| Summary: | Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2017. === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 65-74). === Cancer sequencing efforts have largely focused on profiling somatic variants in the protein-coding genome and characterizing their functional impact. In this study, we develop a computational pipeline to identify non-coding mutational drivers across multiple tumor types. We describe the non-coding mutational profiles of 854 samples, spread across 15 tumor types, in the context of their respective tissue type-specific reference epigenomes, using recent pan-cancer whole-genome sequencing data. We develop a novel method to detect significantly recurrent non-coding mutations by reestimating the background mutation density while adjusting for epigenomic covariates. Existing databases on enhancer-gene links allow us to capture the convergence of disparate mutations onto downstream target genes. We then systematically identify key immunomodulatory and tumor-suppressive genes enriched for non-coding mutations in their regulatory neighborhood and evaluate these in a pan-cancer context. Taken together, we show that low-frequency alterations converge into high-frequency recurrent events on downstream targets through tissue-specific regulatory interactions. === by Karthik Murugadoss. === S.M.
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