Programmatic Precision Oncology Decision Support for Patients With Gastrointestinal Cancer

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.

MatchMiner: an open-source platform for cancer precision medicine

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.

Abstract 2288: Assessing patient trial readiness for precision cancer medicine clinical trials through computational trial matching and rule-based logic applied to genomic and clinical data

More than 1,100 phase I treatment trials are registered with clinicaltrials.cancer.gov. Patient identification and recruitment to these trials is particularly challenging due to the vast molecular data now available for patients and the increasingly complex nature of the molecular eligibility criteria. Additionally, Phase I trial staff often manage a portfolio of dozens of trials, making patient identification, recruitment, screening, and enrollment an arduous task. To address the need for better and more efficient patient identification, we have developed MatchMiner, a novel open source computational platform for matching patient-specific genomic profiles to precision cancer medicine clinical trials, and a pre-screening tool that includes options for filtering and sorting patients based on clinical data elements. MatchMiner excels at identifying patients who are eligible for clinical trials based on genomic, cancer type, age, and gender criteria, but the great majority of patients are not currently ready for a trial. The pre-screening tool allows for the integration of additional clinical data, including genomic testing date, appointments, treatment plan history, and radiology scan impression text, in order to provide an assessment of ‘trial readiness'. To integrate these tools into the clinical workflow, we have implemented a weekly MatchMiner Tumor Board Review process, where together with the DFCI Early-Phase Drug Development Center (EDDC) physicians and staff, a thorough assessment of potential patient-trial matches is performed. Currently we have scaled this initiative to include 18 genomically-driven trials. Each week, ~9000 patient-trial matches are computed across all open trials, which are then computationally filtered to a list of ~300 patient-trial matches utilizing rules-based logic applied to our clinical data sources. The MatchMiner team then performs a manual review of pre-filtered matches based on additional requirements, such as evidence of progression in the radiology scan text impressions. The final list of ~10-20 patients is reviewed with the EDDC, and patients deemed to be ‘trial ready' are flagged. E-mail notifications are then sent out to the patient's oncologist, alerting them to a potential genomically-driven clinical trial opportunity. The combination of computational patient-trial match identification and rules-based computational pre-filtering based on additional clinical data has allowed us to screen thousands of patients per week, which would not be possible otherwise. To evaluate the success of this initiative, we are tracking patient consults and trial enrollments, and capturing feedback on workflow integration and utilization. Additionally, efforts to further automate trial readiness measurements to minimize the need for manual review are ongoing.

Citation Format: Catherine Del Vecchio Fitz, Khanh Do, James Lindsay, Suzanne Hector-Barry, Zachary Zwiesler, Priti Kumari, Tali Mazor, Tamba Monrose, Adem Albayrak, John Methot, Michael Hassett, Geoffrey Shapiro, Ethan Cerami. Assessing patient trial readiness for precision cancer medicine clinical trials through computational trial matching and rule-based logic applied to genomic and clinical data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2288.

Algorithmic matching of genomic profiles to precision cancer medicine clinical trials at DFCI

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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.