Assessing the Transcriptional and Signaling Basis of Heterogeneity in the Epithelial-Mesenchymal Transition in Pancreatic Ductal Adenocarcinoma

评估胰腺导管腺癌上皮-间质转化异质性的转录和信号传导基础

• 批准号: 10751834
• 负责人: Michelle C Barbeau
• 金额: $ 3.91万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10751834
• 关键词: Agonist; Antibodies; Bar Codes; Biochemical; Candidate Disease Gene; Carcinoma; Cell Line; Cell Lineage; Cells; Characteristics; Chemoresistance; Combined Modality Therapy; Computer Models; Computing Methodologies; Data; Data Science; Data Set; Development; Developmental Process; Disease Progression; Epithelium; Exhibits; Gene Expression Profile; Genetic; Genetic Transcription; Growth Factor; Heterogeneity; Human; Hypoxia; Image; Immunofluorescence Immunologic; Indirect Immunofluorescence; Individual; Infection; Information Theory; Lead; Libraries; Maintenance; Malignant Neoplasms; Measures; Mediating; Mesenchymal; Methods; Modeling; Molecular; Neoplasm Metastasis; Oxygen; Pancreatic Ductal Adenocarcinoma; Pathway interactions; Phenotype; Phosphoproteins; Phosphotransferases; Population; Process; Protocols documentation; Regulation; Resistance; Signal Pathway; Signal Transduction; Small Interfering RNA; Sorting; Systems Biology; Testing; Transcript; Treatment Factor; Uncertainty; Variant; Work; cell population study; cell transformation; chemotherapy; cytokine; design; differential expression; epithelial to mesenchymal transition; experimental study; fluorescence imaging; human imaging; information model; insight; interest; knock-down; novel; overexpression; pancreatic ductal adenocarcinoma cell; predictive modeling; prevent; programs; response; single cell analysis; single-cell RNA sequencing; small molecule inhibitor; transcription factor; transcriptome sequencing; transcriptomic profiling; tumor; wound healing

PROJECT SUMMARY The epithelial-mesenchymal transition (EMT) is a developmental process that is aberrantly reactivated in pancreatic ductal adenocarcinoma (PDAC) to promote disease progression and chemoresistance. PDAC tumors and cell lines typically contain a heterogeneous mixture of transformed cells displaying epithelial or mesenchymal characteristics, complicating efforts to understand the regulatory mechanisms that govern this important phenotypic switching. The observation that EMT can be initiated by a variety of different growth factors, low oxygen tension, and matrix-mediated signaling strongly suggests that multiple signaling pathways cooperate to drive robust EMT and raises the possibility that EMT heterogeneity is explained by the ability of only some cells to activate robustly the pathways that cooperate to drive EMT. Another potential, but not necessarily mutually exclusive, explanation for phenotypic heterogeneity is that some PDAC cells are primed to undergo EMT due to transcriptional differences that enable utilization of specific transcription factors or signaling pathways. Based on our preliminary data, we hypothesize that certain PDAC cells are transcriptionally primed to undergo EMT and that EMT heterogeneity further depends upon cell-to-cell variations in kinase-regulated signaling processes within cell populations. The objective of the work proposed here is to test these hypotheses through the development of quantitative systems biology methods to study the basis of EMT heterogeneity regulation via transcriptional and kinase-mediated signaling processes. In Aim 1, an iterative immunofluorescence imaging pipeline will be developed to gather multiplexed signaling data on populations of PDAC cells treated with different EMT agonists. Based on preliminary studies, we propose to measure markers for seven distinct signaling pathway nodes and two EMT markers to create a dataset with nine features measured for thousands of cells for each experimental condition. We will then apply a mutual information data science approach for the quantitative identification of the signaling pathways that cooperate to drive robust EMT. Model predictions will be tested using small molecule inhibitors and siRNA-mediated knockdowns. In Aim 2, we will use genetic barcoding for the transcriptomic profiling of EMT-resistant or -compliant lineages within cell populations. Single-cell RNA sequencing data from cells before and after EMT induction will be analyzed to identify transcriptional states that preferentially enable PDAC cells to undergo the mesenchymal transition. The relevance of candidate transcripts for explaining EMT priming will be tested through knockdown experiments. The methods developed in this work will be broadly applicable to the study of EMT in other cancer settings and to the study of alternative types of phenotypic switching. Moreover, the specific results will have implications for the design of novel combination therapies for PDAC based on the objective of suppressing EMT to promote responsiveness to chemotherapy.

项目摘要 上皮 - 间质转变（EMT）是一个发育过程，在 胰腺导管腺癌（PDAC）以促进疾病进展和化学抗性。 PDAC肿瘤 细胞系通常包含显示上皮或间质的转化细胞的异质混合物 特征，使理解管理这一重要的监管机制的努力变得复杂 表型切换。 EMT可以通过多种不同的生长因子启动的观察结果，低 氧张力和基质介导的信号强烈表明多个信号通路合作 驱动强大的EMT，并提高了EMT异质性的可能性，仅通过某些细胞的能力来解释 为了稳健地激活合作驱动EMT的路径。另一个潜力，但不一定是相互的 独家的，对表型异质性的解释是，由于某些PDAC细胞被启用以进行EMT 转录差异能够利用特定的转录因子或信号通路。基于 我们的初步数据，我们假设某些PDAC细胞已转录启动以进行EMT EMT异质性进一步取决于激酶调节的细胞间变化 细胞种群中的过程。这里提出的工作的目的是通过 定量系统生物学方法的开发，以研究EMT异质性调节的基础 转录和激酶介导的信号传导过程。在AIM 1中，迭代免疫荧光成像 将开发管道以收集有关用不同处理的PDAC细胞种群的多路复用信号数据数据 EMT激动剂。根据初步研究，我们建议测量七个不同信号的标记 途径节点和两个EMT标记，以创建一个数据集，该数据集具有九个针对数千个单元格的特征 每个实验条件。然后，我们将应用一种相互信息数据科学方法进行定量 识别合作驱动强大EMT的信号通路。模型预测将使用 小分子抑制剂和siRNA介导的敲低。在AIM 2中，我们将使用遗传条形码 细胞群体内EMT耐EMT或相结合谱系的转录组分析。单细胞RNA 将分析来自EMT诱导前后细胞的数据的测序数据，以确定转录状态 优先使PDAC细胞能够进行间充质转变。候选成绩单的相关性 为了解释EMT启动，将通过敲低实验进行测试。这项工作中开发的方法 将广泛适用于其他癌症环境中EMT的研究，并研究替代类型的 表型切换。此外，具体结果将对新型组合的设计产生影响 PDAC的疗法基于抑制EMT以促进化学疗法的反应性的目的。