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Vincent (2023-07-31 14:42):
#paper Deep learning-based prediction of the T cell receptor–antigen binding specificity https://doi.org/10.1038/s42256-021-00383-2 2021 nature machine intelligence. 肿瘤新抗原在T细胞识别肿瘤细胞的过程中发挥着重要的作用,肿瘤新抗原与T细胞受体的结合与相互作用预测一直备受关注,然而相关的实验与计算方法一直有诸多不足,可验证性也很差。这篇文章开发了一套基于迁移学习的机器学习方法pMTnet,来预测抗原MHC结合物与T细胞受体的结合能力。通过将pMTnet运用到人的肿瘤基因组数据上,发现肿瘤新抗原比自身抗原的免疫原性更高,拥有对肿瘤新抗原结合能力强的T细胞克隆的病人在免疫治疗中有更好的预后和治疗效果。
Abstract:
Neoantigens play a key role in the recognition of tumor cells by T cells. However, only a small proportion of neoantigens truly elicit T cell responses, and fewer clues exist … >>>
Neoantigens play a key role in the recognition of tumor cells by T cells. However, only a small proportion of neoantigens truly elicit T cell responses, and fewer clues exist as to which neoantigens are recognized by which T cell receptors (TCRs). We built a transfer learning-based model, named pMHC-TCR binding prediction network (pMTnet), to predict TCR-binding specificities of neoantigens, and T cell antigens in general, presented by class I major histocompatibility complexes (pMHCs). pMTnet was comprehensively validated by a series of analyses, and showed advance over previous work by a large margin. By applying pMTnet in human tumor genomics data, we discovered that neoantigens were generally more immunogenic than self-antigens, but HERV-E, a special type of self-antigen that is re-activated in kidney cancer, is more immunogenic than neoantigens. We further discovered that patients with more clonally expanded T cells exhibiting better affinity against truncal, rather than subclonal, neoantigens, had more favorable prognosis and treatment response to immunotherapy, in melanoma and lung cancer but not in kidney cancer. Predicting TCR-neoantigen/antigen pairs is one of the most daunting challenges in modern immunology. However, we achieved an accurate prediction of the pairing only using the TCR sequence (CDR3β), antigen sequence, and class I MHC allele, and our work revealed unique insights into the interactions of TCRs and pMHCs in human tumors using pMTnet as a discovery tool. <<<
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