

I am overwhelmingly thankful for the endless support and guidance I received from my mentors, Brigitte Theriault, Manuel Chan, Matias Casas Selves and Richard Marcellus, without whom this endeavor would not have been feasible. It has been such a humbling experience to be trained by such seasoned scientists who have dedicated so much of their time and patience in educating my peers and me. Each one of these individuals has contributed tremendously to my personal and professional growth, for which I am forever indebted. I would especially like to thank Dr. Brigitte Theriault, who has been such a remarkable teacher and role model. On top of working incredibly hard on her own projects, Brigitte always selflessly took the time to meet with me to discuss my experimental progress, answer my questions and help review my writing. I owe a lot of my success to her and I cannot express how truly grateful I am for her guidance.

I would also like to acknowledge how fortunate I have been to conduct my research in the state of the art laboratories of the OICR and to attend weekly biology meetings that have provided me with a glimpse into the drug discovery pipeline. I got to witness firsthand the collaborative effort that goes into the development of new therapeutics. Fortuities I would not have been afforded in an ordinary academic lab.

I send my thanks and gratitude to my friends and roommate that let me practice my presentations in front of them. Regardless of their scientific background, they always expressed interest, providing me with valuable feedback and words of encouragement.

Finally, I would like to thank my parents and brother who supported me throughout the process. I could not have done this without them and I am so thankful they could be there to talk me through the rougher parts. I am excited to embark on this next journey with all the knowledge I have acquired here.

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Table of Contents

Acknowledgments ...... iii

Table of Contents ...... iv

List of Tables ...... viii

List of Figures ...... x

List of Appendices ...... xii

List of Symbols and Abbreviations ...... xiv

Chapter 1 ...... 1

1 Introduction ...... 1

1.1 Breast Cancer ...... 1

1.1.1 Incidence and Histological Subtypes ...... 1

1.1.2 Molecular Subtypes ...... 2

1.2 Standard of Care and Current Treatments ...... 4

1.3 The WD40 Repeat Family ...... 6

1.3.1 Merits of Targeting the WD40 Family in Drug Discovery ...... 8

1.4 Genome-wide siRNA Screens as Tools for Target Identification ...... 9

1.4.1 Project DRIVE Conducted by Novartis ...... 9

1.4.2 Project Achilles Conducted by Broad Institute ...... 10

1.4.3 Combined (Achilles, DRIVE, and Marcotte) Dataset ...... 12

1.5 WD40-Repeat 12 ...... 14

1.5.1 Ribosome Biogenesis ...... 14

1.5.2 Structure and Binding Partners ...... 15

1.5.3 Localization of WDR12 and its Components ...... 17

1.5.4 The Cell Cycle and Ribosome Biogenesis ...... 18

1.5.5 Nucleolar Morphology as an Indicator of the Rate of Ribosome Biogenesis ...... 20 iv

1.6 Study Rationale and Hypotheses ...... 21

Chapter 2 ...... 23

2 Methods and Materials ...... 23

2.1 Identifying WDR12 as a Potential Drug Target ...... 23

2.1.1 Biomining RNAi Dropout Screen Datasets ...... 23

2.1.2 Analysis of the WDR12 Phenotype using the “Combined” Preloaded Dataset ....24

2.2 Generation of Target shRNA for WDR12 ...... 24

2.2.1 Designing the shRNA sequences ...... 24

2.2.2 Generation of a Lentiviral Expression Vector by Gateway Cloning ...... 30

2.2.3 Generation of Lentiviral Particles ...... 31

2.3 Generating Stable Expression Cell Lines ...... 32

2.3.1 Cell Lines and Routine Passaging ...... 32

2.3.2 Short Tandem Repeat Profiling ...... 33

2.3.3 Qualitative Reverse Transcription PCR ...... 34

2.3.4 Generation of Puromycin Kill Curves ...... 37

2.3.5 Transduction of shRNA Constructs ...... 38

2.4 Induction of shRNA ...... 40

2.4.1 Cell-Proliferation Assay (ATP) ...... 41

2.4.2 Caspase 3/7 Activation Assay ...... 42

2.5 Western Blot ...... 42

2.5.1 Cell lysis ...... 42

2.5.2 Concentration Determination ...... 43

2.5.3 Running the SDS-PAGE Gel ...... 43

2.5.4 Membrane Transfer ...... 44

2.5.5 Band Visualization ...... 44

2.6 Cell Cycle Analysis ...... 45 v

2.6.1 Optimization of BrdU Labelling ...... 45

2.6.2 4 to 7 Day Induction for Cell Cycle Analysis ...... 46

2.6.3 BrdU labelling and Cell Staining ...... 46

2.6.4 Reading Cell Samples on FACS Canto II ...... 47

2.6.5 Flow Cytometry Data Analysis ...... 47

2.7 Nucleolar Staining ...... 48

2.7.1 4 to 7 Day Induction for Nucleolar Staining ...... 48

2.7.2 Staining Cells ...... 48

2.7.3 Cell Imaging ...... 49

2.8 Data and Statistical Analysis ...... 50

2.8.1 Analysis of Cell Proliferation Data ...... 50

2.8.2 Analysis of Trypan Blue Exclusion Determined Cell Counts ...... 50

2.8.3 Statistical Analysis of Caspase 3/7 Activation Data ...... 50

2.8.4 Extrapolation of Cell Number and Doubling Time ...... 51

2.8.5 Analysis of Cell Cycle Data ...... 52

2.8.6 Analysis of Nucleolar Staining ...... 52

Chapter 3 ...... 53

3 Results ...... 53

3.1 Generation of shRNA for WDR12 ...... 53

3.1.1 Verification of PCR product ...... 53

3.2 Generation of Stable Expression Cell Lines ...... 54

3.2.1 STR profiling ...... 54

3.2.2 RTqPCR Breast Cancer Cell Line Subtyping ...... 58

3.3 Transduction and Selection ...... 60

3.3.1 Puromycin Kill Curves ...... 60

3.4 Western Blot ...... 61 vi

3.5 Induction of shWDR12 Knockdown ...... 62

3.5.1 Cell Proliferation Assay (ATPlite and Trypan Blue Exclusion) ...... 62

3.5.2 Cell Number Extrapolation Curves in Diverse Breast Cancer Cell Lines ...... 65

3.5.3 Determination of Doubling Time in Diverse Breast Cancer Cell Lines ...... 66

3.5.4 Apoptotic Cell Death (Caspase 3/7) ...... 74

3.6 Cell Cycle Analysis ...... 75

3.7 Nucleolar Staining ...... 80

3.7.1 Nucleolar Staining After 4 and 7 Day induction in MDA-MB-231 and ZR-75- 1...... 80

3.7.2 Nucleolar Staining of Diverse Breast Cancer Cell Lines ...... 84

Chapter 4 ...... 85

4 Discussion ...... 86

4.1.1 Considerations Taken into Account for shRNA Design and Efficiency ...... 86

4.2 Generation of Stable Expression Cell Lines ...... 87

4.2.1 The Use of Cell Lines as a Model of Breast Cancer ...... 87

4.2.2 STR Profiling Revealed Alterations in Marker Profile ...... 88

4.3 WDR12 Knockdown Results in Decreased Cell Proliferation in Diverse Breast Cancer Cell Lines ...... 89

4.4 Contradictory Results of Apoptosis and Cell Cycle Analysis in Response to WDR12 Knockdown ...... 91

4.5 Changes in Nucleolar Morphology in Response to WDR12 Knockdown in TNBC and non-TNBC cell lines ...... 93

4.6 Intrinsic Differences in Nucleolar Morphology Exist Between Diverse Breast Cancer Cell Lines ...... 94

Chapter 5 ...... 95

5 Conclusions ...... 95

References ...... 97

Appendices ...... 101

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List of Tables

Table 1. Summary of endocrine therapies for hormone receptor positive breast cancers ...... 5

Table 2. Chosen shRNA sequences to target WDR12 mRNA transcript ...... 25

Table 3. shRNA primer sequences for shRNA PCR cloning ...... 26

Table 4. Cell lines used and their corresponding growth media ...... 32

Table 5. TaqMan probes used for breast cancer subtyping experiment ...... 34

Table 6. Seeding density in 96-well plate for generation of puromycin kill curve for each cell line ...... 37

Table 7. Seeding densities and media for transduction in a 6-well plate ...... 39

Table 8. Experimental controls ...... 40

Table 9. Seeding densities for long-term induction ...... 41

Table 10. Conditions for determining optimal BrdU incubation time ...... 45

Table 11. STR profiling results compared to Cellosaurus reports ...... 54

Table 12. Comparison of breast cancer molecular markers between literature and experimental findings...... 59

Table 13. Summary table of puromycin kill curve results ...... 61

Table 14. WDR12 knockdown sensitivity rank based on % cells remaining after 10 days of induction (from most to least sensitive) ...... 66

Table 15. Doubling time ranking of diverse breast cancer cell lines (from fastest to slowest) .... 67

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Table 16. WDR12 knockdown sensitivity rank based on % cells remaining after 7 doublings post- induction (from most to least sensitive) ...... 68

Table 17. Comparison of shRNA dropout screen rank order of breast cancer cell lines’ sensitivity to WDR12 knockdown (from most to least sensitive)...... 68

Table 18. Doubling time determined for MDA-MB-231 ...... 71

Table 19. Doubling time determined for MDA-MB-468 ...... 71

Table 20. Doubling time determined for ZR-75-1 ...... 71

Table 21. Doubling time determined for MCF-7 ...... 72

Table 22. Doubling time determined for SKBR-3 ...... 72

Table 23. Summary of breast cancer cell lines’ doubling times reported in the literature ...... 73

Table 24. Average number of nuclei detected in 7 day DOX-induction conducted on coated plate ...... 81

Table 25. Average number of nuclei detected in 7 day DOX-induction conducted on non-coated plate ...... 81

Table 26. Rank order of nucleolar morphology measurements within breast cancer cell lines tested...... 84

List of Figures

Figure 1. Breast Cancer prognosis based on molecular and intrinsic subtype ...... 3

Figure 2. Various modes of interaction of WD40 repeat proteins...... 7

Figure 3. Diversity of cellular functions played by WD40 family members...... 8

Figure 4. Sensitivity of diverse breast cancer cell lines to WDR12 knockdown according to the Broad Institute's shRNA dropout screen ...... 11

Figure 5. Sensitivity of diverse breast cancer cell lines to WDR12 knockdown according to Novartis' shRNA dropout screen ...... 12

Figure 6. Sensitivity of diverse breast cancer cell lines to WDR12 knockdown according to the Combined shRNA dropout screens ...... 13

Figure 7. Crystal structure of Ytm1 and Erb1 complex (WDR12 and Bop1 yeast homologues). 16

Figure 8. Interdependence of the PeBoW components for cellular localization...... 18

Figure 9. Map of features included in the lentivirus-based, doxycycline-inducible shRNA expression constructs...... 27

Figure 10. PCR-design end result...... 28

Figure 11. Gateway cloning of BP reaction...... 29

Figure 12. Gateway cloning of LR reaction...... 30

Figure 13. Determination of nucleolar morphology of MDA-MB-231 parental cells...... 49

Figure 14. Verification of PCR product...... 53

Figure 15. Breast cancer cell line gene expression relative to HMEC cells...... 59

Figure 16. Puromycin kill curves in diverse cell lines...... 60

Figure 17. Validating protein knockdown by Western Blot...... 62

Figure 18. % 293EMT cells remaining after 4 days of DOX-induction...... 63

Figure 19. % Cells remaining after long term DOX-induction of 293-EMT, MDA-MB-231 and ZR-75-1...... 64

Figure 20. Cell number extrapolation curves in diverse breast cancer cell lines after long-term DOX-induction...... 69

Figure 21. Number of doublings by hours of DOX-induction in diverse breast cancer cell lines.70

Figure 22. Percent cells remaining after long-term induction in diverse breast cancer cell lines...... 74

Figure 23. Caspase activation in diverse breast cancer cell lines after long-term DOX- induction...... 75

Figure 24. Cell cycle analysis of breast cancer cell lines in response to WDR12 knockdown. ... 77

Figure 25. Flow cytometry traces BrdU-labelled MDA-MB-231 cells after four and seven days of WDR12 DOX-induction...... 78

Figure 26 Flow cytometry traces BrdU-labelled ZR-75-1 cells after four and seven days of WDR12 DOX-induction...... 79

Figure 27 Nucleolar staining of MDA-MB-231 and ZR-75-1 cells after 4 and 7 days of DOX- induction...... 81

Figure 28. Nucleolar morphology of MDA-MB-231 after 4 day DOX induction...... 82

Figure 29. Nucleolar morphology of ZR-75-1 after 4 day DOX induction...... 82

Figure 30. Nucleolar morphology of MDA-MB-231 after 7 day DOX induction...... 83

Figure 31. Nucleolar morphology of ZR-75-1 after 7 day DOX induction...... 83

Figure 32. Analysis of nucleolar morphology in diverse parental breast cancer cell lines...... 84

Figure 33. Intrinsic nucleolar morphology of diverse breast cancer cells lines...... 85

List of Appendices

Table A 1. Multiple t-test p-values for MDA-MB-231 after long-term DOX-induction (ATPlite) ...... 101

Table A 2. Multiple t-test p-values for MDA-MB-468 after long-term DOX-induction (ATPlite) ...... 101

Table A 3. Multiple t-test p-values for ZR-75-1 after long-term DOX-induction (ATPlite) ..... 101

Table A 4. Multiple t-test p-values for MCF-7 after long-term DOX-induction (ATPlite) ...... 102

Table A 5. Multiple t-test p-values for SKBR-3 after long-term DOX-induction (ATPlite) ..... 102

Table A 6. Multiple t-test p-values for MDA-MB-231 after long-term DOX-induction (Caspase 3/7) ...... 102

Table A 7. Multiple t-test p-values for MDA-MB-468 after long-term DOX-induction (Caspase 3/7) ...... 103

Table A 8. Multiple t-test p-values for ZR-75-1 after long-term DOX-induction (Caspase 3/7)103

Table A 9. Multiple t-test p-values for MCF-7 after long-term DOX-induction (Caspase 3/7) . 104

Table A 10. Multiple t-test p-values for SKBR-3 after long-term DOX-induction (Caspase 3/7) ...... 104

Table A 11. Multiple t-test p-values for MDA-MB-231 cell cycle analysis after 4 days of DOX- induction ...... 104

Table A 12. Multiple t-test p-values for MDA-MB-231 cell cycle analysis after 7 days of DOX- induction ...... 105

Table A 13. Multiple t-test p-values for ZR-75-1 cell cycle analysis after 4 days of DOX- induction ...... 105

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Table A 14. Multiple t-test p-values for ZR-75-1 cell cycle analysis after 7 days of DOX- induction ...... 105

Table A 15. Multiple t-test p-values for MDA-MB-231 nucleolar morphology analysis after 4 days of induction on a non-coated plate ...... 106

Table A 16. Multiple t-test p-values for MDA-MB-231 nucleolar morphology analysis after 7 days of induction on a non-coated plate ...... 106

Table A 17. Multiple t-test p-values for MDA-MB-231 nucleolar morphology analysis after 7 days of induction on a coated plate ...... 106

Table A 18. Multiple t-test p-values for ZR-75-1 nucleolar morphology analysis after 4 days of induction on a non-coated plate ...... 107

Table A 19. Multiple t-test p-values for ZR-75-1 nucleolar morphology analysis after 7 days of induction on a non-coated plate ...... 107

Table A 20. Multiple t-test p-values for ZR-75-1 nucleolar morphology analysis after 7 days of induction on a coated plate ...... 107

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List of Symbols and Abbreviations

Abbreviation Definition 293EMT Human embryonic kidney cells 3UTR 3' untranslated region 5-FU 5-Fluouracyl 7-AAD 7-amino-actinomycin D Å Angstrom AAAS Aladom ACGT Advanced Center for Genome Technology Ago Argonaute AI Aromatase inhibitor AKT Akt murine thymoma viral oncogene APAF1 Apoptotic protease-activating factor 1 APC Allophycocyanin ARPC1A Actin-related protein 2/3 complex subunit 1A ATARiS Analytical Technique for Assessment of RNAi Similarity ATCC American type culture collection ATP Adenosine Triphosphate B2M Beta-2-microglobulin BL1 Basal-like 1 BL2 Basal-like 2 BLAST Basic Local Alignment Search Tool Bop1 Block of proliferation 1 BrdU Bromodeoxyuridine BSA Bovine serum albumin

CT Threshold cycle CALM2 Calmodulin 2 CDC20 Cell division protein 20 CDK Cyclin-dependent kinase CDK 4/6 Cyclin-dependent kinase 4 and 6 CDK1 Cyclin-dependent kinase 1 CDK2 Cyclin-dependent kinase 2 cDNA Copy deoxyribonucleic acid CDS Coding sequence c-fos Proto-oncogene transcription factor c-fos CHAF1B Chromatin assembly factor 1 subunit B CMV Cytomegalo virus CNV Copy number variation

CO2 Carbon dioxide cPPT Central polypurine tracts CRISPR Clustered regularly interspaced short palindromic repeats CSTF1 Cleavage stimulation factor subunit 1 CTR2 Scrambled control sequence xiv

Cα Carbon linked to functional group D112 Aspartic acid position 112 DAPI 4',6-diamidino-2-phenylindole DAW1 Dynein assembly factor with WDR repeat domain 1 DCAF4 DDB1- and CUL4- associated factor 4 DDB2 DNA damage-binding protein 2 De novo anew dH2O Distilled water DMEM Dulbecco's Modified Eagle Medium DMSO Dimethylsulfoxide DMXL1 DMX-like protein 1 DNAse Deoxyribonuclease DOX- Doxycycline negative DOX+ Doxycycline positive DPBS Dulbecco's Phosphate Buffered Saline dsRNA Double stranded RNA DTL Denticleless protein 2 DYNC1l1 Cytoplasmic dynein 1 intermediate chain 1 E2F E2F transcription factor E481 Glutamic acid position 481 E785 Glutamic acid position 785 EBNA1 Epstein-Barr nuclear antigen 1 ECM Extracellular matrix EED Embryonic ectoderm development protein EGFR Epidermal growth factor receptor ELP2 Elongator complex protein Erb1 Bop1 yeast homologue ERBB2 Erb-B2 receptor tyrosine kinase receptor 2 (Her-2) ERE Estrogen response element ERK Extracellular signal-regulated kinases (original name for MAPK) ERα Estrogen receptor alpha ERβ Estrogen receptor beta ESR Estrogen receptor ESR1 Estrogen receptor gene 1 ESR2 Estrogen receptor gene 2 FBS Fetal bovine serum FBX7 F-Box/WD repeat containing protein 7 FGFR Fibroblast growth factor receptor FSC Forward scatter FZR1 Fizzy-related protein homologue g grams G1 Growth 1 phase G2M Growth 2-Mitotic entry phase GOI Gene of interest xv

H320 Histidine position 320 H3K27me Histone 3 methylated at Lysine position 27 HDM2 Human double minute 2 Her-2 Human epidermal growth factor receptor 2 HMEC Human mammary epithelial cells HPRT1 Hypoxanthine phosphoribosyltransferase 1 HR+ Hormone receptor positive HRP Horseradish peroxidase HSP Heat shock protein IC50 Half maximal inhibitory concentration IDT Integrated DNA technologies IGF-1 Insulin-like growth factor 1 IHC Immunohistochemistry IM Immunomodulatory In vitro In the glass K181 Lysine position 181 KD Knockdown kD Kilo Dalton Ki-67 Marker of cell proliferation KIF11 Kinesin family member 11 L Liter LAR Luminal androgen receptor LRRK2 Leucine-rich repeat serine/threonine-protein kinase 2 LTR Long Terminal Repeats M Mesenchymal MAPK Mitogen-Activated protein Kinases MEBM/MEGM Mammary epithelial cell growth media m Milli MLST8 Target of rapamycin complex subunit LST8 MMTV Mouse mammary tumor virus MRI Magnetic resonance imaging mRNA Messenger RNA MSL Mesenchymal stem-like MSR Macrophage Scavenger Receptors mTOR Mammalian target of rapamycin n Nano nCaspase Normalized caspase NGS Next generation sequencing OICR Ontario Institute for Cancer Research ORF Open reading frame P19arf Cyclin-dependent kinase inhibitor 2A p21 Cyclin-dependent kinase inhibitor 1 p27 Cyclin-dependent kinase inhibitor 1B p35 Early 35 kDa protein xvi

p53 Tumor protein 53 PAAF1 Proteasomal ATPase-associated factor 1 PAFAH1B1 Platelet-activating factor acetylhydrolase 1B subunit α PALB2 Partner and localizer of BRCA2 PAM50 Prediction analysis of microarray 50 PCR2 Polycomb repressive complex 2 PeBoW Pes1-Bop1-WDR12 containing complex Pes 1 Pescadillo 1 PFA Paraformaldehyde PGR Progesterone receptor PI3K Phosphoinositide-3-kinase PI3KR4 Phosphoinositide 3 kinase regulatory protein 4 PKC Protein kinase C PLAA Phospholipase A-2-activating protein PLK1 Polo-like kinase 1 PLRG1 Pleiotropic regulator 1 pLV Lentiviral vector PolI RNA Polymerase I PolII RNA Polymerase II POLR2A RNA polymerase II subunit A polyT Poly-thymine PPII Protein-protein interaction inhibitor PPP2R2B Serine/threonine-protein phosphatase 2A 55kDA regulatory subunit B β isoform PR-A Progesterone receptor isoform A PR-B Progesterone receptor isoform B PR-C Progesterone receptor isoform C PRE Progesterone response element PRPF19 Pre-mRNA-processing factor 19 PTEN phosphatase and tensin homolog deleted in chromosome 10 PVDF Polyvinylidene difluoride R486 Arginine position 486 RAE1 mRNA export factor Rb Retinoblastoma protein RBBP4 Retinoblastoma-binding protein 4 RISC RNA induced silencing complex RMSD Root-mean-square deviation of atomic positions RNA Ribonucleic acid RNAi RNA interference RPMI Roswell park memorial institute RPTOR Regulatory-associated protein of mTOR RRE Rev response element Rrn3 Ribosomal DNA transcription factor rRNA Ribosomal RNA RSA Redundant siRNA activity xvii

