Given the pitfalls of prostate-specific antigen (PSA) testing for screening men with asymptomatic prostate cancer (PCa), a number of novel biomarkers have recently been studied that potentially decrease false-positive PSA results and unnecessary biopsies.
To review the literature on biomarkers with potential diagnostic utility for PCa by guiding the decision for initial or repeat biopsies in patients with elevated PSA.
We conducted a systematic literature review of human clinical studies on diagnostic biomarkers reporting clinicopathologic outcomes. A comprehensive search was performed in the Medline, Scopus, and Web of Science databases for articles from January 2005 through June 2015.
For men presenting with elevated PSA, especially in the 4–10 ng/ml range, who are considered for initial prostate biopsy, two serum-based assays, the Prostate Health Index and the four-kallikrein panel, can help identify patients with an increased risk of significant cancer on biopsy. In the setting of a prior negative biopsy but elevated PSA, urine-based assays detecting prostate cancer antigen 3 and/or transmembrane protease, serine2:v-ets avian erythroblastosis virus E26 oncogene homolog fusion transcript help predict the risk of high-grade cancer on subsequent biopsy. In cases with elevated PSA and an initial negative biopsy, epigenetic analysis can predict cancer diagnosis on subsequent biopsies. The combination of these novel biomarkers with existing nomograms and risk calculators leads to increased predictive accuracy and avoids unnecessary biopsies.
Rapid strides have been made in the discovery of novel biomarkers for guiding biopsy decisions in men suspected of harboring PCa. Although some of them have been approved for specific clinical settings, most of them still await rigorously designed prospective validation studies.
Novel urine-, serum-, and tissue-based biomarkers have been validated for guiding decisions on prostate biopsy in asymptomatic men with elevated prostate-specific antigen. Further exploration in this field may help expand their diagnostic and prognostic roles for prostate cancer.
Keywords: Prostate cancer, Diagnosis, Biomarkers, Genetics, PSA.
Prostate cancer (PCa) is the most common noncutaneous malignancy and the second most common cause of cancer-related deaths in Western men. With an estimated lifetime incidence of 16% in contemporary American men , most cases of PCa are diagnosed in the localized stage, due in large part to widespread prostate-specific antigen (PSA) screening. Notwithstanding its relatively indolent nature, PCa still carries a lifetime mortality risk of 3% , almost entirely due to metastatic disease. In this context, biomarkers (ie, biological markers) for PCa have three distinct but overlapping roles: diagnosis of clinically significant disease (eg, Gleason score [GS] 7–10) that may warrant further treatment, prognosis in the pretreatment (allowing risk stratification and treatment selection) and/or post-treatment setting (deciding on the need for adjuvant treatment and tailoring follow-up regimens), and predicting/monitoring possible response to secondary therapy. The current review focuses on the diagnostic relevance of biomarkers in PCa.
PSA testing has traditionally been used for screening men with asymptomatic PCa, even in the face of suboptimal sensitivity and specificity, variability in the different commercial assays, and conflicting recommendations on population-based PSA screening. To circumvent this problem, researchers have studied numerous novel biomarkers in an attempt to refine the diagnostic accuracy of PSA in predicting the risk of PCa on future biopsy, especially in the PSA grey zone of 4–10 ng/ml. These markers have primarily been studied in urine, serum, or prostate biopsy specimens. Rapid advances in whole genome sequencing, proteomic analyses, and metabolic profiling of PCa have driven the discovery and further exploration of these biomarkers. Nonetheless, most of these novel biomarkers still remain in the investigational phase absent large-scale validation studies, while some of them have been marketed commercially and approved by the US Food and Drug Administration (FDA) for use in specific clinical settings. Owing to the magnitude of research performed in this area, we have primarily focused on biomarkers studied within the past decade (2005–2015).
2. Evidence acquisition
A literature review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis criteria  by two authors (D.D. and F.A.). Figure 1 illustrates the inclusion process of the literature search. Databases including PubMed, Ovid, Scopus, and Web of Science were queried for all articles from January 2005 through June 2015, using the keywords “prostate cancer” AND “biomarkers” AND “diagnosis.” The search was further restricted to human studies and those published in English. Studies focusing on clinicopathologic outcomes—presence of any PCa, presence of significant (GS ≥7) cancer, clinical stage, and/or number of positive cores on biopsy—and those that have had at least one internal/external validation study were prioritized. Reference lists of selected texts were further reviewed, and articles deemed relevant were abstracted. All authors oversaw and approved the final literature review and selection.
