结合多组学数据与深度学习的肿瘤新抗原筛选

肿瘤免疫治疗无疑是近年来癌症治疗领域取得的最令人兴奋的进展，肿瘤新抗原是免疫细胞特异性识别并杀伤肿瘤细胞的主要生物标志物，但新抗原筛选及其免疫原性预测尚缺乏合适的生物信息学方法，为肿瘤免疫治疗的诊断与预后带来巨大挑战。本课题从临床免疫治疗中缺乏可靠的新抗原筛选与伴随诊断指标的实际需求出发，对新抗原筛选涉及的关键内容，包括MHC分子-抗原肽亲和力、抗原递呈效率、T淋巴细胞识别抗原表位以及新抗原负荷等展开深入研究，整合多组学高通量测序数据，结合深度学习、注意力机制与序列嵌入等并提出全新的模型与算法，结合序列嵌入与注意力机制预测MHC分子与抗原肽亲和力与热点残基，运用迁移学习预测肿瘤特异的MHC分子与抗原肽亲和力，基于新抗原负荷的PD-(L)1抑制剂临床治疗生存分析等，建立一系列肿瘤特异新抗原及其免疫原性预测算法与工具，为肿瘤个性化免疫治疗提供更多的机会。

Tumor immunotherapy is the most exciting achievement in the tumor treatments in recent years. Neoantigen is the tumor-specific peptides for T cells to recognize and kill the tumor cells, and is the crucial to the success of various immunotherapies. However, there is no mature and reliable bioinformatics pipeline for the prediction of neoantigen and their immunogenicity, which leads to difficulty for clinical diagnose and evaluation of immune response and prognosis. We refined the crucial problems involved in neoantigen screening, including binding affinity of MHC-peptide, efficiency of antigen presentation, T cell-recognition of epitope and companion diagnostic biomarker of immune checkpoint inhibitors. We will integrate multi-omics high-throughput data, and extend deep learning, attention mechanism and sequence embedding technique and also propose novel methods to develop high-performance computational algorithms for the prediction of tumor-specific and immunogenic neoantigens. In particular, we will combine sequence embedding and attention mechanism to predict the hot spots of MHC I and peptides interactions, and exploit transfer learning to predict the binding affinity of tumor-specific MHC I and antigenic peptides, and also develop neoantigen load to conduct survival analysis for clinical effect of PD-(L)1 inhibitor. We aim to establish the complete bioinformatics pipeline for the screening of tumor-specific neoantigens and evaluation of immunogenicity, so that the patients can benefit more from the immunotherapy.