Department of Urology, Miguel Servet University Hospital, P° Isabel la Catolica 1 y 3, 50.009-Zaragoza, Spain.
BJU Int. 2013 Apr;111(4):549-58. doi: 10.1111/j.1464-410X.2012.11333.x. Epub 2012 Jul 3.
WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: Currently available nomograms to predict preoperative risk of early biochemical recurrence (EBCR) after radical prostatectomy are solely based on classic clinicopathological variables. Despite providing useful predictions, these models are not perfect. Indeed, most researchers agree that nomograms can be improved by incorporating novel biomarkers. In the last few years, several single nucleotide polymorphisms (SNPs) have been associated with prostate cancer, but little is known about their impact on disease recurrence. We have identified four SNPs associated with EBCR. The addition of SNPs to classic nomograms resulted in a significant improvement in terms of discrimination and calibration. The new nomogram, which combines clinicopathological and genetic variables, will help to improve prediction of prostate cancer recurrence.
To evaluate genetic susceptibility to early biochemical recurrence (EBCR) after radical prostatectomy (RP), as a prognostic factor for early systemic dissemination. To build a preoperative nomogram to predict EBCR combining genetic and clinicopathological factors.
We evaluated 670 patients from six University Hospitals who underwent RP for clinically localized prostate cancer (PCa), and were followed-up for at least 5 years or until biochemical recurrence. EBCR was defined as a level prostate-specific antigen >0.4 ng/mL within 1 year of RP; preoperative variables studied were: age, prostate-specific antigen, clinical stage, biopsy Gleason score, and the genotype of 83 PCa-related single nucleotide polymorphisms (SNPs). Univariate allele association tests and multivariate logistic regression were used to generate predictive models for EBCR, with clinicopathological factors and adding SNPs. We internally validated the models by bootstrapping and compared their accuracy using the area under the curve (AUC), net reclassification improvement, integrated discrimination improvement, calibration plots and Vickers' decision curves.
Four common SNPs at KLK3, KLK2, SULT1A1 and BGLAP genes were independently associated with EBCR. A significant increase in AUC was observed when SNPs were added to the model: AUC (95% confidence interval) 0.728 (0.674-0.784) vs 0.763 (0.708-0.817). Net reclassification improvement showed a significant increase in probability for events of 60.7% and a decrease for non-events of 63.5%. Integrated discrimination improvement and decision curves confirmed the superiority of the new model.
Four SNPs associated with EBCR significantly improved the accuracy of clinicopathological factors. We present a nomogram for preoperative prediction of EBCR after RP.
目前用于预测根治性前列腺切除术后早期生化复发（EBCR）的列线图仅基于经典的临床病理变量。尽管提供了有用的预测，但这些模型并不完美。事实上，大多数研究人员都认为，通过纳入新的生物标志物，列线图可以得到改善。在过去的几年中，已经发现了几个与前列腺癌相关的单核苷酸多态性（SNP），但对它们对疾病复发的影响知之甚少。我们已经确定了与 EBCR 相关的四个 SNP。将 SNP 添加到经典列线图中可显著提高区分度和校准度。该新列线图结合了临床病理和遗传变量，将有助于改善前列腺癌复发的预测。
评估遗传易感性对根治性前列腺切除术后（RP）早期生化复发（EBCR）的影响，EBCR 是早期全身播散的预后因素。建立一个用于预测 EBCR 的术前列线图，该列线图结合了遗传和临床病理因素。
我们评估了来自六所大学医院的 670 名接受 RP 治疗的局限性前列腺癌（PCa）患者，并进行了至少 5 年的随访或直至生化复发。EBCR 定义为 RP 后 1 年内前列腺特异性抗原（PSA）>0.4ng/mL；研究的术前变量包括：年龄、PSA、临床分期、前列腺活检 Gleason 评分以及 83 个前列腺癌相关单核苷酸多态性（SNP）的基因型。采用单变量等位基因关联检验和多变量逻辑回归生成 EBCR 预测模型，使用临床病理因素和添加 SNP。我们通过自举法对内部分别进行了验证，并通过曲线下面积（AUC）、净重新分类改善、综合鉴别改善、校准图和 Vickers 决策曲线比较了它们的准确性。
KLK3、KLK2、SULT1A1 和 BGLAP 基因的四个常见 SNP 与 EBCR 独立相关。当 SNP 被添加到模型中时，AUC 显著增加：AUC（95%置信区间）为 0.728（0.674-0.784）与 0.763（0.708-0.817）。净重新分类改善显示事件的概率显著增加了 60.7%，非事件的概率降低了 63.5%。综合鉴别改善和决策曲线证实了新模型的优越性。
与 EBCR 相关的四个 SNP 显著提高了临床病理因素的准确性。我们提出了一种用于预测 RP 后 EBCR 的术前列线图。
