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A Combined Approach on the Impact of EP300 Overexpression on Drug Resistance, Invasion and Stemness in Breast Cancer

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A Combined Approach on the Impact of EP300 Overexpression on Drug Resistance, Invasion and Stemness in Breast Cancer A combined approach on the impact of EP300 overexpression on drug resistance, invasion and stemness in breast cancer Roman Jugov Supervisor: Dr. Ernesto Yagüe Department of Surgery & Cancer Imperial College London ICTEM Building Hammersmith Hospital Campus London W12 0NN A thesis submitted in accordance with the requirements of Imperial College London for the degree of Doctor of Philosophy Faculty of Medicine December 2020 Dedication I dedicate this thesis to my parents. It would not be possible without you. Jevgenia & Oleg Declaration I hereby declare that the contents of this entire thesis including the original data is my own work and that all the resources used in the thesis and all the assistance received at the professional and personal levels, for the completion of the study, have been appropriately acknowledged. Roman Jugov December 2020 Copyright The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work ©. Roman Jugov December 2020 Acknowledgements Foremost, I would like to express my gratitude to my supervisor, Dr Ernesto Yagüe for his continuous support, encouragement and patient guidance for the duration of my PhD project. Many thanks to Prof. Andy Stoker at UCL for providing the laboratory with HEK 293T cells which were used for viral transfections- and who’s lab I had the pleasure of working in. Many thanks also to Dr Stelios Chrysostemou for providing unique insights on methodology for migration and invasion assays, Dr Claire Fletcher for the methodology for miR PCR analysis, Dr Giacomo Corleone and Dr Alex De Giorgio on their advice on computational analysis using R. I would also like to thank Stephen Rothery at the Facility for Imaging by Light Mi- croscopy, for his invaluable expertise on cell imaging and image analysis macro scripts and James Elliot at the MRC Flow Cytometry facility, for his advice on setting up the machines. I would also like to extend a special thanks to Prof. Georgios Giamas, Prof. Justin Stebbing and Dr Aleksandra Filipovic for initially inspiring me to pursue this journey during my MRes programme. I would like to specially thank my fellow laboratory members and previous PhD students whose work this study was built upon. Note of appreciation goes to Leona Keung for motivating me and offering help when in need. Finally, I would like to express my gratefulness to Breast Cancer Now, who funded this work and allowed me to undertake this research successfully. Abstract Triple negative breast cancer, characterized by absence of estrogen and progesterone receptors and human epidermal growth factor receptor 2 (HER2), is very aggressive, and often progresses to metastasis. The absence of druggable targets implies that conventional chemotherapeutic agents do not work. Recent genomic studies show that epigenetics play a complicated role in maintaining healthy gene expression. This thesis attempted to demonstrate a link between E1A binding protein P300 (EP300), epithelial to mesenchymal transition (EMT), drug resistance and cancer stem cell (CSC) traits by using breast cancer cell line models with EP300 overexpression (CAL51 and MDA- MB-231), short hairpin RNA (shRNA) lentiviral knockdown EP300 (T47D and MCF7), and EP300 knockout in colorectal cancer (HCT116). Techniques such as flow cytometry, migration/invasion assays and short/long term drug resistance assays were used to demon- strate these traits. This study also employed reverse transcription quantitative real-time PCR (RTqPCR) and RNA sequencing (RNA-seq) gene expression analysis for evaluating a gene signature and miR expression. The study demonstrated positive regulation of EP300, forkhead box O3a (FOXO3a) and GATA binding protein 3 (GATA3) by miR-25 in minimally transformed mammary epithelial cancer (MTMEC) model. Neither RNA-seq nor RTqPCR demonstrated any correlation between cancer subtype and our gene signature. However, we confirmed that EP300 overex- pression resulted in E-cadherin (CDH1) upregulation in CAL51 and MDA-MB-231. EP300 knockdown in T47D resulted in decreased expression of CDH1 and upregulated expression of FOXO3a. EP300 knockout in HCT116 downregulated CDH1. EP300 overexpression in MDA-MB-231 improved drug sensitivity to paclitaxel and doxorubicin. EP300 overexpres- sion reduced aldehyde dehydrogenase + (ALDH) populations in MDA-MB-231 and CAL-51 cell lines, while EP300 modulation had a variable effect on CD44 antigen (CD44)high/CD24 antigen (CD24)low subpopulations in these cell line models. Finally, overexpression of EP300 in MDA-MB-231 demonstrated a decrease in motility and invasion. Table of contents List of figures xix List of tables xxi Nomenclature xxiii 1 Introduction 1 1.1 Breast Cancer Statistics . 