Rsa4 Ribosome assembly protein 4 (yeast) RTK Receptor tyrosine kinase RTqPCR Quantitative reverse transcription Polymerase chain reaction S Synthesis phase S Svedberg S.O.C. Super optimal broth with catabolite repression SD Standard deviation SDS Sodium dodecyl sulfate SERD Selective estrogen receptor down regulators SERM Selective estrogen receptor modulator SGC Structural Genomics Consortium shRNA Short hairpin RNA siRNA Short interfering RNA SL1 Selectivity Factor 1 SNRNP40 U5 small nuclear ribonucleoprotein 40 kDA protein Src Proto-oncogene protein kinase Src SSC Side scatter STRN4 Striatin-4 SUZ12 Polycomb repressive complex 2 subunit TAF5 Transcription initiation factor TFIID subunit 5 TBP TATA-box binding protein TBST Tris buffered saline solution with 1% tween TCAG The Center for Applied Genomics TetR tetracycline regulator TNBC Triple negative breast cancer TO Tetracycline operator T-Rex Tetracycline inducible system Ub Ubiquitin UBF Upstream binding factor I UBL Ubiquitin-like UPS Ubiquitin-proteasome system VPRBP DDB1- and CUL4- associated factor 1 VSV-G Vesicular stomatitis virus G-protein V Volts W113 Tryptophan position 113 WD Tryptophan aspartate WDH1 WD-repeat and HMG-box DNA-binding protein 1 WDR WD40 Repeat WDR12 WD40-repeat containing protein 12 WDR5 WD40-repeat containing protein 5 WSB1 WD-repeat and SOCS box-containing protein 1 Y151 Tyrosine position 151 Ytm1 WDR12 yeast homologue αMEM Alpha minimal essential media xviii

µ micro °C Degrees Celsius

ΔΔCT Fold Change R2 Coefficient of determination % Percent < Less than > Greater than

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Chapter 1 Introduction 1 Introduction 1.1 Breast Cancer

1.1.1 Incidence and Histological Subtypes

Breast cancer is the most commonly diagnosed cancer in women worldwide [1]. It is the most prevalent cancer in female Canadians, afflicting 25% of women [2]. Characterized as a heterogeneous disease, breast cancer can be divided into three histological subtypes based on receptor expression: hormone receptor positive (HR+) for the estrogen and progesterone receptors (ESR and PGR, respectively), human epidermal growth factor receptor-2 (Her-2) positive and triple negative breast cancer (TNBC), which are negative for the PGR and ESR, as well as for Her-2. Receptors can be thought of as biological switches, such that the dysregulation of the signaling pathways they mediate could lead to one or many of the six hallmarks of cancer: continuous proliferative signaling, induction of angiogenesis, evasion from growth suppressors/apoptosis, replicative senescence and infiltration [3, 4]. Each one of these subtypes displays different prognoses and incidence rates. The most commonly diagnosed breast cancers are the HR+, afflicting 60 to 70% of patients. The endocrine receptor positive breast cancers display the most favorable prognosis relative to the other two subtypes, likely due to the availability of endocrine therapies that either inhibit the hormone from binding to the receptor in breast and peripheral tissues or lower overall hormone levels. A slightly worse prognosis is seen for the Her-2 amplified breast cancers afflicting 20 to 30% of patients. However, Her-2 targeting therapies have proven to be effective treatments [3, 5]. The remaining 10 to 20% of breast cancer diagnoses are negative for all three of these receptors, hence the name triple negative breast cancer (TNBC). This subtype exhibits the least favorable prognosis of all subtypes due to a lack of targeted therapies. These are typically very aggressive cancers that do not respond to currently available treatments and have the highest rate of relapse and death. The biological pathways implicated in the progression of this type of cancer remain obscure and therefore represent an area requiring active research [3].

1.1.2 Molecular Subtypes

Diagnostically, breast cancer tumors are subtyped using staining techniques such as immunohistochemistry (IHC), in situ hybridization and/or by analyzing gene expression levels by microarray or Quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR). The former relies on staining breast tissues using labeled antibodies or probes that bind to specific antigens or genes, respectively. The targets typically analyzed are the endocrine receptors PGR and ESR) and the Her-2 receptors along with markers of proliferation such as Ki- 67. These methods enable the pathologist to classify the tumor as positive or negative for the endocrine receptors and whether it displays an amplification of the Her-2 gene. Although these markers serve as helpful tools in determining treatment and prognosis, breast cancers within each subtype display significant differences in terms of the signaling pathways they exploit to drive the progression of the cancer [6].This presents as a challenge in predicting a patient’s clinical outcome in terms of treatment response. This suggests that the genes and pathways involved in the progression of breast cancer are far more complex than the endocrine and Her-2 receptors. Rather than looking at a singular gene at a time, it became apparent to scientists that the expression pattern of numerous genes painted a better picture of the cancer’s development. With the advent of cDNA microarrays it became possible to look at expression patterns of thousands of genes simultaneously in a time effective manner [7]. In Perou et al.’s seminal study, they classified 65 breast tumor samples into four molecular subtypes based on similarity of expression patterns, using this technology. Luminal breast cancers exhibited high levels of genes characteristic of epithelial cells originating from the mammary duct lumen as well as estrogen receptor genes. Basal-like breast cancer tumors showed little to no expression of the endocrine and Her-2 receptors, and high expression levels of genes characteristic of basal epithelial cells as well as genes playing roles in cell proliferation and survival. A third subtype displayed amplifications of Her-2 and other associated genes. Lastly, the fourth normal-like subtype exhibited genes reminiscent of normal breast epithelium, however these samples may have been contaminated by normal tissues surrounding the malignant tumor cells during excision and is therefore deemed uncertain [6]. A subsequent study conducted by Loi et al. went on to further classify the luminal subtype into luminal A & B based on clinical outcomes. The latter exhibits a more favorable prognosis and is characterized by high levels of ESR with low levels of cell

3 proliferation genes including Ki-67. Ki-67 is a nuclear protein solely expressed in proliferative cells. Higher levels of Ki-67 correlate with greater rates of proliferation and worse prognoses [8]. The former, displaying less advantageous outcomes showed the reverse; lower expression of ESR and high expression levels of proliferative markers [9].Therefore, Luminal B breast cancers typically show greater rates of cell proliferation and slightly worse prognoses than the A type (Figure 1) [10]. This system of classification has since become the gold standard in breast cancer diagnosis.

Figure 1. Breast Cancer prognosis based on molecular and intrinsic subtype. Breast cancer is a heterogeneous disease categorized into four intrinsic subtypes possessing different expression levels of the endocrine receptors (ESR and PGR) and Her-2. The different subtypes exhibit different prognoses: Luminal A shows the better prognosis, while the Basal-like (TNBC) shows the worse prognosis [10].

Gene expression analysis of diverse TNBCs has revealed that there is substantial heterogeneity within the subtype. In fact, 6 subtypes have been identified within TNBCs, based on gene expression patterns revealing dependencies on certain signaling pathways for progression [11, 12]. Lehman et al. identified the following 6 subtypes using 587 TNBC cases via cluster analysis: Basal-like 1 (BL1), Basal-like 2 (BL2), Immunomodulatory (IM), Mesenchymal (M), Mesenchymal-Like (MSL) and Luminal Androgen Receptor (LAR). BL1 displayed increased expression of genes implicated in the cell cycle and DNA damage response. BL2 showed enrichment in genes implicated in growth factor signaling. Both these subtypes showed better

4 responses to anti-mitotic therapies such as taxanes including paclitaxel and docetaxel in comparison to the other TNBC subtypes. IM were enriched for genes implicated in immune response pathways such as immune cell signaling, cytokine signaling, processing and presentation of antigens. M showed increased expression of genes involved in cell motility, extracellular matrix (ECM) receptors, and cell differentiation signaling pathways. MSL also showed similar patterns with the addition of increased expression of genes involved in growth factor signaling and angiogenesis. Furthermore, MSL displayed low expression of genes involved in cell proliferation and an increase in stem-cell genes compared to the M subtype. LAR were characterized by androgen receptor signaling and related genes and respond well to anti-androgenic therapies compared to the other TNBC subtypes. Furthermore, diverse breast cancer cell lines were confirmed to capture all 6 TNBC subtypes [11].

1.2 Standard of Care and Current Treatments

According to Statistics Canada, most breast cancers are diagnosed at an early stage of breast cancer development. This is likely due to early prevention programs that have been put in place in recent years. These early-prevention programs provide mammographies to women at risk of breast cancer development [2]. Typically breast cancers are diagnosed based on screening procedures, such as mammographies, ultrasounds, magnetic resonance imaging (MRI) and can be further confirmed by means of a biopsy. Treatment plans often consist of a combination of surgery, chemotherapy, radiation and hormone therapy personalized to the patients’ demographic characteristics such as age and ethnicity along with medical and family history. When putting together therapeutic regimens a patient’s menopausal status, cancer grade, tumor grade and receptor expression status are carefully considered [13]. Tumors larger than 1 cm are surgically removed followed by radiation therapy and/or chemotherapy [14]. This type of adjuvant therapy ensures that all malignant cells are eradicated. Patients’ with HR+ and/or Her-2+ breast cancers are also typically prescribed targeted therapies, concomitantly or as standalone treatments [13]. Selection of a suitable endocrine therapy depends on the patient’s menopausal status, as this helps determine the body’s main source of estrogen. In pre-menopausal women, the ovaries are responsible for producing a large portion of estrogen. In such cases, endocrine therapies that block estrogen from binding to ESRs or inhibit estrogen production can be used (Table 1). In

5 post-menopausal women, the ovaries decrease their production of estrogen and production depends primarily on the conversion of androstenedione to estrogen. In such cases, women are more likely to be prescribed aromatase inhibitors (AI), which inhibit this reaction from taking place [13, 15]. Currently, no targeted therapies exist for treating TNBC and treatment relies solely on chemotherapeutics [13]. Lack of specific therapies is reflected in the lower survival rates observed in patients with TNBC (80 and 60%, 5 and 10 year survival) in comparison to the Luminal A/B and Her-2 amplified subtypes (90 and 70%, 5 and 10 year survival), regardless of age and stage of cancer at diagnosis [16-18]. The decreased response rate of TNBC patients to endocrine and Her-2 targeting therapies is suggested to be due to the absence of the relevant targets [17]. This emphasizes the importance of investigating the signaling pathways at play in TNBC with the goal of uncovering a potential drug target that could inhibit the progression of the cancer.

Table 1. Summary of endocrine therapies for hormone receptor positive breast cancers [1, 3].

Target Mechanism

Aromatase Aromatase synthesizes estrogen from male hormones (androstenedione Inhibitors and/or testosterone). AIs inhibit the enzymatic activity of aromatase in (AI) peripheral and breast tumor tissue reducing estrogen production.

Selective These drugs bind to the ESR, competitively blocking estrogen from binding. estrogen This may result in the disruption of receptor dimerization, disruption of receptor binding to transcription factors, decreased translocation to the nucleus, as down well as increased degradation and down-regulation of the estrogen receptor. regulators (SERD)

Selective These drugs bind to estrogen receptors both in breast and other tissues, estrogen however it can act as an antagonist in breast tissues and partial agonist in receptor other tissues such as in the bones and uterus. This is important because modulators estrogen plays an important role in bone density, such that antagonizing the (SERM) effects of estrogen in the bones can lead to or aggravate symptoms of

osteoporosis.

1.3 The WD40 Repeat Family The WD40 Repeat (WDR) proteins make up a large family that have recently become interesting drug targets. They contain β-propeller domains that form a deep pore amenable to targeting by small molecule inhibitors. Typically, each blade of the propeller consists of a tryptophan- aspartate (WD) motif, which is most frequently repeated seven times [19]. On average, the length between each WD repeat hovers around 40 amino acids, hence the name WD40. This repeating portion is usually located at the N-terminal of the protein, with a variable length region at its C-terminal [20]. As suggested by their abundance in the eukaryotic genome, the WDR proteins play a pivotal role in connecting proteomic networks by acting as scaffold proteins in higher order protein structures. Although they are implicated in diverse cellular functions, they exhibit no enzymatic activity on their own, making them nonconventional targets for drug discovery.

The WDR proteins have various surfaces of interaction located at the top, bottom and sides, such that they can interact with multiple binding partners and can partake in the formation of various complexes implicated in multiple cellular processes. A single WDR can form a variety of complexes, each implicated in unique processes. Embryonic ectoderm development (EED) is a great example of a WDR that utilizes all of these surfaces to interact with components of the Polycomb repressive complex 2 (PCR2) (Figure 2b). Furthermore, these interactions are not limited to other proteins, but may also occur with nucleic acids, as is seen with the WDR member, DNA Damage-binding protein 2 (DDB2) (Figure 2c). Some members may also detect and bind to post-translational modifications of proteins. This type of interaction is observed with F-box/WD repeat containing protein 7 (FBX7), which recognizes phosphorylated threonine or serine residues belonging to degron motifs by means of its central pore [19]. Although the WDR members are multifunctional, each interaction is unique, each with the potential to be targeted by inhibitors. This ability to interact in a unique way with their binding partners is believed to be due to their low sequence conservation beyond their three-dimensional shape. Evolutionarily, small mutations in a single repeat may have been sufficient to create a new binding pocket to accommodate novel binding partners; such minimal alterations would disrupt the folding efficiency of a single repeat, while leaving the rest of the protein intact. This unhindered portion

7 maintaining its ability to fold would compensate for the folding impediment maintaining the proteins overall donut shape [21]. This property makes them ideal for the development of highly specific drugs. Given that these interaction surfaces possess the suitable physical and chemical properties for the development of small molecules, such inhibitors could restrain multi-protein structures from forming resulting in the disruption of the pathways they partake in. To appreciate the breadth of functions that WDRs are implicated in, Figure 3 lists some of these cellular processes with their corresponding WDR members. The dysregulation of many of these pathways, such as the cell cycle and growth factor signaling, have been implicated in the progression of various pathologies, including cancer [19]. Further validating the WDR protein family as good drug targets, is that protein-protein interaction inhibitors (PPII)s have successfully been developed by the Drug Discovery team at the Ontario Institute for Cancer Research (OICR)/Structural Genomics Consortium (SGC) and by Abbvie/Novartis against WDR5 [22] and EED [23, 24], respectively. Both these proteins are members of the WDR family and play roles in the epigenetic modification of histones that have shown promise as therapies in oncology [19]. Considering the implication of WDR members in a variety of disease-associated pathways and their previous success in being drugged, this provides a good starting point in search of a potential drug target for TNBC, a disease for which targeted therapies are lacking.

Figure 2. Various modes of interaction of WD40 repeat proteins. A) The typical β-propeller structure formed by the WDR domains. B) Interaction of WDR-protein EED with components of the Polycomb repressive complex 2 (PRC2) via its top, bottom and side surfaces. C) Interaction of DNA-damage binding protein 2 (DDB2) with a damaged portion of DNA. D) Interaction of F-box/WD-repeat-containing protein 7 with phosphorylated residues of a Cyclin-E peptide [19].

Figure 3. Diversity of cellular functions played by WD40 family members. The left panel displays the number of WDR proteins involved in each cellular process listed. The enrichment of those proteins relative to the entire proteome is also listed. The right panel lists WDR proteins implicated in pathology-associated pathways [19]. (See abbreviation list for full gene names).

1.3.1 Merits of Targeting the WD40 Family in Drug Discovery Other than the WDR proteins’ implication in a variety of cellular functions associated with disease progression, there are other reasons why targeting this family of genes shows merit. Owing to the low sequence conservation and intrinsic malleability of their β-propeller domains, the binding pockets created by their central pore are chemically diverse. This translates to the development of more diverse chemical inhibitors with greater specificity and less cross- reactivity. This presents as an advantage in comparison to the development of small molecules targeting catalytic sites of enzymes, such as kinases, ubiquitin ligases and de-ubiquitinases, which have been challenging or largely unsuccessful due to lack of specificity and selectivity of inhibitors. These enzymes rely on similar chemical properties for substrate recognition and binding, making the development of highly specific inhibitors very challenging. Drugs designed to target enzymes such as kinases are likely to fit into the catalytic site of many of its family members, increasing off-target effects. As such, going after scaffolding proteins such as the WDR proteins rather than enzymes provides an opportunity to selectively target pathways, some of which have proven to be challenging in the past [19].

Moreover, although the central pore provides an ideal binding pocket for the development of compounds, drug discovery is not limited to those surfaces as suggested by the successful drugging of CDC20’s side pocket [19]. In these types of interactions, inhibitors can bind between the blades of the β-propeller, directly disrupting the interaction between proteins or allosterically by means of conformational change [19]. Another advantage of targeting a scaffold protein rather than an enzyme, is that they may result in less drug resistance over time. Drug resistance may occur due to mutations at the catalytic inhibitor-binding site. These types of resistance mechanisms seem to be avoided with WDR- targeting drugs. A comparison of the effects of drugging the catalytic element of the PRC2 complex (EZH2) versus the scaffolding protein EED provides evidence for this. As mentioned previously, PRC2 is implicated in the epigenetic silencing of genes. Drugs directed at the catalytic component of the complex elicited drug resistance due to mutations at the site of ligand binding, which did not occur with EED-inhibitors over long term treatment of cancer cell lines. This lack of resistance development in WDR proteins is hypothesized to be due to their symmetrical structure, which would require the mutation of many more residues throughout the central pore to disrupt the binding and function of the small molecule inhibitors [19]. Taken together, the WDR proteins provide a platform for the development of inhibitors targeting pathways that were not feasible in the era of catalytic inhibitors. They may also provide a way to circumvent the issue of drug resistance, which has proven to be an issue in breast cancer treatment especially for TNBC [16, 25]. This further validates searching for a novel drug target for TNBC in the gene family of WDR.

1.4 Genome-wide siRNA Screens as Tools for Target Identification

1.4.1 Project DRIVE Conducted by Novartis

Novartis conducted pooled shRNA dropout screens on approximately 400 cancer cell lines to determine the essentiality of approximately 8000 genes in cancer cell survival. In order to accomplish such large-scale loss of function screens, 20 shRNAs targeting different regions of the mRNA transcript for each gene were produced, pooled together and transduced with lentivirus into cancer cell lines. High throughput sequencing was used to generate a log fold

10 change of the shRNA levels before and after infection. This was done by comparing shRNA counts after the 14-day screen relative to the shRNA counts present in the initial plasmid library. The magnitude of the fold change provides an indication of the essentiality of a gene in a given cell line. shRNAs that were no longer detectable were said to have “dropped-out” and deemed to be essential in the survival of a given cancer cell line. The Novartis group used two methods to generate sensitivity scores: Analytical Technique for Assessing of RNAi by Similarity (ATARiS) and Redundant siRNA activity (RSA). In RSA, statistical significance is determined by comparing the effect of a single gene to that of the entire library to determine which genes were essential, active or inert. RSA classifies essential genes as having scores less than or equal to -3 in over 50% of cell lines. On the other hand, the ATARiS scoring system only considers shRNA exhibiting consistent activity in attempt of eliminating inactive or off-target effects. This system essentially standardizes the knockdown effects across the entire dataset, such that cell lines were considered sensitive to knockdown when scores were less than or equal to -1 [26].

1.4.2 Project Achilles Conducted by Broad Institute

The Broad Institute conducted a similar large-scale study, in which approximately 500 cell lines were transduced with a pool of 5 shRNA per gene for a duration of 16 doublings and/or equating up to 40 days. Similar to the Novartis group, they measured shRNA depletion by sequencing the initial shRNA library along with cells 40 days post-infection. A standardized scoring system was established using the DEMETER algorithm in which shRNA off-target effects were minimized by taking into account seed-based effects. In theory shRNA sequences should have 100% complementarity to the target mRNA, however a stretch of 6-7 nucleotides at the 5’ end of the shRNA plays a crucial role in mRNA target recognition and target binding. This seed region can be common to many mRNA transcripts giving rise to different genes, thereby resulting in the silencing of unintended targets and skewing the interpretation of results [27]. The DEMETER method was subject to a series of optimizations to improve the accuracy of results. The Broad Institute qualified a cell line as gene-dependent when its score for a given gene was 6 standard deviations (6σ) from the mean of all the cell lines. Similar to the Novartis scoring system, the

DEMETER scores are derived from log2 (fold changes). A dependency score of -2 or lower

11 corresponds to 6 standard deviations from the mean and are said to be essential for the survival of a given cancer cell line [28].

These results generated by Project Deep RNAi Interrogation of Viability Effects (DRIVE) (Novartis) and Project Achilles (Broad Institute) were made accessible to the general public through online databases [26, 28]. Browsing through these databases led me to the identify WDR12, which among the 21 breast cancer cell lines investigated by the Broad Institute, 19% showed low sensitivity to the knockdown of the gene of interest (GOI) in terms of cell proliferation, 38% showed moderate sensitivity, and 43% showed high sensitivity to the disruption of WDR12. Furthermore, within this subgroup of highly sensitive cell lines, 67% were TNBC; meaning that, the six most sensitive cell lines belonged to this histological subtype, suggesting WDR12 might be an interesting gene to further investigate as a potential drug target (

Figure 4).

Figure 4. Sensitivity of diverse breast cancer cell lines to WDR12 knockdown according to the Broad Institute's shRNA dropout screen [28]. Heat map generated by the Morpheus online visualizer using the Achilles project dataset. Blue range corresponds to high sensitivity to WDR12 knockdown; white corresponds to moderate sensitivity to WDR12 knockdown, and red corresponds to low sensitivity to WDR12 knockdown.

Interestingly, the data provided by Novartis did not corroborate with the Broad Institute results. In fact, as depicted in Figure 5, comparing the data suggested opposing trends such that most breast cancer cell lines (86%) showed moderate or low sensitivity to WDR12 knockdown regardless of subtype.

Figure 5. Sensitivity of diverse breast cancer cell lines to WDR12 knockdown according to Novartis' shRNA dropout screen [27]. Heat map generated by Morpheus online visualizer using Project DRIVE dataset. Blue range corresponds to high sensitivity to WDR12 knockdown; white corresponds to moderate sensitivity to WDR12 knockdown, and red corresponds to low sensitivity to WDR12 knockdown. The histological subtype is provided by color coded arrows (Basal-like = red, Luminal A= blue, Luminal B= green, Her2amp= yellow, No subtype assigned= grey).

1.4.3 Combined (Achilles, DRIVE, and Marcotte) Dataset

Fortunately, an additional dataset was published at a later date, which combined the results of three pooled shRNA drop-out screens. Both of the aforementioned datasets were included, with the addition of results generated by the Marcotte et al. [29] study. The focus of this experiment was solely on uncovering breast cancer vulnerabilities. They surveyed 77 breast cancer cell lines using a genome-wide shRNA library and generated their own scoring system based on the si/shRNA Mixed Effects Model (siMEM) algorithm, which was compared to ATARiS and DEMETER. Analyzing “known positives” such as Her-2 related genes in Her-2 amplified cell lines revealed that the latter algorithms masked a lot of information. For example, when analyzing Her-2+ cell lines, “known positives” such as ERBB2 and its binding partners (ERBB3, members of the PI3K/mTOR signaling pathway) along with associated transcription factors were recovered by siMEM, whereas most of these could not endure the false discovery corrections employed by ATARiS and other metrics [29]. The Broad institute’s continued efforts to optimize the DEMETER algorithm made it possible to combine the results of all three datasets. In DEMETER2, the algorithm included updates accounting for batch effects and variability in screen quality. Combining all three screens provides improved gene dependency estimates and

13 provides a compendium of gene dependencies conducted in the largest number of cancer cell lines to date. The efficacy of DEMETER2 in determining gene essentiality scores using the combined dataset was successfully validated with a list of positive and negative control genes [30]. McFarland et al. [30]’s group even cross-referenced their results to genome-wide CRISPR- Cas9 screening datasets and confirmed concordance of gene dependency scores between gene- silencing methods. In the combined dataset, cell lines with scores less than or equal to -1 were said to be sensitive to the gene in question. Using the combined dataset, I verified the sensitivity of breast cancer cell lines to the knockdown of WDR12. Analysis of cell lines with an official subtype revealed that 47% were Basal-like (TNBC) and 53% were non-Basal-like. Within the Basal-like breast cancer cell lines, 45% showed moderate to high sensitivity in response to WDR12 knockdown. The sensitive Basal-like cell lines represented 56% of all the sensitive cell lines (Figure 6). This suggests that TNBC may exhibit a greater dependency on WDR12 in comparison to the other subtypes and may therefore be an interesting drug target to investigate.

Figure 6. Sensitivity of diverse breast cancer cell lines to WDR12 knockdown according to the Combined shRNA dropout screens [30]. Heat map generated by the Morpheus online visualizer using the combined (Achilles, Drive, Marcotte) dataset. Blue range corresponds to high sensitivity to WDR12 knockdown; white corresponds to moderate sensitivity to WDR12 knockdown, and red corresponds to low sensitivity to WDR12 knockdown. The histological subtypes of each cell line is provided by color coded arrows (Basal-like= red, luminal A= blue, Luminal B= green, Her2Amp= yellow, No subtype assigned=grey).

1.5 WD40-Repeat 12

1.5.1 Ribosome Biogenesis

A literature search revealed WDR12’s implication in the process of ribosome biogenesis as part of the PeBoW (Pes 1, Bop1, WDR12) complex. Ribosome biogenesis refers to the process by which ribosomes are assembled. Ribosomes serve as the protein-synthesizing machinery of the cell. Mature ribosomes consist of two subunits made up of rRNA and protein. Assemblage begins in the nucleolus where transcription of mRNA and the 47S rRNA is catalyzed by RNA Polymerase II (PolII) and a multi-protein complex containing RNA Polymerase I (PolI), respectively. DNA giving rise to these transcripts is located within the nucleolar region. The mRNA transcripts are exported to the cytosol, where they are processed by pre-existing ribosomes producing the protein portions of the subunits. Concomitantly, the 47S rRNA transcript is subjected to a series of coordinated endo- and exo-nucleololytic cleavages giving rise to the rRNA precursors making up the small 40S and large 60S subunit. Once these rRNA species have reached their mature form they are exported to the cytosol, where they can join with the protein portions forming functional ribosomes. The small subunit consists of the 18S and 5.8S rRNAs while the large subunit is composed of the 28S rRNA. The protein portions of the ribosome are catalytically active, stringing amino acids together to form proteins based on codon sequences. The enzymes involved in processing of the 47S transcript have not yet been elucidated both in yeast and in mammals. It is important to note that although the process of ribosome biogenesis possesses similarities to mRNA splicing, it functions independently and employs very different machinery and enzymes [31-33]. Ribosome biogenesis is quite complex, requiring the assistance of over 200 assembly factors and proteins. These proteins and factors are recruited and ejected sequentially, progressively simplifying the structure of the ribosome. Experimental evidence suggests that disruption of any of the PeBoW components results in the accumulation of the 32S rRNA, an intermediate in the maturation of the 28S rRNA. The result is a decrease in functional, fully mature ribosomes. This suggests that WDR12 and its components are implicated in the maturation of this rRNA species and without the formation of the complex, ribosome biogenesis is hindered [34].

1.5.2 Structure and Binding Partners The human protein structure of WDR12 was recently resolved by the Structural Genomics Consortium (SGC), revealing a seven bladed propeller, typical of the WDR family with the addition of an ubiquitin-like (UBL) domain at its N-terminal. Overlaying the crystal structure of the human protein over that of its well established yeast counterpart (ytm1) revealed nearly perfect alignment of key interaction residues with a root-mean-square deviation of atomic positions (RMSD) value of 1.54Å at 294 Cα positions [35]. WDR12 has been shown to interact with its binding partners, Pescadillo 1 (Pes1) and Block of proliferation 1 (Bop1) as part of a larger structure called the PeBoW complex, in a conserved manner. Noteworthy is the fact that Bop1 is also a member of the WDR family. A study by Thoms et al. investigating the interaction between Ytm1 and Erb1 (the WDR12 and Bop1 yeast homologues) revealed a particular type of interaction. The crystallization of an Erb1-Ytm1 complex demonstrated that Erb1 uses two adjacent blades on its side surface to interact with the bottom of Ytm1’s central pore. A rare mode of interaction (cation π-stacking) was discovered, involving Ytm1’s W113 and Y151 residues, in conjunction with R486 in Erb1 (Figure7a). Furthermore, this interaction also relies on multiple salt bridges (E481-K181, R486-D112, E785-H320) between Erb1-Ytm1 (Figure 7a). Interestingly, point mutations inserted at the binding interface of these two proteins resulted in the suppression of cell growth in yeast [36]. Additionally, according to Romes et al., knockdown of any of the PeBoW components leads to a cycle arrest in yeast and a p53-dependent arrest in mammalian cells [37]. This suggests that a shortage of ribosomes could result in a slowing of the cell cycle due to the cells’ inability to keep up with protein demands. Furthermore, recent shRNA knockdown experiments conducted in hepatocellular carcinoma tissues are in concordance with the aforementioned. WDR12 knockdown resulted in decreased cell proliferation and migration accompanied by down regulation of the AKT, mTOR and S6K1 pathways, which have also been implicated in the progression of breast cancers [38, 39]. Taken together, these findings suggest that the interaction between Bop1 and WDR12 might be an interesting target for the development of PPIIs.

As mentioned previously, the process of ribosome biogenesis requires the chronological and sequential recruitment and expulsion of factors and proteins. One such protein helping to control the order of these events is Midasin, a dynein-like protein with a pivoting arm exhibiting

ATPase activity. The base of the protein interacts with the pre-ribosomal molecule bringing proteins and complexes in and out of contact with the maturing ribosomes with a swift power stroke. One of the complexes it interacts with is the PeBoW complex. Midasin binds to WDR12 via its UBL-domain, while Bop1 is hypothesized to interact with rRNA at the opposite end. The role that Bop1 plays at this step in the process has yet to be elucidated, however WDR12 appears to serve as an anchor point enabling the complex to come into contact with the maturing ribosome. Disrupting the interaction between Midasin and WDR12 inhibits the process of ribosome biogenesis, resulting in a decreased cell growth phenotype both in yeast and mammalian cells [36, 37]. Although this interaction does not employ the WD-40 domain of WDR12, it provides another opportunity for the development of small molecules.

Figure 7. Crystal structure of Ytm1 and Erb1 complex (WDR12 and Bop1 yeast homologues). A) Erb1 (the Bop1 yeast homologue) is shown in blue. Ytm1 (the WDR12 yeast homologue is shown in yellow) with its UBL- domain shown in orange. Erb1 interacts with the bottom surface of Ytm1 via its side surface. The interaction consists of various salt bridges between Erb1-Ytm1 (E481-K181, R486-D112, E785-H320). B) Comparison of the crystal structure of Ytm1 (shown in yellow and orange) to that of Rsa4 (another protein that binds to Midasin via its UBL-domain). C) The interaction of Erb1 and Ytm1 spans a very large surface area of both proteins shown in red [36].

1.5.3 Localization of WDR12 and its Components There appears to be an interdependence of the PeBoW components in terms of stability, co- localization and expression levels. The individual components are unstable in the cytoplasm and are predominantly found in the nucleolus as part of the PeBoW complex. After translation, Pes1 and WDR12 migrate to the nucleolus independently. Whereas Bop1 requires Pes1 to translocate to the nucleolus and can hinder the transfer of WDR12 to the nucleolus when overexpressed in mammalian cells. For this reason Bop1 levels are tightly regulated to minimize the formation of these two sub-complexes (Pes1-Bop1 and Bop1-WDR12), which accumulate in different compartments of the cell, thereby hindering the formation of the PeBoW complex. To ensure the optimal formation of the complex, the components must be in a 1:1:1 stoichiometric ratio. In addition, disruption via siRNA of Bop1 leads to a strong depletion of WDR12 and Pes1 protein levels. Whereas WDR12 knockdown results in a modest decrease in Bop1 protein levels and Pes1 manipulations show no effect on the other components (Figure 8) [34]. This suggests that disrupting the PeBoW complex from forming with small molecule inhibitors would result in decreased levels of the other components, further disrupting the process of ribosome biogenesis.

Figure 8. Interdependence of the PeBoW components for cellular localization. Pes1, Bop1 and WDR12 are unstable in the cytoplasm and migrate rapidly to the nucleolus after synthesis. Pes1 and WDR12 can travel to the nucleolus independently. Bop1 depends on Pes1 to translocate to the nucleolus. The over-expression of Bop1 causes the formation of Bop1-WDR12 sub-complex, blocking the migration of WDR12 transport to the nucleolus [34].

1.5.4 The Cell Cycle and Ribosome Biogenesis When cells are preparing to divide, they must grow and synthesize all the genomic and proteomic material required to give rise to viable daughter cells. For this reason, the cell cycle and ribosome biogenesis are tightly linked such that when cells show an increased rate of proliferation, they correspondingly increase their rate of ribosome biogenesis to keep up with inflating protein demands. Consequently, they share many of the same regulators. This is obvious in cancer cells that exhibit uncontrollable growth due to dysregulations in cell cycle progression [40]. Accordingly, Hölzel, M., et al. demonstrated that expression of dominant negative mutants of ytm1 (the WDR12 yeast homologue) results in a reversible cell cycle arrest at the G1-S interphase [41]. This suggests that interfering with ribosome biogenesis by creating a shortage of ribosomes causes cells to slow their progression through the cell cycle due to a decreased ability to synthesize enough protein.

It is unclear how the cell cycle and ribosome biogenesis is linked, however according to Brighenti et al., during cell division CDK1-cyclin B activity inhibits PolI transcription [42-46] (recall that PolI is responsible for the transcription of the 47S rRNA transcript [47]). Conversely, inhibition of CDK1-cyclin B complex causes the re-activation of the PolI multi-protein complex at the end of mitosis. Other components of the PolI complex, the upstream binding factor I (UBF), ribosomal DNA transcription factor (Rrn3) and Selectivity factor 1 (SL1) become activated during G1 upon phosphorylation by G1-Cyclin-CDKs, stimulating rRNA transcription during the progression of this phase [48-52]. Furthermore, during G1, cyclin-D-dependent kinases (CDK4 and CDK6) and cyclin E-CDK2 phosphorylate the tumor suppressor Retinoblastoma protein (Rb), hindering it from binding to UBF and other transcription factors (including E2F), resulting in enhancement of rRNA transcription [53-56]. Contrastingly, unphosphorylated Rb represses rRNA transcription through UBF binding. Disruptions in ribosome biogenesis are linked to the cell-cycle through p53-dependent mechanisms. Under normal circumstances, p53 levels are kept low due to its rapid degradation facilitated by HDM2 (human double minute 2), an E3-ubquiquitin ligase. When a disruption in ribosome synthesis occurs, the unoccupied ribosomal proteins accumulate and bind HDM2 at the site of p53 interaction, thereby inhibiting its degradation and leading to p53 accumulation. The accumulation and stimulation of p53 via phosphorylation at various sites of the protein results in various downstream events, one of which is the enhanced expression of p21, which hinders Rb phosphorylation [15, 57-62]. Unphosphorylated Rb blocks the activity of the E2F transcription factors, which mediate the expression of genes involved in the transition from G1 to S phase. Under normal cell cycle progression, Cyclin D-dependent kinases phosphorylate Rb releasing it from E2F. Events stimulating growth result in the increased activity of Cyclin D-cdk4, which initiates a series of events resulting in E2F accumulation. E2F accumulation results in the transcription of genes required for entry into S-phase. P53 is linked to the Rb-E2F pathway via a protein called p19ARF, which controls the function of HDM2. P19ARF accumulation leads to the inhibition of HDM2, such that p53 cannot be degraded. Interestingly, the expression of p19ARF is induced via E2F activity, thereby linking the two pathways together [63]. These pathways linking the cell cycle and ribosome biogenesis are often mutated in cancers, however there is evidence that even in the presence of these dysregulations, cancer cells do not fare well. When the Rb-p53 pathway is intact, impairments in rRNA synthesis results in G1 cell cycle arrests; in cells with inactivated p53-Rb pathways, cells continue to progress through the cell

20 cycle until the ability to produce protein is exhausted. This results in increased apoptosis, due to the production of inviable daughter cells lacking adequate protein levels for normal functioning. The absence of this pathway in many cancer cells may explain their greater vulnerability to ribosome biogenesis disruptions. Interestingly, breast cancer patients with loss of function mutations in this pathway showed better response to treatments that targeted both DNA and rRNA synthesis (5-Fluorouracyl (5-FU) and methotrexate), than those with intact pathways. Furthermore, these are pathways that are often disrupted in TNBC [11, 64], which might confer a greater vulnerability to WDR12 knockdown. A variety of other pathways link the cell cycle and the process of ribosome biogenesis including the MAPK/ERK, PI3K/AKT, PTEN (phosphatase and tensin homolog deleted in chromosome 10) and mTOR signaling pathways by exerting their effects on PolI rRNA transcription. All of these signaling pathways are hypothesized to link the cell cycle to ribosome biogenesis and have been shown to be dysregulated in various cancers including breast cancer. Therefore, this substantiates the claim that the rate of ribosome biogenesis is enhanced in many cancers in order to maintain an increased rate of cell proliferation [47]. This mandates further investigation to better understand the link between ribosome biogenesis and the cell cycle, in hopes of developing specific inhibitors targeting ribosome synthesis.

1.5.5 Nucleolar Morphology as an Indicator of the Rate of Ribosome Biogenesis Interestingly, aggressive cancers tend to display multiple and enlarged nucleoli; the structure within the nucleus where ribosome biogenesis occurs. Nucleolar size and number has for long been shown to be directly proportional to the rate of ribosome biogenesis. This has become a clinical tool for determining prognosis of colorectal and breast cancers. The larger and more numerous the nucleoli, the more aggressive the cancer and the worse the prognosis [40]. Studies conducted by Derenzini et al. and Trere et al. have revealed a relationship between an increased rate of cell duplication and increased nucleolar size based on silver staining of the nucleolar organizing region (AgNOR), which has become a staining technique used frequently in clinical settings [65-67]. Furthermore, in Ishihara at al.’s study of 308 breast carcinomas obtained from surgeries, they showed that 53.6% of Basal-like breast cancers displayed larger nucleoli, suggesting that this characteristic may be more prominent in a subset of TNBC [68]. Moreover,

21 there is evidence that neoplastic cells show a greater sensitivity to disruptions in ribosome biogenesis than do normal cells. Typical cancer therapies exert their effect by damaging DNA or hindering its synthesis. Such treatments exploit the fact that DNA repair and cell-cycle progression checkpoint mechanisms function sub-optimally in cancer cells, thereby resulting in increased cell death. The caveat however, is that they also exert toxic effects on healthy cells that proliferate rapidly such as those of the hair follicles and intestinal lining. One would expect drugs targeting ribosome biogenesis to have similar adverse effects, considering the process occurs both in proliferating and resting cells. However, Brighenti et al. suggests this isn’t the case. Cancer cells appear to be much more susceptible to disruptions in the synthesis of ribosomes [47]. In fact, Lewinska et al. found that the administration of the three known inducers of nucleolar stress in histologically diverse breast cancer cell lines resulted in the inhibition of cell proliferation along with alterations in levels of ribosome-associated proteins including a decrease in WDR12. Furthermore, these same plant-derived compounds exhibited minimal cytotoxic effects on normal human mammary epithelial cells, suggesting breast cancer cells exhibit a greater vulnerability to disruptions in ribosome biogenesis [69]. This points towards an opportunity for developing more specific cancer drugs by targeting this pathway. Additionally, although gene-dependency analyses [70] revealed no biomarkers of increased reliance on WDR12 in breast cancer cell lines, looking at the size and number of the nucleoli may serve as a good indication of whether a cancer will be susceptible to a WDR12 disruption.

1.6 Study Rationale and Hypotheses

Considering the following three points: firstly, that members of the WDR family have been shown to be highly druggable; secondly, that WDR12 appears to be implicated in ribosome biogenesis; and lastly that TNBC cells exhibit increased and enlarged nucleoli, it may be valuable to explore the disruption of ribosome biogenesis by targeting WDR12, specifically in TNBC cell lines. The long-term goal is to uncover a signaling pathway that might be driving the progression of TNBC, with hopes of developing targeted therapies that could improve the prognosis of patients afflicted with this subtype of breast cancer.

In the present thesis, I began to investigate the role of WDR12 as a potential drug target in TNBC with the use of inducible shRNA and compared knockdown phenotypes between diverse

22 histological breast cancer cell lines. I evaluated the effects of WDR12 knockdown on cell proliferation via an ATP-based assay and cross-referenced those results with Trypan Blue exclusion-determined cell counts. In order to better understand potential decreases in cell proliferation, an apoptosis-activation assay and a cell cycle analysis (via a BrdU incorporation assay paired to flow cytometry) were conducted. This allowed me to answer the question of whether cells were dying or simply slowing down in response to WDR12 knockdown. Lastly, changes in nucleolar morphology in response to WDR12 knockdown were evaluated via nucleolin staining. Nucleolar morphology of diverse breast cancer subtypes were compared by this means. These methods allowed me to validate or refute the following hypotheses: knockdown of WDR12 was anticipated to cause a decrease in cell proliferation in diverse breast cancer cell lines with more pronounced effects in TNBC. The decrease in cell proliferation was anticipated not to be the result of apoptosis, but rather due to a cell cycle arrest at the G1-S interphase. Additionally, I anticipated that WDR12 knockdown would produce morphological changes in nucleoli with more pronounced effects in the TNBC subtypes. The diverse breast cancer subtypes were expected to display differential rates of ribosome biogenesis based on nucleolar size and number. A greater rate of ribosome biogenesis was expected in TNBC in comparison to non-TNBC cell lines.

The mechanisms driving the progression of TNBC are poorly understood, which has slowed the development of effective treatments. This project will begin to elucidate the role that WDR12 plays in the process of ribosome biogenesis and its implication for TNBC cell growth and survival. The validation of WDR12 as a therapeutic target will begin to set the stage for the development of novel PPIIs for patients with TNBC currently lacking positive clinical outcomes.

Chapter 2 Methods and Materials 2 Methods and Materials 2.1 Identifying WDR12 as a Potential Drug Target

2.1.1 Biomining RNAi Dropout Screen Datasets

With the goal of identifying a potential breast cancer drug target, I mined the aforementioned databases (Novartis and Broad), which consist of dependency scores calculated from experimental shRNA drop out screens [26, 28]. I began by sorting through a list of 264 WDR family members focusing on female tissues (breast, endometrial and ovarian) using the Novartis dataset. Data was available for only 235 WDR genes, narrowing the list. Using the ranking system devised by Novartis, I selected genes exhibiting scores less than or equal to -1 in 20 ± 3% in each female tissue cell line (46 genes in total).

Screening the same WDR gene list, 93 gene targets were identified by mining the Achilles Project preloaded dataset generated by the Broad institute. The dependency data was visualized on Morpheus, which provides heat maps corresponding to numerical sensitivity scores (blue being highly sensitive, red being less sensitive). Genes with sensitivity scores less than or equal to -1 in the female tissues were selected for further analysis. These were located in the blue range of the heat map. Scores closer to -1 were in the light blue range and scores less than -2 were in the dark blue range indicating more pronounced decreases in shRNA levels.

Cross-referencing both lists narrowed the potential targets to 16 genes. Interestingly, the 16 genes demonstrated greater sensitivity in breast cancer cell lines in comparison to the other female tissue types (ovarian, endometrial). Therefore, my focus narrowed to breast cancer lines sorted based on histological subtypes (Luminal A, Luminal B, Her2-amplified and Basal-like). Note that both datasets contained limited breast cancer cell line data (Novartis - 24 and Broad - 34). Genes conferring greater dependencies in TNBC cell lines took precedence, due to the lack of targeted treatment for this histological subtype. After selecting proteins based on their sensitivity scores on the online RNAi datasets, information regarding structure, interacting partners, expression patterns, potential biomarkers and clinical implications for each of these genes were reviewed to shed more light on the proteins’ background, and to further refine the

24 target selection list. Based on previous success drugging WDR5, which contains a single propeller domain [22], it is presumed that these simpler structures are easier to develop small molecules against. For this reason single propeller-containing WDR proteins were prioritized over more complex structures. This led me to select WDR12 as a potential drug target.

2.1.2 Analysis of the WDR12 Phenotype using the “Combined” Preloaded Dataset

At a later stage in the progression of my project, new vulnerability scores were published. These were obtained from combining the data from Broad and Novartis in addition to data generated by Marcotte et al. [29]. Marcotte et al. [29]’s study focused solely on breast cancer and surveyed 77 cell lines. To ensure WDR12 was still a relevant target for breast cancer, I re-analyzed the vulnerability scores. I found that of the 77 cell lines, only 54 were tested for WDR12 knockdown. These cell lines were sorted based on histological subtype and sensitivity score to verify whether TNBC cell lines exhibited a greater vulnerability to WDR12 knockdown, when the number of cell lines was increased.

2.2 Generation of Target shRNA for WDR12

2.2.1 Designing the shRNA sequences

The following online tools were used to generate eight constructs targeting different regions of the WDR12 mRNA transcript: - ThermoFisher’s BLOCK-iT RNAi (Designer https://rnaidesigner.thermofisher.com/rnaiexpress/) - Dharmacon’s siDESIGN Center (https://dharmacon.horizondiscovery.com/design center/) - Broad Institute’s shRNA design tool (https://portals.broadinstitute.org/gpp/public/seq/search) - Kay Lab’s siRNA/shRNA/Oligo Optimal Design tool (http://web.stanford.edu/group/markkaylab/cgi-bin/) A Basic Local Alignment Search Tool (BLAST) analysis was applied to each generated

25 sequence to rule out homology to other coding sequences. Each tool provides its own ranking system indicating the effectiveness of the shRNA sequences. Cross-referencing between all the design tools, I chose eight sequences located at different sites on the transcript with the highest scores on all four tools. Because there was significant overlap between the mRNA transcript regions that the high scoring shRNA sequences were targeting generated by the different online tools, I used the sequences generated by the Broad Institute.

Seven of the sequences I selected were targeting central regions of the mRNA transcript, as these are generally most effective. I also included a single sequence targeting the 3’ untranslated region (UTR), in anticipation of a potential rescue experiment.

The eight sequences listed in Table 2 were inserted into an in-house shRNA primer designer Excel template. Inputting the selected shRNA sequences to the spreadsheet generated the forward and reverse primers shown in Table 3. These primers (along with attB1/attB2 site- containing primers) were used to create a lentivirus-based, doxycycline-inducible shRNA expression constructs via Gateway Cloning as depicted in Figure 9.

Table 2. Chosen shRNA sequences to target WDR12 mRNA transcript.

Construct name Location shRNA sequence Region

shWDR12-1 1204 GATAGTTTGTCCTGCTTATTA CDS

shWDR12-2 1054 GACTGGATCAGTTCAATTAAA CDS

shWDR12-3 1342 GATGGCTCAGGAACTAAATTT CDS

shWDR12-4 2067 ATTGGTAGAGAACCATGAAAT 3UTR

shWDR12-5 1819 CAGCTGATTTCAGGATCTTTA CDS

shWDR12-6 1232 CTTCTATGGATCAGACTATTC CDS

shWDR12-7 1612 GGCAGTCTTAAGTCAACTTTG CDS

shWDR12-8 1692 TGGAAGCACAGATAGGCATAT CDS

Table 3. shRNA primer sequences for shRNA PCR cloning.

Construct Forward primer Reverse Primer name shWDR12-1 GTTCATGAGAATGACTAATAAGCAGG GTCATTCTCATGAACTAATAAGCAGGCA ACAAACTATCTTTTTGGGTTGCATGCA AAACTATCGATCTCTATCACTGA GAG shWDR12-2 GTTCATGAGAATGACTTTAATTGAACT GTCATTCTCATGAACTTTAATTGAACGTA GATCCAGTCTTTTTGGGTTGCATGCAG TCCAGTCGATCTCTATCACTGA AG shWDR12-3 GTTCATGAGAATGACAAATTTAGTTCC GTCATTCTCATGAACAAATTTAGTTCAG TGAGCCATCTTTTTGGGTTGCATGCAG GAGCCATCGATCTCTATCACTGA AG shWDR12-4 GTTCATGAGAATGACATTTCATGGTTC GTCATTCTCATGAACATTTCATGGTTAGC TCTACCAATTTTTTGGGTTGCATGCAG TACCAATGATCTCTATCACTGA AG shWDR12-5 GTTCATGAGAATGACTAAAGATCCTG GTCATTCTCATGAACTAAAGATCCTGCC AAATCAGCTGTTTTTGGGTTGCATGCA ATCAGCTGGATCTCTATCACTGA GAG shWDR12-6 GTTCATGAGAATGACGAATAGTCTGA GTCATTCTCATGAACGAATAGTCTGAGA TCCATAGAAGTTTTTGGGTTGCATGCA CATAGAAGGATCTCTATCACTGA GAG shWDR12-7 GTTCATGAGAATGACCAAAGTTGACT GTCATTCTCATGAACCAAAGTTGACTGC TAAGACTGCCTTTTTGGGTTGCATGCA AGACTGCCGATCTCTATCACTGA GAG

shWDR12-8 GTTCATGAGAATGACATATGCCTATCT GTCATTCTCATGAACATATGCCTATCGTT GTGCTTCCATTTTTGGGTTGCATGCAG GCTTCCAGATCTCTATCACTGA AG

EMCV IRES tH1 promoter CMV Promoter Puromycin-Resistance shFLuc RNA Intron TREx HBV PRE

HIV SIN LTR HIV RRE HIV cPPT 6881 bp HIV SIN LTR HIV Psi packaging signal

LV709G shFLuc

Figure 9. Map of features included in the lentivirus-based, doxycycline-inducible shRNA expression constructs. Rev Response Element (RRE) is the binding site for the Rev protein, facilitating the nuclear export of transcripts for packaging. Psi is the packaging site required for the incorporation of transcripts into infectious particles. The central polypurine tracts (cPPT) is required for efficient reverse transcription and integration in non- dividing cell types. LTR are responsible for the integration into the host genome. tH1 promoter was used as it provides more flexibility in shRNA design. TREx refers to the tetracycline-inducible system, which will allow for the timing of induction. The CMV promoter drives the expression of the puromycin-resistance gene, serving as a selective marker.

The following steps were employed to clone the shRNA constructs:

A PCR master mix was prepared for each reaction consisting of 1 µL H2O, 1 µL of 4 µg/mL pBS-tH1, 1 µL of 5 µM attB1 and attB2 primers (Integrated DNA technologies) and 5µL of HotStart Taq Plus Master Mix (Qiagen, Cat. # 203643). Subsequently, a 9µl aliquot of this mixture was transferred to a PCR strip tube (VWR, Cat. # 53509-304). 1 µL of 0.1 µM of forward and reverse shRNA primers (IDT) for each construct was then added. The strip tubes were inserted into a T100 Thermocycler (BioRad). The reaction was ran for 35 cycles at 95°C for 5 minutes, then 50°C for 45 seconds and finally at 72°C for 45 seconds. Generation of the PCR product was confirmed by running 5 µL of the PCR reaction (combined with 2µL loading dye (Invitrogen, Cat. #R0611)) on a 1% agarose gel (Invitrogen, Cat. # 16500- 100). The gel was stained with SYBR safe dye (Invitrogen Cat. # S33102) and visualized using a

UV light imager (AlphaImager HP, Alpha Innotech MultiImage II). The product was confirmed (~300 bp) by running 5µL of 1kB Plus Ladder alongside samples (Invitrogen, Cat. #10787018). As depicted in Figure 10, the PCR design end result includes a tH1 promoter, which helps drive the transcription of the shRNA hairpin (forward-loop-reverse sequence). The design also included a polyT tail, which increases transcript stability once expressed.

attB1 Primer shRNA Forward Primer

attB2 tH1 Promoter pT attB1 loop

shRNA Reverse Primer attB2 Primer

Figure 10. PCR-design end result. The end result of the cloning reaction includes the following features: attB1 and B2 sites for Gateway cloning into a pDonor vector. A tH1 promoter (containing the tet-operator for inducible gene expression), driving the expression of the shRNA sequence with a hairpin loop portion. A poly-T tail, which increases the constructs stability once expressed.

After the verification of the PCR products, the latter (containing attB1 and B2 sites) were cloned (using BP Clonase) into pDonor vectors containing P1 & P2 sites for Gateway cloning along with a Kanamycin resistance gene. Upon the addition of BP Clonase the sequence flanked by attB1 & B2 sites gets inserted into the pDONOR vector between its attP1 and P2 sites (Figure 11). Each BP reaction consisted of 1µL of 150 µg/mL pDONOR, 1 µL of the BP Clonase II enzyme and 3 µL of the PCR reaction. Once all ingredients were combined, the reaction was incubated at room temperature for one hour before transformation into E. Coli cells. 50 µL of TOP10 cells (Invitrogen, Cat. #C404003) were directly added to the cloning mix and left to incubate on ice for 15 to 30 minutes. To ensure vector transference, the bacterial cells were heat shocked at 42°C for 30 seconds using the PCR machine. S.O.C. media was added to each reaction as delineated by the One shot Top10 chemically competent E. Coli (Invitrogen, Cat. #C404003) protocol, and left to shake at 37°C for 45 minutes to an hour. Subsequently, a diluted amount of the reaction was spread onto warm 50µg/mL kanamycin plates and left to incubate over night at 37°C. Cells not successfully transfected did not acquire the kanamycin resistance gene and died, leaving behind only cells containing the vector.

Figure 11. Gateway cloning of BP reaction. The Gateway cloning BP reaction is catalyzed by a BP Clonase enzyme, which allows for a gene (shown in red) flanked by attB1 and B2 sites (shown in green) to be inserted into a Donor vector containing attP1 and P2 sites (shown in orange). The Donor vector contains a ccdB gene (shown in black), which is lethal to E. coli cells. Cells containing a plasmid with the intact ccdB gene perish, leaving behind only those that have successfully been transformed with the plasmid containing the PCR product.

The following day, in order to amplify the bacterial DNA, four colonies per plate were picked

and diluted in 50 µL of H2O in a 96-well plate. 5 µL of TempliPhi sample buffer was aliquoted into strip tubes, to which 0.5 µL of the colony suspension was added. Using a PCR machine, the reactions were heated to 95°C for three minutes then cooled to room temperature. A TempliPhi reaction mix was prepared consisting of 5 µL of reaction buffer and 0.2 µL of enzyme mix for each reaction. 5 µL of this mixture was added to each reaction and heated in the PCR machine at 30°C overnight. The next day, 40 µL of TE buffer was added to each reaction and 20-30 µL was sent for sequencing at the Advanced Center for Genome Technology (ACGT). 1 µL of this same reaction was used to quantify DNA using a NanoDrop spectrophotometer.

Note that each reaction was conducted in multiples of four. The ACGT sequences were compared to the original shRNA sequences I designed, using Vector NTI (Invitrogen). Reactions with 100% alignment were selected to use in LR cloning to generate pLV vectors.

2.2.2 Generation of a Lentiviral Expression Vector by Gateway Cloning

Similarly to the pDONOR vector used in the BP reaction, the pLV709 vector contains attL sites for Gateway cloning, as well as an ampicillin resistance gene for selection (Figure 12). An LR Master mix was prepared consisting of 1 µL of pLV709 and 1 µL of LR Clonase II. 2 µL of this Master mix was used per reaction with the addition of 3 µL of each chosen PCR TempliPhi reaction mentioned above. The tubes were left to incubate at room temperature for 1 hour before being transformed into bacterial cells. TOP10 cells (Invitrogen, Cat. #C404003) were added to the LR cloning mix and incubated on ice for 15 to 30 minutes. Cells were heat shocked for 30 seconds using the PCR machine, then incubated on ice for two minutes. Cells were transferred to a 96-well plate containing 500 µL of S.O.C. media, covered and left to incubate at 37°C on a shaker for an hour. The reactions were then spread onto carbenicillin plates and left to incubate at 37°C overnight. Carbenicillin is an analog of ampicillin that is more stable and better for long- term storage, which is why the latter were used instead of ampicillin plates. The following day, a single colony was picked from each plate and transferred to 11mL of Terrific Broth (Invitrogen) with 2 µl/ml of ampicillin. The latter were placed on a shaker and left to grow overnight at 37°C.

Figure 12. Gateway cloning of LR reaction. The Gateway cloning LR reaction is catalyzed by a LR Clonase enzyme, which allows for a gene (shown in red) flanked by attL1 and L2 sites (shown in yellow) to be inserted into a Destination vector containing attR1 and R2 sites (purple). The Donor vector contains a ccdB gene (shown in black), which is lethal to E. coli cells. Cells containing a plasmid with the intact ccdB gene perish, leaving behind only those that have successfully been transformed with the plasmid containing the PCR product.

2.2.3 Generation of Lentiviral Particles

Once the plasmids containing the shRNA constructs were generated, the next step was to transfer the lentiviral particles to mammalian cells. In order to generate the lentiviral particles for transduction, the plasmid amplified in the previous step had to be isolated. This was done using Qiagen’s Plasmid Plus Midi Kit (Qiagen, Cat. # 12943) according to their high-yield protocol. In brief, cells were lysed in the appropriate lysis buffer containing RNAase A solution (an endoribonuclease that cleaves single stranded RNA) followed by neutralization to precipitate cellular debris and chromosomal DNA. The precipitate was cleared, then the lysate was filtered through a DNA-binding column, washed and then eluted into labeled tubes with the appropriate elution buffer. The resulting DNA was quantified using a NanoDrop spectrophotometer and stored at -20°C until needed for transfection to generate the lentiviral particles.

In order to transfer the packaging and lentiviral vectors into mammalian cells, a cationic lipid was employed to transfect 293-EMT packaging cells (cell culture methods described below). A master mix was prepared containing the following reagents such that each reaction received 50 µl of LipofectAMINE 2000 (Invitrogen, Cat. # 11668-019) diluted in 2.5mL of OptiMEM (Invitrogen, Cat. # 31985-070). 2.5 ml of the LipofectAMINE/OptiMEM master mix was added to the plasmid DNA for each reaction. The plasmid DNA consisted of 1.0 µg pBaculo-p35 plasmid, 1.0 µg pVSV-G plasmid, 2.5 µg pCMV-ΔR8.2 plasmid and 1.5 µg of the pLV expression construct isolated in the previous step. The pVSV-G plasmid encodes for mammalian envelope proteins, while the pCMV-ΔR8.2 plasmid encodes for genes that will drive the expression of the viral components necessary for packaging and integration into the host genome. The p35 plasmid confers an anti-apoptotic gene originating from insect cells. This ensures that transfected cells will not increase their rate of apoptosis in response to high levels of protein expression, thereby maximizing viral particle production. Once the lipid-based transfection reagent and the plasmid DNA were combined, the solution was left to incubate for 20 minutes at room temperature. The solution was added to 90% confluent 293EMT cells with 3.5mL of OptiMEM. The flasks were gently mixed by tilting and left to incubate overnight at

37°C and 5% CO2. The following day, media was replaced with 8.5mL of Dulbecco’s Modified Eagle Medium (DMEM) (Invitrogen, Cat. # 11965092) with 10% fetal bovine serum (FBS) (Invitrogen, Cat. # 10437-028) and antibiotics to prevent bacterial and fungal contamination (discussed in cell culture methods below). Media containing live virus was harvested and

32 replaced with fresh culture media on the two following days. The collected media was filtered and concentrated 30-fold using an Amicon Ultra-15 centrifugal filter. The concentrate was aliquoted and stored at -80°C until transduction of cancer cell lines of interest.

2.3 Generating Stable Expression Cell Lines

2.3.1 Cell Lines and Routine Passaging

Each cell line is adapted for growth in specific media, supplemented with 10% FBS (Invitrogen Cat. # 10437-028) as summarized in Table 4. To prevent bacterial and fungal contaminations, 50µg/ml Gentamicin (Invitrogen, Cat. # 15750-060) and 100µg/ml Normocin (Invivogen, Cat. # ant-nr-2) were added to the media, respectively. Furthermore, 293-EMT cells are a model cancer cell line requiring the following selection antibiotics to maintain its genetically engineered features: 750 µg/mL of Geneticin/G418 (Invivogen, Cat. # ant-gn-1) was added to preserve EBNA1, a gene overexpressed to facilitate episomal replication of oriP (Epstein-Barr virus origin of replication) allowing for the replication of this plasmid in bacterial and/or mammalian cells); 200 µg/mL of Hygromycin B (Invivogen, Cat. # ant-hg-1) was added for the maintenance of Macrophage Scavenger Receptors (MSR), which help cells adhere to the culture flasks; finally, 250 µg/mL of Zeocin (Invivogen, Cat. # ant-zn-1) was added to maintain the expression of TetR.

Table 4. Cell lines used and their corresponding growth media.

Cell line Media

293EMT DMEM (Invitrogen, Cat. # 11965092)

HMEC MEBM basal media (Lonza Cat.# CC-3151) + MEGM Single quots (Cat.# CC-4136) cell culture supplements

ZR-75-1 RPMI (Invitrogen, Cat. # 11875-093)

MCF-7 DMEM (Invitrogen, Cat. # 11965092)

SK-BR-3 McCoy’s (Invitrogen, Cat. # 16600-082)

MDA-MB-231 α-MEM (Invitrogen, Cat. # 41061-029)

MDA-MB-468 α-MEM (Invitrogen, Cat. # 41061-029)

Cells were grown on polystyrene flasks treated for cell-adherence. Once cells reached 90% confluence, cells were passaged as follows: Media was aspirated and cells were rinsed with Dulbecco’s Phosphate Buffered Saline (DPBS) (Invitrogen, Cat. # 10437-028). The wash solution was aspirated, replaced with TrypLE Express (Invitrogen, Cat. # 12604-013), a phenol red-free trypsin analogue and incubated at 37°C and 5% CO2 for 3-5 minutes to lift cells. After incubation, the flask was tapped to dislodge cells, and re-suspended with their corresponding media. 1 mL of cell suspension was used to count cells by trypan-blue exclusion using a Vi-Cell XR counter (Beckman Coulter). This method of cell counting relies on the fact that the cell membranes of viable cells are impermeable to dyes such as trypan blue. Via video imaging, the software distinguishes between dead cells, live cells and background based on the difference in grey scale, excluding cells that have taken up the dye. Based on the viable cell count, the appropriate amount of cell suspension was transferred to a new flask and re-suspended in media. A baseline count between 0.1 to 0.2 x 106 cells/mL was maintained, depending of the growth rate of the cell line in question. Some cell lines grow more slowly and seeding them too sparsely can stunt their growth, such is the case for ZR-75-1, SKBR3 and HMEC cells.

2.3.2 Short Tandem Repeat Profiling

Cell lines were short tandem repeat (STR) profiled to ensure no cross-contamination between cell lines had occurred throughout the experimentation process. Genomic DNA was prepared according to Qiagen DNeasy (Qiagen, Cat. # 69504) protocol. Briefly, cell pellets of 3-5 million cells were collected by centrifugation and stored at -80 until DNA isolation. Cells lysed using the kit-provided buffers followed by incubation with proteinase K. Subsequently, the Genomic-tip 500/G column was equilibrated with the appropriate buffer, after which the lysed cell sample was loaded into it and allowed to filter by gravity flow. The DNA binding column was washed and the genomic DNA was eluted using the corresponding buffer. Lastly, the eluted DNA was precipitated and collected by adding room temperature isopropanol, inverting the tube and

34 spooling the DNA out using a pipette tip. The genomic DNA was submerged in 200 µl EB buffer (Qiagen, Cat. #19086). Samples were dissolved at 55°C on a heating block for 1 hour, after which yield was determined using the Nanodrop spectrophotomer and diluted to 20 ng/µl. Samples were sent to TCGA for profiling and gene marker results were inputted to the STR similarity search on Cellosaurus (https://web.expasy.org/cellosaurus-str-search/) [71].

2.3.3 Qualitative Reverse Transcription PCR

Mutations and cross-contamination in cell lines can vary quite significantly from lab to lab, therefore, rather than blindly relying on the breast cancer subtypes provided in the literature, I chose to confirm their receptor subtype expression levels, using quantitative reverse transcription PCR (RTqPCR).

For the purpose of this experiment the Taqman probes (LifeTech) listed in Table 5 were used to verify what histological subtype each breast cancer cell line belongs to. ESR1, PGR, ERBB2 probes were used to target the ESR, PGR and HER-2 mRNA transcripts, Ki-67 to target the marker of proliferation; CALM2, HPRT1, B2M and TBP were used as reference genes for normalization purposes.

Table 5. TaqMan probes used for breast cancer subtyping experiment.

TaqMan Probe Provider Catalogue # Gene name

ESR1 Taqman probe LifeTech Hs01046816 Estrogen receptor

PGR Taqman probe LifeTech Hs01556702 Progesterone receptor

ERBB2 Taqman Probe LifeTech Hs01001580 Human epidermal growth receptor 2

MKi67 Taqman probe LifeTech Hs01032437 Ki-67 (marker of cell proliferation)

CALM2 Taqman probe LifeTech Hs04187148_G1 Calmodulin 2

HPRT1 Taqman probe LifeTech Hs9999909 Hypoxanthine phosphoribosyltransferase 1

B2M Taqman probe LifeTech Hs00187842 Beta-2-microglobulin

TBP Taqman probe LifeTech Hs00427621 TATA-box binding protein

Firstly, the amplification efficiency of each Taqman probe was verified in 293EMT cells, where a RNA titration curve confirmed that the relation between CT values and amount of RNA was linear and proportional. Each sample was run in technical duplicates.

Subsequent to verifying the TaqMan probe efficiencies, 100 ng of RNA was used for each sample, with two technical replicates.

2.3.3.1 RNA extraction and qPCR

A pellet of 1-3 million cells was extracted for each cell line, by rinsing, trypsinizing and re- suspending with media. Cells were counted to determine the appropriate volume of cell suspension required. Cells were pelleted and rinsed with 1 mL of DPBS, then pelleted once again. Cell pellets were kept at -80°C until RNA extraction. The manufacturer’s protocol was followed to extract RNA from each cell line using the QIAGEN RNeasy Plus mini kit (Cat. # 74136) and QIAShredder column (Cat. # 79654), which is used to homogenize the cell sample in preparation for cell lysis. In brief, cells were lysed using Qiagen’s LRT lysis buffer, filtered through a column that binds genomic DNA. Ethanol was added to the eluate, spun down and filtered through another column that binds RNA. RNA was eluted using RNase-free water. Note that, RNase-free consumables were used throughout this protocol to prevent RNA degradation. RNA was quantified using the NanoDrop spectrophotometer. 293EMT cells were diluted tenfold from 100 ng to 0 ng with RNase-free water. RNA from the breast cancer cell lines and HMECs were all diluted to 100 ng. Samples were kept at -80°C until qPCR was performed. On the day of qPCR, samples were thawed on ice and reagents kept cold while preparing. A qPCR master mix was prepared, such that each reaction contained 10 µL EXPRESS qPCR SuperMix Universal (Invitrogen, Cat. #11785200), 1µl 20X TaqMan Gene Expression Assay, 0.04 µL Rox Reference Dye (25µM), 2 µL RNase-free water (LifeTech Cat. # 10977-015), 2 µL

EXPRESS SuperScript Mix for One-Step qPCR (LifeTech Cat. # 11781-01K) and 5 µL of Diluted RNA template.

The master mix was vortexed and centrifuged, before dispensing 15 µL into a MicroAmp® Optical 96-Well Reaction Plate (LifeTech, Cat. #4306737) on a plate cooler. Subsequently, 5 µL of the diluted RNA template was carefully added to its corresponding well. A MicroAmp® Optical Adhesive Film (LifeTech, Cat. #4313663) was used to seal the plate before centrifuging at 1200 rpm for 5 minutes at 4°C. The plate was inserted in the ViiA7 Real Time PCR machine (LifeTech) and run according to the following qRT-PCR cycling protocol: a. 50°C for 15min (cDNA synthesis) b. 95°C for 2min (Inactivate RT and activate Taq) c. 40 cycles of 95°C – 15s, 60°C - 1 min

2.3.3.2 qPCR Data Analysis of the TaqMan Probe Amplification Efficiency Experiment

Data was analyzed using GraphPad Prism. CT values were plotted on the y-axis and the amount of RNA in ng was plotted on the x-axis. Data were transformed to a Log-base 10 scale and a linear regression was applied to determine the slope and R2.

2.3.3.3 qPCR Data Analysis of the Breast Cancer Subtyping Experiment

Data was analyzed using Microsoft Excel 2013. A relative quantification method is used to compare the expression level between cell lines. In this experiment HMEC were used as a ‘reference cell line’ as they are a model of normal breast cells. The formula used to calculate fold change (ΔΔCT) in gene expression is the following [72]:

��! = �! !"#" !" !"#$%$&# !"#!!" !"## !!"# !" !"#$%$&#

− �! !"#"!"$%" !"#" !"#!!" !"## !"#$ !" !"#$%$&# − [ �! !"#" !" !"#$%$&# !"#!!" !"#$

− �! !"#"!"$%" !"#" !"#!!" !"#$ ]

!∆∆!" �� ℎ�� = 2

Seeing as four reference genes were used, this yielded four fold change values for each gene of interest. An average fold change value was calculated for each gene of interest within each cell line of interest. Values were transformed to a log-base 2 scale and plotted using Excel.

2.3.4 Generation of Puromycin Kill Curves

As stated previously, CMV promoters were incorporated in the design of lentiviral expression constructs. This drives the expression of a puromycin-resistance gene enabling for the selection of cells that have acquired the plasmid along with the antibiotic resistance. Cells not successfully transduced will not acquire this resistance and will not survive the addition of puromycin. To determine the optimal concentration of puromycin required for selection in each cell line, a puromycin kill curve was generated to determine the half maximal inhibitory concentration (IC50). Once determined, a dose 10-times greater than the IC50 was used for selection to ensure that only cells having acquired the viral plasmid remain. On day one of the experiment, cells were seeded in a black clear bottom 96-well plate (Corning Cat. # CLS3904-100EA) according to the seeding densities listed in Table 6. Outer wells were omitted as these are more susceptible to alterations in growth caused by liquid evaporation. These wells were filled with 100µl of plain media. The plate was incubated overnight at 37°C,

5% CO2. On the subsequent day, an eight point 1 in 3 serial dilution of puromycin was prepared, from 0 µg/mL to 3 µg/mL. Media was removed and replaced with 100 µl of the corresponding puromycin dilution using a multichannel pipette. 72 hours after administration of puromycin, cell counts were determined using an ATPlite 1 step luminescence assay (Perkin Elmer Cat. #6016736). Refer to cell proliferation assay for ATPlite 1 step luminescence protocol.

Table 6. Seeding density in 96-well plate for generation of puromycin kill curve for each cell line.

Cell line Plate/Flask Seeding density

293 EMT 4000 cells/well

ZR751 8000 cells/well 96-Well (100 µl) MDAMB468 15000 cells/well

MDAMB231 4000 cells/well

MCF7 4000 cells/well

SKBR3 10 000 cells/well

2.3.4.1 Puromycin Kill Curve Data Analysis

Data was analyzed with GraphPad Prism using an XY-plot. Puromycin concentrations were plotted on the x-axis and transformed to log base 10 form. The percent of cells surviving was plotted on the Y-axis and was calculated as follows:

�� [��] % �� = 100 × �� 0 µg/ml ��

The data was analyzed using a nonlinear regression for a dose-response – inhibition curve to determine the IC50 and R2.

2.3.5 Transduction of shRNA Constructs

In order to generate stable expression cell lines, the shRNA expression constructs were introduced into the host using the lentiviral particles I generated. To increase efficiency of transduction, polybrene was added to the transduction media.

The same general protocol was used to transduce breast cancer cell lines of interest. On day one of the experiment, cells were seeded in 6-well plates according to the seeding densities listed in

Table 7 and incubated overnight at 37°C, 5% CO2. On the subsequent day, a phenol-red free transduction media was prepared with FBS screened for tetracycline (Corning Cat. #35075CV) and 8 µg/mL of polybrene (EMD Milipore Cat. # TR-1003-G) Recall that a TetR inducible system was integrated in the design of the shRNA constructs. Phenol-red contains phenol-rings resembling those found in tetracycline and may contribute to leaky expression of the construct by binding to TetR and releasing it from TO, thereby dis-inhibiting transcription of the shRNA

construct. Similarly, FBS often contains traces of antibiotics, which can also result in the unwanted induction of the shRNA construct. Media was aspirated from each well and replaced with 2 mL of transduction media (screened FBS + 8 µg/mL polybrene). 20 µL of the lentivirus was added to its corresponding well. The 6-well plates were incubated for 72 hours at 37°C, 5%

CO2. Following the incubation period, media was replaced with tetracycline-screened media containing the appropriate concentration of puromycin. Biosafety level 2 precautions were taken for the disposal of material and media used in this experiment, until cells had been selected. A Mock positive control was included for puromycin selection. This consisted of a sample of cells that were not transduced with any construct, but were treated with the same transduction media and puromycin. These cells were expected to display cell death due to the absence of the puromycin resistance gene.

Table 7. Seeding densities and media for transduction in a 6-well plate.

Cell line Plate/Flask # Cells seeded Media

50 000 cells/ DMEM (Phenol red-free) 293EMT well (Invitrogen, Cat. # 21063029)

400 000 cells RPMI (Phenol red-free) ZR751 /well (Invitrogen, Cat. # 11835-030)

400 000 cells alpha-MEM MDAMB231 /well (Invitrogen, Cat. #41061-029) 6-well plate 100 000 DMEM (Phenol red-free) MCF7 (2ml/well) cells/well (Invitrogen, Cat. # 21063029)

alpha-MEM 400 000 MDAB468 (Invitrogen Cat. # 41061-029) cells/well

400 000 cells McCoy's 5A (not available in phenol-red SKBR3 /well free) (Invitrogen, Cat. # 16600-082)

2.4 Induction of shRNA

To compare the effects of WDR12 knockdown in breast cancer cell lines, cells were induced for 0, 4, 7 and 10 days. Table 8 lists the controls used in this experiment. Each cell line was transduced with two to three toxic controls (shKIF11, shPLK1, shPOLR2A) to select the gene that was most essential for that particular cell lines’ survival. When knocked down, these toxic controls display pronounced effects on cell proliferation, resulting in strong cell death phenotypes. Additionally, a non-targeting control (shCTR2) was included as a negative control. This shRNA construct consists of a pool of scrambled sequences that do not target any mRNA sequence in the mammalian genome. It serves as a control of experimental manipulation.

On day 0, cells were seeded according to the seeding densities listed in Table 9. Each shRNA construct was seeded in two flasks: a control flask without doxycycline treatment (DOX-) and an experimental flask with 0.1 µg/mL doxycycline added to the growth media (DOX+). On subsequent induction days, cells were passaged and re-seeded to the initial density. The experimental flask received media with 0.1 µg/mL doxycycline. Cell counts were determined by trypan blue exclusion using a Vi-Cell XR reader (Beckman Coulter) on day 0, 4, 7 and 10. 100 µl of this same cell suspension was transferred to two 96-well plates with five technical replicates. These plates were subject to two assays: ATPlite 1 step luminescence assay (Perkin Elmer Cat. #6016736) and Caspase 3/7 Glo (Promego, Cat. # G8092). Note that each experiment was repeated two to four times within each cell line.

Table 8. Experimental controls.

Control Control Description type shKIF11 Positive Kinesin Family Member 11 is an essential gene required for centrosome separation and the establishment of bipolar spindles in mitosis, without it cells cannot divide properly leading to cell death. shPLK1 Positive Polo-like Kinase 1 is a ser/thr kinase that phosphorylates a number of proteins during the cell cycle. Without this essential gene, cells cannot

move through the cell cycle properly leading to cell death. shPOLR2A Positive RNA polymerase subunit A is responsible for the synthesis of mRNA. Knocking down this gene would result in a decrease in proliferation phenotype. shCTR2 Negative Non-targeting construct that does not target any mRNA transcript of the mammalian genome. The induction of this construct should not result in an anti-proliferative phenotype.

Table 9. Seeding densities for long-term induction.

Cell Line Flask/Plate # Cells x106/ml

293EMT 0.1

MDA-MB-231 0.12

MDA-MB468 0.15 T25/ 6-well plate MCF-7 0.15

SKBR-3 0.2

ZR-75-1 0.2

2.4.1 Cell-Proliferation Assay (ATP)

All metabolically active cells use ATP. As cells die, levels of ATP are depleted rapidly and therefore can be exploited to monitor cell proliferation and viability. 100 µl of the reagent (ATPlite 1 step luminescence assay (Perkin Elmer Cat. #6016736) is added to each well using a multichannel pipette; the plate is left to incubate on a shaker for 5 minutes at room temperature and inserted into the Biotek Cytation 3 plate reader to measure intensity of luminescence.

2.4.2 Caspase 3/7 Activation Assay

Caspases 3 and 7 are activated during later stages of apoptosis, and can therefore serve as appropriate markers of cells undergoing programmed cell death. The Caspase 3/7 Glo assay (Promega, Cat. # G8092) provides a substrate for caspases 3 and 7, such that when it is cleaved a luminescence signal is emitted. 100 µl of the reagent is added to each well of a 96-well plate. The plate is incubated on a shaker for five minutes at room temperature and then incubated for another 25 minutes in the dark. This allows for the reagent to lyse the cells allowing for the substrate to come into contact with cytosolic contents (i.e. the caspases). The luminescence signal is then quantified using the Biotek Cytation 3 plate reader. The intensity of the luminescence signal is proportional to the amount of activated caspase 3/7.

2.5 Western Blot

To ensure that the shRNA constructs were knocking down WDR12, a Western blot was employed to look at protein levels before and after DOX induction. In this method, protein samples are loaded onto a polyacrylamide gel and separated based on size by electrophoresis. The bands created by these proteins are visualized by transfer onto a membrane and labeling the protein of interest with a specific antibody. This technique is said to be quasi-quantitative as it does not allow for the exact quantification of protein levels, but provides a relative estimate.

2.5.1 Cell lysis

The cell lines of interest were induced for four days and cell pellets were collected. Depending on pellet size, cells were lysed with 50-200µl of lysis buffer (1% SDS in M-Per buffer (Thermo Scientific, Cat. # 78501) with a 1:100 dilution of a 100X protease/phosphatase inhibitor cocktail (Thermo Scientific, Cat. # 1861281) targeting the major classes of proteases and phosphatases that have for target aminopeptidases, cysteine and serine proteases, serine/threonine and protein tyrosine phosphatases. Additionally, each sample was sonicated twice for ten seconds.

2.5.2 Concentration Determination

Preparing samples with the same concentration of protein is crucial in order to make accurate estimates. A Quick Start Bradford protein assay (BioRad Cat. #500-0207) was used to interpolate the sample’s protein concentrations. This kit provides seven pre-diluted concentrations to generate a standard curve.

In Brief, 5 µl of the 7 pre-diluted concentrations of bovine serum albumin (BSA) protein standards (2.0, 1.5, 1.0, 0.75, 0.5, 0.25, 0.125 µg/ml) were added in ascending order to a 96-well plate in triplicates. 5 µl of the cell lysates were added to the following wells in triplicate. A multichannel pipette was used to add the necessary reagents (reagent A+S followed by reagent B containing the Brilliant Blue G-250 dye. This dye upon reacting with protein adapts an unprotonated anionic form and absorbs light at 595 nm. After incubating the plate on a shaker for ten minutes, the plate was measured using the Biotek Cytation 3 microplate reader at a wavelength of 595 nm. The data points were inserted into GraphPad Prism as a XY-plot. The concentrations corresponding to the pre-diluted standard curve were plotted on the X-axis with the corresponding emission values plotted on the Y-axis. The emission values corresponding to the cell lysates were added below and their X-values were interpolated. These concentrations were used to determine the correct dilution for each sample such that each sample would contain the same mass of protein (~10 µg). Samples were diluted with dH2O and 15 µL of 4X Laemmli loading buffer (BioRad, Cat. #161-0747) to 1 µg/µl. The samples were heated at 100°C on a heat block for 5 minutes and centrifuged to collect any condensation that may have formed during heating at 100°C.

2.5.3 Running the SDS-PAGE Gel

An electrophoresis buffer was prepared with a 1:10 dilution of Tris/glycine/SDS (Biorad, Cat.#

161-0732) in dH2O. Any kD precast gels with 10-15 wells (BioRad, Cat. #456-1094) were used. These are suitable for proteins with molecular weights between 10-100 kD. The polyacrylamide gel gradient makes the separation of such a broad range of sizes possible. The gel was inserted into the BioRad Electrophoresis chamber and filled with the running buffer. The first well was loaded with 5 µl of the Spectra Multicolor Broad Range Protein Ladder (Thermofisher, Cat. #

26634). The following wells were loaded with 10 µl of each sample, such that 10 µg of protein was contained in each well. Note that empty wells were filled with 2µl of laemmli buffer to ensure bands migrated down uniformly. 80V was applied to the gel for ten minutes ensuring the dye was stacking evenly and increased to 200V for approximately twenty minutes.

2.5.4 Membrane Transfer

A transfer buffer was prepared according to the manufacturer’s protocol diluting the 10X transfer buffer 1:10 with 1:5 ethanol, (BioRad, Cat. # 161-0734) in dH2O. Prior to using the Invitrolon PVDF Filter Paper Sandwich membrane (Novex/Life Technologies, Cat. # LC2005), it was activated in ethanol for a few seconds and equilibrated in transfer buffer. The transfer sandwich was prepared in the cassette, inserted into the electrophoresis chamber with an icepack and filled with transfer buffer. 100 V was applied to the blot and run for one hour with constant stirring.

2.5.5 Band Visualization

To verify proper transfer and protein loading, the membrane was stained with the protein binding dye Ponceau S (SigmaAldrich, Cat. # 6226-79-5). The latter was incubated on a shaker until bands became visible. Ponceau was removed and rinsed in dH2O, until clear. WDR12 has a molecular weight of 48 kD and would appear on the central region of the membrane. The membrane was cut at the 100 kD ladder mark for simultaneous detection of WDR12 and the loading control vinculin (117 kD). Prior to incubation with primary antibodies, the membrane was immersed in Odyssey blocking solution (absent of mammalian blocking agents in tween buffered saline (PBS)) (LiCor, Cat. # 927-40000) for an hour, to minimize unspecific binding sites. The cut membranes were incubated on a shaker overnight at 4°C in a 1:1000 dilution of a rabbit polyclonal antibodies against WDR12 (AbCam, Cat. # ab11955) and vinculin (CST, Cat. # 13901S) in Odyssey blocking buffer. The following day, the primary antibody solution was removed and the membrane was rinsed in Tris buffered saline solution with 1% Tween (TBST). The Tris buffer helps maintain a pH of 7.4 and the tween detergent serves to minimize nonspecific interactions between the antibody and off-target surfaces. 1:10 000 dilution of an HRP- conjugated secondary antibody (Millipore, Cat. # 12-348) in SuperBlock solution (lacking immunoglobulin, albumin and endogenous biotin) (Thermofisher, Cat. # 37515) was applied to

45 the membrane for 50 minutes on a shaker. After the incubation period, the membrane was rinsed 3 times for 5 minutes in TBST and treated with the Immobilon Western Chemiluminescent HRP substrate (Millipore, Cat. # WBKLS0500) for imaging with the BioRad Chemidoc XRS imaging system. Chemiluminescent detection is based on the horseradish peroxidase enzymatic reaction, in which peroxide oxidizes luminol, producing light. Protein levels were analyzed in three cell lines: 293EMT, ZR-75-1 and MDA-MB-231 to ensure knockdown consistency.

2.6 Cell Cycle Analysis

To investigate the impact of knocking down WDR12 on cell cycle progression, cells were labeled with bromodeoxyuridine (BrdU), a thymine analog, and analyzed using flow cytometry.

To capture the position of cells in the cell cycle, two parameters were labeled and measured using the BD Pharmingen BrdU flow kit following the vendor’s protocol (Cat. #557891). Firstly, cells are labeled with BrdU, which allows monitoring of de novo DNA synthesis. As cells progress through the cell cycle and synthesize DNA, they incorporate BrdU instead of thymine. Secondly, the entire DNA content is labeled with 7-amino-actinomycin D (7-AAD).

2.6.1 Optimization of BrdU Labelling

Cell cycle analysis was performed in MDA-MB-231 and ZR-75-1 cells as these were predicted to be the most and least sensitive to WDR12 knockdown according to the Broad online dataset [28], respectively. These cell lines divide at different rates and therefore the incubation period for BrdU-labelling differs. To determine the optimal time of BrdU incubation, the shCTR2 construct for each cell line was seeded into four T25 flasks according to the seeding densities used for induction listed in Table 9. Each flask corresponded to different conditions listed in Table 10. The traces produced suggested that for optimal BrdU-labelling, MDA-MB-231 should be incubated for 60 minutes and ZR-75-1 for 120 minutes. A typical trace of cells progressing through the cell cycle normally, should contain ~60% cells in G1, ~20% of cells in G1 and ~20% of cells in G2M.

Table 10. Conditions for determining optimal BrdU incubation time.

Cell line Incubation time for MDA-MB-231 Incubation time for ZR-75-1

Flask 1 No treatment No treatment

Flask 2 60 minutes 60 minutes

Flask 3 75 minutes 120 minutes

Flask 4 90 minutes 180 minutes

Green shaded areas represent the incubation optimal incubation times for each cell line.

2.6.2 4 to 7 Day Induction for Cell Cycle Analysis

Once these incubation periods were determined, cells were seeded in triplicates in a 6-well plate at 0.2 x 106 cells per well and induced for 4 to 7 days. A DOX- control for each construct was included in triplicate as well. Note that the 4 day induction was conducted entirely in 6-well plates. For cells induced for 7 days, they were first seeded and induced in T25 flasks for 4 days and then subsequently re-seeded in 6-well plates and induced for an additional 3 days.

2.6.3 BrdU labelling and Cell Staining

On the day of extraction, a media with 10 µM of BrdU was prepared using the appropriate growth media. Such small amounts of BrdU are used as it can be toxic to cells in larger quantities. The induction media was replaced by 2 mL of the BrdU solution and incubated at

37°C and 5% CO2. Cells were collected following the same procedure as in induction, by trypsinizing and centrifugation.

Cell staining was conducted in a 96-well round-bottom plate according to the vendor’s protocol. Briefly, cells were rinsed with BSA stain buffer (BD Pharmingen, Cat. # 554657) and centrifuged. The supernatant was discarded and cells were fixed with Fix/permeabilization buffer included in the BrdU staining kit on ice. Afterwards, cells were spun down and rinsed with BSA stain buffer. Subsequently, cells were re-suspended in freezing media consisting of 90% FBS and 10% DMSO. Cells were stored at -80°C in microfuge tubes until staining could be performed.

On the day of flow cytometry analysis, samples were thawed on ice and pelleted. Cells were rinsed in BSA stain buffer and re-fixed in fix/permeabilization buffer. Cells were washed with 1X Permeabilization/wash buffer (prepared according to the manufacturer’s protocol). Subsequently, 300µg/ml of DNAse was added to each sample and incubated at 37°C for an hour. After washing cells, BrdU was labelled with a 1:50 dilution of APC anti-BrdU antibody in the dark. Subsequently, samples were pelleted and re-suspended with 7-AAD and passed through a filter-cap tube (Corning™ 352235) immediately followed by the addition of BSA stain buffer. The samples were read on the FACS Canto II flow cytometry device.

2.6.4 Reading Cell Samples on FACS Canto II

The following filter sets were selected for detection of stained cells: 1) APC – detector for BrdU 2) PerCP-Cy5.5 – detector for 7-AAD (total DNA content) 3) FSC – cell size (forward scatter) 4) SSC- cell granularity (side scatter)

Prior to data collected the appropriate voltages for each detector were determined, to ensure each phase of the cell cycle could be clearly differentiated. Each sample was vortexed before being inserted into the flow cytometer. A total of 10 000 events were recorded for each sample with a flow rate less than 400 events per second.

2.6.5 Flow Cytometry Data Analysis

Data was analyzed using the FlowJo v.10 program. Scatter plots with PerCP-Cy5.5 (total DNA content) on the x-axis and APC (BrdU) on the y-axis were constructed. Gates were applied to the cell populations corresponding to each phase of the cell cycle: G1, S and G2M, with the addition of a sub-G1 phase representing dying cells. The program calculates the percent of cells within each gate.

2.7 Nucleolar Staining

Nucleoli were visualized by immunofluorescence using an antibody against nucleolin conjugated to Alexa 647 (Abcam, Cat. # ab202709) and 4’,6-diamidino-2-phenylindole (DAPI) (ThermoFisher, Cat.# 62248) nuclear counterstain. This method was used to compare nucleolar size between the different breast cancer cell lines listed in Table 4, with the exclusion of 293EMT cells. Additionally, the effect of WDR12 knockdown on nucleolar morphology was also analyzed using this technique.

2.7.1 4 to 7 Day Induction for Nucleolar Staining

In order to evaluate the effect of knocking down WDR12 on the nucleolus, MDA-MB-231 and ZR-75-1 cells were induced for 4 and 7 days. Cells induced for 4 days were seeded directly in 96-well Cell Carrier plates (Perkin Elmer, Cat. # 6055302) at a density of 0.03-0.04 x106 cells per well. Cells induced for 7 days, were first induced in T25 flasks for 4 days, then passaged and seeded in a 96-well plate at a density of 0.03-0.04 x106 cells per well for 3 additional days. Note that the 7 day induction was repeated twice, once on a non-coated plate and once on a poly-D- lysine coated-plate (Cellcarrier, Cat. #6005450) for increased cell adherence.

