Background: Molecular profiling of cancers is now routine at many cancer centers, and the number of precision cancer medicine clinical trials, which are informed by profiling, is steadily rising. Additionally, these trials are becoming increasingly complex, often having multiple arms and many genomic eligibility criteria. Currently, it is a challenging for physicians to match patients to relevant clinical trials using the patient's genomic profile, which can lead to missed opportunities. Automated matching against uniformly structured and encoded genomic eligibility criteria is essential to keep pace with the complex landscape of precision medicine clinical trials.Results: To meet these needs, we built and deployed an automated clinical trial matching platform called MatchMiner at the Dana-Farber Cancer Institute (DFCI). The platform has been integrated with Profile, DFCI's enterprise genomic profiling project, which contains tumor profile data for >20,000 patients, and has been made available to physicians across the Institute. As no current standard exists for encoding clinical trial eligibility criteria, a new language called Clinical Trial Markup Language (CTML) was developed, and over 158 genomically-driven clinical trials were encoded using this language. The platform is open source and freely available for adoption by other institutions. Conclusion:MatchMiner is the first open platform developed to enable computational matching of patient-specific genomic profiles to precision cancer medicine clinical trials. Creating MatchMiner required developing clinical trial eligibility standards to support genome-driven matching and developing intuitive interfaces to support practical use-cases. Given the complexity of tumor profiling and the rapidly changing multi-site nature of genome-driven clinical trials, open source software is the most efficient, scalable, and economical option for matching cancer patients to clinical trials.
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.
PURPOSE With the growing number of available targeted therapeutics and molecular biomarkers, the optimal care of patients with cancer now depends on a comprehensive understanding of the rapidly evolving landscape of precision oncology, which can be challenging for oncologists to navigate alone. METHODS We developed and implemented a precision oncology decision support system, GI TARGET, (Gastrointestinal Treatment Assistance Regarding Genomic Evaluation of Tumors) within the Gastrointestinal Cancer Center at the Dana-Farber Cancer Institute. With a multidisciplinary team, we systematically reviewed tumor molecular profiling for GI tumors and provided molecularly informed clinical recommendations, which included identifying appropriate clinical trials aided by the computational matching platform MatchMiner, suggesting targeted therapy options on or off the US Food and Drug Administration–approved label, and consideration of additional or orthogonal molecular testing. RESULTS We reviewed genomic data and provided clinical recommendations for 506 patients with GI cancer who underwent tumor molecular profiling between January and June 2019 and determined follow-up using the electronic health record. Summary reports were provided to 19 medical oncologists for patients with colorectal (n = 198, 39%), pancreatic (n = 124, 24%), esophagogastric (n = 67, 13%), biliary (n = 40, 8%), and other GI cancers. We recommended ≥ 1 precision medicine clinical trial for 80% (406 of 506) of patients, leading to 24 enrollments. We recommended on-label and off-label targeted therapies for 6% (28 of 506) and 25% (125 of 506) of patients, respectively. Recommendations for additional or orthogonal testing were made for 42% (211 of 506) of patients. CONCLUSION The integration of precision medicine in routine cancer care through a dedicated multidisciplinary molecular tumor board is scalable and sustainable, and implementation of precision oncology recommendations has clinical utility for patients with cancer.
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.
6620 Background: Genomic profiling and access to precision medicine clinical trials are now standard at leading cancer institutes and many community practices. Interpreting patient-specific genomic information and tracking the complex criteria for precision medicine trials requires specialized computational tools, especially for multi-institutional basket studies such as NCI-MATCH and TAPUR. Methods: To address this challenge we have developed an open source computational platform for patient-specific clinical trial matching at Dana-Farber Cancer Institute (DFCI) called MatchMiner, which aides in both patient recruitment to precision medicine trials, as well as decision support for oncologists. Trial matches are computed based on genomic criteria, including mutations, CNAs, and SVs, as well as clinical and demographic information, including cancer type, age, and gender. A formal standard called clinical trial markup language (CTML) to encode complex clinical trial eligibility criteria has also been created. Results: MatchMiner is now available at DFCI. Currently 123 precision medicine clinical trials have been transformed into CTML and 13,000 patient records are available, with over 88% of current patients having at least 1 match (average 2.6). A total of 103 genes are specified as criteria for at least 1 trial. KRAS, TP53, PTEN, PIK3CA and BRAF are the genes driving the most number of matches. General usage statistics and trial enrollment rates are currently being monitored to determine the system effectiveness. As this is an open source initiative, the software is also now publically available at https://github.com/dfci/matchminer. Conclusions: We have developed an open source computational platform that enables patient-specific matching and recruitment to precision medicine clinical trials at DFCI. We are actively seeking collaborators and plan to make CTML a multi-institution standard for encoding complex clinical trial eligibility in a computable form.
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