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

应用深度学习预测新抗原的 T 细胞受体结合特异性以及对检查点抑制剂的反应

• 批准号: 10393020
• 负责人: Tao Wang
• 金额: $ 35.97万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10393020
• 关键词: Address; Benchmarking; Binding; Biological Assay; Biological Markers; Biological Process; COVID-19; Clinical; Clinical Data; Communities; Complement; Complex; Data; Data Set; Databases; Disease; Extramural Activities; Feedback; Genomics; Goals; Health Insurance Portability and Accountability Act; Immune; Immune checkpoint inhibitor; Immune response; Immunogenomics; Immunologics; Immunology; Immunotherapy; Institutional Review Boards; Knowledge; Laboratories; Lead; Learning; Link; MHC antigen; Malignant Neoplasms; Methodology; Methods; Modeling; Mutation; Patient Care; Patient-Focused Outcomes; Patients; Performance; Play; Policies; Prediction of Response to Therapy; Problem Solving; Process; Property; Publications; Records; Regulation; Reporting; Reproducibility; Research; Research Personnel; Resistance; Resources; Science; Services; Source; Specificity; Standardization; T cell response; T-Cell Receptor; T-Lymphocyte; Validation; Work; analysis pipeline; base; cancer care; cancer immunotherapy; clinical application; clinical practice; cloud based; cohort; computing resources; data sharing; data sharing networks; deep learning; deep learning model; enzyme linked immunospot assay; exome sequencing; experience; immunogenic; immunogenicity; improved; laboratory experiment; learning algorithm; neglect; neoantigen vaccine; neoantigens; neoplastic cell; patient response; personalized medicine; phenotypic data; predicting response; predictive modeling; receptor binding; response; statistics; transcriptome sequencing; transfer learning; treatment response; tumor; vaccine development; web portal

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.

项目摘要 背景：对新抗原的深入研究将促进我们对新抗原基本机制的了解。 基础免疫学和免疫相关疾病过程，如对癌症免疫治疗的反应。 新抗原在T细胞识别肿瘤细胞中起着关键作用，并且越来越多地被证明是 检查点抑制剂诱导免疫反应的靶点。然而，在新抗原中存在几个缺失的环节。 研究。(1)只有一小部分新抗原能诱导T细胞反应。更不清楚的是， 新的抗原将被识别通过哪个特定的T细胞受体(TCR)。(2)尽管新抗原很重要 在免疫治疗的作用过程中，新抗原谱系数据如何用于预测患者 人们对此的反应还知之甚少。(3)缺乏标准化的分析管道，共享有限 新的抗原数据阻碍了肿瘤免疫基因组学领域的有效和一致的研究。 目的1：建立基于转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 数量有限的报告建立了预测模型，确定新抗原/MHC复合体是否可以 引发任何T细胞反应。其中能够预测TCR结合特异性的就更少了 新抗原。然而，预测总的免疫原性和TCR结合特异性的能力 新抗原对于提高免疫治疗的效益至关重要。AIM 1通过以下方式应对这一挑战 先进的迁移学习算法，随后是基准测试和实验室验证。 目的2：结合免疫原性和其他特性预测对检查点抑制剂的反应 患者体内的所有新抗原，通过贝叶斯多实例学习模型。到目前为止，大多数研究已经 重点研究新抗原/突变负载方法与患者免疫治疗反应的相关性 行政管理。这种简单化的方法遗漏了包含在整个 仅在少数几项研究中取得成功，而在其他研究中则没有成功。AIM 2解决了这一问题 通过创建充分考虑各种质量的贝叶斯多实例学习模型来实现重要不足 患者体内所有新抗原的特征，包括免疫原性，用于预测治疗反应。 目标3：创建一个门户网站，以提供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 新抗原和在目标1和2中开发的先进分析方法。网络服务器将开放 根据IRB和HIPAA的规定，共享新抗原/TCR和患者表型数据。 预期影响：(1)该项目将预测新抗原的免疫原性，这可能会为 新抗原疫苗的开发。(2)该项目将预测对检查点抑制剂和其他形式的反应 根据患者的新抗原特征进行免疫治疗。(3)新抗原数据库将推动研究和 还导致癌症和其他免疫相关疾病的临床应用，如新冠肺炎。