Potential impact of adding genetic markers to clinical parameters in predicting prostate biopsy outcomes in men following an initial negative biopsy: findings from the REDUCE trial

Abstract

Background: Several germline single nucleotide polymorphisms (SNPs) have been consistently associated with prostate cancer (PCa) risk.

Objective: To determine whether there is an improvement in PCa risk prediction by adding these SNPs to existing predictors of PCa.

Design, setting, and participants: Subjects included men in the placebo arm of the randomized Reduction by Dutasteride of Prostate Cancer Events (REDUCE) trial in whom germline DNA was available. All men had an initial negative prostate biopsy and underwent study-mandated biopsies at 2 yr and 4 yr. Predictive performance of baseline clinical parameters and/or a genetic score based on 33 established PCa risk-associated SNPs was evaluated.

Outcome measurements and statistical analysis: Area under the receiver operating characteristic curves (AUC) were used to compare different models with different predictors. Net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess changes in risk prediction by adding genetic markers.

Results and limitations: Among 1654 men, genetic score was a significant predictor of positive biopsy, even after adjusting for known clinical variables and family history (p = 3.41 × 10(-8)). The AUC for the genetic score exceeded that of any other PCa predictor at 0.59. Adding the genetic score to the best clinical model improved the AUC from 0.62 to 0.66 (p<0.001), reclassified PCa risk in 33% of men (NRI: 0.10; p=0.002), resulted in higher net benefit from DCA, and decreased the number of biopsies needed to detect the same number of PCa instances. The benefit of adding the genetic score was greatest among men at intermediate risk (25th percentile to 75th percentile). Similar results were found for high-grade (Gleason score ≥ 7) PCa. A major limitation of this study was its focus on white patients only.

Conclusions: Adding genetic markers to current clinical parameters may improve PCa risk prediction. The improvement is modest but may be helpful for better determining the need for repeat prostate biopsy. The clinical impact of these results requires further study.

Copyright © 2012 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Figures

Fig. 1

Four-year observed detection rates of prostate cancer (PCa) among men who were classified at low (1st quartile), intermediate (2nd and 3rd quartiles), or high (4th quartile) estimated risk for PCa in two sequential prediction models. The observed detection rates for (a) any PCa and (b) high-grade PCa (Gleason score ≥7) are shown. In both figures, the top panel presents PCa detection rates of the initial prediction model based on the best clinical model, consisting of five clinical variables: age, family history, free-to-total prostate-specific antigen ratio, number of cores at base biopsy, and prostate volume. The bottom panel presents PCa detection rates of the revised prediction model by adding the genetic score estimated from 33 single nucleotide polymorphisms to the best clinical model. In subsets of men, PCa risk categories either remained the same (dotted bars) or were reclassified (hatched bars). The reclassified risk correlated better with the observed PCa detection rates. PCa = prostate cancer.

Fig. 2

Decision curves for predicting prostate cancer at prostate biopsy. The y-axis represents the net benefit calculated using the methods proposed by Vickers et al. [25,26]. The x-axis represents the threshold probability (percentage) estimated from prediction models. The two vertical lines represent the 25th and 75th percentiles of estimated risk (intermediate risk) in the population, corresponding to 17% and 31% of threshold probabilities, respectively. The dotted line represents the prediction model based on the best clinical model, consisting of five clinical variables: age, family history, free-to-total prostate-specific antigen ratio, number of cores at base biopsy, and prostate volume. The dashed line represents the prediction model based on the best clinical model plus the genetic score. GS = genetic score.

Fig. 3

Curves demonstrating the number of men needed to undergo prostate biopsy to identify a given number of (a) overall or (b) high-grade disease based on the clinical model alone (blue) or with the genetic score (orange). Horizontal hatched lines represent the number of biopsies required to identify two-thirds of cancers (represented by the vertical blue line) based on the clinical model alone (blue) or with the genetic score (orange). PCa = prostate cancer; GS = genetic score.