Abstract 6455: Investigating clinical trial eligibility criteria to improve MatchMiner trial matching

Abstract As the number of precision medicine (PM) trials and the volume of patient genomic data have grown, it has become challenging for clinicians and trial staff to identify PM trial options for patients. At Dana-Farber Cancer Institute (DFCI), we have addressed this challenge by developing our own open source institutional trial matching software called MatchMiner. MatchMiner algorithmically matches patient genomic and clinical data with PM trial eligibility data. PM trial eligibility data is manually curated into a human- and computer-readable structured format called clinical trial markup language (CTML), for trial matching with DFCI’s institutional genomic assay OncoPanel. MatchMiner has 2 main modes of clinical use: (1) patient-centric, where clinicians can search for trial matches for individual patients and (2) trial-centric, where trial staff can identify patients that match their trial’s genomic eligibility. Thus far, more than 300 DFCI patients have enrolled onto trials facilitated by MatchMiner. In its current implementation, MatchMiner uses limited clinical data (gender, age, and cancer type) to match patients to trials. Although this limited data gives clinicians a head start on assessing available trial options, incorporating more clinical data into MatchMiner would provide more precise trials options and reduce additional work to assess patient eligibility. Here, we investigated clinical trial eligibility criteria that were available for trials at DFCI with the goal of improving specificity of trial matching for MatchMiner. We attempted to answer 2 main research questions 1) what categories of clinical data comprise the eligibility criteria for precision medicine trials at DFCI and 2) which categories are most relevant for trial matching? We first investigated the clinical eligibility criteria across several PM lung cancer trials. After extracting the inclusion and exclusion criteria from the protocol documents, we found criteria could be classified into 10 distinct categories. The most common categories making up almost half of the eligibility criteria were “Prior Therapy” and “Prior or Concurrent Non-Cancerous Disease”. Within some categories, we found it appropriate to also make subcategories to make our analysis more informative such as “Prior Therapy, Surgery” or “Disease Status, CNS”. We next broadened our search to non-lung trials to see if criteria was consistent across other cancer types. We found similar proportions of clinical criteria for the non-lung trials compared to the lung trials, but did find some additional subcategories. Overall, having similar categories of criteria across trials suggests that common clinical data could be used for many different PM trials. Currently, we are investigating which categories are most relevant for trial matching and the use of AI for structuring unstructured clinical data. Citation Format: Harry Klein, Tali Mazor, Matthew Galvin, Jason Hansel, Emily Mallaber, Pavel Trukhanov, James Provencher, James Lindsay, Michael Hassett, Ethan Cerami. Investigating clinical trial eligibility criteria to improve MatchMiner trial matching [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6455.