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

项目摘要 胰腺导管腺癌(Pdac)是一种传统上非免疫原性的肿瘤类型，已显示出局限性。 对免疫治疗的有益反应。我们是第一个证明将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)前体的扩增和三级淋巴聚集体(Tlas)的形成。然而，免疫 监管机制正在阻止任何显著的临床益处。DC匮乏导致功能障碍 免疫监测，并可在PDAC中建立免疫抑制TME，以防止淋巴样细胞 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。我们建议生成单个单元和空间 转录组和蛋白质组学数据用于研究疫苗的系统反应和局部免疫活性 预装PDAC。具体地说，我们假设DC状态转换以及与其他TME细胞类型的相互作用 可以在疫苗预置的PDAC中描述对免疫治疗的免疫应答。为了解决这一假设， 我们提出了两个具体目标。我们将首先确定疫苗和免疫的不同免疫效果。 检查点抑制联合方案对PDAC患者外周血树突状细胞状态转变的影响(目标1)。至 为此，我们将开发一种新型的计算细胞的单细胞蛋白质组轨迹分析流水线 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换 在离散细胞类型分析中不易辨别的方法(技术分目标)。然后我们会申请 我们通过实施质量细胞术来捕获外周血中DC状态转变的DC流水线 通过评估基线和治疗中的样本来自疫苗临床试验的单个核细胞(PBMC) (生物子目标)。探讨影响疫苗免疫后TLA形成的空间因素 我们将评估DC在PDAC淋巴聚集的形成和调节中的作用(AIM 2)。为了实现这一点，我们将在RNA上空间解析疫苗(GVAX)激活的人PDAC肿瘤中的Tlas 和蛋白质水平使用维西姆空间转录组和成像质量细胞术(IMC)。我们将采用矩阵 在两种空间数据模式中学习基因和蛋白质表达模式的因式分解方法 识别Tlas特有的基因表达模式，并评估其DC标记的表达。通过 了解直接在TME中的DC状态转换，来自这一目标的发现将与AIM 1协同。 这些目标的完成将为PDAC患者提供潜在的新的免疫治疗策略，以及 开发新的开放源码的质量细胞仪分析软件。我从这份工作中学到的技能将为 我要追求的职业是交叉培训的癌症免疫学家和计算生物学家，描绘免疫 对精准免疫治疗的反应。

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.