Predicting patient-specific responses to personalize androgen deprivation therapy for prostate cancer

项目摘要 前列腺癌(PCA)是美国男性中最常见的癌症。PCA治疗的一个主要障碍是 以最大耐受量持续治疗往往会使肿瘤产生抗药性。PCA由以下几部分组成 雄激素非依赖性肿瘤干细胞(PCASC)和分化程度更高、雄激素依赖的PCA细胞 (PCaC)，构成了肿瘤的大部分。治疗诱导的PCASC浓缩似乎提供了 治疗抵抗。持续治疗忽略了进化动态，在那里竞争、适应 在治疗敏感和耐药细胞之间进行选择是导致治疗失败的原因之一。间歇性 雄激素剥夺治疗(IADT)的治疗周期和非治疗周期可能会抵消竞争性释放 雄激素非依赖性癌细胞和延缓进展时间(TTP)。成功地在临床上实施 IADT需要识别耐药机制，预测反应，并确定临床 用于暂停和恢复IADT周期的可操作触发器。我们建议将我们的数学、 生物学、临床和统计学专业知识，以检验PCASC动态反应基础的假设 IADT耐药的治疗和演变。通过将不同的机械数学模型拟合到 在训练数据集中我们可以确定临床上可信的个体患者的纵向回顾数据 模型参数分布。从早期治疗周期的治疗反应动态来看，我们的目标是 在验证中模拟和可靠地预测单个患者对后续治疗周期的反应 数据集。然后，我们将使用验证后的模型对不同周期间隔的IADT协议进行仿真 每一位病人。将模拟PSA水平暂停和恢复IADT的名义和相对截止值，并 将确定TTP。最大化TTP的PSA截止时间将与模型派生的PCASC相关 动力学，并用于确定最佳的患者特定的IADT方案。与雄激素剥夺相比 单独使用多西他赛(DOC)联合治疗可提高患者存活率，其存活率取决于 治疗时机。我们将模拟在不同的IADT周期中初始化的DOC治疗，以确定DOC的时间- 依赖TTP与患者特定PCASC动力学参数的相关性进一步改善PCASC 治疗结果。这个探索性的高风险和高回报的项目可能会提供一个重要的概念性 PCA治疗的进展，远离在最大耐受剂量下持续雄激素剥夺，直到 肿瘤对IADT方案产生抵抗力，使用基于患者个体反应的触发器 动力学。如果成功，这项建议的结果将告知最佳方案和所需样本量 随后进行的第一次个性化适应性IADT临床试验推迟了TTP。

Project Summary Prostate cancer (PCa) is the most prevalent cancer in men in the US. A major obstacle in PCa therapy is that continuous treatment at maximum tolerable doses often renders the tumor resistant. PCa is comprised of androgen-independent cancer stem cells (PCaSC) and more differentiated, androgen-dependent PCa cells (PCaC) that make up the bulk of the tumor. Treatment-induced enrichment in PCaSC appears to confer therapy resistance. Continuous treatment neglects the evolutionary dynamics where competition, adaptation and selection between treatment-sensitive and -resistant cells contribute to therapy failure. Intermittent androgen deprivation therapy (IADT) with on-and off-treatment cycles may counteract competitive release of androgen-independent cancer cells and delay time to progression (TTP). Successful clinical implementation of IADT requires identification of resistance mechanisms, prediction of responses, and determination of clinically actionable triggers for pausing and resuming IADT cycles. We propose to integrate our mathematical, biological, clinical, and statistical expertise to test the hypothesis that PCaSC dynamics underlie response to therapy and evolution of resistance in IADT. By fitting different mechanistic mathematical models to retrospective longitudinal data of individual patients in a training data set we can determine clinically plausible model parameter distributions. From treatment response dynamics in early treatment cycles, we aim to simulate and reliably forecast an individual patient's response to subsequent treatment cycles in a validation data set. Then, we will use the validated model to simulate IADT protocols with different cycle intervals for each patient. Nominal and relative cutoffs for PSA levels to pause and resume IADT will be simulated and TTP will be determined. PSA cutoffs that maximize TTP will be correlated with model-derived PCaSC dynamics and used to identify optimal patient-specific IADT protocols. Compared to androgen deprivation alone, co-treatment with docetaxel (DOC) improves patient survival with the survival benefit dependent on treatment timing. We will simulate DOC therapy initialized at different IADT cycles to determine DOC timing- dependent TTP in correlation to patient-specific PCaSC dynamics parameters to further improve PCa treatment outcomes. This exploratory high-risk and high-reward project may provide a significant conceptual advance in PCa treatment, away from continuous androgen deprivation at maximum tolerable dose until the tumor becomes resistant towards an IADT protocol, using triggers based on individual patients' response dynamics. If successful, the findings of this proposal will inform the optimal protocol and required sample size of a subsequent first-in-kind clinical trial of personalized adaptive IADT that delays TTP.