From Text to Translation: Using Language Models to Prioritize Variants for Clinical Review.

Despite rapid advances in genomic sequencing, most rare genetic variants remain insufficiently characterized for clinical use, limiting the potential of personalized medicine. When classifying whether a variant is pathogenic, clinical labs adhere to diagnostic guidelines that comprehensively evaluate many forms of evidence including case data, computational predictions, and functional screening. While a substantial amount of clinical evidence has been developed for these variants, the majority cannot be definitively classified as 'pathogenic' or 'benign', and thus persist as 'Variants of Uncertain Significance' (VUS). We processed over 2.4 million plaintext variant summaries from ClinVar, employing sentence-level classification to remove content that does not contain evidence and removing uninformative summaries. We developed ClinVar-BERT to discern clinical evidence within these summaries by fine-tuning a BioBERT-based model with labeled records. When validated classifications from this model against orthogonal functional screening data, ClinVar-BERT significantly separated estimates of functional impact in clinically actionable genes, including (p = × ), (p = × ), and (p = × ). Additionally, ClinVar-BERT achieved an AUROC of 0.927 in classifying ClinVar VUS against this functional screening data. This suggests that ClinVar-BERT is capable of discerning evidence from diagnostic reports and can be used to prioritize variants for re-assessment by diagnostic labs and expert curation panels.