<p>Neoantigens, tumor-specific peptides with immunogenic potential, represent pivotal targets for cancer immunotherapy. Existing methods prioritize HLA-peptide binding but often fail to adequately address immunogenicity, limiting their clinical utility. This study introduces TrambaHLApan, a novel neoantigen prediction framework that integrates Transformer and Mamba architectures to concurrently predict antigen presentation likelihood (TrambaHLApan-EL) and immunogenic potential (TrambaHLApan-IM). A Transformer-based encoding module is employed to generate unique representations for HLA molecules and peptides. Subsequently, a hybrid fusion module, which combines merged-attention mechanisms with Mamba-based sequential modeling, is deployed to evaluate interaction patterns. TrambaHLApan-IM incorporates antigen presentation scores derived from TrambaHLApan-EL to explicitly model the interplay between antigen presentation and immunogenic potential, thereby enhancing the identification of neoantigens with high confidence. Experimental results on independent datasets demonstrate that TrambaHLApan outperforms state-of-the-art methods, establishing it as a reliable tool for advancing personalized cancer immunotherapies.</p> Graphical Abstract <p></p>