Clinical-Grade Somatic Variant Interpretation Performance via a Rule-Constrained Large Language Model Framework (Oncology Logic-Informed Variant Evaluator).
Summary
This article introduces OLIVE, a rule-constrained large language model (LLM) framework designed for somatic variant interpretation in clinical oncology. The system integrates gene-specific biology, tumor context, and multiple evidence sources to classify variants. Evaluated on 200 clinical next-generation sequencing cases, OLIVE demonstrated a mean concordance of 97.5% with historical laboratory classifications. Observed discordances were primarily attributed to interpretive ambiguity or evolving evidence rather than model instability, and were deemed equally valid by expert adjudication. These findings indicate that OLIVE can reproducibly support expert somatic variant interpretation in a real-world clinical setting.
Analysis
🔴 CLINIQUE: OLIVE represents a significant advancement for clinical decision support in oncology, offering reproducible assistance for somatic variant interpretation. The high concordance of 97.5% with historical classifications suggests a reliability that could accelerate the integration of such tools into molecular pathology laboratories. By standardizing interpretation and reducing variability, OLIVE could potentially improve diagnostic accuracy and guide targeted therapies, with a clinical impact expected within 3 to 5 years. 🟢 BIOMOL: The OLIVE framework utilizes next-generation sequencing (NGS) as the primary data source for identifying somatic variants. The biomolecular approach is strengthened by applying explicit gene-specific guidance files, ensuring consistent and evidence-based interpretation. The system's reproducibility, demonstrated across multiple replicates, is crucial for its analytical validity and translation into clinical applications, where reliable variant classifications are paramount for patient management. 🔵 BIOINFO: OLIVE is an innovative application of LLMs, not for a radically new architecture, but for its rule-constrained framework that addresses concerns about reproducibility and opacity. The system was benchmarked against existing laboratory classifications on a real-world clinical dataset, demonstrating robust performance. Its clinical deployment could transform the molecular pathology workflow by assisting experts in variant review and ensuring a more uniform application of interpretation guidelines, which is essential for precision medicine.