Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes
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 ge...
| Main Authors: | , |
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| Format: | Article |
| Language: | en_US |
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Public Library of Science
2016
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| Online Access: | http://hdl.handle.net/1721.1/105412 https://orcid.org/0000-0003-4610-1707 |
| _version_ | 1811084883829719040 |
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| author | Pritchard, Justin R. Zhao, Boyang |
| author2 | Massachusetts Institute of Technology. Computational and Systems Biology Program |
| author_facet | Massachusetts Institute of Technology. Computational and Systems Biology Program Pritchard, Justin R. Zhao, Boyang |
| author_sort | Pritchard, Justin R. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:59:01Z |
| format | Article |
| id | mit-1721.1/105412 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:59:01Z |
| publishDate | 2016 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/1054122022-09-28T11:19:12Z Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes Pritchard, Justin R. Zhao, Boyang Massachusetts Institute of Technology. Computational and Systems Biology Program Zhao, Boyang 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. 2016-11-22T17:45:37Z 2016-11-22T17:45:37Z 2016-06 2015-11 Article http://purl.org/eprint/type/JournalArticle 1553-7404 1553-7390 http://hdl.handle.net/1721.1/105412 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. https://orcid.org/0000-0003-4610-1707 en_US http://dx.doi.org/10.1371/journal.pgen.1006081 PLOS Genetics Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science PLOS |
| spellingShingle | Pritchard, Justin R. Zhao, Boyang Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes |
| title | Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes |
| title_full | Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes |
| title_fullStr | Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes |
| title_full_unstemmed | Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes |
| title_short | Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes |
| title_sort | inherited disease genetics improves the identification of cancer associated genes |
| url | http://hdl.handle.net/1721.1/105412 https://orcid.org/0000-0003-4610-1707 |
| work_keys_str_mv | AT pritchardjustinr inheriteddiseasegeneticsimprovestheidentificationofcancerassociatedgenes AT zhaoboyang inheriteddiseasegeneticsimprovestheidentificationofcancerassociatedgenes |