Department of Urology, University of California, San Francisco, California, USA.
Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
Cancer. 2024 May 15;130(10):1766-1772. doi: 10.1002/cncr.35215. Epub 2024 Jan 27.
The challenge of distinguishing indolent from aggressive prostate cancer (PCa) complicates decision-making for men considering active surveillance (AS). Genomic classifiers (GCs) may improve risk stratification by predicting end points such as upgrading or upstaging (UG/US). The aim of this study was to assess the impact of GCs on UG/US risk prediction in a clinicopathologic model.
Participants had favorable-risk PCa (cT1-2, prostate-specific antigen [PSA] ≤15 ng/mL, and Gleason grade group 1 [GG1]/low-volume GG2). A prediction model was developed for 864 men at the University of California, San Francisco, with standard clinical variables (cohort 1), and the model was validated for 2267 participants from the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry (cohort 2). Logistic regression was used to compute the area under the receiver operating characteristic curve (AUC) to develop a prediction model for UG/US at prostatectomy. A GC (Oncotype Dx Genomic Prostate Score [GPS] or Prolaris) was then assessed to improve risk prediction.
The prediction model included biopsy GG1 versus GG2 (odds ratio [OR], 5.83; 95% confidence interval [CI], 3.73-9.10); PSA (OR, 1.10; 95% CI, 1.01-1.20; per 1 ng/mL), percent positive cores (OR, 1.01; 95% CI, 1.01-1.02; per 1%), prostate volume (OR, 0.98; 95% CI, 0.97-0.99; per mL), and age (OR, 1.05; 95% CI, 1.02-1.07; per year), with AUC 0.70 (cohort 1) and AUC 0.69 (cohort 2). GPS was associated with UG/US (OR, 1.03; 95% CI, 1.01-1.06; p < .01) and AUC 0.72, which indicates a comparable performance to the prediction model.
GCs did not substantially improve a clinical prediction model for UG/US, a short-term and imperfect surrogate for clinically relevant disease outcomes.
区分惰性和侵袭性前列腺癌（PCa）的挑战使得考虑主动监测（AS）的男性的决策变得复杂。基因组分类器（GCs）可以通过预测升级或升级（UG/US）等终点来改善风险分层。本研究的目的是在临床病理模型中评估 GCs 对 UG/US 风险预测的影响。
参与者患有低危 PCa（cT1-2，前列腺特异性抗原[PSA]≤15ng/ml，Gleason 分级组 1[GG1]/低体积 GG2）。在加利福尼亚大学旧金山分校为 864 名男性建立了预测模型（队列 1），并在癌症前列腺战略泌尿科研究 Endeavor（CaPSURE）登记处的 2267 名参与者中验证了该模型（队列 2）。使用逻辑回归计算受试者工作特征曲线下的面积（AUC），以建立前列腺切除术时 UG/US 的预测模型。然后评估基因组分类器（Oncotype Dx 基因组前列腺评分[GPS]或 Prolaris）以改善风险预测。
预测模型包括活检 GG1 与 GG2（比值比[OR]，5.83；95%置信区间[CI]，3.73-9.10）；PSA（OR，1.10；95%CI，1.01-1.20；每增加 1ng/ml），阳性核心百分比（OR，1.01；95%CI，1.01-1.02；每增加 1%），前列腺体积（OR，0.98；95%CI，0.97-0.99；每毫升）和年龄（OR，1.05；95%CI，1.02-1.07；每年），AUC 为 0.70（队列 1）和 AUC 为 0.69（队列 2）。GPS 与 UG/US 相关（OR，1.03；95%CI，1.01-1.06；p<0.01）和 AUC 为 0.72，表明其性能与预测模型相当。
GCs 并没有显著改善 UG/US 的临床预测模型，UG/US 是一种短期的、不完美的替代临床相关疾病结果的指标。
