Prostate Cancer Gene 3 (PCA3): Development and Internal Validation of a Novel Biopsy Nomogram

doi:10.1016/j.eururo.2009.03.029

European Urology

Volume 56, Issue 4, October 2009, Pages 659-668

https://doi.org/10.1016/j.eururo.2009.03.029 Get rights and content

Abstract

Background

Urinary prostate cancer gene 3 (PCA3) represents a promising novel marker of prostate cancer detection.

Objective

To test whether urinary PCA3 assay improves prostate cancer (PCa) risk assessment and to construct a decision-making aid in a multi-institutional cohort with pre–prostate biopsy data.

Design, setting, and participants

PCA3 assay cut-off threshold analyses were followed by logistic regression models which used established predictors to assess PCa-risk at biopsy in a large multi-institutional data set of 809 men at risk of harboring PCa.

Measurements

Regression coefficients were used to construct four sets of nomograms. Predictive accuracy (PA) estimates of biopsy outcome predictions were quantified using the area under the curve of the receiver operator characteristic analysis in models with and without PCA3. Bootstrap resamples were used for internal validation and to reduce overfit bias. The extent of overestimation or underestimation of the observed PCa rate at biopsy was explored graphically using nonparametric loss-calibration plots. Differences in PA were tested using the Mantel-Haenszel test. Finally, nomogram-derived probability cut-offs were tested to assess the ability to identify patients with or without PCa.

Results and limitations

PCA3 was identified as a statistically independent risk factor of PCa at biopsy. Addition of a PCA3 assay improved bootstrap-corrected multivariate PA of the base model between 2% and 5%. The highest increment in PA resulted from a PCA3 assay cut-off threshold of 17, where a 5% gain in PA (from 0.68 to 0.73, p = 0.04) was recorded. Nomogram probability–derived risk cut-off analyses further corroborate the superiority of the PCA3 nomogram over the base model.

Conclusions

PCA3 fulfills the criteria for a novel marker capable of increasing PA of multivariate biopsy models. This novel PCA3-based nomogram better identifies men at risk of harboring PCa and assists in deciding whether further evaluation is necessary.

Introduction

Development of biomarkers by genomic and proteomic high-throughput platforms has garnered great expectations of improving cancer screening, early detection, staging, and prognosis. Recently, the urinary prostate cancer gene 3 (PCA3) assay has shown promising results for prostate cancer (PCa) detection. This assay measures PCA3–messenger ribonucleic acid (mRNA) and prostate-specific antigen (PSA)–mRNA concentrations in post–digital rectal examination (post-DRE) urine [1]. PCA3 is highly overexpressed (median: 66-fold) in malignant prostate tissue compared with benign and normal tissues [2]. Several studies demonstrated superior sensitivity and specificity of the PCA3 assay score over that of PSA level [3], [4], [5]. These findings could translate into improved identification of men at risk of harboring PCa and reduction in the number of unnecessary biopsies. Consequently, a urinary PCA3 assay was developed and was made available for clinical use as a Conformité Européenne (CE)–marked product [1].

Beyond univariate and highly discriminatory ability, improvement of sensitivity and specificity, and confirmation of its independent predictor status, it is mandatory for a novel marker to increase the combined multivariate predictive accuracy (PA) of established risk factors. Furthermore, the increase in multivariate PA should not only be statistically significant but should also address a significant number of individuals. If these criteria are met, the novel marker may be considered clinically meaningful, and its application in clinical practice can be justified [6], [7]. PCA3 has never been tested in a multivariate biopsy nomogram setting. To address this void, we tested several cut-off thresholds of urinary PCA3 assay scores in the largest reported PCA3 biopsy data set to date; we applied stringent analytic methods in addition to testing the multivariate independent status of PCA3; and we quantified the increment in PA related to its inclusion to established risk factors in risk models for biopsy outcome.

