Determining Dendritic Cell Responses to Vaccine-based Immunotherapy in PDAC at Single Cell Resolution.

以单细胞分辨率确定 PDAC 中树突状细胞对基于疫苗的免疫疗法的反应。

• 批准号: 10386271
• 负责人: Dimitrios N Sidiropoulos
• 金额: $ 4.68万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-05 至 2025-06-04
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10386271
• 关键词: Adaptive Immune System; Address; Adjuvant; Allogenic; Atlases; B-Lymphocytes; Biological; CD8-Positive T-Lymphocytes; Cancer Etiology; Cell Lineage; Cell physiology; Cells; Cessation of life; Clinical; Clinical Trials; Computer software; Computing Methodologies; Cytometry; Data; Data Scientist; Data Set; Dendritic Cells; Future; GVAX Cancer Vaccine; Gene Expression; Gene Expression Profile; Goals; Granulocyte-Macrophage Colony-Stimulating Factor; Human; Hybrids; Image; Immune; Immune checkpoint inhibitor; Immune response; Immunologic Surveillance; Immunologics; Immunologist; Immunotherapeutic agent; Immunotherapy; Incidence; Interdisciplinary Study; Learning; Link; Lymphoid; Lymphoid Cell; Malignant Neoplasms; Malignant neoplasm of pancreas; Mediating; Methods; Modality; Modeling; Monitor; Myelogenous; Nature; Pancreatic Ductal Adenocarcinoma; Patients; Pattern; Peripheral; Peripheral Blood Mononuclear Cell; Phenotype; Physiological; Play; Population; Process; Proteins; Proteomics; RNA; Regimen; Regulation; Regulatory T-Lymphocyte; Research; Resistance; Resolution; Role; Sampling; Surveys; T cell response; T-Lymphocyte; Technology; Training; Tumor Antigens; Tumor Immunity; Tumor-infiltrating immune cells; United States; Vaccination; Vaccine Clinical Trial; Vaccine Therapy; Vaccines; Work; adaptive immune response; analysis pipeline; base; career; cell type; checkpoint inhibition; clinical efficacy; computational pipelines; effector T cell; exhaustion; high dimensionality; immunogenic; immunoregulation; immunotherapy clinical trials; indexing; multidisciplinary; novel; open source; pancreatic cancer patients; pancreatic neoplasm; patient response; peripheral blood; personalized immunotherapy; prevent; prospective; protein expression; response; single-cell RNA sequencing; skills; spatiotemporal; targeted treatment; training opportunity; transcriptomics; tumor; tumor immunology; tumor microenvironment; tumor-immune system interactions; vaccine immunotherapy

Project Summary Pancreatic ductal adenocarcinoma (PDAC), a traditionally non-immunogenic tumor type, has shown limited beneficial response to immunotherapy. We were the first to demonstrate that it is possible to convert PDAC to an immunogenic state following GM-CSF-secreting allogeneic vaccine (GVAX) treatment which promotes Dendritic Cell (DC) precursor expansion and formation of tertiary lymphoid aggregates (TLAs). However, immune regulatory mechanisms are preventing any significant clinical benefit. DC paucity gives rise to dysfunctional immune surveillance and can establish an immunosuppressive TME in PDAC that prevents lymphoid cell activation and immune invasion. With recently emerged single cell and spatial omics, we now have the ability to study cancer immunology at unprecedented scale and resolution. We propose to generate single cell and spatial transcriptomic and proteomic data to study systemic responses and local immunological activities in vaccine primed PDAC. Specifically, we hypothesize that DC state transitions and interactions with other TME cell types can delineate immunologic responses to immunotherapies in vaccine primed PDAC. To address this hypothesis, we propose two specific aims. We will first determine the distinct immunologic effects of vaccine and immune checkpoint inhibition combination regimens on peripheral DC state transitions in PDAC patients (Aim 1). To accomplish this we will develop a novel single cell proteomic trajectory analysis pipeline that computes cell phenotypes using continuous variables. This will allow us to study phenotypic transitions using unsupervised approaches that are less discernible in discrete cell type analyses (technological sub-aim). We will then apply our pipeline on DCs by implementing mass cytometry to capture DC state transitions in peripheral blood mononuclear cells (PBMCs) from vaccine clinical trials by assessing baseline and on-treatment samples (biological sub-aim). To delineate spatial factors influencing the immune dynamics of TLA formation after vaccine priming, we will evaluate the role of DCs in the formation and regulation of lymphoid aggregates in PDAC (Aim 2). To achieve this, we will spatially resolve TLAs in vaccine (GVAX) primed human PDAC tumors at the RNA and protein levels using Visium spatial transcriptomics and imaging mass cytometry (IMC). We will employ matrix factorization methods to learn gene and protein expression patterns in both of the spatial data modalities to discern gene expression patterns unique to TLAs and evaluate their expression of DC markers. By understanding DC state transitions directly within the TME, the findings from this Aim will synergize with Aim 1. Completion of these aims will deliver potential new immunotherapy strategies in PDAC patients, as well as develop novel open-source software for mass cytometry analysis. The skills I obtain from this work will prepare me to pursue a career as a cross-trained cancer immunologist and computational biologist, delineating immune responses to empower precision immunotherapy.

项目摘要 胰腺导管腺癌（PDAC）是一种传统的非免疫原性肿瘤类型， 对免疫疗法的有益反应。我们是第一个证明可以将PDAC转换为 分泌GM-CSF的同种异体疫苗（GVAX）治疗后的免疫原性状态， 树突状细胞（DC）前体扩增和三级淋巴聚集体（TLAs）的形成。然而，免疫 调节机制阻止了任何显著的临床益处。DC缺乏导致功能失调 并可在PDAC中建立免疫抑制性TME， 激活和免疫入侵。随着最近出现的单细胞和空间组学，我们现在有能力 以前所未有的规模和分辨率研究癌症免疫学。我们建议生成单细胞和空间 研究疫苗中全身反应和局部免疫活性的转录组学和蛋白质组学数据 准备好了PDAC具体来说，我们假设DC状态转换和与其他TME细胞类型的相互作用 可以描述疫苗引发的PDAC中对免疫疗法的免疫应答。为了解决这个假设， 我们提出两个具体目标。我们将首先确定疫苗和免疫的不同免疫作用， 检查点抑制联合方案对PDAC患者外周DC状态转换的影响（目的1）。到 为了实现这一点，我们将开发一种新的单细胞蛋白质组轨迹分析管道， 使用连续变量的表型。这将使我们能够研究表型转换使用无监督 在离散细胞类型分析中不易辨别的方法（技术子目标）。然后我们将申请 我们通过实施质谱细胞术来捕获外周血中的DC状态转换， 通过评估基线和治疗期间样本， （生物学目标）。描述影响疫苗接种后TLA形成的免疫动力学的空间因素 我们将评估DC在PDAC中淋巴聚集体的形成和调节中的作用（目的 2）。为了实现这一点，我们将在RNA水平上对疫苗（GVAX）致敏的人PDAC肿瘤中的TLA进行空间解析。 和蛋白质水平。我们将使用矩阵 因子分解方法来学习基因和蛋白质表达模式的空间数据模式， 辨别TLAs特有的基因表达模式，并评估其DC标志物的表达。通过 直接在TME内理解DC状态转换，来自该目标的发现将与目标1协同作用。 这些目标的实现将为PDAC患者提供潜在的新免疫治疗策略， 开发用于质谱分析的新型开源软件。我从这项工作中获得的技能将为 我追求的职业生涯作为一个交叉训练的癌症免疫学家和计算生物学家，描绘免疫 为精准免疫治疗提供了可能。