利用一组联合评分系统的血浆和尿液生物标志物预测前列腺活检结果。
Predicting Prostate Biopsy Results Using a Panel of Plasma and Urine Biomarkers Combined in a Scoring System.
机构信息
1. NeoGenomics Laboratories, Irvine, CA;
2. Departments of Urology, Odense University Hospital, Odense, Denmark;
出版信息
J Cancer. 2016 Feb 2;7(3):297-303. doi: 10.7150/jca.12771. eCollection 2016.
BACKGROUND
Determining the need for prostate biopsy is frequently difficult and more objective criteria are needed to predict the presence of high grade prostate cancer (PCa). To reduce the rate of unnecessary biopsies, we explored the potential of using biomarkers in urine and plasma to develop a scoring system to predict prostate biopsy results and the presence of high grade PCa.
METHODS
Urine and plasma specimens were collected from 319 patients recommended for prostate biopsies. We measured the gene expression levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, ERG, GAPDH, B2M, AR, and PTEN in plasma and urine. Patient age, serum prostate-specific antigen (sPSA) level, and biomarkers data were used to develop two independent algorithms, one for predicting the presence of PCa and the other for predicting high-grade PCa (Gleason score [GS] ≥7).
RESULTS
Using training and validation data sets, a model for predicting the outcome of PCa biopsy was developed with an area under receiver operating characteristic curve (AUROC) of 0.87. The positive and negative predictive values (PPV and NPV) were 87% and 63%, respectively. We then developed a second algorithm to identify patients with high-grade PCa (GS ≥7). This algorithm's AUROC was 0.80, and had a PPV and NPV of 56% and 77%, respectively. Patients who demonstrated concordant results using both algorithms showed a sensitivity of 84% and specificity of 93% for predicting high-grade aggressive PCa. Thus, the use of both algorithms resulted in a PPV of 90% and NPV of 89% for predicting high-grade PCa with toleration of some low-grade PCa (GS <7) being detected.
CONCLUSIONS
This model of a biomarker panel with algorithmic interpretation can be used as a "liquid biopsy" to reduce the need for unnecessary tissue biopsies, and help to guide appropriate treatment decisions.
背景
确定是否需要进行前列腺活检通常较为困难，因此需要更客观的标准来预测是否存在高级别前列腺癌（PCa）。为了降低不必要的活检率，我们探索了使用尿液和血浆中的生物标志物来开发评分系统以预测前列腺活检结果和高级别 PCa 存在的可能性。
方法
从 319 名被推荐进行前列腺活检的患者中收集了尿液和血浆标本。我们测量了血浆和尿液中 UAP1、PDLIM5、IMPDH2、HSPD1、PCA3、PSA、TMPRSS2、ERG、GAPDH、B2M、AR 和 PTEN 的基因表达水平。使用患者年龄、血清前列腺特异性抗原（sPSA）水平和生物标志物数据来开发两种独立的算法，一种用于预测 PCa 的存在，另一种用于预测高级别 PCa（Gleason 评分[GS]≥7）。
结果
使用训练和验证数据集，开发了一种用于预测前列腺活检结果的模型，其受试者工作特征曲线下面积（AUROC）为 0.87。阳性和阴性预测值（PPV 和 NPV）分别为 87%和 63%。然后，我们开发了第二种算法来识别患有高级别 PCa（GS≥7）的患者。该算法的 AUROC 为 0.80，PPV 和 NPV 分别为 56%和 77%。使用两种算法均得出阳性结果的患者对预测高级别侵袭性 PCa 的敏感性为 84%，特异性为 93%。因此，使用两种算法可使预测高级别 PCa 的 PPV 达到 90%，NPV 达到 89%，同时可检测到一些低级别 PCa（GS<7）。
结论
该生物标志物面板模型与算法解释可作为“液体活检”用于减少不必要的组织活检，并有助于指导适当的治疗决策。
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