Section snippets

Patient populations

Data were collected from 1206 men subjected to ≥10 cores at initial or repeat prostate biopsy from two prospective, multicenter studies from Europe and North America. Men receiving medical therapy affecting PSA levels, men with symptoms of urinary tract infection, and men with a history of PCa or invasive treatment for benign prostatic hyperplasia (BPH) were not recruited for the studies. After exclusion of 397 men due to missing variables, 809 men remained in the cohort to develop a biopsy

Results

Patient characteristics are shown in Table 1. PCa was detected in 316 men (39.1%). Median PCA3 score was 25.9 (0.2–366.9). Mean and median PCA3 scores were significantly higher in the PCa versus the biopsy negative group (56.5 and 37.4 vs 34.6 and 19.5, respectively) (p < 0.001). Mean and median ages were 65 yr and 66 yr, respectively (p > 0.05). PSA levels ranged from 0.1 ng/ml to 48.5 ng/ml (mean: 7.4; median: 6.3). Most patients exhibited a normal DRE (n = 586, 72.4%) and a history of previous

Discussion

In this study, we used the most stringent methodologic criteria suggested by Kattan, for which, in addition to demonstrating its independent predictor status, the candidate marker should enhance the overall PA of established predictors [6], [7]. We added this methodology to the standard univariate and multivariate tests of the candidate marker PCA3. In univariate analyses predicting PCa at biopsy, all forms of PCA3 coding represented a statistically significant predictor (all p < 0.001), and they

Conclusions

In conclusion, PCA3 was identified as a statistically independent and informative novel marker that is capable of increasing the PA of multivariate biopsy models. We constructed a novel PCA3-based individual risk stratification tool to identify men at risk of harboring PCa. It may assist patients and clinicians in deciding whether further prostatic evaluations are necessary.

