Hospital Universitario Vall d'Hebron, Barcelona, Spain.
J Urol. 2010 Aug;184(2):506-11. doi: 10.1016/j.juro.2010.03.144. Epub 2010 Jun 17.
Single nucleotide polymorphisms are inherited genetic variations that can predispose or protect individuals against clinical events. We hypothesized that single nucleotide polymorphism profiling may improve the prediction of biochemical recurrence after radical prostatectomy.
We performed a retrospective, multi-institutional study of 703 patients treated with radical prostatectomy for clinically localized prostate cancer who had at least 5 years of followup after surgery. All patients were genotyped for 83 prostate cancer related single nucleotide polymorphisms using a low density oligonucleotide microarray. Baseline clinicopathological variables and single nucleotide polymorphisms were analyzed to predict biochemical recurrence within 5 years using stepwise logistic regression. Discrimination was measured by ROC curve AUC, specificity, sensitivity, predictive values, net reclassification improvement and integrated discrimination index.
The overall biochemical recurrence rate was 35%. The model with the best fit combined 8 covariates, including the 5 clinicopathological variables prostate specific antigen, Gleason score, pathological stage, lymph node involvement and margin status, and 3 single nucleotide polymorphisms at the KLK2, SULT1A1 and TLR4 genes. Model predictive power was defined by 80% positive predictive value, 74% negative predictive value and an AUC of 0.78. The model based on clinicopathological variables plus single nucleotide polymorphisms showed significant improvement over the model without single nucleotide polymorphisms, as indicated by 23.3% net reclassification improvement (p = 0.003), integrated discrimination index (p <0.001) and likelihood ratio test (p <0.001). Internal validation proved model robustness (bootstrap corrected AUC 0.78, range 0.74 to 0.82). The calibration plot showed close agreement between biochemical recurrence observed and predicted probabilities.
Predicting biochemical recurrence after radical prostatectomy based on clinicopathological data can be significantly improved by including patient genetic information.
单核苷酸多态性是一种遗传变异，可使个体易患或对临床事件具有保护作用。我们假设，单核苷酸多态性分析可能会提高前列腺癌根治术后生化复发的预测能力。
我们对 703 例接受前列腺癌根治术治疗局限性前列腺癌的患者进行了回顾性、多机构研究，这些患者在手术后至少有 5 年的随访。所有患者均采用低密寡核苷酸微阵列技术对 83 个前列腺癌相关的单核苷酸多态性进行基因分型。采用逐步逻辑回归法，对基线临床病理变量和单核苷酸多态性进行分析，以预测术后 5 年内的生化复发。采用 ROC 曲线 AUC、特异性、敏感性、预测值、净再分类改善和综合鉴别指数来衡量鉴别能力。
总的生化复发率为 35%。拟合效果最佳的模型包含 8 个协变量，包括前列腺特异抗原、Gleason 评分、病理分期、淋巴结受累和切缘状态等 5 个临床病理变量，以及 KLK2、SULT1A1 和 TLR4 基因的 3 个单核苷酸多态性。模型预测能力的定义为阳性预测值 80%、阴性预测值 74%和 AUC 为 0.78。基于临床病理变量和单核苷酸多态性的模型显示，与不包含单核苷酸多态性的模型相比，具有显著的改善，表现为净再分类改善 23.3%（p = 0.003）、综合鉴别指数（p <0.001）和似然比检验（p <0.001）。内部验证证明了模型的稳健性（bootstrap 校正 AUC 为 0.78，范围为 0.74 至 0.82）。校准图显示，观察到的生化复发与预测的概率之间具有紧密的一致性。
基于临床病理数据预测前列腺癌根治术后的生化复发，通过纳入患者的遗传信息，可以显著提高预测能力。
