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UBC Theses and Dissertations

Computational prioritization of cancer driver genes for precision oncology Shrestha, Raunak

Abstract

Advances in high-throughput sequencing technologies has drastically increased the efficiency to access different alterations in the genome, transcriptome, proteome, and epigenome of a cancer cell. This has increased the computational burden to analyze these “big data” making the translation of the knowledge into insightful and impactful patient outcomes extraordinarily challenging. Among these alterations, only a few “driver” alterations are expected to confer crucial growth advantage. These are greatly outnumbered by functionally inconsequential “passenger” alterations. This poses a significant challenge for the identification of driver alterations, requiring solutions to novel algorithmic problems. Although, the insight on driver alterations is critical to guide selection of appropriate drug therapies for the patient, no specific tools exist to help clinicians contextualize the enormous genomic information when making therapeutic decisions. In this thesis we describe novel algorithms for the identification and prioritization of cancer driver genes. First we describe, HIT’nDRIVE, a combinatorial algorithm measuring the impact of genomic aberration to global changes of gene expression pattern to prioritize cancer driver genes. We also demonstrate its application on large multi-omics cancer datasets to guide precision oncology. We further describe integrative multi-omics characterization of peritoneal mesothelioma, a rare cancer of abdomen. Here using HIT’nDRIVE, we identified peritoneal mesothelioma with BAP1 loss to form a distinct molecular subtype characterized by distinct gene expression patterns of chromatin remodeling, DNA repair pathways, and immune checkpoint receptor activation. We demonstrate that this subtype is correlated with an inflammatory tumor microenvironment and thus is a candidate for immune checkpoint blockade therapies. Finally, we describe, cd-CAP, a combinatorial algorithm to identify subnetworks with conserved molecular alteration pattern across a large subset of a tumor sample cohort. Notably, we demonstrate that many of the largest highly conserved subnetworks within a tumor type solely consist of genes that have been subject to copy number gain, typically located on the same chromosomal arm and thus likely a result of a single, large scale copy number amplification.

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Attribution-NonCommercial-NoDerivatives 4.0 International

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