Dataset Information

Emerging patterns of somatic mutations in cancer.

ABSTRACT: Recent advances in technological tools for massively parallel, high-throughput sequencing of DNA have enabled the comprehensive characterization of somatic mutations in a large number of tumour samples. In this Review, we describe recent cancer genomic studies that have assembled emerging views of the landscapes of somatic mutations through deep-sequencing analyses of the coding exomes and whole genomes in various cancer types. We discuss the comparative genomics of different cancers, including mutation rates and spectra, as well as the roles of environmental insults that influence these processes. We highlight the developing statistical approaches that are used to identify significantly mutated genes, and discuss the emerging biological and clinical insights from such analyses, as well as the future challenges of translating these genomic data into clinical impacts.

SUBMITTER: Watson IR

PROVIDER: S-EPMC4014352 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Emerging patterns of somatic mutations in cancer.

Watson Ian R IR Takahashi Koichi K Futreal P Andrew PA Chin Lynda L

Nature reviews. Genetics 20130911 10

Recent advances in technological tools for massively parallel, high-throughput sequencing of DNA have enabled the comprehensive characterization of somatic mutations in a large number of tumour samples. In this Review, we describe recent cancer genomic studies that have assembled emerging views of the landscapes of somatic mutations through deep-sequencing analyses of the coding exomes and whole genomes in various cancer types. We discuss the comparative genomics of different cancers, including ...[more]

PMID: 24022702

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Project description:BackgroundLarge-scale tumor sequencing projects are now underway to identify genetic mutations that drive tumor initiation and development. Most studies take a gene-based approach to identifying driver mutations, highlighting genes mutated in a large percentage of tumor samples as those likely to contain driver mutations. However, this gene-based approach usually does not consider the position of the mutation within the gene or the functional context the position of the mutation provides. Here we introduce a novel method for mapping mutations to distinct protein domains, not just individual genes, in which they occur, thus providing the functional context for how the mutation contributes to disease. Furthermore, aggregating mutations from all genes containing a specific protein domain enables the identification of mutations that are rare at the gene level, but that occur frequently within the specified domain. These highly mutated domains potentially reveal disruptions of protein function necessary for cancer development.ResultsWe mapped somatic mutations from the protein coding regions of 100 colon adenocarcinoma tumor samples to the genes and protein domains in which they occurred, and constructed topographical maps to depict the "mutational landscapes" of gene and domain mutation frequencies. We found significant mutation frequency in a number of genes previously known to be somatically mutated in colon cancer patients including APC, TP53 and KRAS. In addition, we found significant mutation frequency within specific domains located in these genes, as well as within other domains contained in genes having low mutation frequencies. These domain "peaks" were enriched with functions important to cancer development including kinase activity, DNA binding and repair, and signal transduction.ConclusionsUsing our method to create the domain landscapes of mutations in colon cancer, we were able to identify somatic mutations with high potential to drive cancer development. Interestingly, the majority of the genes involved have a low mutation frequency. Therefore, the method shows good potential for identifying rare driver mutations in current, large-scale tumor sequencing projects. In addition, mapping mutations to specific domains provides the necessary functional context for understanding how the mutations contribute to the disease, and may reveal novel or more refined gene and domain target regions for drug development.

Dataset Information

Emerging patterns of somatic mutations in cancer.

Publications

Emerging patterns of somatic mutations in cancer.

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