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Nat Biotechnol DOI:10.1038/s41587-022-01353-8

Genome-wide mapping of somatic mutation rates uncovers drivers of cancer.

Publication TypeJournal Article
Year of Publication2022
AuthorsSherman, MA, Yaari, AU, Priebe, O, Dietlein, F, Loh, P-R, Berger, B
JournalNat Biotechnol
Date Published2022 Jun 20
ISSN1546-1696
Abstract

Identification 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.

DOI10.1038/s41587-022-01353-8
Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/35726091?dopt=Abstract

Alternate JournalNat Biotechnol
PubMed ID35726091
Grant ListF31 MH124393 / MH / NIMH NIH HHS / United States
DP2 ES030554 / ES / NIEHS NIH HHS / United States
U01CA250554 / / U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) /