Prostate cancer gene 3 (PCA3): development and internal validation of a novel biopsy nomogram

The role of biomarkers in the assessment of prostate cancer risk prior to prostate biopsy: which markers matter and how should they be used?

Prostate cancer (PCa) screening has been substantially influenced by the clinical implementation of serum prostate-specific antigen (PSA). In this context, improvement of early PCa detection and stage migration as well as reduced PCa mortality were achieved, and up-to-date PSA represents the gold standard biomarker of PCa diagnosis together with clinical findings. Nonetheless, PSA shows weakness in discriminating between malign and benign prostatic disease or indolent and aggressive cancers. As a result, the expansion of PSA screening is extensively debated with regard to overdetection and ultimately overtreatment, keeping in mind that PCa is still the third leading cause of cancer-specific mortality in the Western male population. Consequently, today's task is to increase the accuracy of PCa detection and furthermore to allow stratification for indolent PCa that might permit active surveillance and to filter out aggressive cancers necessitating treatment. Thus, novel biomarkers, especially in combination with approved clinical risk factors (e.g., age or family history of PCa), within multivariable prediction models carry the potential to improve many aspects of PCa diagnosis and to enable risk classification in clinical practice. Multivariable models lead to superior accuracy for PCa prediction instead of the use of a single risk factor. The aim of this article was to present an overview of known risk factors for PCa together with new promising blood- and urine-based biomarkers and their application within risk models that may allow risk stratification for PCa prior to prostate biopsy. Risk models may optimize PCa detection and classification with regard to improved PCa risk assessment and avoidance of unnecessary prostate biopsies.

Optimizing prostate cancer screening; prospective randomized controlled study of the role of PSA and PCA3 testing in a sequential manner in an opportunistic screening program

OBJECTIVES

To reduce unnecessary biopsies (Bx) in an opportunistic screening programme of prostate cancer.

MATERIAL AND METHODS

We perform a prospective evaluation of PCA3 as a second line biomarker in an opportunistic screening for prostate cancer (PCa). From September-2010 until September-2012, 2,366 men, aged 40-74 years and with >10 years life expectancy, were initially screened with PSA/digital rectal examination (DRE). Men with previous Bx or with recent urine infections were excluded. Men with abnormal DRE and/or PSA >3 ng/ml were submitted for PCA3. All men with PCA3 ≥ 35 underwent an initial biopsy (IBx) -12cores-. Men with PCA3 < 35 were randomized 1:1 to either IBx or observation. Re-biopsy(16-18 cores) criteria were PSA increase >.5 ng/ml at 4-6 months or PSAv > .75 ng/ml/year.

RESULTS

With median follow-up (FU) of 10.1 months, PCA3 was performed in 321/2366 men (13.57%), 289 at first visit and 32 during FU. All 110 PCA3+ men (34.3%) were biopsied and PCa was identified in 43 men in IBx (39.1%). In the randomized arm, 110 were observed and 101 underwent biopsy, finding 12 PCa (11.9%), showing a statistically significant reduction of PCa detection rate in this cohort (P<.001). Global PCa detection rates were 40.9% and 9.5% for the PCA3+ and PCA3- branches, respectively (P<.001). Area under the curve for PSA and PCA3 were .601 and .74, respectively. This is an ongoing prospective study limited by its short follow-up period and still limited enrolment.

CONCLUSIONS

PCA3 as a second line biomarker within an opportunistic dual screening protocol, can potentially avoid 65.7% and 50.1% biopsies at first round and at median FU of 10.1 months, respectively, just missing around 3.2% of high grade PCa.

Prostate-specific antigen and other serum and urine markers in prostate cancer

Prostate-specific antigen (PSA) is one of the most widely used tumor markers, and strongly correlates with the risk of harboring from prostate cancer (PCa). This risk is visible already several years in advance but PSA has severe limitations for PCa detection with its low specificity and low negative predictive value. There is an urgent need for new biomarkers especially to detect clinically significant and aggressive PCa. From all PSA-based markers, the FDA-approved Prostate Health Index (phi) shows improved specificity over percent free and total PSA. Other serum kallikreins or sarcosine in serum or urine show more diverging data. In urine, the FDA-approved prostate cancer gene 3 (PCA3) has also proven its utility in the detection and management of early PCa. However, some aspects on its correlation with aggressiveness and the low sensitivity at very high values have to be re-examined. The detection of a fusion of the androgen regulated TMPRSS2 gene with the ERG oncogene (from the ETS family), which acts as transcription factor gene, in tissue of ~50% of all PCa patients was one milestone in PCa research. When combining the urinary assays for TMPRSS2:ERG and PCA3, an improved accuracy for PCa detection is visible. PCA3 and phi as the best available PCa biomarkers show an equal performance in direct comparisons.

Development, validation and comparison of two nomograms predicting prostate cancer at initial 12-core biopsy

AIM

Our aim was to establish, validate and compare two nomograms in an Iranian population for the first time using clinical, laboratory and transrectal ultrasonography (TRUS) findings for predicting prostate cancer at initial biopsy.

METHODS

Data were collected on a total of 916 men referred for an initial prostate biopsy in our center in a 7-year period. Variables analyzed included age, prostate-specific antigen (PSA), free/total PSA (%fPSA), digital rectal examination (DRE) findings, prostate volume (PV) and presence of hypoechoic lesion on TRUS. Univariate logistic regression models were fitted to test cancer predictors. Two multivariate logistic regression models were fitted to create nomograms. Both models were internally validated. Calibration of nomograms was assessed graphically. The area under the receiver operating characteristic curve (AUC) was calculated as a scale of discrimination and predictive accuracy and also used to compare models.

RESULTS

Prostate cancer was detected in 221/669 (33%) men. Based on univariate logistic regression, all of variables except DRE were significant predictors of prostate cancer, with highest AUC for PV (AUC 0.696, 95% CI 0.653-0.738).AUC of nomogram with and without TRUS findings and PSA alone were 0.791, 0.721 and 0.624, respectively. In internal validation, both nomograms had acceptable calibration plots.

CONCLUSION

Our nomogram based on age, DRE, PSA, %fPSA and TRUS finding was significantly more accurate in predicting initial prostate biopsy outcome in men.

Development of a nomogram for predicting a positive repeat prostate biopsy

AIM: To find risk factors of cancer in patients who had a repeat biopsy and to develop the nomogram using our cohort.

METHODS: Among 3500 patients who had a prostate biopsy over 11 years between 2000 and 2010 at our hospital, we studied a total of 807 repeat biopsy sessions in 459 patients who had at least 1 initial negative biopsy. At each biopsy session, we recorded patient age, number of previous biopsy sessions, number of biopsy cores, number of previously negative biopsy cores, months from the initial biopsy, months from the previous biopsy, serum PSA, PSA slope, digital rectal examination findings, hypoechoic lesions suspicious for a cancer on transrectal ultrasonography, total prostate volume, transitional zone (TZ) volume, PSA density, PSA TZ density and history of high grade prostatic intraepithelial neoplasia (HGPIN) or atypical small acinar proliferation (ASAP). Clinical and pathological variables were correlated with the outcome of repeat biopsies. A nomogram was developed based on logistic regression analyses and calibration was performed.

