Preclinical analyses of advanced prostate cancer in genetically-engineered mice

描述（由申请人提供）：我们的提案侧重于通过调查研究改善晚期前列腺癌男性的治疗选择。我们已经开发了一种新的实验范式，它结合了临床前分析的基因工程小鼠（GEM）模型的去势抵抗性前列腺癌与复杂的跨物种计算分析，以确定主调节药物反应。我们将应用这一策略来研究阿比特龙，这是一种雄激素生物合成抑制剂，已被证明可以提高晚期前列腺癌患者的生存率。然而，尽管阿比特龙在临床试验中显示出有希望的结果，但并非所有患者对阿比特龙都有反应 大多数最初有反应的人最终治疗失败。因此，迫切需要进行临床前研究，以了解阿比特龙在特定肿瘤背景下的作用机制，并确定预测反应和耐药性的生物标志物。 为此，我们的研究将集中在以下关键问题：（目的1）：阿比特龙在哪些肿瘤背景下有反应？我们将使用我们的GEM模型进行临床前研究，目的是识别反应性和非反应性肿瘤。利用这些反应性和非反应性肿瘤的基因表达特征，我们将进行跨物种计算分析，以确定预测阿比特龙反应的主要调控因子。将验证候选主调节剂对人前列腺癌的作用，以及评估其对阿比特龙体内应答的功能相关性。这些研究的目的是阐明阿比特龙反应的机制和预测其疗效的生物标志物。(Aim 2）：阿比特龙的疗效是否可以通过与抗雄激素药物共靶向来提高？我们将在GEM模型中进行临床前研究，以评价阿比拉酮与抗雄激素MDV 3100联合给药的疗效。使用来自用单独与组合药物治疗的小鼠的分子特征，我们将鉴定告知药物相互作用机制的主调节剂，并作为多药物反应的生物标志物。候选主调节剂将被验证用于人前列腺癌，以及在功能研究中评估其体内药物作用的功效。这些研究的目的是 了解阿比特龙和抗雄激素药物相互作用的机制，并确定多药反应的生物标志物。(Aim 3）：为什么阿比特龙治疗失败？考虑到内在耐药与获得性耐药的机制，我们将建立对阿比特龙治疗耐药的GEM模型。使用阿比特龙治疗前或治疗后立即或肿瘤复发后小鼠的分子特征，我们将鉴定耐药性的主要调节因子。这些将被验证为人类前列腺癌，并评估其影响药物治疗耐药性的能力。这些研究的目的是阐明阿比特龙耐药的机制，并鉴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.