Integrating genomic, transcriptomic and epigenetic data to identify phenotypically impactful driver pathways in glioblastoma

Glioblastoma multiforme (GBM), a highly aggressive brain tumor characterized by molecular hetero-geneity, necessitates integrative approaches to decipher driver pathways and therapeutic vulnerabilities. Here, we present gtePIDP , a computational framework that systematically integrates multi-omics data (genomic, transcriptomic, epigenetic) to identify Phenotypically Impactful Driver Pathways through dual evaluation of mutation patterns (coverage/exclusivity) and downstream regulatory cascades. Lever-aging mutation profiles and expression data from The Cancer Genome Atlas (TCGA), combined with transcription factor (TF) and miRNA regulatory networks, we constructed a systems biology model to map somatic alterations to phenotypic outcomes. By iteratively pruning candidate driver genes and quantifying resultant perturbations in gene expression and regulatory activities, gtePIDP prioritizes gene sets that maximize mutational significance and phenotypic impact. Our analysis uncov-ered pivotal GBM driver pathways involving TP53, CDKN2A, MDM2 , and RB1 , which disrupt key cancer-associated regulators (e.g., oncogenic: NFATC2, MIR-370 ; tumor-suppressive: MIR-506, MIR-9, FOXJ2 ), driving dysregulation of angiogenesis, apoptosis, and proliferation. Notably, we identified ther-apeutic axes such as TP53/MIR-185/VEGFA and MDM2/SMAD4/VEGFA , suggesting anti-angiogenic targeting strategies. Furthermore, a PI3K-Akt signaling regulatory module revealed actionable targets ( CDKN2A/CREBBP/OSMR and TP53/MIR-9/FGF12 ) for intervention. By bridging mutational landscapes with transcriptional/epigenetic dysregulation, gtePIDP advances mechanistic driver pathway discovery and prioritizes targets with clinical relevance. This study highlights the power of multi-omics integration to unravel GBM pathogenesis and accelerate precision oncology strategies.