LAUSR

Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient’s tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data.Informative pathways driving cancer pathogenesis and subtypes can be difficult to identify in the presence of many gene interactions irrelevant to cancer. Here, the authors describe an approach for cancer gene pathway analysis based on key molecular interactions that drive cancer in relevant tissue types, and they assemble a focused map of Evolutionarily Selected Pathways (ESP) with interactions supported by both protein–protein binding and genetic epistasis during somatic tumor evolution.