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

项目摘要 开发有效的癌症治疗方法仍然是医疗保健和T细胞面临的最重要挑战之一 以免疫为基础的治疗在癌症治疗方面取得了一些非常积极的进展。细胞毒T淋巴细胞- 巨噬细胞(CTL)可以在体内循环，能够识别和清除致瘤细胞。 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 肿瘤细胞表面的白细胞抗原(HL A)I类分子，它结合并展示源自的多肽 来自细胞内的蛋白质。多肽-人类白细胞抗原复合体是由所有有核细胞呈递的，构成了一个有效的 免疫系统识别体内细胞内异常变化的监视机制。阿尔- 尽管CTL监测可能是为了消除病毒感染的细胞而发展起来的，但这个系统也提供了非常有希望的 癌症治疗的机会，特别是基于免疫的治疗方法的发展。然而，这样的 治疗必须高度个性化，因为这些肿瘤相关多肽大多是针对患者的。这 主要是由于人类群体中的高水平的人类白细胞抗原多样性，结合每个 人的肿瘤会获得独特的基因异常。另一个挑战是识别肿瘤特异性的PEP-- 潮汐也不是由正常细胞表达的，这可能会确保在治疗过程中减少偏离目标的影响。我们的 长期目标是对具有免疫学潜力的肿瘤来源的多肽进行结构导向选择。 APY，它还将允许对特定T细胞识别的不同多肽-人类白细胞抗原复合体进行结构分析；这 知识将有助于防止危险的脱靶毒物。该项目的目标是开发计算机-- 支持基于对接的多肽-人类白细胞抗原复合体建模的常规工具，从人类白细胞抗原变体(同种异型)开始 在人类群体中非常普遍，并向其他不太普遍的人转移(对于人- 规范化治疗)。我们的初步数据支持需要一个结构框架来改进选择 免疫治疗的靶点，因为目前的方法有重要的局限性，特别是在较少的 普遍存在的HLA。中心假设是基于结构的分析可以用来改进多肽靶标。 选择个体的人类白细胞抗原同种异型，从而促进个性化免疫疗法的发展 所有癌症患者。设计了两个特定的目标来检验这一假设。在具体目标1中，一种对接方法 将专门定制以预测肿瘤来源的多肽与两个频繁和良好的- 研究了人类白细胞抗原，即人类白细胞抗原-A*2402和人类白细胞抗原*A1101，这两种基因由55%的世界人口共同表达。具体而言 目的2，人类40%的人口共同表达的人类白细胞抗原-A3超家族将被用作模型 将这些方法扩展到研究较少的高密度脂蛋白。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将 促进个性化、抗原特异性免疫疗法的发展，这将使 癌症患者的数量。

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.