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

该提案结合了一个在衰老，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与计算团队有很大的 在分析和模拟免疫系统老化方面的专业知识。我们将研究年龄对PD-L1/PD-1的影响 信号在宿主和肿瘤集中在黑色素瘤与一些膀胱癌的工作，两个肿瘤， 作为概念验证，对αPD-1和/或αPD-L1高度反应，并存在于不同的解剖隔室中。 在目的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的内在影响 使用可移植的B16和可诱导的Nras/Cdk 2n黑色素瘤模型的癌症，以及可移植的MB 49和 用于膀胱癌研究的BBN诱导的肿瘤。我们还使用新的黑色素瘤和BC模型，肿瘤细胞- 特异性PD-L1 KO。我们研究了3个队列，老年人与年轻人接受αPD-L1或αPD-1治疗黑色素瘤 或者膀胱癌来进行人体验证。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色流式细胞仪从人类样本和小鼠收集的信息， CyTOF、Luminex、Nanostring和其他方法。在目标2中，我们使用所有上述模型和分析 在年轻和老年PD-L1 KO小鼠和WT或骨髓嵌合体中测试造血和非造血干细胞的策略 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Aim 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子，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衰老的轨迹， 系统水平的综合分析协作交叉和BL 6小鼠的衰老， 纵向和横向研究。这一轨迹将用于了解肿瘤反应如何， 治疗结果作为年龄的函数而变化，并且为了建立简单的低参数（即，易于测试， 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 在新型机器中接受αPD-L1和αPD-1癌症免疫治疗的老年患者与年轻患者 我们开创了一种学习方法，用于从小鼠数据中识别与人类相关的见解。 将这些疾病信息与我们最近定义的健康人类衰老轨迹相结合， 调整我们的小鼠数据，根据时间和免疫老化来预测人类的最佳治疗方法。 这种综合的跨学科方法将确定减少PD-L1/PD-1的常见年龄相关残疾 基于免疫治疗反应，并建议量身定制的治疗方法，以获得最佳疗效， 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。

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.