OncoVar

OncoVar prioritizes and identifies oncogenic driver mutations and driver genes across cancer genomes by integrating published bioinformatics algorithms and reanalysis of 10,769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1,942 genomes from 18 cancer types in the International Cancer Genome Consortium (ICGC).

Key Features:

• Integrated database and analysis: Combines curated known driver events with published bioinformatics algorithms for systematic driver prioritization.
• Large-scale reanalysis: Reanalyzed 10,769 exomes from 33 cancer types (TCGA) and 1,942 genomes from 18 cancer types (ICGC).
• Driver identification: Identified 20,162 cancer driver mutations, 814 driver genes, and 2,360 pathogenic pathways.
• Driver versus passenger distinction: Systematically distinguishes oncogenic driver mutations from passenger mutations.
• Multi-perspective analysis: Provides four analytical perspectives — Mutation, Gene, Pathway, and Cancer — to characterize relationships among variants, genes, pathways, and cancer types.
• Actionable alteration detection: Identifies actionable driver alterations to inform drug development and repurposing for combinational treatment strategies.
• Multi-scale detection: Detects cancer drivers at both individual cohort levels and pan-cancer scales to support precision medicine research.

Scientific Applications:

• Driver discovery and prioritization: Prioritizes candidate driver mutations and genes for functional validation and therapeutic targeting.
• Therapeutic target identification: Identifies actionable alterations to guide drug development, repurposing, and combinational therapy strategies.
• Pan-cancer and cohort analyses: Enables pan-cancer and cancer-type–specific analyses using TCGA and ICGC exome and genome data.
• Pathway and genotype–phenotype mapping: Maps pathogenic pathways to link genetic alterations with cancer phenotypes.

Methodology:

Reanalysis of TCGA exomes and ICGC genomes using published bioinformatics algorithms and incorporation of known driver events to identify and prioritize cancer driver mutations, genes, and pathogenic pathways.