The Effect of Multi-Task Learning on the Prediction of Neoantigen-MHC Class II Binding

Neoepitopes are significant therapeutic cancer vaccine candidates, given that tumor neoepitopes induce an immune response to eliminate cancer cells. This immune activation depends on the binding affinity between the antigen peptide and the major histocompatibility complex (MHC). The epitope-MHC binding assay is a technologically difficult, time-consuming, and expensive technique. Therefore, prediction methods for these binding affinities have been developed using computational prediction approaches. However, these predictive models are trained on datasets biased toward viral peptides and some MHC alleles and are limited in their prediction of neoepitopes. In particular, because of the wide variety of MHC class II binding formats, the performance of MHC class II prediction must be improved. Here, we propose a novel deep learning model that consists of multi-task bidirectional long short-term memory (Bi-LSTM) models. Our multi-task model can predict neoepitope-MHC class II bindings from limited training data by sharing MHC class I and II training parameters. We confirm that multi-task learning significantly enhances the prediction performances of cancer antigens. Our model achieves an area under the receiver operating characteristic curve (AUC-ROC) of 82.2%, outperforming existing state-of-the-art single allele neoantigen prediction models while maintaining its strong generalization performance.