Identifying Combinations of Cancer Drivers in Individual Patients

Abstract

Identifying the subset of genetic alterations present in individual tumors that are essential and collectively sufficient for cancer initiation and progression would advance the development of effective personalized treatments. We present CRSO for inferring the combinations of alterations, i.e., rules, that cooperate to drive tumor formation in individual patients. CRSO prioritizes rules by integrating patient-specific passenger probabilities for individual alterations along with information about the recurrence of particular combinations throughout the population. We present examples in glioma, liver cancer and melanoma of significant differences in patient outcomes based on rule assignments that are not identifiable by consideration of individual alterations.