Single Cell Dissection of Epigenetic and Tumor Ecosystem Dynamics During Pancreatic Cancer Progression

胰腺癌进展过程中表观遗传和肿瘤生态系统动力学的单细胞剖析

• 批准号: 9907515
• 负责人: Cassandra Burdziak
• 金额: $ 4.55万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-28 至 2023-06-27
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9907515
• 关键词: ATAC-seq; Address; Algorithms; Automobile Driving; Binding Sites; Biological Assay; Cancer Etiology; Cancerous; Carcinoma; Cell Lineage; Cells; Cessation of life; Chromatin; Chronic; Collaborations; Computing Methodologies; Data; Detection; Development; Disease; Disease Progression; Dissection; Ecosystem; Epigenetic Process; Epithelial; Epithelium; Event; Genetic; Genetic Transcription; Genetically Engineered Mouse; Genomics; Goals; Hematopoiesis; Heterogeneity; High-Risk Cancer; Immune Targeting; Immune system; Immuno-Chemotherapy; Individual; Inflammation; Inflammatory; Knowledge; Lesion; Maintenance; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of pancreas; Measurement; Methodology; Methods; Modeling; Molecular; Natural regeneration; Neoplasm Metastasis; Normal Cell; Oncogenic; Outcome; Pancreas; Pancreatic Ductal Adenocarcinoma; Pancreatitis; Pathway interactions; Patient-Focused Outcomes; Patients; Pattern; Phenotype; Population; Population Dynamics; Premalignant Cell; Process; Proteins; Recurrence; Refractory; Regulation; Regulator Genes; Research; Resistance; Resolution; Sampling; Series; Signal Transduction; System; Target Populations; Technology; Time; Tissues; base; cancer initiation; cancer stem cell; cell type; clinically relevant; computer framework; computing resources; druggable target; falls; improved; insight; malignant state; mutant; neoplastic; new therapeutic target; novel; outcome forecast; premalignant; programs; response; single-cell RNA sequencing; stem; stem cell population; targeted treatment; therapeutic target; tool; transcription factor; transcriptomics; treatment strategy; tumor; tumor progression; tumorigenesis; tumorigenic; wound healing

Project Summary Previous studies of Pancreatic Ductal Adenocarcinoma (PDAC) have failed to inform treatment strategies providing sizeable improvements in patient outcomes, and as such this disease is predicted to be the second leading cause of cancer –related death by the year 2020. A major reason for this shortcoming stems from the extensive cellular heterogeneity that arises during PDAC tumorigenesis, which is largely ignored by bulk genomic studies. I hypothesize that a single cell dissection of the epithelial compartment and tumor ecosystem along disease progression will reveal novel druggable targets and clarify the specific cellular drivers of the disease. I further hypothesize that integration of transcriptional and epigenetic data within computational frameworks will improve the prospects of target detection, as initial evidence suggests a strong impact of epigenetic dysregulation on tumorigenesis. To this end, the proposed project leverages single cell transcriptomic (scRNA-seq) and bulk chromatin accessibility measurements (ATAC-seq) collected in collaboration with the Scott Lowe lab from genetically engineered mice modeling PDAC progression from the moment of initiation through metastasis. For my doctoral research, I propose to develop and apply novel computational methodology to integrate scRNA-seq and ATAC-seq to infer cell type –specific regulatory programs in subpopulations of PDAC, such that we may identify dysregulated mechanisms comparing to normal pancreas epithelium (Aim 1). I then propose to model dynamics of phenotypic shifts over the time course of PDAC progression with a novel method to orient “velocity” of cellular states, again drawing information from both epigenetic and transcriptomic data (Aim 2). This latter aim will allow identification of potential stem cell populations and the phenotypes they give rise to, thus providing a basis for targeting populations driving recurrence or resistance to treatment. In summary, our proposed approach to studying regulation in cancer will provide predictions which are unobtainable with existing methods based on either bulk data or single cell data alone, and which are well-poised to uncover cancer regulators in particular phenotypic niches.

项目摘要 胰腺导管腺癌（PDAC）的既往研究未能提供治疗信息 战略提供了相当大的改善患者的结果，因此，这种疾病是 预计到2020年将成为癌症相关死亡的第二大原因。一个主要 这一缺点的原因是由于细胞的广泛异质性， PDAC肿瘤发生，这在很大程度上被大量基因组研究所忽视。我假设 上皮区室和肿瘤生态系统的单细胞解剖沿着疾病 研究进展将揭示新的可药物靶点，并阐明特定的细胞驱动因素， 疾病我进一步假设，转录和表观遗传数据的整合， 计算框架将改善目标探测的前景，作为初步证据， 表明表观遗传失调对肿瘤发生的强烈影响。为此中央 拟议的项目利用单细胞转录组学（scRNA-seq）和大量染色质 与Scott Lowe实验室合作收集的可访问性测量（ATAC-seq）， 基因工程小鼠建模PDAC进展从开始的时刻到 转移对于我的博士研究，我建议开发和应用新的计算 整合scRNA-seq和ATAC-seq以推断细胞类型特异性调节的方法 PDAC亚群的程序，这样我们就可以确定失调的机制， 与正常胰腺上皮比较（目的1）。然后，我建议建立动态模型， 表型变化随着PDAC进展的时间过程，用一种新的方法来定位 细胞状态的“速度”，再次从表观遗传学和转录组学中提取信息， 数据（目标2）。后一个目标将允许鉴定潜在的干细胞群体， 它们产生的表型，从而为靶向推动复发的人群提供了基础 或对治疗的抵抗。总之，我们提出的研究癌症调控的方法 将提供现有方法无法获得的预测， 或单细胞数据，并且特别准备揭示癌症调节因子， 表型生态位