3. Evidence synthesis
3.1. Landscape of available biomarkers for the diagnosis of prostate cancer
Currently explored biomarkers for PCa may be broadly classified in one of five categories based on their origin: genome, epigenome, transcriptome, proteome, or metabolome . Table 1 shows some selected biomarkers from each of these categories.
|Biomarker||Specimen||Performance metrics and comments||Commercial availability|
|Copy number alteration (loss of heterozygosity)|
|• 7q, 8p, 10q, 12p, 13p, 16p, 17p, and 18q ||Urine (PCa patients)||• Sensitivity: 87%|
• 53% of histologically normal cells near the tumor showed the same chromosomal deletions as the malignant cells: “field effect”
|• Tumor-specific cfDNA ||Serum (PCa patients)||• Evaluated number of sequence reads of cfDNA at 100-kb intervals|
• AUC 0.92 in final validation model
• Parallel sequencing of cfDNA may allow distinction between malignant and nonmalignant settings
|3.4-kB mitochondrial genome deletion (3.4 mtΔ) ||Prostate biopsy (histopathologically negative)||• Sensitivity: 84%, specificity: 54%, negative predictive value: 91%|
• A cycle threshold of 31 allows prediction of missed cancer in biopsy performed 1 yr after initial negative biopsy
• Field effect: histopathologically normal cells contiguous to tumor foci show genetic abnormalities
|Yes (Prostate Core Mitomic Test)|
|Hypermethylation of CpG islands in promoter regions|
|• p16, ARF, MGMT, and GSTP1||Post-DRE urine||• Sensitivity: 87%; specificity: 89–100%|
• QMSP chain reaction–detected hypermethylation in at least one of four genes in 87% of patients
• Role of four-gene panel for stratifying patients into low/high risk of PCa and need for initial/repeat prostate biopsies
|• GSTP1, APC, RASSF1, and RARB2||Post-DRE urine||No|
|• GSTP1, APC, and RASSF1 and ||Prostate biopsy (histopathologically negative)||• Negative predictive value: 88–90%|
• QMSP on three-gene panel strongly associated with repeat biopsy outcome, done within 13–30 mo of prior negative biopsy
• Field effect for epigenetic changes: aids diagnosis in face of tumor multifocality and sampling error on biopsy
|PCA3: noncoding mRNA on chromosome 9q21–22 , , , , and ||Urine, following “attentive” DRE||• AUC: 0.66–0.69|
• AUC (when combined with other predictive factors for PCa at biopsy): 0.71–0.75
• Third-generation assays calculate PCA3 score (ratio of PCA3 mRNA to PSA mRNA). Cut-off scores of 20–35 have been suggested for optimal accuracy
• Predictive accuracy depends on clinical setting (prediction of PCa at initial or repeat biopsy), combination with other predictive variables (such as PSA, DRE, %fPSA), and study population (American vs European cohorts)
• Unlike PSA, not greatly affected by age, inflammation, recent trauma to the prostate, or use of 5α-reductase inhibitors
|Gene fusion transcripts: TMPRSS2 and ERG and ||Urine||• Sensitivity about 50%; specificity about 95%|
|Other long-noncoding RNA|
• FR0348383 
|Urine||• FR0348383 score independent predictor of PCa, after controlling for the effect of PSA, age, and prostate volume|
• AUC 0.815 (compared with 0.562, 0.6, and 0.64 for PSA, %fPSA, and PSA density, respectively) in PSA 4–10 ng/ml range
• DCA: probability threshold of 30% avoided 52% unnecessary biopsies, without missing any high-grade (GS ≥7) PCa
|miRNA signature||Benign and malignant prostate tissue|
|• Meta-analysis showed pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of 74%, 73%, 2.7, and 0.35. Pooled AUC was 0.79 |
• miRNA 21 showed sensitivity of 90% and specificity of 90% (in PSA 4–10 ng/ml range) for detecting PCa 
|MD miniRNA ||Serum||• At cut-off of 867.8 MD-miniRNA copies/μl, sensitivity and specificity were 58.6% and 84.8%, respectively, for discriminating PCa from non-PCa||No|
|Proteome-based biomarkers (other than PSA, PSA isoforms, and kallikreins)|
|NPY ||Plasma (from healthy BPH and PCa patients)||• Novel mass spectrometry techniques used to enrich low molecular weight proteins like NPY|
