ArticleNature communications2020
Extreme differences between human germline and tumor mutation densities are driven by ancestral human-specific deviations.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Evolution of termination codons of proteins and the TAG-TGA paradox.Scientific reports · 2023Article
- Enabling Inference for Context-Dependent Models of Mutation by Bounding the Propagation of Dependency.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Prevalence and spectrum of DNA mismatch repair gene variation in the general Chinese population.Journal of medical genetics · 2022Article
- Article
- Nature or Nurture? Role of the Bone Marrow Microenvironment in the Genesis and Maintenance of Myelodysplastic Syndromes.Cancers · 2021Review
- Use of signals of positive and negative selection to distinguish cancer genes and passenger genes.eLife · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Mutations do not accumulate uniformly across the genome. Human germline and tumor mutation density correlate poorly, and each is associated with different genomic features. Here, we use non-human great ape (NHGA) germlines to determine human germline- and tumor-specific deviations from an ancestral-like great ape genome-wide mutational landscape. Strikingly, we find that the distribution of mutation densities in tumors presents a stronger correlation with NHGA than with human germlines. This effect is driven by human-specific differences in the distribution of mutations at non-CpG sites. We propose that ancestral human demographic events, together with the human-specific mutation slowdown, disrupted the human genome-wide distribution of mutation densities. Tumors partially recover this distribution by accumulating preneoplastic-like somatic mutations. Our results highlight the potential utility of using NHGA population data, rather than human controls, to establish the expected mutational background of healthy somatic cells.
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