Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes

Boyang Zhao, Justin Pritchard

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere1006081
JournalPLoS genetics
Volume12
Issue number6
DOIs
StatePublished - Jun 1 2016

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Inborn Genetic Diseases
Neoplasm Genes
genetic disorders
cancer
Exome
neoplasms
gene
mutation
Neoplasms
genes
Mutation
filter
orphan
Noonan Syndrome
Genetic Databases
pathogenicity
somatic mutation
therapeutics
tumor
Drug Discovery

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics
  • Genetics(clinical)
  • Cancer Research

Cite this

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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.",
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Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes. / Zhao, Boyang; Pritchard, Justin.

In: PLoS genetics, Vol. 12, No. 6, e1006081, 01.06.2016.

Research output: Contribution to journalArticle

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