Analysis of drug response in organoids and mouse models

项目摘要 我们建议的研究将解决与区别药物治疗相关的主要临床挑战。 反应，重点是晚期前列腺癌。我们的总体方法是基于联合临床试验。 范式，其中对基因工程小鼠(GEM)模型进行药物反应分析，以提供 可纳入患者治疗方案的信息。在这里，我们提出了一种新的增广联合 使用组织特定器官分析和GEM模型的临床范式以加快 不同药物反应和药物协同作用的研究，以及复杂的计算系统 鉴定小鼠模型中保守的药物反应分子调控因子的生物学方法 与人类癌症有关。在初步研究中，我们已经建立了新的立体培养方法。 肿瘤有机化合物，它们显示了GEM模型的药物反应特征 已经成立了。此外，我们还使用计算机系统方法来生成基因调控。 针对小鼠和人类前列腺癌的网络(交互作用)，并已证明它们对 肿瘤侵袭性的候选主要调控因子的跨物种识别。我们已经扩展了这些 用系统生物学方法预测临床前研究中的药物反应并外推这些数据 人类前列腺癌。 基于这些初步研究，我们假设对药物反应的系统分析在 基因工程小鼠(GEM)模型和跨物种系统分析可以为人类提供信息 通过在不同的肿瘤背景下对最佳药物治疗进行系统评估来进行癌症治疗 以及通过确定最有可能对治疗有反应的患者。因此，我们提议的研究将 追求广泛的目标，即确定不同类型药物反应的潜在机制 前列腺癌相关知识。我们的具体计划是：目标1：寻找针对特定肿瘤的最佳药物治疗 背景下，我们将使用来自前列腺癌的一系列GEM模型的有机类线来检测反应。 到一系列药物，包括目前用于治疗晚期前列腺癌的药物。这些发现 来自有机模型的数据将在体内的宝石模型中进行实验验证。目标2：分析分子 药物反应和药物协同作用的机制，我们将使用跨物种计算系统 确定调节人类前列腺癌药物反应的基因和途径的方法。这些 结果将在有机体、宝石和异种移植模型中得到实验验证。 影响：我们建议的研究直接涉及肿瘤学模型论坛的广泛目标，因为 他们为有效翻译小鼠模型引入了新的实验范式，以实现未满足的目标 翻译需求。我们最新开发的有机模型和跨物种计算分析 验证将对肿瘤学论坛的成员具有广泛的价值，并可通过NCIP中心共享。

Project Summary Our proposed studies will address major clinical challenges associated with differential drug treatment response, focusing on advanced prostate cancer. Our overall approach is based upon the co-clinical trial paradigm, in which genetically-engineered mouse (GEM) models are assayed for drug response to provide information that can be incorporated into patient treatment regimens. Here, we propose a novel augmented co- clinical paradigm that uses analyses of tissue-specific organoids together with GEM models to expedite investigation of differential drug response and drug synergy, as well as sophisticated computational systems biology approaches to identify molecular regulators of drug response that are conserved from mouse models to human cancer. In preliminary studies, we have established methods for novel three-dimensional culture of tumor organoids, which display drug responses characteristic of the GEM models from which they were established. Furthermore, we have used computational systems methods to generate gene regulatory networks (interactomes) for both mouse and human prostate cancer, and have demonstrated their utility for cross-species identification of candidate master regulators of tumor aggressiveness. We have expanded these systems biology approaches for prediction of drug response in preclinical studies and to extrapolate these data to human prostate cancer. Based on these preliminary studies, we hypothesize that systematic analysis of drug response in genetically-engineered mouse (GEM) models followed by cross-species systems analyses can inform human cancer treatment by enabling the systematic evaluation of optimal drug treatments in distinct tumor contexts as well as by identifying patients who are most likely to respond to treatment. Thus, our proposed research will pursue the broad objective of identifying the underlying mechanisms of differential drug response in distinct prostate tumor contexts. Our specific plans are: Aim 1: To identify optimal drug treatments for specific tumor contexts, we will use organoid lines derived from a series of GEM models of prostate cancer to assay response to a range of drugs, including agents currently used for treatment of advanced prostate cancer. These findings from organoid models will be experimentally validated in GEM models in vivo. Aim 2: To analyze molecular mechanisms of drug response and drug synergy, we will use cross-species computational systems approaches to identify genes and pathways that regulate drug response in human prostate cancer. These results will be experimentally validated in organoid, GEM, and xenograft models. Impact: Our proposed studies directly address the broad goals of the Oncology Models Forum, since they introduce new experimental paradigms for effective translation of mouse models to achieve unmet translational needs. Our newly developed organoid models and cross-species computational analyses and validation will be of broad value to members of the Oncology Forum and are sharable through the NCIP Hub.