ArticleDiscover oncology2024
Identification of BRCA new prognostic targets and neoantigen candidates from fusion genes.
Article in Discover oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 authors.
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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.
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