RESULTS: Overall, 17% of repeat biopsies had a cancer. With receiver operating characteristics analyses, the highest area under the curve (AUC) was obtained based on all available 13 variables, which were age, PSA, digital rectal examination, PSA density, prostate volume, TZ volume, PSA TZ density, cumulative number of biopsy cores, HGPIN, ASAP, months from previous negative biopsy, initial negative biopsy and number of biopsy cores. Based on multivariable logistic regression analysis, a nomogram was constructed with an AUC of 0.74, which was greater than that of any single risk factor. The calibration plot seemed to be good.

CONCLUSION: Our nomogram for predicting a positive repeat biopsy can provide probabilities for cancer and may help clinical judgment on whether to do a repeat prostate biopsy.

A Quest to Identify Prostate Cancer Circulating Biomarkers with a Bench-to-Bedside Potential

Prostate cancer (PCA) is a major health concern in current times. Ever since prostate specific antigen (PSA) was introduced in clinical practice almost three decades ago, the diagnosis and management of PCA have been revolutionized. With time, concerns arose as to the inherent shortcomings of this biomarker and alternatives were actively sought. Over the past decade new PCA biomarkers have been identified in tissue, blood, urine, and other body fluids that offer improved specificity and supplement our knowledge of disease progression. This review focuses on superiority of circulating biomarkers over tissue biomarkers due to the advantages of being more readily accessible, minimally invasive (blood) or noninvasive (urine), accessible for sampling on regular intervals, and easily utilized for follow-up after surgery or other treatment modalities. Some of the circulating biomarkers like PCA3, IL-6, and TMPRSS2-ERG are now detectable by commercially available kits while others like microRNAs (miR-21, -221, -141) and exosomes hold potential to become available as multiplexed assays. In this paper, we will review some of these potential candidate circulating biomarkers that either individually or in combination, once validated with large-scale trials, may eventually get utilized clinically for improved diagnosis, risk stratification, and treatment.

Prostate cancer biomarkers: an update

Many aspects of prostate cancer diagnosis and treatment could be greatly advanced with new, effective biomarkers. Prostate-specific antigen (PSA) has multiple weaknesses as a biomarker, such as not distinguishing well between cancer and benign prostatic hyperplasia or between indolent and aggressive cancers, thus leading to overtreatment, especially unnecessary biopsies. PSA also often fails to indicate accurately which patients are responding to a given treatment. Yet PSA is the only prostate cancer biomarker routinely used by urologists. Here, we provide updated information on the most relevant of the other biomarkers currently in use or in development for prostate cancer. Recent research shows improvement over using PSA alone by comparing total PSA (tPSA) or free PSA (fPSA) with new, related markers, such as prostate cancer antigen (PCA) 3, the individual molecular forms of PSA (proPSA, benign PSA, and intact PSA), and kallikreins other than PSA. Promising results have also been seen with the use of the fusion gene TMPRSS2:ERG and with various forms of the urokinase plasminogen activation receptor. Initially, there were high hopes for early PCA, but those data were not reproducible and thus research on early PCA has been abandoned. Much work remains to be done before any of these biomarkers are fully validated and accepted. Currently, the only markers discussed in this paper with Food and Drug Administration-approved tests are PCA 3 and an isoform of proPSA, [-2]proPSA. Assays are in development for most of the other biomarkers described in this paper. While the biomarker validation process can be long and filled with obstacles, the rewards will be great-in terms of both patient care and costs to the health care system.

Multicenter European external validation of a prostate health index-based nomogram for predicting prostate cancer at extended biopsy

BACKGROUND

External validation of a prediction tool is mandatory to assess the tool's accuracy and generalizability within different patient cohorts.

OBJECTIVE

To externally validate a previously developed Prostate Health Index (PHI)-based nomogram for predicting the presence of prostate cancer (PCa) at biopsy.

DESIGN, SETTING, AND PARTICIPANTS

The study population consisted of 883 patients who were scheduled for a prostate biopsy at one of five European tertiary care centers. Total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), and [-2]pro-prostate-specific antigen (p2PSA) levels were determined. The fPSA-to-tPSA ratio (%fPSA), p2PSA, and PHI ([p2PSA / fPSA] × √tPSA) were calculated.

INTERVENTION

Extended initial and repeat prostate biopsy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Logistic regression models were fitted to test the predictors of PCa and to determine their predictive accuracy. A calibration plot was used to evaluate the extent of overestimation or underestimation between nomogram predictions and observed PCa rate. Decision curve analysis (DCA) provided an estimate of the net benefit obtained by using the PHI-based nomogram.

RESULTS AND LIMITATIONS

Of 833 patients, 365 (41.3%) were diagnosed with PCa at extended prostate biopsy. In accuracy analyses, PHI was the most informative predictor of PCa (0.68), outperforming tPSA (0.51) and %fPSA (0.64). The predictive accuracy of the previously developed nomogram was 75.2% (95% confidence interval, 71.4-78.1). Calibration of the nomogram was good in patients at a low to intermediate predicted probability of PCa, while calibration was suboptimal, with a tendency to overestimate the presence of PCa, in high-risk patients. Finally, DCA demonstrated that the use of the PHI-based nomogram resulted in the highest net benefit. The main limitation of the study is the fact that only Caucasian patients were included.

CONCLUSIONS

At external validation, the previously developed PHI-based nomogram confirmed its ability to determine the presence of PCa at biopsy. These findings provide further evidence supporting the potential role of the nomogram in the biopsy decision pathway for European men with suspected PCa.

PATIENT SUMMARY

In the current study, we externally validated a Prostate Health Index-based nomogram to predict the presence of prostate cancer (PCa) at biopsy. This tool may help clinicians determine the need for a prostate biopsy in European patients with suspected PCa.

Urinary PCA3 as a predictor of prostate cancer in a cohort of 3,073 men undergoing initial prostate biopsy

PURPOSE

PCA3 is a urinary marker that has shown promise in predicting the presence of prostate cancer in men undergoing repeat prostate biopsy. We studied PCA3 before initial prostate biopsy.

MATERIALS AND METHODS

Records from a single organization were retrospectively reviewed. The predictive value of PCA3 was explored using nonparametric receiver operating characteristic curve analysis (ROC) and multivariable logistic regression analysis.

