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

基本信息

批准号：
10084163
负责人：
Cassandra Burdziak
金额：
$ 4.6万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
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/10084163
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 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 Prognosis 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 pancreatic ductal adenocarcinoma model 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

项目摘要

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.

项目概要先前对胰腺导管腺癌 (PDAC) 的研究未能为治疗提供信息策略可显着改善患者的治疗结果，因此这种疾病预计到 2020 年将成为癌症相关死亡的第二大原因。造成这一缺点的原因源于在过程中出现的广泛的细胞异质性 PDAC 肿瘤发生，在很大程度上被大量基因组研究所忽视。上皮区室和肿瘤生态系统沿疾病的单细胞解剖将揭示新的可药物进展靶标并阐明该进展的特定细胞驱动因素我进一步追求转录和表观遗传数据的整合。作为初步证据，计算框架将改善目标检测的前景表明表观遗传失调对肿瘤发生有强烈影响。拟议的项目利用单细胞转录组 (scRNA-seq) 和批量染色质与 Scott Lowe 实验室合作收集的可达性测量 (ATAC-seq) 基因工程小鼠模拟 PDAC 从起始时刻到对于我的博士研究，我建议开发和应用新的计算方法。整合 scRNA-seq 和 ATAC-seq 来推断细胞类型特定监管的方法 PDAC 亚群中的程序，以便我们可以识别失调机制与正常胰腺上皮细胞相比（目标 1）。通过一种新的方法来定向 PDAC 进展过程中的表型变化细胞状态的“速度”，再次从表观遗传和转录组中获取信息数据（目标 2）。后一个目标将允许识别潜在的干细胞群体和它们引起的表型，从而为针对导致复发的人群提供基础总之，我们提出的研究癌症调节的方法。将提供基于批量数据的现有方法无法获得的预测或单独的单细胞数据，这些数据特别适合揭示癌症调节因子表型生态位。