OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

Area of Science:

• Genomics
• Bioinformatics
• Cancer Biology

Background:

• Distinguishing oncogenic driver mutations from neutral passenger mutations in cancer is a significant challenge.
• Accurate identification of cancer drivers is crucial for understanding cancer development and for precision medicine.

Purpose of the Study:

• To develop a systematic platform, OncoVar, for prioritizing oncogenic somatic mutations and cancer genes.
• To provide a comprehensive resource for identifying and analyzing cancer driver mutations, genes, and pathways.

Main Methods:

• Reanalyzed exome and genome data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC).
• Employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and genes.
• Integrated data from multiple cancer types to identify high-confidence driver events and pathways.

Main Results:

• Identified 20,162 high-confidence cancer driver mutations, 814 driver genes, and 2360 pathogenic pathways.
• OncoVar platform provides multi-faceted views (Mutation, Gene, Pathway, Cancer) for data visualization and analysis.
• Identified actionable driver alterations with potential for druggable targets and combination therapies.

Conclusions:

• OncoVar is a valuable resource for researchers and clinicians to identify and rank cancer drivers.
• The platform facilitates the discovery of novel therapeutic targets and repurposing opportunities for cancer treatment.
• OncoVar supports precision medicine by enabling better decision-making in cancer patient management.