Improved prediction of biochemical recurrence after radical prostatectomy by genetic polymorphisms.
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.
Morote
Juan
J
Hospital Universitario Vall d'Hebron, Barcelona, Spain. jmorote@vhebron.net
Del Amo
Jokin
J
Borque
Angel
A
Ars
Elisabet
E
Hernández
Carlos
C
Herranz
Felipe
F
Arruza
Antonio
A
Llarena
Roberto
R
Planas
Jacques
J
Viso
María J
MJ
Palou
Joan
J
Raventós
Carles X
CX
Tejedor
Diego
D
Artieda
Marta
M
Simón
Laureano
L
Martínez
Antonio
A
Rioja
Luis A
LA
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