Exploring the Past and Current Landscape of Biomarker-Driven Clinical Trials Through Large Language Models

A survey of our clinical genomics showed that many individuals likely have mutations that would make them eligible for biomarker-driven trials. We used the NCI Clinical Trials database to show that the distribution of biomarker trials across the United States limits access for many patients and likely leads to the frequent trial termination because of inadequate accrual. Finally, we built an automated publicly available tool that can improve patient-to-trial biomarker-based matching.

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Publication Details

Journal
JCO Clinical Cancer Informatics
Published
2026-02-01
DOI
https://doi.org/10.1200/cci-25-00028
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
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article

Exploring the Past and Current Landscape of Biomarker-Driven Clinical Trials Through Large Language Models

Margaret Guo, Brenda Y. Miao, Atul J. Butte, Evan Passalacqua et al.
JCO Clinical Cancer Informatics
Topic Modeling
article

Exploring the Past and Current Landscape of Biomarker-Driven Clinical Trials Through Large Language Models

Margaret Guo, Brenda Y. Miao, Atul J. Butte, Evan Passalacqua, Erik L. Bao, Travis Zack
article en

Abstract

A survey of our clinical genomics showed that many individuals likely have mutations that would make them eligible for biomarker-driven trials. We used the NCI Clinical Trials database to show that the distribution of biomarker trials across the United States limits access for many patients and likely leads to the frequent trial termination because of inadequate accrual. Finally, we built an automated publicly available tool that can improve patient-to-trial biomarker-based matching.

JCO Clinical Cancer InformaticsVol. 10(10)
Harvard University (US), Dana-Farber Cancer Institute (US), UCSF Helen Diller Family Comprehensive Cancer Center (US), University of California, Berkeley (US)
Openalex Percentile: Top 70%
Topic Modeling
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