Antigen-independent prediction and biomarker identification of cancer-specific T cells

癌症特异性 T 细胞的抗原独立预测和生物标志物鉴定

• 批准号: 10248560
• 负责人: Bo Li
• 金额: $ 37.49万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10248560
• 关键词: Adopted; Amino Acid Sequence; Animal Model; Antigens; Autoimmune; Autoimmunity; Autologous; BRAF gene; Binding; Biochemical; Biological Assay; Biological Markers; CD28 gene; Cancer Patient; Cancer cell line; Cell Line; Cell Separation; Cellular immunotherapy; Classification; Clinical; Clinical Trials; Computer software; Computing Methodologies; Data; Data Set; Development; Diagnosis; Future; Genes; Goals; HLA-A gene; Hematopoietic stem cells; Human; IL2RA gene; Immune; Immune response; Immunodeficient Mouse; Immunotherapy; Individual; Innate Immune System; Lead; Machine Learning; Malignant Neoplasms; Melanoma Cell; Methods; Oncogenes; Open Reading Frames; Outcome; Patients; Post-Translational Protein Processing; Prognosis; Safety; Sampling; Sorting - Cell Movement; Source; T-Cell Receptor; T-Lymphocyte; Testing; Tissue-Specific Gene Expression; Tissues; Training; Treatment Efficacy; Tumor Antigens; Tumor Expansion; Tumor stage; Umbilical Cord Blood; Validation; Xenograft procedure; anti-cancer; anticancer treatment; antigen binding; antigen-specific T cells; base; biomarker identification; cancer biomarkers; cancer cell; cancer diagnosis; cancer genomics; cancer immunotherapy; clinical application; complementarity-determining region 3; deep learning; design; gag Gene Products; genetic signature; genomic data; humanized mouse; improved; in vivo; learning strategy; machine learning method; neoantigens; neoplastic cell; novel; peripheral blood; predictive marker; receptor; reconstitution; response; side effect; single cell sequencing; single-cell RNA sequencing; software development; success; therapy development; tool; transcriptome; transcriptome sequencing; tumor; tumor immunology; tumor microenvironment; unsupervised learning

Project Summary/Abstract Cancer immunotherapy has achieved remarkable clinical success treating late-stage tumors, yet the response rates remain low and the side effects are often severe. Designing effective immunotherapies relies on accurate identification of tumor-reactive T cells. This is an extremely difficult task because 1) most of the cancer antigens are unknown; 2) the majority of the tumor-infiltrating T cells (TIL) does not recognize cancer cells; and 3) without known antigens, the only approach to acquire such T cells is to perform ex vivo expansion of TILs stimulated by autologous cancer cells, which generates non-specific T cells and is infeasible to many patients. Nonetheless, this strategy is widely adopted in current clinical trials for anti-cancer treatment, despite its reduced therapeutic efficacy and unpredictable side effects of autoimmunity. Therefore, unbiased, antigen- independent identification of tumor-reactive T cells, if possible, will be a major clinical priority as it will significantly increase the efficiency and safety of T cell based immunotherapies. Here we propose to achieve this goal through the development of novel machine learning methods. Such approach has not yet been explored because the fundamental difference between cancer and non-cancer T cells lies in their receptor sequences (TCR), and training data of cancer-specific TCRs is currently unavailable. To prepare for this task, we have developed the software TRUST, to extract the T cell antigen-binding CDR3 regions from bulk tumor RNA-seq data, and the software iSMART to group these CDR3s into antigen-specific clusters. These tools allowed us to develop a new rationale for producing large training sets of tumor-reactive TCRs, even without knowing cancer antigens. In our preliminary analysis, we observed that TCRs from the training data can be matched to tumor antigens that bind to HLA-A*02:01 and elicit immune response in vivo. The cancer-specific CDR3 amino acid sequences also show significantly different biochemical features from non-cancer ones, based on which we further developed software DeepCAT to demonstrate the feasibility of de novo prediction of cancer TCRs. These exciting results highlighted the importance to develop better computational method to track the tumor-reactive T cells for clinical applications. Accordingly, we propose the following Specific Aims: In Aim 1, we will deliver a new machine learning method for accurate classification of tumor-reactive T cells using the CDR3 sequences. In Aim 2, we will derive a set of biomarkers for the cancer-specific T cells for fast and accurate flow sorting of these T cells from TILs. In Aim 3, we will perform single cell sequencing and functional validation of cancer-specific T cells using humanized animal model to validate the predicted genes, and to produce a prioritized list of promising targets for cancer diagnosis, prognosis and therapy development. These Aims will be accomplished with the great support from the excellent collaborators specialized in cancer immunology at UTSW. Successful completion of this proposal will provide an exciting new paradigm to identify tumor-reactive T cells for precision cancer immunotherapies.

项目总结/摘要 癌症免疫疗法在治疗晚期肿瘤方面取得了显着的临床成功， 发病率仍然很低，副作用往往很严重。设计有效的免疫疗法依赖于准确的 肿瘤反应性T细胞的鉴定。这是一个非常困难的任务，因为1）大多数癌症 抗原是未知的; 2）大多数肿瘤浸润性T细胞（TIL）不识别癌细胞;和 3)在没有已知抗原的情况下，获得这种T细胞的唯一方法是进行TIL的离体扩增 由自体癌细胞刺激，产生非特异性T细胞，对许多患者来说是不可行的。 尽管如此，这种策略在目前的抗癌治疗临床试验中被广泛采用，尽管其 降低的治疗效果和自身免疫的不可预测的副作用。因此，无偏的，抗原- 如果可能的话，肿瘤反应性T细胞的独立鉴定将是主要的临床优先事项，因为它将 显著提高基于T细胞的免疫疗法的效率和安全性。在这里，我们建议实现 通过开发新的机器学习方法来实现这一目标。这种方法尚未 因为癌症和非癌症T细胞之间的根本区别在于它们的受体 目前，癌症特异性TCR序列（TCR）的训练数据是不可用的。为了准备这项任务， 我们开发了TRUST软件，从肿瘤组织中提取T细胞抗原结合CDR 3区 RNA-seq数据和软件iSMART将这些CDR 3分组为抗原特异性簇。这些工具 这使得我们能够开发出一种新的原理来生产大量的肿瘤反应性TCR训练集，即使没有 了解癌症抗原。在我们的初步分析中，我们观察到来自训练数据的TCR可以是 与结合HLA-A*02：01并在体内引发免疫应答的肿瘤抗原相匹配。癌症特异 CDR 3氨基酸序列也显示出与非癌症氨基酸序列显着不同的生化特征， 在此基础上，我们进一步开发了软件DeepCAT，以证明从头预测的可行性。 癌症TCR。这些令人兴奋的结果突出了发展更好的计算方法的重要性， 跟踪肿瘤反应性T细胞用于临床应用。因此，我们提出以下具体目标： 目标1，我们将提供一种新的机器学习方法，用于肿瘤反应性T细胞的准确分类， CDR 3序列。在目标2中，我们将推导出一组癌症特异性T细胞的生物标志物，用于快速和特异性地检测癌症。 这些T细胞从TIL中准确流式分选。在目标3中，我们将进行单细胞测序和功能分析。 使用人源化动物模型验证癌症特异性T细胞以验证预测的基因，以及 为癌症诊断、预后和治疗开发提供有希望的靶点的优先列表。这些 我们的目标将在癌症领域的优秀合作者的大力支持下实现 UTSW的免疫学成功完成此提案将提供一个令人兴奋的新范例， 肿瘤反应性T细胞用于精确的癌症免疫治疗。