1 1.1.1 Breast Cancer Incidence . 1 1.1.2 Breast Cancer Risk Factors . 1 1.1.3 Molecular Subtypes of Breast Cancer . 2 1.1.4 Methods of Detection, Diagnosis and Classification . 2 1.1.5 Therapy and Management of Breast Cancer . 5 1.1.6 Hallmarks of Cancer . 8 1.2 Epithelial to Mesenchymal Transition (EMT) . 11 1.2.1 Transcription Factors . 13 1.2.2 Plasticity and CSC Characteristics . 13 1.2.3 Invasion and Migration . 15 1.2.4 Intravasation . 16 1.2.5 Circulating Tumour Cells -the Seed Hypothesis . 17 1.2.6 Micrometastasis, Drug Resistance and Relapse . 19 1.3 Markers and Predictors of Drug Resistance and Metastasis . 20 1.3.1 Stem Cells . 20 1.3.2 Cancer Stem Cells (CSCs) . 21 1.3.3 Cancer Stem Cell Maintenance . 21 1.3.4 Cancer Stem Cell, EMT and Microenvironment . 22 1.3.5 Cancer Stem Cell and Drug Resistance . 23 1.3.6 Relevance of Markers for Stemness, EMT and Drug Resistance . 23 xiv Table of contents 1.3.7 Regulatory Pathways Involved in EMT and Stem Cell Phenotypes . 24 1.3.8 Relevance of Pathways to EMT . 30 1.3.9 Relevance of Pathways to CSCs . 33 1.4 EP300 and its Involvement in Transcription . 35 1.4.1 Hypothesis . 35 1.4.2 Transcription Factors and Drug Resistance . 35 1.4.3 EP300 Function as a Transcriptional Co-activator . 35 1.4.4 Structure of the EP300 Gene . 42 1.4.5 Protein Structure of EP300 . 43 1.4.6 Functions of EP300 . 46 1.4.7 EP300 Mutations in Cancer . 49 1.4.8 Aim . 49 2 Methods 51 2.1 Cell Culture and Maintenance . 51 2.1.1 Cell Freezing and Thawing Procedure . 51 2.1.2 Generating Knock-down Cell Lines with RNA Interference (RNAi) 52 2.1.3 Generating Overexpression Cell Lines . 53 2.1.4 Cloning Vectors . 53 2.1.5 Plasmid DNA Isolation, Digestion and Purification . 54 2.1.6 Plasmid Isolation . 54 2.1.7 Sequencing . 54 2.1.8 Transfection Methods . 55 2.1.9 Antibiotic Selection and Cell Culture Maintenance . 55 2.2 Analysis of Protein Expression . 56 2.2.1 Cell Lysis Procedure . 56 2.2.2 Protein Concentration Estimation Using the BCA Assay . 57 2.2.3 SDS-Polyacrylamide Gel Electrophoresis (SDS-PAGE) . 57 2.2.4 Electroblotting by Wet Transfer Method . 57 2.2.5 Western Blotting . 57 2.3 Analysis of mRNA Expression by Real Time Quantitative Polymerase Chain Reaction (RTq PCR) . 58 2.3.1 mRNA Extraction Method (RNeasy) . 58 2.3.2 Quantification of mRNA Using NanoDrop® . 58 2.3.3 Reverse Transcription of RNA for cDNA Synthesis . 59 2.3.4 Real Time Quantitative Polymerase Chain Reaction (RTqPCR) . 59 2.3.5 Data Analysis . 60 Table of contents xv 2.4 RNA-seq Data Analysis . 60 2.4.1 Survival Analysis . 60 2.4.2 RNA Sequencing Data Analysis . 62 2.4.3 Statistics and Plots . 62 2.5 Short- and Long-Term Drug Sensitivity Assays . 63 2.5.1 Cell Viability Assay for Drug Sensitivity . 63 2.5.2 Clonogenic Assay for Drug Resistance . 63 2.5.3 Sulphorhodamine B (SRB) Assay for Population Doubling Time . 64 2.6 Flow Cytometry Analysis by FACS . 64 2.6.1 CD44-APC and CD24-PE Assay . 64 2.6.2 Aldefluor Dehydrogenase (ALDH) Assay . 65 2.7 Cell Migration and Invasion Assay . 65 2.7.1 Cell Tracking Assay . 65 2.7.2 Wound Healing Assay . 65 2.7.3 Matrigel Cell Invasion via Chemotaxis . 66 2.8 Statistical Analysis . 66 3 Micro RNA and its Effect on EP300 69 3.1 Introduction . 69 3.1.1 What is micro RNA? . 69 3.1.2 Structure and Biogenesis . 69 3.1.3 Mechanism of Action . 70 3.1.4 General Overview of miR Function . 71 3.1.5 MiRs Involved in Epithelial to Mesenchymal Transition . 72 3.1.6 MiRs Involved in Chemoresistance . 73 3.1.7 MiR-25 . 74 3.1.8 MiR-27a . 76 3.1.9 MiR-23a . 80 3.2 Analysis of miRs 23a,25,27a . 82 3.2.1 Background . 82 3.2.2 Hypothesis . 84 3.2.3 Aim . 84 3.2.4 Results . 85 3.2.5 Discussion . 103 3.2.6 Future Potential Work . 107 3.2.7 Overall Conclusion . 108 xvi Table of contents 4 Gene Signature Linked to EP300 109 4.1 Introduction . 109 4.1.1 ABCG2 . 109 4.1.2 ARHGAP20 . 111 4.1.3 BCL2 . 113 4.1.4 BMP4 and BMP7 . 114 4.1.5 CAPN9 . 116 4.1.6 Cadherins . 118 4.1.7 CEACAM5 . 121 4.1.8 CNN2 . 122 4.1.9 EFEMP1 . 124 4.1.10 EPHA4 . 125 4.1.11 FGFR2 .
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