AN INVESTIGATION INTO THE NON-CODING GENOMIC LANDSCAPE AND EFFECTS OF CHEMOTHERAPEUTICS IN PRE-TREATED ADVANCED CANCERS by Harwood Kwan B.Sc., The University of British Columbia, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Medical Genetics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) March 2020 © Harwood Kwan, 2020 The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: An investigation into the non-coding genomic landscape and effects of chemotherapeutics in pre-treated advanced cancers submitted by Harwood Kwan in partial fulfillment of the requirements for the degree of Master of Science in Medical Genetics Examining Committee: Steven Jones, Professor, Medical Genetics, UBC Supervisor Inanc Birol, Professor, Medical Genetics, UBC Supervisory Committee Member Peter Stirling, Associate Professor, Medical Genetics, UBC Supervisory Committee Member Philipp Lange, Assistant Professor, Pathology and Laboratory Medicine, UBC Additional Examiner ii Abstract Cancer is a disease which arises due to somatic alterations in the genome. However, most studies on cancer genetics only explore the impact that coding mutations have on the progression of the disease. Furthermore, many genomic inquiries on cancer only implicate primary untreated tumours, which misses the impact of metastasis and treatment. Here we present a cohort of 638 advanced cancer patients with whole genomic, transcriptomic and clinical information. Through this cohort, we attempt to better characterize the non-coding region of metastatic cancers as well as attempt to understand the mutational impact of chemotherapeutics. Using a positional clustering method, we identified 1,567 significant mutational hotspots in the genome. 86 genes were identified as being affected by a hotspot in a regulatory region, including in the TERT promoter, a region with well-known driving mutations. To characterize the biological function of the hotspots, we analyzed the impact of mutation on corresponding gene expression. We show an increased expression for TERT and AP2A1 when their respective promoter regions are mutated, the latter being a novel association. Mutational clusters affecting non-coding RNAs were also examined for any functional impact, but no significant associations were seen. Large non-coding mutational events such as kataegis were seen in multiple cancer types and across all chromosomes. However, little recurrence was seen for kataegis. Additionally, using observed mutational frequencies, we attempt to identify any mutations that may be treatment-induced. Examining the breast, lung, colon and pancreas and ovarian cohorts, we were able to extract known resistance mutations such as ESR1 mutations after aromatase inhibitor treatment and EGFR T790M mutations post anti-EGFR therapy. Further insights are required to confirm the expressional change seen in the cohort. Additional studies to determine AP2A1’s role in cancer would help understand this correlation. Overall, our study shows the presence of important iii mutations in the non-coding space of metastatic cancers, and the power of whole genome sequencing. Furthermore, we display the need for similar datasets to extrapolate mutations which correlate to resistance. iv Lay Summary Cancers are caused by changes in our genetic information that result in uncontrolled growth of our cells. Most of our information about cancers come from early-stage cancers that have not undergone any treatment, limiting our understanding of how the disease progresses. Additionally, much is still unknown about how mutations in non-coding DNA regions can affect cancer progression. Here we present a study on the genomic landscape advanced cancers, where we attempt to garner a better understanding of metastatic mutations and the effects of chemotherapies on our DNA. We show that the most prevalent mutations reside in the coding regions, but functionally relevant mutations can be seen in the non-coding regions as well. However, there does not appear to be a singular mutation responsible for metastasis. We also show that using clinical data, we can identify mutations that arise due to chemotherapeutics. v Preface Under the supervision of Dr. Steven Jones, I, Harwood Kwan, designed the experiments and studies described within the thesis with the assistance of Dr. Erin Pleasance. This work was approved by and conducted under the University of British Columbia – British Columbia Cancer Agency Research Ethics Board (H12-00137, H14-00681), and approved by the institutional review board (IRB). The POG program is registered under clinical trial number NCT02155621. Patients were referred to the POG program through their treating oncologist and enrolled into the program through a POG trained oncologist or study nurse. Sample collection was performed by the overseeing surgical oncologist. Dr. Andrew Mungall was responsible for the processing and library construction of the samples. Dr. Richard Moore oversaw the sequencing of the samples. Eric Chuah, Karen Mungall, Tina Wong and Reanne Bowlby supervised the alignment and variant calling of the samples. Each person named oversaw multiple individuals who contributed to some part of the overall pipeline. A version of Chapter 2 is published in “Pleasance, E.D., Titmuss E., Williamson L., et al. (2020). Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nature Cancer. In press.”. I performed all the computational experiments described. Aside from the clustering algorithm, I wrote the remainder of the code for the positional clustering of mutations, including determination of statistical significance and expression analysis. I analyzed the results and selected candidate mutations for downstream analysis. Dr. Jahanshah Ashkani was responsible for the STAR alignment of RNA-seq used in expression analysis. I also called the variants in the TCGA samples and performed all the expressional analysis in that dataset. I also wrote all the code pertaining to kataegis identification vi and performed all the computational analysis regarding kataegis events. The portion of the manuscript pertaining to this work was co-written by myself, Dr. Erin Pleasance, Dr. Laura Williamson and Emma Titmuss. A version of Chapter 3 is also published in “Pleasance, E.D., Titmuss E., Williamson L., et al. (2020). Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nature Cancer. In press.”. I wrote and performed all computational experiments described. Biological relevance was examined by both Dr. Erin Pleasance and me. The portion of the manuscript pertaining to this work was co-written by myself and Dr. Erin Pleasance, Dr. Laura Williamson and Emma Titmuss. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................. v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ viii List of Tables ................................................................................................................................ xii List of Figures ............................................................................................................................. xiii List of Abbreviations .................................................................................................................. xiii Acknowledgements ................................................................................................................... xviii Dedication ..................................................................................................................................... xx Chapter 1: Introduction ................................................................................................................ 1 1.1 Research Aims ................................................................................................................. 1 1.2 Background ...................................................................................................................... 1 1.2.1 Cancer is a Global Health and Economic Issue ....................................................... 1 1.2.2 Genetics of Cancer .................................................................................................. 2 History of Cancer Genetics ................................................................................. 2 Cancerous Mutations Arise from Multiple Sources ............................................ 3 DNA Repair Plays an Important Role in Cancer Progression ............................ 5 Properties of Oncogenic Mutations ..................................................................... 6 Non-coding DNA Play Functional Roles in Cancer ............................................ 8 Kataegis ............................................................................................................. 14 Cancer Evolves