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dc.contributor.advisorManolis Kellis.en_US
dc.contributor.authorMurugadoss, Karthiken_US
dc.contributor.otherMassachusetts Institute of Technology. Computation for Design and Optimization Program.en_US
dc.date.accessioned2017-09-15T15:37:21Z
dc.date.available2017-09-15T15:37:21Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/111510
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2017.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 65-74).en_US
dc.description.abstractCancer 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.en_US
dc.description.statementofresponsibilityby Karthik Murugadoss.en_US
dc.format.extent74 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectComputation for Design and Optimization Program.en_US
dc.titleConvergence of regulatory mutations into oncogenic pathways across multiple tumor typesen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computation for Design and Optimization Program
dc.identifier.oclc1003324173en_US


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