Systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinicogenomics data

Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Analysis of the Flatiron Health-Foundation Medicine clinicogenomc database resulted in a large-scale computational analysis of 40,903 US patients with cancer who had detailed mutation profiles, treatment sequences and outcomes. The analysis identified 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents, or targeted therapies across eight common cancer types. The analysis further characterized mutation-mutation interactions that impact the outcomes of targeted therapies.The authors conclude that this work demonstrates how computational analysis of real-world data generates insights, hypotheses, and resources to enable precision oncology.