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。

基本信息

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
项目类别：
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

项目摘要

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.

项目概要胰腺导管腺癌（PDAC）是一种传统的非免疫原性肿瘤类型，目前已显示出有限的对免疫治疗的有益反应。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，可促进树突状细胞 (DC) 前体扩张和三级淋巴聚集体 (TLA) 的形成。然而，免疫监管机制正在阻碍任何重大的临床益处。 DC 缺乏会导致功能失调免疫监视，并可以在 PDAC 中建立免疫抑制 TME，防止淋巴细胞激活和免疫侵袭。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用于研究疫苗中的系统反应和局部免疫活性的转录组和蛋白质组数据已涂底漆的 PDAC。具体来说，我们假设 DC 状态转换以及与其他 TME 细胞类型的相互作用可以描绘疫苗引发的 PDAC 中对免疫疗法的免疫反应。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学作用检查点抑制联合方案对 PDAC 患者外周 DC 状态转变的影响（目标 1）。到为了实现这一目标，我们将开发一种新颖的单细胞蛋白质组轨迹分析管道，用于计算细胞使用连续变量的表型。这将使我们能够使用无监督的方法研究表型转变在离散细胞类型分析中不太明显的方法（技术子目标）。然后我们将申请我们的 DC 管道通过实施质谱流式分析来捕获外周血中 DC 状态的转变通过评估基线和治疗样本，从疫苗临床试验中获得单核细胞 (PBMC) （生物子目标）。描述影响疫苗接种后 TLA 形成免疫动态的空间因素启动后，我们将评估 DC 在 PDAC 中淋巴聚集体的形成和调节中的作用（目标 2）。为了实现这一目标，我们将在 RNA 处空间解析疫苗 (GVAX) 引发的人类 PDAC 肿瘤中的 TLA 使用 Visium 空间转录组学和成像质量细胞术 (IMC) 检测蛋白质水平。我们将采用矩阵分解方法来学习两种空间数据模式中的基因和蛋白质表达模式辨别 TLA 特有的基因表达模式并评估其 DC 标记的表达。经过直接在 TME 内理解 DC 状态转换，该目标的发现将与目标 1 产生协同作用。这些目标的完成将为 PDAC 患者提供潜在的新免疫治疗策略，以及开发用于质谱流式分析的新型开源软件。我从这项工作中获得的技能将准备我的职业生涯是作为一名经过交叉训练的癌症免疫学家和计算生物学家，描绘免疫增强精准免疫治疗的反应。