(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: 10475260
• 负责人: Tyler J. Curiel
• 金额: $ 60.15万
• 依托单位: DARTMOUTH-HITCHCOCK CLINIC
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10475260
• 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.

该提案结合了一支在衰老、肿瘤免疫学、肿瘤免疫治疗、特异性 转基因动物模型和早期临床试验，计算团队拥有出色的能力 分析和模拟免疫系统衰老的专业知识。我们将研究年龄对 PD-L1/PD-1 的影响 宿主和肿瘤中的信号传导主要针对黑色素瘤和膀胱癌，这两种肿瘤是 对 αPD-1 和/或 αPD-L1 作为概念验证高度敏感，并且位于不同的解剖隔室中。 在目标 1 中，我们研究肿瘤 PD-L1 对黑色素瘤和膀胱中 αPD-L1 和 αPD-1 治疗的内在影响 使用可移植 B16 和诱导型 Nras/Cdk2n 黑色素瘤模型以及可移植 MB49 和 用于膀胱癌研究的 BBN 诱导肿瘤。我们还使用带有肿瘤细胞的新型黑色素瘤和 BC 模型 特异性 PD-L1KO。我们研究了 3 组老年人与年轻人接受 αPD-L1 或 αPD-1 治疗黑色素瘤的情况 或膀胱癌用于人体验证。我们测量高维细胞表型和信号反应， 蛋白质和基因，以最大限度地利用 23 色 FACS 从人类样本和小鼠中收集的信息， CyTOF、Luminex、Nanostring 等方法。在目标 2 中，我们使用上述所有模型和分析 在年轻和老年 PD-L1KO 小鼠和 WT 或骨髓嵌合体中测试造血和非造血功能的策略 造血（宿主）PD-L1 信号对黑色素瘤和膀胱癌治疗结果的影响。在目标 3 中 系统免疫学团队将利用他们创新且成功的计算模型来识别年龄 免疫治疗反应的相关共同预测因子，并确定反应者和非反应者的候选机制 响应者。我们将通过执行超高分辨率定义小鼠免疫系统衰老的轨迹 Collaborative Cross 和 BL6 小鼠衰老的系统级综合分析 纵向和横断面研究。该轨迹将用于了解肿瘤反应和 治疗结果随年龄而变化，并建立一个简单的低参数（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 来自在新型机器中接受 αPD-L1 和 αPD-1 癌症免疫治疗的老年患者与年轻患者的比较 我们开创的学习方法是为了从与人类相关的小鼠数据中识别见解。 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起来将使我们能够 调整我们的小鼠数据，根据时间和免疫衰老来预测人类的最佳治疗方法。 这种综合跨学科方法将识别常见的与年龄相关的残疾，从而减少 PD-L1/PD-1 基于免疫治疗反应并建议量身定制的治疗方案以达到最佳疗效，并在以后进行测试 在验证集中。这些数据也可以应用于其他类型的免疫疗法，我们也将进行测试。