Preclinical analyses of advanced prostate cancer in genetically-engineered mice

描述(由申请人提供)：我们的建议集中于通过调查研究改善晚期前列腺癌男性的治疗选择。我们开发了一种新的实验范式，将去势耐受前列腺癌的基因工程小鼠(GEM)模型的临床前分析与复杂的跨物种计算分析相结合，以确定药物反应的主要调节因素。我们将应用这一策略来研究阿比拉特龙，它是一种雄激素生物合成的抑制剂，已被证明可以提高晚期前列腺癌患者的存活率。然而，尽管阿比拉特龙在临床试验中显示出有希望的结果，但并不是所有的患者对阿比拉特龙都有反应 而大多数最初有反应的人最终都未能通过治疗。因此，迫切需要进行临床前研究，以了解阿比拉特酮在特定肿瘤环境中的作用机制，并定义预测反应和耐药的生物标记物。为此，我们的研究将集中在以下关键问题上：(目标1)：在哪些肿瘤情况下阿比拉特龙有反应？我们将使用我们的GEM模型进行临床前研究，目标是识别响应性和非响应性肿瘤。利用这些有反应和无反应的肿瘤的基因表达特征，我们将进行跨物种的计算分析，以确定预测阿比拉特龙反应的主要调节因子。候选的MASTER调节器将被验证用于人类前列腺癌，以及评估它们在体内对阿比拉特龙的反应的功能相关性。这些研究的目的是阐明阿比拉特酮的反应机制和预测其疗效的生物标志物。(目标2)：阿比拉特酮能否通过与抗雄激素药物共同靶向来提高疗效？我们将在GEM模型中进行临床前研究，以评估阿比瑞酮联合抗雄激素药物MDV3100的疗效。使用单独药物与联合药物治疗的小鼠的分子签名，我们将识别告知药物相互作用机制的主要调节因子，并将其作为多药物反应的生物标志物。候选的MASTER调节剂将在人体前列腺癌以及功能研究中得到验证，以评估它们在体内的药物作用效果。这些研究的目标是 告知阿比拉特酮和抗雄激素相互作用的潜在机制，并识别多种药物反应的生物标记物。(目标3)：为什么阿比拉特龙治疗失败？考虑到内在耐药和获得性耐药的机制，我们将建立对阿比拉特龙耐药的GEM模型。使用阿比拉特龙治疗前或治疗后立即或肿瘤复发后小鼠的分子签名，我们将识别耐药的主要调节因素。这些药物将被验证为人类前列腺癌，并评估它们影响药物治疗耐药性的能力。这些研究的目的是为了揭示阿比拉通耐药的机制，并确定耐药的生物标志物。

DESCRIPTION (provided by applicant): Our proposal is focused on improving treatment options for men with advanced prostate cancer through investigative research. We have been developed a novel experimental paradigm, which combines preclinical analyses in genetically-engineered mouse (GEM) models of castration-resistant prostate cancer with sophisticated cross-species computational analyses to identify master regulators of drug response. We will apply this strategy to study Abiraterone, which is an inhibitor of androgen biosynthesis that has been shown to improve survival of men with advanced prostate cancer. However, although Abiraterone has shown promising results in clinical trials, not all patients respond to Abiraterone and most of those who do respond initially eventually fail treatment. Therefore, there is a critica need to pursue preclinical investigations to understand the mechanisms of action of Abiraterone in specific tumor contexts, and to define biomarkers predictive of response and resistance. Toward this end, our studies will focus on the following critical questions: (Aim 1): In which tumor contexts is Abiraterone responsive? We will perform preclinical studies using our GEM models, with the goal of identifying responsive and non-responsive tumors. Using gene expression signatures from these responsive and non-responsive tumors, we will perform cross-species computational analyses to identify master regulators that predict response to Abiraterone. Candidate master regulators will be validated to human prostate cancer, as well as to assess their functional relevance for response to Abiraterone in vivo. The goal of these studies is to elucidate mechanisms of Abiraterone response and biomarkers that predict its efficacy. (Aim 2): Can the efficacy of Abiraterone be improved by co-targeting with anti-androgens? We will perform preclinical studies in GEM models to evaluate the efficacy of Abireratone in combination with the anti-androgen, MDV3100. Using molecular signatures from mice treated with the individual versus combinatorial agents, we will identify master regulators that inform on the mechanisms of the drug interaction and as biomarkers of multi-drug response. Candidate master regulators will be validated to human prostate cancer, as well as in functional studies to asses their efficacy for drug action in vivo. The goal of these studies is to inform on mechanisms underlying the interaction of Abiraterone and anti-androgens, and to identify biomarkers of multi-drug response. (Aim 3): Why does treatment with Abiraterone fail? Considering mechanisms of intrinsic versus acquired resistance, we will establish GEM models that are resistant to Abiraterone treatment. Using molecular signatures from mice prior to or immediately following Abiraterone treatment or following tumor recurrence, we will identify master regulators of drug resistance. These will be validated to human prostate cancer, and to assess their ability to affect resistance to drug treatment. The goal of these studies is to eludicate mechanisms of Abiraterone resistance, and to identify biomarkers of resistance.