Cytokine-driven immunogenicity prediction: integrating HLA binding and cytokine induction

Cytokines are central to the immune response, determining whether T-cell activation leads to protective immunity or tolerance. The secretion of key cytokines such as IFN-$\gamma$, IL-2, IL-4, and IL-10 shapes the immune response, influencing its presence, pathways, and magnitude. While most existing computational methods for predicting peptide immunogenicity typically focus on either cytokine induction or histocompatibility complex binding affinity separately, they fail to capture the full complexity of immune activation. In this study, we introduce a two-stage computational framework that integrates both cytokine induction and HLA binding affinity predictions to provide a more comprehensive prediction of peptide immunogenicity. First, we develop machine learning models EpiLAMA-IL to predict cytokine secretion based on peptide and parent protein sequences. These models outperform current state-of-the-art cytokine release prediction methods, achieving ROC AUC from 0.79 up to 0.92 depending on the task. These cytokine scores are then combined with HLA binding affinity predictions and peptide physico-chemical descriptors to develop a composite immunogenicity score prediction model EpiLAMA-IM. This approach bridges the gap between sequence-based antigen presentation and functional immune outcomes, offering improved accuracy in predicting immune responses and achieves MCC 0.47 on the CD4Episcore dataset.