Abstract
AbstractIdentification of cancer driver mutations that confer a proliferative advantage is central to understanding cancer; however, searches have often been limited to protein-coding sequences and specific non-coding elements (for example, promoters) because of the challenge of modeling the highly variable somatic mutation rates observed across tumor genomes. Here we present Dig, a method to search for driver elements and mutations anywhere in the genome. We use deep neural networks to map cancer-specific mutation rates genome-wide at kilobase-scale resolution. These estimates are then refined to search for evidence of driver mutations under positive selection throughout the genome by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and applied these maps to identify putative drivers within intronic cryptic splice regions, 5′ untranslated regions and infrequently mutated genes. Our high-resolution mutation rate maps, available for web-based exploration, are a resource to enable driver discovery genome-wide.
Funder
U.S. Department of Health & Human Services | NIH | National Institute of Mental Health
U.S. Department of Health & Human Services | National Institutes of Health
Burroughs Wellcome Fund
Alfred P. Sloan Foundation
U.S. Department of Health & Human Services | NIH | National Cancer Institute
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering,Molecular Medicine,Applied Microbiology and Biotechnology,Bioengineering,Biotechnology
Cited by
20 articles.
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