Single Cell Deconvolution of the Pancreatic Tumor Microenvironment

项目概要/摘要： 胰腺导管腺癌（PDAC）保持其作为最致命的实体癌之一的地位， 5年生存率为8%。微小的改善归功于早期发现，但绝大多数 如果没有有效的治疗干预，患者面临严峻的预后。患者样本的分子分析 经常被混合的生物样品混淆，导致重现性的挑战。此前，我们 实验室对大量RNA-seq患者样本进行虚拟显微切割，建立稳健的预后基因 描述侵袭性基底样和药物反应性经典肿瘤亚型的特征，突出了 患者之间癌症异质性的重要性。使用这些签名作为患者分类器已经是一种 在患者来源的类器官的初步临床试验和治疗分析中的重要用途。 建立证据表明患者独特的TME组成影响PDAC进展和抵抗 标准治疗。虽然使用批量测量的患者组织表征提供了关键的见解， 分析复杂的肿瘤微环境需要更高的分辨率，因为 广泛的间质受累和稀疏的肿瘤群。单细胞测序提供了 分析能力，以帮助确定患者之间的可变TME元素，导致不同的预后， 治疗反应。因此，了解TME异质性在PDAC中的程度和作用， 肿瘤亚型对于拓宽肿瘤学中的个体化医学的大门至关重要。 在这个建议中，我将建立一个全面的人类PDAC TME单细胞图谱， 降低研究人员和复杂的单细胞转录组学数据之间的障碍，以探索新的预后 和协同治疗靶点。我将使用PDAC组织的本地和公共单细胞RNA-seq数据， 研究患者肿瘤亚型中细胞异质性的程度和作用。具体来说，我将定义 分子标记，并绘制出间质、淋巴细胞、髓系内功能细胞类型的相互作用组 前所未有的空间分辨率。最终，通过整合来自单细胞的高维数据， RNA-seq和空间转录组学，这项工作将阐明复杂的组织病理学，同时奠定了 一个广泛的框架，用于理解多种疾病进展和耐药性背后的多轴细胞相互作用， 癌症类型。

Project Summary/Abstract: Pancreatic ductal adenocarcinoma (PDAC) maintains its status as one of most lethal solid cancers with a 5-year survival of 8%. Minor improvements have been attributed to early detection, but the vast majority of patients face a grim prognosis without effective therapeutic intervention. Molecular analysis of patient samples has often been confounded by mixed biological samples, leading to reproducibility challenges. Previously, our lab performed virtual microdissection on bulk RNA-seq patient samples establishing robust prognostic gene signatures describing an aggressive basal-like and drug-responsive classical tumor subtypes highlighting the importance of cancer heterogeneity across patients. Using these signatures as patient classifiers has been an important utility in preliminary clinical trials and therapeutic profiling of patient derived organoids. Building evidence suggests patient unique TME composition impacts PDAC progression and resistance to standard treatments. While patient tissue characterization with bulk measurements has provided key insights into cancer biology, parsing the complex tumor microenvironments requires higher resolution due to the widespread stromal involvement and sparse neoplastic populations. Single-cell sequencing delivers the analytical power to help identify variable TME elements between patients that lead to the distinct prognostic and therapeutic responses. Thus, understanding the extent and role of TME heterogeneity in PDAC across patient tumor subtypes is paramount to widen the door for personalized medicine in oncology. In this proposal, I will establish a comprehensive single-cell atlas of human PDAC TME to significantly lower the barrier between researchers and complex single-cell transcriptomics data to explore novel prognostic and synergistic therapeutic targets. I will use local and public single cell RNA-seq data of PDAC tissue to investigate the extent and role of cellular heterogeneity across patient tumor subtypes. Specifically, I will define molecular signatures and map out the interactome of functional cell types within stromal, lymphocytic, myeloid populations at unprecedented spatial resolution. Ultimately, by integrating high-dimensional data from single-cell RNA-seq and Spatial Transcriptomics, this work will shed light on the intricate tissue pathology while laying down a broad framework for understanding multi-axis cell interactions behind progression and resistance in diverse cancer types.