Large language models for tumor genomic interpretation
Presenter: Jennifer Yu, MS Session: AACR Project GENIE: Genomic Characterization Time: 4/21/2026 9:00:00 AM → 4/21/2026 12:00:00 PM
Authors
Jennifer Yu 1 , Madison Darmofal 2 , Michele Waters 2 , January Choy 2 , Thinh N. Tran 2 , Chenlian Fu 1 , Leah Morales 1 , Kaicheng U 1 , Ross L. Levine 2 , Nikolaus Schultz 2 , Michael F. Berger 2 , Quaid Morris 2 , Justin Jee 2 1 Tri-Institutional Computational Biology & Medicine, Weill Cornell Medicine, New York, NY, 2 Memorial Sloan Kettering Cancer Center, New York, NY
Abstract
Introduction: Algorithms trained on real-world data aid in tumor genomic prediction tasks, such as identifying cancer driver mutations and inferring cancer type. The extent to which generalist large language models (LLMs) trained on large natural language corpora can replace or supplement such domain-specific algorithms with zero-shot inference is unknown. Methods: We evaluated the zero-shot performance of proprietary (GPT-5, o3-mini, GPT-4o and Claude 3.7 Sonnet), open weight (DeepSeek and Qwen3) and domain-specialized medical (MedGemma) LLMs on three tasks: (i) Distinguishing tumor-somatic mutations from clonal hematopoietic (CH) variants in patients with matched tumor-whole blood profiling (N=37,179 patients; 54,807 samples), (ii) Classifying oncogenic variants using the OncoKB dataset as a positive control (N=10,489 patients; 10,752 samples; 13,470 variants), and (iii) Predicting cancer type from tumor genomic profiles in the multi-institution AACR GENIE dataset (N=97,074 patients; 102,791 samples). Results: Multiple LLMs approached the accuracy of MetaCH, a supervised model for distinguishing somatic tumor mutations from CH variants. o3-mini achieved the highest accuracy for distinguishing oncogenic driver from benign passenger mutations. Among patients with non-small cell lung cancer and mutations in KEAP1 , those with VUSs classified as oncogenic by GPT-5 had worse overall survival than those with VUSs classified as benign. GPT-5, o3-mini, and Claude 3.7 Sonnet had accuracy approaching that of a supervised model, GDD-ENS, at classifying 34 cancer types using tumor genomic profiles. Ensemble approaches combining prediction results from GPT-5 and GDD-ENS improved cross-institutional generalizability and performance by an average of 20%. In their reasoning, LLMs discussed clinically relevant genomic features consistent with feature importances from GDD-ENS. Conclusion: Without task-specific training, LLMs achieved performance comparable to specialized supervised models across all tasks. F1 score comparison across three cancer genomics prediction tasks. Mutation status (Tumor-somatic vs. CH) Oncogenic Variants (Benign vs. Oncogenic) Cancer Type (34 types) GPT-5 0.94 0.80 0.49 GPT-4o 0.96 0.75 0.32 o3-mini 0.93 0.82 0.43 Claude 3.7 Sonnet 0.95 0.79 0.43 DeepSeek 0.86 0.79 0.23 Qwen3 0.95 0.79 0.26 MedGemma 0.86 0.77 0.19 MetaCH 0.98 n/a n/a AlphaMissense n/a 0.90 n/a GDD-ENS n/a n/a 0.57
Disclosure
J. Yu, None.. M. Darmofal, None.. M. Waters, None.. J. Choy, None. T. N. Tran, Natera Employment. C. Fu, None.. L. Morales, None.. K. U, None. R. L. Levine, Qiagen g., Board of Directors, non-salaried role), Stock. Ajax Therapeutics, Inc. g., Board of Directors, non-salaried role), Stock, ). The Mark Foundation for Cancer Research g., Board of Directors, non-salaried role). Mission Bio g., Board of Directors, non-salaried role), Stock. Kurome Therapeutics, Inc. g., Board of Directors, non-salaried role), Stock. Syndax g., Board of Directors, non-salaried role), Stock. Scorpion Therapeutics, Inc. g., Board of Directors, non-salaried role), Stock. Zentalis Pharmaceuticals g., Board of Directors, non-salaried role), Stock, ). Jubilant Therapeutics Inc. g., Board of Directors, non-salaried role), Stock. Auron Therapeutics, Inc. g., Board of Directors, non-salaried role), Stock. Prelude Therapeutics g., Board of Directors, non-salaried role), Stock. C4 Therapeutics g., Board of Directors, non-salaried role), Stock. Cure Breast Cancer Foundation ). Calico ). ECOG-ACRIN Cancer Research Group Independent Contractor. Genome Canada Independent Contractor. Goldman Sachs Independent Contractor. Astra Zeneca Independent Contractor. N. Schultz, Innovation in Cancer Informatics g., Board of Directors, non-salaried role). Stand Up to Cancer Independent Contractor. M. F. Berger, AstraZeneca Independent Contractor. Paige.AI, Inc. Independent Contractor. JCO Precision Oncology g., Board of Directors, non-salaried role). SOPHiA GENETICS S.A. g., Board of Directors, non-salaried role). Journal of Molecular Diagnostics g., Board of Directors, non-salaried role). Q. Morris, None. J. Jee, Microsoft Stock. AstraZeneca Travel.
Cited in
Control: 174 · Presentation Id: 3411 · Meeting 21436