Abstract 5469: iCVA- A knowledge-based cancer variation annotation application

A typical genomic sequencing experiment results in a long list of statistically significant variant candidates among protein-coding genes without any unifying biological theme. This leads to a daunting task of identifying the causal variants and genes to accurately diagnose the disease for clinical or research utility. While a number of downstream tools exist to aid in cancer genome annotation and interpretation, including Clinical Interpretations of Variants in Cancer (CIViC), ClinGen, Database of Curated Mutations (DoCM), Oncotator, and Variant Interpretation for Cancer (VIC), these only provide selective information (somatic/germline/gene-variant specific) or are time/resource consuming. To address this challenge, we developed a software called iCVA using the following methodology: 1) we obtained a priori defined gene sets for different cancer-inducing mechanisms such as pro-oncogenesis, tumor-suppression, DNA repair, angiogenesis, inflammation, metabolism, hypoxia, cell cycle and immune system. 2) genomic variation data were integrated from 1000 Genome, Exome Sequencing Project, Exome Aggregation Consortium (ExAC), ClinVar, dbSNP, GNOMAD, The Cancer Genome Atlas (TCGA), the Catalogue of Somatic Mutations in Cancer (COSMIC) and the International Cancer Gene Census (ICGC), and re-classified into a consensus classification according to ACMG guidelines. 3) Finally, we devised a reporting system to process mutation data from a sequencing experiment to utilize the harmonized pathway and mutational information integrated in a local database to identify and classify cancer-specific gene mutations. The iCVA reporting system classifies mutations as germline or somatic and then sub-classifies these into different cancer-specific mechanisms, including those known to be targetable through existing therapies. To our knowledge, iCVA is the first tool to provide a comprehensive report on cancer variants in a simplified and faster manner, to accelerate the genomic characterization of cancer samples analyzed by high-throughput DNA sequencing. Citation Format: Gaurav Kumar, Melanie Kelly, Paolo Fortina, Adam Ertel. iCVA- A knowledge-based cancer variation annotation application [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5469.