Systems analysis of aggressive prostate cancer pathology

项目概要 组织病理学是定义癌症的基因组畸变的表现（即阶段、级别、类型）。在 前列腺癌，最好的预后标志物是格里森评分，这是一种综合肿瘤分级系统， 总结原发肿瘤形态。患有局部疾病的患者亚群不可避免地会发展 转移，此时进行治疗往往为时已晚。因此，我们建议建模和 治疗目标是驱动侵袭性原发性前列腺癌的分子亚型。我们从大处开始， –组学数据集并使用组织病理学和基因表达作为疾病发展的读数。至 评估侵袭性原发性疾病分子驱动因素的直接影响，我们使用一种新型前列腺干细胞， 概括前列腺发育和组织病理学的组织重组小鼠模型。模型 针对特定的分子亚型，我们将对小鼠前列腺干细胞进行基因改造，导入基因 敲除或 Tet 调控的基因。然后这些工程细胞将与胎儿泌尿生殖系统结合 间充质并移植到小鼠肾囊下以产生前列腺结构。我们将评估 组织病理学并分离具有侵袭性组织形态的感兴趣区域（例如筛状图案） 用于 RNA 测序。使用这种方法来模拟三种互斥的分子亚型，我们可以 直接比较和对比基因表达和途径变化以识别常见和亚型 具体效果。使用这些数据以及来自患者的组学数据，我们将使用两个基于网络的 用于识别新型治疗方法并捕获系统级基因调控的计算模型 和功能关系。治疗预测将使用工程小鼠进行体外筛选 具有前景的治疗方法的人类细胞正在组织重组小鼠模型中进行测试。 总的来说，通过这个项目，我们将研究侵袭性原发性前列腺癌的驱动因素，表征 疾病发展机制，并确定临床前药物治疗策略。 计算模型和前列腺干细胞将成为 CSBC 和更大规模研究的宝贵资源 社区。

Project Summary Tissue pathology is a manifestation of the genomic aberrations that define cancer (i.e. stage, grade, type). In prostate cancer, the best prognostic marker is Gleason score, the composite tumor grading system that summarizes primary tumor morphology. Subsets of patients with localized disease inevitably develop metastases, a point where it is often too late for curative treatment. Thus, we propose to model and therapeutically target molecular subtypes that drive aggressive primary prostate cancer. We start from large, –omic datasets and use tissue pathology and gene expression as the readout of disease development. To evaluate the direct effect of molecular drivers of aggressive primary disease, we use a novel prostate stem cell, tissue recombination mouse model that recapitulates prostate development and tissue pathology. To model specific molecular subtypes, we will genetically engineer mouse prostate stem cells to introduce gene knockouts or Tet-regulated genes. These engineered cells will then be combined with fetal urogenital mesenchyme and engrafted under the mouse kidney capsule to produce prostate structures. We will evaluate tissue pathology and isolate regions of interest with aggressive tissue morphology (e.g., cribriform patterning) for RNA sequencing. Using this approach to model three mutually exclusive molecular subtypes, we can directly compare and contrasts gene expression and pathways changes to identify common and subtype- specific effects. Using these data, along with –omic data from patients, we will use two network-based computational models to identify novel therapeutic treatments and capture the systems-level gene regulation and functional relationships. The therapeutic predictions will be screened in vitro using engineered mouse and human cells with promising treatments being promoted for testing in the tissue recombination mouse model. Overall, through this project, we will study drivers of aggressive primary prostate cancer, characterize mechanisms of disease development, and identify pre-clinical pharmacological treatment strategies. Computational models and the prostate stem cells will be valuable resources for the CSBC and larger research communities.