RESULTS

A total of 3,073 men underwent PCA3 analysis before initial prostate biopsy sampling of 12 to 14 areas. Mean PCA3 was 27.2 and 52.5 for patients without and with cancer, respectively. Prostate cancer was identified in 1,341 (43.6%) men. Overall 54.5% had Gleason 6 disease and 45.5% had Gleason 7 or greater (high grade prostate cancer). Mean PCA3 was 47.5 and 58.5 for the patients with Gleason 6 and 7 or greater disease, respectively. On multivariable logistic analysis PCA3 was statistically significantly associated with prostate cancer and high grade prostate cancer after adjusting for prostate specific antigen (p<0.001 for both), free prostate specific antigen (p=0.04 and p=0.01, respectively), age (p<0.001 for both), family history (p<0.001 and p=0.59, respectively), abnormal digital rectal examination (p=0.31 and p<0.001, respectively), prostate volume (p<0.001 for both) and body mass index (p<0.001 for both). Using ROC analysis PCA3 outperformed prostate specific antigen in the prediction of prostate cancer (AUC 0.697 vs 0.599, p<0.01) but not for high grade prostate cancer (AUC 0.682 vs 0.679, p=0.702).

CONCLUSIONS

PCA3 proved a useful tool in identifying patients at risk for prostate cancer before initial prostate biopsy. To our knowledge this is the largest PCA3 study in the initial biopsy population. These results suggest that further exploration of the value of PCA3 is warranted.

Clinical use of novel urine and blood based prostate cancer biomarkers: a review

In the era of upcoming techniques for molecular profiling, breakthroughs led to new discoveries in the field of prostate cancer (PCa) biomarkers. Since the early 1990s a tremendous increase in PCa incidence is seen, dedicated to the introduction of prostate specific antigen (PSA) testing. However, due to its lack of specificity many men undergo unnecessary biopsies, resulting in a rising incidence of clinically insignificant PCa. To overcome this drawback, cancer specific biomarkers are needed to identify patients who are at high risk of harbouring PCa and to distinguish patients with aggressive disease from patients with insignificant cancer. The most non-invasive, easy to obtain substrate for biomarker measurement is urine. The most promising markers to date are PCA3 and TMPRSS2-ERG. Both markers demonstrate to have a higher specificity and diagnostic accuracy for PCa outcome compared to serum PSA. This might better predict the presence of PCa and therefore reduce the number of unnecessary biopsies. Combining both markers in a panel might result in an even higher diagnostic accuracy, given the heterogeneity of the disease. In PCa management, circulating tumour cells (CTCs) detected in the blood seem a promising tool to predict treatment response and survival benefit. Although results appear to be encouraging, the biggest challenge about new markers in PCa is to validate them in large clinical trials and subsequently implement these markers into clinical practice. In this review we discuss the clinical usefulness of novel, non-invasive tests in PCa management.

PCA3 and PCA3-based nomograms improve diagnostic accuracy in patients undergoing first prostate biopsy

While now recognized as an aid to predict repeat prostate biopsy outcome, the urinary PCA3 (prostate cancer gene 3) test has also been recently advocated to predict initial biopsy results. The objective is to evaluate the performance of the PCA3 test in predicting results of initial prostate biopsies and to determine whether its incorporation into specific nomograms reinforces its diagnostic value. A prospective study included 601 consecutive patients addressed for initial prostate biopsy. The PCA3 test was performed before ≥12-core initial prostate biopsy, along with standard risk factor assessment. Diagnostic performance of the PCA3 test was evaluated. The three available nomograms (Hansen's and Chun's nomograms, as well as the updated Prostate Cancer Prevention Trial risk calculator; PCPT) were applied to the cohort, and their predictive accuracies were assessed in terms of biopsy outcome: the presence of any prostate cancer (PCa) and high-grade prostate cancer (HGPCa). The PCA3 score provided significant predictive accuracy. While the PCPT risk calculator appeared less accurate; both Chun's and Hansen's nomograms provided good calibration and high net benefit on decision curve analyses. When applying nomogram-derived PCa probability thresholds ≤30%, ≤6% of HGPCa would have been missed, while avoiding up to 48% of unnecessary biopsies. The urinary PCA3 test and PCA3-incorporating nomograms can be considered as reliable tools to aid in the initial biopsy decision.

Assays for prostate cancer : changing the screening paradigm?

Prostate cancer (PCa) screening and detection have changed dramatically since the introduction of serum prostate-specific antigen (PSA) testing. Despite the resulting improvement in early PCa detection and stage migration, in clinical practice the use of PSA testing may cause overdetection and ultimately overtreatment. As a consequence, novel biomarkers are needed to increase the specificity of PCa detection. The aim of this article is to present an overview of novel blood- and urine-based biomarkers that may optimize PCa detection, with improved identification of patients with significant PCa and avoidance of unnecessary prostate biopsies. A systematic and comprehensive PubMed search was performed using the MeSH search terms 'prostate cancer', 'biomarker', 'marker', and 'detection'. Results were restricted to the English language. Several blood- and urine-based biomarkers have the potential to improve prediction of the presence and/or significance of PCa. Ideally, biomarkers should be used in combination within multivariate models, leading to superior accuracy for prediction of any PCa or clinically significant PCa, compared with the use of a single marker.

Urinary biomarkers for prostate cancer: a review

Although the routine use of serum prostate-specific antigen (PSA) testing has undoubtedly increased prostate cancer (PCa) detection, one of its main drawbacks is its lack of specificity. As a consequence, many men undergo unnecessary biopsies or treatments for indolent tumours. PCa-specific markers are needed for the early detection of the disease and the prediction of aggressiveness of a prostate tumour. Since PCa is a heterogeneous disease, a panel of tumour markers is fundamental for a more precise diagnosis. Several biomarkers are promising due to their specificity for the disease in tissue. However, tissue is unsuitable as a possible screening tool. Since urine can be easily obtained in a non-invasive manner, it is a promising substrate for biomarker testing. This article reviews the biomarkers for the non-invasive testing of PCa in urine.

PCA3 in the detection and management of early prostate cancer

Although widely used, the value of prostate-specific antigen (PSA) in the detection of prostate cancer is controversial. The percentage of free PSA increases the specificity of PSA, but results are not enough. Prostate cancer gene 3 (PCA3) has been proposed as an option that may complement these markers in the detection and management of early prostate cancer. Our aim has been to review the value of PCA3 as tumor marker. The available results suggest that PCA3 is particularly useful to select in which patients the biopsy should be repeated when the first biopsy was negative. However, some points should be specified with further studies, including the most appropriate PCA3 cutoff level and the significance of a high PCA3 score in patients with negative biopsy. False-negative results are also a conflictive point in the use of PCA3, because prostate cancer, including aggressive tumors, may be present in patients with a low PCA3 score. Probably, a proper interpretation of this test requires its management together with other tests, through multivariate models for the detection of prostate cancer. On the other hand, several studies showed the relation between PCA3 score and Gleason score, and also the utility of PCA3 to select patients for active surveillance. To summarize, the available studies show the utility of PCA3 in the detection and management of early prostate cancer, although some aspects referred to its use need to be retested after further studies to confirm the actual value and the limitations of this test.

