Single Cell Deconvolution of the Pancreatic Tumor Microenvironment

项目摘要/摘要： 胰腺导管腺癌(PDAC)是最致命的实体癌之一，与 5年存活率为8%。细微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，患者将面临严峻的预后。患者样本的分子分析 经常被混合的生物样本混淆，导致重复性方面的挑战。此前，我们的 实验室对大量RNA-seq患者样本进行了虚拟显微解剖，建立了强大的预后基因 描述侵袭性基底细胞样和药物敏感的经典肿瘤亚型的特征突出 癌症患者异质性的重要性。使用这些签名作为患者分类器是一种 在患者衍生的有机化合物的初步临床试验和治疗概况中的重要用途。 越来越多的证据表明，患者独特的TME成分影响PDAC的进展和耐药性 到标准治疗。虽然患者的组织特征通过整体测量提供了关键的见解 对于癌症生物学，解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体。单细胞测序提供了 分析能力，以帮助确定患者之间的变量TME元素，导致不同的预后和 治疗反应。因此，跨患者了解TME异质性在PDAC中的程度和作用 肿瘤亚型对于打开肿瘤学个性化药物的大门是至关重要的。 在这项建议中，我将建立一个全面的人类PDAC TME单细胞图谱，以显著 降低研究人员和复杂单细胞转录组数据之间的障碍，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数据来 研究不同肿瘤亚型患者细胞异质性的程度和作用。具体地说，我将定义 分子特征，并绘制出间质、淋巴细胞、髓系中功能细胞类型的相互作用组 人口以前所未有的空间分辨率。最终，通过集成来自单个单元的高维数据 RNA-SEQ和空间转录组学，这项工作将揭示复杂的组织病理 一个广泛的框架，用于理解不同疾病进展和耐药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.