Integrating molecular profiles into clinical frameworks through the Molecular Oncology Almanac to prospectively guide precision oncology
Brendan Reardon, Nathanael D. Moore, Nicholas Moore, Eric Kofman, Saud H. AlDubayan, Alexander T. M. Cheung, Jake R. Conway, Haitham Elmarakeby, Alma Imamović, Sophia C. Kamran, Tanya E. Keenan, Daniel Keliher, David J. Konieczkowski, David Liu, Kent W. Mouw, Jihye Park, Natalie I. Vokes, Felix Dietlein, Eliezer M. Van Allen
Abstract
Tumor molecular profiling of single gene-variant ('first-order') genomic alterations informs potential therapeutic approaches. Interactions between such first-order events and global molecular features (for example, mutational signatures) are increasingly associated with clinical outcomes, but these 'second-order' alterations are not yet accounted for in clinical interpretation algorithms and knowledge bases. We introduce the Molecular Oncology Almanac (MOAlmanac), a paired clinical interpretation algorithm and knowledge base to enable integrative interpretation of multimodal genomic data for point-of-care decision making and translational-hypothesis generation. We benchmarked MOAlmanac to a first-order interpretation method across multiple retrospective cohorts and observed an increased number of clinical hypotheses from evaluation of molecular features and profile-to-cell line matchmaking. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 47% of patients. Overall, we present an open-source computational method for integrative clinical interpretation of individualized molecular profiles.