e-MutPath

e-MutPath identifies candidate 'edgetic' cancer somatic mutations that perturb biological signaling networks by analyzing interaction profiles and informative network paths using a network-based computational approach implemented as an R library to link genotype and phenotype in precision oncology.

Key Features:

• Implementation: Implemented as an R library for network-based analysis of cancer somatic mutations.
• Network-Based Computational Approach: Analyzes interaction profiles mediated by genomic mutations to establish links between genotype and phenotype.
• Edgetic Mutation Identification: Identifies mutations that specifically perturb functional pathways within biological signaling networks.
• Informative Path Identification and Subtype Stratification: Detects informative paths within networks to distinguish disease risk factors from neutral variants and to stratify disease subtypes.
• Enrichment in Cancer Vulnerability Genes and Drug Targets: Predicted targets are enriched for known cancer vulnerability genes and recognized drug targets.
• Depletion of Side Effect-Associated Proteins: Predicted targets are depleted for proteins associated with side effects.
• Systematic Function Assignment: Systematically assigns functions to genetic mutations with emphasis on their pathway perturbation effects.

Scientific Applications:

• Precision oncology: Provides functional interpretation of somatic mutations to inform targeted therapy development and clinical research.
• Disease subtype stratification: Enables stratification of disease subtypes based on pathway-perturbing mutations with clinical relevance.
• Therapeutic target identification: Facilitates identification of candidate therapeutic targets enriched in cancer vulnerability genes and drug targets.
• Adverse-effect minimization: Helps prioritize mutations and targets that are less associated with proteins linked to side effects.

Methodology:

Uses network-based computational methods implemented in R that analyze interaction profiles mediated by genomic mutations, identify informative network paths to detect edgetic mutations, and systematically assign functions to mutations based on pathway perturbation effects.