Urine TMPRSS2:ERG fusion transcript integrated with PCA3 score, genotyping, and biological features are correlated to the results of prostatic biopsies in men at risk of prostate cancer

BACKGROUND

Detection of fusion gene TMPRSS2:ERG transcripts in urine have been recently described in order to refine urine-based detection of prostate cancer (PCa), but data its clinical impact remain scarce. We aimed at investigating the correlation of TMPRSS2:ERG, prostate cancer antigen 3 (PCA3), prostate specific antigen (PSA) density, genetic variants, and androgenic status with outcome and pathological findings at prostatic biopsy.

METHODS

Between 2007 and 2011, 291 patients at risk of PCa because of PSA > 3.0 ng/ml (55%) or candidate to active surveillance protocol justifying restaging biopsy management (45%) were recruited. TMPRSS2:ERG was detected by urine assay (Progensa™). PCA3-score, PSA level, bioavailable testosterone level, prostate volume, rs1447295 and rs6983267 genotypes were prospectively assessed. Univariate and multivariate analysis by logistic regression model (logit) were conducted to study the correlation of TMPRSS2:ERG status, PCA3, and PSA density with biopsy results, and Gleason score.

RESULTS

Of 291 patients, 173 had PCa and 118 had negative biopsy. PCA3 score, PSA density and TMPRSS2:ERG-score were correlated with presence of PCa (P < 0.0001, P = 0.046, and P < 0.0001, respectively). This correlation remained strong on multivariable analysis model (area under curve 0.743). PCA3 score and PSA density were significantly associated with presence of Grade 4 through multivariable analysis. PCA3 score was also correlated to the percentage of positive cores at biopsy (P = 0.008).

CONCLUSIONS

Integration of levels TMPRSS2:ERG transcripts in urine, with PCA3-score, androgenic status, genetic status and traditional clinical variables could significantly increase detection of high risk localized PCa.

Artificial neural networks and prostate cancer--tools for diagnosis and management

Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.

Clinical utility of the prostate cancer gene 3 (PCA3) urine assay in Japanese men undergoing prostate biopsy

WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: It is known that a prostate cancer gene 3 (PCA3) urine assay is superior to serum PSA level or PSA-related indices for predicting a positive biopsy result in European and US men. This is the first report on PCA3 in a large cohort of Japanese men. The diagnostic value of the PCA3 score in Japanese men was similar to those reported in European and US men. The study concludes that a combination of PSA density and PCA3 score may be useful for selecting patients who could avoid an unnecessary biopsy.

OBJECTIVE

To examine the diagnostic performance of the prostate cancer gene 3 (PCA3) score for prostate cancer in Japanese men undergoing prostate biopsy.

PATIENTS AND METHODS

This Japanese, multicentre study included 647 Asian men who underwent extended prostate biopsy with elevated prostate-specific antigen (PSA) and/or abnormal digital rectal examination (DRE). Urine samples were collected after DRE. The PCA3 score was determined using a PROGENSA PCA3 assay and correlated with biopsy outcome. Its diagnostic accuracy was compared with that of serum PSA level, prostate volume (PV), PSA density (PSAD), and free/total PSA ratio (f/t PSA).

RESULTS

A total of 633 urine samples were successfully analysed (the informative rate was 98%). Median PSA was 7.6 ng/mL. Biopsy revealed cancer in 264 men (41.7%). The PCA3 score for men with prostate cancer was significantly higher than that for men with negative biopsies (median PCA3 score: 49 vs. 18; P < 0.001). The rate of positive biopsy was 16.0% in men with a PCA3 score of <20 and 60.6% in those with a PCA3 score of ≥50. Using a PCA3 score threshold of 35, sensitivity and specificity were 66.5 and 71.6%, respectively. The area under the curve of the PCA3 score was significantly higher than that of the f/t PSA in men with PSA 4-10 ng/mL (0.742 vs 0.647; P < 0.05). In men with PSAD < 0.15 and PCA3 < 20, only three (4.2%) out of 72 men had prostate cancer.

CONCLUSIONS

The PCA3 score was significantly superior to f/t PSA in predicting a positive biopsy result for prostate cancer in Japanese men with PSA 4-10 ng/mL. The combination of PSAD and PCA3 score may be useful for selecting patients who could avoid an unnecessary biopsy.

Prediction of the risk of harboring prostate cancer by a prebiopsy nomogram based on extended biopsy protocol

OBJECTIVE

We aimed to build a nomogram allowing to predict the probability of prostate cancer (PC) after an initial 21-core biopsy and with readily available clinical data.

METHODS

1,490 screened men who underwent an initial 21-core biopsy protocol were included. A multivariate logistic regression was realized including age, prostate volume, prostate-specific antigen (PSA) level, digital rectal examination (DRE) and transrectal ultrasonography (TRUS). Receiver-operating characteristic estimates were used to quantify accuracy of each model.

RESULTS

PC was detected in 41.3% of the patients. Median PSA, age and prostate volume were 6.2 ng/ml (range 0.2-50), 64.6 years (range 33-87) and 40 ml (range 10-270), respectively. Abnormal TRUS findings were detected in 14.7% of patients. Age, PSA level, prostate volume, DRE and TRUS were significantly associated with PC (all p ≤ 0.004) in univariable logistic regression analysis. In multivariate logistic regression analysis, significant associations were found for age, PSA level, prostate volume and DRE. Predictive accuracy estimate of this model was equal to 0.70. TRUS was not an independent predictor of PC.

CONCLUSIONS

We constructed the first prebiopsy predictive nomogram based on an extended 21-core biopsy procedure with age, PSA level, DRE and prostate volume which are readily available clinical data to urologists.

Use of PCA3 in detecting prostate cancer in initial and repeat prostate biopsy patients

BACKGROUND

The PCA3 urinary assay has shown promise in predicting the presence of prostate cancer. We evaluated the value of this test in patients undergoing initial and repeat prostate biopsy.

METHODS

PCA3 and PSA levels were obtained from 456 men with no known personal history of prostate cancer prior to prostate biopsy. Two hundred eighty-nine men underwent an initial prostate biopsy and 167 underwent a repeat prostate biopsy. PCA3 and PSA levels were compared to the prostate biopsy results.

RESULTS

PCA3 score was shown to be independent of prostate volume (P = 0.162) and PSA level (P = 0.959). PCA3 scores were significantly higher in patients with cancer on prostate biopsy compared to patients with negative biopsy results (P < 0.0001). In logistic regression, PCA3 showed a significantly higher AUC than PSA (0.726 vs. 0.512, P = 0.0001). This difference persisted when examining the initial biopsy subgroup, with PCA3 out-performing PSA (AUC 0.772 vs. AUC = 0.552, P < 0.0001), but not in the repeat biopsy subgroup (AUC = 0.605 vs. AUC = 0.500, P = 0.2488).

