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治疗的主要障碍是， 以最大耐受剂量连续治疗通常使肿瘤具有抗性。PCa包括 雄激素非依赖性癌症干细胞（PCaSC）和分化程度更高的雄激素依赖性PCa细胞 （PCaC），构成肿瘤的大部分。治疗诱导的PCaSC富集似乎赋予 治疗抵抗连续治疗忽略了进化动力学，竞争，适应 以及在治疗敏感细胞和治疗抗性细胞之间的选择导致治疗失败。间歇 雄激素剥夺治疗（IADT）与治疗周期和停药周期可能会抵消竞争性释放， 雄激素非依赖性癌细胞和延迟进展时间（TTP）。成功的临床实施 IADT需要鉴定耐药机制，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。我们建议整合我们的数学， 生物学、临床和统计学专业知识，以检验PCaSC动力学是对 IADT的治疗和耐药性演变。通过拟合不同的机械数学模型， 在训练数据集中，我们可以确定临床上合理的个体患者的回顾性纵向数据 模型参数分布从早期治疗周期的治疗反应动力学，我们的目标是 在验证中模拟并可靠地预测个体患者对后续治疗周期的反应 数据集然后，我们将使用经过验证的模型来模拟具有不同周期间隔的IADT协议， 每一个病人。将模拟暂停和恢复IADT的PSA水平的标称和相对临界值， 将确定TTP。使TTP最大化的PSA临界值将与模型推导的PCaSC相关 动态，并用于确定最佳的患者特异性IADT协议。与雄激素剥夺相比 单用多西他赛（DOC）联合治疗可改善患者生存期，生存获益取决于 治疗时机我们将模拟在不同IADT周期初始化的DOC治疗，以确定DOC时间- 与患者特异性PCaSC动力学参数相关的依赖性TTP，以进一步改善PCa 治疗结果。这种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 前列腺癌治疗的进展，远离以最大耐受剂量持续雄激素剥夺，直到 肿瘤对IADT方案产生耐药性，使用基于个体患者反应的触发器 动力学如果成功，本提案的结果将为最佳方案和所需样本量提供信息 随后的第一个同类临床试验的个性化自适应IADT，延迟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.