title: Improved prediction of biochemical recurrence after radical prostatectomy by genetic polymorphisms.
abstract: OBJECTIVE
  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.
  
  
  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.
authors.affiliation: Hospital Universitario Vall d'Hebron, Barcelona, Spain. jmorote@vhebron.net
authors.forename: Juan
authors.initials: J
authors.lastname: Morote
authors.forename: Jokin
authors.initials: J
authors.lastname: Del Amo
authors.forename: Angel
authors.initials: A
authors.lastname: Borque
authors.forename: Elisabet
authors.initials: E
authors.lastname: Ars
authors.forename: Carlos
authors.initials: C
authors.lastname: Hernández
authors.forename: Felipe
authors.initials: F
authors.lastname: Herranz
authors.forename: Antonio
authors.initials: A
authors.lastname: Arruza
authors.forename: Roberto
authors.initials: R
authors.lastname: Llarena
authors.forename: Jacques
authors.initials: J
authors.lastname: Planas
authors.forename: María J
authors.initials: MJ
authors.lastname: Viso
authors.forename: Joan
authors.initials: J
authors.lastname: Palou
authors.forename: Carles X
authors.initials: CX
authors.lastname: Raventós
authors.forename: Diego
authors.initials: D
authors.lastname: Tejedor
authors.forename: Marta
authors.initials: M
authors.lastname: Artieda
authors.forename: Laureano
authors.initials: L
authors.lastname: Simón
authors.forename: Antonio
authors.initials: A
authors.lastname: Martínez
authors.forename: Luis A
authors.initials: LA
authors.lastname: Rioja
mesh_terms: D000368:Aged; D006801:Humans; D008297:Male; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000601:surgery*; D012189:Retrospective Studies