MatchMiner: An open source platform for cancer precision medicine

The systematic deployment of next generation sequencing means patient tumors can be genomically profiled and specific genetic alterations can be targeted with precision medicine (PM) drugs. More therapeutic clinical trials are needed to test new PM drugs to advance precision medicine, however, the availability of comprehensive patient sequencing data coupled with complex clinical trial eligibility has made it challenging to match patients to PM trials. To facilitate enrollment onto PM trials, we developed MatchMiner. MatchMiner is an open-source platform to computationally match genomically profiled cancer patients to PM trials. Here, we describe MatchMiner’s capabilities, outline its deployment at Dana-Farber Cancer Institute (DFCI), and characterize its impact on PM trial enrollment. MatchMiner’s two primary goals are to (1) facilitate PM trial options for all patients, and (2) accelerate trial enrollment onto PM trials. MatchMiner has 3 main modes of use: (1) patient-centric, where a clinician looks up trial options for an individual patient, (2) trial-centric, where a trial team identifies candidate patients for their trial by setting up a filter, and (3) trial search, where a clinician can find trial options for patients that have external genomic reports. From the time MatchMiner was first deployed at DFCI in March 2016 through March 2021, we curated 354 PM trials containing a broad range of genomic and clinical eligibility criteria and MatchMiner facilitated 166 trial consents (MatchMiner consents, MMC) for 159 patients. To quantify MatchMiner’s impact on trial consent, we retrospectively measured time from genomic sequencing report date to trial consent date for the 166 MMC compared to trial consents not facilitated by MatchMiner (non-MMC). We found MMC consented to trials 55 days (22%) earlier than non-MMC. MatchMiner has enabled our clinicians to match patients to PM trials and accelerated the trial enrollment decision making process. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by DFCI and the Fund for Innovation in Cancer Informatics (ICI). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Institutional Review Board of Dana-Farber Cancer Institute/Harvard Cancer Center gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present work are contained in the manuscript