In Silico Methods for Assessing Cancer Immunogenicity—A Comparison Between Peptide and Protein Models

Identifying and characterizing putative tumor antigens is essential to cancer vaccine development. Given the impracticality of isolating and evaluating each potential antigen individually, in silico prediction algorithms, especially those employing machine learning (ML) techniques, are indispensable. These algorithms substantially decrease the experimental workload required for discovering viable vaccine candidates, thereby accelerating the development process and enhancing the efficiency of identifying promising immunogenic targets. In this study, we employed six supervised ML methods on a dataset containing 546 experimentally validated immunogenic human tumor proteins and 548 non-immunogenic human proteins to develop models for immunogenicity prediction. These models included k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). After validation through internal cross-validation and an external test set, the best-performing models (QDA, RF, and XGBoost) were selected for further evaluation. A comparison between the chosen protein models and our previously developed peptide models for tumor immunogenicity prediction revealed that the peptide models slightly outperformed the protein models. However, since both proteins and peptides can be subject to tumor immunogenicity assessment, evaluating each with the respective models is prudent. The three selected protein models are set to be integrated into the new version of the VaxiJen server.