CONCLUSIONS

PCA3 was found to be a better predictor of prostate cancer than PSA in the total population as well as the initial biopsy population, but was not superior to PSA in the repeat biopsy population. Prostate 73: 48-53, 2013. © 2012 Wiley Periodicals, Inc.

Epithelial cell adhesion molecule is an independent prognostic marker in clear cell renal carcinoma

Epithelial cell adhesion molecule (EPCAM) has recently attained a renewed interest as a candidate protein in diagnosis, prognostication and therapy of various tumor entities. The molecular epidemiology and prognostic relevance of EPCAM in renal cell carcinoma (RCC) and amongst the histological subtypes of RCC are unclear. We analyzed the prevalence and prognostic significance of EPCAM in a tumor tissue microarray composed of 1,088 independent RCCs samples by immunohistochemistry (IHC). We found significant variations of EPCAM IHC staining intensities in between the RCC subtypes: in papillary and chromophobe RCC, the majority of tumors (89-93%) showed an at least weak EPCAM protein expression. In the largest subgroup, the clear cell (cc)RCC (n = 767), a negative EPCAM IHC was found in 1/3 of the patients and was associated with high-grade disease and nodal metastases. Kaplan-Meier analyses demonstrated a significant association between positive EPCAM IHC and prolonged overall survival, even in a subset of low-risk ccRCC. In multivariable analyses, EPCAM represented an independent risk factor of survival throughout all subgroups. For localized, low-grade ccRCC, information of EPCAM IHC raised predictive accuracy of a multivariate model by ∼5%, compared to T-stage and grade alone. Our findings indicate that EPCAM is an independent prognostic molecular marker in ccRCC and, especially in localized ccRCC, might be able to provide auxiliary information for a better prognostication.

Molecular diagnostic trends in urological cancer: biomarkers for non-invasive diagnosis

The early detection of urological cancers is pivotal for successful patient treatment and management. The development of molecular assays that can diagnose disease accurately, or that can augment current methods of evaluation, would be a significant advance. Ideally, such molecular assays would be applicable to non-invasively obtained body fluids, enabling not only diagnosis of at risk patients, but also asymptomatic screening, monitoring disease recurrence and response to treatment. The advent of advanced proteomics and genomics technologies and associated bioinformatics development is bringing these goals into focus. In this article we will discuss the promise of biomarkers in urinalysis for the detection and clinical evaluation of the major urological cancers, including bladder, kidney and prostate. The development of urine-based tests to detect urological cancers would be of tremendous benefit to both patients and the healthcare system.

Comparative assessment of urinary prostate cancer antigen 3 and TMPRSS2:ERG gene fusion with the serum [-2]proprostate-specific antigen-based prostate health index for detection of prostate cancer

BACKGROUND

We compared urinary prostate cancer antigen 3 (PCA3), transmembrane protease, serine 2 (TMPRSS2):v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG) gene fusion (T2:ERG), and the serum [-2]proprostate-specific antigen ([-2]proPSA)-based prostate health index (Phi) for predicting biopsy outcome.

METHODS

Serum samples and first-catch urine samples were collected after digital rectal examination (DRE) from consented outpatients with PSA 0.5-20 μg/L who were scheduled for prostate biopsy. The PCA3 score (PROGENSA PCA3, Hologic Gen-Probe) and T2:ERG score (Hologic Gen-Probe) were determined. Measurements of serum PSA, free PSA, and [-2]proPSA (Beckman Coulter) were performed, and the percentages of free PSA (%fPSA) and Phi ([-2]proPSA/fPSA × √PSA) were determined.

RESULTS

Of 246 enrolled men, prostate cancer (PCa) was diagnosed in 110 (45%) and there was no evidence of malignancy (NEM) in 136 (55%). A first set of biopsies was performed in 136 (55%) of all men, and 110 (45%) had ≥1 repeat biopsies. PCA3, Phi, and T2:ERG differed significantly between men with PCa and NEM, and these markers showed the largest areas under the ROC curve (AUCs) (0.74, 0.68, and 0.63, respectively). PCA3 had the largest AUC of all parameters, albeit not statistically different from Phi. Phi showed somewhat lower specificities than PCA3 at 90% sensitivity. Combination of both markers enhanced diagnostic power with modest AUC gains of 0.01-0.04. Although PCA3 had the highest AUC in the repeat-biopsy cohort, the highest AUC for Phi was observed in DRE-negative patients with PSA in the 2-10 μg/L range.

CONCLUSIONS

PCA3 and Phi were superior to the other evaluated parameters but their combination gave only moderate enhancements in diagnostic accuracy for PCa at first or repeat prostate biopsy.

Beyond prostate-specific antigen - future biomarkers for the early detection and management of prostate cancer

Prostate-specific antigen is currently commonly used as a screening biomarker for prostate cancer, but it has limitations in both sensitivity and specificity. The development of novel biomarkers for early cancer detection has the potential to improve survival, reduce unnecessary investigations and benefit the health economy. Here we review the use and limitations of prostate-specific antigen and its subtypes, urinary biomarkers including PCA3, alpha-methylacyl-CoA racemase, the TMPRSS2-ERG fusion gene and microseminoprotein-beta, and other novel markers in both serum and urine. Many of these biomarkers are at early stages of development and require evaluation in prospective trials to determine their potential usefulness in clinical practice. Genetic profiling may allow for the targeting of high-risk populations for screening and may offer the opportunity to combine biomarker results with genotype to aid risk assessment.

Toward the detection of prostate cancer in urine: a critical analysis

PURPOSE

Prostate specific antigen and digital rectal examination have low specificity for detecting prostate cancer and they poorly predict the presence of aggressive disease. Urine is readily available and noninvasive, and it represents a promising source of biomarkers for the early detection and prediction of prostate cancer prognosis. We identified promising biomarkers for urine based prostate cancer, examined trends and outlined potential pitfalls.

MATERIALS AND METHODS

We performed PubMed® and Web of Science® database searches of the peer reviewed literature on urine based testing for prostate cancer. Original studies of this subject as well as a small number of reviews were analyzed, including the strengths and weaknesses. We provide a comprehensive review of urine based testing for prostate cancer that covers the technical aspects, including the methodology of urine collection, as well as recent developments in biomarkers spanning the fields of genomics, epigenetics, transcriptomics, proteomics and metabolomics.

RESULTS

The process of urine collection is subject to variability, which may result in conflicting clinical results. Detecting prostate cancer in urine is technically feasible, as demonstrated by numerous proof of principle studies, but few markers have been validated in multiple large sample sets. Biomarker development using urine has been accelerating in recent years with numerous studies identifying DNA, RNA, protein and metabolite based biomarkers in urine. Advanced clinical studies have identified PCA3 and TMPRSS2:ERG fusion transcripts as promising RNA markers for cancer detection and possibly prognosis. DNA methylation analysis of multiple genes improves specificity and represents a promising platform for developing clinical grade assays.

