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Background:
Review

Active Surveillance in Prostate Cancer: Role of Available Biomarkers in Daily Practice

by
Belén Pastor-Navarro
1,2,
José Rubio-Briones
3,
Ángel Borque-Fernando
4,
Luis M. Esteban
5,
Jose Luis Dominguez-Escrig
3 and
José Antonio López-Guerrero
1,2,6,*
1
Laboratory of Molecular Biology, Fundación Instituto Valenciano de Oncología (IVO), 46009 Valencia, Spain
2
Príncipe Felipe Research Center (CIPF), IVO-CIPF Joint Research Unit of Cancer, 46012 Valencia, Spain
3
Department of Urology, Fundación Instituto Valenciano de Oncología (IVO), 46009 Valencia, Spain
4
Department of Urology, University Hospital Miguel Servet, IIS-Aragón, 50009 Zaragoza, Spain
5
Department of Applied Mathematics, Engineering School of La Almunia, University of Zaragoza, 50100 Zaragoza, Spain
6
Department of Pathology, School of Medicine, Catholic University of Valencia ‘San Vicente Martir’, 46001 Valencia, Spain
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2021, 22(12), 6266; https://doi.org/10.3390/ijms22126266
Submission received: 13 April 2021 / Revised: 3 May 2021 / Accepted: 8 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Advances in Molecular Research on Prostate Cancer)
Browse Figure
Versions Notes

Abstract

:
Prostate cancer (PCa) is the most commonly diagnosed cancer in men. The diagnosis is currently based on PSA levels, which are associated with overdiagnosis and overtreatment. Moreover, most PCas are localized tumours; hence, many patients with low-/very low-risk PCa could benefit from active surveillance (AS) programs instead of more aggressive, active treatments. Heterogeneity within inclusion criteria and follow-up strategies are the main controversial issues that AS presently faces. Many biomarkers are currently under investigation in this setting; however, none has yet demonstrated enough diagnostic ability as an independent predictor of pathological or clinical progression. This work aims to review the currently available literature on tissue, blood and urine biomarkers validated in clinical practice for the management of AS patients.

1. Introduction

In 2020, 19.3 million new cases of cancer were diagnosed worldwide, according to the International Agency for Research of Cancer, of which 1,414,259 were of prostate cancer (PCa). These figures place PCa in the fourth position in incidence when combining both sexes, preceded by breast, lung and colorectum cancer [1]. When only men are considered, PCa represents the second most frequent malignancy after lung cancer (14.1% and 14.3%, respectively), being considered a global health problem, due to both the volume of population affected and its economic impact on Health Systems.
Almost 90% of new PCa are localized and they are clinically classified in three risk groups, based on the serum prostate-specific antigen (PSA) levels, local clinical stage and histological aggressiveness provided by the Gleason Score (GS): high risk, intermediate risk, low risk and very low risk [2]. All clinical guidelines, such as European Society of Medical Oncology (ESMO) [3], National Comprehensive Cancer Network (NCCN) [4] and 2020 EAU-EANM-ESTRO-ESUR-SIOG [5], follow this classification with very little variation.
A recent study, which aimed to characterize PCa features, identified five clinical risk groups: low-risk localised (LRL), intermediate-risk localised (IRL), high-risk localised (HRL), positive nodal (N+) and metastatic (M1). Among 635,733 PCa cases analysed, the overall risk group distribution at diagnosis was: LRL 10.5% (n = 66,959), IRL 49.7% (n = 316,227), HRL 34.8% (n = 221 494), N+ 1.5% (n = 9150) and M1 3.5% (21,903). Furthermore, the proportions of patients who died from PCa in the five groups were 3.2%, 5.1%, 10.2%, 19% and 57.2%, respectively [6]
Before active surveillance (AS) appeared, the conventional therapeutic approach was to offer active treatment, either with surgery or radiotherapy, achieving high cure rates. However, they are not exempt from side effects at 5 years, including urinary incontinence, erectile dysfunction and bowel toxicity [7].
PCa occupies the fifth position in terms of mortality, with 375,304 cases in 2020 worldwide, preceded by lung, liver, stomach and colorectal cancers [1]. This difference between incidence and mortality is due to both the favourable impact of the treatments and the heterogeneous aggressiveness of these tumours. Furthermore, PCa has a slow evolution, even latent, and most tumours are diagnosed well before they are considered as clinically significant PCa (csPCa), characterized by International Society of Urological Pathology (ISUP) histopathology grade ≥2 and/or volume ≥0.5 cc and/or extraprostatic extension [8]. In fact, one-third of screening-detected PCa never becomes a health problem. Reinforcing this evidence, it is well known that 59% of necropsies of men over 79 years of age who died from other causes have latent PCa [9].
The effects of overdiagnosis, a consequence of current diagnostic pathways, could be minimized by progressive improvements in the characterization of indolent PCa, as well as, the implementation of less aggressive therapeutic strategies such as AS, with promising results after long follow-up, of up to 8 years [10].
In only a decade, AS has moved from uncertainty and low acceptability among urologists to representing the first therapeutic option in every clinical guideline [4,5] for very low/low risk and even some de novo intermediate-risk tumours [3,11]. However, the trend today is that AS protocols should pursue the reduction of strict follow-up biopsy schemes [12], and personalised biopsy schedules have been proposed based on the risk of Gleason upgrading. This reduction could also be favoured by improving the selection of low-risk PCa both with biomarkers or multiparametric magnetic resonance imaging (mpMRI) [13].
The new taxonomic classification of PCa in seven molecular biotypes has given some light to the biologic and molecular characterization of such a heterogeneous disease, but it is still far from changing daily practice [14]. AS has evolved with the refinement of imaging, where multiparametric magnetic resonance imaging (mpMRI) has been key, but it is still limited when used alone for clinical decision-making, together with its availability, cost and dependency on the radiologists experience as collateral drawbacks [15,16]. We have focused on commercially available biomarkers for AS management in our review, although we recognize that mpMRI is already an established tool that has optimized AS management; hence, potential synergies between mpMRI and some biomarkers have also been addressed.
As mentioned above, biomarkers would constitute our second tool. They should “scan” the whole prostate and be widely available, reproducible, inexpensive, objective and with adjusted predictions. However, these are characteristics that not all biomarkers demonstrate, and the search continues to find the “ideal biomarker”, the “Holy Grail” studying serum, urine or prostatic tissue [17,18]. Knowing that the molecular classification of PCa will probably pave the road for more specific biomarkers in different molecular biotypes, the aim of this study is to review the present literature in clinically available biomarkers that could help to optimize AS in daily practice.