• Combination of NPY and PSA showed sensitivity and specificity of 81.5% and 82.2%, respectively
|SPON2 ||Serum (of both PCa patients and healthy controls)||• AUC of SPON2, sarcosine, %fPSA, and tPSA were 0.95, 0.67, 0.81, and 0.56, respectively|
• Patients with low-grade cancer had higher median SPON2 levels (p = 0.001)
|Volatile organic compounds|
|Cornu et al ||Urine (66 men with elevated PSA/abnormal DRE)||• Urine “sniffed” by trained Belgian Malinois dog to discriminate between cancerous and noncancerous specimens|
• Sensitivity 91%; specificity 91%
|Roine et al ||Urine (50 with PCa, 15 with BPH)||• “Electronic nose” in urine headspace|
• Sensitivity, specificity, and AUC of 78%, 67%, and 0.77, respectively
%fPSA = ratio of free to total prostate-specific antigen; AUC = area under the curve; BPH = benign prostatic hyperplasia; cfDNA = cell-free DNA; DCA = decision curve analysis; DRE = digital rectal examination; ERG = ETS transcription factors; fPSA = free prostate-specific antigen; GS = Gleason score; MD = metastasis associated in lung adenocarcinoma transcript (MALAT-1) derived; miRNA = microRNA; mRNA = messenger RNA; NPY = neuropeptide Y; PCa = prostate cancer; PCA3 = prostate cancer antigen 3; PSA = prostate-specific antigen; QMSP = quantitative methylation-specific polymerase; SPON2 = spondin-2; TMPRSS2 = transmembrane protease serine 2 gene; tPSA = total prostate-specific antigen.
3.1.1. Genome-based markers
220.127.116.11. Germline risk
Along with age and race, family history is a well-established risk factor for PCa, suggesting an important role of germline genetic variants in PCa risk . This genetic risk can be accounted for by a small number of highly penetrant germline mutations (eg, the previously identified hotspot mutation in HOXB13 or by lower penetrance risk alleles ). Together with family history, such germline genomic variants can have a significant cumulative risk association with PCa and might be clinically helpful in early risk stratification  and .
18.104.22.168. Somatic changes
Recent large-scale sequencing efforts cataloguing the somatic changes in PCa have dramatically expanded our understanding of the genomic landscape of PCa and shed new light on disease biology . Compared with other solid tumors, PCa genomes show a high level of recurrent structural rearrangements and focal copy-number changes (eg, loss of the tumor suppressor PTEN) but a lower rate of somatic mutations . Among the mutations most frequently found in primary PCa, single-nucleotide variants in SPOP, TP53, PTEN, MED12, ATM, FOXA1, COL5A1, and PIK3CA have been shown to occur at frequencies of around 1–10% . This overall low frequency of recurrent mutations in PCa greatly limits their use as diagnostic biomarkers.
The most characteristic and common somatic alterations in PCA genomes are recurrent structural rearrangement. As first described by Tomlins et al, recurrent rearrangements often involve androgen-regulated genes such as TMPRSS2 fused to a family of ETS transcription factor genes  and . Through such rearrangements, PCa cells coopt androgen receptor signaling to overexpress oncogenic transcription factors. It has been shown that ERG overexpression results in increased invasion and profound transcriptional changes in prostate cells, establishing TMPRSS2:ERG fusions as a driver alteration in PCa . Importantly, androgen receptor signaling plays a major role not only in the transcriptional control of oncogenic fusion transcript, but it has also been implemented in the genesis of PCa-specific rearrangements .
Recent efforts have also focused on using molecular classifiers to subtype PCa based on genomic changes . Such PCa subtypes are often characterized by mutually exclusive alterations, suggesting a distinct biology and likely therapeutic vulnerability of individual molecular subtypes. It remains to be shown in future studies how these molecular classifiers can be implemented in clinical management.
