Evaluating Underrepresentation in Cancer Clinical Trial Enrollment Across Three National Cancer Institute–Designated Cancer Centers: A Retrospective Demographic Study

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Abstract
PURPOSE: To assess the generalizability of cancer clinical trials by comparing trial enrollment with baseline cancer populations across three National Cancer Institute (NCI)–designated cancer centers and to identify institutional barriers and best practices. METHODS: We conducted a retrospective analysis (2020-2024) of three NCI-designated Comprehensive Cancer Centers using Epic electronic health records and Clinical Trial Management Systems. Baseline cancer populations were compared with enrolled patients across solid tumors and hematologic malignancies by age, race/ethnicity, language, geography, and insurance status. We additionally evaluated the impact of an automated clinical trial prescreening tool implemented at Yale Cancer Center on enrollment demographics. RESULTS: Among 311,186 patients with solid tumor and 62,988 with hematology malignancies, non–English-speaking and Medicaid-insured populations were consistently underrepresented. Vanderbilt-Ingram Cancer Center enrolled the largest cohorts (12,053 solid tumor; 2,214 hematology) and demonstrated strong rural representation (23.2% enrolled v 17.8% baseline in solid tumors) but limited enrollment of non-English speakers (0.7% v 2.3% baseline in hematology). Mays Cancer Center at UT Health San Antonio achieved the highest racial/ethnic (68.5% v 57.5% baseline) and Medicaid-insured representation (28.3% v 10.4% baseline) in hematology despite smaller volumes. Yale Cancer Center maintained strong enrollment of older adults but underrepresented young adults; implementation of automated prescreening increased young adult consent (1.2%-5.2%) and minority participation (18.2%-23.8%) in solid tumors. CONCLUSION: Persistent enrollment disparities—particularly among non–English-speaking and Medicaid populations—were observed across institutions, alongside distinct institutional strengths. Technology-enabled interventions and cross-institutional knowledge sharing targeting language, socioeconomic, and geographic barriers are critical to improving equitable trial participation in precision oncology.

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