Improved prediction of biochemical recurrence after radical prostatectomy by genetic polymorphisms.

Abstract

PURPOSE 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. MATERIALS AND METHODS 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. RESULTS 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. CONCLUSIONS Predicting biochemical recurrence after radical prostatectomy based on clinicopathological data can be significantly improved by including patient genetic information.

DOI: 10.1016/j.juro.2010.03.144
01002003002011201220132014201520162017
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@article{Morote2010ImprovedPO, title={Improved prediction of biochemical recurrence after radical prostatectomy by genetic polymorphisms.}, author={Juan Morote and Jokin Del Amo and Angel Borque and Elisabet Ars and Carlos Hern{\'a}ndez and Felipe Herranz and Antonio Arruza and Roberto Llarena and Jacques Planas and Mar{\'i}a J Viso and Joan Palou and Carles X Ravent{\'o}s and Diego Tejedor and Marta Artieda and Laureano Sim{\'o}n and Antonio Mart{\'i}nez and Luis A Rioja}, journal={The Journal of urology}, year={2010}, volume={184 2}, pages={506-11} }