3.1.2. Epigenome-based markers
Epigenetic changes, encompassing alterations in histone modifications, chromatin organization, and covalent DNA modifications (eg, cytosine methylation) causing heritable changes in gene expression without changing DNA coding sequence, are frequently observed in PCa . In particular, DNA methylation changes have been shown to arise at early steps of transformation and persist throughout invasion and metastatic dissemination . Cancer-specific changes in CpG island methylation can be found in promoter regions of DNA damage repair, tumor suppressor, cell cycle control, cell adhesion, and signal transduction genes and are often associated with transcriptional silencing. Previous studies have validated several methylated loci such as GSTP1, APC, RASSF1A, and PTGS2 as diagnostic and/or prognostic biomarkers , , and . More recent insights from large-scale investigations of the PCa epigenome suggest that thousands of genomic loci undergo cancer-specific methylation changes . Many of these changes show a very high prevalence in PCa (with frequencies reported up to 95%), suggesting that DNA methylation changes can be used as highly specific and sensitive diagnostic biomarkers. Importantly, while the detection of single epigenetic changes has shown good specificity for PCa, detection of methylation across gene panels has been utilized to improve their sensitivity and negative predictive value (NPV) , , , and . This is especially important in deciding the need for rebiopsy in patients with a prior negative biopsy.
3.1.3. Transcriptome-based markers
Perhaps the most well-known transcriptome-based marker is prostate cancer antigen 3 (PCA3), a noncoding RNA transcribed from chromosome 9 , , , , and . With the development of urinary assays for the detection of RNA transcripts, PCA3, often in conjunction with TMPRSS2:ERG fusion messenger RNA (mRNA)  and , is increasingly being used to guide the decision for repeat biopsy in men with elevated PSA and prior negative biopsies. More recently, a promising three-gene panel was tested in a prospective multicenter study to identify patients with significant cancer, particularly in those with low PSA values (3–10 ng/ml) . Other noncoding mRNA , microRNA  and , and mini-RNA  have been explored for diagnostic accuracy in predicting PCa. In addition, several commercially available tissue-based clinical assays have been developed as prognostic tools .
3.1.4. Proteome-based markers
Located on the long arm of chromosome 19, the human kallikrein (hK) family comprises 15 genes (each coding for a serine protease), the best known of which is PSA (or hexokinase 3 [white cell] [hK3]). In the absence of PCa, serum PSA levels are determined by androgens, age, race, and prostate volume. Normally present in low concentrations in serum (<2.5–4 ng/ml), most PSA (about 70%) in sera is rendered physiologically inactive by complexing with antiproteases. The remaining unbound fraction (free PSA [fPSA]) undergoes proteolytic inactivation and consists of proenzyme PSA (proPSA) (precursor PSA and its variably truncated forms), benign PSA, and intact PSA . Although PCa cells actually synthesize less PSA than their benign counterparts , elevated PSA in men with PCa may be secondary to loss of barrier function from the disruption of the basal layer and basement membrane . Further, PSA released from malignant cells may escape proteolysis, allowing a greater proportion of PSA to be available for antiprotease complexing and decreasing the fPSA-to-total PSA (tPSA) ratio (%fPSA) . However, loss of barrier function may also allow partially cleaved forms of proPSA (eg, [−2] or [−4] proPSA) to escape, and these may constitute the predominant fraction of fPSA in circulation .
Increasing proteomic profiling and high-throughput assays have increased interest in proteins selectively expressed in malignant prostate cells, such as neuropeptide Y  and spondin-2 . In situ detection of protein expression changes in biopsy specimens using immunohistochemistry can be used as a diagnostic (eg, α-methylacyl-CoA racemase; selectively expressed by prostatic carcinoma cells ) or prognostic (eg, PTEN, loss of expression associated with upgrading and worse outcomes ) aid.
3.1.5. Metabolomic biomarkers
Cancer cell–specific metabolic perturbations may result in the increased production of specific organic compounds  and  (eg, phosphatidyl-inositol and glycine, among others). Detection of such metabolites in urine and serum from patients may be used as an additional test for the initial diagnosis of PCa.
3.2. Biomarkers to guide decision for initial biopsy in men with elevated prostate-specific antigen
Subsequent to the 1994 FDA approval of PSA testing for screening men for PCa, data from the Prostate Cancer Prevention Trial demonstrated a lack of a single cut-off that would show both high sensitivity and specificity (sensitivity 20–83% and specificity 39–94%, depending on the cut-off values from 1.1 to 4.1 ng/ml ). The specificity of PSA testing is compromised by the inability to discriminate between benign prostatic conditions that lead to elevated PSA, indolent PCa, and aggressive PCa. This issue is particularly relevant in the grey zone of 4.0–10.0 ng/ml. Researchers have therefore focused on PSA kinetics (eg, PSA growth velocity and doubling time), PSA density, and age-specific PSA cut-offs as triggers for initial biopsy .
