FusionPath: Gene fusion pathogenicity prediction using protein structural data and contextual protein embeddings

Abstract Accurate prediction of gene fusion pathogenicity is critical for understanding oncogenic mechanisms and advancing precision oncology. While existing computational methods provide valuable insights, their performance remains limited by incomplete integration of multi-scale biological features and insufficient model interpretability for clinical translation. We present FusionPath, a novel deep learning framework for gene fusion pathogenicity prediction. FusionPath uniquely integrates embeddings from multiple pretrained protein language models, including FusON-pLM and ProtBERT, alongside retained protein domains and Gene Ontology (GO) functional annotations. The model was trained and validated on a large-scale dataset of annotated pathogenic and benign fusions. The model was trained and validated on a rigorously curated dataset of 100,433 gene fusions (78,115 benign, 22,318 pathogenic) derived from FusionPDB, ChimerDB4.0, and 27 RNA-seq datasets of normal tissues. FusionPath significantly outperformed state-of-the-art methods, achieving higher AUC scores of 0.95 and 0.87 on independent test sets. By synergistically leveraging sequence, structural, and functional information with explicit modeling of wild-type sequence context, FusionPath establishes a new standard for gene fusion pathogenicity prediction by effectively leveraging complementary sequence, structural, and functional information.