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

Structure-based selection of tumor-antigens for T-cell based immunotherapy

基于结构的 T 细胞免疫治疗肿瘤抗原选择

基本信息

批准号：
9332344
负责人：
Lydia E. Kavraki
金额：
$ 20.24万
依托单位：
RICE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9332344
关键词：
3-Dimensional Adoptive Transfer Algorithms Alleles Antigen Targeting Antigens Autoantigens Base Sequence Binding Biochemical Cancer Patient Cancer Vaccines Cell surface Cells Cessation of life Characteristics Clinical Trials Complement Complex Computing Methodologies Cytotoxic T-Lymphocytes Dangerousness Data Data Set Development Diagnosis Docking Doctor of Medicine Eligibility Determination Ensure Family Genetic Glean Goals Gold HLA Antigens Healthcare Histocompatibility Antigens Class I Human Immune Immune system Immunotherapy Individual Knowledge Ligands Link Malignant Neoplasms Malignant neoplasm of ovary Malignant neoplasm of pancreas Mass Spectrum Analysis Methods Modeling Normal Cell Normal tissue morphology Outcome Patients Peptide Vaccines Peptides Persons Population Proteins Public Health Reporting Research Resources Structural Models Structure Surface System T-Cell Receptor T-Lymphocyte Testing Therapeutic Toxic effect Training Tumor Antigens Tumor Expansion Tumor Specific Peptide Tumor-Derived Tumorigenicity Variant Viral antigen binding base cancer immunotherapy cancer therapy colon cancer patients computer framework computerized tools cross reactivity design improved individual patient innovation killings melanoma neoplastic cell next generation novel patient population personalized immunotherapy personalized medicine prediction algorithm prevent response tumor tumorigenic

项目摘要

Project Summary Developing effective cancer treatments remains one of the most important challenges for healthcare, and T-cell based immunotherapy has provided some very positive recent advances in cancer treatment. Cytotoxic T lym- phocytes (CTLs) can circulate through the body and are capable of identifying and eliminating tumorigenic cells. The recognition of tumor depends on the specific interaction between the T-cell receptor of CTLs and Human Leucocyte Antigen (HLA) class I molecules at the tumor cell surface, which binds and displays peptides derived from intracellular proteins. Peptide-HLA complexes are presented by all nucleated cells, constituting an efficient surveillance mechanism by which the immune system can recognize aberrant changes within cells of the body. Al- though CTL surveillance likely evolved to eliminate virally-infected cells, this system also provides very promising opportunities for cancer treatment and specifically the development of immune-based therapies. However, such therapies must be highly personalized since most of these tumor-associated peptides are patient-specific. This is due mainly to the high level of HLA diversity within the human population, combined with the fact that each person’s tumor acquires unique genetic aberrations. A further challenge is the identification of tumor-specific pep- tides that are not also expressed by normal cells, which will likely ensure less off-target effects during therapy. Our long-term goal is to perform structure-guided selection of tumor-derived peptides with potential for immunother- apy, which will also allow structural analysis of different peptide-HLA complexes recognized by a given T-cell; this knowledge will help to prevent dangerous off-target toxicities. The objective of this project is to develop computa- tional tools to enable docking-based modeling of peptide-HLA complexes, starting with HLA variants (allotypes) that are highly prevalent within human population and moving toward others that are less prevalent (for person- alized treatment). Our Preliminary Data supports the need for a structural framework to improve the selection of targets for immunotherapy, since current methods have important limitations, particularly with regard to less prevalent HLAs. The central hypothesis is that structure-based analysis can be used to improve peptide target selection for individual HLA allotypes and thus facilitate the development of personalized immunotherapies for all cancer patients. Two specific aims were designed to test this hypothesis. In Specific Aim 1, a docking method will be specifically tailored to make binding predictions of tumor-derived peptides to two highly frequent and well- studied HLAs, HLA-A*2402 and HLA*A1101, collectively expressed by >55% of the world population. In Specific Aim 2, the HLA-A3 superfamily, collectively expressed by >40% of the human population, will be used as a model for extending the methods towards less well-studied HLAs. Innovative computational methods will be applied in this project and cutting-edge experimental resources will be used to train and validate computational methods. The underlying rationale is that developing a computational framework for these prevalent HLA allotypes will facilitate the development of personalized, antigen-specific immunotherapies, which would benefit a much larger number of cancer patients.

项目摘要开发有效的癌症治疗方法仍然是医疗保健最重要的挑战之一，而T细胞基于免疫疗法的癌症治疗提供了一些非常积极的最新进展。细胞毒性T淋巴细胞巨噬细胞（CTL）可以在体内循环，并且能够识别和消除致瘤细胞。肿瘤的识别依赖于CTL的T细胞受体与人的特异性相互作用，白细胞抗原（HLA）I类分子在肿瘤细胞表面，结合并展示肽衍生从细胞内蛋白质。肽-HLA复合物由所有有核细胞呈递，构成有效的免疫调节系统。免疫系统可以识别身体细胞内异常变化的监视机制。铝- 尽管CTL监视可能进化为消除病毒感染的细胞，但该系统也提供了非常有前途的癌症治疗的机会，特别是基于免疫的疗法的发展。但这种治疗必须高度个性化，因为这些肿瘤相关肽中的大多数是患者特异性的。这这主要是由于人类群体中HLA多样性的高水平，结合每个人都有一个事实，一个人的肿瘤获得独特的遗传畸变。另一个挑战是识别肿瘤特异性pep，这可能会确保在治疗期间较少的脱靶效应。我们长期目标是对具有免疫治疗潜力的肿瘤衍生肽进行结构导向选择，这也将允许由给定T细胞识别的不同肽-HLA复合物的结构分析; 知识将有助于防止危险的脱靶毒性。该项目的目标是开发计算- 从HLA变体（同种异型）开始的基于对接的肽-HLA复合物建模的标准工具在人群中高度流行，并向其他不太流行的（对人来说，治疗）。我们的初步数据支持需要一个结构框架，以改善选择由于目前的方法具有重要的局限性，特别是在免疫治疗的靶点较少的情况下，流行的HLA。中心假设是基于结构的分析可以用于改进肽靶向选择个体HLA同种异型，从而促进个性化免疫疗法的发展，所有癌症患者。设计了两个具体目标来检验这一假设。在具体目标1中，对接方法将专门定制，以预测肿瘤衍生肽与两种高度频繁和良好的- 研究了HLA，HLA-A*2402和HLA*A1101，它们共同由>55%的世界人口表达。在特定目标2，由>40%的人类群体共同表达的HLA-A3超家族将被用作模型用于将方法扩展到研究较少的HLA。创新的计算方法将应用于该项目和尖端实验资源将用于培训和验证计算方法。其基本原理是，为这些流行的HLA同种异型开发一个计算框架，促进个性化，抗原特异性免疫疗法的发展，这将有利于更大的癌症患者的数量。