Identifying Key Genes in Cancer Networks Using Persistent Homology

TL;DR

This paper introduces a novel approach using Persistent Homology to analyze higher-order network structures in cancer gene networks, improving the identification of driver genes beyond traditional methods.

Contribution

It presents a new topological method to distinguish driver and cancer-associated genes from passengers by analyzing higher-order structures in cancer networks.

Findings

Known driver genes influence topological voids in networks

Passenger genes do not affect higher-order structures

Combining topological analysis with traditional metrics enhances driver gene identification

Abstract

Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks. This study presents a novel method using Persistent Homology to analyze the role of driver genes in higher-order structures within Cancer Consensus Networks derived from main cellular pathways. We integrate mutation data from six cancer types and three biological functions: DNA Repair, Chromatin Organization, and Programmed Cell Death. We systematically evaluated the impact of gene removal on topological voids ($\beta_2$ structures) within the Cancer Consensus Networks. Our results reveal that only known driver genes and cancer-associated genes influence these structures, while passenger genes do not. Although centrality measures alone proved insufficient to fully characterize impact genes, combining higher-order topological analysis with traditional network metrics can improve the precision of distinguishing between drivers and passengers. This work shows that cancer genes play an important role in higher-order structures, going beyond pairwise measures, and provides an approach to distinguish drivers and cancer-associated genes from passenger genes.