Abstract
Classifying cancer genetic variants based on clinical actionability is crucial yet challenging in precision oncology. Large language models (LLMs) offer potential solutions, but their performance remains underexplored. This study evaluates GPT-4o, Llama 3.1, and Qwen 2.5 in classifying genetic variants from the OncoKB and CIViC databases, as well as a real-world dataset derived from FoundationOne CDx reports. GPT-4o achieved the highest accuracy (0.7318) in distinguishing clinically relevant variants from variants of unknown clinical significance (VUS), outperforming Qwen 2.5 (0.5731) and Llama 3.1 (0.4976). LLMs demonstrated better concordance with expert annotations for variants with strong clinical evidence but exhibited greater inconsistencies for those with weaker evidence. All three models showed a tendency to assign variants to higher evidence levels, suggesting a propensity for overclassification. Prompt engineering significantly improved accuracy, while retrieval-augmented generation (RAG) further enhanced performance. Stability analysis across 100 iterations revealed greater consistency with the CIViC system than with OncoKB. These findings highlight the promise of LLMs in cancer genetic variant classification while underscoring the need for further optimization to improve accuracy, consistency, and clinical applicability.