Genetic predisposition to early recurrence in clinically localized prostate cancer

作者：Borque Angel^*; del Amo Jokin; Esteban Luis M; Ars Elisabet; Hernandez Carlos; Planas Jacques; Arruza Antonio; Llarena Roberto; Palou Joan; Herranz Felipe; Raventos Carles X; Tejedor Diego; Artieda Marta; Simon Laureano; Martinez Antonio; Carceller Elena; Suarez Miguel; Allue Marta; Sanz Gerardo; Morote Juan

来源：BJU International, 2013, 111(4): 549-558.

DOI：10.1111/j.1464-410X.2012.11333.x

• prostate cancer
• nomograms
• progression-free survival
• genetics
• single nucleotide polymorphisms

Objectives %26lt;br%26gt;To evaluate genetic susceptibility to early biochemical recurrence (EBCR) after radical prostatectomy (RP), as a prognostic factor for early systemic dissemination. %26lt;br%26gt;To build a preoperative nomogram to predict EBCR combining genetic and clinicopathological factors. %26lt;br%26gt;Patients and Methods %26lt;br%26gt;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. %26lt;br%26gt;EBCR was defined as a level prostate-specific antigen %26gt;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). %26lt;br%26gt;Univariate allele association tests and multivariate logistic regression were used to generate predictive models for EBCR, with clinicopathological factors and adding SNPs. %26lt;br%26gt;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%26apos; decision curves. %26lt;br%26gt;Results %26lt;br%26gt;Four common SNPs at KLK3, KLK2, SULT1A1 and BGLAP genes were independently associated with EBCR. %26lt;br%26gt;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). %26lt;br%26gt;Net reclassification improvement showed a significant increase in probability for events of 60.7% and a decrease for non-events of 63.5%. %26lt;br%26gt;Integrated discrimination improvement and decision curves confirmed the superiority of the new model. %26lt;br%26gt;Conclusions %26lt;br%26gt;Four SNPs associated with EBCR significantly improved the accuracy of clinicopathological factors. %26lt;br%26gt;We present a nomogram for preoperative prediction of EBCR after RP.

• 出版日期2013-4