Abstract 3382: MatchMiner: An open-source computational platform for genomically-driven matching of cancer patients to precision medicine clinical trials
Authors: Tali Mazor, Rachel B. Keller, Priti Kumai, James Lindsay, Eric Marriott, Andrea Ovalle, Ethan Siegel, Joyce Yu, Michael Hassett, Ethan Cerami
Journal: Cancer Research (2020)

Abstract To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials based on clinical and genomic features. MatchMiner supports two distinct workflows: (1) patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) trial-centric mode, in which a clinical trial investigator can identify and recruit patients for a specific trial. MatchMiner has been operational at Dana-Farber Cancer Institute since early 2017. There are currently 275+ trials curated in the system and genomic data from 26,000+ patients. Over 80% of living patients match to at least one open clinical trial, with an average of 6 trial matches per patient. At least 98 patients have enrolled on a clinical trial as a result of MatchMiner. To enable computational matching, we developed clinical trial markup language (CTML), a structured format to encode detailed information about a trial. CTML utilizes boolean logic to define clinical (e.g. cancer type), demographic (e.g. age) and genomic (e.g. specific mutations, copy number alterations, structural variants or mutational signatures) eligibility, which can be applied to individual arms of a trial. MatchMiner is an open-source two-tier web application with a Python-based REST API server and an AngularJS front-end. MatchMiner utilizes Security Assertion Markup Language (SAML)-based authentication and is fully HIPAA-compliant when hosted behind a secure institutional firewall. MatchMiner ingests clinical and genomic data, and connects to existing clinical systems, including clinical trial management systems for real-time trial status. We recently refactored the core matching algorithm (the matchengine), which improves upon the original matchengine in several ways: (1) increased granularity in reporting the reason for a match; (2) can match all patients/trials or individual patients/trials; (3) easily extensible to match based on additional data types. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). We are committed to supporting MatchMiner as an open-source software; to our knowledge, at least five cancer centers are implementing MatchMiner at their own institutions. In summary, we have defined a standard for encoding clinical trial information in a structured and computable form, and we have developed an open-source computational trial matching platform to support patient-specific trial identification as well as trial-specific patient recruitment. We are actively collaborating with clinical groups at Dana-Farber Cancer Institute and other institutions to understand the role of MatchMiner in their clinical workflows, and we are committed to continuing to evolve MatchMiner to meet clinical needs. Citation Format: Tali Mazor, Rachel B. Keller, Priti Kumai, James Lindsay, Eric Marriott, Andrea Ovalle, Ethan Siegel, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source computational platform for genomically-driven matching of cancer patients to precision medicine clinical trials [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3382.