2. Evidence Acquisition

A comprehensive literature search from January 2013 to March 2021 was performed in PubMed, including articles written in English language, reporting on PCa diagnosis and follow-up biomarkers. A specific search strategy was designed combining the following keywords: “prostate cancer”, “active surveillance”, “tissue biomarkers”, “blood biomarkers”, “urine biomarkers” and “clinical significance”. In particular, the following search blocks were used for the PubMed database (active surveillance) AND (prostate cancer) AND (biomarker) AND [(tissue) OR (blood) OR (urine)] AND (clinical significance). Due to the exigent search criteria used, mainly based on the addition of the “clinical significance” label, secondary sources were also examined in a descriptive manner. Cross-referenced potentiality relevant articles, not identified in the primary search, were also considered and hand-picked. Case reports, editorials, letters, congress abstract and congress communications were not eligible.
Original research articles were curated based on favouring large sample sizes, independent validation and patients directly included in AS programs and not just as per its role in optimizing PCa diagnosis. When biomarkers were studied in diagnosis, we focused our interest in those cohorts already in AS and its role in its clinical management. All abstracts were reviewed by expert urologists (J.R.-B., A.B.-F., J.L.D.-E.).
After exclusion of duplicates and articles unrelated to the topic of this review, 267 full-text records were screened, and finally, 26 papers accomplished final eligibility. Then, 39 other papers were added that focus on commercially available markers related to the topic. The article selection process is shown in a workflow diagram (Figure 1).

3. Evidence Synthesis

3.1. Blood Biomarkers

○
PSA
Nobody questions the role of PSA in PCa screening, with the latest publication of the European Randomized Screening in Prostate Cancer setting its irreplaceable importance [19]. However, once diagnosed, in those PCa candidates for AS, its role as biomarker has been questioned due to its variability and has been preferably studied related to its changes with time, prostate volume or other isoforms.
○
PSA kinetics
Diagnostic and follow-up of PCa patients based on PSA measurements are still the most common strategies, although PSA has been demonstrated as an unspecific biomarker in the AS setting. The rate of PSA change over time, known as PSA dynamics or kinetics (PSAk), was conceptually introduced by Carter in 1992 [20]. PSAk has been shown to overcome PSA limitations and to predict PCa reclassification in men enrolled in AS programs.
Cooperberg et al., calculated PSAk using a linear mixed-effect model, in which the natural logarithm of PSA (ln[PSA]) was modelled as a linear function of time since the diagnosis, with a random intercept indicating the individual-specific ln[PSA] at diagnosis and a random slope reflecting the individual-specific rate of change over time. It was tested in a multicentre cohort with long-term follow-up, suggesting that collecting PSA measurements over time could be clinically useful at predicting outcomes in men with PCa on AS [21].
Related to PSAk, PSA doubling-time (PSADT) is the number of years over which a certain level of PSA increases by a factor of two and is calculated as DT = ln(2)/m, where m is the slope of the regression of ln[PSA] over time [22]. In the prospective multicentre Canary Prostate Active Surveillance Study (PASS), PSADT < 36 months was originally a criterion for progression, and some authors recommended this parameter for the detection of aggressive tumours during AS [23]. However, it was found to be unspecific [24] and must be evaluated as a part of the PSAk linear model.
On the other hand, PSA velocity (PSAV) represents a change in PSA level over time and is calculated by linear regression of untransformed PSA values. By calculating the number of times that serial PSAV measurements pass a threshold, Patel et al. developed the PSAV Risk Count (PSAV RC) score. When used in a cohort of very low-risk PCa, it was associated with an increased risk of biopsy reclassification due to any unfavourable pathology finding; hence, PSAV RC is proposed for monitoring patients on AS and to decrease the frequency of biopsies in the long term [25].
In summary, higher PSAV and shorter PSADT kinetics might be useful in differentiating between PCa with more aggressive potential. The association between pre-treatment PSAk and PCa biology is supported by multiple studies that found a strong association between the PSAV and PSADT and various pathological features of aggressive PCa [22].
○
PSA density (PSAD)
The PSA density (PSAD), calculated by dividing the preoperative PSA by the prostate volume (without seminal vesicles), was introduced in the early 1990s by Benson et al. [26]. Its role in predicting upgrading and reclassification in men with low-risk PCa enrolled in AS has been assessed in several studies.
A study recently published by Yusim et al. including 992 men with a median age of 66 years concluded that patients with PSAD higher than 0.34 ng/mL2 have a 56.4% chance of being diagnosed with a csPCa, with risks estimated at 4%, 8.5% and 31.5%, for PSADs <0.09 ng/mL2, between 0.09 and 0.19 ng/mL2 and between 0.19 and 0.34 ng/mL2, respectively [27]. Moreover, it has been demonstrated that PSAD values differ between African American (AA) men and Caucasian men, being lower in AA patients in cases of similar tumour volumes [28].
In summary, PSAD is proposed to be tested prior to prostate biopsy as it is an inexpensive and widely available tool, which may avoid unnecessary biopsies. Its inclusion in AS protocols could improve inclusion criteria and follow-up of PCa patients. Furthermore, the more accurate prostate volume calculation by MRI might potentially make its use more reliable [27,29].
○
Prostate Health Index (PHI)
PHI, from Beckman Coulter [30], is a diagnostic blood test that combines free (fPSA), total PSA (tPSA) and isoform [-2]proPSA into a single score, calculated according to the formula [-2]proPSA/fPSA x √tPSA, developed to maximize specificity at high sensitivity [31]. Approved by the USA Food and Drug Administration (FDA), it is now considered by the NCCN guidelines in the diagnostic setting [4].
Chiu et al., demonstrated that PHI and %p2PSA are predictors of RP pathologic outcomes such as pT3, pGS, GS upgrade, tumour volume >0.5 mL, and Epstein criteria for significant tumours [32]. Moreover, Hirama et al. showed the high diagnostic ability of p2PSA-related parameters, such as %p2PSA and PHI, for discriminating patients with non-significant cancer from those with significant cancer, being useful markers for AS [23].
Several studies compared the predicting value of PHI and PCA3. Cantiello et al., found a superior predictive accuracy of PHI over PCA3 (AUC 0.92 vs. AUC 0.77) in discriminating clinically significant disease in men eligible for AS, outperforming PCA3 performance, resulting in a higher net benefit [33,34].
The use of these markers may reduce the incidence of underestimation at initial diagnosis, enabling a more accurate selection of candidates suitable for AS. PHI measurement could be clinically useful in discriminating the presence of insignificant PCa in AS candidates [23,31,32,33]. Furthermore, its utility is increased by adding mpMRI to discriminate the presence of pathologically confirmed significant PCa, as demonstrated in a cohort of patients who underwent RP but were eligible for AS [35].
○
4Kscore®
The 4Kscore® is a prebiopsy blood test developed by OPKOlab that incorporates a panel of four kallikreins (tPSA, fPSA, intact PSA [iPSA] and human kallikrein 2 [hK2]) combined with clinical features such as the 4Kscore®, which determines a man’s risk for high-grade aggressive PCa (GS ≥ 7) [36], improving decision making regarding prostate biopsy.
The 4Kscore® provides a score ranging from 0 to100%, reflecting the probability of finding a significant PCa at biopsy; hence, 100% minus the 4Kscore result is the personalized negative predictive value (NPV) or probability that a patient will not have Gleason ≥7 cancer on prostate biopsy [37].
Lin et al. evaluated the 4Kscore® in a cohort of patients candidates for AS, already diagnosed with cancer, demonstrating that the addition of 4Kscore® to a model containing clinical information significantly improves the prediction of the outcome in the first surveillance biopsy, which is associated with reclassification [38].
Similarly, in a prospective evaluation by Borque-Fernando et al. in an AS scenario, concluded that the 4Kscore®, at a cut-off 7.5%, was significantly associated with tumour reclassification at the confirmatory biopsy, while the previously used %fPSA/tPSA ratio did not show this association [39].
Due to the ability of the 4Kscore® to discriminate between men who are likely to harbour clinically relevant PCa and those with indolent tumours or no cancer, it has been proposed as a good marker to identify patients who are more likely to benefit from biopsy, due to a higher risk of csPCa requiring active treatment [39,40] potentially implemented in an AS setting [37,38,39,40,41].

