PT  - JOURNAL ARTICLE
AU  - Li, Quan
AU  - Ren, Zilin
AU  - Zhou, Yunyun
AU  - Wang, Kai
TI  - CancerVar: a web server for improved evidence-based clinical interpretation of cancer somatic mutations and copy number abnormalities
AID  - 10.1101/2020.10.06.323162
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.10.06.323162
4099  - http://biorxiv.org/content/early/2020/10/10/2020.10.06.323162.short
4100  - http://biorxiv.org/content/early/2020/10/10/2020.10.06.323162.full
AB  - Several knowledgebases, such as CIViC, CGI and OncoKB, have been manually curated to support clinical interpretations of somatic mutations and copy number abnormalities (CNAs) in cancer. However, these resources focus on known hotspot mutations, and discrepancies or even conflicting interpretations have been observed between these knowledgebases. To standardize clinical interpretation, AMP/ASCO/CAP/ACMG/CGC jointly published consensus guidelines for the interpretations of somatic mutations and CNAs in 2017 and 2019, respectively. Based on these guidelines, we developed a standardized, semi-automated interpretation tool called CancerVar (Cancer Variants interpretation), with a user-friendly web interface to assess the clinical impacts of somatic variants. Using a semi-supervised method, CancerVar interpret the clinical impacts of cancer variants as four tiers: strong clinical significance, potential clinical significance, unknown clinical significance, benign/likely benign. CancerVar also allows users to specify criteria or adjust scoring weights as a customized interpretation strategy, and allows phenotype-driven scoring for specific types of cancer. Importantly, CancerVar generates automated texts to summarize clinical evidence on somatic variants, which greatly reduces manual workload to write interpretations that include relevant information from harmonized knowledgebases. CancerVar can be accessed at http://cancervar.wglab.org and it is open to all users without login requirements. The command line tool is also available at https://github.com/WGLab/CancerVar.Competing Interest StatementThe authors have declared no competing interest.