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A novel nomogram combined PIRADS v2 and neutrophil-to-lymphocyte ratio to predict the risk of clinically significant prostate cancer in men with PSA < 10 ng/ml at first biopsy
2020, Urologic Oncology: Seminars and Original Investigations
To determine whether Prostate Imaging-Reporting and Data System version 2 (PIRADS v2) and neutrophil-to-lymphocyte ratio(NLR) improve the detection of clinically significant prostate cancer(csCaP) in men with prostate-specific antigen (PSA) <10 ng/ml at first biopsy.
Univariable and multivariable binary logistic regression analysis were used to screen for independent risk factors of csCaP. The multivariable model based on the risk factors was to build the nomogram predicting csCaP and assessed by receiver operator characteristic curve analysis, calibration plot, and decision curve analysis.
This retrospective study included 335 men with PSA < 10 ng/ml who underwent initial biopsy. A total of 78 (23.3%) men had csCaP. The nomogram was built based on the multivariable model including age, digital rectal examination, free prostate-specific antigen, PIRADS v2, and NLR. It had high area under the curve of 0.876 and was well calibrated in internal validation. Decision curve analysis also demonstrated that it would improve the prediction of csCaP.
PIRADS v2 and NLR improve the detection of csCaP in men with PSA < 10 ng/ml at first biopsy. Due to lack of external validation, relatively small cohort and homogenous population, the study has several limitations. Despite of this, the nomogram based on our study is a promising tool for patients to understand their risk of csCaP and for urologists to make clinical decisions.
Application of Urinary Volatile Organic Compounds (VOCs) for the Diagnosis of Prostate Cancer
2019, Clinical Genitourinary Cancer
Prostate cancer (PCa) screening using serum prostate-specific antigen (PSA) testing has caused unnecessary biopsies and overdiagnosis owing to its low accuracy and reliability. Therefore, there is an increased interest in identifying better PCa biomarkers. Studies showed that trained dogs can discriminate patients with PCa from unaffected men by sniffing urine. We hypothesized that urinary volatile organic compounds (VOCs) may be the source of that odor and could be used to develop urinary VOC PCa diagnosis models.
Urine samples from 55 and 53 biopsy proven PCa-positive and -negative patients respectively were initially obtained for diagnostic model development. Urinary metabolites were analyzed by gas chromatography-mass spectrometry. A PCa diagnosis model was developed and validated using innovative statistical machine-learning techniques. A second set of samples (53 PCa-positive and 22 PCa-negative patients) were used to evaluate the previously developed PCa diagnosis model.
The analysis resulted in 254 and 282 VOCs for their significant association (P < .05) with either PCa-positive or -negative samples respectively. Regularized logistic regression analysis and the Firth method were then applied to predict PCa prevalence, resulting in a final model that contains 11 VOCs. Under cross-validation, the area under the receiver operating characteristic curve (AUC) for the final model was 0.92 (sensitivity, 0.96; specificity, 0.80). Further evaluation of the developed model using a testing cohort yielded an AUC of 0.86. As a comparison, the PSA-based diagnosis model only rendered an AUC of 0.54.
The study describes the development of a urinary VOC-based model for PCa detection.
Analysis of competing endogenous RNA network to identify the key RNAs associated with prostate adenocarcinoma
2018, Pathology Research and Practice
Citation Excerpt :
PCA3 is overexpressed in most PRAD cases [19], and it produces a prostate-specific noncoding mRNA that can act as a biomarker for PRAD diagnosis [17]. Urinary PCA3 is a potential marker for detection of PRAD; a PCA3-based nomogram can better recognize men harboring high-risk PRAD and confirm whether further evaluation is needed [9,11]. UCA1 acts as an oncogenic lncRNA in some malignant tumors such as bladder cancer [40] and renal cell carcinoma [30], and it may be used as a promising biomarker for human cancers.
Prostate adenocarcinoma (PRAD) is the most common cancer in men. The aim of this study was to reveal the critical long non-coding RNA (lncRNAs), microRNA (miRNAs) and mRNAs involved in the pathogenesis of PRAD.
The level 3 mRNA and miRNA sequencing data of PRAD were downloaded from The Cancer Genome Atlas database. Using the edgeR package of R, the differentially expressed mRNAs (DEGs), lncRNAs (DE-lncRNAs) and miRNAs (DE-miRNAs) between PRAD and normal tissues were screened. The Cox proportional hazards regression method in the survival package was used to select the lncRNAs significantly related to clinical characteristics. After the miRNA-lncRNA and miRNA-mRNA pairs were predicted, a regulatory network was constructed by the Cytoscape software. For the DEGs involved in the network, enrichment analysis was conducted by the Fisher algorithm.
