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Video: Genetic predisposition to early recurrence in clinically localized prostate cancer




Genetic predisposition to early recurrence in clinically localized prostate cancer

Ángel Borque, Jokin del Amo, Luis M. Esteban*, Elisabet Ars§, Carlos Hernández**, Jacques Planas, Antonio Arruza††, Roberto Llarena, Joan Palou§, Felipe Herranz**, Carles X. Raventós, Diego Tejedor, Marta Artieda, Laureano Simon, Antonio Martínez, Elena Carceller, Miguel Suárez, Marta Allué, Gerardo Sanz* and Juan Morote

‘Miguel Servet’ University Hospital, *University of Zaragoza, Zaragoza, Spain, Progenika Biopharma S.A., University Hospital of Cruces, Bilbao, §Puigvert Foundation, ‘Vall d’Hebron’ University Hospital, Barcelona, **‘Gregorio Marañón’ University Hospital, Madrid, and ††Hospital of Txagorritxu, Vitoria, Spain

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• 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.

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