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

预测患者对前列腺癌个体化雄激素剥夺疗法的特异性反应

• 批准号: 9810308
• 负责人: Heiko Enderling
• 金额: $ 20.03万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9810308
• 关键词: Acetates; Address; Androgens; Biological; Blood; Calibration; Cancer Patient; Castration; Cells; Clinical; Clinical Trials; Complex; Computer Simulation; Data; Data Set; Development; Diagnosis; Disease; Disease Resistance; Dose; Early treatment; Evolution; Goals; Hormones; Individual; Malignant Neoplasms; Malignant neoplasm of prostate; Mathematics; Methodology; Modeling; Molecular; Operative Surgical Procedures; Outcome; Patients; Phenotype; Population; Prostate Cancer therapy; Prostate specific antigen measurement; Prostate-Specific Antigen; Protocols documentation; Radiation; Resistance; Rewards; Sample Size; Serum; Stem cells; Study models; Testing; Time; Toxic effect; Training; Treatment Failure; Treatment outcome; Validation; abiraterone; androgen deprivation therapy; androgen independent prostate cancer; androgen sensitive; base; cancer cell; cancer stem cell; castration resistant prostate cancer; clinical implementation; clinically actionable; deprivation; docetaxel; high reward; high risk; hormone therapy; improved; individual patient; individual response; innovation; mathematical model; men; neglect; patient response; phase II trial; predicting response; prospective; prostate cancer cell; resistance mechanism; response; response biomarker; standard care; treatment response; tumor

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.

项目摘要 前列腺癌（PCA）是美国男性最普遍的癌症。 PCA疗法的主要障碍是 最大耐受剂量的连续治疗通常会耐肿瘤。 PCA由 雄激素非依赖性的癌症干细胞（PCASC）和更多分化的雄激素依赖性PCA细胞 （PCAC）组成大部分肿瘤。治疗引起的PCASC富集似乎可以赋予 治疗抗性。连续治疗忽略了竞争，适应的进化动态 和治疗敏感和耐药细胞之间的选择有助于治疗衰竭。间歇性 雄激素剥夺疗法（IADT）具有在外和治疗周期 雄激素独立的癌细胞和延迟进展时间（TTP）。成功的临床实施 IADT需要鉴定电阻机制，预测反应以及临床上的确定 可操作的触发器，用于暂停和恢复IADT周期。我们建议整合我们的数学， 生物学，临床和统计专业知识，以检验PCASC动力学的假设是对 IADT抗药性的治疗和进化。通过将不同的机械数学模型拟合到 在训练数据集中，个别患者的回顾性纵向数据我们可以确定临床上的合理 模型参数分布。从早期治疗周期的治疗反应动态中，我们的目标是 在验证中模拟并可靠地预测单个患者对随后的治疗周期的反应 数据集。然后，我们将使用经过验证的模型模拟具有不同周期间隔的IADT协议 每个患者。将模拟PSA级别暂停和恢复IADT的名义和相对截止 将确定TTP。最大化TTP的PSA截止将与模型衍生的PCASC相关 动力学，用于识别最佳患者特异性IADT方案。与雄激素剥夺相比 单独，与多西他赛（DOC）共同治疗可改善患者的生存，并取决于生存收益 治疗时机。我们将模拟在不同IADT周期初始初始初始化的DOC治疗，以确定DOC PISTING- 依赖性TTP与特定于患者的PCASC动力学参数相关，以进一步改善PCA 治疗结果。这个探索性的高风险和高回报项目可能会提供重要的概念 在PCA治疗中前进，从最大耐受剂量下远离连续的雄激素剥夺直到 使用基于个别患者的反应的触发因素，肿瘤对IADT方案具有抵抗力 动力学。如果成功，该提案的发现将为最佳协议和所需的样本量提供信息 随后的个性化自适应IADT的最初的临床试验，该试验延迟了TTP。