Articles

Platinum Priority – Prostate Cancer
Editorial by Matthew R. Cooperberg on pp. 962–963 of this issue

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

By: A. Karim Kadera f 1, Jielin Suna b 1, Brian H. Reckg 1, Paul J. Newcombei, Seong-Tae Kima b, Fang-Chi Hsua d, Ralph B. D’Agostino Jr.d, Sha Taoa b, Zheng Zhanga b, Aubrey R. Turnera b, Greg T. Platekh, Colin F. Spraggsi, John C. Whittakeri, Brian R. Lanej, William B. Isaacsk, Deborah A. Meyersb, Eugene R. Bleeckerb, Frank M. Tortie, Jeffery M. Trentl, John D. McConnellc, S. Lilly Zhenga b, Lynn D. Condreayg, Roger S. Rittmasterh and Jianfeng Xua b c e l lowast

European Urology, Volume 62 Issue 1, December 2012, Pages 953-961

Published online: 01 December 2012

Keywords: Prostate cancer, Genetics, AUC, Detection rate, Reclassification, SNPs, Prospective study, Clinical trial

Abstract Full Text Full Text PDF (1,0 MB)

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.

Take Home Message

A single nucleotide polymorphism-based genetic score is a prostate cancer (PCa) risk factor. This study suggests that those men with intermediate clinical risk following an initial negative prostate biopsy for PCa may benefit most from this form of testing.

Keywords: Prostate cancer, Genetics, AUC, Detection rate, Reclassification, SNPs, Prospective study, Clinical trial.

Footnotes

a Center for Cancer Genomics, Wake Forest University School of Medicine, Winston-Salem, NC, USA

b Department of Genomics and Personalized Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA

c Department of Urology, Wake Forest University School of Medicine, Winston-Salem, NC, USA

d Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA

e Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC, USA

f Division of Urology, University of California, San Diego, San Diego, CA, USA

g Genetics, GlaxoSmithKline, Research Triangle Park, NC, USA

h Oncology Development, GlaxoSmithKline, Research Triangle Park, NC, USA

i Genetics, GlaxoSmithKline, Harlow, United Kingdom

j Division of Urology, Spectrum Health, Grand Rapids, MI, USA

k Johns Hopkins Medical Institutions, Baltimore, MD, USA

l Van Andel Research Institute, Grand Rapids, MI, USA

lowast Corresponding author. Center for Cancer Genomics, Medical Center Blvd, Winston-Salem, NC 27157, USA. Tel. +1 336 713 7500; Fax: +1 336 713 7566.

1 These authors contributed equally to this work.

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