The telomere biomarker as a tool to inform decision-making for aggressive salvage therapy in men with rising PSA post prostatectomy

接受挽救放射治疗(RT)的前列腺摘除术后生化复发的男性可能受益于增加 通过降低进展到远处转移和死亡的可能性进行抗雄激素治疗(AAT)。 然而，在RTOG 9601中，并不是所有的人都受益。目前还没有预测生物标记物来识别谁是 更有可能或更不可能从积极的抢救治疗(RT AAT)中受益。为了满足这一未得到满足的需求 精确治疗决策，我们将评估端粒生物标记物作为预测生物标记物 在这种情况下的治疗反应。我们的概念创新假设是端粒生物标记物-- 癌细胞间端粒长度的变异性与间质细胞端粒长度的组合- 捕获有关肿瘤行为的信息，而不是当前使用的指标，从而识别出 更有可能或更不可能从积极的抢救治疗中受益。我们发现端粒 生物标记物是手术治疗男性致命疾病的独立预后标记物，识别出3 预后分类：良、中、差。端粒生物标记物还没有被测试为具有预测性 在任何环境下的治疗反应。我们将在试验和临床两个相辅相成的环境中解决这些目标 练习，总计839人和165个转移事件。在试验设置中，我们将使用RTOG 9601，其中 均随机接受RT/-AAT治疗。在临床实践环境中，我们使用接受RT/-AAT治疗的队列 约翰霍普金斯大学或波士顿医学中心，并拥有组织微阵列；在分析中，我们将按 倾向性评分，以最大限度地减少患者和肿瘤因素造成的偏见。我们将对这些目标进行分层评估 端粒生物标记物：1.在标准化设置中，测试是否进展到转移和死亡的比率 前列腺癌在RT-AAT和RT之间只有不同。2.在临床实践环境中，测试是否有 前列腺癌的进展到转移和死亡在RT-AAT和RT之间是不同的。我们会弄脏的 用于端粒和细胞类型的特异性免疫荧光标记，并执行图像捕获和定量 图像分析，并得出每个人的端粒生物标志物。我们将按生物标志物类别分层并使用 COX模型估计RT AAT和进展之间的关联，并确定生物标记物是否增加 对RT-AAT应答的预测能力超过目前使用的生化后复发预后 指标。在具有与中期预后相关的生物标志物类别的男性中，我们假设 与仅接受RT的男性相比，接受RT-AAT的男性的进展率较低。在男性中 生物标志物类别与预后好坏有关，我们假设男性的进展率 接受RT-AAT治疗的患者与仅接受RT治疗的男性患者的发生率相似。在RTOG 9601中，RT AAT较多 仅在某些亚组中疗效优于RT。为了优化决策，我们将确定生物标记物 在亚群中具有预测性。如果我们的假设得到证实，下一步将是前瞻性验证和 商业合作伙伴关系，为自动化平台生成基于套件的系统。

Men with biochemical recurrence after prostatectomy receiving salvage radiation (RT) may benefit from added anti-androgen therapy (AAT) by decreasing their likelihood of progressing to distant metastasis and death. However, in RTOG 9601, not all men benefitted. No predictive biomarker currently exists to identify who is more likely or less likely to benefit from aggressive salvage therapy (RT+AAT). To address this unmet need for precision treatment decision-making, we will evaluate the telomere biomarker as a predictive biomarker for treatment response in this setting. Our conceptually innovative hypothesis is that the telomere biomarker – the combination of cancer cell-to-cell variability in telomere length coupled with stromal cell telomere length – captures information about tumor behavior beyond currently used indicators and thus, identifies men who are more likely or less likely to benefit from aggressive salvage therapy. We discovered that the telomere biomarker is an independent prognostic marker for lethal disease in surgically-treated men, identifying 3 prognostic categories: good, intermediate, and poor. The telomere biomarker has not been tested as predictive of treatment response in any setting. We will address the aims in 2 complementary settings, trial and clinical practice, totaling 839 men and 165 metastatic events. In the trial setting, we will use RTOG 9601, in which men were randomized to RT+/-AAT. In the clinical practice setting, we use cohorts who received RT+/-AAT at Johns Hopkins or Boston Medical Center and have tissue microarrays; in the analysis, we will weight by a propensity score to minimize bias due to patient and tumor factors. We will evaluate these aims stratified by the telomere biomarker: 1. In the standardized setting, test if rate of progression to metastasis and death from prostate cancer differs between RT+AAT and RT only. 2. In the clinical practice setting, test if rate of progression to metastasis and death from prostate cancer differs between RT+AAT and RT only. We will stain for telomeres and cell-type specific immunofluorescence markers and perform image capture and quantitative image analysis, and derive each man’s telomere biomarker. We will stratify by biomarker category and use Cox models to estimate associations between RT+AAT and progression, and determine if the biomarker adds to predictive capability for response to RT+AAT beyond currently used post-biochemical recurrence prognostic indicators. In men with the biomarker category associated with intermediate prognosis, we hypothesize that the progression rate is lower in men who received RT+AAT compared to men who received RT only. In men with biomarker categories associated with good or poor prognosis, we hypothesize that the progression rate in men who received RT+AAT is similar to the rate in men who received RT only. In RTOG 9601, RT+AAT was more efficacious than RT only in some subgroups. For optimized decision-making, we will determine if the biomarker is predictive in subgroups. If our hypothesis is confirmed, next steps would be prospective validation and commercial partnership to generate a kit-based system for automated platforms.