Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors

项目摘要 背景：对新生抗原的深入研究将有助于我们更好地了解肿瘤的发生机制， 基础免疫学和免疫相关疾病过程，如对癌症免疫疗法的反应。 新抗原在T细胞识别肿瘤细胞中起关键作用，并且越来越多地显示其在肿瘤细胞中的作用。 检查点通道诱导的免疫应答的靶点。然而，新抗原中存在几个缺失的环节， research. (1)只有一小部分新抗原可以引发T细胞应答。更不清楚的是， 新抗原将被哪种特异性T细胞受体（TCR）识别。(2)虽然新抗原很重要， 在免疫疗法的作用过程中，如何使用新抗原库数据来预测患者 反应只是知之甚少。(3)缺乏标准化的分析渠道， 新抗原数据阻碍了肿瘤免疫基因组学领域的高效和一致的研究。 目的1：建立一个基于迁移学习的新抗原免疫原性预测模型。到目前为止，只有一个非常 有限数量的报告已经建立了预测模型，以确定新抗原/MHC复合物是否可以 引发任何T细胞反应。其中甚至很少有能够预测TCR-binding特异性的。 新抗原然而，预测免疫球蛋白的总体免疫原性和TCR结合特异性的能力是不确定的。 新抗原对于改善免疫治疗的益处是至关重要的。目标1通过以下方式应对这一挑战： 先进的迁移学习算法，其次是基准测试和实验室验证。 目的2：通过整合免疫原性和其他特性，预测对检查点抑制剂的反应。 患者中的所有新抗原，通过贝叶斯多实例学习模型。迄今为止，大多数研究 重点关注与患者对免疫治疗的应答相关的新抗原/突变负荷方法 局这种简单化的方法错过了包含在整个剧目的丰富信息， 新抗原，并且仅在少数研究中成功，而不是其他研究。目标2解决了这个问题 通过创建一个贝叶斯多实例学习模型，充分考虑各种质量的重要不足 在一些实施方案中，本发明提供了用于预测患者中所有新抗原的特征（包括免疫原性）的方法，以预测治疗应答。 目标3：创建一个门户网站，提供与新抗原相关的计算服务并共享新抗原 数据PI将建立一个公共网络服务器，提供基于云的标准化服务，包括预测 新抗原和先进的分析方法在目标1和2中开发。网站服务器将公开 根据IRB和HIPAA法规共享新抗原/TCR和患者表型数据。 预期影响：（1）本项目将预测新抗原的免疫原性，这可以为新抗原的免疫原性提供信息。 新抗原疫苗开发。(2)该项目将预测对检查点抑制剂和其他形式的反应 免疫治疗的基础上病人的新抗原谱。(3)新抗原数据库将推动研究， 还可用于癌症和其他免疫相关疾病（例如COVID-19）的临床应用。

Project Summary Background: In-depth study of neoantigens will promote our knowledge of the fundamental mechanisms of basic immunology and immune-related disease processes, such as response to cancer immunotherapy. Neoantigens play a key role in the recognition of tumor cells by T cells and are increasingly shown to be targets of checkpoint inhibitor-induced immune response. However, several missing links exist in neoantigen research. (1) Only a small proportion of neoantigens can elicit T cell responses. It is even less clear which neoantigens will be recognized by which specific T cell receptor (TCR). (2) Although neoantigens are important during the course of action of immunotherapies, how neoantigen repertoire data can be used to predict patient response is only poorly understood. (3) The lack of standardized analysis pipelines and limited sharing of neoantigen data have hindered efficient and consistent research in the tumor immunogenomics field. Aim 1: Build a transfer learning-based model to predict immunogenicity of neoantigens. So far, only a very limited number of reports have created predictive models determining whether a neoantigen/MHC complex can elicit any T cell response. Even fewer of them are capable of predicting the TCR-binding specificity of neoantigens. However, the capability to predict the overall immunogenicity and the TCR-binding specificity of neoantigens is critical for improving the benefit of immunotherapy. Aim 1 addresses this challenge with advanced transfer learning algorithms, followed by benchmarking and laboratory validations. Aim 2: Predict response to checkpoint inhibitors by integration of the immunogenicity and other properties of all neoantigens in a patient, through a Bayesian multi-instance learning model. To date, most studies have focused on the neoantigen/mutation load approach in correlation with response of patients to immunotherapy administration. This simplistic approach misses the rich information contained in the whole repertoire of neoantigens per patient and has been successful in only a few studies, but not others. Aim 2 addresses this important inadequacy by creating a Bayesian multi-instance learning model that fully considers various quality features, including immunogenicity, of all neoantigens in a patient for prediction of treatment response. Aim 3: Create a web portal to provide neoantigen-related computational services and to share neoantigen data. The PI will establish a public webserver providing cloud-based standardized services, including prediction of neoantigens and the advanced analysis methods developed in Aim 1 and 2. The webserver will openly share neoantigen/TCR and patient phenotype data, in accordance with IRB and HIPAA regulations. Expected impact: (1) This project will predict the immunogenicity of neoantigens, which could inform neoantigen vaccine development. (2) This project will predict response to checkpoint inhibitors and other forms of immunotherapy based on patient neoantigen profiles. (3) The neoantigen database will propel research and also lead to clinical applications for cancers and other immune-related diseases, such as COVID-19.