PITCH: A Pathway-Induced Prioritization of Personalized Cancer Driver Genes Based on Higher-Order Interactions.

Abstract

Cancer is driven by specific mutations known as cancer driver genes, whose identification is crucial for advancing cancer therapy. Although many computational methods have been proposed with this purpose, most provide a single driver gene list ignoring the high heterogeneity of drivers across patients in cohort. Besides, they often fail to capture the higher-order interactions among genes at the patient level. Here we introduce a novel method PITCH to prioritize personalized cancer driver genes by assessing the higher-order propagation dynamics among genes. PITCH constructs a patient-specific hypergraph model that represents higher-order interactions well-characterized in signaling pathways, enabling a more comprehensive assessment of gene influence in cancer development. PITCH does not require paired case-control data, simplifying its application in clinical practice. We evaluated our approach using data from four different types of cancers, demonstrating its superior performance in identifying cancer driver genes compared to existing methods. Importantly, PITCH is shown to identify both common and rare drivers. The results were validated against well-studied cancer gene databases, confirming the accuracy of the identified drivers. Additionally, most of PITCH-identified personalized driver genes were actionable and druggable for most patients, offering significant potential for guiding personalized treatment strategies. Our approach represents a significant advancement in the field of cancer driver genes discovery, providing a powerful tool for the precise identification of therapeutic targets in cancer research.