Compared to the normal samples, 25 DE-lncRNAs, 1421 DEGs and 68 DE-miRNAs were identified in the PRAD samples. The down-regulated MESTIT1 had a significantly negative correlation with overall survival. A total of 44 DE-miRNA-DE-lncRNA pairs were predicted, including the PCA3-miR-96 and UCA1-miR-96. Meanwhile, 33 DEGs targeted by miRNAs (for example, miR-96-CYP19A1) were found to correlate with cancers.
Functional enrichment analysis showed that the reproductive development process (which involved TDRD1) was enriched for the DEGs implicated in the lncRNA-miRNA-mRNA regulatory network. The lncRNAs MESTIT1, PCA3, and UCA1; mRNAs CYP19A1 and TDRD1; as well as miR-96 might affect the pathogenesis of PRAD.
Toward an MRI-based nomogram for the prediction of transperineal prostate biopsy outcome: A physician and patient decision tool
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To develop and internally validate a nomogram using biparametric magnetic resonance imaging (B-MRI)–derived variables for the prediction of prostate cancer at transperineal sector-guided prostate biopsy (TPSB).
Consecutive patients referred to our institution with raised prostate-specific antigen (PSA), abnormal prostate examination, or persistent suspicion of prostate cancer after previous transrectal biopsy between July 2012 and November 2015 were reviewed from a prospective database.
All patients underwent prebiopsy B-MRI with T2-weighted and diffusion-weighted imaging sequences, followed by 24 to 40 core TPSB with additional targeted cores using cognitive registration.
Univariable and multivariable logistic regression analysis was used to determine predictors of prostate cancer outcomes. Multivariable coefficients were used to construct 2 MRI-based nomograms to predict any and significant (Gleason 4 or maximum cancer core length ≥6 mm) prostate cancer at TPSB. Bootstrap resamples were used for internal validation. Accuracy was assessed by calculating the concordance index.
In total, 615 men were included in the study. Prostate cancer was diagnosed in 317 (51.5%) men with significant cancer diagnosed in 237 (38.5%) men.
Age, Prostate Imaging Reporting and Data System (PI-RADS) score, PSA, PSA density, and primary biopsy were predictors of prostate cancer at TPSB on univariable analysis (P<0.0001). PSA showed strong correlation with PSA density and was excluded. The remaining variables were all independent predictors of prostate cancer on multivariable analysis (P<0.0001) and used to generate the nomograms.
Both nomograms showed good discrimination for prostate cancer, with a concordance index of 87% for any cancer and 92% for significant disease. Using a nomogram-derived probability threshold of<15%, 111 (18.0%) biopsies can be saved, at the expense of 3 missed significant prostate cancers.
These internally validated MR-based nomograms were able to accurately predict TPSB outcomes for prostate cancer, especially significant disease. Our findings support the combination of prebiopsy MRI results and clinical factors as part of the biopsy decision-making process.
Prostate Cancer Antigen 3 Score Does Not Predict for Adverse Pathologic Features at Radical Prostatectomy or for Progression-free Survival in Clinically Localized, Intermediate- and High-risk Prostate Cancer
2017, Urology
To evaluate whether preoperative urinary prostate cancer antigen 3 (PCA3) scores predict for adverse pathologic features (APFs) or progression-free survival (PFS) in men with intermediate- or high-risk prostate cancer (PCa) undergoing radical prostatectomy (RP).
One hundred nine men with intermediate- (n = 52) or high-risk (n = 57) PCa who underwent RP were retrospectively identified. Logistic regression analysis was performed to evaluate the association of PCA3 score with various APFs (eg, extracapsular extension, seminal vesicle invasion, etc.). Among 78 men with ≥1 year of follow-up, the association between PCA3 score and PFS was assessed using Cox regression analysis.
At RP, 52% of patients had at least 1 APF, and with median follow-up of 2.3 years, overall 3-year PFS was 70%. PCA3 was not a significant predictor of any APF on multivariate analysis (MVA), whereas canonical predictors (eg, biopsy Gleason score and initial prostate-specific antigen) remained predictive of various APFs. No significant predictors for PFS were found on MVA, although certain canonical predictors (eg, National Comprehensive Cancer Network risk group) were significant predictors of PFS on univariate analysis (UVA). PCA3 score was not a significant predictor of PFS on either UVA or MVA.
