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Evolutionary dynamics of ovarian cancer microenvironments and tumour cells

University of British Columbia

High-grade serous ovarian cancer (HGSC) is the most common and lethal histotype of epithelial ovarian cancer. Often presenting as multi-site disease, HGSC exhibits extensive malignant clonal diversity with widespread but non-random patterns of disease dissemination. The proclivity of HGSC toward clonally heterogeneous disease is thought to underlie the prevalence of treatment-resistant disease. Yet, the factors that influence the spatial distribution of cancer clones in HGSC remain largely uncharacterized. Hypothesizing that distinct peritoneal niches formed by microenvironmental cell types shape the observed patterns of clonal dynamics in HGSC, the primary aim of this thesis was to understand how microenvironmental factors influence malignant cell evolutionary dynamics. To establish the experimental substrate for this thesis, I led the construction of a cohort of 148 tumour samples from 41 HGSC cases (Chapter 2). In addition to coordinating clinical case identification, I oversaw and learned how to create patient-derived xenograft models and conduct single cell experiments from patient tumours. Leveraging this resource, I explored whether local immune microenvironment factors shape tumor progression properties at the interface of tumor-infiltrating lymphocytes and cancer cells (Chapter 3). Through multi-region study with whole-genome sequencing, immunohistochemistry, image analysis, gene expression profiling, and T- and B-cell receptor sequencing, I identified three immunologic subtypes across samples associated with patterns of malignant clonal diversity. These findings were consistent with immunological pruning of tumor clones. Finally, in order to explore the non-lymphocytic components of the tumour microenvironment, I developed an automated approach to cell type identification from single cell RNA-seq data that eliminates the manual work involved in traditional workflows reliant on post-hoc expert annotation (Chapter 4). I demonstrated how this method performs superiorly to state-of-the-art workflows for cell type identification and applied the method to profile the HGSC microenvironment. Collectively, this work highlights multiple interfaces of evolutionary interplay between malignant and non-malignant cells in the HGSC microenvironment, identifying novel mechanisms by which tumour cells escape from immune recognition. These results will inform the interpretation of results from immunotherapy clinical trials and set the stage for comprehensive microenvironment profiling in large HGSC cohorts and other cancers.

Text; Dataset

2020-05-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/70469

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/