2.7.2 Staining Cells

As previously mentioned, cells were seeded in 96-well Cell Carrier plates (Perkin Elmer, Cat. # 6055302) at a density of 0.03-0.04 x106 cells per well. Plates were left to incubate at 37°C, 5%

CO2 for 72 to 96 hours to ensure proper adherence. Subsequently, cells were rinsed with 100 µl per well of DPBS (Invitrogen, Cat. # 10437-028) on a shaker at room temperature for 5 minutes. This was repeated for a total of 3 times. Cells were then fixed with 100 µl of 4% Paraformaldehyde (PFA) (Santa Cruz Biotechnology, Cat. #30525-89-4) on a shaker for 10 minutes at room temperature. In the same manner as the previous step, cells were rinsed 3 times with DPBS. To permeabilize cells, 100 µl of 0.1% Triton X-100 (Promega, Cat. # H5142) in DPBS was added to each well and left to incubate on a shaker for 10 minutes at room temperature. Subsequently, cells were blocked with 100 µl of blocking buffer consisting of 1% BSA (BD Pharmingen, Cat. # 554657) in DPBS, 0.1% Tween 20 (Biorad, Cat. #1610781) and 22.5mg/mL Glycine (BioShop, Cat. # GLN001.1) for 30 minutes at room temperature, while

49 shaking gently. A 1:400 dilution of anti-nucleolin antibody (Abcam, Cat. # ab202709) was prepared in blocking buffer and 50 µl was added to each well before incubating in the dark for 1 hour on a shaker at room temperature. After incubation, cells were rinsed 3 times in DPBS in the same manner as previously stated. Subsequently, 50 µl of a 1:1000 dilution of DAPI in DPBS was added to each well and left to shake for 5 minutes in the dark. Prior to imaging, cells were rinsed in DPBS retaining the last rinse.

2.7.3 Cell Imaging

The plate was inserted into the Perkin Elmer Operetta High Content Screening system. Using the Harmony high content analysis software (Perkin Elmer), an assay was set up such that each well was imaged at 25 different locations. The same software was used to analyze nucleolar number, intensity and size. The Harmony software digitally delineates the nucleus by detecting areas stained with DAPI. Subsequently, the software identifies nucleoli stained with the anti-nucleolin antibody found within the delineated nuclei (Figure 13). The data provides information on the average number per nucleus, relative intensity and area of nucleoli. The software counts the number of nucleolin stained spots within the nucleus to provide an average value. Relative nucleolar intensity is determined based on the quantity of antibody detected at a single location.

Figure 13. Determination of nucleolar morphology of MDA-MB-231 parental cells. Right panel displays how the Harmony software delineates nuclei (blue) and nucleoli (red).

2.8 Data and Statistical Analysis

2.8.1 Analysis of Cell Proliferation Data

The cell proliferation data obtained from the induction experiments was analyzed using GraphPad Prism. Each data point in the DOX- condition was normalized to the average of the five replicates and expressed in percentage form using the following formula:

�� − % �� − �� = 100× �� − ��

This normalization was done in order to preserve the variance observed in the DOX- condition. Each data point in the DOX+ condition was normalized to the DOX- condition and expressed in percentage form according to the following formula:

�� − �� + % = 100× �� + �� −

Note that these normalizations were applied for data within each construct. These normalized datasets were entered in a Grouped table and analyzed using a multiple T-test with an α-value of .05. Note that each experiment was replicated twice, an average of the two trials was used for all statistical analyses and figures.

2.8.2 Analysis of Trypan Blue Exclusion Determined Cell Counts

Biological replicates (N=2 or 3) were subject to the same normalizations applied to the ATPlite data mentioned above.

2.8.3 Statistical Analysis of Caspase 3/7 Activation Data

The caspase 3/7 activation data obtained from the induction experiments was analyzed using GraphPad Prism. Each data point was divided by its corresponding ATPlite reading yielding a normalized caspase value (nCaspase). As cells undergo apoptosis, fewer cells remain, therefore normalizing to the number of proliferating cells will reveal the amount of caspase activation per

51 cell. Subsequently, the nCaspase values were subject to the same normalizations and statistical tests applied to the ATPlite data mentioned above. Note that each experiment was replicated twice, an average of the two trials was used for all statistical analyses and figures.

2.8.4 Extrapolation of Cell Number and Doubling Time

Adherent cells growing in a two-dimensional environment need to be passaged at regular intervals to prevent contact inhibition. To extrapolate cell number, these routine passages must be taken into account. To determine how many times cells doubled in between passages, cell counts determined by trypan blue exclusion and ATPlite readings were normalized to initial seeding density (or day 0) as follows:

�� # �� = ��

Where Day X represents the cell count at a particular day of induction. The number of doublings that occurred between day 0 to day 4, day 4 to day 7 and day 7 to day 10 were determined in this manner for each replicate. Multiplying the day 0 values by the # of doublings that occurred between day 0 and day 4, provides the same value obtained experimentally at day 4. Multiplying the cell counts obtained on day 4 by the # of doublings that occurred between day 4 and day 7, provides a hypothetical value for the number of cells that would be present had growth not been interrupted between day 4 and 7. Similarly, the value obtained in the previous step was multiplied by the # of doublings between day 7 and day 10 to obtain the hypothetical number of cells that would be present at day 10. The following are the mathematical formulas used[73]:

�� 4 = # �� 0 �� 4 ×(�� 0 )

ℎ��ℎ�� 7 = # �� 4 �� 7 ×(�� 4)

ℎ��ℎ�� 10 = # �� 7 �� 10 ×(ℎ��ℎ�� 7)

Both the raw cell number extrapolation and the percent DOX+/DOX- normalization were inserted into GraphPad Prism as an XY-plot with time of induction in hours on the x-axis and cell count on the y-axis. Alternatively, an XY-plot was constructed with hours of induction on the x-axis and # of doublings on the y-axis for both the DOX – and DOX+ condition. A linear regression curve-fit was applied to determine if doubling time was linear. The inverse of the slope was noted, providing doubling time. The doubling time obtained through this method was compared to the calculated doubling time, which was obtained as follows: Doubling time was calculated for each replicate by dividing the hours of growth by the # of doublings between days. For comparison, a total doubling time was also determined by summing the total hours spent growing, divided by the total # of doublings in 10 days of growth. Note that each experiment was replicated twice, an average of the two trials was used for all statistical analyses and figures.

2.8.5 Analysis of Cell Cycle Data

The data generated by FlowJo was analyzed using GraphPad Prism. Data was normalized in the same manner as the cell proliferation ATPlite data and the same statistical analyses were applied.

2.8.6 Analysis of Nucleolar Staining

The data generated by Harmony was analyzed using GraphPad Prism. A multiple T-test was applied, comparing DOX- and DOX+ condition of each cell cycle phase.

Chapter 3 Results 3 Results 3.1 Generation of shRNA for WDR12

3.1.1 Verification of PCR product

The PCR product for the cloning reaction was verified by size partitioning on an agarose gel. As depicted in Figure 14, the 8 PCR cloning reactions were verified using a 1% agarose gel based on size partitioning. Bands appear at ~ 300bp confirming products have formed. Recall that these products include the attB sites, tH1 promoter, forward/reverse primers and a poly-T tail. The band corresponding to construct 5, appears fainter relative to the others, however sequencing by ACGT confirmed the presence of a product.

Figure 14. Verification of PCR product. The 8 PCR cloning reactions were verified using a 1% agarose gel based on size partitioning. Bands appear at ~300 bp confirming products have formed.

3.2 Generation of Stable Expression Cell Lines

3.2.1 STR profiling

Table 11 displays the results from the STR profiling, which suggests that some mutations may have occurred in response to induction and/or prolonged passaging. Light orange boxes represent areas that did not match to the Cellosaurus reports 100%.

Table 11. STR profiling results compared to Cellosaurus reports.

STR profiling Cellosaurus % results match

Cell Line Marker Allele 1 Allele 2

Amelogenin X X

CSF1PO 12 13

D13S317 13 13

D16S539 12 12 100% match to MDA-MB-231 shWDR12-4 D5S818 12 12 MDA-MB-231 D7S820 8 8

THO1 7 9.3

TPOX 8 9

vWA 15 18

Amelogenin X X

CSF1PO 12 13

D13S317 13 13

D16S539 12 12

MDA-MB-231 shPLK1 D5S818 12 12 100% match to MDA-MB-231 D7S820 8 8

THO1 7 9.3

TPOX 8 9

vWA 15 18

Amelogenin X X

CSF1PO 12 13

D13S317 13 13

D16S539 12 12 100% match to MDA-MB-231 shCTR2 D5S818 12 12 MDA-MB-231 subline 1833 D7S820 8 8

THO1 7 9.3

TPOX 8 9

vWA 15 15

MDA-MB-468 shWDR12-4 Amelogenin X X 96% match to

MDA-MB-468 CSF1PO 12 13

D13S317 13 13

D16S539 12 12

D5S818 12 12

D7S820 8 9

THO1 7 9.3

TPOX 8 9

vWA 15 18

Amelogenin X X

CSF1PO 12 13

D13S317 13 13

D16S539 12 12 96% match to MDA-MB-468 shKIF11 D5S818 12 12 MDA-MB-468 D7S820 8 9

THO1 7 9.3

TPOX 8 9

vWA 15 18

Amelogenin X X 96% match to MDA-MB-468 shCTR2 MDA-MB-468 CSF1PO 12 13

D13S317 13 13

D16S539 12 12

D5S818 12 12

D7S820 8 9

THO1 7 9.3

TPOX 8 9

vWA 15 18

Amelogenin X X

CSF1PO 10 11

D13S317 9 9

D16S539 11 11 100% match to ZR- ZR-75-1 shWDR12-4 D5S818 13 13 75-1 D7S820 10 11

THO1 7 9.3

TPOX 8 8

vWA 16 18

Amelogenin X X 100% match to ZR- ZR-75-1 shPOLR2A CSF1PO 10 11 75-1 D13S317 9 9

D16S539 11 11

D5S818 13 13

D7S820 10 11

THO1 7 9.3

TPOX 8 8

vWA 16 18

Amelogenin X X

CSF1PO 10 11

D13S317 9 9

D16S539 11 11 100% match to ZR- ZR-75-1 shCTR2 D5S818 13 13 75-1 D7S820 10 11

THO1 7 9.3

TPOX 8 8

vWA 16 18

Light orange boxes represent areas that did not match to the Cellosaurus reports 100%

3.2.2 RTqPCR Breast Cancer Cell Line Subtyping

Breast cancer subtype was verified by RTqPCR. Figure 15 displays the RTqPCR data normalized to HMEC, which is in accordance with what is reported in the literature about the

59 breast cancer cell line histological subtypes (Table 12). Note that, although MDA-MB-468 and SKBR-3 reveal some expression of ESR1, I deemed them to be negative. This is due to the fact that in comparison to known HR+ cell lines (ZR-75-1 and MCF-7) they show a much lower expression of these receptors. Additionally, levels of the KI-67 marker of proliferation was relatively constant between the cell lines relative to HMEC.

-3 ESR1 PGR ERBB2 KI67 relativeHMECto Geneexprerssion -8

-13 MDAMB231 MDAMB468 ZR751 MCF7 SKBR3 Figure 15. Breast cancer cell line gene expression relative to HMEC cells. Gene expression in diverse breast cancer cell lines relative to HMEC cells determined by RTqPCR. N=2 (technical replicates), 100ng RNA.

Table 12. Comparison of breast cancer subtypes reported in literature to RTqPCR experimental data.

Literature [42] Experiment

Cell Line ESR1 PGR ERBB2 ESR1 PGR ERBB2

MDA-MB-231 ------

MDA-MB-468 ------

ZR-75-1 + +/- - + + -

MCF-7 + + - + + -

SKBR-3 - - + - - +

3.3 Transduction and Selection

3.3.1 Puromycin Kill Curves

Figure 16 displays the puromycin kill curves conducted in diverse cell lines in order to determine suitable concentrations for the selection of transduced cells. Table 13 summarizes the half maximal inhibitory concentrations (IC50), R squared (R2) values and the puromycin concentrations ([Puro] µg/ml) used to select for transduced cells. In general, data points revealed good-curve fits with R2 values > 0.90, with the exception of MDA-MB468 and SKBR3, demonstrating R2 values approximating 0.80. Typically, a dose 10 times greater than the IC50 is used for selection, as was the case for MDA-MB-231, MDA-MB-468, MCF-7 and SKBR-3. ZR751 and 293EMT received greater doses of puromycin as these kill curves were conducted after cell transduction. The doses used were based on previous literature findings and in-house recommendations. Furthermore, these higher doses did not affect the selection process, as all cells selected properly.

Table 13. Summary table of puromycin kill curve results.

Cell Line IC50 R2 Final [Puro] µg/ml

293-EMT 0.08 0.99 2.5

MDA-MB-231 0.15 0.98 1.5

MDA-MB-468 0.10 0.77 1.0

ZR-75-1 0.02 0.98 2.5

MCF-7 0.12 0.95 1.0

SKBR-3 0.07 0.81 1.0

3.4 Western Blot

Figure 17 displays the Western Blots conducted to validate protein knockdown efficiency of the shRNA constructs. As shown in Figure17A, the induction of the shWDR12 constructs in 293EMT cells resulted in different degrees of protein knockdown: shWDR12-1, 2, 4, 5 displayed the strongest phenotypes, shWDR12-3, 7, 8 displayed more moderate phenotypes and shWDR12-6 demonstrated little to no protein knockdown. The toxic controls (shPLK1 and shPOLR2A) and the non-targeting control (shCTR2) were included in this blot and displayed no WDR12 protein knockdown, as was expected. Despite the protein loading inconsistencies revealed by the vinculin loading control, this data confirms that the constructs are effectively knocking down the gene of interest at the protein level. However, under loading may falsely suggest the presence of protein knockdown as might be the case for shWDR12-7 DOX+.

Figure17B displays protein knockdown of the two strongest constructs (shWDR12-2 and 4) in two breast cancer cell lines (MDA-MB-231 and ZR-75-1). These constructs reveal that protein knockdown is consistent between cell lines. These two cell lines were used based on Broad’s shRNA dropout screen findings, which reported them to be the most and least sensitive to WDR12 knockdown.

Figure 17. Validating protein knockdown by Western Blot. Cell pellets were collected after four days of DOX- inductions. The boxed images represent the protein blots (run separately) comparing the DOX- and DOX+ conditions in terms of WDR12 protein knockdown. ~10µg of protein was loaded per well. 1° antibodies targeting WDR12 and Vinculin were used to label the protein of interest and as a loading control, respectively. WDR12 has a molar mass of 48kDa; bands appear slightly higher between the 55kDa and 72KDa mark. A. Blot conducted in 293- EMT cells testing shWDR12-1, 2, 3, 4, 5, 6, 7, 8, shPLK1, shPOLR2A and shCTR2. B. Blot conducted in MDA- MB-231 and ZR-75-1 cells testing shWDR12-2, 4 and shCTR2.

3.5 Induction of shWDR12 Knockdown

3.5.1 Cell Proliferation Assay (ATPlite and Trypan Blue Exclusion)

Figure 18 displays the percent 293EMT cells remaining normalized to DOX- after four days of DOX-induction. The eight constructs revealed different degrees of effects on cell proliferation: Constructs 1 and 7 showed little to no effect with ~100% cells remaining after 4 days of induction. Constructs 2, 4, 5, 6 and 8 displayed moderate cell growth phenotypes with 40-80% cells remaining after the induction. Contrastingly, the toxic controls (shPLK1 and POLR2A) showed very pronounced decreases in cell proliferation with less than 30% of cells remaining.

Comparing the phenotype of WDR12 knockdown to that of the toxic controls, suggests that the gene of interest may not be an essential gene required for cell survival. As anticipated, the non- targeting control showed no effect on cell proliferation in response to WDR12 knockdown. These results along with the protein knockdown data, allowed me to narrow the number of constructs to test in breast cancer cell lines. Constructs 1 and 7, which showed decreases in protein level with no effect on cell proliferation were eliminated. Constructs 3 and 6 were also discarded as they showed low levels of protein knockdown relative to the other constructs, with similar effects on cell proliferation as the constructs 2, 4, 5 and 8 suggesting potential off-target effects. Because constructs 2, 4, 5 and 8, showed consistent protein knockdown levels and moderate effects on cell proliferation, I chose to carry on my experiments with the following three constructs: shWDR12-2, 4 and 8. Construct 2 displayed a slightly stronger phenotype with less than 40% cells remaining after 4 days of induction in contrast to the other constructs with closer to 60% of cells remaining. As mentioned previously, construct 4 was designed to target the 3’UTR of the mRNA transcript, proving to be useful for potential rescue experiments. Lastly, Construct-8 which displayed a similar cell growth phenotype as shWDR12-4 was included to confirm similar effects in multiple cell lines.

Figure 18. % 293EMT cells remaining after 4 days of DOX-induction. Cells were seeded in 96-well plates induced for 4 days (N=10). Cell counts were determined via an ATP-based assay. DOX- normalized results are presented.

Figure 19 shows that MDA-MB-231 (TNBC) and ZR-75-1 (HR+) breast cancer cell lines exhibit different degrees of sensitivity to WDR12 knockdown with MDA-MB-231 displaying a greater sensitivity to WDR12 knockdown (< 3% cells remaining after 10 days of induction) than

ZR-75-1 (<15-40% cells remaining after 10 days of induction). This comparison however does not take growth rate and seeding density into consideration. ZR-75-1 display a much lower rate of proliferation, which may be masking the effects of WDR12 knockdown. Furthermore, under- seeding cells may hinder the growth of some cell lines, as appears to be the case for ZR-75-1 cells. Recall from the methods section that I address these issues by utilizing constant seeding and by calculating each cell line’s doubling time. All constructs (2, 4 and 8) displayed very similar trends, with constructs 2 and 4 showing almost identical phenotypes in all three cell lines. Interestingly, Construct 8 displayed a slightly less pronounced phenotype than the two other shWDR12 constructs in ZR-75-1 cells. Based on these results, I further reduced constructs to include shWDR12- 4, a toxic control and the non-targeting control for testing in diverse breast cancer cell lines.

Figure 19. % Cells remaining after long term DOX-induction of 293-EMT, MDA-MB-231 and ZR-75-1. Cell lines were seeded on day 0 at 1 million cells per T25 flask and induced for 10 days. Cells were passaged on day 4, 7 and 10. Cell counts were determined by trypan blue exclution (N=1). DOX- normalized results are presented.

3.5.2 Cell Number Extrapolation Curves in Diverse Breast Cancer Cell Lines

Diverse breast cancer cell lines belonging to different histological subtypes were DOX-induced for a duration of 10 days to evaluate their sensitivity to WDR12 knockdown. As stated previously, two different measurements of cell proliferation were used in each experiment. Figure 20A displays the DOX(-)-normalized cell number extrapolation curves obtained for each cell line via trypan blue exclusion, while Figure 20B displays those obtained via the ATP-based assay. Comparison of these two methods reveals that although data determined by trypan blue exclusion appear slightly more pronounced, a similar trend is observed within each cell line for each construct. Upon induction of shWDR12-4, a statistically significant decrease in cell proliferation arises after 4 days in all cell lines tested except MDA-MB-231, which reveal significant decreases after 7 days of induction (see Table A1-Table A5). Contrastingly, induction of the toxic controls result in much more pronounced decreases in cell proliferation with <50% of cells remaining by the 4th day of induction. This suggests that WDR12 knockdown is less cytotoxic than the latter in all breast cancer cell lines. Interestingly, the shCTR2 construct consistently resulted in decreases in cell proliferation leveling off with increasing days of induction in the MDA-MB-231 and MDA-MB-468 cell lines. To verify whether this was due to DOX treatment, the parental cell lines were treated with 0.1 µg/ml of DOX and measured on day 0, 4, and 7. This experiment revealed no effect on cell proliferation (data not shown), suggesting that the shCTR2 construct might be producing off-target effects in these cell lines. However, the effect on cell proliferation is minor in comparison to the experimental and toxic control constructs. Furthermore, the shCTR2 reveals consistent cell counts between the DOX+ and DOX- conditions in the other cell lines tested (ZR-75-1 and MCF-7), suggesting that these off- target effects may be limited to certain cell lines. Induction data for SKBR-3 was collected on a single occasion. Cells transduced with shCTR2 may have been over seeded in the DOX+ condition resulting in values >100. Tables A1 to A5 provide the p-values (α= 0.05) obtained via multiple T-tests analyzing cell proliferation data determined via an ATP-based assay.

Taken together, these breast cancer cell lines exhibit different degrees of sensitivities to WDR12 knockdown with MCF-7 (HR+) cells displaying the lowest percentage of cells remaining (<10%) by 10 days of induction and SKBR-3 (HER-2+) cells displaying the greatest percentage of cells remaining (~60%) after 10 days of induction. Interestingly, the TNBC cell lines (MDA-MB-231

66 and MDA-MB-468) displayed similar sensitivities (~19% of cells remaining after 10 days) and did not appear to be much more vulnerable to WDR12 disruption in comparison to non-TNBC. As I eluded to previously, differential growth rates may mask cell proliferation phenotypes; the rank provided in Table 14 does not take each cell line’s doubling time into account. Furthermore, as mentioned previously, each cell line was seeded at a different density depending on their rate of growth to prevent contact inhibition from occurring. This may have further impacted the rate of cell proliferation, further masking the true effects of WDR12 knockdown.

Table 14. WDR12 knockdown sensitivity rank based on % cells remaining after 10 days of induction (from most to least sensitive).

Cells Breast cancer remaining after cell line 10 days of induction (%)

MCF-7 7 MDA-MB-231 19 MDA-MB-468 19 ZR-75-1 29 SKBR-3 58

3.5.3 Determination of Doubling Time in Diverse Breast Cancer Cell Lines

In order to account for differences in growth rate between cell lines, doubling time was determined using the cell proliferation data of the long-term inductions. As shown in Table 18- 22, three methods were used to determine and compare doubling time values. Results suggest that some cell lines show greater consistency between methods such as MCF-7 (Mean=17 hours, SD=2.64) and MDA-MB-468 (Mean=25 hours, SD=3.04) compared to the other cell lines (SD>4). MDA-MB-231 (Mean=25, SD=5.1) and ZR-75-1 (Mean= 33, SD= 5.81) cells show the most variance between methods. Doubling times calculated using trypan blue exclusion data were slightly lower than those determined using data from the ATP-based assay, which was also

67 used to determine doubling time by linear regression (Figure 21). This is in accordance to what was seen in the cell proliferation data discussed previously (Figure 20). Moreover, DOX- induction of the shWDR12-4 and the toxic control constructs result in increases in doubling time, which is also in accordance with cell proliferation data. Table 23 lists the doubling times for each cell line, reported in the literature revealing a wide range of variation. The values I obtained fall within these ranges. Interestingly, comparing the rank order of the breast cancer cell line sensitivity to WDR12 knockdown (Table 14) versus the doubling time rank (Table 15) reveal the same order. This suggests that when growth rate is not considered, the slowest growing cells appear to have the weakest phenotypes in response to WDR12 knockdown; the reverse is true from the fastest growing cells.

