MutEx

MutEx integrates somatic mutation, gene expression, survival, and functional annotation data from 11,315 subjects across 33 cancer types to analyze relationships between somatic mutations and gene expression dysregulation in pan-cancer studies.

Key Features:

• Cohort and data composition: Contains genomic and clinical data from 11,315 subjects across 33 cancer types, including somatic mutations, gene expression, and survival.
• Integrative associations: Records associations among gene expression levels, somatic mutations, and survival to enable within-cancer and pan-cancer analyses.
• Functional annotation: Incorporates Gene Ontology (GO) annotations and several pathway databases to provide functional context for mutations and expression changes.
• Survival analysis methods: Implements elastic net regression and computes a gene expression composite score for survival analyses.
• Analytical application examples: Supports identification of top somatic mutations associated with significant expression dysregulation, analysis of differential mutational burdens downstream of DNA mismatch repair gene mutations, and evaluation of survival differences using composite gene expression scores in breast cancer.
• Hypothesis generation: Provides corroborating evidence across its comprehensive dataset to facilitate generation of hypotheses linking genetic alterations and gene expression changes.

Scientific Applications:

• Mutation–expression association analysis: Analyzing how somatic mutations influence gene expression within and across cancer types.
• Biomarker and prognostic discovery: Identifying mutations and gene expression composite scores associated with survival to prioritize biomarkers and prognostic signatures.
• Mismatch repair impact studies: Assessing downstream differential mutational burdens following DNA mismatch repair gene mutations.
• Cancer-type specific investigations: Investigating mutation-linked expression dysregulation and survival differences in breast cancer and other individual cancer types.

Methodology:

Integration of genomic and clinical data from 11,315 subjects across 33 cancer types, incorporation of Gene Ontology and several pathway annotations, and use of elastic net regression plus a gene expression composite score for survival analysis.