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Urol Oncol. 2013 Jul;31(5):566-71. doi: 10.1016/j.urolonc.2011.04.001. Epub 2011 May 19.

Combining urinary detection of TMPRSS2:ERG and PCA3 with serum PSA to predict diagnosis of prostate cancer.

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  • 1Division of Urology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.

Abstract

OBJECTIVES:

We sought to develop a clinical algorithm combining serum PSA with detection of TMPRSS2:ERG fusion and PCA3 in urine collected after digital rectal exam (post-DRE urine) to predict prostate cancer on subsequent biopsy.

MATERIALS AND METHODS:

Post-DRE urine was collected in 48 consecutive patients before prostate biopsy at 2 centers; quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to detect PCA3 and TMPRSS2:ERG fusion transcript expression. Serum PSA was measured by clinical assay. The performance of TMPRSS2:ERG fusion, PCA3, and serum PSA as biomarkers predicting prostate cancer at biopsy was measured; a clinically practical algorithm combining serum PSA with TMPRSS2:ERG and PCA3 in post-DRE urine to predict prostate cancer was developed.

RESULTS:

Post-DRE urine sediment provided informative RNA in 45 patients; prostate cancer was present on subsequent biopsy in 15. TMPRSS2:ERG in post-DRE urine was associated with prostate cancer (OR = 12.02; P < 0.001). PCA3 had the highest sensitivity in predicting prostate cancer diagnosis (93%), whereas TMPRSS2:ERG had the highest specificity (87%). TMPRSS2:ERG had the greatest discriminatory value in predicting prostate cancer (AUC = 0.77 compared with 0.65 for PCA3 and 0.72 for serum PSA alone). Combining serum PSA, PCA3, and TMPRSS2:ERG in a multivariable algorithm optimized for clinical utility improved cancer prediction (AUC = 0.88; specificity = 90% at 80% sensitivity).

CONCLUSIONS:

A clinical algorithm specifying biopsy for all patients with PSA ≥ 10 ng/ml, while restricting biopsy among those with PSA <10 ng/ml to only those with detectable PCA3 or TMPRSS2:ERG in post-DRE urine, performed better than the individual biomarkers alone in predicting prostate cancer.

Copyright © 2013 Elsevier Inc. All rights reserved.

PMID:
21600800
[PubMed - indexed for MEDLINE]
PMCID:
PMC3210917
Free PMC Article

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