Academic Urology Group, Department of Surgery, University of Cambridge, Cambridge, UK.
Department of Urology, Cambridge University Hospitals NHS Trust, Cambridge, UK.
BJU Int. 2019 Nov;124(5):758-767. doi: 10.1111/bju.14800. Epub 2019 Jun 2.
To test whether using disease prognosis can inform a rational approach to active surveillance (AS) for early prostate cancer.
We previously developed the Cambridge Prognostics Groups (CPG) classification, a five-tiered model that uses prostate-specific antigen (PSA), Grade Group and Stage to predict cancer survival outcomes. We applied the CPG model to a UK and a Swedish prostate cancer cohort to test differences in prostate cancer mortality (PCM) in men managed conservatively or by upfront treatment in CPG2 and 3 (which subdivides the intermediate-risk classification) vs CPG1 (low-risk). We then applied the CPG model to a contemporary UK AS cohort, which was optimally characterised at baseline for disease burden, to identify predictors of true prognostic progression. Results were re-tested in an external AS cohort from Spain.
In a UK cohort (n = 3659) the 10-year PCM was 2.3% in CPG1, 1.5%/3.5% in treated/untreated CPG2, and 1.9%/8.6% in treated/untreated CPG3. In the Swedish cohort (n = 27 942) the10-year PCM was 1.0% in CPG1, 2.2%/2.7% in treated/untreated CPG2, and 6.1%/12.5% in treated/untreated CPG3. We then tested using progression to CPG3 as a hard endpoint in a modern AS cohort (n = 133). During follow-up (median 3.5 years) only 6% (eight of 133) progressed to CPG3. Predictors of progression were a PSA density ≥0.15 ng/mL/mL and CPG2 at diagnosis. Progression occurred in 1%, 8% and 21% of men with neither factor, only one, or both, respectively. In an independent Spanish AS cohort (n = 143) the corresponding rates were 3%, 10% and 14%, respectively.
Using disease prognosis allows a rational approach to inclusion criteria, discontinuation triggers and risk-stratified management in AS.
检验利用疾病预后能否为早期前列腺癌的主动监测（AS）提供合理的方法。
我们之前开发了剑桥预后分组（CPG）分类，这是一个五层次模型，使用前列腺特异性抗原（PSA）、分级组和分期来预测癌症生存结局。我们将 CPG 模型应用于英国和瑞典的前列腺癌队列，以检验在 CPG2 和 3（将中间风险分类进一步细分）和 CPG1（低风险）中接受保守治疗或早期治疗的男性的前列腺癌死亡率（PCM）差异。然后，我们将 CPG 模型应用于一个具有最佳基线疾病负担特征的当代英国 AS 队列，以确定真正预后进展的预测因素。结果在西班牙的一个外部 AS 队列中进行了重新测试。
在英国队列（n=3659）中，CPG1 的 10 年 PCM 为 2.3%，CPG2 治疗/未治疗的 PCM 为 1.5%/3.5%，CPG3 治疗/未治疗的 PCM 为 1.9%/8.6%。在瑞典队列（n=27942）中，CPG1 的 10 年 PCM 为 1.0%，CPG2 治疗/未治疗的 PCM 为 2.2%/2.7%，CPG3 治疗/未治疗的 PCM 为 6.1%/12.5%。然后，我们将进展为 CPG3 作为现代 AS 队列（n=133）的一个硬终点进行测试。在随访期间（中位数 3.5 年），仅有 6%（133 人中的 8 人）进展为 CPG3。进展的预测因素是 PSA 密度≥0.15ng/mL/mL 和诊断时的 CPG2。分别有 6%、8%和 21%的男性没有、只有一个或两个因素，其进展发生率分别为 1%、8%和 21%。在一个独立的西班牙 AS 队列（n=143）中，相应的发生率分别为 3%、10%和 14%。
利用疾病预后可以为 AS 的纳入标准、停药触发因素和风险分层管理提供合理的方法。