3.2. Tissue Biomarkers

○
Oncotype DX® Genomic Prostate Score (GPS)
The Oncotype Dx® Genomic Prostate Score (GPS), from Genomic Health® [42], is an RNA based expression assay that includes 12 PCa related genes normalized to five housekeeping genes, which can be performed on needle core biopsy tissues. This test uses real-time quantitative polymerase chain reaction (RT-qPCR) to measure expression levels of genes related to four tumour aggressiveness pathways, including androgen signalling (AZGP1, KLK2, SRD5A2, and FAM13C), cellular organization (FLNC, GSN, TPM2, and GSTM2), stromal response (BGN, COL1A1, and SFRP4) and cellular proliferation (TPX2) [43].
GPS has been shown to predict adverse surgical pathology (AP) and biochemical recurrence (BR) in men diagnosed with low- and intermediate-risk PCa treated with immediate surgery [44,45]. It has been used as a tool to inform the decision making of immediate treatment versus AS in men newly diagnosed with low- or favourable intermediate-risk PCa and was recently included in the NCCN guidelines [46]. Some studies have demonstrated that GPS is associated with an increased risk of AP findings in AS patients who later underwent radical prostatectomy (RP), being also associated with BR following surgery in such patients [46,47]. The GPS ranges from 0 to 100, with higher scores indicating a greater risk of aggressive disease.
Eure et al. reported higher rates of AS uptake (62% vs. 40%) and persistence on AS at 1 year (55% vs. 35%) among men who underwent the GPS testing compared to those who did not [48]. The GPS test has been recommended in men with early-stage PCa [49]. In this way, after obtaining a GPS, more patients were re-classified as very low-risk compared to the initial distribution determined by the NCCN risk category alone [42]. Regarding reproducibility, the initial test is the most informative, and serial testing seems to have limited benefit [43].
Although higher GPS has been associated with an increased risk of AP and BR, it is reasonable to use the GPS in conjunction with other known clinical risk factors when selecting patient candidates for AS [47].
○
Genome DX Decipher® Genomic Classifier
Decipher® Prostate Cancer Test, from Genome Dx Biosciences [50], is a 22-feature RNA biomarker assay that has been developed to predict metastasis risk at 5- and 10-years after RP [51]. This test incorporates 22 coding and non-coding genes that cover seven cancer pathways, including angiogenesis, invasion and metastasis, or growth and differentiation [50]. It generates a score ranging from 0 to 1, with higher values indicating an increased probability for both AP and poorer oncologic outcomes [52].
Decipher® is a significant predictor of AP when used alone or combined with clinical risk stratification systems [53], and when applied to prostatectomy tissue from NCCN very low-/low- and favourable intermediate-risk patients, it has identified patients inappropriately selected for AS.
It has been demonstrated that Decipher® at the biopsy cores outperformed NCCN clinical risk grouping, biopsy GS and preoperative PSA; for every 10% increase in the Decipher® score, the high-risk increased by 1.72 [53]. Notably, combining Decipher® with NCCN risk groups increased the concordance index (C-index) from 0.75 to 0.88, suggesting that Decipher® captures a considerable proportion of PCa available on diagnostic needle biopsies obtained in routine clinical practice. Decipher® scores on biopsies were also predictive of other key outcomes, including the presence of primary Gleason pattern 4/5 and an elevated risk of metastasis within 5 years [54]. Applying the Decipher ® test appears to better guide treatment recommendations [55].
○
Prolaris® (Cell Cycle Progression Score)
The Cell Cycle Progression (CCP) score, from Myriad [56], is a validated prognostic RNA expression signature, based on the expression levels of 31 genes related to cell cycle progression and 15 housekeeping genes. Several studies have demonstrated that the CCP score is strongly associated with progression to metastatic disease after both surgery and radiotherapy [57,58,59].
The CCP score threshold was developed in men who might typically be considered for AS based on having low- or favourable intermediate-risk disease according to a conservative interpretation of NCCN guidelines [46]. The combination of CCP score with clinico-pathologic risk factors from the Cancer of the Prostate Risk Assessment (CAPRA) model [60] results in the cell cycle risk (CCR) test, with a better classification performance. The CCR-based risk stratification might guide the identification of patient candidates to AS from those who would require more active intervention [58]. Both the molecular score and the derived predicted risk might be used in both AA and non-AA patients with newly diagnosed PCa [59].
Lin et al., validated this test in conservatively managed men and showed that the CCR score threshold significantly dichotomized men with high-risk (CCR >0.8) and low-risk (≤0.8) of 10-year PCa mortality, indicating that the threshold can be safely used to identify candidates for AS. Application of the CCR score threshold should result in a substantial increase in men who would be considered candidates for AS that would have traditionally been excluded [57].

