Evidence map›Paper›PMID 25985884›Full record

ArticleEuropean urology2016

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

Scott A Tomlins, John R Day, Robert J Lonigro, Daniel H Hovelson, Javed Siddiqui, L Priya Kunju, Rodney L Dunn, Sarah Meyer, Petrea Hodge, Jack Groskopf and 2 more

Open access · greenAbstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European urology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 207 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
207citing papers in PubMed, 5 pooled it
29.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

207 citing papers in PubMed, 5 syntheses or guidelines pooled it, 388 citations in OpenAlex.

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  18. Bioinformatics Approaches in Noncoding RNAs Research.Experientia supplementum (2012) · 2026
    Review
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147 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 1 country.

Scott A TomlinsMichigan Center for Translational Pathology, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA; Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: tomlinss@umich.edu.
John R DayHologic/Gen-Probe Inc., San Diego, CA, USA.
Robert J LonigroMichigan Center for Translational Pathology, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA.
Daniel H HovelsonDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
Javed SiddiquiMichigan Center for Translational Pathology, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA.
L Priya KunjuMichigan Center for Translational Pathology, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA.
Rodney L DunnDepartment of Urology, University of Michigan Medical School, Ann Arbor, MI, USA.
Sarah MeyerHologic/Gen-Probe Inc., San Diego, CA, USA.
Petrea HodgeHologic/Gen-Probe Inc., San Diego, CA, USA.
Jack GroskopfHologic/Gen-Probe Inc., San Diego, CA, USA.
John T WeiDepartment of Urology, University of Michigan Medical School, Ann Arbor, MI, USA.
Arul M ChinnaiyanMichigan Center for Translational Pathology, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA; Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Howard Hughes Medical Institute, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: arul@umich.edu.
Hologic (United States) · USUniversity of Michigan–Ann Arbor · USMichigan Center for Translational Pathology · US

Funding

Tissue/InformaticsP50CA186786 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ganesh S Palapattu · 2014 to 2026
$27.6M
Prostate Cancer Biomarker and Imaging Validation Alliance: Emory University, University of Alabama Birmingham, and University of Texas SouthwesternU01CA113913 · NCI · EMORY UNIVERSITY · PI Martin G. Sanda · 2005 to 2026
$16.4M
Epitomic Biomarkers of Prostate CancerU01CA111275 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHINNAIYAN, ARUL M, RUBIN, MARK A. · 2004 to 2015
$6.8M
Molecuar Sub-typing of Prostate Cancer Based on Recurrent Gene FusionsR01CA132874 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHINNAIYAN, ARUL M · 2009 to 2013
$1.2M
NCI NIH HHS P50 CA186786NCI NIH HHS R01 CA132874NCI NIH HHS U01 CA111275NCI NIH HHS U01 CA113913
6 · The paper itself

Abstract

backgroundTMPRSS2:ERG (T2:ERG) and prostate cancer antigen 3 (PCA3) are the most advanced urine-based prostate cancer (PCa) early detection biomarkers.

objectiveValidate 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

participantsT2: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.

conclusionsIncorporating 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.

Indexed as

AgedAntigens, NeoplasmArea Under CurveBiomarkers, TumorBiopsyHumansLogistic ModelsMaleMiddle AgedNeoplasm GradingOncogene Proteins, FusionPredictive Value of TestsProstateProstate-Specific AntigenProstatic NeoplasmsRisk AssessmentAntigens, NeoplasmBiomarkers, TumorOncogene Proteins, Fusionprostate cancer antigen 3, humanProstate-Specific AntigenTMPRSS2-ERG fusion protein, humanEarly detectionGene fusionsPCA3Prostate cancerUrine biomarkers

Identifiers

PMID25985884
PMCPMC4644724
OpenAlexW1573867559

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.