The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations.
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BORIS DOI
Publisher DOI
PubMed ID
27789569
Description
Objective
This paper describes the Precision Medicine Knowledge Base (PMKB; https://pmkb.weill.cornell.edu ), an interactive online application for collaborative editing, maintenance, and sharing of structured clinical-grade cancer mutation interpretations.
Materials and Methods
PMKB was built using the Ruby on Rails Web application framework. Leveraging existing standards such as the Human Genome Variation Society variant description format, we implemented a data model that links variants to tumor-specific and tissue-specific interpretations. Key features of PMKB include support for all major variant types, standardized authentication, distinct user roles including high-level approvers, and detailed activity history. A REpresentational State Transfer (REST) application-programming interface (API) was implemented to query the PMKB programmatically.
Results
At the time of writing, PMKB contains 457 variant descriptions with 281 clinical-grade interpretations. The EGFR, BRAF, KRAS, and KIT genes are associated with the largest numbers of interpretable variants. PMKB's interpretations have been used in over 1500 AmpliSeq tests and 750 whole-exome sequencing tests. The interpretations are accessed either directly via the Web interface or programmatically via the existing API.
Discussion
An accurate and up-to-date knowledge base of genomic alterations of clinical significance is critical to the success of precision medicine programs. The open-access, programmatically accessible PMKB represents an important attempt at creating such a resource in the field of oncology.
Conclusion
The PMKB was designed to help collect and maintain clinical-grade mutation interpretations and facilitate reporting for clinical cancer genomic testing. The PMKB was also designed to enable the creation of clinical cancer genomics automated reporting pipelines via an API.
This paper describes the Precision Medicine Knowledge Base (PMKB; https://pmkb.weill.cornell.edu ), an interactive online application for collaborative editing, maintenance, and sharing of structured clinical-grade cancer mutation interpretations.
Materials and Methods
PMKB was built using the Ruby on Rails Web application framework. Leveraging existing standards such as the Human Genome Variation Society variant description format, we implemented a data model that links variants to tumor-specific and tissue-specific interpretations. Key features of PMKB include support for all major variant types, standardized authentication, distinct user roles including high-level approvers, and detailed activity history. A REpresentational State Transfer (REST) application-programming interface (API) was implemented to query the PMKB programmatically.
Results
At the time of writing, PMKB contains 457 variant descriptions with 281 clinical-grade interpretations. The EGFR, BRAF, KRAS, and KIT genes are associated with the largest numbers of interpretable variants. PMKB's interpretations have been used in over 1500 AmpliSeq tests and 750 whole-exome sequencing tests. The interpretations are accessed either directly via the Web interface or programmatically via the existing API.
Discussion
An accurate and up-to-date knowledge base of genomic alterations of clinical significance is critical to the success of precision medicine programs. The open-access, programmatically accessible PMKB represents an important attempt at creating such a resource in the field of oncology.
Conclusion
The PMKB was designed to help collect and maintain clinical-grade mutation interpretations and facilitate reporting for clinical cancer genomic testing. The PMKB was also designed to enable the creation of clinical cancer genomics automated reporting pipelines via an API.
Date of Publication
2017-05-01
Publication Type
article
Subject(s)
500 - Science
500 - Science::570 - Life sciences; biology
Keyword(s)
application-programming interface cancer genomics clinical reporting database pathology precision medicine
Language(s)
en
Contributor(s)
Huang, Linda | |
Fernandes, Helen | |
Zia, Hamid | |
Tavassoli, Peyman | |
Rennert, Hanna | |
Pisapia, David | |
Imielinski, Marcin | |
Sboner, Andrea | |
Kluk, Michael | |
Elemento, Olivier |
Additional Credits
Department for BioMedical Research, Forschungsgruppe Präzisionsonkologie
Series
Journal of the American Medical Informatics Association
Publisher
Oxford University Press
ISSN
1067-5027
Access(Rights)
open.access