Characterizing selection signatures in coding and noncoding regions of 14,886 cancer genomes

Clonal selection drives cancer development, but quantifying selection on noncoding somatic mutations remains largely unexplored. Here, we introduce dNdS-Fun, an extension of the dN/dS framework to quantify selection of both coding and noncoding somatic mutations, thereby enhancing the discovery of driver genes. Applying dNdS-Fun to whole-genome sequencing data from 14,886 cancer patients across 31 cancer types, we identify 175 genes under positive selection across multiple cancer types or datasets, as well as 20 previously known driver genes detected through noncoding mutations. Of these, 69 are previously unrecognized as drivers, and 30 are identified solely through noncoding mutations. Furthermore, we observe evidence of negative selection throughout the genome, with significant enrichment in essential and cancer-dependent genes. Sixteen genes exhibit an overall signature of negative selection but show positive selection in noncoding elements, indicating both their conserved functions and adaptive regulatory roles in tumorigenesis. Our study reveals evidence consistent with negative selection of noncoding mutations, providing important insights for future research on their roles in cancer progression.