Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes
Name
Zhao-2016-Inherited Disease Ge.pdf
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2.67 MB
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Adobe PDF
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Author(s)
Pritchard, Justin R.
Zhao, Boyang
Date Issued
June 2016
Journal
PLOS Genetics
Publisher
Public Library of Science
Citation
Zhao, Boyang, and Justin R. Pritchard. “Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes.” Ed. Gregory M. Cooper. PLOS Genetics 12.6 (2016): e1006081.
Version
Final published version
Abstract
The identification of biologically significant variants in cancer genomes is critical to therapeutic discovery, but it is limited by the statistical power needed to discern driver from passenger. Independent biological data can be used to filter cancer exomes and increase statistical power. Large genetic databases for inherited diseases are uniquely suited to this task because they contain specific amino acid alterations with known pathogenicity and molecular mechanisms. However, no rigorous method to overlay this information onto the cancer exome exists. Here, we present a computational methodology that overlays any variant database onto the somatic mutations in all cancer exomes. We validate the computation experimentally and identify novel associations in a re-analysis of 7362 cancer exomes. This analysis identified activating SOS1 mutations associated with Noonan syndrome as significantly altered in melanoma and the first kinase-activating mutations in ACVR1 associated with adult tumors. Beyond a filter, significant variants found in both rare cancers and rare inherited diseases increase the unmet medical need for therapeutics that target these variants and may bootstrap drug discovery efforts in orphan indications.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Terms of Use
Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1371/journal.pgen.1006081
