Identification of BRCA new prognostic targets and neoantigen candidates from fusion genes.

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). Amo

SUBMITTER: Zhang P 

PROVIDER: S-EPMC11655931 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature