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CancerVar: a web server for improved evidence-based clinical interpretation of cancer somatic mutations and copy number abnormalities

Quan Li, Zilin Ren, Yunyun Zhou, Kai Wang
doi: https://doi.org/10.1101/2020.10.06.323162
Quan Li
1Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, M5G2C1, Canada
2Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
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  • For correspondence: wangk@email.chop.edu leequan@gmail.com
Zilin Ren
2Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
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Yunyun Zhou
2Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
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Kai Wang
2Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
3Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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  • For correspondence: wangk@email.chop.edu leequan@gmail.com
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ABSTRACT

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 Statement

The authors have declared no competing interest.

Footnotes

  • We made some changes in main text,corrected the tiers names.

  • http://cancervar.wglab.org

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 10, 2020.
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CancerVar: a web server for improved evidence-based clinical interpretation of cancer somatic mutations and copy number abnormalities
Quan Li, Zilin Ren, Yunyun Zhou, Kai Wang
bioRxiv 2020.10.06.323162; doi: https://doi.org/10.1101/2020.10.06.323162
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CancerVar: a web server for improved evidence-based clinical interpretation of cancer somatic mutations and copy number abnormalities
Quan Li, Zilin Ren, Yunyun Zhou, Kai Wang
bioRxiv 2020.10.06.323162; doi: https://doi.org/10.1101/2020.10.06.323162

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