HyperNetWalk: A Unified Framework for Personalized and Population-Level Cancer Driver Gene Identification via Multi-Network Hypergraph Diffusion

TL;DR

HyperNetWalk is a new computational framework that integrates multiple biological networks and hypergraph diffusion to identify cancer driver genes at both individual patient and population levels, improving accuracy and biological insight.

Contribution

It introduces a unified multi-network hypergraph diffusion approach for personalized and cohort-level cancer driver gene identification, surpassing existing methods.

Findings

Achieves superior performance on 12 TCGA cancer types

Identifies known driver genes with high precision

Reveals cancer type-specific driver genes

Abstract

Identifying cancer driver genes is crucial for understanding tumor biology and developing precision therapies. However, existing computational methods often rely on single biological networks or population-level mutation patterns, limiting their ability to identify patient-specific drivers and leverage the complementary information from multiple network types. Here, we present HyperNetWalk, a novel computational framework that integrates multiple biological networks and hypergraph diffusion to identify driver genes at both personalized and cohort levels. In the first stage, HyperNetWalk integrates protein-protein interaction networks, gene regulatory networks, and dynamic co-expression networks through sample-independent random walks on patient-specific subnetworks to capture topological importance and expression perturbation effects. In the second stage, it refines predictions through hypergraph-based random walks that leverage cross-sample information while preserving individual mutational contexts. Comprehensive evaluation on 12 TCGA cancer types demonstrates that HyperNetWalk achieves superior or competitive performance compared to state-of-the-art methods in both personalized and cohort-level predictions. Notably, HyperNetWalk successfully identifies known driver genes with high precision while revealing cancer type-specific drivers that reflect distinct biological mechanisms. Our framework provides a unified solution for personalized and population-based driver gene identification, offering valuable insights for precision oncology and therapeutic target discovery.