ActiveDriver

ActiveDriver identifies post-translational modification (PTM) sites that are significantly mutated in cancer genomes, focusing on phosphorylation-associated single nucleotide variants (pSNVs) that alter kinase networks and signaling pathways relevant to oncogenesis and therapeutic targeting.

Key Features:

• Identification of phosphorylation-associated SNVs (pSNVs): Detects SNVs that specifically target phosphorylation sites within proteins to prioritize mutations affecting phosphorylation machinery.
• Large-scale cancer genome analysis: Analyzes extensive cancer genome sequencing data, including a cohort of 800 cancer genomes across eight cancer types.
• Signaling pathway and network analysis: Maps phospho-mutated pathways, kinase networks, and signaling modules to interpret how pSNVs influence cellular signaling.
• Clinical correlation and survival insights: Associates pSNVs with clinical outcomes, reporting examples such as pSNVs in TP53 and kinase/immune network modules linked to prolonged survival in ovarian cancer.
• Actionable candidate identification: Nominally prioritizes actionable gene candidates and complexes, including FLNB, GRM1, POU2F1, HCF1, ASF1, and kinases such as PRKCZ.
• Driver versus passenger prioritization: Focuses on the functional impact of SNVs on protein phosphorylation to distinguish putative tumor driver genes from passenger mutations.

Scientific Applications:

• Functional interpretation of cancer mutations: Prioritizes SNVs that perturb phosphorylation to reveal mechanistic links between mutations and altered signaling in cancer.
• Identification of therapeutic targets and network modules: Highlights kinases, kinase modules, and protein complexes as candidate targets for drug development and personalized medicine.
• Clinical outcome association: Enables correlation of phospho-mutated pathways and pSNVs with patient survival and other clinical endpoints.

Methodology:

Applies computational algorithms to process large-scale cancer genome sequencing data and identifies genes with significant phosphorylation-associated SNVs by testing for an unexpectedly high frequency of mutations in phosphorylation-related sites.