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

项目摘要 以往对胰腺导管腺癌(PDAC)的研究未能提供治疗信息。 为患者预后提供显著改善的策略，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。一位少校 这一缺陷的原因是由于在 PDAC的肿瘤发生，这在很大程度上被大量基因组研究所忽视。我假设一个 单细胞解剖上皮室和肿瘤生态系统 进展将揭示新的可药物靶点，并澄清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在 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很大影响。为此， 拟议的项目利用单细胞转录(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) 基因工程小鼠模拟PDAC从启动到进展的过程 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控的方法学 在PDAC亚群中的程序，以便我们可以识别失调的机制 与正常胰腺上皮细胞比较(目标1)。然后，我提议对 用一种新的方法定位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本中提取信息 数据(目标2)。后一个目标将允许识别潜在的干细胞群体和 它们产生的表型，从而为针对导致复发的人群提供了基础 或对治疗产生抗药性。总而言之，我们提出的研究癌症调控的方法 将提供现有方法基于任一批量数据无法获得的预测 或者仅仅是单细胞数据，这些数据很有可能发现特别是癌症监管机构 表型利基。

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.