1. DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.
- Author
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Wu J, Wang W, Zhang J, Zhou B, Zhao W, Su Z, Gu X, Wu J, Zhou Z, and Chen S
- Subjects
- CD8-Positive T-Lymphocytes immunology, Epitopes, T-Lymphocyte immunology, HLA Antigens genetics, Humans, Mutation, Neoplasms genetics, Neoplasms immunology, Peptides genetics, Antigens, Neoplasm immunology, Deep Learning, HLA Antigens immunology, Peptides immunology
- Abstract
Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan., (Copyright © 2019 Wu, Wang, Zhang, Zhou, Zhao, Su, Gu, Wu, Zhou and Chen.)
- Published
- 2019
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