权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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：
10900208
负责人：
Bo Li
金额：
$ 39.9万
依托单位：
CHILDREN'S HOSP OF PHILADELPHIA
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 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10900208
关键词：
Adaptive Immune System 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 Infiltration Innate Immune System Machine Learning Malignant Neoplasms Melanoma Cell Methods Oncogenes Open Reading Frames Outcome Patients Post-Translational Protein Processing Prognosis Safety Sampling Sorting Source T cell infiltration 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 biomarker identification cancer biomarkers cancer cell cancer diagnosis cancer genomics cancer immunotherapy cancer infiltrating T cells 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.

项目总结/文摘

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Quantifiable TCR repertoire changes in pre-diagnostic blood specimens among high-grade ovarian cancer patients.

高级别卵巢癌患者诊断前血液样本中可量化的 TCR 谱变化。

DOI：
10.1101/2023.10.12.562056
发表时间：
2023
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Yu,Xuexin;Ye,Jianfeng;Hathaway,CassandraA;Tworoger,Shelley;Lea,Jayanthi;Li,Bo
通讯作者：
Li,Bo

Concurrent delivery of immune checkpoint blockade modulates T cell dynamics to enhance neoantigen vaccine-generated antitumor immunity.

DOI：
10.1038/s43018-022-00352-7
发表时间：
2022-04
期刊：
NATURE CANCER
影响因子：
22.7
作者：
Liu, Longchao;Chen, Jiahui;Zhang, Hongyi;Ye, Jianfeng;Moore, Casey;Lu, Changzheng;Fang, Yan;Fu, Yang-Xin;Li, Bo
通讯作者：
Li, Bo

T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies.