Blood-based and Urinary Prostate Cancer Biomarkers: A Review and Comparison of Novel Biomarkers for Detection and Treatment Decisions

BACKGROUND: The diagnosis of prostate cancer (PCa) is currently based on serum PSA testing and/or abnormal digital rectal examination and histopathologic evaluation of prostate biopsies. The main drawback of PSA testing is the lack of specificity for PCa. To improve early detection of PCa more specific biomarkers are needed. In the past few years, many new promising biomarkers have been identified; however, to date, only a few have reached clinical practice.

METHODS: In this review, we discuss new blood-based and urinary biomarker models that are promising for usage in PCa detection, follow-up and treatment decision-making. These include Prostate Health Index (PHI), prostate cancer antigen 3 (PCA3), four-kallikrein panel (4K), transmembrane protease serine 2-ERG (TMPRSS2-ERG), ExoDx Prostate Intelliscore, SelectMDx and the Mi-Prostate score. Only few head-to-head studies are available for these new fluid-based biomarkers and/or models. The blood-based PHI and urinary PCA3 are two US Food and Drug Administration-approved biomarkers for diagnosis of PCa. In the second part of this review, we give an overview of published studies comparing these two available biomarkers for prediction of (1) PCa upon prostate biopsy, (2) pathological features in radical prostatectomy specimen and (3) significant PCa in patients eligible for active surveillance.

RESULTS: Studies show opposing results in comparison of PHI with PCA3 for prediction of PCa upon initial and repeat prostate biopsy. PHI and PCA3 are able to predict pathological findings on radical prostatectomy specimen, such as tumor volume and Gleason score. Only PHI could predict seminal vesicle invasion and only PCA3 could predict multifocality. There is some evidence that PHI outperforms PCA3 in predicting significant PCa in an active surveillance population.

CONCLUSIONS: Future research should focus on independent validation of promising fluid-based biomarkers/models, and prospective comparison of biomarkers with each other.

Prostate Cancer and Prostatic Diseases (2017) 20, 12–19; doi:10.1038/pcan.2016.59; published online 6 December 2016 


Prostate cancer (PCa) is the second most frequently diagnosed malignancy in males worldwide, with 1.1 million estimated new cases in 2012.1 Early detection of PCa is based on serum PSA testing and/or abnormal digital rectal examination (DRE), and histopathologic evaluation of prostate biopsies. In the past decades, the incidence of PCa increased because of prolonged life expectancy, the use of PSA testing as a detection method and a larger number of men undergoing prostate biopsy. PSA is a kallikrein serine protease encoded by the KLK3 gene. PSA can be elevated because of PCa and also because of a large prostate volume, BPH or prostatitis. The main drawback is this lack of specificity leading to unnecessary (repeat) biopsies and the diagnosis of indolent PCa, and therefore a high risk for overdiagnosis and overtreatment. The incidence of metastatic disease has decreased since the use of PSA, whereas the incidence of local regional disease has increased. Today clinical stage T1c tumors represent 40–50% of newly diagnosed cases.2

For the individual patient, the PSA level does not correlate directly with clinical and pathological tumor stage. PSA levels of >4.0 ng ml−1 are commonly used as a threshold value for prostate biopsy. However, PSA has a 25–40% positive predictive value to detect PCa, and eventually 65–70% of men presenting with increased PSA between 4.0 and 10.0 ng ml−1 have a negative prostate biopsy.3, 4 Additionally, up to 15% of men with PCa have PSA levels below 4.0 ng ml−1, and therefore many cases will be left undetected.5

To improve the early diagnosis of PCa and to reduce the overdiagnosis and overtreatment of insignificant tumors, there is an urgent need for a specific test to detect clinically significant PCa.


The increasing knowledge of molecular biology considering carcinogenesis and PCa has led to the identification of new biomarkers. The challenges in developing ideal early detection markers for PCa are widespread. First of all the biomarkers should be specific for PCa and should not be altered or expressed in other tissues or tumors. The method of collection should be noninvasive. In terms of preservation, analytical procedures needed and costs, the biomarkers should possibly be applicable for use in large-scale screening programs. Besides, it is desirable that these biomarkers should not only distinguish patients with and without PCa but also differentiate between clinically significant tumors and indolent disease. For PCa several body fluids would be adequate for testing in a noninvasive manner, including prostate serum, semen, plasma and urine.6

In this review, we discuss new blood-based and urinary biomarker models that are promising for usage in PCa diagnostics (see Table 1). Moreover, we give an overview of the published studies in which a comparison was made of two well-studied commercially available biomarkers, the blood-based Prostate Health Index (PHI) and urinary prostate cancer antigen 3 (PCA3).

PSA-based assays

In the past decades, tests with molecular isoforms of PSA have been developed. Part of PSA in the blood is complexed (with proteins) and the greater part circulates in an unbound form. The unbound form is called freePSA (or fPSA) and the free-to-total PSA ratio significantly improves differentiation between PCa and benign conditions in the PSA ‘grey area’, 4.0–10.0 ng ml−1.7 More recently, PCa-specific fPSA isoforms, proPSAs, have been identified to improve specificity in detection.8 Especially [−2]proPSA (p2PSA) is associated with PCa and has been demonstrated to significantly outperform the use of total PSA and fPSA alone. Besides, p2PSA seemed to be related to the risk of aggressive disease.9, 10

Blood-based and urinary prostate cancer biomarkers_UroToday


In 2011 Catalona et al.11 published the results of a large multicenter trial on the PHI for PCa detection. PHI combines total PSA, fPSA and p2PSA, and is calculated using the following formula: (2pPSA/fPSA) × √PSA. In other words, men are more at risk of having significant PCa when they have a higher total PSA and p2PSA, and a lower fPSA.12 The score can be used in decision-making regarding prostate biopsies, and in the PSA ‘grey area’. PHI is now commercially available, and has been approved by the US Food and Drug Administration (FDA) for use in the 4.0–10 ng ml−1 PSA range.11 In the study of Catalona et al.,11 892 patients with PSA levels of 2.0–10 ng ml−1 and a normal DRE underwent prostate biopsies. For discrimination of PCa on prostate biopsies, PHI had a sensitivity of 80–95% and greater specificity than total PSA or fPSA. Besides, PHI had shown an association with biopsy Gleason score (GS)⩾7. Moreover, the PHI test may also have a role in monitoring men under active surveillance (AS).12


Several cancer products are found to be released directly into urine through prostate ducts as cell-free markers or carried in prostate cells.6

Prostate cancer antigen 3
PCA3, formerly known as differential display code 3 (DD3), was discovered by Bussemakers et al.13 in 1999. It is a prostate-specific noncoding messenger RNA (mRNA). PCA3 was found to be highly overexpressed in 95% of PCa tissue compared with normal prostate tissue of the same patient and in PCa metastasis.14 In 2003, Hessels et al.15 reported a median of 66-fold upregulation of PCA3 in PCa tissue compared with normal prostate tissue. Unlike PSA, PCA3 expression appears to be less influenced by patient age, prostate volume, inflammation, trauma or prior biopsies.4, 6 Although PCA3 does not encode a protein, PCA3 mRNA transcripts originating from prostate cells are detectable and quantifiable in urine.4

PCA3 was the first possible option for molecular diagnostics in clinical urological practice.16 In 2006, Groskopf et al.17 developed a quantitative PCA3 urine test for use in clinical settings. The Progensa PCA3 test (Hologic, Marlborough, MA, USA) is a commercially available test and has been approved by the US FDA for men with a previous negative biopsy and a persistently elevated PSA level to aid in decision-making regarding repeat biopsies.18 This molecular diagnostic assay quantitatively detects PCA3 mRNA expression in whole urine after DRE using transcription-mediated amplification.19 The PCA3 score was developed to determine the likelihood of PCa detection on prostate biopsy. To generate this quantitative PCA3 score, the ratio PCA3 mRNA/PSA mRNA × 1000 is used, meaning that PCA3 expression is normalized with PSA expression.4, 6 In 2003, Hessels et al.15 showed a sensitivity of 67% and a specificity of 83% for PCA3 in 108 voided post-DRE urine samples for the presence of a tumor using prostate biopsies as the gold standard. Moreover, this test had a negative predictive value of 90%, which indicated that the quantitative determination of PCA3 mRNA transcripts in urinary sediments had the potential in reducing the number of biopsies. In men undergoing repeated biopsy, PCA3 was superior to PSA in predicting whether PCa was present upon prostate biopsy.20 Studies on the value of PCA3 in the prediction of clinical–pathological features of PCa, including GS, tumor volume (TV), stage and extraprostatic extension, are contradictory.18, 21

Transmembrane protease serine 2-ERG gene fusion

Gene fusions are most often caused by genomic chromosomal rearrangements. These gene fusions are thought to be an initiating event in oncogenesis and have a role in the development of certain tumor types. In 2005, Tomlins et al.22 used a new biostatistical method to identify gene fusions in PCa. These chromosomal rearrangements included transmembrane protease serine 2 (TMPRSS2) that can be fused to several ETS transcription factor genes (erythroblastosis virus E26 transformation-specific transcription factor family), including ERG, ETV1, ETV4, ETV5 and ELK4. ETS transcription factors have an important role in several biological processes, including cell growth and proliferation, apoptosis, stress responses, angiogenesis and invasiveness. TMPRSS2-ERG gene fusions are the most common variant in ~50% of patients with PCa.23 The genes for TMPRSS2 and ERG are both located on the same chromosome, 21q22.3.4

TMPRSS2-ERG gene fusion seemed to be specific for PCa in tissue-based studies,23 and can also be detected in urine after prostate massage.24 According to Hessels et al.,24 this gene fusion has a 93% specificity and 94% positive predictive value for detection of PCa in post-DRE urine samples in a cohort of 108 men undergoing prostate biopsy. TMPRSS2-ERG gene fusions are not yet approved as a PCa biomarker to predict the prostate biopsy outcome. Regarding the predictive value for aggressive disease, there still is a lot of uncertainty. In 2007, Rajput et al.25 found a higher frequency of TMPRSS2-ERG gene fusions in moderate to poorly differentiated tumors compared with well-differentiated PCa. There was a positive correlation found between TMPRSS2-ERG fusion transcripts in urine and a high PSA level, pathological stage and GS.26 This was not confirmed by a large study of 1180 men in which overexpression of TMPRSS2-ERG gene fusion was found in 49% of patients, and no significant correlation with GS or tumor grade.27 The combination of gene fusions with other markers in a risk algorithm is discussed later in this review.

ExoDx Prostate Intelliscore

In 2009, Nilsson et al.28 showed that urinary exosomes are a promising substrate for PCa biomarkers. Exosomes are small vesicles that are secreted from (tumor) cells containing cellular protein and RNA, and are highly representative for their cell origin.28, 29 Donovan et al.30 used exosomal RNA and developed the EXO106 score (the sum of normalized PCA3 and ERG exosomal RNA), which had negative and positive predictive values for prediction of high-grade PCa of 97.5% and 34.5%, respectively. McKiermann et al.29 showed that the combination of exosomal PCA3 and ERG with normalization of RNA levels with SPDEF (SAM pointed domain-containing ETS transcription factor) derived from non-DRE urine samples could predict high-grade PCa upon initial biopsy with an area under the curve (AUC) of 0.73 (95% confidence interval (CI): 0.68–0.77). This is called the ExoDx Prostate Intelliscore (ExosomeDx) and aims to reduce the number of unnecessary biopsies.

Four-kallikrein panel

To improve the clinical value of PSA-based tests, Vickers et al.31 studied the combination of a four-kallikrein panel (4K) (total PSA, fPSA, intact PSA and human kallikrein-related peptidase 2) in blood samples from 740 men in Goteborg, Sweden, undergoing biopsy as part of the European Randomized study of Screening for Prostate Cancer (ERSPC). This four-kallikrein model was able to predict the biopsy outcome more accurately than total PSA and age alone. The 4K score test (OPKO Health, Miami, FL, USA) combines measurement of the four prostate-specific kallikreins in blood with clinical information in an algorithm that calculates the probability of significant (GS⩾7) PCa before biopsy. To validate these findings, Vickers et al.32 used an independent large, population-based cohort, the Rotterdam section of the ERSPC. In this cohort of 2186 men, the laboratory base model (PSA and age) had an AUC of 0.637, which increased to 0.764 for the full laboratory model (age plus kallikrein panel). The clinical models included DRE findings and the comparison demonstrated a difference between the base model (age, DRE and PSA) with an AUC of 0.695 vs 0.776 for the full model (age, DRE and four-kallikrein panel).32 This was a confirmation of the previously found predictive value in the Goteborg ERSPC cohort. In terms of predicting aggressive disease, Parekh et al.33 showed in a cohort of 1012 men scheduled for prostate biopsy a good diagnostic performance (AUC 0.82) in detecting significant PCa. Nordstrom et al.34 compared the 4K score with PHI and showed that both similarly increased predictive accuracy for high-grade disease and all PCa. The 4K score could save 44% of the biopsies when using a 15% chance for high-grade PCa, with the risk of missing ~20% high-grade tumors.34


In 2015, Leyten et al.35 described the identification of a novel urinary gene panel for the early diagnosis of PCa. A three-gene panel (HOXC6, TDRD1 and DLX1) with an AUC of 0.77 (95% CI: 0.71–0.83) to predict GS⩾7 PCa upon biopsy outperformed PCA3 (AUC 0.68) and PSA (AUC 0.72). Van Neste et al.36 developed a multimodal model, incorporating two of the previously identified biomarkers (HOXC6 and DLX1) and traditional clinical risk factors that could be used to identify patients with high-grade PCa (GS⩾7) upon prostate biopsy. The combination of biomarkers HOXC6 and DLX1 had the best performance with an AUC of 0.76 (95% CI: 0.71–0.81) in the training cohort. Using the risk factors age, PSA, PSA density, family history of PCa, DRE, history of prostate biopsy in combination with HOXC6 and DLX1 expression levels resulted in an AUC of 0.90 (95% CI: 0.87–0.93) for high-grade PCa.36 The AUC of the model for predicting high-grade PCa was significantly higher than the AUC of the Prostate Cancer Prevention Trial risk calculator (AUC 0.77) (P=0.015).36 Moreover, in men with a PSA level of <10 ng ml−1, the risk score remained the strongest predictor with an AUC of 0.78, compared with Prostate Cancer Prevention Trial risk calculator with an AUC of 0.66.36 The two-gene risk score, named SelectMDx (MDxHealth, Irvine, CA, USA), could be used in decision-making, reducing the number of unnecessary prostate biopsies and potential overtreatment. At a cutoff with an negative predictive value of 98% for high-grade PCa, a total reduction of biopsies by 42% could be obtained.36

Blood-based and urinary prostate cancer biomarkers_UroToday2

Mi-Prostate score

Considering PCa heterogeneity, combining biomarkers or the use of a panel of biomarkers is likely the way forward. Earlier studies showed that the combined use of PCA3 and TMPRSS2-ERG in urine had additional diagnostic and prognostic value in the prediction of PCa.24, 37 The validated Mi-Prostate score (MiPS) (University of Michigan MLabs, Ann Arbor, MI, USA) combines measurement of PCA3 and TMPRSS2-ERG in post-DRE urine samples together with serum PSA levels.38 Cornu et al.38 showed that PCA3 score, PSA density and TMPRSS2-ERG score were independently associated with prostate biopsy outcome in multivariable analysis with an AUC of 0.734. In multiple logistic regression model, PCA3 score and PSA density were significantly associated with the presence of Gleason grade 4 upon biopsy and there was a positive trend for TMPRSS2-ERG score. Salami et al.39 combined serum PSA, PCA3 and TMPRSS2-ERG in a multivariable algorithm to predict PCa upon biopsy with an AUC of 0.88 (95% CI: 0.75–0.98). A recent publication of Tomlins et al.40 concluded that the MiPS test could improve prediction of PCa and of high-grade PCa (GS>6) (AUC 0.772). Decision curve analysis demonstrated a net benefit of the MiPS test together with the multivariate Prostate Cancer Prevention Trial risk calculator. The MiPS test is promising for risk stratification of (high-grade) PCa while avoiding unnecessary biopsies.38, 39, 40, 41


Publications in which new biomarker tests are compared head-to-head are limited. Comparative data is needed to determine the best pathway for detection, prognosis and follow-up of PCa. The two commercially available tests, PHI and PCA3, are both promising to improve overdiagnosis and overtreatment. Up-to-date seven articles have been published comparing PHI with PCA3 in the initial and/or repeat biopsy setting (see Table 2). The first comparison of the two tests was made by Ferro et al.42 in 2012. In 151 men with initial prostate biopsies, the accuracy of PHI and PCA3 was assessed to predict benign, malignant and HG-PIN diagnosis. Receiver operating characteristic (ROC) curve analysis showed that PHI and PCA3 were good indicators of malignancies (AUC 0.77 and 0.71, respectively). PHI had the highest AUC but there was no significant difference with PCA3 (P=0.368), indicating comparable ability to discriminate benign for malignant condition. On the contrary, Seisen et al.43 showed PCA3 was the most accurate predictor of PCa in the initial biopsy setting compared with PHI (AUC 0.71 vs 0.65; P=0.03). Scattoni et al.44 compared PHI and PCA3 in a cohort of patients in the initial and repeat biopsy setting. In the whole group, ROC analyses revealed that PHI had the highest AUC (0.70, 95% CI: 0.63–0.76) compared with PCA3 (AUC 0.59, 95% CI: 0.52–0.66; P=0.043). Moreover, PHI was slightly more accurate than PCA3 in the repeat setting alone (AUC 0.72 vs 0.63).44 According to the study of Stephan et al.,45 PCA3 was the most accurate predictor of PCa in candidates for repeat biopsy compared to PHI (AUC 0.77 vs 0.69), although the AUCs were not statistically different.

Porpiglia et al.46 evaluated the diagnostic accuracy of PCA3, PHI and multiparametric magnetic resonance imaging (mpMRI) in patients undergoing repeat biopsy. The multivariate logistic regression analysis showed only mpMRI was a significant independent predictor of PCa diagnosis on repeat biopsy. Interestingly, the results of missing PCa were listed as well: mpMRI missed 5 of 52 (9.6%) tumors (3 GS 6 and 2 GS 7). PCA3 missed 22 of 52 (42.3%) tumors (10 GS 6, 10 GS 7 and 2 GS⩾8), whereas PHI missed 30 of 52 (57.7%) tumors (16 GS 6, 12 GS 7 and 2 GS⩾8).

Perdona et al.47 evaluated using the combination of PCA3 and PHI in predicting biopsy results in 160 men with initial biopsy. ROC analyses showed that PHI outperformed PCA3 for high specificity level, whereas PCA3 outperformed PHI for high sensitivity level. Multivariable analysis showed that the combination of PHI with PCA3 overall performed better than the single biomarkers. In an initial biopsy cohort in the PSA grey area (2–10 ng ml−1), Ferro et al.48 showed that PHI and PCA3 are the strongest predictors of PCa with no significant differences in pairwise comparison. The combination of the two tests did not further improve diagnostic power in this cohort, in contrast with the results of Stephan et al.45 and Perdona et al.47

Blood-based and urinary prostate cancer biomarkers_UroToday3


To evaluate the prognostic accuracy of PCA3 and PHI, these tests were also studied in patients who underwent radical prostatectomy (RP) (see Table 3). Cantiello et al.49 included 156 patients with biopsy-proven, clinically localized PCa and showed that inclusion of PHI significantly increased the accuracy of a base multivariate model (which included age, total PSA, fPSA, rate of positive cores, clinical stage, prostate volume, body mass index and biopsy GS), in predicting TV>0.5 ml, extra capsular extension (ECE), seminal vesicles invasion (SVI), pathologic GS⩾7 and pathologically confirmed significant PCa. Although both PHI and PCA3 significantly improved accuracy independently (all P's<0.01) to predict ECE compared with the base model, only PHI led to a significant improvement in the prediction of SVI (AUC 92.2, P<0.05). Moreover, in the study of Tallon et al.,50 PHI and PCA3 were both predictors of a TV⩾0.5 ml. Only PHI predicted GS⩾7 and ECE, and multifocality was predicted by PCA3 only. A smaller study of Ferro et al.51 showed that the largest AUC’s were obtained with PHI compared with PCA3 for TV⩾0.5 ml (0.94 vs 0.86), GS⩾7 (0.94 vs 0.78) and tumor stage (0.85 vs 0.74). Furthermore, Fossati et al.52 used the PROMETHEUS database to select 489 patients who underwent RP for localized PCa, and to test the correlation between p2PSA, %p2PSA and PHI with pathological features of the RP specimen. For prediction of pT3 disease and/or pathologic GS⩾7, PHI was the most accurate biomarker (AUC 0.74 and 0.69, respectively). Moreover, PHI significantly increased the predictive accuracy of the used base model (PSA, DRE, biopsy GS and percentage of positive biopsy cores) with 2.4% (P=0.01) when considering pT3 disease and pathologic GS⩾7. Models including %p2PSA and PHI, however, did not result in a greater net benefit when plotted against various threshold probabilities in the decision curve analysis.52

Blood-based and urinary prostate cancer biomarkers_UroToday4


Patients are currently selected for AS instead of active treatment based on clinical and pathological characteristics (e.g., total PSA, PSA density, biopsy GS, number of positive cores, percentage of core involvement, clinical stage).53 Unfortunately, the current stratification risk schemes are not perfect. There is limited accuracy in correctly selecting patients with insignificant PCa and limited tools for predicting progress or need for active treatment during follow-up. Bul et al.53 showed the updated results from the PRIAS (Prostate Cancer Research International Active Surveillance) study, in which 28% of the cohort disease was reclassified (defined as GS>6 and/or >2 positive cores) at the first repeated biopsy during follow-up. Moreover, selection criteria for AS can exclude patients who would actually benefit from expectant management. Therefore, the long-term safety and effectiveness of AS depends on the ability to select appropriate patients and there is an urgent need for better selection and follow-up tools for improving the risk assessment. A recently published systematic review included 30 studies on MRI, serum biomarkers (2pPSA, PHI) and urinary markers (PCA3) for the selection and monitoring of patients on AS.54 Van den Bergh et al.54 concluded that the addition of a PSA isoform to the current AS criteria could benefit the outcomes. Furthermore, the use of mpMRI is very promising in this domain because of a high negative predictive value with respect to significant PCa. Besides, the mpMRI should be able to guide in decision-making regarding the need for repeat biopsy during AS. There were two retrospective studies published regarding the comparison of PHI and PCA3 in an AS cohort (see Table 4).55, 56 Cantiello et al.55 showed that the outcomes of using the Epstein and PRIAS protocols for selecting patients for AS could be improved by adding PHI or PCA3, with an increase in the predictive accuracy that ranged from 17 to 39%. In a direct comparison and decision curve analysis, PHI outperformed PCA3 performance resulting in higher net benefit. In the study of Porpiglia et al.56 in 55 patients (45.8%) pathologically confirmed reclassification was observed. On multivariate analysis, the inclusion of both PHI and mpMRI significantly increased the accuracy in prediction of significant PCa, whereas PCA3 did not add net benefit.


To date, many novel promising biomarkers for PCa have been identified, which have been shown to outperform the use of PSA alone. Most studied are the two commercially available biomarkers PHI and PCA3. After reviewing the current literature wherein PHI and PCA are compared head-to-head, we are not able to give a clear recommendation about how and when to use PHI and/or PCA3 in the biopsy setting and treatment selection. In the initial biopsy setting, some studies showed that PHI was a better predictor for PCa and high-grade PCa,42, 44, 46 whereas other studies showed that PCA3 was the most accurate predictor.43, 45 In the repeat biopsy setting, there were opposing results as well, and there were no statistically significant differences between PHI and PCA3.44, 45 As for the combination of the two biomarker tests, there is some evidence for improving the diagnostic accuracy,45, 47 although the third study could not confirm these findings.48 Regarding prediction of pathological features of prostatectomy specimen, PHI and PCA3 both improved the prediction of tumor stage as well as TV.49, 50, 51 Furthermore, only PHI led to a significant improvement for prediction of SVI,49 whereas PCA3 was the only predictor for tumor multifocality.50 For selection of eligible patients for AS and follow-up, PHI and/or PCA3 could be used to improve predictive accuracy. According to the decision curve analysis, PHI outperforms PCA3, and the use of mpMRI in this group is very promising.55, 56

Although many studies have shown that novel biomarkers outperform PSA, they are not yet part of daily clinical practice and guidelines. We would recommend that before using new biomarkers as tools for risk stratification, biopsy decisions and treatment selection in patients with PCa, the biomarkers should be validated and prospectively compared with each other. Especially models in which biomarkers are combined with clinical risk factors should be compared, in particular the 4K score, SelectMDx test and MiPS Score. It should be given the highest priority to compare these risk scores head-to-head in large prospective studies to find out the clinical value and benefit.

Future research should also focus on use of random transrectal ultrasound biopsies as golden standard, because of a high false-negative biopsy rate and the chance of missing clinically significant tumors in the anterior and apical part of the prostate.57, 58 Options to overcome this limitation would be targeted biopsy techniques such as mpMRI-guided biopsies, transrectal ultrasound/MRI fusion biopsies and mapping biopsies, and of course RP specimen. It would also be interesting to study the biomarkers in longer follow-up in the same cohort, to see if there are fluctuations that should be taken into account when interpreting the results. De Luca et al.59 demonstrated that PCA3 scores showed clinically notable changes in ~20% of patients when measured multiple times. Besides the assessment of clinical effectiveness, the cost-effectiveness should be the main focus of future research as well. Nicholson et al.60 performed an economic evaluation of PCA3 and PHI in the diagnosis of PCa for the National Institute for Health Research. Unfortunately, despite a systemic search no published literature met the inclusion criteria for the review for cost-effectiveness. They presented a de novo economic model that showed that neither PHI nor PCA3 is likely to be cost-effective when identifying patients for second biopsy compared with clinical assessment alone (e.g., DRE, total PSA, PSA density, age, family history) or clinical assessment plus mpMRI. In addition, the authors suggested that there is a higher risk of identifying more patients as potentially having PCa when PHI or PCA3 are used, compared with if clinicians had only relied on their clinical assessment. On the other hand, PHI and PCA3 could be cost-effective if the tests had higher sensitivity for detecting clinically significant PCa.60

To conclude this review, longitudinal studies are required following men from initial investigation through to diagnosis and treatment of PCa to determine clinical effectiveness and cost-effectiveness to guide doctor and patient in decision-making regarding PCa diagnostics and treatment selection.


JAS and IMvO have consultancy with honoraria for Astellas, Janssen and Sanofi. The remaining author declares no conflict of interest.

1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin 2015; 65: 87–108. 
2. Schroder FH, Hugosson J, Roobol MJ, Tammela TL, Ciatto S, Nelen V et al. Screening and prostate-cancer mortality in a randomized European study. N Engl J Med 2009; 360: 1320–1328. 
3. Draisma G, Etzioni R, Tsodikov A, Mariotto A, Wever E, Gulati R et al. Lead time and overdiagnosis in prostate-specific antigen screening: importance of methods and context. J Natl Cancer Inst 2009; 101: 374–383. 
4. Salagierski M, Schalken JA. Molecular diagnosis of prostate cancer: PCA3 and TMPRSS2:ERG gene fusion. J Urol 2012; 187: 795–801. 
5. Thompson IM, Pauler DK, Goodman PJ, Tangen CM, Lucia MS, Parnes HL et al. Prevalence of prostate cancer among men with a prostate-specific antigen level <or =4.0 ng per milliliter. N Engl J Med 2004; 350: 2239–2246. 
6. Truong M, Yang B, Jarrard DF. Toward the detection of prostate cancer in urine: a critical analysis. J Urol 2013; 189: 422–429. 
7. Catalona WJ, Partin AW, Slawin KM, Brawer MK, Flanigan RC, Patel A et al. Use of the percentage of free prostate-specific antigen to enhance differentiation of prostate cancer from benign prostatic disease: a prospective multicenter clinical trial. JAMA 1998; 279: 1542–1547. 
8. Mikolajczyk SD, Catalona WJ, Evans CL, Linton HJ, Millar LS, Marker KM et al. Proenzyme forms of prostate-specific antigen in serum improve the detection of prostate cancer. Clin Chem 2004; 50: 1017–1025. 
9. Guazzoni G, Nava L, Lazzeri M, Scattoni V, Lughezzani G, Maccagnano C et al. Prostate-specific antigen (PSA) isoform p2PSA significantly improves the prediction of prostate cancer at initial extended prostate biopsies in patients with total PSA between 2.0 and 10 ng/ml: results of a prospective study in a clinical setting. Eur Urol 2011; 60: 214–222. 
10. Sokoll LJ, Sanda MG, Feng Z, Kagan J, Mizrahi IA, Broyles DL et al. A prospective, multicenter, National Cancer Institute Early Detection Research Network study of [−2]proPSA: improving prostate cancer detection and correlating with cancer aggressiveness. Cancer Epidemiol Biomarkers Prev 2010; 19: 1193–1200. 
11. Catalona WJ, Partin AW, Sanda MG, Wei JT, Klee GG, Bangma CH et al. A multicenter study of [−2]pro-prostate specific antigen combined with prostate specific antigen and free prostate specific antigen for prostate cancer detection in the 2.0 to 10.0 ng/ml prostate specific antigen range. J Urol 2011; 185: 1650–1655. 
12. Loeb S, Catalona WJ. The Prostate Health Index: a new test for the detection of prostate cancer. Ther Adv Urol 2014; 6: 74–77. 
13. Bussemakers MJ, van Bokhoven A, Verhaegh GW, Smit FP, Karthaus HF, Schalken JA et al. DD3: a new prostate-specific gene, highly overexpressed in prostate cancer. Cancer Res 1999; 59: 5975–5979. 
14. de Kok JB, Verhaegh GW, Roelofs RW, Hessels D, Kiemeney LA, Aalders TW et al. DD3(PCA3), a very sensitive and specific marker to detect prostate tumors. Cancer Res 2002; 62: 2695–2698. 
15. Hessels D, Klein Gunnewiek JM, van Oort I, Karthaus HF, van Leenders GJ, van Balken B et al. DD3(PCA3)-based molecular urine analysis for the diagnosis of prostate cancer. Eur Urol 2003; 44: 8–15 discussion 15-16. 
16. Hessels D, Schalken JA. Urinary biomarkers for prostate cancer: a review. Asian J Androl 2013; 15: 333–339. 
17. Groskopf J, Aubin SM, Deras IL, Blase A, Bodrug S, Clark C et al. APTIMA PCA3 molecular urine test: development of a method to aid in the diagnosis of prostate cancer. Clin Chem 2006; 52: 1089–1095. 
18. Marks LS, Fradet Y, Deras IL, Blase A, Mathis J, Aubin SM et al. PCA3 molecular urine assay for prostate cancer in men undergoing repeat biopsy. Urology 2007; 69: 532–535. 
19. Schalken JA, Hessels D, Verhaegh G. New targets for therapy in prostate cancer: differential display code 3 (DD3(PCA3)), a highly prostate cancer-specific gene. Urology 2003; 62 (Suppl 1): 34–43. 
20. Hessels D, Schalken JA. Recurrent gene fusions in prostate cancer: their clinical implications and uses. Curr Urol Rep 2013; 14: 214–222. 
21. Ploussard G, Haese A, Van Poppel H, Marberger M, Stenzl A, Mulders PF et al. The prostate cancer gene 3 (PCA3) urine test in men with previous negative biopsies: does free-to-total prostate-specific antigen ratio influence the performance of the PCA3 score in predicting positive biopsies? BJU Int 2010; 106: 1143–1147. 
22. Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 2005; 310: 644–648. 
23. Perner S, Mosquera JM, Demichelis F, Hofer MD, Paris PL, Simko J et al. TMPRSS2-ERG fusion prostate cancer: an early molecular event associated with invasion. Am J Surg Pathol 2007; 31: 882–888. 
24. Hessels D, Smit FP, Verhaegh GW, Witjes JA, Cornel EB, Schalken JA. Detection of TMPRSS2-ERG fusion transcripts and prostate cancer antigen 3 in urinary sediments may improve diagnosis of prostate cancer. Clin Cancer Res 2007; 13: 5103–5108. 
25. Rajput AB, Miller MA, De Luca A, Boyd N, Leung S, Hurtado-Coll A et al. Frequency of the TMPRSS2:ERG gene fusion is increased in moderate to poorly differentiated prostate cancers. J Clin Pathol 2007; 60: 1238–1243. 
26. Rostad K, Hellwinkel OJ, Haukaas SA, Halvorsen OJ, Oyan AM, Haese A et al. TMPRSS2:ERG fusion transcripts in urine from prostate cancer patients correlate with a less favorable prognosis. APMIS 2009; 117: 575–582. 
27. Pettersson A, Graff RE, Bauer SR, Pitt MJ, Lis RT, Stack EC et al. The TMPRSS2:ERG rearrangement, ERG expression, and prostate cancer outcomes: a cohort study and meta-analysis. Cancer Epidemiol Biomarkers Prev 2012; 21: 1497–1509. 
28. Nilsson J, Skog J, Nordstrand A, Baranov V, Mincheva-Nilsson L, Breakefield XO et al. Prostate cancer-derived urine exosomes: a novel approach to biomarkers for prostate cancer. Br J Cancer 2009; 100: 1603–1607. 
29. McKiernan J, Donovan MJ, O'Neill V, Bentink S, Noerholm M, Belzer S et al. A novel urine exosome gene expression assay to predict high-grade prostate cancer at initial biopsy. JAMA Oncol 2016; 2: 882–889. 
30. Donovan MJ, Noerholm M, Bentink S, Belzer S, Skog J, O'Neill V et al. A molecular signature of PCA3 and ERG exosomal RNA from non-DRE urine is predictive of initial prostate biopsy result. Prostate Cancer Prostatic Dis 2015; 18: 370–375. 
31. Vickers AJ, Cronin AM, Aus G, Pihl CG, Becker C, Pettersson K et al. A panel of kallikrein markers can reduce unnecessary biopsy for prostate cancer: data from the European Randomized Study of Prostate Cancer Screening in Goteborg, Sweden. BMC Med 2008; 6: 19. 
32. Vickers A, Cronin A, Roobol M, Savage C, Peltola M, Pettersson K et al. Reducing unnecessary biopsy during prostate cancer screening using a four-kallikrein panel: an independent replication. J Clin Oncol 2010; 28: 2493–2498. 
33. Parekh DJ, Punnen S, Sjoberg DD, Asroff SW, Bailen JL, Cochran JS et al. A multi-institutional prospective trial in the USA confirms that the 4 K score accurately identifies men with high-grade prostate cancer. Eur Urol 2015; 68: 464–470. 
34. Nordstrom T, Vickers A, Assel M, Lilja H, Gronberg H, Eklund M. Comparison between the Four-kallikrein Panel and Prostate Health Index for predicting prostate cancer. Eur Urol 2015; 68: 139–146. 
35. Leyten GH, Hessels D, Smit FP, Jannink SA, de Jong H, Melchers WJ et al. Identification of a candidate gene panel for the early diagnosis of prostate cancer. Clin Cancer Res 2015; 21: 3061–3070. 
36. Van Neste L, Hendriks RJ, Dijkstra S, Trooskens G, Cornel EB, Jannink SA et al. Detection of high-grade prostate cancer using a urinary molecular biomarker-based risk score. Eur Urol 2016; 70: 740–748. 
37. Leyten GH, Hessels D, Jannink SA, Smit FP, de Jong H, Cornel EB et al. Prospective multicentre evaluation of PCA3 and TMPRSS2-ERG gene fusions as diagnostic and prognostic urinary biomarkers for prostate cancer. Eur Urol 2014; 65: 534–542. 
38. Cornu JN, Cancel-Tassin G, Egrot C, Gaffory C, Haab F, Cussenot O. 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. Prostate 2013; 73: 242–249. 
39. Salami SS, Schmidt F, Laxman B, Regan MM, Rickman DS, Scherr D et al. Combining urinary detection of TMPRSS2:ERG and PCA3 with serum PSA to predict diagnosis of prostate cancer. Urol Oncol 2013; 31: 566–571. 
40. Tomlins SA, Day JR, Lonigro RJ, Hovelson DH, Siddiqui J, Kunju LP et al. Urine TMPRSS2:ERG plus PCA3 for individualized prostate cancer risk assessment. Eur Urol 2016; 70: 45–53. 
41. Tonry CL, Leacy E, Raso C, Finn SP, Armstrong J, Pennington SR. The role of proteomics in biomarker development for improved patient diagnosis and clinical decision making in prostate cancer. Diagnostics 2016; 6: 27. 
42. Ferro M, Bruzzese D, Perdona S, Mazzarella C, Marino A, Sorrentino A et al. Predicting prostate biopsy outcome: prostate health index (PHI) and prostate cancer antigen 3 (PCA3) are useful biomarkers. Clin Chim Acta 2012; 413: 1274–1278. 
43. Seisen T, Roupret M, Brault D, Leon P, Cancel-Tassin G, Comperat E et al. Accuracy of the prostate health index versus the urinary prostate cancer antigen 3 score to predict overall and significant prostate cancer at initial biopsy. Prostate 2015; 75: 103–111. 
44. Scattoni V, Lazzeri M, Lughezzani G, De Luca S, Passera R, Bollito E et al. Head-to-head comparison of prostate health index and urinary PCA3 for predicting cancer at initial or repeat biopsy. J Urol 2013; 190: 496–501. 
45. Stephan C, Jung K, Semjonow A, Schulze-Forster K, Cammann H, Hu X et al. 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. Clin Chem 2013; 59: 280–288. 
46. Porpiglia F, Russo F, Manfredi M, Mele F, Fiori C, Bollito E et al. The roles of multiparametric magnetic resonance imaging, PCA3 and prostate health index-which is the best predictor of prostate cancer after a negative biopsy? J Urol 2014; 192: 60–66. 
47. Perdona S, Bruzzese D, Ferro M, Autorino R, Marino A, Mazzarella C et al. Prostate health index (PHI) and prostate cancer antigen 3 (PCA3) significantly improve diagnostic accuracy in patients undergoing prostate biopsy. Prostate 2013; 73: 227–235. 
48. Ferro M, Bruzzese D, Perdona S, Marino A, Mazzarella C, Perruolo G et al. Prostate health index (Phi) and prostate cancer antigen 3 (PCA3) significantly improve prostate cancer detection at initial biopsy in a total PSA range of 2-10 ng/ml. PLoS ONE 2013; 8: e67687. 
49. Cantiello F, Russo GI, Ferro M, Cicione A, Cimino S, Favilla V et al. Prognostic accuracy of Prostate Health Index and urinary Prostate Cancer Antigen 3 in predicting pathologic features after radical prostatectomy. Urol Oncol 2015; 33: 163 e115–123. 
50. Tallon L, Luangphakdy D, Ruffion A, Colombel M, Devonec M, Champetier D et al. Comparative evaluation of urinary PCA3 and TMPRSS2: ERG scores and serum PHI in predicting prostate cancer aggressiveness. Int J Mol Sci 2014; 15: 13299–13316. 
51. Ferro M, Lucarelli G, Bruzzese D, Perdona S, Mazzarella C, Perruolo G et al. Improving the prediction of pathologic outcomes in patients undergoing radical prostatectomy: the value of prostate cancer antigen 3 (PCA3), prostate health index (PHI) and sarcosine. Anticancer Res 2015; 35: 1017–1023. 
52. Fossati N, Buffi NM, Haese A, Stephan C, Larcher A, McNicholas T et al. Preoperative prostate-specific antigen isoform p2PSA and its derivatives, %p2PSA and prostate health index, predict pathologic outcomes in patients undergoing radical prostatectomy for prostate cancer: Results from a Multicentric European Prospective Study. Eur Urol 2015; 68: 132–138. 
53. Bul M, Zhu X, Valdagni R, Pickles T, Kakehi Y, Rannikko A et al. Active surveillance for low-risk prostate cancer worldwide: the PRIAS study. Eur Urol 2013; 63: 597–603. 
54. van den Bergh RC, Ahmed HU, Bangma CH, Cooperberg MR, Villers A, Parker CC. Novel tools to improve patient selection and monitoring on active surveillance for low-risk prostate cancer: a systematic review. Eur Urol 2014; 65: 1023–1031. 
55. Cantiello F, Russo GI, Cicione A, Ferro M, Cimino S, Favilla V et al. PHI and PCA3 improve the prognostic performance of PRIAS and Epstein criteria in predicting insignificant prostate cancer in men eligible for active surveillance. World J Urol 2016; 34: 485–493. 
56. Porpiglia F, Cantiello F, De Luca S, Manfredi M, Veltri A, Russo F et al. In-parallel comparative evaluation between multiparametric magnetic resonance imaging, prostate cancer antigen 3 and the prostate health index in predicting pathologically confirmed significant prostate cancer in men eligible for active surveillance. BJU Int 2015; 118: 527–534.
57. Roehl KA, Antenor JA, Catalona WJ. Serial biopsy results in prostate cancer screening study. J Urol 2002; 167: 2435–2439. 
58. Wolters T, van der Kwast TH, Vissers CJ, Bangma CH, Roobol M, Schroder FH et al. False-negative prostate needle biopsies: frequency, histopathologic features, and follow-up. Am J Surg Pathol 2010; 34: 35–43. 
59. De Luca S, Passera R, Cappia S, Bollito E, Randone DF, Milillo A et al. Fluctuation in prostate cancer gene 3 (PCA3) score in men undergoing first or repeat prostate biopsies. BJU Int 2014; 114: E56–E61. 
60. Nicholson A, Mahon J, Boland A, Beale S, Dwan K, Fleeman N et al. The clinical effectiveness and cost-effectiveness of the PROGENSA(R) prostate cancer antigen 3 assay and the Prostate Health Index in the diagnosis of prostate cancer: a systematic review and economic evaluation. Health Technol Assess 2015; 19: 1–191.

Prostate Cancer and Prostatic Diseases (2017) 20, 12–19 © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved 1365-7852/17 

Written By: R JHendriks1Hendriks, R J I Mvan Oort1van Oort, I M J ASchalken1Schalken, J A Professor JA Schalken 

1Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands Department of Urology, Radboud University Medical Center, Postbus 9101, Nijmegen 6500 HB, The Netherlands.

Read More:
A letter from the desk of the associate editor: Ashley Evan Ross, MD, PhD