Immunophenotype Integration for Monitoring T Cell Dynamics in Pancreatic Cancers

项目摘要 我们提案的首要目标是加速发现生物标志物，以识别罕见的 胰腺导管腺癌（PDAC）患者通过采用一种新的高- 吞吐量免疫分析流水线。大多数PDAC患者存在转移性疾病， 治疗选择有限，5年生存率只有8%。尽管最近取得了显著进展， 在许多癌症类型的免疫治疗中，迄今为止的研究未能在这方面产生实质性的益处。 典型的免疫限制性疾病。我们的研究小组长期以来一直是免疫策略的先驱 针对PDAC的免疫应答，表明刺激针对PDAC的免疫应答实际上是可能的。值得注意的是， 观察到发生异常临床反应的情况。一个关键的挑战是发现 识别这些罕见反应者的生物标志物必须进行高参数表征， 尽管缺乏具有成本效益的高通量方法，但在大多数阴性试验中，免疫应答仍然存在。我们 拟议的研究特别适合应对这一挑战，原因如下。首先，我们的团队 建立了一个无与伦比的PDAC患者的生物标本资源，这些患者经历了各种 - 免疫疗法，包括PDAC特异性疫苗、检查点抑制剂（抗PD-1、抗CTLA-4）， 表观遗传修饰剂（恩替司他）和IDO 1抑制剂（epacadostat）。第二，我们的工作已经导致了 发现PDAC患者免疫治疗应答的关键决定因素：特定的骨髓细胞类型和 生殖系遗传学，例如错配修复缺陷。第三，我们最近开发了高参数（30+标记） 使用质谱细胞术（CyTOF）的T和髓样细胞类型的免疫谱板。我们的CyTOF工作流程 涉及样品的多路复用，显著降低了分析过程中的成本负担和与批次相关的偏差。 第四，我们最近开发了一种新的计算管道，将基于CyTOF的高- 参数T细胞谱转化为反映给定样品中T细胞状态的简化的基于伪时间的度量。 该方法克服了分析瓶颈，避免了迭代，详细的细胞注释的需要 类型，并促进与其他免疫学参数和不同临床试验的比较。 我们的进展现在提示我们的中心假设，这种基于CyTOF的管道将增强对 T细胞对（i）不同免疫疗法，（ii）临床结果和（iii）其他免疫调节因子的应答。 因此，使用我们的生物样本库代表PDAC患者的七项早期免疫治疗临床试验，我们将 建立我们的管道在确定和比较每种免疫疗法特异性T细胞动力学方面的效用 方案以及它们与临床结果的相关性。使用基于CyTOF的骨髓细胞谱和已经 从PDAC患者中获得的生殖系变异数据集，我们还将确定其他 免疫调节因子对T细胞应答的影响。这项工作也将为生物标志物的发现提供新的工具 提出了一个广泛的PDAC患者的免疫概况数据库，这将使未来的调查。

PROJECT SUMMARY The overarching goal of our proposal is to accelerate the discovery of biomarkers that identify the rare group of pancreatic ductal adenocarcinoma (PDAC) patients who respond to immunotherapy by employing a novel high- throughput immune analysis pipeline. The majority of PDAC patients present with metastatic disease, and treatment options are limited, yielding a dismal 5-year survival of 8%. Despite the recent remarkable progress in immunotherapy in many cancer types, studies have failed thus far to yield substantial benefit in this stereotypically immune-restricted disease. Our research group has long pioneered immunotherapeutic strategies against PDAC, demonstrating that inciting immune responses against PDAC is in fact possible. Notably, we have observed instances in which exceptional clinical responses take place. A critical challenge in discovering biomarkers that identify these rare responders is having to undertake high-parameter characterization of the immune responses in largely negative trials despite the lack of cost-effective high-throughput methods. Our proposed study is uniquely suited to address this challenge for the following reasons. First, our team has established an unparalleled resource of biospecimens from PDAC patients who have undergone a variety of immunotherapeutic modalities including PDAC-specific vaccines, checkpoint inhibitors (anti-PD-1, anti-CTLA-4), an epigenetic modifier (entinostat), and an IDO1 inhibitor (epacadostat). Second, our work has already led to the discovery of key determinants of immunotherapy responses in PDAC patients: specific myeloid cell types and germline genetics, e.g. mismatch repair defects. Third, we have recently developed high-parameter (30+ marker) immune profiling panels for T and myeloid cell types using mass cytometry (CyTOF). Our CyTOF workflow involves multiplexing of samples, significantly reducing the cost burden and batch-related biases during analysis. Fourth, we have recently developed a novel computational pipeline to integrate the CyTOF-based high- parameter T cell profiles into simplified pseudotime-based metrics that reflect the T cell states in a given sample. This method overcomes the analytic bottleneck by obviating the need for iterative, detailed annotation of cell types, and also by facilitating comparisons with other immunologic parameters and across disparate clinical trials. Our progress now prompts our central hypothesis that this CyTOF-based pipeline will enhance the understanding of T cell responses to (i) distinct immunotherapies, (ii) clinical outcomes, and (iii) other immunomodulatory factors. Thus, using our biobank representing seven early-phase immunotherapy clinical trials in PDAC patients, we will establish the utility of our pipeline in determining and comparing T cell dynamics specific to each immunotherapy regimen and how they correlate with clinical outcomes. Using CyTOF-based myeloid cell profiles and an already available germline variant dataset from PDAC patients, we will also determine the effects of other immunomodulatory factors on T cell responses. This work will provide a new tool for biomarker discovery as well as bring forth an extensive immune profile database of PDAC patients that will empower future inquiries.