Abstract
Cancer-associated gene fusions serve as a potential source of highly immunogenic neoantigens. In this study, we identified fusion proteins from fusion genes and extracted fusion peptides to accurately predict Breast cancer (BRCA) neo-antigen candidates by high-throughput artificial intelligence computation. Firstly, Deepsurv was used to evaluate the prognosis of patients, providing a landscape of prognostic fusion genes in BRCA. Next, AGFusion was utilized to generate full-length fusion protein sequences and annotate functional domains. Advanced neural networks and Transformer-based analyses were implemented to predict the binding of fusion peptides to 112 types of HLA, thereby forming a new immunotherapy candidates' library of BRCA neo-antigens (n = 7791, covering 88.41% of patients). Among them, 15 neo-antigens were validated and factually translated into mass spectrometry data of BRCA patients. Finally, AlphaFold2 was applied to predict the binding sites of these neo-antigens to MHC (HLA) molecules. Notably, we identified a prognostic neoantigen from the TBC1D4-COMMD6 fusion that significantly improves patient prognosis and extensively binds to 16 types of HLA alleles. These highly immunogenic and tumor-specific neoantigens offer emerging targets for personalized cancer immunotherapies and act as prospective predictors for tumor survival prognosis and responses to immune checkpoint therapies.