Brendan Reardon is a computational scientist in the Van Allen laboratory at Dana-Farber Cancer Institute focusing on clinical interpretation of individual patient molecular profiles
Brendan Reardon, Eliezer Van Allen
Dana-Farber Cancer Institute, Boston, MA, USA
Background: MOAlmanac is an open source clinical interpretation algorithm and paired knowledge base for precision cancer medicine used to rapidly characterize and identify genomic features related to therapeutic sensitivity and resistance and of prognostic relevance.
Methods: MOAlmanac assesses tumor actionability by evaluating individual genomic features (e.g. somatic or germline variants, copy number alterations, or fusions), interactions between them, and secondary features (such as mutational burden, mutational signatures, and MSI status) relative to our knowledge base. In addition, it also performs profile-to-cell line matchmaking to nominate genomically similar cell lines for hypothesis expansion. All clinically relevant findings are summarized into a web-based report. The underlying knowledge base can be accessed through our API endpoints and web browser, and we developed a cloud-based web portal to increase accessibility of the algorithm.
Results: 32,108 samples from 66 cancer types received targeted sequencing with Oncopanel and were evaluated by MOAlmanac. Using Oncopanel’s tier 1 and tier 2 criteria for clinical actionability, we observed that 8,285 samples harbored at least one alteration suggesting therapeutic sensitivity based on FDA approvals or clinical guidelines, increasing to 18,117 when considering clinical trials, clinical, preclinical, and inferential evidence.
Conclusion: Clinical actionability of molecular tumor data was increased in individual patients by expanding the set of evidence considered. Source code and a web portal for this project are available at moalmanac.org.