Table 15. Doubling time ranking of diverse breast cancer cell lines (from fastest to slowest).

Average Breast cancer cell doubling time line (hours)

MCF-7 17 MDA-MB-231 25 MDA-MB-468 24 ZR-75-1 33 SKBR-3 35

Furthermore, accounting for doubling time changes the rank order of the breast cancer cell lines’ sensitivity to WDR12 knockdown (Figure 22): MDA-MB-468 cells are promoted to the most sensitive rank and MCF-7 demoted to the least sensitive rank (Table 16). Table 17 compares the ranks determined by the shRNA dropout screens discussed previously, conducted by different institutions. Interestingly, all ranks place MDA-MB-468 cells as most sensitive, which is in accordance with my findings. There is no clear consistence in ranking for the other cell lines, however this may be due to the tightness of their sensitivity scores in the combined dataset.

Recall that the original datasets provided by Broad and Novartis tested fewer breast cancer cell lines, which is why some of the cell lines I tested are not included in the rankings.

Table 16. WDR12 knockdown sensitivity rank based on % cells remaining after 7 doublings post- induction (from most to least sensitive).

Cells Breast cancer remaining cell line after 7 doublings (%)

MDA-MB-468 33% ZR-75-1 45% SKBR-3 60% MDA-MB-231 60% MCF-7 65%

Table 17. Comparison of shRNA dropout screen rank order of breast cancer cell lines’ sensitivity to WDR12 knockdown (from most to least sensitive).

Novartis Broad Combined

MDA-MB-468 MDA-MB-468 MDA-MB-468 MCF-7

MCF-7 MDA-MB-231 SKBR-3 MDA-MB-231 ZR-75-1 MDA-MB-231 ZR-75-1

Figure 20. Cell number extrapolation curves in diverse breast cancer cell lines after long-term DOX- induction. Cell lines were seeded in T25 flasks with 1 to 2 million cells per flask and induced for 10 days. Cells were passaged on day 4, 7 and 10. Cell counts were determined by trypan blue exclution (N=1) (panel A) and via an ATP-based cell proliferation assay (N=5)(Panel B). Data is presented normalized to the DOX- condition (marked by the horizontal black line at 100%). Each experiment was conducted twice, the data shown is the average of both trials (excluding SKBR-3, which was induced a single time) (* p <0.05).

Figure 21. Number of doublings by hours of DOX-induction in diverse breast cancer cell lines. Cell proliferation data (ATP) was used to determine the number of doublings occurring between each passage (day 4, 7, 10). The cumulative number of doublings at each day of DOX-induction is plotted against the hours of induction. The inverse of the slope provides the doubling time (hours) for each breast cancer cell line.

Table 18. Doubling time determined for MDA-MB-231.

Calculated using Calculated using trypan Blue Calculated using Construct linear regression exclusion data ATPlite data (hours) (hours) (hours) shWDR12-4 27 25 20 shPLK1 32 26 18 shCTR2 30 27 17

Average doubling time of all DOX- constructs: 25, SD: 5.1

Table 19. Doubling time determined for MDA-MB-468.

Calculated using Calculated using trypan Blue Calculated using Construct linear regression exclusion data ATPlite data (hours) (hours) (hours) shWDR12-4 24 29 31 shKIF11 23 24 21 shCTR2 24 26 27

Average doubling time of all DOX- constructs: 25, SD: 3.04

Table 20. Doubling time determined for ZR-75-1.

Calculated using Calculated using Calculated using Construct trypan Blue ATPlite data (hours) linear regression

exclusion data (hours) (hours) shWDR12-4 23 37 36 shPOLR2A 28 40 41 shCTR2 27 35 32

Average doubling time of all DOX- constructs: 33, SD: 5.81

Table 21. Doubling time determined for MCF-7.

Calculated using Calculated using trypan Blue Calculated using Construct linear regression exclusion data ATPlite data (hours) (hours) (hours) shWDR12-4 16 16 18 shPOLR2A 12 20 18 shCTR2 12 17 17

Average doubling time of all DOX- constructs: 16, SD: 2.64

Table 22. Doubling time determined for SKBR-3.

Calculated using Calculated using trypan Blue Calculated using Construct linear regression exclusion data ATPlite data (hours) (hours) (hours) shWDR12-4 36 39 39

shPOLR2A 42 29 29 shCTR2 40 32 32

Average doubling time of all DOX- constructs: 35 , SD: 4.55

Table 23. Summary of breast cancer cell lines’ doubling times reported in the literature.

Cell Line Doubling times reported in literature [71]

MDA-MB-231 31.2 hours 41.9 hours 38 hours 25-30 hours 38 hours

MDA-MB-468 62 hours 30-40 hours 47 hours

ZR-75-1 80 hours 54 hours

MCF-7 43.2 hours 80 hours 31.2 hours 25.4 hours 50 hours 30-72 hours

SKBR-3 48-72 hours 30 hours

Figure 22. Percent cells remaining after long-term induction in diverse breast cancer cell lines. Cell lines were induced for 10 days. Cells were seeded on day 0 and passaged on day 4, 7 and 10. Cell counts were determined by trypan blue exclution (N=1) (panel A) and via an ATP-based cell proliferation assay (N=5)(Panel B). Data is presented normalized to the DOX- condition (marked by the horizontal black line at 100%) and plotted against the number of doublings. Each experiment was conducted twice, the data shown is an average of two trials (excluding SKBR-3, which was induced a single time).

3.5.4 Apoptotic Cell Death (Caspase 3/7)

An apoptotic cell death assay revealed that the decrease in cell proliferation in response to shWDR12 induction was not due to apoptosis. As shown in Figure 23, the level of caspase activation remains constant throughout the long-term induction of shWDR12-4 and shCTR2. Although these levels of caspase activation were deemed statistically significant by multiple T-tests (Table A6-Table A10), these low levels likely correspond to basal rates of apoptosis and cannot be assumed to be biologically responsible for the decrease in cell proliferation. Furthermore, in contrast to the toxic controls showing

75 over 4 fold increases in caspase 3/7 activation in all breast cancer cell lines, the effects of WDR12 appear to be minimal.

Figure 23. Caspase activation in diverse breast cancer cell lines after long-term DOX- induction. Cell lines were induced for 10 days. Cells were seeded on day 0 and passaged on day 4, 7 and 10. Cell proliferation (ATP) and caspase activation data were collected on day 0, 4, 7 and 10 in 96-well plates (N=5). Caspase activation data was normalized to the total number of live cells (ATP). Data is presented normalized to the DOX- condition (marked by the horizontal black line at 100%). Each experiment was conducted twice, the data shown is an average of two trials (excluding SKBR-3, which was induced a single time) (* p <0.05).

3.6 Cell Cycle Analysis

Cell cycle analysis was conducted in MBA-MB-231 and ZR-75-1 cell lines as these were hypothesized to be the most and least sensitive to WDR12 knockdown according to the Broad Institute dataset [28]. As displayed in Figure 24, there is no evidence of a cell cycle arrest after

76 the four and seven day induction of shWDR12-4 in both cell lines. No significant difference in cell cycle phase distribution was observed after the four day induction of shWDR12-4 in MBA- MB-231 and ZR-75-1 cells (Table A11 and Table A13). In contrast, induction of shPLK1 and shPOLR2A revealed a statistically significant difference in all four phases after the 4 days induction. A statistically significant increase of cells in sub-G1 and G2M is observed in both cell lines in response to toxic control knockdown after 4 days. This may suggest a G2M arrest, however cells appear to be dying for the most part based on the pronounced increase of cells in sub-G1.

After seven days of induction, a statistically significant increase of cells in sub-G1 is observed in both cell lines in response to WDR12 knockdown. In MDA-MB-231 this increase in sub-G1 is accompanied by decreases of cells in G1 and in S-phase. The increase of cells in sub-G1 implies enhanced cell death, which may in turn result in a decrease of cells in later phases of the cycle. The seven day induction of shPLK1 in MDA-MB-231 produced statistically significant differences in all phases of the cycle, with the most pronounced increase in sub-G1. These results suggest that the majority of cells induced with shPLK1 are on their way to die and are no longer in an arrested state. Similarly, ZR-75-1 display a pronounced increase of cells in sub-G1 suggested an increase in cell death. Cells induced with shCTR2 showed no difference in cell cycle distribution after four and seven days of induction (Table A12 and Table A14).

Figure 24. Cell cycle analysis of breast cancer cell lines in response to WDR12 knockdown. ZR-75-1 and MDA-MB-231 construct-containing cells were seeded in 6-well plate at ~0.002 x106cells/well with N=3 and induced for 4 and 7 days before BrdU-labelling and staining. MDA-MB-231 were labelled with BrdU for 1 hour, while ZR-75-1 were labelled for 2 hours. Cells were subsequently, fixed and incubated with an APC anti-BrdU antibody and total DNA content was stained with 7-AAD, which was detected via flow cytometry (* p <0.05).

Figure 25. Flow cytometry traces BrdU-labelled MDA-MB-231 cells after four and seven days of WDR12 DOX-induction. Cells were DOX-induced in 6-well plates for 4 and 7 days (N=3), then labelled with a BrdU-incorporation assay. MDA-MB-231 were labelled with BrdU for 1 hour. Cells were subsequently fixed and incubated with an APC anti-BrdU antibody and total DNA content was stained with 7-AAD, which was detected via flow cytometry. Gates were applied encompassing phase sub-G1, G1, S and G2M. APC-A corresponds to the level of BrdU incorporation and PerCP-Cy5-5-A refers to total DNA staining.

Figure 26 Flow cytometry traces BrdU-labelled ZR-75-1 cells after four and seven days of WDR12 DOX- induction. Cells were DOX-induced in 6-well plates for 4 and 7 days (N=3), then labelled with a BrdU- incorporation assay. MDA-MB-231 were labelled with BrdU for 2 hours. Cells were subsequently fixed and incubated with an APC anti-BrdU antibody and total DNA content was stained with 7-AAD, which was detected via flow cytometry. Gates were applied encompassing phase sub-G1, G1, S and G2M. APC-A corresponds to the level of BrdU incorporation and PerCP-Cy5-5-A refers to total DNA staining.

3.7 Nucleolar Staining

3.7.1 Nucleolar Staining After 4 and 7 Day induction in MDA-MB-231 and ZR-75-1

Analysis of nucleolar morphology on a non-coated plate after 4 and 7 days of induction revealed changes in morphology in MDA-MB-231 and ZR-75-1 cells. MDA-MB-231 cells displayed statistically significant decreases in nucleolar intensity, while ZR-75-1 displayed an increase in number of nucleoli per nucleus and a decrease in the nucleolar area after 4 days of induction. The effect on nucleolar intensity is similar in both cell lines. Interestingly, the toxic control also shows the same trends after 4 days of induction (an increase in number of nucleoli per nucleus and decrease in nucleolar intensity), suggesting this may be a cellular response to stress (Table A16, Table A17, Table A19 and Table A20). After 7 days of induction, the MDA-MB-231 cells exhibit statistically significant increases and decreases in the number of nucleoli per nucleus and nucleolar area, respectively when analyzed on a coated plate (Table A18). The same phenotype is observed in ZR-75-1 cells, however to a lesser degree (Table A21). Interestingly, the average number of nuclei detected reveals that many MDA-MB-231 cells were lifted during the staining protocol, diminishing the sample size (Table 24-25). The ZR-75-1 cells revealed a similar effect on the non-coated plate.

Figure 27 Nucleolar staining of MDA-MB-231 and ZR-75-1 cells after 4 and 7 days of DOX-induction. Cells were seeded in a 96-well plate at 3-4 x104 cells per well (N=5). Cells induced for 7 days were first induced in T25 flasks for 4 days, then seeded in 96-well plates for 3 additional days. A, B, D, E were conducted on non-coated plates, while C and F were conduced on coated plates for increased cell adherence. Nucleoli were stained with nucleolin (1:400) and the nucleus was stained with DAPI (1:1000). Results were normalized to DOX- (* p<0.05).

Table 24. Average number of nuclei detected in 7 day DOX-induction conducted on coated plate.

MDA-MB-2311 ZR-75-12 shRNA construct DOX- DOX+ DOX- DOX+ shWDR12-4 425.4 335.6 2909.8 1252.4 shPLK11/shPOLR2A2 306.8 14.4 979.2 226.2 shCTR2 77.8 100.8 1048.8 1252.6

1 PLK1 was used as a toxic control in MDA-MB-231

2 POLR2A was used as a toxic control in ZR-75-1

Table 25. Average number of nuclei detected in 7 day DOX-induction conducted on non-coated plate.

MDA-MB-2311 ZR-75-12 shRNA construct DOX- DOX+ DOX- DOX+ shWDR12-4 2697 1435.4 484.2 43.8 shPLK11/shPOLR2A2 1166.2 23.2 794 68.8 shCTR2 826.4 668.4 56.2 117.8

1 PLK1 was used as a toxic control in MDA-MB-231

2 POLR2A was used as a toxic control in ZR-75-1

Figure 28. Nucleolar morphology of MDA-MB-231 after 4 day DOX induction. MDA-MB-231 cells DOX- induced for 4 days in a non-coated 96-well plate. Nucleolus detected via an anti-Nucleolin antibody. Cell nuclei stained with DAPI. N=5, Scale bar = 50 µm.

Figure 29. Nucleolar morphology of ZR-75-1 after 4 day DOX induction. ZR-5-1 cells DOX-induced for 4 days in a non-coated 96-well plate.Nucleolus detected via an anti-Nucleolin antibody. Cell nuclei stained with DAPI.

N=5, Scale bar = 50 µm.

Figure 30. Nucleolar morphology of MDA-MB-231 after 7 day DOX induction. MDA-MB-231 cells DOX- induced for 4 days in a T25 flask, then seeded (and DOX-induced) in a non-coated 96-well plate for an additional 3 days. Nucleolus detected via an anti-Nucleolin antibody. Cell nuclei stained with DAPI. N=5, Scale bar = 50 µm.

Figure 31. Nucleolar morphology of ZR-75-1 after 7 day DOX induction. ZR-5-1 cells DOX-induced for 4 days in a T25 flask, then seeded (and DOX-induced) in a coated 96-well plate for an additional 3 days. Nucleolus detected via an anti-Nucleolin antibody. Cell nuclei stained with DAPI. N=5, Scale bar = 50 µm.

3.7.2 Nucleolar Staining of Diverse Breast Cancer Cell Lines

Comparing between the different breast cancer cell lines revealed that intrinsic differences exist in nucleolar morphology. Table 26 provides a rank order of the diverse breast cancer cell lines for each nucleolar feature measured. As depicted in Figure 30, little to no difference exists between the breast cancer cell lines in terms of nucleolar intensity. Differences are observed in terms of nucleolar area, with the ZR-75-1 cells exhibiting the largest nucleoli and MCF-7 the smallest. Moreover, SKBR-3 and MCF-7 possess the greatest number of nucleoli per nucleus in comparison to the other cell lines that exhibit similar numbers. Interestingly, MDA-MB-468 which exhibited the greatest sensitivity to WDR12 knockdown displayed the second smallest nucleolar area after MCF-7 cells. This reveals a trend opposite to what was anticipated (i.e. that cell lines with larger nucleoli would be more sensitive to WDR12 knockdown). Furthermore, the TNBC cell lines (MDA-MB-231 and MDA-MB-468) do not reveal greater nucleolar size and number compared to the non-TNBC.

Figure 32. Analysis of nucleolar morphology in diverse parental breast cancer cell lines. Red bars depict the Average number of nucleoli per nucleus in breast cancer cell lines. Blue bars depict the relative nucleoli intensity in breast cancer cell lines. Green bars depicts the average nucleolar area in breast cancer cell lines.

Table 26. Rank order of diverse breast cancer cell lines in three nucleolar morphology measurements from greatest to least.

Average number of Relative nucleoli Average nucleolar area nucleoli per nucleus intensity

SKBR-3 MCF-7

ZR-75-1 MCF-7 ZR-75-1 SKBR-3 MDA-MB-231 MDA-MB-231 MDA-MB-231 MDA-MB-468 SKBR-3 MDA-MB-468 ZR-75-1 MDA-MB-468 MCF-7

Figure 33. Intrinsic nucleolar morphology of diverse breast cancer cells lines. Cells were seeded in a 96-well plate. Nucleoli were stained with nucleolin. Nuclei were stained with DAPI. N= 10, Scale bar = 50 µm

Chapter 4 Discussion

4 Discussion

4.1.1 Considerations Taken into Account for shRNA Design and Efficiency

While designing the shRNA constructs, a variety of considerations were taken into account to increase efficiency of constructs and minimize off-target effects. According to Taxman et al., the most effective RNAi sequences are 19-27 nucleotides in length. Increasing length tends to decrease specificity of the construct, thereby enhancing the risk of nonspecific silencing caused by seed-based effects [27]. Despite all eight constructs being designed within this range, shWDR12-1, 7 and 8 exhibited inconsistent protein knockdown levels and cell proliferation phenotypes. Construct 1 and 7 exhibited decreased protein levels after induction as determined by Western blot, accompanied by no effect on cell proliferation. Contrastingly, shWDR12-6 displayed the reverse (no protein knockdown, with a decrease in cell growth). This suggests decreased efficiency of those constructs and/or off-target effects. Western blot analysis of protein level is quasi-quantitative. Other methods commonly used to determine knockdown efficiency of shRNA constructs include RTqPCR, which quantifies levels of mRNA. Although the mRNA transcript provides the template for protein translation, mRNA levels may not be reflective of protein levels. For instance, some proteins may have longer half-lives, exerting their functions even once mRNA levels have been depleted. It would be interesting to verify whether decreases in mRNA levels are consistent with that of proteins, while providing a more quantitative measure of knockdown.

More than one construct targeting different regions of the mRNA transcript of the gene of interest were designed, as some regions may be more effective in producing knockdown. This occurs because mRNA can be bound by a variety of regulatory proteins, which could hinder accessibility of the RISC complex. This would result in the decreased effectiveness of the RNAi machinery resulting in different degrees of knockdown. This may explain the differential effects observed with the eight constructs I designed in terms of protein knockdown and cell proliferation phenotypes. Moreover, including more than one construct allows for monitoring of off-target effects [27], which is why I initially designed eight and reduced the number of constructs to one (shWDR12-4) based on consistency of protein knockdown and cell growth effects in diverse cell lines.

4.2 Generation of Stable Expression Cell Lines

As mentioned in the methods and materials section, an H1 promoter was included in the design of the shRNA constructs. There are a variety of promoters to choose from that drive the expression of the construct. However, the choice of the H1 promoter was motivated by its flexibility in design; it can transcribe sequences regardless of the base located at position +1 [74]. Recall that this promoter also contains the tetracycline regulatory sequences necessary for the tetracycline inducible system, allowing to control the timing of shRNA expression. In the absence of DOX, the repressor forms a homodimer and binds to the TO, repressing transcription. When bound to DOX, the repressor changes conformation and is released from the operator, thereby inducing the transcription of the gene of interest. Many chemicals found in culture media can mimic the shape of DOX and displace the repressor from the operator. This is why tetracycline-screened FBS and phenol red-free reagents were used for most cell lines. Unfortunately, the growth media used for the SKBR-3 cells (McCoy’s 5A) is not available as phenol-red free. Interestingly, these cells took a very long time to select with puromycin. They required 10 passages in contrast to 3-5 passages in other cell lines. This may have been due to leaky expression caused by the phenol-rings found in the media. The phenol-rings can mimic those found in the chemical structure of tetracycline binding to the operator, and disinhibiting the repressor. This suggests that cells more resistant to WDR12 knockdown would have been selected for. This would in turn result in dampened effects in response to WDR12 knockdown. When taking doubling time into account, SKBR-3 cells showed similar cell proliferation phenotypes in response to WDR12 knockdown as MCF-7 and MDA-MB-231. Perhaps had they been transduced and selected in a phenol-red free media, a greater sensitivity might have been observed. Repeating this experiment using a phenol red-free alternative would help rule out this hypothesis.

4.2.1 The Use of Cell Lines as a Model of Breast Cancer

Breast cancer cell lines represent the primary model to investigate the biology of breast cancer in vitro. Like any model in science there are caveats; these are cells that have been optimized to grow on plastic culture dishes in a 2 dimensional environment and therefore may exhibit a multitude of differences from the tumor they originated from. Due to the genetically unstable nature of breast cancers, I chose to validate the histological subtypes of the breast cancer cell

88 lines I was working with by RTqPCR in comparison to HMEC (a model of normal breast epithelial tissue). Typically, normal proliferative breast tissue cells in adults are steroid receptor negative for the estrogen (ESR) and progesterone (PGR) receptors [3]. Only 7-10% of normal breast epithelial cells are HR+, however these are non-proliferative cells locked into a non- dividing state by various inhibitory molecules. This may explain why the HMEC cells displayed some expression of endocrine receptors. In a cancer setting, these receptor-possessing cells may evolve escape mechanisms to maneuver passed these inhibitions enabling them to proliferate and express greater levels of the receptor [39]. The MDA-MB-468 cell line is reported to be a model of TNBC, however data revealed some expression of ESR relative to HMEC. That being said I still categorized the cell line as being TNBC as it expressed much lower levels in comparison to known HR+ cell lines (ZR-75-1 and MCF-7), which expressed 2 to 5 fold more of the receptor.

