A molecular oncology almanac for integrative clinical interpretation of molecular profiles to guide precision cancer medicine

Background: Tumor molecular profiling is increasingly used to detect first-order genomic alterations associated with therapeutic actions (e.g. BRAF V600E & RAF/MEK inhibition). Simultaneously, more complex molecular features are being discovered and applied to clinical scenarios (e.g. mutational signatures, somatic-germline interactions). As patients receive expanded profiling, such as clinical whole-exome and RNA sequencing, novel algorithms are needed to integrate interpretation of multiple data modalities. Furthermore, the clinical-preclinical gap continues to widen as data from high-throughput screens of cancer cell lines are generated without accessibility at the point of care. Here, we introduce a paired interpretation algorithm and knowledge system for cancer genomic data, the Molecular Oncology Almanac, to inform treatment decisions through rapid assessment of tumor actionability. Methods: We implemented a cloud-based interpretation algorithm that annotates and evaluates variants from WES and RNA-seq (SNVs from WES and RNA-seq, InDels, CNAs, and fusions) and infers additional features such as mutational burden, mutational signatures, MSI, somatic-germline interactions, and aneuploidy. Predictive implication levels were assigned to reflect confidence in the database9s catalogued relationships to therapeutic response and prognosis for each molecular feature. We also developed a patient-preclinical matchmaker function to expand the theoretical therapeutic modalities for any given patient. Towards timeliness of updates and knowledge system accessibility, we developed API endpoints, a browser extension for suggesting citations, and workflows in the FireCloud framework. Results: A total of 260 patients with metastatic castration-resistant prostate cancer (n=150) and metastatic melanoma (n=110) were evaluated with 569 alteration-action relationships catalogued in the Molecular Oncology Almanac. Overall 80% of patients had at least one alteration suggesting therapeutic sensitivity based on FDA approval, clinical trials, or studies in humans; which increased to 95.8% by also considering preclinical and inferential associations. Per patient, the matchmaker function on average highlighted 1.56 additional therapies that would not have otherwise been nominated. At least one feature associated with resistance or prognosis was observed in 85% and 90% of patients, respectively. Conclusion: Clinical actionability of sequence data was increased by including integrative molecular profiling of DNA and RNA, global molecular features, and preclinical alteration-action relationships. Increased accessibility of clinical interpretation through our cloud-based web portals and API endpoints may aid in sample contextualization. Source code and a web portal for this project are available at moalmanac.org. Citation Format: Brendan Reardon, Nicholas Moore, Nathanael Moore, Eric Kofman, Eliezer Van Allen. A molecular oncology almanac for integrative clinical interpretation of molecular profiles to guide precision cancer medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2470.