Abstract 2751: Large language model-derived molecular patient similarity from real-world MTB data.

Abstract Background: Molecular tumor boards (MTBs) frequently evaluate patients with rare molecular profiles where prospective evidence is scarce. To address this challenge, we investigated whether large language models (LLMs) can structure real-world MTB documentation into analyzable representations to identify molecularly similar patients. Methods: MTB documentation from 2,788 patients and pathology reports from 506 patients discussed at the Charité MTB (2020-2025) were structured using NVIDIA Nemotron-49B. Textual summaries and structured features on prior targeted therapies, immunohistochemistry (IHC), and molecular alterations (MOL; CiViC-weighted) were extracted, yielding 1,736 textual, binary and numeric features. The LLM output was vector embedded using bge-multilingual-gemma2. Patient similarity was computed using cosine distance for embeddings, and Jaccard (binary) and absolute error (numeric) for structured features. Similarity rankings were ensembled via reciprocal-rank fusion and evaluated using MRR and nDCG, against BM25 and embedding baselines on raw MTB documentation. As a proxy for ground truth, MTB recommendations were structured into course of action, drug, agent class, and clinical trial elements and used as similarity targets. Results: LLM-based information extraction achieved F1 scores of 96% for mutation detection and 92% for IHC, evaluated against 30 manually annotated pathology reports. The ensemble similarity method - combining structured-feature similarity, summary-embedding similarity, and BM25 via reciprocal-rank fusion - showed the highest alignment with MTB recommendations (MRR@1000 = 25.8, nDCG@10 = 10.1), outperforming BM25 alone (22.8, 8.6) or text-embedding similarity of MTB documentation (22.2, 8.3), with all improvements statistically significant (p 0.01). Structured features derived from MTB documentation (20.2, 9.2) outperformed those derived from pathology reports (17.9, 8.0). Together, these findings indicate that LLM-derived representation improved therapy-aligned patient similarity over text- and embedding-based baselines. Across all cases, 486 unique therapeutic entities were identified; among 290 drug entities, 163 were recommended to more than one patient, and 65% of patients shared at least one recommendation with another case. Conclusion: LLM-derived clinical-molecular representations enabled scalable retrieval of molecularly matched cases in real-world MTB datasets. This approach supports institutional case library formation and systematic case series generation, enhancing evidence generation in precision oncology. O.S. and S.L. contributed equally to this work. Citation Format: Sophie Lugani, Oğuz Serbetci, Alexander Reinicke, Benedikt Körtum, Berkay Özdin, Thomas Debertshäuser, Dominik Modest, Ulrich Keilholz, Ulf Leser, Manuela Benary. Large language model-derived molecular patient similarity from real-world MTB data abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2751.