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

项目摘要/摘要 癌症免疫疗法在治疗晚期肿瘤方面取得了显著的临床成功，但其反应 利率仍然很低，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。这是一项极其困难的任务，因为大多数癌症 抗原未知；2)大多数肿瘤浸润性T细胞(TIL)不识别癌细胞；以及 3)在没有已知抗原的情况下，获得这种T细胞的唯一途径是进行TIL的体外扩增 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。 然而，这一策略在目前的抗癌治疗临床试验中被广泛采用，尽管它 自身免疫的治疗效果降低和不可预测的副作用。因此，不偏不倚，抗原- 如果可能，独立鉴定肿瘤反应性T细胞将是临床的主要优先事项 显著提高以T细胞为基础的免疫疗法的效率和安全性。在这里，我们建议实现 这一目标通过开发新的机器学习方法来实现。这样的方法还没有被 因为癌症和非癌症T细胞的根本区别在于它们的受体 目前还没有癌症特异性TCR的序列(TCR)和训练数据。为准备这项任务， 我们已经开发了软件TRUST，用于从实体瘤中提取T细胞抗原结合CDR3区域 RNA-seq数据，以及软件iSmart将这些CDR3分组为抗原特异性簇。这些工具 使我们开发了一种新的理论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 与与HLA-A*02：01相结合的肿瘤抗原相匹配，并在体内引发免疫反应。特定于癌症的 CDR3氨基酸序列也表现出与非癌症序列显著不同的生化特征， 在此基础上，我们进一步开发了软件DeepCAT，以验证从头预测的可行性 癌症TCR。这些令人振奋的结果突显了开发更好的计算方法来 追踪肿瘤反应性T细胞，用于临床应用。为此，我们提出了以下具体目标： 目标1，我们将提供一种新的机器学习方法，用于对肿瘤反应性T细胞进行准确分类 CDR3序列。在目标2中，我们将为癌症特异性T细胞推导出一组生物标记物，用于快速和 从TIL中准确地对这些T细胞进行流动分类。在目标3中，我们将进行单细胞测序和功能 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，并 为癌症诊断、预后和治疗发展制定一份有希望的目标的优先列表。这些 在癌症领域的优秀合作者的大力支持下，目标将得以实现 威斯康星大学免疫学专业。这一提议的成功完成将提供一种令人兴奋的新范式来确定 肿瘤反应性T细胞用于精确的癌症免疫治疗。

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.