In addition, %fPSA <25% showed a sensitivity of 95% for PCa diagnosis in patients with PSA 4–10 ng/ml and a normal digital rectal examination (DRE) , and it has been approved by the FDA for clinical use in this setting. Likewise, area under curve (AUC) for %[−2]proPSA ([−2]proPSA/t PSA × 100) was 0.73 (compared with 0.52 for PSA and 0.53 for %fPSA) for the detection of PCa in the PSA range of 2–10 ng/ml .
3.2.1. Prostate Health Index
Developed and marketed by Beckman Coulter Inc. (Brea, CA, USA), the Prostate Health Index (PHI) combines PSA isoforms in a mathematical formula ([−2]proPSA × square root tPSA (tPSA) / fPSA) and builds on a body of evidence supporting the role of [−2] proPSA to discriminate between malignant and nonmalignant disease  and . Le et al showed that PHI had a higher predictive accuracy than total PSA or %fPSA (AUC: 0.77, 0.50, and 0.68, respectively) for men with PSA 2.5–10 ng/ml and normal DRE . In a multicenter double-blind case-control trial, Catalona et al  reported that at any given sensitivity, the specificity of PHI was significantly greater than total PSA, %fPSA, or [−2]proPSA, thus avoiding more unnecessary biopsies. PHI also outperformed %fPSA at predicting GS ≥4 disease (AUC: 0.72 vs 0.67, respectively). This was recently corroborated in two independent studies. In a multicenter European cohort of 489 consecutive patients treated with radical prostatectomy , %[−2]proPSA and PHI levels were significant predictors of pT3 disease and/or pathologic GS ≥7. Likewise, data from a multi-institutional American study suggested PHI had the highest AUC compared with %fPSA, [−2]proPSA, and tPSA for GS ≥7 cancer (AUC: 0.70, 0.66, 0.59, and 0.55, respectively) and Epstein-significant PCa (AUC: 0.70, 0.65, 0.55, and 0.55, respectively). At a 90% sensitivity cut-off for PHI, specificity of 30.1% was noted, compared with 21.7% with %fPSA . PHI was approved by the FDA for clinical use in men with a PSA 4–10 ng/ml, with higher summary scores indicating greater probability of PCa on biopsy.
3.2.2. Four-kallikrein panel score
A combination of tPSA, fPSA, intact PSA, and hK2, the four-kallikrein panel (4K) predicts the risk of GS ≥7 cancers. Vickers et al  studied men participating in the Malmo Diet and Cancer Study and reported that for patients with tPSA >3.0 ng/ml and <20% prediction of PCa using the 4K panel, the probability of advanced disease (cT3–T4 or M1) was 0.05% over a 15-yr period. The addition of the 4K panel to the base model (tPSA, DRE, and age) improved the AUC from 0.70 to 0.78 for predicting positive biopsy among men with PSA >3.0 ng/ml in the European Randomized Study of Screening for Prostate Cancer (ERSPC), along with reducing unnecessary biopsies by 51.3%, at the cost of missing 12% high-grade cancers (GS ≥7) . These results were corroborated in a recent prospective multi-institutional trial in the United States on 1012 men undergoing prostate biopsy (regardless of PSA or DRE findings): the 4K score showed near perfect calibration and significantly greater discriminant accuracy (AUC: 0.82 vs 0.74) and higher net benefit across all threshold probabilities compared with the modified Prostate Cancer Prevention Trial (PCPT) calculator v.2.0 at predicting GS ≥7 disease . At a 4K threshold of 9%, 43% of biopsies could be avoided, at the cost of delayed diagnosis of 2.4% of GS ≥7 PCa. Head-to-head comparison of 4K score with PHI showed both improved base model (PSA and age) and had comparable accuracies when predicting high-grade PCa (GS ≥7) (71.8 vs 71.1, respectively). Estimation of net benefit was contingent on accepted risk threshold for biopsy (10% probability of high-grade cancer by 4K score or a PHI score of 39 would allow avoiding 29% of biopsies and miss 10% of high-grade cancers . The 4K score is not currently approved by the FDA but is offered as a laboratory developed test by Opko Labs.
Recently, a combination of three gene mRNAs (HOXC6, TDRD1, and DLX1), all of which are upregulated in PCa, were shown to have higher accuracy (AUC: 0.77; 95% CI, 0.71–0.83] to predict GS ≥7 PCa in biopsies compared with PCA3 (AUC: 0.68; 95% CI, 0.62–0.75) or serum PSA (AUC: 0.72; 95% CI, 0.65–0.78) alone. Importantly, even at serum PSA levels <10 ng/ml, the three-gene panel showed optimal performance with an accuracy of 73–74% (vs 57–58% for serum PSA) .
3.3. Biomarkers to guide decision for repeat biopsy in men with elevated prostate-specific antigen and prior negative biopsy
Men with elevated PSA but negative biopsy frequently present a diagnostic dilemma to physicians: Is there a need to rebiopsy? If yes, when should the rebiopsy be done? The development and FDA approval of two biomarker-based tests have helped answer these questions.
3.3.1. Prostate cancer antigen 3
Identified nearly 15 yr ago following a multi-institutional collaboration and initially named the DD3 gene, PCA3 RNA shows significantly higher expression levels in malignant compared with benign prostate cells . The expression of PCA3, which encodes for a noncoding RNA with unknown function, is highly specific to cancerous prostate epithelium. It can be reliably detected in urine with third-generation assay platforms, and its yield is increased by attentive DRE (three to eight firm horizontal strokes on each lobe) . The PCA3 score is calculated after normalizing PCA3 RNA in the urine sample to PSA mRNA.
The performance of PCA3 was evaluated by Marks et al, who reported greater accuracy compared with PSA alone (AUC: 0.68 vs 0.52; p = 0.008) and a sensitivity of 58% and specificity of 72% at a threshold score of 35 . Inclusion of the PCA3 score in a multivariate regression model (with prostate volume, age, and family history) predicting PCa at repeat biopsy increased the AUC from 0.72 to 0.75, and PCA3 score at year 2 predicted biopsy outcome at year 4 with 63% accuracy . Utilizing a cohort of men presenting for initial or repeat biopsy, Chun et al  developed a novel nomogram integrating PCA3 into a multivariate model: the Chun nomogram improved predictive accuracy of the base model (age, PSA, DRE, prostate volume, and biopsy history) by 5% (from 0.68 to 0.73) at a PCA3 score cut-off of 17, and external validation studies noted high accuracy (0.75) and excellent calibration . The incorporation of PCA3 into prevalidated risk calculators (RCs), such as the PCPT RC (comprising age, race, family history, PSA, DRE, and biopsy history) increased AUC from 0.64 to 0.69 for the prediction of any cancer, with a NPV of 88% at PCA3 cut-off <20 . Importantly, adding PCA3 to the PCPT calculator also increased the accuracy of predicting high-grade (GS ≥7) cancer by 5% (from 0.74 to 0.79). When tested in the Rotterdam cohort of the ERSPC study , including PCA3 significantly improved the performance of the ERSPC RC (70–73%). Head-to-head comparisons of PCA3 with PHI  or the 4K score  have, however, shown comparable performance metrics, with the combined AUC of 0.77 and greatest net benefit seen on multiplexing different biomarkers in urine (PCA3) with blood (PHI and 4K score) . However, some researchers have postulated  that while PCA3 more accurately predicted presence of any cancer (71% vs 65%), PHI had a greater AUC (0.80 vs 0.55; both p = 0.03) for significant PCa (GS ≥7, three or more positive cores, or >50% positivity in any core), with optimal performance metrics seen at a PCA3 score >35 and a PHI score >40 accordingly.
PCA3 recently demonstrated good validity even in the initial biopsy setting. Hansen et al developed a novel initial biopsy-specific PCA3-based nomogram, comprising age, PSA, DRE, prostate volume, and PCA3 . PCA3 increased the predictive accuracy of this model by 4.5–7%, and 55% of unnecessary biopsies could be avoided at the cost of missing <2% high-grade (GS ≥7) PCa at a probability threshold of ≤30%. Wei et al have shown a positive predictive value (PPV) of 80% in men undergoing first biopsy at a PCA3 score >60 . A simulation study of several PSA-PCA3 screening strategies, based on an existing model of PSA growth, disease progression, and survival, reported that PCA3 >35 for biopsy referral in men with PSA between 4.0 and 10.0 ng/ml retained 85% of lives saved while approximately halving false positives and reducing overdiagnoses by 25% .
PCa genomes frequently show recurrent genomic translocations resulting in chimeric fusion transcripts. The most common prostate-specific rearrangement encompassed the 5′ region of TMPRSS2 fused to the ets transcription factor ERG. This rearrangement leads to overexpression of a TMPRSS2:ERG fusion transcript and can be detected in approximately 50% of all PCa cases across American, European, and Asian cohorts  and . The fusion transcript is exquisitely specific for PCa and has not been found in any other cancer or normal tissue, making it a valuable PCa-specific biomarker. Development of urine-based assay for the fusion transcript has yielded a specificity and PPV of 93% and 94%, respectively . Utilizing the same platform as for the PCA3 assay, combination of TMPRSS2:ERG gene fusions increased the sensitivity of PCA3 from 68% to 76% in predicting PCa, and AUC for the ERSPC RC (0.8 for the base model, 0.83 for ERSPC RC plus PCA3, and 0.842 for ERSPC plus PCA3 plus TMPRSS2:ERG) . Similar increments were observed with PCPT RC, with an AUC of 0.71 in the base model and 0.78 for PCPT and PCA3 and TMPRSS2:ERG, for predicting high-grade (GS ≥7) PCa, and offered more favorable net benefit across relevant threshold probabilities . Notably, both of the aforementioned were performed on post-DRE urine in a predominantly biopsy-naive cohort of men presenting with elevated PSA. Currently, however, PCA3 (commercially available as Progensa; Hologic Inc., Bedford, MA, USA) is FDA approved for use in men with elevated PSA but a prior negative biopsy.
The ConfirmMDx assay (MDxHealth, Irvine, CA, USA) is designed to detect cancer-specific CpG island hypermethylation in promoter regions of GSTP1, APC, and RASSF1 genes in histopathologically normal biopsy tissue. In this assay, detection of cancer-specific methylation changes indicates the presence of occult tumor cells that were missed by histomorphologic analysis and potentially points to aberrant malignant “field effect” changes. Therefore this assay increases the sensitivity of cancer diagnosis on benign or suspicious prostate biopsies. Stewart et al studied 498 archived negative biopsy specimens for occult prostate cancer (GSTP1, APC, and/or RASSF1 methylation). The sensitivity, specificity, NPV, and PPV were 67%, 64%, 90%, and 29%, respectively, and the methylation status was a significant independent predictor of repeat biopsy outcome (odds ratio [OR]: 3.17) up to 30 mo after initial negative biopsy . The results were externally validated in 350 subjects with negative biopsy core samples across five urologic centers in the United States in a case-control study design: a NPV of 88% was noted, with the test independently predicting biopsy outcome within 24 mo (OR: 2.69) after controlling for standard clinical and histopathologic variables . In its current commercial form, the test results have an added advantage of documenting the presence of abnormal epigenetic alterations on one or more of 12 locations in the prostate gland, allowing for targeted biopsy of the suspicious region.
3.3.3. Prostate Core Mitomic (Not FDA approved) Test
Robinson et al observed large-scale mitochondrial deletions, spanning 3.4-kilobase pairs (3.4 mtΔ) that were selectively present in malignant cells, as well as histologically normal contiguous prostate tissue . Using an empirically defined cycle threshold cut-off of 31, they studied negative needle biopsy specimens from 101 patients who underwent repeat biopsy within 1 yr. Sensitivity and specificity of the cut-off was 84% and 54%, respectively, with AUC 0.75 and NPV of 91% for detecting PCa at future biopsy. However, although this test is available commercially (MDNA Life Sciences, Inc., Broomfield, CO, USA), limited diagnostic and validation studies preclude its usage in specific clinical situations.
3.4. Prostate cancer biomarkers in other diagnostic settings and other miscellaneous markers
A wide array of potential PCa biomarkers has been explored in diagnostic settings, some of which are mentioned briefly. For men with high-grade prostatic intraepithelial neoplasia in a first biopsy, Sequeiros et al  sought to identify a panel of urinary biomarkers that could predict PCa in repeat biopsies. A combination of prostate-specific membrane antigen, PCA3, prostate-specific G-protein couple receptor (PSGR), GOLM, KLK3, CDH1, and SPINK1 had greater AUC (0.81–0.86) than single genes, and the multiplex polymerase chain reaction model allowed avoidance of 33–47% of unnecessary biopsies. Immunohistochemical detection of ERG expression in insolated high-grade prostatic intraepithelial neoplasia lesions was shown to be associated with increased rates of cancer diagnosis on subsequent biopsies .