3.3. Urine Biomarkers

○
Prostate Cancer Antigen 3 (PCA3)
PCA3 was first described in 1999 by Bussemakers et al. [61] as a non-coding mRNA only expressed in human prostate tissue but significantly overexpressed in PCa and is used in clinical practice as Progensa® PCA3 assay, an in vitro nucleic acid amplification test, developed by Hologic [62], approved by the FDA to be used in conjunction with other patient information to aid the decision for repeat biopsy in men 50 years of age or older with one or more previous negative prostate biopsies.
This PCA3 score is generated as a ratio of PCA3 mRNA to PSA mRNA in urine multiplied by 1000, and a PCA3 score <25 is associated with a decreased likelihood of positive biopsy [63,64].
Tosoian et al., evaluated it in a cohort of men with favourable-risk PCa included in AS programs and who underwent tumour reclassification, demonstrating that patients who reclassified had significantly higher PCA3 scores at both initial (48.0 vs. 24.5, p = 0.007) and subsequent (63.5 vs. 36.0, p = 0.002) measurements, indicating an association between PCA3 and grade reclassification [65].
However, despite the demonstrated PCA3 utility for PCa diagnosis, several studies suggest that additional biomarkers should be incorporated into PCA3 to ensure detection of high grade [66].
The addition of mpMRI to PCA3 reduces overdetection and overtreatment of indolent PCa by improving diagnostic accuracy. Hence, the use of urinary PCA3 testing in men with low or equivocal suspicion mpMRI allows unnecessary biopsies, the refinement of risk stratification and the optimization of high-grade cancer detection to be reduced [64].
○
TMPRSS2:ERG (T2E) fusion gene
The T2E fusion gene was first discovered in 2005 by Tomlins et al. [67], and it is present in approximately 50% of PCas. It constitutes a highly specific biomarker that can be detected both in FFPE and urine samples [68,69].
In a prospective study by Lin et al., post-digital rectal examination (post-DRE) urine samples were collected and T2E levels were analysed, indicating an association with higher tumour volume and higher GS in subsequent biopsies [70]. Furthermore, high T2E expression levels have been associated with an increased risk of tumour upgrading and upstaging in AS candidates [71].
In an AS context, Whelan et al., assessing the effectiveness of T2E expression in prostatic secretions, showed that non-invasive T2E measurement in urine may refine patient acceptance into AS programs [72].
However, a comparative study between urinary PCA3 and T2E measures during multiple times at surveillance found that these markers add little or no improvement in clinical variables in predicting biopsy reclassification due to the high PCa heterogeneity [73,74]. Hence, both PCA3 and T2E are proposed to be combined in a risk calculator, MiProstate Score (MiPS), which shows higher specificity and sensitivity [75,76].
○
SelectMDx
Related to urinary biomarkers useful in predicting csPCa, a novel urinary assay-based risk score called SelectMDx from MDXHealth® was developed by combining serum PSA, PSAD and clinical factors such as age and prior negative biopsy with two mRNA signatures, namely urinary homeobox C6 (HOXC6) and distal-less homeobox 1 (DLX1), and KLK3 gene as a reference [77].
Leyten et al. developed the test based on detecting increased mRNA levels HOXC6, DLX and tudor-domain-containing 1 (TDRD1), which have been selected between eight candidates because of having independent additional predictive value of PSA for the detection of biopsy GS ≥7. These genes have been associated with PCa development [78]. In this study, it was shown that the combination of urinary HOXC6 and DLX1 was superior to Progensa® PCA3 in the diagnosis of GS ≥ 7 PCa.
Based on the urinary biomarker panel previously defined, Van Neste et al. proposed a new test by combining HOXC6 and DLX1 mRNA expression levels with traditional clinical risk factors, such as PSAD, DRE, PSA, age, history of prostate biopsy and family history, which is able to detect high-grade csPCa accurately and consequently could be used in AS decision making, reducing unnecessary biopsies and potential overtreatment [79].
We have just published our results with SelectMDx and PCA3 in an AS setting, showing that SelectMDx showed statistically significant differences related to pathological progression-free survival (HR: 1.035; 95% CI: 1.012–1.057) (p = 0.002) with a C-index of 0.670 (95% CI: 0.529–0.810) and AUC of 0.714(95% CI: 0.603–0.825) at 5 years. The combination of both biomarkers did not improve the prediction of PP and C-index 0.630 (95% CI: 0.455–0.805) [80]. Despite the high ability of SelectMDx in predicting the likelihood of finding high-grade PCa, it has been demonstrated that its combination with mpMRI could better select candidates to AS, identifying men who harbour csPCa, hence improving the cost-effectiveness. Pepe P et al., found that mpMRI and SelectMDx missed 3/9 (33.3%) and 4/9 (44.5%) of csPCa, respectively. Moreover, mpMRI combined with SelectMDx diagnosed 7/9 (77.8%) csPCa, outperforming SelectMDx alone, in a cohort of men enrolled in an AS protocol [81,82]. Nonetheless, several studies have demonstrated the potential cost-effectiveness of SelectMDx alone and instead of using mpMRI. In Spain, the cost-saving result of using the SelectMDx strategy has been estimated at EUR 247 per patient, which is EUR 20 million per yearly cohort, mostly by preventing the detection of insignificant cancers and, to a lesser extent, by reducing the number of biopsies [83]. Similarly, Dijkstra et al. showed that SelectMDx could improve PCa patients’ quality of life and detect high-grade tumours while saving cost compared to the current standard of care [84].

4. Conclusions

Active Surveillance (AS) is increasingly used in all health systems, balancing screening benefits with lowering overtreatment in PCa. Since this strategy was introduced, AS has evolved from initial protocols a decade ago, allowing inclusion without any reliable imaging, to more refined contemporary management pathways, guided by mpMRI imaging and fusion biopsy. Nevertheless, mpMRI is not widely available everywhere, and it is still very dependent on radiologist expertise and hence lacks homogeneity.
Biomarkers might play a role, probably combined with mpMRI and mainly in PIRADs 3 lesions, the “grey box”, where results are unclear. Their potential to give information from the scan the whole gland, their homogeneity, reproducibility and potential for comparison make them very attractive in the AS setting, but their prices should come down in order to be generalized, mainly tissular, markers. Most of them have shown in pilot studies their complementary role with mpMRI, but none is going to replace it when a biopsy is needed, at both the confirmatory and follow-up phases.
The main limitation that we have found in this review is that there are few studies specifically focused on applying these biomarkers in AS series. However, given the applicability that many of these tests have in identifying csPCa, they are good candidates for being tested in an AS scenario (Table 1).
Tissular biomarkers, mainly those analysing independent molecular markers not related to classical clinic-pathological variables, could have a role in the future of AS management. However, issues in terms of high costs and difficulties in handling are drawbacks that should overcome before widespread implementation is possible. Therefore, at present, we can just recommend them in doubtful cases such as high-volume Gleason 3 + 3 or favourable intermediate-risk PCa where a more conservative management could be considered in case of favourable tissular marker scoring.
Prospective and longitudinal studies with different biomarkers focused on AS cohorts are still missing. Prospective, well-designed studies assessing predefined clinically relevant endpoints are needed to fully assess the real potential of these biomarkers and to compare them with other diagnostic tools, such as novel predictors of grade progression and mpMRI- PRECISE criteria [85]. Robust clinical evidence derived from such studies will support and guide clinical decision-making in the selection, management and long-term follow-up of PCa patients in AS schemes.

Author Contributions

Conceptualization: B.P.-N., J.R.-B. and J.A.L.-G.; Methodology: B.P.-N.; Validation: Á.B.-F. and L.M.E.; Formal Analysis: B.P.-N.; Investigation: B.P.-N.; Resources: J.R.-B. and J.A.L.-G.; Data Curation: B.P.-N., J.R.-B. and J.A.L.-G.; Writing—Original Draft Preparation: B.P.-N., J.R.-B.; Writing—review and editing: J.L.D.-E., Á.B.-F., L.M.E. and J.A.L.-G.; Visualization: J.A.L.-G.; Supervision: J.R.-B. and J.A.L.-G.; Project Administration: J.R.-B.; Funding Acquisition: J.A.L.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Rafael Mollá y Rodrigo grant from Fundación para la Investigación en Urología 2021 (FIU) and a PhD grant (APRO2019 to BP-N) from the Spanish Association against Cancer (AECC).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

Authors declare no conflict of interest.

