Systems analysis of aggressive prostate cancer pathology

项目摘要 组织病理学是定义癌症的基因组畸变的表现（即阶段、等级、类型）。在 前列腺癌，最好的预后标志物是格里森评分，这是一种复合肿瘤分级系统， 总结了原发性肿瘤形态学。局部疾病患者的亚群不可避免地发展为 转移，这是一个点，它往往是为时已晚的治愈性治疗。因此，我们建议建立模型， 治疗靶向分子亚型，驱动侵袭性原发性前列腺癌。我们从大的开始， -omic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。到 为了评估侵袭性原发性疾病的分子驱动因素的直接作用，我们使用了一种新的前列腺干细胞， 组织重组小鼠模型，重现前列腺发育和组织病理学。建模 特定的分子亚型，我们将对小鼠前列腺干细胞进行基因工程， 敲除或Tet-regulated基因。然后，这些工程细胞将与胎儿泌尿生殖系统结合， 间充质细胞并移植到小鼠肾包膜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个相互排斥的分子亚型，我们可以 直接比较和对比基因表达和途径的变化，以确定共同和亚型， 具体效果。使用这些数据，沿着来自患者的组学数据，我们将使用两个基于网络的 计算模型，以确定新的治疗方法和捕获系统水平的基因调控 和功能关系。治疗预测将在体外使用工程小鼠和 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， 疾病发展的机制，并确定临床前药理学治疗策略。 计算模型和前列腺干细胞将是CSBC和更大研究的宝贵资源 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.