Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington.
Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina.
Prostate. 2019 Oct;79(14):1589-1596. doi: 10.1002/pros.23882. Epub 2019 Aug 2.
Molecular studies have tried to address the unmet need for prognostic biomarkers in prostate cancer (PCa). Some gene expression tests improve upon clinical factors for prediction of outcomes, but additional tools for accurate prediction of tumor aggressiveness are needed.
Based on a previously published panel of 23 gene transcripts that distinguished patients with metastatic progression, we constructed a prediction model using independent training and testing datasets. Using the validated messenger RNAs and Gleason score (GS), we performed model selection in the training set to define a final locked model to classify patients who developed metastatic-lethal events from those who remained recurrence-free. In an independent testing dataset, we compared our locked model to established clinical prognostic factors and utilized Kaplan-Meier curves and receiver operating characteristic analyses to evaluate the model's performance.
Thirteen of 23 previously identified gene transcripts that stratified patients with aggressive PCa were validated in the training dataset. These biomarkers plus GS were used to develop a four-gene (CST2, FBLN1, TNFRSF19, and ZNF704) transcript (4GT) score that was significantly higher in patients who progressed to metastatic-lethal events compared to those without recurrence in the testing dataset (P = 5.7 × 10 ). The 4GT score provided higher prediction accuracy (area under the ROC curve [AUC] = 0.76; 95% confidence interval [CI] = 0.69-0.83; partial area under the ROC curve [pAUC] = 0.008) than GS alone (AUC = 0.63; 95% CI = 0.56-0.70; pAUC = 0.002), and it improved risk stratification in subgroups defined by a combination of clinicopathological features (ie, Cancer of the Prostate Risk Assessment-Surgery).
Our validated 4GT score has prognostic value for metastatic-lethal progression in men treated for localized PCa and warrants further evaluation for its clinical utility.
分子研究试图解决前列腺癌（PCa）中未满足的预后生物标志物需求。一些基因表达测试提高了临床因素对预后的预测能力，但需要额外的工具来准确预测肿瘤侵袭性。
基于先前发表的一组 23 个基因转录本，这些转录本区分了转移性进展的患者，我们使用独立的训练和测试数据集构建了一个预测模型。使用验证的信使 RNA 和 Gleason 评分（GS），我们在训练集中进行模型选择，以定义一个最终的锁定模型，将发生转移性致死事件的患者与保持无复发的患者进行分类。在独立的测试数据集中，我们将我们的锁定模型与已建立的临床预后因素进行比较，并利用 Kaplan-Meier 曲线和接收者操作特征分析来评估模型的性能。
在训练数据集中验证了之前确定的 23 个基因转录本中有 13 个可分层患者的侵袭性 PCa。这些生物标志物加上 GS 用于开发一个四基因（CST2、FBLN1、TNFRSF19 和 ZNF704）转录本（4GT）评分，该评分在进展为转移性致死事件的患者中明显高于在测试数据集中无复发的患者（P=5.7×10-4）。4GT 评分提供了更高的预测准确性（ROC 曲线下面积 [AUC] = 0.76；95%置信区间 [CI] = 0.69-0.83；部分 ROC 曲线下面积 [pAUC] = 0.008）比 GS 单独（AUC = 0.63；95% CI = 0.56-0.70；pAUC = 0.002），并且它改善了根据临床病理特征组合定义的亚组的风险分层（即前列腺癌风险评估手术）。
我们验证的 4GT 评分对接受局部 PCa 治疗的男性的转移性致死进展具有预后价值，值得进一步评估其临床实用性。
