Detection of High-grade Prostate Cancer Using a Urinary Molecular Biomarker-Based Risk Score
Detection of High-grade Prostate Cancer Using a Urinary Molecular Biomarker-Based Risk Score
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DOI：
10.1016/j.eururo.2016.04.012
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发表时间：
2016-11-01
期刊：
影响因子：
23.4
通讯作者：
Schalken, Jack A.
中科院分区：
文献类型：
--
作者：
Van Neste, Leander;Hendriks, Rianne J.;Schalken, Jack A.
背景资料：为了减少过度诊断和过度治疗，迫切需要一种检测临床显著前列腺癌（PCa）的方法。目的：建立一种多模式模型，将先前鉴定的信使RNA（mRNA）生物标志物和传统危险因素结合起来，用于识别高级别PCa患者。（Gleason评分>= 7）。设计、设置和参与者：在两项前瞻性多中心研究中，在直肠指检（DRE）后和前列腺活检前收集尿液进行mRNA分析。在第一个队列（n = 519）中开发多模式风险评分，随后在独立队列（n = 386）中进行临床验证。结果测量和统计分析：使用逆转录定量聚合酶链反应测量mRNA水平。Logistic回归用于模拟患者风险和联合收割机风险因素。模型进行了比较，使用曲线下面积（AUC）的受试者工作特性，并与决策曲线分析（DCA）的临床效用进行了评估。结果和局限性：HOXC 6和DLX 1 mRNA水平被证明是很好的预测高级别PCa的检测。多模式方法的总体AUC为0.90（95%置信区间[CI]，0.85-0.95）（训练组群中AUC 0.86），mRNA特征、前列腺特异性抗原（PSA）密度和既往癌症阴性前列腺活检作为最强、最显著的组分，此外PSA、年龄、和家族史对于另一个模型，其中包括DRE作为额外的风险因素，获得的AUC为0.86（95% CI，0.80-0.92）（训练队列中的AUC为0.90）。这两种模型都得到了成功验证，验证队列中的AUC没有显著变化，与其他临床决策工具（如前列腺癌预防试验风险计算器和PCA 3测定）相比，DCA显示了强大的净效益和最佳的不必要活检减少。基于mRNA液体活检检测的风险评分结合传统的临床风险因素，确定了男性存在高风险的风险，分级PCa，并导致更好的患者风险分层相比，目前的方法在临床实践中。因此，风险评分可以减少不必要的前列腺活检的数量。患者摘要：本研究评估了一种新的尿为基础的测定，可用作高级别前列腺癌（PCa）的非侵入性诊断援助。当该测定的结果与传统的临床风险因素相结合时，高级别PCa的风险分层和活检决策得到改善。(C)2016年欧洲泌尿外科协会。Elsevier B. V.出版，保留所有权利。
Background: To reduce overdiagnosis and overtreatment, a test is urgently needed to detect clinically significant prostate cancer (PCa).Objective: To develop a multimodal model, incorporating previously identified messenger RNA (mRNA) biomarkers and traditional risk factors that could be used to identify patients with high-grade PCa (Gleason score >= 7) on prostate biopsy.Design, setting, and participants: In two prospective multicenter studies, urine was collected for mRNA profiling after digital rectal examination (DRE) and prior to prostate biopsy. The multimodal risk score was developed on a first cohort (n = 519) and subsequently validated clinically in an independent cohort (n = 386).Outcome measurements and statistical analysis: The mRNA levels were measured using reverse transcription quantitative polymerase chain reaction. Logistic regression was used to model patient risk and combine risk factors. Models were compared using the area under the curve (AUC) of the receiver operating characteristic, and clinical utility was evaluated with a decision curve analysis (DCA).Results and limitations: HOXC6 and DLX1 mRNA levels were shown to be good predictors for the detection of high-grade PCa. The multimodal approach reached an overall AUC of 0.90 (95% confidence interval [CI], 0.85-0.95) in the validation cohort (AUC 0.86 in the training cohort), with the mRNA signature, prostate-specific antigen (PSA) density, and previous cancer-negative prostate biopsies as the strongest, most significant components, in addition to nonsignificant model contributions of PSA, age, and family history. For another model, which included DRE as an additional risk factor, an AUC of 0.86 (95% CI, 0.80-0.92) was obtained (AUC 0.90 in the training cohort). Both models were successfully validated, with no significant change in AUC in the validation cohort, and DCA indicated a strong net benefit and the best reduction in unnecessary biopsies compared with other clinical decision-making tools, such as the Prostate Cancer Prevention Trial risk calculator and the PCA3 assay.Conclusions: The risk score based on the mRNA liquid biopsy assay combined with traditional clinical risk factors identified men at risk of harboring high-grade PCa and resulted in a better patient risk stratification compared with current methods in clinical practice. Therefore, the risk score could reduce the number of unnecessary prostate biopsies.Patient summary: This study evaluated a novel urine-based assay that could be used as a noninvasive diagnostic aid for high-grade prostate cancer (PCa). When results of this assay are combined with traditional clinical risk factors, risk stratification for high-grade PCa and biopsy decision making are improved. (C) 2016 European Association of Urology. Published by Elsevier B.V. All rights reserved.