Abbreviations

AAAfrican American
APadverse surgical pathology
ASactive surveillance
AUCarea under the curve
BRbiochemical recurrence
CAPRACancer of the Prostate Risk Assessment
CCPCell Cycle Progression
CCRcell cycle risk
C-indexconcordance-index
CsPCaclinically significant prostate cancer
DLX1distal-less homeobox 1
DREdigital rectal examination
ESMOEuropean Society of Medical Oncology
FDAFood and Drug Administration
FFPEformalin-fixed paraffin-embedded
fPSAfree PSA
GPSGenomic Prostate Score
GSGleason score
HOXC6urinary homeobox C6
HRLhigh-risk localised
iPSAintact PSA
IRLintermediate-risk localised
ISUPInternational Society of Urological Pathology
ln(PSA)logarithm of PSA
LRLlow-risk localised
M1metastatic
MiPSMiProstate Score
mpMRImultiparametric magnetic resonance imaging
N+nodal positive
NCCNNational Comprehensive Cancer Network
PASSCanary Prostate Active Surveillance Study
PCaprostate cancer
PCA3prostate cancer antigen 3
PCPTRCProstate Cancer Prevention Trial risk calculator
PHIProstate Health Index
PSAprostate-specific antigen
PSADprostate-specific antigen density
PSADTprostate-specific antigen doubling-time
PSAkprostate-specific antigen kinetics
PSAVprostate-specific antigen velocity
PSAV RCPSAV Risk Count
RT-qPCRreal-time quantitative polymerase chain reaction
T2ETMPRSS2:ERG
TDRD1tudor domain containing 1
tPSAtotal PSA

