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Platinum Priority – Prostate Cancer
Editorial by XXX on pp. x–y of this issue

Urine TMPRSS2:ERG Plus PCA3 for Individualized Prostate Cancer Risk Assessment

By: Scott A. Tomlins a b c lowast , John R. Day d , Robert J. Lonigro a c , Daniel H. Hovelson e , Javed Siddiqui a , L. Priya Kunju a , Rodney L. Dunn b , Sarah Meyer d , Petrea Hodge d , Jack Groskopf d , John T. Wei b and Arul M. Chinnaiyan a b c e f lowastlowast

European Urology, Volume 70 Issue 1, July 2016, Pages 45-53

Published online: 01 July 2016

Keywords: Prostate cancer, Urine biomarkers, Early detection, Gene fusions, PCA3

Abstract Full Text Full Text PDF (450 KB) Patient Summary

Abstract

Background

TMPRSS2:ERG (T2:ERG) and prostate cancer antigen 3 (PCA3) are the most advanced urine-based prostate cancer (PCa) early detection biomarkers.

Objective

Validate logistic regression models, termed Mi-Prostate Score (MiPS), that incorporate serum prostate-specific antigen (PSA; or the multivariate Prostate Cancer Prevention Trial risk calculator version 1.0 [PCPTrc]) and urine T2:ERG and PCA3 scores for predicting PCa and high-grade PCa on biopsy.

Design, setting, and participants

T2:ERG and PCA3 scores were generated using clinical-grade transcription-mediated amplification assays. Pretrained MiPS models were applied to a validation cohort of whole urine samples prospectively collected after digital rectal examination from 1244 men presenting for biopsy.

Outcome measurements and statistical analysis

Area under the curve (AUC) was used to compare the performance of serum PSA (or the PCPTrc) alone and MiPS models. Decision curve analysis (DCA) was used to assess clinical benefit.

Results and limitations

Among informative validation cohort samples (n = 1225 [98%], 80% from patients presenting for initial biopsy), models incorporating T2:ERG had significantly greater AUC than PSA (or PCPTrc) for predicting PCa (PSA: 0.693 vs 0.585; PCPTrc: 0.718 vs 0.639; both p < 0.001) or high-grade (Gleason score >6) PCa on biopsy (PSA: 0.729 vs 0.651, p < 0.001; PCPTrc: 0.754 vs 0.707, p = 0.006). MiPS models incorporating T2:ERG score had significantly greater AUC (all p < 0.001) than models incorporating only PCA3 plus PSA (or PCPTrc or high-grade cancer PCPTrc [PCPThg]). DCA demonstrated net benefit of the MiPS_PCPTrc (or MiPS_PCPThg) model compared with the PCPTrc (or PCPThg) across relevant threshold probabilities.

Conclusions

Incorporating urine T2:ERG and PCA3 scores improves the performance of serum PSA (or PCPTrc) for predicting PCa and high-grade PCa on biopsy.

Patient summary

Incorporation of two prostate cancer (PCa)-specific biomarkers (TMPRSS2:ERG and PCA3) measured in the urine improved on serum prostate-specific antigen (or a multivariate risk calculator) for predicting the presence of PCa and high-grade PCa on biopsy. A combined test, Mi-Prostate Score, uses models validated in this study and is clinically available to provide individualized risk estimates.

Take Home Message

Using a large contemporary cohort, we validated individualized risk models incorporating serum prostate-specific antigen (or the Prostate Cancer Prevention Trial risk calculators) and urine TMPRSS2:ERG and PCA3 scores for predicting prostate cancer and high-grade prostate cancer risk.

Keywords: Prostate cancer, Urine biomarkers, Early detection, Gene fusions, PCA3.

Footnotes

a Michigan Center for Translational Pathology, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA

b Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA

c Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA

d Hologic/Gen-Probe Inc., San Diego, CA, USA

e Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA

f Howard Hughes Medical Institute, University of Michigan Medical School, Ann Arbor, MI, USA

Corresponding author. University of Michigan Medical School, 1524 BSRB, 109 Zina Pitcher Place, Ann Arbor, MI 48109-2200, USA. Tel. +1 734 764 1549.

⁎⁎ Corresponding author. University of Michigan Medical School, 1400 E. Medical Center Drive, 5316 CCGC, Ann Arbor, MI 48109-0602, USA. Tel. +1 734 615 4062.

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