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Classification and characterization of microsatellite instability across 18 cancer types
Classification and characterization of microsatellite instability across 18 cancer types
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Classification and characterization of microsatellite instability across 18 cancer types
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Classification and characterization of microsatellite instability across 18 cancer types
Classification and characterization of microsatellite instability across 18 cancer types

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Classification and characterization of microsatellite instability across 18 cancer types
Classification and characterization of microsatellite instability across 18 cancer types
Journal Article

Classification and characterization of microsatellite instability across 18 cancer types

2016
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Overview
Systematic analysis of more than 5,900 human tumor exomes yields a new genomic classifier of microsatellite instability and insight into its prevalence and biological implications. Microsatellite instability (MSI), the spontaneous loss or gain of nucleotides from repetitive DNA tracts, is a diagnostic phenotype for gastrointestinal, endometrial, and colorectal tumors, yet the landscape of instability events across a wider variety of cancer types remains poorly understood. To explore MSI across malignancies, we examined 5,930 cancer exomes from 18 cancer types at more than 200,000 microsatellite loci and constructed a genomic classifier for MSI. We identified MSI-positive tumors in 14 of the 18 cancer types. We also identified loci that were more likely to be unstable in particular cancer types, resulting in specific instability signatures that involved cancer-associated genes, suggesting that instability patterns reflect selective pressures and can potentially identify novel cancer drivers. We also observed a correlation between survival outcomes and the overall burden of unstable microsatellites, suggesting that MSI may be a continuous, rather than discrete, phenotype that is informative across cancer types. These analyses offer insight into conserved and cancer-specific properties of MSI and reveal opportunities for improved methods of clinical MSI diagnosis and cancer gene discovery.