Utilizing the humoral response to a variety of PCa-associated antigens (PCAAs), Wang and colleagues  constructed a phage-peptide library to characterize autoantibody signature in PCa: for men with PSA 4–10 ng/ml, the AUC for the phage-peptide detector was 0.93 (95% CI, 0.86–1.00) versus 0.56 (95% CI, 0.38–0.74) for PSA. Another group combined PSA and autoantibodies against six PCAAs into a logistic regression model, increasing the predictive accuracy from 66% (with PSA alone) to 91% .
Exosomes, small vesicles secreted from most cells, are an enriched and stable source of proteins, RNAs, and lipids involved in oncogenic pathways . In a recently concluded prospective multicenter study of 519 men presenting for initial biopsy with PSA 2–10 ng/ml, a novel “liquid biopsy” urine exosome gene signature (EXO106) showed high discriminant accuracy for GS ≥4 + 3 PCa when combined with standard of care (SOC; PSA, age, race, and family history) with AUC of 0.72 (95% CI, 0.68–0.77), compared with the SOC alone (AUC: 0.63; 95% CI, 0.58–0.68; p < 0.001). Further, the assay led to a 27% reduction of prostate needle biopsies while missing only 5% of higher grade ≥ 4 + 3 cancers . Altered serum levels of chemokines were observed in PCa patients, with higher CCL2 and lower CCL5, CCL20, and CX3CL1 levels compared with controls , and their diagnostic role warrants further investigation.
The field of diagnostic biomarker for of PCa is rapidly evolving. The greatly improved understanding of the genomic underpinnings of PCa, molecular pathways involved in carcinogenesis, and the resultant proteomic and metabolomic changes, together with the development of sophisticated high-throughput assay platforms, have led to an unprecedented evolution of this field in the past decade. To date, however, only a few of the biomarkers have been shown to have potential clinical utility, and even fewer are approved for clinical use (PCA3, PHI, ConfirmMDx), not least because of intrinsic biases associated with study designs, difficulty in prospective validation, and lack of clearly demonstrable changes in survival and quality of life. Given the recent controversial recommendations against PSA screening, there is an urgent need to develop more biomarkers with enough specificity to prevent overdiagnosis and overtreatment, and explore options to expand the current role of existing biomarkers. One major problem for biomarker development for localized PCa is the high level of morphologic, clonal, and molecular heterogeneity  and . Because PCa often presents as multifocal and multiclonal lesions within one diseased gland with diverse propensities for metastatic dissemination that do not necessarily correlate with the size of the individual lesion, the next generation of biomarkers will need to capture this heterogeneity to allow risk stratification based on molecular phenotypes  and . The multiplicity of biomarkers further warrants the need to individualize risk assessment: the development of nomograms and RCs incorporating these biomarkers to modulate biopsy and treatment decisions is a step in the right direction.
Author contributions: Deepansh Dalela had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Abdollah, Dalela, Haffner, Culig, Schalken.
Acquisition of data: Dalela, Abdollah.
Analysis and interpretation of data: Dalela, Abdollah, Haffner, Culig, Schalken.
Drafting of the manuscript: Dalela.
Critical revision of the manuscript for important intellectual content: Dalela, Abdollah, Haffner, Culig, Schalken.
Statistical analysis: Dalela, Abdollah.
Obtaining funding: Haffner, Culig, Schalken.
Administrative, technical, or material support: Haffner, Culig, Schalken.
Supervision: Haffner, Culig, Schalken.
Other (specify): None.
Financial disclosures: Deepansh Dalela certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: Jack Schalken is an inventor on an PCA3-related IP. His employer (Radboud University Medical Center [RUMC]) has licensed the IP, and RUMC receives royalty payments. He provides consulting services to Hologic, relating to PCA3 and TMPRSS22:ETS gene fusions. Firas Abdollah is a consultant for GenomeDx Biosciences.
Funding/Support and role of the sponsor: None.
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a VUI Center for Outcomes Research, Analytics and Evaluation, Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA
b Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
c Medical University of Innsbruck, Innsbruck, Austria
d Radboud University Medical Center, Nijmegen, The Netherlands
Corresponding author. VUI Center for Outcomes Research, Analytics and Evaluation, Vattikuti Urology Institute, 2799 West Grand Boulevard K-9, Henry Ford Hospital, Detroit, MI 48202, USA. Tel. +1 313 916 7129; Fax: +1 313 916 9539.
© 2015 European Association of Urology, Published by Elsevier B.V.