CancerVar: a web server for improved evidence-based clinical interpretation for cancer somatic mutations

CancerVar is a bioinformatics software tool for clinical interpretation of somatic variants by evidence from AMP/ASCO/CAP/CGC 2017-2019 guideline. The input to CancerVar can be genomic coordinate,dbSNP ID, gene with cDNA change, gene with Protein change, while the output of CancerVar is the interpretation of variants as clinical significance: 'Tier I/Pathogenic: Variants of Strong Clinical Significance','Tier II/Likely Pathogenic: Variants of Potential Clinical Significance','Tier III: Variants of Unknown Clinical Significance' and 'Tier IV: Benign or Likely Benign Variants', together with detailed evidence.

The clinical sequencing of tumours has become mainstay in cancer care, and generated explosive cancer variants. However, how to standardly, quickly and easily interpret these variants remains to be an urgent need for clinicians and researchers in oncology. The clinical significance interpretation in one-stop depends greatly on the evidence which should be precisely derived from multiple databases and annotations. CancerVar (Cancer Variants interpretation) is a bioinformatics tool for interpreting or predicting clinical impacts of cancer variants. CancerVar classifies cancer variants according to AMP/ASCO/CAP/CGC 2017-2019 guidelines for assessing variant pathogenicity. In CancerVar’s interpretation process, user can specify criteria or scoring weights, as a customized interpretation strategy. CancerVar is written in Python script and http://cancervar.wglab.org is a companion web server of CancerVar, implemented in PHP framework—Zend OPcache v7.2, MongoDB v1.3.4 and Apache v2.4.29, with all major browsers supported.
For clinical significance interpretation, the CancerVar take the Cancer evidence as CBPs:
(for evidence detail, please check the reference)
CBP_1 "Therapeutic: FDA approved or investigational with strong evidence";
CBP_2 "Diagnostic: In Professional guideline or reported evidence with consensus";
CBP_3 "Prognostic: In Professional guideline or reported evidence with consensus";
CBP_4 "Mutation type: Activating, LOF (missense, nonsense, indel, splicing), CNVs, fusions";
CBP_5 "Variant frequencies:Mostly mosaic";
CBP_6 "Potential germline: Mostly nonmosaic";
CBP_7 "Population databases: Absent or extremely low MAF";
CBP_8 "Germline databases: may be present in HGMD/ClinVar";
CBP_9 "Somatic databases: Most present in COSMIC, My Cancer Genome, TCGA";
CBP_10 "Predictive from: SIFT, PolyPhen2,MutationTaster, CADD, MetaSVM,MetaLR,FATHMM,GERP++_RS, and mostly as";
CBP_11 "Pathway: involve in Disease-associated pathways or pathogenic pathways";
CBP_12 "Publications: Convincing evidence from Functional study, population study, other";
CBP_13 "Additional: user specified";


Reference

1. Li MM, et al. Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn. 2017 Jan;19(1):4-23. doi: 10.1016/j.jmoldx.2016.10.002.
2.Mikhail FM, et al. Technical laboratory standards for interpretation and reporting of acquired copy-number abnormalities and copy-neutral loss of heterozygosity in neoplastic disorders: a joint consensus recommendation from the American College of Medical Genetics and Genomics (ACMG) and the Cancer Genomics Consortium (CGC). Genet Med. 2019 Sep;21(9):1903-1916. doi: 10.1038/s41436-019-0545-7.
3.Quan Li and Kai Wang. InterVar: Clinical interpretation of genetic variants by ACMG-AMP 2015 guideline(The American Journal of Human Genetics 100, 1-14, February 2, 2017,http://dx.doi.org/10.1016/j.ajhg.2017.01.004)
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