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
Authors: 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
Journal: Cancer Research (2018)

Abstract 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.