DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity

• Published in: Frontiers in immunology 2019-11, Vol.10, p.2559-2559
• Main Authors: Wu, Jingcheng, Wang, Wenzhe, Zhang, Jiucheng, Zhou, Binbin, Zhao, Wenyi, Su, Zhixi, Gu, Xun, Wu, Jian, Zhou, Zhan, Chen, Shuqing
• Format: Article
• Language: English
• Subjects: cancer immunology; deep learning; human leukocyte antigen; Immunology; neoantigen; recurrent neural network
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2019.02559

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.