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

• 批准号: 9186273
• 负责人: Lydia E. Kavraki
• 金额: $ 18.2万
• 依托单位: RICE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9186273
• 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; Data; Data Set; Development; Diagnosis; Docking; Doctor of Medicine; Ensure; Family; Genetic; Glean; Goals; Gold; HLA Antigens; Healthcare; 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; Staging; Structural Models; Structure; Surface; System; T-Cell Receptor; T-Lymphocyte; Testing; Toxic effect; Training; Tumor Antigens; Tumor Specific Peptide; Tumor-Derived; Variant; 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; prediction algorithm; prevent; response; tool; tumor; tumorigenic

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 发展有效的癌症治疗仍然是医疗保健的最重要挑战之一，T细胞 基于免疫疗法为癌症治疗提供了一些非常积极的进步。细胞毒性T-Lym- 植物（CTL）可以通过体内循环，并能够识别和消除肿瘤细胞。 肿瘤的识别取决于CTL和人的T细胞受体之间的特定相互作用 肿瘤细胞表面上的白细胞抗原（HLA）I类分子，该肿瘤细胞表面结合并显示衍生的肽 来自细胞内蛋白。所有核细胞都呈现肽-HLA复合物，构成有效的 免疫系统可以识别体内细胞内异常变化的监视机制。 al 尽管CTL监视可能会进化为消除几乎受感染的单元，但该系统也提供了非常有希望的 癌症治疗，特别是基于免疫疗法的发展机会。但是，这样 疗法必须高度个性化，因为这些与肿瘤相关的宠物大多数都是患者特异性的。这 主要是由于人口中HLA多样性的高水平，再加上每个人 人的肿瘤获得了独特的遗传畸变。另一个挑战是鉴定肿瘤特异性PEP- 正常细胞也未表达的潮汐，这可能会确保治疗过程中脱靶效应较小。我们的 长期目标是进行结构引导选择的肿瘤衍生的宠物，并具有免疫剂的潜力 APY，还将允许对给定T细胞识别的不同肽-HLA复合物进行结构分析；这 知识将有助于防止危险的脱靶毒性。该项目的目的是开发计算 - 从HLA变体（同型）开始 在人口中非常普遍，朝着其他较少普遍的人迈进（对于人 - 艾滋病治疗）。我们的初步数据支持需要一个结构性框架来改善选择 免疫疗法的靶标的，因为当前方法具有重要的局限性，尤其是在较少方面 普遍的HLA。中心假设是基于结构的分析可用于改善肽目标 选择单个HLA同种型，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。在特定的目标1中，一种对接方法 将专门针对肿瘤来源的宠物的结合预测，以使两个高度频繁，良好 研究了HLA，HLA-A*2402和HLA*A1101，占世界人口> 55％的共同表达。具体 AIM 2，HLA-A3超家族统称以> 40％的人口表达，将被用作模型 用于将方法扩展到较少良好的HLA。创新的计算方法将用于 该项目和尖端的实验资源将用于培训和验证计算方法。 根本的理由是为这些普遍的HLA同型开发计算框架 有助于开发个性化的，抗原特异性的免疫疗法，这将使更大的受益 癌症患者的数量。