Immunophenotype Integration for Monitoring T Cell Dynamics in Pancreatic Cancers

用于监测胰腺癌 T 细胞动态的免疫表型整合

基本信息

批准号：
10300626
负责人：
Won Jin Ho
金额：
$ 42.1万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10300626
关键词：
Address Adopted Antigens Biological Markers CD80 gene Cancer Etiology Cells Cessation of life Clinical Clinical Trials Computer software Cytometry DNA Repair Data Data Set Databases Defect Development Disease Epigenetic Process Future GVAX Cancer Vaccine Genetic Genetic Polymorphism Goals HLA-DR Antigens Immune Immune checkpoint inhibitor Immune response Immune system Immunologics Immunophenotyping Immunotherapeutic agent Immunotherapy Malignant neoplasm of pancreas Marker Discovery Measurement Mediating Methods Mismatch Repair Modality Monitor Myelogenous Myeloid Cells Outcome Pancreatic Ductal Adenocarcinoma Patients Phase Phase I/II Clinical Trial Phenotype Population Process Proteins Regimen Research Resources Sampling Systemic Therapy T cell response T-Lymphocyte Testing Tumor Immunity United States Vaccines Variant Work analysis pipeline anti-CTLA4 anti-PD-1 anti-cancer anticancer research base biobank biomarker development biomarker discovery cancer immunotherapy cancer type cell type checkpoint therapy chemokine receptor computational pipelines cost cost effective cytotoxic design empowered experience immunoregulation immunotherapy clinical trials improved inhibitor/antagonist multidimensional data novel novel marker patient subsets peripheral blood predict clinical outcome predictive marker prevent programmed cell death ligand 1 response single cell analysis single-cell RNA sequencing success tool treatment strategy

项目摘要

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.

项目概要我们提案的总体目标是加速发现识别罕见群体的生物标志物胰腺导管腺癌 (PDAC) 患者通过采用新型高吞吐量免疫分析管道。大多数 PDAC 患者存在转移性疾病，并且治疗选择有限，5 年生存率仅为 8%。尽管最近在这方面取得了显着进展免疫疗法可用于多种癌症类型，但迄今为止的研究未能在这方面产生实质性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治疗策略的先驱对抗 PDAC，证明激发针对 PDAC 的免疫反应实际上是可能的。值得注意的是，我们有观察到发生异常临床反应的实例。发现的关键挑战识别这些罕见反应者的生物标志物必须对这些罕见反应者进行高参数表征尽管缺乏具有成本效益的高通量方法，但免疫反应在很大程度上是负面的试验。我们的由于以下原因，拟议的研究特别适合应对这一挑战。首先，我们团队有从经历过各种治疗的 PDAC 患者中建立了无与伦比的生物样本资源免疫治疗方式，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（entinostat）和 IDO1 抑制剂（epacadostat）。其次，我们的工作已经导致发现 PDAC 患者免疫治疗反应的关键决定因素：特定的骨髓细胞类型和种系遗传学，例如错配修复缺陷。三、我们最近开发了高参数（30+marker）使用质谱流式技术 (CyTOF) 对 T 细胞和骨髓细胞类型进行免疫分析。我们的 CyTOF 工作流程涉及样品的多重分析，显着降低分析过程中的成本负担和批次相关偏差。第四，我们最近开发了一种新颖的计算管道来集成基于 CyTOF 的高将 T 细胞概况参数转化为简化的基于伪时间的指标，反映给定样本中的 T 细胞状态。该方法消除了对细胞进行迭代、详细注释的需要，从而克服了分析瓶颈类型，并通过促进与其他免疫学参数和不同临床试验的比较。我们的进展现在提出了我们的中心假设，即这种基于 CyTOF 的管道将增强理解 T 细胞对 (i) 不同免疫疗法、(ii) 临床结果和 (iii) 其他免疫调节因素的反应。因此，利用我们代表 PDAC 患者的七项早期免疫治疗临床试验的生物库，我们将确定我们的管道在确定和比较每种免疫疗法特有的 T 细胞动力学方面的效用治疗方案以及它们与临床结果的关系。使用基于 CyTOF 的骨髓细胞谱和已经可用 PDAC 患者的种系变异数据集，我们还将确定其他因素的影响 T 细胞反应的免疫调节因子。这项工作也将为生物标志物发现提供新工具我们将建立一个广泛的 PDAC 患者免疫特征数据库，为未来的查询提供支持。