Cancer Genomics

Generalist large language models complement tailor-made predictors for tumor genomics interpretation
General-purpose large language models, used without any fine-tuning, match or complement specialized predictors on three tumor genomic interpretation tasks, with the largest gains on data from new assays and institutions.
Jennifer Yu, Madison Darmofal, Michele Waters, January Choy, Thinh N. Tran, Chenlian Fu, Leah Morales, Kaicheng U, Ross L. Levine, Nikolaus Schultz, Michael F. Berger, Quaid Morris, Justin Jee
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Allostatic load connects tumor genomics, disease trajectory, and pre-diagnosis wearable activity
Cancer-modulated allostatic load, a stress index computed from routine labs and vitals in 12,689 MSK cancer patients, rises with disease progression, tracks EGFR and KRAS mutation status in lung cancer, and is linked to fewer pre-diagnosis daily steps in both MSK and All of Us wearable cohorts.
Christopher J. Fong, Kaicheng U, Cheryl Phua, Xuechun Bai, Karl Pichotta, Kathryn Tsai, Anzhi Chen, Meixuan Zhang, Jennifer Yu, Whitney Underwood, Chenlian Fu, Michele Waters, Sanna Goyert, Adam Schoenfeld, Nikolaus Schultz, Justin Jee, Jessica Scott, Luke Pike, Jian Carrot-Zhang
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Large language models for tumor genomic interpretation
Without any task-specific training, general-purpose large language models approach the accuracy of specialized supervised models at separating tumor mutations from clonal hematopoiesis variants, flagging oncogenic variants and predicting cancer type from tumor genomic profiles, and ensembling them with a supervised classifier improves generalization across institutions.
Jennifer Yu, Madison Darmofal, Michele Waters, January Choy, Thinh N. Tran, Chenlian Fu, Leah Morales, Kaicheng U, Ross L. Levine, Nikolaus Schultz, Michael F. Berger, Quaid Morris, Justin Jee
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