A context-augmented large language model for accurate precision oncology medicine recommendations

The rapid expansion of molecularly informed therapies in oncology, coupled with evolving regulatory food and drug administration (FDA) approvals, poses a challenge for oncologists seeking to integrate precision oncology medicine into patient care. Large language models (LLMs) have clinical potential, but their reliance on general knowledge limits their ability to provide up-to-date and niche treatment recommendations. Here, we developed a retrieval-augmented generation (RAG)-LLM workflow using the molecular oncology almanac (MOAlmanac) and benchmarked it against an LLM-only approach for biomarker-driven treatment recommendations. Our RAG-LLM achieved up to 95% accuracy on synthetic queries and 93% on real-world queries collected from practicing oncologists. Finally, our study explored several prompting and retrieval strategies to enhance performance. Taken together, this approach may serve as valuable guidance for deploying LLMs to support cancer patients' treatment decisions in precision oncology clinical settings.

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Publication Details

Journal
Cancer Cell
Published
2026-01-15
DOI
https://doi.org/10.1016/j.ccell.2025.12.017
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

A context-augmented large language model for accurate precision oncology medicine recommendations

David D. Yang, Erica Pimenta, Dan Y. Gui, Tess A. O’Meara et al.
Cancer Cell
Computational Drug Discovery Methods
article

A context-augmented large language model for accurate precision oncology medicine recommendations

David D. Yang, Erica Pimenta, Dan Y. Gui, Tess A. O’Meara, Shreya Johri, Hyeji Jun, Alok Tewari, Alexander C. Jordan, Θεοδώρα Παππά, Brendan Reardon, Samantha Martin, Matthew Nagy, Sabrina Y. Camp, Yutaro Tanaka, Eddy Saad, Filipe L.F. Carvalho, Eliezer Van Allen, Chris Labaki, Erik L. Bao, Riaz Gillani
article en

Abstract

The rapid expansion of molecularly informed therapies in oncology, coupled with evolving regulatory food and drug administration (FDA) approvals, poses a challenge for oncologists seeking to integrate precision oncology medicine into patient care. Large language models (LLMs) have clinical potential, but their reliance on general knowledge limits their ability to provide up-to-date and niche treatment recommendations. Here, we developed a retrieval-augmented generation (RAG)-LLM workflow using the molecular oncology almanac (MOAlmanac) and benchmarked it against an LLM-only approach for biomarker-driven treatment recommendations. Our RAG-LLM achieved up to 95% accuracy on synthetic queries and 93% on real-world queries collected from practicing oncologists. Finally, our study explored several prompting and retrieval strategies to enhance performance. Taken together, this approach may serve as valuable guidance for deploying LLMs to support cancer patients' treatment decisions in precision oncology clinical settings.

Cancer CellVol. 44(3)
Broad Institute (US), Brigham and Women's Hospital (US), Beth Israel Deaconess Medical Center (US), Dana-Farber Cancer Institute (US), Dana-Farber Brigham Cancer Center (US)
Openalex Percentile: Top 85%
Computational Drug Discovery Methods
DOIOpen Access PDFOpenAlex
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