Morris David S, Woods J Scott, Edwards Byard, Lenz Lauren, Logan Jennifer, Flake Darl D, Mabey Brent, Bishoff Jay T, Cohen Todd, Stone Steven
Urology Associates, PC, Nashville, TN.
Urology Associates, PC, Nashville, TN.
Urol Oncol. 2021 Jun;39(6):366.e19-366.e28. doi: 10.1016/j.urolonc.2020.11.016. Epub 2020 Nov 27.
To compare the prognostic capabilities and clinical utility of the cell cycle progression (CCP) gene expression classifier test, multiparametric magnetic resonance imaging (mpMRI) with Prostate Imaging Reporting and Data System (PI-RADS) scoring, and clinicopathologic data in select prostate cancer (PCa) medical management scenarios.
Retrospective, observational analysis of patients (N = 222) ascertained sequentially from a single urology practice from January 2015 to June 2018. Men were included if they had localized PCa, a CCP score, and an mpMRI PI-RADS v2 score. Cohort 1 (n = 156): men with newly diagnosed PCa, with or without a previous negative biopsy. Cohort 2 (n = 66): men who initiated active surveillance (AS) without CCP testing, but who received the test during AS. CCP was combined with the UCSF Cancer of the Prostate Risk Assessment (CAPRA) score to produce a clinical cell-cycle risk (CCR) score, which was reported in the context of a validated AS threshold. Spearman's rank correlation test was used to evaluate correlations between variables. Generalized linear models were used to predict binary Gleason score category and medical management selection (AS or curative therapy). Likelihood-ratio tests were used to determine predictor significance in both univariable and multivariable models.
In the combined cohorts, modest but significant correlations were observed between PI-RADS score and CCP (r = 0.22, P = 8.1 × 10), CAPRA (r= 0.36, P = 4.8 × 10), or CCR (r = 0.37, P = 2.0 × 10), suggesting that much of the prognostic information captured by these measures is independent. When accounting for CAPRA and PI-RADS score, CCP was a significant predictor of higher-grade tumor after radical prostatectomy, with the resected tumor approximately 4 times more likely to harbor Gleason ≥4+3 per 1-unit increase in CCP in Cohort 1 (Odds Ratio [OR], 4.10 [95% confidence interval [CI], 1.46, 14.12], P = 0.006) and in the combined cohorts (OR, 3.72 [95% CI, 1.39, 11.88], P = 0.008). On multivariable analysis, PI-RADS score was not a significant predictor of post-radical prostatectomy Gleason score. Both CCP and CCR were significant and independent predictors of AS versus curative therapy in Cohort 1 on multivariable analysis, with each 1-unit increase in score corresponding to an approximately 2-fold greater likelihood of selecting curative therapy (CCP OR, 2.08 [95% CI, 1.16, 3.94], P = 0.014) (CCR OR, 2.33 [95% CI, 1.48, 3.87], P = 1.5 × 10). CCR at or below the AS threshold significantly reduced the probability of selecting curative therapy over AS (OR, 0.28 [95% CI, 0.13, 0.57], P = 4.4 × 10), further validating the clinical utility of the AS threshold.
CCP was a better predictor of both tumor grade and subsequent patient management than was PI-RADS. Even in the context of targeted biopsy, molecular information remains essential to ensure precise risk assessment for men with newly diagnosed PCa.
比较细胞周期进程(CCP)基因表达分类器检测、采用前列腺影像报告和数据系统(PI-RADS)评分的多参数磁共振成像(mpMRI)以及临床病理数据在特定前列腺癌(PCa)医疗管理场景中的预后评估能力和临床实用性。
对2015年1月至2018年6月从单一泌尿外科诊所连续确诊的患者(N = 222)进行回顾性观察分析。纳入标准为患有局限性PCa、具有CCP评分以及mpMRI PI-RADS v2评分的男性。队列1(n = 156):新诊断为PCa的男性,无论之前活检结果是否为阴性。队列2(n = 66):在未进行CCP检测的情况下开始主动监测(AS),但在AS期间接受该检测的男性。CCP与加州大学旧金山分校前列腺癌风险评估(CAPRA)评分相结合,得出临床细胞周期风险(CCR)评分,并在经过验证的AS阈值背景下进行报告。采用Spearman等级相关检验评估变量之间的相关性。使用广义线性模型预测二元Gleason评分类别和医疗管理选择(AS或根治性治疗)。采用似然比检验确定单变量和多变量模型中预测指标的显著性。
在合并队列中,观察到PI-RADS评分与CCP(r = 0.22,P = 8.1×10)、CAPRA(r = 0.36,P = 4.8×10)或CCR(r = 0.37,P = 2.0×10)之间存在适度但显著的相关性,这表明这些指标所捕获的大部分预后信息是独立的。在考虑CAPRA和PI-RADS评分时,CCP是根治性前列腺切除术后高级别肿瘤的显著预测指标,在队列1中,CCP每增加1个单位,切除的肿瘤出现Gleason≥4 + 3的可能性大约增加4倍(比值比[OR],4.10 [95%置信区间[CI],1.46,14.12],P = 0.006),在合并队列中也是如此(OR,3.72 [95% CI,1.39,11.88],P = 0.008)。在多变量分析中,PI-RADS评分不是根治性前列腺切除术后Gleason评分的显著预测指标。在队列1的多变量分析中,CCP和CCR都是AS与根治性治疗的显著且独立的预测指标,评分每增加1个单位,选择根治性治疗的可能性大约增加2倍(CCP OR,2.08 [95% CI,1.16,3.94],P = 0.014)(CCR OR,2.33 [95% CI,1.48,3.87],P = 1.5×10)。处于或低于AS阈值的CCR显著降低了选择根治性治疗而非AS的概率(OR,0.28 [95% CI,0.13,