References

  1. Cancer Today. Available online: https://gco.iarc.fr/today/online-analysis-multi-bars?v=2020&mode=cancer&mode_population=countries&population=900&populations=900&key=total&sex=0&cancer=39&type=0&statistic=5&prevalence=0&population_group=0&ages_group%5B%5D=0&ages_group%5B%5D=17&nb_items=1 (accessed on 12 January 2021).
  2. American Cancer Society. Prostate Cancer Early Detection, Diagnosis. American Cancer Society, 2017; pp. 1–25. Available online: https://www.cancer.org/content/dam/CRC/PDF/Public/8795.00.pdf (accessed on 12 April 2021).
  3. Parker, C.; Gillessen, S.; Heidenreich, A.; Horwich, A. Cancer of the prostate: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 2015, 26, v69–v77. [Google Scholar] [CrossRef]
  4. Aslam, N.; Nadeem, K.; Noreen, R. JAC Prostate Cancer Prostate Cancer. Abeloff’s Clin. Oncol. 5/e 2015, 8, 938–944. [Google Scholar]
  5. Mottet, N.; Bellmunt, J.; Briers, E.; van den Bergh, R.; Bolla, M.; van Casteren, N.; Cornford, P.; Culine, S.; Joniau, S.; Lam, T.; et al. Pocket Guidelines on Prostate Cancer. European Association of Urology, 2015. Available online: https://uroweb.org/wp-content/uploads/09-Prostate-Cancer_LR.pdf (accessed on 12 April 2021).
  6. Roy, S.; Morgan, S.C. Who Dies from Prostate Cancer? An Analysis of the Surveillance, Epidemiology and End Results Database. Clin. Oncol. 2019, 31, 630–636. [Google Scholar] [CrossRef]
  7. Hashine, K.; Kakuda, T.; Iuchi, S.; Tomida, R.; Matsumura, M. Patient-reported outcomes after open radical prostatectomy, laparoscopic radical prostatectomy and permanent prostate brachytherapy. Jpn. J. Clin. Oncol. 2019, 49, 1037–1042. [Google Scholar] [CrossRef] [PubMed]
  8. Weinreb, J.C.; Barentsz, J.O.; Choyke, P.L.; Cornud, F.; Haider, M.A.; Macura, K.J.; Margolis, D.; Schnall, M.D.; Shtern, F.; Tempany, C.M.; et al. PI-RADS Prostate Imaging-Reporting and Data System: 2015, Version 2. Eur. Urol. 2016, 69, 16–40. [Google Scholar] [CrossRef] [PubMed]
  9. Bell, K.J.L.; Del Mar, C.; Wright, G.; Dickinson, J.; Glasziou, P. Prevalence of incidental prostate cancer: A systematic review of autopsy studies. Int. J. Cancer 2015, 137, 1749–1757. [Google Scholar] [CrossRef] [PubMed]
  10. Tosoian, J.J.; Mamawala, M.; Epstein, J.I.; Landis, P.; Macura, K.J.; Simopoulos, D.N.; Carter, H.B.; Gorin, M.A. Active Surveillance of Grade Group 1 Prostate Cancer: Long-term Outcomes from a Large Prospective Cohort. Eur. Urol. 2020, 77, 675–682. [Google Scholar] [CrossRef] [PubMed]
  11. Kim, S.P.; Shah, N.D.; Meropol, N.J.; Tilburt, J.C.; Nguyen, P.L.; Yu, J.B.; Abouassaly, R.; Kim, A.; Gross, C.P. Recommendations of Active Surveillance for Intermediate-risk Prostate Cancer: Results from a National Survey of Radiation Oncologists and Urologists. Eur. Urol. Oncol. 2019, 2, 189–195. [Google Scholar] [CrossRef] [PubMed]
  12. Tomer, A.; Nieboer, D.; Roobol, M.J.; Bjartell, A.; Steyerberg, E.W.; Rizopoulos, D. Personalised biopsy schedules based on risk of Gleason upgrading for patients with low-risk prostate cancer on active surveillance. BJU Int. 2020, 127, 96–107. [Google Scholar] [CrossRef]
  13. Thomsen, F.B.; Jakobsen, H.; Langkilde, N.C.; Borre, M.; Jakobsen, E.B.; Frey, A.; Lund, L.; Lunden, D.; Dahl, C.; Helgstrand, J.T.; et al. Active Surveillance for Localized Prostate Cancer: Nationwide Observational Study. J. Urol. 2019, 201, 520–526. [Google Scholar] [CrossRef]
  14. Abeshouse, A.; Ahn, J.; Akbani, R.; Ally, A.; Amin, S.; Andry, C.D.; Annala, M.; Aprikian, A.; Armenia, J.; Arora, A.; et al. The Molecular Taxonomy of Primary Prostate Cancer. Cell 2015, 163, 1011–1025. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Stabile, A.; Giganti, F.; Rosenkrantz, A.B.; Taneja, S.S.; Villeirs, G.; Gill, I.S.; Allen, C.; Emberton, M.; Moore, C.M.; Kasivisvanathan, V. Multiparametric MRI for prostate cancer diagnosis: Current status and future directions. Nat. Rev. Urol. 2020, 17, 41–61. [Google Scholar] [CrossRef]
  16. Stamatakis, L.; Siddiqui, M.M.; Nix, J.W.; Logan, J.; Rais-Bahrami, S.; Walton-Diaz, A.; Hoang, A.N.; Vourganti, S.; Truong, H.; Shuch, B.; et al. Accuracy of multiparametric magnetic resonance imaging in confirming eligibility for active surveillance for men with prostate cancer. Cancer 2013, 119, 3359–3366. [Google Scholar] [CrossRef]
  17. López-Guerrero, J.A.; Casanova-Salas, I.; Fernández-Serra, A.; Rubio, L.; Calatrava, A.; García-Flores, M.; García-Casado, Z.; Rubio-Briones, J. Nuevos biomarcadores para optimizar selección y seguimiento de pacientes con cáncer de próstata en vigilancia activa. Arch. Esp. Urol. 2014, 67, 462–472. [Google Scholar]
  18. Rubio-Briones, J.; Casanova-Salas, I.; Fernández-Serra, A.; Casanova Ramón-Borja, J.; Ramírez Backhaus, M.; Collado Serra, A.; Domínguez-Escrig, J.; Gómez-Ferrer, A.; López-Guerrero, J.A. Diagnóstico no invasivo del cáncer de próstata; marcadores séricos y en orina. Arch. Esp. Urol. 2013, 66, 440–452. [Google Scholar]
  19. Hugosson, J.; Roobol, M.J.; Månsson, M.; Tammela, T.L.J.; Zappa, M.; Nelen, V.; Kwiatkowski, M.; Lujan, M.; Carlsson, S.V.; Talala, K.M.; et al. A 16-yr Follow-up of the European Randomized study of Screening for Prostate Cancer(Figure presented.). Eur. Urol. 2019, 76, 43–51. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Carter, H.B.; Pearson, J.D.; Metter, E.J.; Brant, L.J.; Fozard, J.L. Longitudinal Evaluation of Prostate-Specific Antigen Levels in Men With and Without Prostate Disease. JAMA J. Am. Med. Assoc. 1992, 267, 2215–2220. [Google Scholar] [CrossRef] [Green Version]
  21. Cooperberg, M.R.; Brooks, J.D.; Faino, A.V.; Newcomb, L.F.; Kearns, J.T.; Carroll, P.R.; Dash, A.; Etzioni, R.; Fabrizio, M.D.; Gleave, M.E.; et al. Refined Analysis of Prostate-Specific Antigen Kinetics to Predict Prostate Cancer Active Surveillance Outcomes. Eur. Urol. 2018, 74, 211–217. [Google Scholar] [CrossRef]
  22. Iremashvili, V.; Barney, S.L.; Manoharan, M.; Kava, B.R.; Parekh, D.J.; Punnen, S. Prediagnostic prostate-specific antigen kinetics and the risk of biopsy progression in active surveillance patients. Int. J. Urol. 2016, 23, 313–317. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Hirama, H.; Sugimoto, M.; Ito, K.; Shiraishi, T.; Kakehi, Y. The impact of baseline [-2] proPSA-related indices on the prediction of pathological reclassification at 1 year during active surveillance for low-risk prostate cancer: The Japanese multicenter study cohort. J. Cancer Res. Clin. Oncol. 2014, 140, 257–263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Klotz, L.; Vesprini, D.; Sethukavalan, P.; Jethava, V.; Zhang, L.; Jain, S.; Yamamoto, T.; Mamedov, A.; Loblaw, A. Long-term follow-up of a large active surveillance cohort of patients with prostate cancer. J. Clin. Oncol. 2015, 33, 272–277. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Patel, H.D.; Feng, Z.; Landis, P.; Trock, B.J.; Epstein, J.I.; Carter, H.B. Prostate specific antigen velocity risk count predicts biopsy reclassification for men with very low risk prostate cancer. J. Urol. 2014, 191, 629–637. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Benson, M.C.; Whang, I.S.; Pantuck, A.; Ring, K.; Kaplan, S.A.; Olsson, C.A.; Cooner, W.H. Prostate specific antigen density: A means of distinguishing benign prostatic hypertrophy and prostate cancer. J. Urol. 1992, 147, 815–816. [Google Scholar] [CrossRef]