CONCLUSIONS

Urine based testing is noninvasive and represents a rich source of novel biomarkers for prostate cancer. Although urine shows promise for detecting cancer, the ability to identify aggressive subsets of prostate cancer needs further development.

Diagnostic performance of PCA3 to detect prostate cancer in men with increased prostate specific antigen: a prospective study of 1,962 cases

PURPOSE

The detection of prostate cancer relies primarily on abnormal digital rectal examination or increased serum prostate specific antigen concentration. However, low positive predictive values result in many men with increased prostate specific antigen and/or suspicious digital rectal examination having a negative biopsy. We investigated the value of the PCA3 (prostate cancer gene 3) urine test in predicting the likelihood of diagnosis of cancer before biopsy.

MATERIALS AND METHODS

We performed a prospective, community based clinical trial to evaluate PCA3 score before any biopsy. This trial was conducted at 50 urology practices in the United States. Samples were obtained from 1,962 men with increased serum prostate specific antigen (greater than 2.5 ng/ml) and/or abnormal digital rectal examination before transrectal prostate needle biopsy. Study samples (urinary PCA3 and biopsies) were processed and analyzed by a central laboratory. Sensitivity-specificity analyses were conducted.

RESULTS

A total of 1,913 urine samples (97.5%) were adequate for PCA3 testing. Of 802 cases diagnosed with prostate cancer 222 had high grade prostatic intraepithelial neoplasia or atypical small acinar proliferation and were suspicious for cancer, whereas 889 cases were benign. The traditional PCA3 cutoff of 35 reduced the number of false-positives from 1,089 to 249, a 77.1% reduction. However, false-negatives (missed cancers) increased significantly from 17 to 413, an increase of more than 2,300%. Lowering the PCA3 cutoff to 10 reduced the number of false-positives 35.4% and false-negatives only increased 5.6%.

CONCLUSIONS

Urinary PCA3 testing in conjunction with prostate specific antigen has the potential to significantly decrease the number of unnecessary prostate biopsies.

How accurate is our prediction of biopsy outcome? PCA3-based nomograms in personalized diagnosis of prostate cancer

PURPOSE

The sensitivity and specificity of prostate-specific antigen (PSA) alone to select men for prostate biopsy remain suboptimal. This review aims at presenting a review of current prostate cancer (PCa) nomograms that incorporate Prostate Cancer Gene 3 (PCA3), which was designed to outperform PSA at predicting biopsy outcome.

MATERIALS AND METHODS

The PubMed database and current literature search was conducted for reports on PCA3-based nomograms and tools for examining the risk of a positive prostate biopsy in a man without a known PCa diagnosis.

RESULTS AND CONCLUSIONS

The introduction of PCA3 into clinical practice has led to the development of a set of PCA3-based nomograms to predict biopsy outcome. Combining PCA3 results with established PCa risk factors has produced significant improvements over PSA alone in predicting the risk of a positive prostate biopsy for cancer.

Development and internal validation of a Prostate Health Index based nomogram for predicting prostate cancer at extended biopsy

PURPOSE

We developed and validated a Prostate Health Index (Beckman Coulter, Brea, California) based nomogram to predict prostate cancer at extended prostate biopsy.

MATERIALS AND METHODS

The study population consisted of 729 patients who were scheduled for prostate biopsy following suspicious digital rectal examination and/or increased prostate specific antigen. Total and free prostate specific antigen, percent free-to-total prostate specific antigen, [-2]proPSA and the prostate health index [([-2]proPSA/free prostate specific antigen) × √total prostate specific antigen)] were determined. Logistic regression models were fitted to test prostate cancer predictors. Predictive accuracy estimates of biopsy outcome predictions were quantified. Regression coefficients were used to create a decision making tool to predict prostate cancer. A calibration plot was used to evaluate the extent of overestimating or underestimating the observed prostate cancer rate. Decision curve analysis provided an estimate of the net benefit obtained using the prostate health index based nomogram.

RESULTS

Overall 280 of 729 patients (38.4%) were diagnosed with prostate cancer at extended prostate biopsy. On accuracy analyses prostate health index emerged as the most informative predictor of prostate cancer (AUC 0.70) compared to established predictors, such as total prostate specific antigen (0.51) and percent free-to-total prostate specific antigen (0.62). Including the prostate health index in a multivariable logistic regression model based on patient age, prostate volume, digital rectal examination and biopsy history significantly increased predictive accuracy by 7% from 0.73 to 0.80 (p <0.001). Nomogram calibration was good. Decision curve analysis showed that using the prostate health index based nomogram resulted in the highest net benefit.

CONCLUSIONS

The prostate health index based nomogram can assist clinicians in the decision to perform biopsy by providing an accurate estimation of an individual risk of prostate cancer.

Initial prostate biopsy: development and internal validation of a biopsy-specific nomogram based on the prostate cancer antigen 3 assay

BACKGROUND

Urinary prostate cancer antigen 3 (PCA3) assay in combination with established clinical risk factors improves the identification of men at risk of harboring prostate cancer (PCa) at initial biopsy (IBX).

OBJECTIVE

To develop and validate internally the first IBX-specific PCA3-based nomogram that allows an individual assessment of a man's risk of harboring any PCa and high-grade PCa (HGPCa).

DESIGN, SETTING, AND PARTICIPANTS

Clinical and biopsy data including urinary PCA3 score of 692 referred IBX men at risk of PCa were collected within two prospective multi-institutional studies.

INTERVENTION

IBX (≥ 10 biopsy cores) with standard risk factor assessment including prebiopsy urinary PCA3 measurement.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

PCA3 assay cut-off thresholds were investigated. Regression coefficients of logistic risk factor analyses were used to construct specific sets of PCA3-based nomograms to predict any PCa and HGPCa at IBX. Accuracy estimates for the presence of any PCa and HGPCa were quantified using area under the curve of the receiver operator characteristic analysis and compared with a clinical model. Bootstrap resamples were used for internal validation. Decision curve analyses quantified the clinical net benefit related to the novel PCA3-based IBX nomogram versus the clinical model.

RESULTS AND LIMITATIONS

Any PCa and HGPCa were diagnosed in 46% (n=318) and 20% (n=137), respectively. Age, prostate-specific antigen, digital rectal examination, prostate volume, and PCA3 were independent predictors of PCa at IBX (all p<0.001). The PCA3-based IBX nomograms significantly outperformed the clinical models without PCA3 (all p<0.001). Accuracy was increased by 4.5-7.1% related to PCA3 inclusion. When applying nomogram-derived PCa probability thresholds ≤ 30%, only a few patients with HGPCa (≤ 2%) will be missed while avoiding up to 55% of unnecessary biopsies. External validation of the PCA3-based IBX-specific nomogram is warranted.

CONCLUSIONS

The internally validated PCA3-based IBX-specific nomogram outperforms a clinical prediction model without PCA3 for the prediction of any PCa, leading to the avoidance of unnecessary biopsies while missing only a few cases of HGPCa. Our findings support the concepts of a combination of novel markers with established clinical risk factors and the superiority of decision tools that are specific to a clinical scenario.

The use of the finasteride-adjusted Prostate Cancer Prevention Trial Prostate Cancer Risk Calculator in a Mexican referral population: a validation study

OBJECTIVES

To perform the first validation study of the finasteride-adjusted Prostate Cancer Prevention Trial Prostate Cancer Risk Calculator (finPCPTRC) in a contemporary referral population in Mexico.

METHODS

837 patients referred to the Instituto Nacional de Cancerología, Mexico City, Mexico, between 2005 and 2009 were used to validate the finPCPTRC by examining various measures of discrimination and calibration. Net benefit curve analysis was used to gain insight into the use of the finPCPTRC for clinical decisions.

RESULTS

Prostate cancer (PCa) incidence (72.8%) was high in this Mexican referral cohort and 45.7% of men who were diagnosed with PCa had high-grade lesions (HGPCa, Gleason score >6). 1.3% of the patients were taking finasteride. The finPCPTRC was a superior diagnostic tool compared to prostate-specific antigen alone when discriminating patients with PCa from those without PCa (AUC = 0.784 vs. AUC = 0.687, p < 0.001) and when discriminating patients with HGPCa from those without HGPCa (AUC = 0.768 vs. AUC = 0.739, p < 0.001). The finPCPTRC underestimated the risk of PCa but overestimated the risk of HGPCa (both p < 0.001). Compared with other strategies to opt for biopsy, the net benefit would be larger with utilization of the finPCPTRC for patients accepting higher risks of HGPCa.

CONCLUSIONS

Rates of biopsy-detectable PCa and HGPCa were high and 1.3% of this referral cohort in Mexico was taking finasteride. The risks of PCa or HGPCa calculated by the finPCPTRC were not well calibrated for this referral Mexican population and new clinical diagnostic tools are needed.

The risk of biopsy-detectable prostate cancer using the prostate cancer prevention Trial Risk Calculator in a community setting

MATERIALS AND METHODS

Risks of prostate cancer (CaP) and high-grade CaP were evaluated using the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) in an age-stratified random sample of 1,021 Caucasian and African-American men with no previous diagnosis of CaP, aged 55-74 years, residing in King County, WA, USA.

RESULTS

Median PCPTRC risks of CaP (high-grade CaP) were 15.6% (1.2%), 18.7% (2.0%), 18.5% (2.2%), and 26.4% (5.1%) for 55-59, 60-64, 65-69, and 70-74-year-old men, respectively; 25.2% of men aged 55-59 had a 25% or greater PCPTRC risk of CaP; this increased to 53.1% in men aged 70-74; 9.4% of men aged 55-59 had a 6% or greater PCPTRC risk of high-grade CaP, increasing to 44.1% in men aged 70-74.

CONCLUSIONS

PCPTRC risk of CaP in a community of US males is high and confounded with overdetected cancers. In contrast, average community PCPTRC risk of high-grade disease is low and increases gradually by age and may better serve for counseling purposes.

Prostate cancer risk prediction in a urology clinic in Mexico

OBJECTIVES

To evaluate factors affecting the risk of prostate cancer (CaP) and high-grade disease (HGCaP, Gleason score ≥ 7) in a Mexican referral population, with comparison to the Prostate Cancer Prevention Trial Prostate Cancer Risk Calculator (PCPTRC).

METHODS AND MATERIALS

From a retrospective study of 826 patients who underwent prostate biopsy between January 2005 and December 2009 at the Instituto Nacional de Cancerología, Mexico, logistic regression was used to assess the effects of age, prostate-specific antigen (PSA), digital rectal exam (DRE), first-degree family history of CaP, and history of a prior prostate biopsy on CaP and HGCaP, separately. Internal discrimination, goodness-of-fit, and clinical utility of the resulting models were assessed with comparison to the PCPTRC.

RESULTS

Rates of both CaP (73.2%) and HGCaP (33.3%) were high among referral patients in this Mexican urology clinic. The PCPTRC generally underestimated the risk of CaP but overestimated the risk of HGCaP. Four factors influencing CaP on biopsy were logPSA, DRE, family history and a prior biopsy history (all P < 0.001). The internal AUC of the logistic model was 0.823 compared with 0.785 of the PCPTRC for CaP (P < 0.001). The same 4 factors were significantly associated with HGCaP as well and the AUC was 0.779 compared with 0.766 of the PCPTRC for HGCaP (P = 0.13).

CONCLUSIONS

Lack of screening programs or regular urologic checkups in Mexico imply that men typically first reach specialized clinics with a high cancer risk. This renders diagnostic tools developed on comparatively healthy populations, such as the PCPTRC, of lesser utility. Continued efforts are needed to develop and externally validate new clinical diagnostic tools specific to high-risk referral populations incorporating new biomarkers and more clinical characteristics.

Molecular diagnosis of prostate cancer: PCA3 and TMPRSS2:ERG gene fusion

PURPOSE

Widespread prostate specific antigen screening together with the increase in the number of biopsy cores has led to increased prostate cancer incidence. Standard diagnostic tools still cannot unequivocally predict prostate cancer progression, which often results in a significant overtreatment rate. We present recent findings on PCA3 and TMPRSS:ERG fusion, and describe their clinical implications and performance.

MATERIALS AND METHODS

The PubMed® database was searched for reports on PCA3 (130 articles), TMPRSS:ERG and ETS fusion (180 publications) since 1999.

RESULTS

In recent years advances in genetics and biotechnology have stimulated the development of noninvasive tests to detect prostate cancer. Serum and urine molecular biomarkers have been identified, of which PCA3 has already been introduced clinically. The identification of prostate cancer specific genomic aberrations, ie TMPRSS2:ERG gene fusion, might improve diagnosis and affect prostate cancer treatment.

CONCLUSIONS

Although several recently developed markers are promising, often showing increased specificity for prostate cancer detection compared to that of prostate specific antigen, their clinical application is limited. The only 2 true prostate cancer specific biomarkers identified to date remain PCA3 and TMPRSS2:ERG gene fusion.

[Outcomes of expanded use of PCA3 testing in a Spanish population with clinical suspicion of prostate cancer]

OBJECTIVES

DD3(PCA3) (PCA3) gene expression is prostate cancer-specific. Routine use of this biomarker has resulted in a 35-67% reduction in the number of required biopsies. The aim of this study is to evaluate our outcomes in its routine use and to establish in which group of patients this is the most efficient, depending on the number of previous PCA3 biopsies.

