(PQ2) PD-L1/PD-1 signals in aged hosts undergoing cancer immunotherapy

(PQ2) 接受癌症免疫治疗的老年宿主体内的 PD-L1/PD-1 信号

• 批准号: 10247570
• 负责人: Tyler J. Curiel
• 金额: $ 56.53万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10247570
• 关键词: Address; Affect; Age; Aging; Anatomy; Animal Model; Antineoplastic Agents; Appearance; Bladder Neoplasm; Blocking Antibodies; Bone Marrow; Cancer Model; Cancer Patient; Cells; Chimera organism; Chronology; Clinical; Clinical Trials; Color; Complement; Computer Models; Coupling; Cross-Sectional Studies; Data; Detection; Disease; Effectiveness; Elderly; Genes; Genetically Modified Animals; Hematopoietic; Human; Immune; Immune System Diseases; Immune system; Immunity; Immunology; Immunotherapy; Individual; Longitudinal Studies; Machine Learning; Malignant Neoplasms; Malignant neoplasm of urinary bladder; Measures; Metabolism; Modeling; Morphologic artifacts; Mus; Organ; Outcome; PD-1/PD-L1; Patients; Pharmaceutical Preparations; Phenotype; Preclinical Testing; Prediction of Response to Therapy; Proteins; Publishing; Regulatory T-Lymphocyte; Research; Risk; Risk Factors; Sampling; Signal Transduction; System; Systems Biology; T-Lymphocyte Subsets; Testing; Translating; Transplantation; Treatment Efficacy; Treatment Protocols; Treatment outcome; Tumor Immunity; Validation; Work; age effect; age related; aged; anti-PD-1; anti-PD-L1; base; cancer immunotherapy; cell dimension; cohort; comorbidity; dimensional analysis; disability; early phase clinical trial; high dimensionality; human disease; immune checkpoint; immune function; improved; individual patient; individualized medicine; innovation; insight; mathematical model; melanoma; metagenome; mouse model; nano-string; neoplasm immunotherapy; neoplastic cell; novel; optimal treatments; preclinical study; prediction algorithm; predictive modeling; programmed cell death ligand 1; responders and non-responders; response; tool; treatment optimization; treatment response; treatment strategy; tumor; tumor immunology; tumor microenvironment; ultra high resolution

This proposal combines a team with expertise in aging, tumor immunology, tumor immunotherapy, specific genetically modified animal models and early phase clinical trials with a computational team having great expertise in analyzing and modeling aging of the immune system. We will study age effects on PD-L1/PD-1 signaling in the host and the tumor focusing on melanoma with some bladder cancer work, two tumors that are highly responsive to αPD-1 and/or αPD-L1 as proofs-of-concept, and residing in distinct anatomic compartments. In Aim 1 we study tumor PD-L1 intrinsic effects on αPD-L1 and αPD-1 treatment in melanoma and bladder cancer using transplantable B16 and inducible Nras/Cdk2n melanoma models, and transplantable MB49 and BBN-induced tumors for bladder cancer studies. We also use novel melanoma and BC models with tumor cell- specific PD-L1KO. We study 3 cohorts of elderly versus younger humans getting αPD-L1 or αPD-1 for melanoma or bladder cancer for human validation. We measure high-dimensional cell phenotypes and signaling responses, proteins and genes to maximize the information collected from human samples and mice using 23-color FACS, CyTOF, Luminex, Nanostring and other approaches. In Aim 2 we use all the above models and analytic strategies in young and aged PD-L1KO mice and WT or bone marrow chimeras to test hematopoietic and non- hematopoietic (host) PD-L1 signals in treatment outcomes in melanoma and bladder cancer. In Aim 3 the Systems Immunology team will use their innovative and successful computational modeling to identify age- related co-predictors of immunotherapy response and to identify candidate mechanisms for responders and non- responders. We will define a trajectory of immune system aging in mice at ultra-high resolution by performing a systems level integrative analysis of aging in Collaborative Cross and BL6 mice tracked in a combined longitudinal and cross-sectional study. This trajectory will be used to understand how tumor response and treatment outcomes vary as a function of age, and to build a simple, low parameter (i.e., easily testable and clinically translated), predictive models of treatment response. We will test insights by analyzing immune data from aged versus young patients undergoing αPD-L1 and αPD-1 cancer immunotherapy in novel machine learning approaches that we pioneered to identify insights from mouse data that are relevant to humans. Coupling this disease information with the healthy human aging trajectory that we recently defined will allow us to adapt our mouse data to predict optimal treatments in humans based on chronological and immune aging. This combined trans-disciplinary approach will identify common age-related disabilities that reduce PD-L1/PD-1 based immunotherapy responses and suggest tailored treatments for optimal efficacy that could later be tested in validation sets. These data can also be applied to other types of immunotherapy as we will also test.

该建议将一个团队与衰老，肿瘤免疫学，肿瘤免疫疗法，特定方面的专业知识相结合 具有良好计算团队的转基因动物模型和早期临床试验 分析和建模免疫系统衰老方面的专业知识。我们将研究年龄对PD-L1/PD-1的影响 宿主中的信号传导和肿瘤以一些膀胱癌作用为重点是黑色素瘤，两种肿瘤是 对αPD-1和/或αPD-L1作为概念证明，并居住在不同的解剖区室中。 在AIM 1中，我们研究了黑色素瘤和膀胱肿瘤对αPD-L1和αPD-1处理的肿瘤PD-L1固有效应 使用可移植的B16和诱导NRA/CDK2N黑色素瘤模型以及可移植MB49和 BBN诱导的肿瘤用于膀胱癌研究。我们还使用具有肿瘤细胞的新型黑色素瘤和BC模型 特定的PD-L1KO。我们研究了3个老年人群与年轻的人，以获得黑色素瘤的αPD-L1或αPD-1 或用于人类验证的膀胱癌。我们测量高维细胞表型和信号反应， 蛋白质和基因使用23色FACS最大化从人类样品和小鼠收集的信息， Cytof，Luminex，纳米串和其他方法。在AIM 2中，我们使用以上所有模型和分析 年轻和老化的PD-L1KO小鼠以及WT或骨髓嵌合体的策略，以测试造血和非 - 黑色素瘤和膀胱癌治疗结果中的造血（宿主）PD-L1信号。在目标3中 系统免疫学团队将使用其创新和成功的计算建模来确定年龄 - 免疫疗法反应的相关联合提出者，并确定响应者和非候选机制 响应者。我们将通过执行A的超高分辨率来定义小鼠中免疫系统衰老的轨迹 在合并中跟踪的协作交叉和BL6小鼠中衰老的系统级集成分析 纵向和横断面研究。该轨迹将用于了解肿瘤反应和 治疗结果随着年龄的函数而变化，并构建一个简单的低参数（即易于测试和 临床翻译），治疗反应的预测模型。我们将通过分析免疫数据来测试见解 在新机器中接受αPD-L1和αPD-1癌症免疫疗法的老年人与年轻患者 我们开创的学习方法是从与人类相关的鼠标数据中识别出的见解。 将这些疾病信息与我们最近定义的健康人类衰老轨迹结合在一起将使我们 适应我们的小鼠数据以根据年代和免疫老化预测人类中的最佳治疗方法。 这种联合跨学科方法将确定与年龄相关的残疾，以减少PD-L1/PD-1 基于免疫疗法的反应，并建议量身定制的治疗方法以稍后进行测试 在验证集中。这些数据也可以应用于其他类型的免疫疗法，因为我们还将测试。