  27. Yusim, I.; Krenawi, M.; Mazor, E.; Novack, V.; Mabjeesh, N.J. The use of prostate specific antigen density to predict clinically significant prostate cancer. Sci. Rep. 2020, 10, 20015. [Google Scholar] [CrossRef] [PubMed]
  28. Kryvenko, O.N.; Balise, R.; Soodana Prakash, N.; Epstein, J.I. African-American Men with Gleason Score 3 + 3 = 6 Prostate Cancer Produce Less Prostate Specific Antigen than Caucasian Men: A Potential Impact on Active Surveillance. J. Urol. 2016, 195, 301–306. [Google Scholar] [CrossRef]
  29. Umbehr, M.H.; Platz, E.A.; Peskoe, S.B.; Bhavsar, N.A.; Epstein, J.I.; Landis, P.; Partin, A.W.; Carter, H.B. Serum prostate-specific antigen (PSA) concentration is positively associated with rate of disease reclassification on subsequent active surveillance prostate biopsy in men with low PSA density. BJU Int. 2014, 113, 561–567. [Google Scholar] [CrossRef] [Green Version]
  30. Beckman Coulter. Available online: https://www.beckmancoulter.com/es/products/immunoassay/phi (accessed on 11 February 2021).
  31. Loeb, S.; Sanda, M.G.; Broyles, D.L.; Shin, S.S.; Bangma, C.H.; Wei, J.T.; Partin, A.W.; Klee, G.G.; Slawin, K.M.; Marks, L.S.; et al. The prostate health index selectively identifies clinically significant prostate cancer. J. Urol. 2015, 193, 1163–1169. [Google Scholar] [CrossRef] [Green Version]
  32. Chiu, P.K.F.; Lai, F.M.M.; Teoh, J.Y.C.; Lee, W.M.; Yee, C.H.; Chan, E.S.Y.; Hou, S.M.; Ng, C.F. Prostate Health Index and % p2PSA Predict Aggressive Prostate Cancer Pathology in Chinese Patients Undergoing Radical Prostatectomy. Ann. Surg. Oncol. 2016, 23, 2707–2714. [Google Scholar] [CrossRef]
  33. Cantiello, F.; Russo, G.I.; Cicione, A.; Ferro, M.; Cimino, S.; Favilla, V.; Perdonà, S.; De Cobelli, O.; Magno, C.; Morgia, G.; 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. [Google Scholar] [CrossRef]
  34. Cantiello, F.; Russo, G.I.; Ferro, M.; Cicione, A.; Cimino, S.; Favilla, V.; Perdonà, S.; Bottero, D.; Terracciano, D.; De Cobelli, O.; et al. Prognostic accuracy of Prostate Health Index and urinary Prostate Cancer Antigen 3 in predicting pathologic features after radical prostatectomy. Urol. Oncol. Semin. Orig. Investig. 2015, 33, e15–e163. [Google Scholar] [CrossRef] [Green Version]
  35. Porpiglia, F.; Cantiello, F.; De Luca, S.; Manfredi, M.; Veltri, A.; Russo, F.; Sottile, A.; Damiano, R. 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. 2016, 118, 527–534. [Google Scholar] [CrossRef]
  36. Prostate Cancer Test 4Kscore OPKO Health. Available online: https://4kscore.com/ (accessed on 26 February 2021).
  37. Parekh, D.J.; Punnen, S.; Sjoberg, D.D.; Asroff, S.W.; Bailen, J.L.; Cochran, J.S.; Concepcion, R.; David, R.D.; Deck, K.B.; Dumbadze, I.; et al. A Multi-institutional Prospective Trial in the USA Confirms that the 4Kscore Accurately Identifies Men with High-grade Prostate Cancer. Eur. Urol. 2015, 68, 464–470. [Google Scholar] [CrossRef]
  38. Lin, D.W.; Newcomb, L.F.; Brown, M.D.; Sjoberg, D.D.; Dong, Y.; Brooks, J.D.; Carroll, P.R.; Cooperberg, M.; Dash, A.; Ellis, W.J.; et al. Evaluating the Four Kallikrein Panel of the 4Kscore for Prediction of High-grade Prostate Cancer in Men in the Canary Prostate Active Surveillance Study. Eur. Urol. 2017, 72, 448–454. [Google Scholar] [CrossRef]
  39. Borque-Fernando, Á.; Rubio-Briones, J.; Esteban, L.M.; Dong, Y.; Calatrava, A.; Gómez-Ferrer, Á.; Gómez-Gómez, E.; Gil Fabra, J.M.; Rodríguez-García, N.; López González, P.; et al. Role of the 4Kscore test as a predictor of reclassification in prostate cancer active surveillance. Prostate Cancer Prostatic Dis. 2019, 22, 84–90. [Google Scholar] [CrossRef]
  40. Haese, A.; Tin, A.L.; Carlsson, S.V.; Sjoberg, D.D.; Pehrke, D.; Steuber, T.; Huland, H.; Graefen, M.; Scardino, P.T.; Schlomm, T.; et al. A pre-specified model based on four kallikrein markers in blood improves predictions of adverse pathology and biochemical recurrence after radical prostatectomy. Br. J. Cancer 2020, 123, 604–609. [Google Scholar] [CrossRef] [PubMed]
  41. Bryant, R.J.; Sjoberg, D.D.; Vickers, A.J.; Robinson, M.C.; Kumar, R.; Marsden, L.; Davis, M.; Scardino, P.T.; Donovan, J.; Neal, D.E.; et al. Predicting High-Grade Cancer at Ten-Core Prostate Biopsy Using Four Kallikrein Markers Measured in Blood in the ProtecT Study. J. Natl. Cancer Inst. 2015, 107, djv095. [Google Scholar] [CrossRef] [Green Version]
  42. About the Oncotype DX Genomic Prostate Score® Test Oncotype IQ®. Available online: https://www.oncotypeiq.com/en-US/prostate-cancer/healthcare-professionals/oncotype-dx-genomic-prostate-score/about-the-test (accessed on 5 February 2021).
  43. Cedars, B.E.; Washington, S.L.; Cowan, J.E.; Leapman, M.; Tenggara, I.; Chan, J.M.; Cooperberg, M.R.; Carroll, P.R. Stability of a 17-gene genomic prostate score in serial testing of men on active surveillance for early stage prostate cancer. J. Urol. 2019, 202, 696–700. [Google Scholar] [CrossRef] [PubMed]
  44. Van Den Eeden, S.K.; Lu, R.; Zhang, N.; Quesenberry, C.P.; Shan, J.; Han, J.S.; Tsiatis, A.C.; Leimpeter, A.D.; Lawrence, H.J.; Febbo, P.G.; et al. A Biopsy-based 17-gene Genomic Prostate Score as a Predictor of Metastases and Prostate Cancer Death in Surgically Treated Men with Clinically Localized Disease [Figure presented]. Eur. Urol. 2018, 73, 129–138. [Google Scholar] [CrossRef] [PubMed]
  45. Lin, D.W.; Zheng, Y.; McKenney, J.K.; Brown, M.D.; Lu, R.; Crager, M.; Boyer, H.; Tretiakova, M.; Brooks, J.D.; Dash, A.; et al. 17-Gene Genomic Prostate Score Test Results in the Canary Prostate Active Surveillance Study (PASS) Cohort. J. Clin. Oncol. 2020, 38, 1549–1557. [Google Scholar] [CrossRef]
  46. Mohler, J.L.; Antonarakis, E.S.; Armstrong, A.J.; D’Amico, A.V.; Davis, B.J.; Dorff, T.; Eastham, J.A.; Enke, C.A.; Farrington, T.A.; Higano, C.S.; et al. Prostate cancer, version 2. 2019. JNCCN J. Natl. Compr. Cancer Netw. 2019, 17, 479–505. [Google Scholar] [CrossRef] [Green Version]
  47. Kornberg, Z.; Cooperberg, M.R.; Cowan, J.E.; Chan, J.M.; Shinohara, K.; Simko, J.P.; Tenggara, I.; Carroll, P.R. A 17-gene genomic prostate score as a predictor of adverse pathology in men on active surveillance. J. Urol. 2019, 202, 702–708. [Google Scholar] [CrossRef]
  48. Eure, G.; Germany, R.; Given, R.; Lu, R.; Shindel, A.W.; Rothney, M.; Glowacki, R.; Henderson, J.; Richardson, T.; Goldfischer, E.; et al. Use of a 17-Gene Prognostic Assay in Contemporary Urologic Practice: Results of an Interim Analysis in an Observational Cohort. Urology 2017, 107, 67–75. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Eggener, S.; Karsh, L.I.; Richardson, T.; Shindel, A.W.; Lu, R.; Rosenberg, S.; Goldfischer, E.; Korman, H.; Bennett, J.; Newmark, J.; et al. A 17-gene Panel for Prediction of Adverse Prostate Cancer Pathologic Features: Prospective Clinical Validation and Utility. Urology 2019, 126, 76–82. [Google Scholar] [CrossRef] [Green Version]
  50. Prostate Treatment Planning Patient Decipher Bio. Available online: https://decipherbio.com/prostate/ (accessed on 29 January 2021).
