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

这项建议结合了一个团队，在老龄化，肿瘤免疫学，肿瘤免疫治疗，特别是 转基因动物模型和早期临床试验，由一个计算团队进行 具有分析和模拟免疫系统老化的专业知识。我们将研究年龄对PD-L1/PD-1的影响 宿主和肿瘤中的信号转导集中在黑色素瘤和一些膀胱癌工作上，这两种肿瘤是 对αPD-1和/或αPD-L1高度敏感，作为概念验证，并位于不同的解剖分区中。 在目标1中，我们研究了肿瘤PD-L1对αPD-L1和αPD-1治疗黑色素瘤和膀胱癌的内在影响 使用可移植的B16和可诱导的NRAS/CDK2n黑色素瘤模型和可移植的MB49和 BBN诱导的肿瘤用于膀胱癌研究。我们还使用了新的黑色素瘤和BC模型来处理肿瘤细胞- 特定的PD-L1KO。我们研究了3组接受αPD-L1或αPD-1治疗黑色素瘤的老年人和年轻人 或膀胱癌以供人类验证。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色流式细胞仪从人类样本和小鼠中收集的信息， CyTOF、Luminex、纳米线等方法。在目标2中，我们使用了所有上述模型和分析 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的策略 造血(宿主)PD-L1信号在黑色素瘤和膀胱癌治疗结果中的作用。在目标3中， 系统免疫学团队将使用他们创新和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素，并确定应答者和非应答者的候选机制 响应者。我们将在超高分辨率下定义小鼠免疫系统老化的轨迹 联合追踪的协作型杂交和BL6小鼠衰老的系统水平综合分析 纵向和横断面研究。这一轨迹将被用来理解肿瘤的反应和 治疗结果随年龄的变化而变化，并建立一个简单、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洞察力 在新型机器上接受αPD-L1和αPD-1癌症免疫治疗的老年患者和年轻患者的比较 我们首创的学习方法是从与人类相关的老鼠数据中识别洞察力。 将这种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 使我们的小鼠数据能够根据时间顺序和免疫老化预测人类的最佳治疗方案。 这种综合的跨学科方法将确定降低PD-L1/PD-1的常见年龄相关残疾 基于免疫治疗反应，并建议量身定做的治疗方案，以获得最优疗效，可在以后进行测试 在验证集中。这些数据也可以应用于其他类型的免疫疗法，因为我们也将进行测试。

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.