SYS-Mut

SYS-Mut infers the functional significance of rare somatic mutations in cancer by integrating multi-omic profiles from primary tumors using a systems biology approach to predict their biological consequences.

Key Features:

• Systems biology integration: Integrates multi-omic profiles from primary tumors to infer downstream biological effects of somatic mutations.
• Rare mutation analysis: Detects and interprets the functional impact of infrequently mutated genes across cancers.
• PanCancer validation: Leverages PanCancer datasets to recapitulate known functional impacts of driver genes for accuracy assessment.
• Application to HNSC: Applied to low-frequency gene mutations in Head and Neck Cancers (HNSC) to identify actionable biology.
• Lipidogenic network discovery: Identified the lipidogenic network as a novel therapeutic vulnerability in aggressive HNSC.
• Empirical validation linkage: Predictions were supported by molecular and pharmacogenetic validation studies.
• Robustness and scalability: Described as a robust and scalable framework for interpreting somatic mutations using multi-omic data.

Scientific Applications:

• Functional annotation of mutations: Assigns biological consequences to rare somatic mutations across cancer types.
• Therapeutic target prioritization: Prioritizes candidate targets, exemplified by identifying the lipidogenic network in HNSC.
• Benchmarking of mutation effects: Validates inference performance by recapitulating known driver gene effects in PanCancer datasets.
• Expansion of precision oncology: Supports expanding personalized cancer therapy beyond highly recurrent driver genes by highlighting understudied alterations.

Methodology:

Integrates routine multi-omic profiles from primary tumors and leverages PanCancer datasets to recapitulate known driver gene functional impacts for accuracy assessment.