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

基本信息

批准号：
10413251
负责人：
Bo Li
金额：
$ 37.52万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10413251
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

项目摘要

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.

项目概要/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，但反应发生率仍然很低，而且副作用往往很严重。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 1) 大多数癌症抗原未知； 2）大多数肿瘤浸润T细胞（TIL）不识别癌细胞；和 3）在没有已知抗原的情况下，获得此类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，该策略在当前的抗癌治疗临床试验中被广泛采用，尽管其效果不佳。治疗效果降低和自身免疫的不可预测的副作用。因此，公正的、抗原的如果可能的话，独立鉴定肿瘤反应性 T 细胞将成为主要的临床优先事项，因为它将显着提高基于 T 细胞的免疫疗法的效率和安全性。这里我们建议实现通过开发新颖的机器学习方法来实现这一目标。这种方法目前还没有之所以进行探索，是因为癌症 T 细胞和非癌症 T 细胞的根本区别在于它们的受体序列（TCR），并且目前无法获得癌症特异性 TCR 的训练数据。为了准备这项任务，我们开发了软件 TRUST，用于从大块肿瘤中提取 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。这些工具使我们能够开发出一种新的原理来生产大量肿瘤反应性 TCR 训练集，即使没有了解癌症抗原。在我们的初步分析中，我们观察到训练数据中的 TCR 可以是与结合 HLA-A*02:01 并引发体内免疫反应的肿瘤抗原相匹配。癌症特异性 CDR3氨基酸序列也表现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DeepCAT软件来论证从头预测的可行性癌症 TCR。这些令人兴奋的结果凸显了开发更好的计算方法的重要性追踪肿瘤反应性 T 细胞的临床应用。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，利用该方法对肿瘤反应性 T 细胞进行准确分类 CDR3 序列。在目标 2 中，我们将获得一组癌症特异性 T 细胞的生物标志物，以快速、准确地检测癌症。从 TIL 中准确流式分选这些 T 细胞。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，并制定癌症诊断、预后和治疗开发有前景目标的优先列表。这些在癌症领域优秀合作者的大力支持下，目标将得以实现 UTSW 的免疫学。该提案的成功完成将提供一个令人兴奋的新范式来确定用于精准癌症免疫疗法的肿瘤反应性 T 细胞。