Systems analysis of aggressive prostate cancer pathology

项目摘要 组织病理学是定义癌症(即分期、分级、类型)的基因组异常的一种表现。在……里面 前列腺癌，最好的预后标志是格里森评分，这是一个综合的肿瘤分级系统， 总结了原发肿瘤的形态。局部疾病患者的亚群不可避免地会发生 转移，这一点往往为时已晚，无法根治。因此，我们建议建立模型并 在治疗上针对导致侵袭性原发性前列腺癌的分子亚型。我们从大做起， -基因组数据集，并使用组织病理学和基因表达作为疾病发展的读数。至 评估侵袭性原发疾病的分子驱动因素的直接影响，我们使用了一种新的前列腺干细胞， 重述前列腺发育和组织病理学的组织重组小鼠模型。建模 特定的分子亚型，我们将基因工程小鼠前列腺干细胞引入基因 基因敲除或Tet调控的基因。然后，这些工程细胞将与胎儿的泌尿生殖系统结合 并将其植入小鼠肾被膜下，形成前列腺结构。我们将评估 组织病理学和分离具有侵袭性组织形态的感兴趣区(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通路变化，以确定共同的和亚型- 特定效果。使用这些数据，以及来自患者的基因组数据，我们将使用两个基于网络的 识别新的治疗方法和捕捉系统水平基因调控的计算模型 和职能关系。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推广用于组织重组小鼠模型的测试。 总体而言，通过这个项目，我们将研究侵袭性前列腺癌的驱动因素，表征 疾病发展的机制，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.