MatchMiner: an open-source platform for cancer precision medicine

Abstract

Widespread, comprehensive sequencing of patient tumors has facilitated the usage of precision medicine (PM) drugs to target specific genomic alterations. Therapeutic clinical trials are necessary to test new PM drugs to advance precision medicine, however, the abundance of 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, 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 primary goals are to facilitate PM trial options for all patients and accelerate trial enrollment onto PM trials. MatchMiner can help clinicians find trial options for an individual patient or provide trial teams with candidate patients matching their trial’s eligibility criteria. From 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 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 process.

Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow

“…Commercial products such as IBM Watson or open-source platforms such as MatchMiner would be limited to the inherent workflow found in these products along with challenges of data mapping and integration. As an example, MatchMiner is well suited for the prescreening workflow but not for the screening workflow [ 9 ]. A limitation of this report is that the feedback collected from our group of users is part of an iterative user-centered design process and not a formal qualitative research study.…”

Unlocking digitally enabled research in oncology: the time is now

“…A software can be trained to recognize eligibility requirements for systemic therapy trials and also match complex genomic characteristics for targeted therapies. 15 , 16 , 17 For example, the use of such software in a community-based breast cancer care unit demonstrated agreement with manually generated eligibility assessments (>80%) and a reduction in the time burden of eligibility assessments from 110 min using manual review to 24 min using system-assisted eligibility determinations. 15 Recently Klein et al.…”

Challenges and solutions to system-wide use of precision oncology as the standard of care paradigm

“…While MTBs have been useful for clinical trial enrolment, they cannot address the trial-matching challenge because they typically focus only on certain cancer types (Larson et al, 2021 ; Farhangfar et al, 2022 ). In an effort to solve this problem, academic cancer centres (Klein et al, 2022 ; Keller et al, 2023 ) and industries (Gardner et al, 2021 ; Haddad et al, 2021 ) invested in the development and implementation of a number of clinical trial matching solutions that can automate the process. However, the proprietary nature of these solutions limited their widespread adoption.…”

“…However, the proprietary nature of these solutions limited their widespread adoption. Therefore, open-source trial matching solutions are perceived as valuable capabilities that can be leveraged cost-effectively by many institutions for patient enrolment in precision oncology trials (Klein et al, 2022 ).…”