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Hypermutation in human cancer genomes: footprints and mechanisms

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Roberts, Steven A., Gordenin, Dmitry A.·2014·Nature Reviews Cancer
DOI 10.1038/nrc3816
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Abstract

A role for somatic mutations in carcinogenesis is well accepted, but the degree to which mutation rates influence cancer initiation and development is under continuous debate. Recently accumulated genomic data have revealed that thousands of tumour samples are riddled by hypermutation, broadening support for the idea that many cancers acquire a mutator phenotype. This major expansion of cancer mutation data sets has provided unprecedented statistical power for the analysis of mutation spectra, which has confirmed several classical sources of mutation in cancer, highlighted new prominent mutation sources (such as apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) enzymes) and empowered the search for cancer drivers. The confluence of cancer mutation genomics and mechanistic insight provides great promise for understanding the basic development of cancer through mutations.

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Cited by

See all 405

Metadata

Authors
Roberts, S. A., & Gordenin, D. A.
Venue
Nature Reviews Cancer
Publisher
Springer Science and Business Media LLC
Published
2014-11-24
Volume
14
Issue
12
Pages
786-800
ISSN
  • 1474-175X (ISSN-L)
  • 1474-1768
DOI
10.1038/nrc3816
OpenAlex
W2044563319
Cited by
405
References
29
FWCI
3.44
Type
journal article
Language
en
Update notices
erratum (2015-09-28)
Open access
green
Licence

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This page
CoChat. (2026). Citation check: Hypermutation in human cancer genomes: footprints and mechanisms. https://app.cochat.ai/papers/10.1038/nrc3816
The paper
Roberts, S. A., & Gordenin, D. A. (2014). Hypermutation in human cancer genomes: footprints and mechanisms. Nature Reviews Cancer. https://doi.org/10.1038/nrc3816

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.