Genome-wide mapping of somatic mutation rates uncovers drivers of cancer
Open accessAbstract
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.
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Dig outperformed multiple other methods for driver identification in accuracy and power and ran multiple orders of magnitude faster.
“The method outperformed multiple other methods for driver identification in accuracy and power and ran multiple orders of magnitude faster.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [127]
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- Authors
- Sherman, M. A., Yaari, A. U., Priebe, O., Dietlein, F., Loh, P.-R., & Berger, B.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Loh, Po-Ru
- Berger, Bonnie
- Venue
- Nature Biotechnology
- Published
- 2022-06-20
- Volume
- 40
- Issue
- 11
- Pages
- 1634-1643
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC9646522
- OpenAlex
- W4283163503
- Cited by
- 80
- References
- 79
- Citations by year
- 2026: 15 · 2025: 18 · 2024: 31 · 2023: 12 · 2022: 4
- FWCI
- 6.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Biotechnology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (F31 MH124393)
- U.S. Department of Health & Human Services | National Institutes of Health (DP2 ES030554)
- Burroughs Wellcome Fund
- Alfred P. Sloan Foundation
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (U01CA250554)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- somatic mutation rate, cancer driver mutations, positive selection, deep neural network, 5' untranslated region, cancer genome
- MeSH terms
- Humans, Mutation, Neoplasms, Promoter Regions, Genetic, Open Reading Frames, Mutation Rate
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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