Interpreting cancer genomes: Computational methods for uncovering the genomic basis of transcriptional dysregulation in cancer

Abstract

Though somatic mutations play a critical role in driving cancer initiation and progression, the functional impacts of these mutations—particularly, how they alter expression patterns across the genome and give rise to cancer hallmarks—are not yet well-understood, even for mutations in well-studied cancer driver genes like KRAS or PIK3CA. Given the rise in cancer therapies designed to target commonly mutated driver genes, such as mutant KRAS inhibitors, the need for a nuanced understanding of the molecular effects of these mutations has become urgent. This thesis describes a suite of computational methods that integrate multiomic data from patient tumors—including somatic mutation, copy number alteration, methylation, and germline variation—to discern the effects of mutated cancer driver genes on expression across the genome. This begins with a linear regression-based framework, Dyscovr, which we apply both pan-cancer and individually across 19 cancer types in the Cancer Genome Atlas (TCGA), obtaining thousands of broad and cancer type-specific links. To hone in on driver-target pairs with the most potential clinical relevance, we developed a pipeline to predict which of Dyscovr's significant pairings are most likely to exhibit negative genetic interactions, such as synthetic lethality (SL). In collaboration with the Rabinowitz lab at Princeton, we experimentally validated some of these putative SL pairs in cell lines, identifying novel pairings with clinical potential. The output of Dyscovr is available in a user-friendly website, dyscovr.princeton.edu, which we anticipate will prove a valuable tool to precipitate further experimental and clinical innovations in cancer. Finally, we put forth a complementary framework, DyscovrSNP, which systematically uncovers the role that germline variants play in modulating the transcriptional effects of mutant driver genes. DyscovrSNP identifies hundreds of novel somatic-germline interactions pan-cancer, many of which also relate to patient outcomes such as survival and drug response. Altogether, this work introduces integrative computational approaches to uncover the transcriptional impacts of somatically mutated cancer driver genes, germline SNPs, and their interactions, a critical step forward in developing personalized cancer therapies.