MATERIAL AND METHODS

A total of 474 consecutive patients who had previously undergone a biopsy (group A, n=337) or not (group B, n=134) for whom a PCA3 was requested were analyzed. We subdivided group A into A(1) (a previous biopsy, n=182) and A(2) (<1 previous biopsy, n=155). The recommendation of whether to perform a biopsy or not was made independently by each of the 11 clinicians and guided by prostatic specific antigen (PSA) levels and digital rectal examination.

RESULTS

Median age was 65 years (range 38 to 84). PCA3 score had an informative ratio of 99.6%, with a median of 29 (range 1-3245). The percentage of biopsy sparing was 49% of the cases. ROC analysis demonstrated an AUC for PSA and PCA3 of 0.532 (P=.417) and 0.672 (P<.0001), respectively. Sensitivities of PSA≥ 4 and PCA3≥ 35 were 87% vs. 85%, with specificities of 12% vs. 33%, PPV 34% vs. 39% and NPV 63% vs. 81%, respectively. The PCA3 score showed direct correlation with the percentage of positive biopsies (P<.0001).

CONCLUSIONS

Routine use of PCA3, due to its high NPV, results in a significant reduction in the number of biopsies. PCA3 appears to be more efficient in biopsy-naive patients. Among patients already biopsied, the results are superior in those biopsied only once.

Utility of PCA3 in patients undergoing repeat biopsy for prostate cancer

BACKGROUND

Men with persistently elevated and/or rising PSA levels after negative prostate biopsy often undergo multiple repeat biopsies. Prostate cancer antigen 3 (PCA3) has emerged as a predictor of prostate cancer.

METHODS

We sought to define the utility of PCA3 in combination with other clinical data in predicting the risk of prostate cancer on repeat biopsy. We retrospectively obtained PCA3, PSA, PSA density (PSAD), digital rectal examination (DRE) and transrectal ultrasound (TRUS) findings from 103 patients at a single institution who had at least one prior negative prostate biopsy. The sensitivity and specificity of PCA3 in detecting prostate cancer was determined. Receiver operating characteristics curves were produced for each variable individually and in multivariable analysis, controlling for PCA3, PSAD, TRUS, PSA and DRE. A nomogram was created, internally validated and compared to another recently published nomogram.

RESULTS

Of the 103 patients, 37 (31%) had prostate cancer on repeat biopsy. The sensitivity and specificity of PCA3 (using a cut point of 25) was 0.67 and 0.64, respectively. In multivariable analyses, PCA3 was independently associated with prostate cancer (odds ratio: 1.02, 95% confidence interval: 1.01-1.04), with area under the curve (AUC) of 0.64. A multivariable model containing PCA3, PSAD, PSA, DRE and TRUS findings showed the most diagnostic accuracy (AUC: 0.82).

CONCLUSIONS

In the setting of prior negative biopsies, PCA3 was independently associated with prostate cancer in a multivariable model. In combination with other clinical data, PCA3 is a valuable tool in assessing the risk of prostate cancer on repeat biopsy.

Age-specific PCA3 score reference values for diagnosis of prostate cancer

PURPOSE

We evaluated the impact of age on PCA3 score and the utility of age-specific reference values in predicting initial prostate biopsy (pBx) outcomes.

PATIENTS AND METHODS

This single-center, retrospective study included 205 men who underwent an initial 14-core TRUS-guided pBx due to PSA > 3.0 ng/ml or suspicious digital-rectal examination (DRE). PCA3 scores were measured with the Progensa assay. Linear regression models were fit to identify factors that impact PCA3 score and to determine age-specific reference values. Predictive accuracies of logistic regression models predicting presence of prostate cancer (PCa) were analyzed.

RESULTS

The positive biopsy rate was 37%. In multivariable linear regression, age (P < 0.001), presence of PCa (P < 0.001), and multifocal HG-PIN (P = 0.012) were independent predictors of PCA3 score. Age showed the strongest impact on PCA3 score (T = 4.77). The upper 95% confidence interval of PCA3 score in each age category was defined as the age-specific limit. A PCA3-score over the age-specific limit (PCA3-age) was associated with an 4.17-fold increased odds of being diagnosed with PCa (P < 0.001). In multivariable logistic regression models predicting the presence of PCa, predictive accuracy of a base model (age, DRE, PSA, volume) increased from 69.6 to 75.4% (P = 0.037) by adding the continuous PCA3 score, to 73.9% (P = 0.098) with the 35 cutoff (PCA3-35) and to 77.1% (P = 0.008) with PCA3-age.

CONCLUSIONS

PCA3 score increases with age, independent of PCa presence. Age-specific PCA3 score reference values are superior to PSA, continuous PCA3 score, and PCA3-35 in predicting initial pBx outcome. Therefore, an age-adjusted PCA3 score should be used for interpretation of the results.

Contemporary role of prostate cancer antigen 3 in the management of prostate cancer

CONTEXT

Newly discovered biomarkers ideally should prove clinical usefulness, provide additional detection, staging, and prognosis information to improve individual risk assessment, and potentially permit targeted cancer therapy.

OBJECTIVE

To review, display, and evaluate the current evidence regarding the biologic and analytic approach of urinary prostate cancer gene 3 (PCA3) in prostate cancer (PCa) detection, staging, and prognosis, and its therapeutic potential.

EVIDENCE ACQUISITION

A systematic and comprehensive Medline search was performed using the Medical Subject Headings search terms PCA3, DD3, UPM3, prostate cancer, cell-lines, prostate tissue, prostate biopsy, detection, diagnosis, radical prostatectomy, staging, grading, progression, and gene therapy. Results were restricted to English-language papers published within the period 1999-2011.

EVIDENCE SYNTHESIS

The PCA3 gene is highly overexpressed in specific PCa cell lines and prostatic tumours. In 2006, a simple and robust urine test (Progensa) became commercially available. Despite its costs, prostate cancer antigen 3 (PCA3) is superior to prostate-specific antigen (PSA) and percent free PSA in the early detection of PCa. PCA3 improves the diagnostic accuracy of externally validated nomograms among men with an elevated PSA undergoing biopsy. PCA3 independently predicts low-volume disease and pathologically insignificant PCa but is not associated with locally advanced disease and is limited in the prediction of aggressive cancer. Preliminary data demonstrate that combining PCA3 with other new biomarkers further improves diagnostic and prognostic accuracy. Finally, findings of the first PCA3-Gene-ViroTherapy study suggest therapeutic potential by exploiting PCA3 overexpression.

CONCLUSIONS

PCA3, integrated in novel biopsy nomograms or risk stratification tools, can be used to counsel or confirm biopsy indications. If confirmed in further studies, using PCA3 together with established staging risk factors could assist clinicians in specific pretreatment decision making. So far no evidence for the usefulness of PCA3 in active surveillance programs has been presented.