GUST

GUST classifies genes as oncogenes (OGs), tumor suppressor genes (TSGs), or passenger genes (PGs) by analyzing somatic single nucleotide variants (SNVs) across tumor exomes to identify context-specific cancer drivers.

Key Features:

• Context-Aware Classification: Uses the direction and magnitude of somatic selection on protein-coding mutations to differentiate OGs, TSGs, and PGs in a cancer-type specific manner.
• High Accuracy: Achieves 92% accuracy in predicting OGs and TSGs as reported, validated using strict cross-validation techniques.
• Comprehensive Tumor Exome Analysis: Applied to 10,172 tumor exomes to identify known and novel cancer driver genes with high tissue-specificity.
• Functional Domain Insights: Reveals that oncogenes shared among multiple cancer types often engage different functional domains selectively across cancers.

Scientific Applications:

• Driver Gene Identification: Enables precise identification and classification of cancer driver genes tailored to specific cancer types.
• Targeted Therapy and Precision Medicine: Discerns tissue-specific oncogenic mechanisms that can inform development of targeted therapies and personalized medicine approaches.
• Discovery of Novel Drivers: Supports uncovering novel cancer drivers and the discovery of new therapeutic targets to advance understanding of tumor biology.

Methodology:

Analyzes somatic SNVs and quantifies the direction and magnitude of somatic selection on protein-coding mutations to classify genes as OGs, TSGs, or PGs, with validation by strict cross-validation and application to 10,172 tumor exomes.