CIViC: Crowdsourcing the Clinical Interpretation of Variants in Cancer
Creators: name: Kilannin Krysiak
name: Spies, Nicholas
name: McMichael, Josh
name: Coffman, Adam
name: Arpad Danos
name: Ainscough, Benjamin
name: Ramirez, Cody
name: Rieke, Damian
name: Lynzey Kujan
name: Barnell, Erica
name: Wagner, Alex
name: Skidmore, Zachary
name: Wollam, Amber
name: Liu, Connor
name: Jones, Martin
name: Bilski, Rachel
name: Lesurf, Robert
name: Yan-Yang Feng
name: Nakul Shah
name: Bonakdar, Melika
name: Trani, Lee
name: Matlock, Matthew
name: Ramu, Avinash
name: Campbell, Katie
name: Spies, Gregory
name: Graubert, Aaron
name: Gangavarapu, Karthik
name: Eldred, James
name: Larson, David
name: Walker, Jason
name: Good, Benjamin
name: Chunlei Wu
name: Su, Andrew
name: Dienstmann, Rodrigo
name: Margolin, Adam
name: Tamborero, David
name: Lopez-Bigas, Nuria
name: Jones, Steven
name: Bose, Ron
name: Spencer, David
name: Wartman, Lukas
name: Wilson, Richard
name: Mardis, Elaine
name: Griffith, Malachi
name: Obi Griffith
Publisher: name: F1000Research
Year: 2017

description: The genomic landscape of tumorigenesis has been systematically surveyed in recent years, identifying thousands of potential cancer-driving alterations. Interpreting the events identified in data from even a single sequenced sample requires both extensive bioinformatics expertise as well as an understanding of cancer biology and clinical paradigms. To best integrate genomics into the practice of medicine, results must be placed in the context of therapeutic response and diagnostic, predisposing or prognostic associations. The evidence for these associations must be curated so that we can achieve a principled consensus among genomic experts, pathologists, and oncologists on how best to interpret a genomic alteration in a clinical context. This curation and interpretation now represents a significant bottleneck, preventing the realization of personalized medicine. To this end, we present CIViC (www.civicdb.org) as a forum for the clinical interpretation of variants in cancer. We believe that to succeed, such a resource must be comprehensive, current, community-based, and above all, open-access. CIViC allows massively-scalable curation of structured evidence coupled with free-form discussion for user-friendly interpretation of clinical actionability of genomic alterations without any barriers to access. CIViC supports multiple lines of evidence, stratified based on the type of study, from in vitro studies to large clinical trials. Standardization of this crowd-sourced data requires clear and concise guidelines. To minimize ambiguity, the CIViC data model establishes a formal structure for categorizing the information in evidence items, while ontologies such as the Disease Ontology and Sequence Ontology allow for standardization of language. This structure will allow for computational techniques to be applied to the data for novel hypothesis generation. CIViC currently contains clinical interpretations for over 733 genomic alterations in 286 genes spanning 205 cancer types and summarizing the evidence from over 1090 publications. The CIViC interface facilitates both discovery and collaboration by allowing a user to not only search and browse the current state-of-the art interpretations but also to join the community discussion by adding, editing, or commenting on genomic events, evidence for their clinical actionability and the resulting community consensus interpretation.