Pan-Cancer Driver Mutation Signatures Define a Molecular Taxonomy of Tumors

Abstract

Cancers with similar histology often exhibit divergent clinical behavior, reflecting molecular heterogeneity not captured by current classification systems. Although driver mutations are central to tumorigenesis, their broader systems-level consequences have not been systematically leveraged. We integrated genomic and transcriptomic data across cancers to define driver mutation signatures (DMS), coordinated transcriptional programs associated with cancer driver mutations. From 121 candidate drivers, we derived 90 robust signatures and quantified their activity in individual tumors using mutation signature scores (MSS). DMS analysis revealed a hierarchical organization of tumors into molecular subgroups that transcended tissue boundaries while preserving driver-associated features. Continuous MSS profiles further defined high-resolution molecular fingerprints for individual tumors. DMS provides a quantitative framework for tumor classification and patient stratification and links driver-associated programs to potential therapeutic vulnerabilities. Together, these findings establish a pan-cancer molecular taxonomy that bridges genotype and phenotype and may inform precision oncology.