4.2.2 STR Profiling Revealed Alterations in Marker Profile

The STR profile of MDA-MB-231 for shCTR2 revealed a 100% match to an MDA-MB-231 subclone. This subclone was generated by injecting MDA-MB-231 cells in mice resulting in tumor growth [71]. These tumor cells were subsequently re-adapted to 2D culture and STR profiled revealing slight alterations in markers (TPOX and VWA). Considering that all constructs were generated using the same MDA-MB-231 cells, these alterations may be adaptations resulting from prolonged passaging or transduction with shCTR2. Similarly, the MDA-MB-468 cells also showed a slightly lower STR profile match (~96%) for all constructs, due to a difference of 1 for the CSF1PO marker at a single allele. It is unlikely that these small discrepancies are due to cross-contamination between cell lines. A cross-contamination would display much greater variations at all markers. Furthermore, these slight differences may also be due to limitations of the profiling technique. STR profiling is based on the notion that a “DNA fingerprint” arises from variable number tandem repeats (VNTR). These are hypervariable regions of DNA that have hybridized to various loci throughout the genome creating a unique pattern within a cell line. Counting these markers at each loci and allele allows to authenticate a given cell line [75]. The STR profile is determined by the PCR amplification of the markers listed in Table 11. Subsequently, the amplicons are size partitioned via capillary electrophoresis, then transformed into alleles by comparing results to allelic ladders, which are then converted to

89 numerical values used to determine cell line identity. The variations observed showed a difference of 1 on a single allele, suggesting this may be due to some experimental or technical error.

4.3 WDR12 Knockdown Results in Decreased Cell Proliferation in Diverse Breast Cancer Cell Lines

As stated previously, two measures of cell proliferation were used to determine cell counts: trypan blue exclusion and an ATP-based assay. Results determined by these two methods revealed slightly different results. Trypan blue determined counts were consistently slightly lower than those determined using the ATP-based assay. This may be due to the fact that an N=1 was used when measuring via trypan blue exclusion versus an N=5 when determined via the ATP-based assay. As such, including more than one replicate allows to account for variance of measurements. However, experiments were repeated twice allowing for two biological replicates within each method. Based on these two biological replicates cell counts determined via trypan blue exclusion and the ATP-based assay showed consistency between measures within methods. This suggests that differences between methods may be due to the fact that they are measuring different properties. As mentioned previously, the Vi-Cell automated cell counter is based on the notion that viable cells are impermeable to certain dyes such as trypan blue. The Vi-Cell software uses video imaging to distinguish between cells that have been penetrated by the dye and those that have not. In contrast, the ATPlite 1 step luminescence assay is based on the notion that ATP is present in all metabolically active cells. Levels drop sharply in dying cells and therefore serves as good model for cell count. That being said, it is possible that cells on their way to death progressively lose membrane integrity, more rapidly than ATP levels are depleted, which may explain observed differences. Despite these differences, similar trends of decreased cell count were observed on successive induction days between methods. This suggests that WDR12 knockdown is in fact resulting in decreased cell proliferation to different degrees in diverse breast cancer cell lines. One of the TNBC cell lines (MDA-MB-468) showed the highest sensitivity to WDR12 knockdown after doubling time normalization. These results are in accordance with the hypothesis that TNBC cell lines exhibit a greater survival dependency on this gene. However, a second TNBC (MDA-MB-231) cell line showed a more moderate

90 vulnerability to WDR12 knockdown similar to what was observed in the non-TNBC cell lines. Interestingly, according to Lehman et al.’s categorization of TNBC, the MDA-MB-231 belongs to the MSL subset, while MDA-MB-468 belong to the BL1 subtype [11]. As mentioned previously, these subsets are characterized by unique expression patterns. Recall that the BL1 subtype is characterized by increased expression of genes implicated in the cell cycle and DNA damage, while the MSL subtype displays an increased expression of genes responsible for cell motility, ECM receptors and cell differentiation [11]. This may explain why the MDA-MB-468 exhibited a greater sensitivity to WDR12 knockdown, as they display a greater dependence on processes such as the cell cycle and DNA damage, which have both been linked to the rate of ribosome biogenesis. This suggests that the heterogeneity of TNBC cell lines might not have been captured in this experiment warranting further testing with diverse TNBC cell lines belonging to all 6 TNBC subsets delineated by Lehman et al. It is possible that only a subset of TNBC cell lines might exhibit increased dependence on WDR12 for survival. Interestingly, I began my experiments culturing another TNBC BL1 cell line (HCC1937), however these cells could not withstand puromycin selection. This could be due to HCC1937 cells being less amenable to transduction, the virus being inefficient in this cell line or leaky expression. However, HCC1937 cells transduced with the shWDR12 constructs revealed more rapid cell death in response to the addition of puromycin than those transduced with shCTR2, suggesting the observed increase in cell death may be due to leaky expression. This may imply an increased sensitivity of this cell line to knockdown of the gene of interest, however further testing would be required to validate this hypothesis.

Another limitation in the interpretation of results is that doubling time was determined based on cell number extrapolation curves. As such, the results revealed variability of doubling time within each cell line between methods. These inaccuracies may have impacted the rank order of cell lines’ sensitivity to WDR12 knockdown after being normalized for doubling time. Proper growth curves would have to be determined for each cell line by seeding cells on 96-well plates and measuring cell counts via the ATP-based assay after 1, 2, 3 and 4 days of growth. Interestingly, doubling time reported in the literature reveals a large range of variation (Table 23), fortunately the doubling times I determined via growth extrapolation fall within these ranges. This variation may be due to the impact of seeding density on cell growth. Hafner, M., et al. showed that the division rate of MDA-MB-231 and MCF-7 cells decreased as density

91 increased whereas SKBR-3 cells revealed the inverse trend. Furthermore they found that some cell lines exhibited differences in drug response depending on seeding density [73]. This highlights the importance of controlling for seeding density when trying to validate a gene as a potential drug target via knockdown. All my experiments were conducted at the same seeding density within each cell line. Moreover, the linear regression determined by plotting number of doublings versus hours of induction revealed a linear trend suggesting that the selected seeding densities were not affecting division rate. Determining growth rate using the previously delineated method at different densities would help confirm the optimal seeding density for each cell line. Furthermore, the Broad institute did control for doubling time whereas Novartis did not. Additionally, all studies implicated in these shRNA drop out screens, passaged cells maintaining a constant density however whether they determined the optimal seeding for each cell line is unknown. This could explain why sensitivity ranking from these datasets may not correspond to those I obtained experimentally. This suggests that information derived from these databases should be taken with a grain of salt, and should always be validated.

4.4 Contradictory Results of Apoptosis and Cell Cycle Analysis in Response to WDR12 Knockdown

Apoptosis is a type of programmed cell death that can be initiated from various pro-apoptotic stimuli and result in the excretion of cytochrome c from the mitochondria [76]. Cytochrome c is a key player in the electron transfer chain taking place on the inner mitochondrial membrane feeding into the synthesis of ATP. When cytochrome c makes its way into the cytosol it activates a death signal cascade. Within this pathway, a number of caspases are involved. These make up a family of enzymes with proteolytic activity that cleave their substrates at a particular sequence. There are many functions these enzymes can have in the apoptotic pathway, but two major roles are that of the activator and effector. At earlier stages of apoptosis many caspases such as caspase-2, -8, -9 and -10 are implicated in the activation of enzymes downstream, including other caspases. These enzymes exist as xymogens, an inactive form of the enzyme activated upon cleavage. At later stages, caspases -3, -6 and -7 are involved in the degradation of cell components ultimately resulting in cell death [77]. The assay used to measure apoptosis quantified the level of activation of some of these late apoptotic markers, namely caspase 3 and 7. As was expected, results suggest that caspase activation was minimal in response to WDR12 knockdown when compared to toxic controls. Interestingly, cell cycle analysis painted a different

92 picture. Cells were expected to exhibit a cell cycle arrest at the G1-S interphase in response to WDR12 knockdown. Instead both cell lines tested showed an increased population of cells in sub-G1 after 7 days of induction, but not four. Perhaps a shorter time course at 5 to 6 days of induction might reveal a cell cycle arrest and would have to be repeated. Furthermore, the flow cytometry traces obtained for both cell lines revealed a cell distribution beyond the 4N chromosomal content. This is likely due to the fact that both cell lines exhibit aneuploidy [64], which makes it more difficult to obtain the typical “horse-shoe” shaped distribution of cells. An increase of cells in sub-G1 indicates an enhancement of cell death, however this method cannot distinguish between the various types of cell death (apoptosis, necrosis and autophagy). Therefore, based on these results I cannot confirm what type of death is occurring in response to WDR12 knockdown. Moreover, the method used to measure caspase activation only evaluated two of the effector caspases, omitting analysis of caspase-6 (another effector caspase implicated in degrading cellular contents) [77]. Other measures of apoptosis could be used to detect different components of the process such as Annexin V. Phosphatidylserine (PS) is a lipid confined to the inner leaflet of the membrane, upon apoptosis it becomes exposed on the cell surface and bound by Annexin V. This also occurs in necrotic cells, however co-staining with propidium iodide (PI) and analysis via flow cytometry can help distinguish between the two processes [78]. As mentioned previously cancer cells lacking a functional p53 pathway have been shown to produce inviable daughter cells in response to disruptions in ribosome biogenesis [47]. Analysis of the functionality of this pathway in breast cancer cell lines could help explain why no G1-S cell cycle arrest was detected. According to the ATCC breast cancer panel, MDA- MB-231 cells possess a mutated version of p53 gene [64], however it would have to be determined whether this mutation results in loss of function. One way to verify the functionality of this pathway is to induce P53 activation with the use of drugs such as low doses of Actinomycin D and detecting the upregulation of downstream components such as the cell cycle protein P21 via RTqPCR or Western blot [79, 80]. Contrastingly, Huovinen et al. conducted a study showing that ZR-75-1 cells possess wild-type p53 [81].

4.5 Changes in Nucleolar Morphology in Response to WDR12 Knockdown in TNBC and non-TNBC cell lines

In accordance with my hypothesis, after seven days of induction nucleolar area appeared to have shrunk in response to WDR12 knockdown in both cell lines tested. This suggests that disrupting this gene results in inhibition of ribosome biogenesis, which is reflected by decreased nucleolar area. Interestingly, nucleolar size was affected to a greater extent in the TNBC MDA-MB-231 cells compared to the non-TNBC ZR-75-1 cells, suggesting an increased reliance of the process of ribosome biogenesis in TNBC. A greater number of TNBC cell lines would have to be tested to confirm this hypothesis. Moreover, in order to confirm WDR12’s role in stunting ribosomal maturation, analysis of rRNA modulation could be determined in response to WDR12 knockdown. Burger et al. delineates a nonradioactive method that could be employed, where nascent rRNA species are labelled and extracted using click-chemistry and streptavidin beads. The labelled rRNA is subsequently run on a gel and the rRNA species are identified based on size partitioning [82]. This experiment would confirm whether an accumulation of the 32s rRNA species is occurring in response to WDR12 knockdown. Recall that the 32S rRNA species is an intermediate in the maturation of the 28s rRNA component of ribosomes. Such results would help explain what might be causing these changes in nucleolar morphology providing evidence that WDR12 knockdown results in a disruption of ribosome biogenesis.

Furthermore, an increase in the number of nucleoli per nucleus was observed in both cell lines tested (MDA-MB-231 and ZR-75-1) and an increase in nucleolar intensity was also observed only in ZR-75-1 cells. These changes may suggest an adaptive mechanism in response to WDR12 knockdown. Perhaps cells are attempting to counter balance the decreased rate of ribosome biogenesis by increasing the number of organelles where ribosome biogenesis could occur. Comparison of results obtained on the coated versus the non-coated plate suggests that cells exhibiting stronger phenotypes in response to WDR12 induction might lose adherence and be washed away during the staining procedure. Replicating these results a second time on a coated plate would help validate this hypothesis. Furthermore, results obtained using the coated plate revealed much smaller error bars suggesting less variance between cells, especially in ZR- 75-1 cells, which were especially susceptible to being washed away. Despite increased adherence provided by the coated plate, the number of MDA-MB-231 nuclei detected (Table 24) revealed a

94 greater loss of cells for constructs shCTR2 and shPLK1. This may be due to issues in permeabilization. This further warrants the repetition of this experiment on a coated plate.

4.6 Intrinsic Differences in Nucleolar Morphology Exist Between Diverse Breast Cancer Cell Lines

Intrinsic differences in nucleolar morphology exist between the diverse breast cancer cell lines tested. Interestingly, these differences are most pronounced in terms of nucleolar area, however there does not appear to be a clear trend between nucleolar size and sensitivity to WDR12 knockdown. This refutes the hypothesis that cell lines with larger, more numerous nucleoli would exhibit a greater dependency on the gene of interest. This suggests that intrinsic nucleolar morphology could not be used as a predictor of response to WDR12 disruptions. Furthermore, this also suggests that although increased size and number of nucleoli is linked to the rate of ribosome biogenesis, a more direct measurement of the rate of ribosome biogenesis would have to be devised. Perhaps using the rRNA modulation method described previously could allow to quasi-quantitatively determine how much rRNA is being processed in diverse cell lines.

Chapter 5 Conclusions 5 Conclusions

In conclusion, all breast cancer cell lines tested revealed different degrees of sensitivity to WDR12 knockdown. This suggests that it may be a good therapeutic target for breast cancer. Interestingly, one of the TNBC (MDA-MB468) cell lines tested exhibited the greatest sensitivity in response to WDR12 disruptions. However the second TNBC cell line displayed similar growth phenotypes as the non-TNBC cell lines. This suggests that two cell lines could not capture the heterogeneity of the subtype and an increased number of cell lines would have to be screened to validate the importance of this gene in TNBC. Furthermore, the sensitivity ranks determined experimentally revealed a strong discordance with those determined by the large- scale shRNA dropout screens. These results demonstrate the importance of validation. Alterations in experimental methods and materials may lead to different outcomes. For example the use of different shRNA sequences, seeding densities and normalization procedures, may be responsible for these discrepancies. Moreover, STR profiling of three cell lines revealed slight alterations in marker profile, suggesting that cell lines can vary between labs, exhibiting different characteristics.

As anticipated, the anti-proliferative effects in response to WDR12 knockdown were not caused by an increase in apoptosis. Interestingly, they were also not due to a G1-S cell cycle arrest as was suggested in the literature. Further investigation is required to rule out the possibility of other types of programmed cell death and a shorter time course for cell cycle analysis is to be repeated to confirm the absence of a cell cycle arrest.

Nucleolar morphology analysis revealed alterations in response to WDR12 knockdown. Most compellingly, nucleolar area showed a decrease after the induction of the gene of interest in both cell lines tested. Interestingly, effects were more pronounced in the MDA-MB-231 TNBC cells. This suggests that phenotypes observed after disruption of the gene of interest are due to disruptions in ribosome biogenesis. A more direct measure of rRNA modulation is required to confirm WDR12’s role in triple negative cell lines.

Taken together, this suggests that WDR12 may be an interesting drug target to further investigate for the development of ribosome biogenesis inhibitors. Furthermore, this study is the first to study the role of WDR12 in the survival of breast cancer cell lines, which has revealed some dependency. More research on the implication of ribosome biogenesis in TNBC cell lines is required, to validate WDR12 as a potential drug target in this subtype.

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Appendices

Table A 1. Multiple t-test p-values for MDA-MB-231 after long-term DOX-induction (ATPlite).

Induction days

Construct Day 0 Day 4 Day 7 Day 10 shWDR12-4 0.285186 0.081073 5.14E-06 2.29E-07 shPLK1 0.16871 7.1E-07 1.51E-06 7.43E-06 shCTR2 0.028272 0.011924 0.007152 0.003265

Table A 2. Multiple t-test p-values for MDA-MB-468 after long-term DOX-induction (ATPlite).

Induction days

Construct Day 0 Day 4 Day 7 Day 10 shWDR12-4 0.139191 0.002619 0.002082 0.009852 shKIF11 0.803155 3.5E-10 1.7E-08 9.4E-07 shCTR2 0.046416 0.672768 0.27176 0.74274

Table A 3. Multiple t-test p-values for ZR-75-1 after long-term DOX-induction (ATPlite).

Induction days

Construct Day 0 Day 4 Day 7 Day 10 shWDR12-4 0.587612 0.001139 0.000177 9.04E-06

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shPOLR2A 0.960701 4.09E-08 3.38E-05 6.72E-06 shCTR2 0.031819 0.195692 0.010448 0.054725

Table A 4. Multiple t-test p-values for MCF-7 after long-term DOX-induction (ATPlite).

Induction days

Construct Day 0 Day 4 Day 7 Day 10 shWDR12-4 0.042468 5.89E-06 1.72E-06 1.64E-06 shPOLR2A 0.234055 2.25E-10 3.57E-08 4.6E-06 shCTR2 0.841946 0.005111 0.184926 0.707335

Table A 5. Multiple t-test p-values for SKBR-3 after long-term DOX-induction (ATPlite).

Induction days

Construct Day 0 Day 4 Day 7 Day 10 shWDR12-4 0.150573 0.000695 0.012091 0.000152 shPOLR2A 0.352956 0.000199 1.24E-07 9.62E-08 shCTR2 0.18652 0.00777 4.09E-05 0.000136

Table A 6. Multiple t-test p-values for MDA-MB-231 after long-term DOX-induction (Caspase 3/7).

Induction Days

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Construct Day 0 Day 4 Day 7 Day 10 shWDR12-4 0.089305 0.566168 0.000168 0.000460353 shPLK1 0.665936 2.53E-04 1.49E-03 2.29E-01 shCTR2 0.409903 0.001986 7.11E-02 0.0730047

Table A 7. Multiple t-test p-values for MDA-MB-468 after long-term DOX-induction (Caspase 3/7).

Induction Days

Construct Day 0 Day 4 Day 7 Day 10 shWDR12-4 0.356629 0.001611 0.000326 0.000266 shKIF11 0.002766 7.11E-09 0.001284 0.038413 shCTR2 0.027632 0.010641 0.254325 0.745726

Table A 8. Multiple t-test p-values for ZR-75-1 after long-term DOX-induction (Caspase 3/7).

Induction Days

Construct Day 0 Day 4 Day 7 Day 10 shWDR12-4 0.489794 0.062705 0.130012 0.351689 shPOLR2A 0.950003 5.14E-01 3.99E-01 1.81E-01 shCTR2 0.163003 0.058444 8.96E-01 0.463037

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Table A 9. Multiple t-test p-values for MCF-7 after long-term DOX-induction (Caspase 3/7).

Induction Days

Construct Day 0 Day 4 Day 7 Day 10 shWDR12-4 0.133802 0.71415 0.005519 0.595826 shPOLR2A 0.022363 1.52E-02 1.06E-01 9.17E-03 shCTR2 0.702056 0.079988 8.47E-01 0.000143

Table A 10. Multiple t-test p-values for SKBR-3 after long-term DOX-induction (Caspase 3/7).

Induction Days

Construct Day 0 Day 4 Day 7 Day 10 shWDR12-4 0.136069 0.331593 0.215768 0.796194 shPOLR2A 0.080938 6.39E-05 4.10E-01 5.64E-02 shCTR2 0.168277 0.03082 6.73E-01 0.130862

Table A 11. Multiple t-test p-values for MDA-MB-231 cell cycle analysis after 4 days of DOX-induction.

Construct subG1 G1 S G2M shWDR12-4 0.071679 0.380167 0.061338 0.126672 shPLK1 0.003815 2.23451E-05 0.053573 0.014756 shCTR2 0.238386 0.94743 0.206299 0.194728

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Table A 12. Multiple t-test p-values for MDA-MB-231 cell cycle analysis after 7 days of DOX-induction.

Construct subG1 G1 S G2M shWDR12-4 0.010703 0.00745169 0.002061 0.158834 shPLK1 0.009253 7.23867E-05 0.023171 0.031887 shCTR2 0.050332 0.180791 0.097431 0.916624

Table A 13. Multiple t-test p-values for ZR-75-1 cell cycle analysis after 4 days of DOX-induction.

Construct subG1 G1 S G2M shWDR12-4 0.099963 0.695784 0.397161 0.590168 shPLK1 0.000363 3.24706E-05 0.003749 0.001039 shCTR2 0.99424 0.163514 0.76541

Table A 14. Multiple t-test p-values for ZR-75-1 cell cycle analysis after 7 days of DOX-induction.

Construct subG1 G1 S G2M shWDR12-4 0.0074 0.702418 0.504412 shPLK1 0.046463 0.0326475 0.090583 0.990495 shCTR2 0.068329 0.987269 0.77995 0.484658

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Table A 15. Multiple t-test p-values for MDA-MB-231 nucleolar morphology analysis after 4 day induction on a non-coated plate.

Construct Average number of Average relative Average nucleolar nucleoli per nucleus nucleolar intensity area shWDR12-4 0.111697 0.023395 0.098729 shPLK1 0.000595 0.005658 0.015882 shCTR2 0.04299 0.245996 0.141678

Table A 16. Multiple t-test p-values for MDA-MB-231 nucleolar morphology analysis after 7 day induction on a non-coated plate.

Construct Average number of Average relative Average nucleolar nucleoli per nucleus nucleolar intensity area shWDR12-4 0.354318 0.004906 0.299771 shPLK1 0.191978 0.144769 0.0585503 shCTR2 0.038474 0.052807 0.0541688

Table A 17. Multiple t-test p-values for MDA-MB-231 nucleolar morphology analysis after 7 day induction on a coated plate.

Construct Average number of Average relative Average nucleolar nucleoli per nucleus nucleolar intensity area shWDR12-4 0.000602 0.338328 0.0059646 shPLK1 0.732628 0.628453 0.146129

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shCTR2 0.5578 0.693124 0.864108

Table A 18. Multiple t-test p-values for ZR-75-1 nucleolar morphology analysis after 4 day induction on a non-coated plate.

Construct Average number of Average relative Average nucleolar nucleoli per nucleus nucleolar intensity area shWDR12-4 0.030631 0.009572 0.055452 shPLK1 0.003526 0.045045 0.001688 shCTR2 0.057604 0.156014 0.224456

Table A 19. Multiple t-test p-values for ZR-75-1 nucleolar morphology analysis after 7 day induction on a non-coated plate.

Construct Average number of Average relative Average nucleolar nucleoli per nucleus nucleolar intensity area shWDR12-4 0.94054 0.434319 0.725104 shPLK1 0.23092 0.000562 0.508864 shCTR2 0.037621 0.433107 0.488469

Table A 20. Multiple t-test p-values for ZR-75-1 nucleolar morphology analysis after 7 day induction on a coated plate.

Construct Average number of Average relative Average nucleolar nucleoli per nucleus nucleolar intensity area

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shWDR12-4 0.004983 9.25E-05 0.000622 shPLK1 0.382385 2.33E-05 0.415462 shCTR2 0.103199 0.007382 0.034264

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