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bioRxiv · 10.1101/059824

The Precision Medicine Knowledge Base: an online application for collaborative editing, maintenance and sharing of structured clinical-grade cancer mutations interpretations

Abstract

ObjectiveThis 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 mutations interpretations.\n\nMaterials and MethodsPMKB was built using the Ruby on Rails Web application framework. Leveraging existing standards such as Human Genome Variation Society (HGVS) 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, detailed activity history. A REpresentational State Transfer (REST) application-programming interface (API) was implemented to query the PMKB programmatically.\n\nResultsAt 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. The PMKBs interpretations have been used in over 1,500 AmpliSeq tests and 750 whole exome sequencing tests. The interpretations are accessed either directly via the Web interface or programmatically via the existing API.\n\nDiscussionAn 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.\n\nConclusionThe PMKB was designed to help collect and maintain clinical-grade mutation interpretations and facilitates 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.

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Linda Huang, Helen Fernandes, Hamid Zia, Peyman Tavassoli, Hanna Rennert, David Pisapia, Marcin Imielinski, Andrea Sboner, Mark A Rubin, Michael Kluk, Olivier Elemento. 2016-06-19. The Precision Medicine Knowledge Base: an online application for collaborative editing, maintenance and sharing of structured clinical-grade cancer mutations interpretations. https://doi.org/10.1101/059824

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