Reardon, BrendanMoore, Nathanael D.Moore, Nicholas S.Kofman, EricAlDubayan, Saud H.Cheung, Alexander T.M.Conway, JakeElmarakeby, HaithamImamovic, AlmaKamran, Sophia C.Keenan, TanyaKeliher, DanielKonieczkowski, David J.Liu, DavidMouw, Kent W.Park, JihyeVokes, Natalie I.Dietlein, FelixVan Allen, Eliezer M.2023-06-222023-06-222021Reardon B, Moore ND, Moore NS, et al. Integrating molecular profiles into clinical frameworks through the Molecular Oncology Almanac to prospectively guide precision oncology. Nat Cancer. 2021;2(10):1102-1112. doi:10.1038/s43018-021-00243-3https://hdl.handle.net/1805/33931Tumor 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.en-USPublisher PolicyGenomicsNeoplasmsPrecision medicineArticle