NatureGenomicsPeer-reviewed journal article
Germline determinants of the somatic mutation landscape in 2,642 cancer genomes
Lauri A. AaltoneniD, Federico AbascaliD, Adam A. AbeshouseiD, Hiroyuki AburataniiD, David J. AdamsiD, Nishant AgrawaliD, Keun Soo AhniD, Sung-Min Ahn, Hiroshi AikataiD, Rehan AkbaniiD, Kadir Caner AkdemiriD, Hikmat A Al‐AhmadieiD, Sultan T. Al‐Sedairy, Fátima Al‐ShahrouriD, Malik AlawiiD, Monique Albert, Kenneth AldapeiD, Ludmil B. AlexandroviD, Adrian Ally, Kathryn AlsopiD, Eva G. ÁlvareziD, Fernanda M AmaryiD, Samirkumar B. AminiD, Brice Aminou, Ole AmmerpohliD, , Johann Sebastian De BonoiD
Abstract
Cancers develop through somatic mutagenesis, however germline genetic variation can markedly contribute to tumorigenesis via diverse mechanisms. We discovered and phased 88 million germline single nucleotide variants, short insertions/deletions, and large structural variants in whole genomes from 2,642 cancer patients, and employed this genomic resource to study genetic determinants of somatic mutagenesis across 39 cancer types. Our analyses implicate damaging germline variants in a variety of cancer predisposition and DNA damage response genes with specific somatic mutation patterns. Mutations in the MBD4 DNA glycosylase gene showed association with elevated C>T mutagenesis at CpG dinucleotides, a ubiquitous mutational process acting across tissues. Analysis of somatic structural variation exposed complex rearrangement patterns, involving cycles of templated insertions and tandem duplications, in BRCA1-deficient tumours. Genome-wide association analysis implicated common genetic variation at the APOBEC3 gene cluster with reduced basal levels of somatic mutagenesis attributable to APOBEC cytidine deaminases across cancer types. We further inferred over a hundred polymorphic L1/LINE elements with somatic retrotransposition activity in cancer. Our study highlights the major impact of rare and common germline variants on mutational landscapes in cancer.
- organism: human
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
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- FWCI
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- Field percentile
- top 1%
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