Unlike in lower risk cohorts, increasing PCA3 score was not associated with any APF in this higher risk cohort, despite enrichment for APFs, nor was it associated with PFS. Notably, multiple known preoperative predictors for APFs were significant on MVA, and multiple predictors were associated with PFS on UVA. Therefore, PCA3 may not be a useful adjunct predictive marker in men with intermediate- or high-risk PCa.
PCA3 as a second-line biomarker in a prospective controlled randomized opportunistic prostate cancer screening programme
2017, Actas Urologicas Espanolas
Determinar el comportamiento del PCA3 como un marcador de segunda línea en un programa de cribado oportunista de cáncer de próstata (CaP) y su comparación con la calculadora de riesgo 3 del cribado aleatorizado europeo en cáncer de próstata (ERSPC RC-3).
Un total de 5.199 hombres de 40-75 años se hicieron la prueba del antígeno prostático específico (PSA) y un tacto rectal (TR). Aquellos con TR normal y PSA ≥ 3 ng/ml se realizaron un PCA3. Todos los hombres con PCA3 ≥ 35 se hicieron biopsia inicial (BxI) —12 cilindros—. Aquellos con PCA3 < 35 se aleatorizaron 1:1 a BxI u observación. Los resultados se comparan con los obtenidos con la aplicación de la calculadora ERSPC RC-3.
PCA3 se testó en 838 hombres (16,1%). En los grupos PCA3(+) y PCA3(–), las tasas de detección global de CaP fueron del 40,9 y del 14,7% a una mediana de seguimiento de 21,7 meses (p < 0,001. En el grupo PCA3(+) (n = 301, 35,9%), se identificó CaP en 115 hombres en BxI (38,2%). En el brazo aleatorizado, 256 se hicieron BxI y se objetivó CaP en 46 (18,0%) (p < 0,001). La potencial tasa de ahorro de biopsias siguiendo el corte PCA3 = 35 hubiera sido de 64,1% frente a la de 76,6% si hubiéramos usado ERSPC RC-3. Sin embargo, la tasa estimada de falsos negativos de CaP de alto grado (CaPAG = Gleason ≥ 7) se hubiera reducido un 37,1% (de 89 a 56 pacientes) al usar el PCA3. Si hubiéramos usado el corte 35 de PCA3 para no realizar BxI, hubiésemos dejado de diagnosticar un 14,7% de CaP y un 9,1% de CaP clínicamente significativo, a un seguimiento medio aproximado de 2 años.
Cuando se usa PCA3-35 como biomarcador de segunda línea en hombres con PSA ≥ 3 ng/ml y TR normal, se puede obviar la BxI un 12,5% menos que usando la ERSPC RC-3, pero reduciendo los falsos negativos un 36,2%. A un seguimiento de 21,7 meses, este protocolo dual no hubiera detectado un 9,1% de CaP clínicamente significativo, por lo que el seguimiento con estrictos criterios de biopsia basados en PSA y TR es obligatorio en casos con PCA3 < 35.
PCA3 performance as a single second line biomarker is compared to the European Randomised Study of Screening for Prostate Cancer risk calculator model 3 (ERSPC RC-3) in an opportunistic screening in prostate cancer (PCa).
5,199 men, aged 40-75y, underwent prostate-specific antigen (PSA) screening and digital rectal examination (DRE). Men with a normal DRE and PSA ≥ 3 ng/ml had a PCA3 test done. All men with PCA3 ≥ 35 underwent an initial biopsy (IBx) —12 cores—. Men with PCA3 < 35 were randomized 1:1 to either IBx or observation. We compared them to those obtained with ERSPC RC-3.
PCA3 test was performed on 838 men (16.1%). In PCA3(+) and PCA3(–) groups, global PCa detection rates were 40.9% and 14.7% with a median follow-up (FU) of 21.7 months (P < .001). In the PCA3(+) arm (n = 301, 35.9%), PCa was identified in 115 men at IBx (38.2%). In the randomized arm, 256 underwent IBx and PCa was found in 46 (18.0%) (P < .001). The biopsy-sparing potential would have been 64.1% as opposed to 76.6% if we had used ERSPC RC-3. However, the estimated false negative cases for HGPCa would have been reduced by 37.1% (89 to 56 patients). Moreover, if we had applied PCA3-35 to avoid IBx, 14.7% PCa and 9.1% of clinical significant PCa patients would not have been diagnosed during this FU.
When PCA3-35 is used as a second-line biomarker when PSA ≥ 3 ng/ml and DRE is normal, IBx could be avoided in 12.5% less than if ERSPC RC-3 is used and would reduce the false negative cases by 36.2%. At a FU of 21.7 months, this dual protocol would miss 9.1% of clinically significant PCa, so strict FU is mandatory with established biopsy criteria based on PSA and DRE in cases with PCA3 < 35.

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¹: Both authors contributed equally to the manuscript.

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Prostate CancerProstate Cancer Gene 3 (PCA3): Development and Internal Validation of a Novel Biopsy Nomogram

Abstract

Background

Objective

Design, setting, and participants

Measurements

Results and limitations

Conclusions

Introduction

Section snippets

Patient populations

Results

Discussion

Conclusions

Eur Urol

Urology

J Urol

J Urol

J Urol

Eur Urol

J Urol

Eur Urol

J Urol

J Urol

Eur J Cancer

APTIMA PCA3 molecular urine test: development of a method to aid in the diagnosis of prostate cancer

Clin Chem

Prostate Cancer
Prostate Cancer Gene 3 (PCA3): Development and Internal Validation of a Novel Biopsy Nomogram