  51. Klein, E.A.; Haddad, Z.; Yousefi, K.; Lam, L.L.C.; Wang, Q.; Choeurng, V.; Palmer-Aronsten, B.; Buerki, C.; Davicioni, E.; Li, J.; et al. Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk. Urology 2016, 90, 148–152. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Herlemann, A.; Huang, H.C.; Alam, R.; Tosoian, J.J.; Kim, H.L.; Klein, E.A.; Simko, J.P.; Chan, J.M.; Lane, B.R.; Davis, J.W.; et al. Decipher identifies men with otherwise clinically favorable-intermediate risk disease who may not be good candidates for active surveillance. Prostate Cancer Prostatic Dis. 2020, 23, 136–143. [Google Scholar] [CrossRef] [Green Version]
  53. Nguyen, P.L.; Haddad, Z.; Ross, A.E.; Martin, N.E.; Deheshi, S.; Lam, L.L.C.; Chelliserry, J.; Tosoian, J.J.; Lotan, T.L.; Spratt, D.E.; et al. Ability of a Genomic Classifier to Predict Metastasis and Prostate Cancer-specific Mortality after Radiation or Surgery based on Needle Biopsy Specimens. Eur. Urol. 2017, 72, 845–852. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Kim, H.L.; Li, P.; Huang, H.C.; Deheshi, S.; Marti, T.; Knudsen, B.; Abou-Ouf, H.; Alam, R.; Lotan, T.L.; Lam, L.L.C.; et al. Validation of the Decipher Test for predicting adverse pathology in candidates for prostate cancer active surveillance. Prostate Cancer Prostatic Dis. 2019, 22, 399–405. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Badani, K.; Thompson, D.J.S.; Buerki, C.; Davicioni, E.; Garrison, J.; Ghadessi, M.; Mitra, A.P.; Wood, P.J.; Hornberger, J. Impact of a genomic classifier of metastatic risk on postoperative treatment recommendations for prostate cancer patients: A report from the DECIDE study group. Oncotarget 2013, 4, 600–609. [Google Scholar] [CrossRef] [Green Version]
  56. Prolaris Prolaris. Available online: https://prolaris.com/ (accessed on 8 February 2021).
  57. Lin, D.W.; Crawford, E.D.; Keane, T.; Evans, B.; Reid, J.; Rajamani, S.; Brown, K.; Gutin, A.; Tward, J.; Scardino, P.; et al. Identification of men with low-risk biopsy-confirmed prostate cancer as candidates for active surveillance. Urol. Oncol. Semin. Orig. Investig. 2018, 36, e7–e310. [Google Scholar] [CrossRef]
  58. Canter, D.J.; Freedland, S.; Rajamani, S.; Latsis, M.; Variano, M.; Halat, S.; Tward, J.; Cohen, T.; Stone, S.; Schlomm, T.; et al. Analysis of the prognostic utility of the cell cycle progression (CCP) score generated from needle biopsy in men treated with definitive therapy. Prostate Cancer Prostatic Dis. 2020, 23, 102–107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Canter, D.J.; Reid, J.; Latsis, M.; Variano, M.; Halat, S.; Rajamani, S.; Gurtner, K.E.; Sangale, Z.; Brawer, M.; Stone, S.; et al. Comparison of the Prognostic Utility of the Cell Cycle Progression Score for Predicting Clinical Outcomes in African American and Non-African American Men with Localized Prostate Cancer. Eur. Urol. 2019, 75, 515–522. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Cooperberg, M.R.; Pasta, D.J.; Elkin, E.P.; Litwin, M.S.; Latini, D.M.; Duchane, J.; Carroll, P.R. The University of California, San Francisco Cancer of the Prostate Risk Assessment score: A straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy. J. Urol. 2005, 173, 1938–1942. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Bussemakers, M.J.G.; Van Bokhoven, A.; Verhaegh, G.W.; Smit, F.P.; Karthaus, H.F.M.; Schalken, J.A.; Debruyne, F.M.J.; Ru, N.; Isaacs, W.B. DD3: A new prostate-specific gene, highly overexpressed in prostate cancer. Cancer Res. 1999, 59, 5975–5979. [Google Scholar]
  62. Progensatm PCA3-Southgenetics. Available online: https://southgenetics.com/progensa-pca3/ (accessed on 23 March 2021).
  63. Chevli, K.K.; Duff, M.; Walter, P.; Yu, C.; Capuder, B.; Elshafei, A.; Malczewski, S.; Kattan, M.W.; Jones, J.S. Urinary PCA3 as a predictor of prostate cancer in a cohort of 3073 men undergoing initial prostate biopsy. J. Urol. 2014, 191, 1743–1748. [Google Scholar] [CrossRef]
  64. Fenstermaker, M.; Mendhiratta, N.; Bjurlin, M.A.; Meng, X.; Rosenkrantz, A.B.; Huang, R.; Deng, F.M.; Zhou, M.; Huang, W.C.; Lepor, H.; et al. Risk Stratification by Urinary Prostate Cancer Gene 3 Testing Before Magnetic Resonance Imaging-Ultrasound Fusion-targeted Prostate Biopsy Among Men With No History of Biopsy. Urology 2017, 99, 174–179. [Google Scholar] [CrossRef]
  65. Tosoian, J.J.; Patel, H.D.; Mamawala, M.; Landis, P.; Wolf, S.; Elliott, D.J.; Epstein, J.I.; Carter, H.B.; Ross, A.E.; Sokoll, L.J.; et al. Longitudinal assessment of urinary PCA3 for predicting prostate cancer grade reclassification in favorable-risk men during active surveillance. Prostate Cancer Prostatic Dis. 2017, 20, 339–342. [Google Scholar] [CrossRef] [Green Version]
  66. Alshalalfa, M.; Verhaegh, G.W.; Gibb, E.A.; Santiago-Jiménez, M.; Erho, N.; Jordan, J.; Yousefi, K.; Lam, L.L.C.; Kolisnik, T.; Chelissery, J.; et al. Low PCA3 expression is a marker of poor differentiation in localized prostate tumors: Exploratory analysis from 12,076 patients. Oncotarget 2017, 8, 50804–50813. [Google Scholar] [CrossRef]
  67. Tomlins, S.A.; Rhodes, D.R.; Perner, S.; Dhanasekaran, S.M.; Mehra, R.; Sun, X.W.; Varambally, S.; Cao, X.; Tchinda, J.; Kuefer, R.; et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 2005, 310, 644–648. [Google Scholar] [CrossRef]
  68. Fernández-Serra, A.; Rubio, L.; Calatrava, A.; Rubio-Briones, J.; Salgado, R.; Gil-Benso, R.; Espinet, B.; García-Casado, Z.; López-Guerrero, J.A. Molecular characterization and clinical impact of TMPRSS2-ERG rearrangement on prostate cancer: Comparison between FISH and RT-PCR. Biomed. Res. Int. 2013, 2013, 465179. [Google Scholar] [CrossRef] [Green Version]
  69. Laxman, B.; Tomlins, S.A.; Mehra, R.; Morris, D.S.; Wang, L.; Helgeson, B.E.; Shah, R.B.; Rubin, M.A.; Wei, J.T.; Chinnaiyan, A.M. Noninvasive detection of TMPRSS2:ERG fusion transcripts in the urine of men with prostate cancer. Neoplasia 2006, 8, 885–888. [Google Scholar] [CrossRef] [Green Version]
  70. Lin, D.W.; Newcomb, L.F.; Brown, E.C.; Brooks, J.D.; Carroll, P.R.; Feng, Z.; Gleave, M.E.; Lance, R.S.; Sanda, M.G.; Thompson, I.M.; et al. Urinary TMPRSS2:ERG and PCA3 in an active surveillance cohort: Results from a baseline analysis in the canary prostate active surveillance study. Clin. Cancer Res. 2013, 19, 2442–2450. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Berg, K.D.; Vainer, B.; Thomsen, F.B.; Røder, M.A.; Gerds, T.A.; Toft, B.G.; Brasso, K.; Iversen, P. ERG protein expression in diagnostic specimens is associated with increased risk of progression during active surveillance for prostate cancer. Eur. Urol. 2014, 66, 851–860. [Google Scholar] [CrossRef]
  72. Whelan, C.; Kawachi, M.; Smith, D.D.; Linehan, J.; Babilonia, G.; Mejia, R.; Wilson, T.; Smith, S.S. Expressed prostatic secretion biomarkers improve stratification of NCCN active surveillance candidates: Performance of secretion capacity and TMPRSS2:ERG models. J. Urol. 2014, 191, 220–226. [Google Scholar] [CrossRef] [PubMed]
  73. Leyten, G.H.J.M.; Hessels, D.; Jannink, S.A.; Smit, F.P.; De Jong, H.; Cornel, E.B.; De Reijke, T.M.; Vergunst, H.; Kil, P.; Knipscheer, B.C.; 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. [Google Scholar] [CrossRef] [PubMed]
  74. Newcomb, L.F.; Zheng, Y.; Faino, A.V.; Bianchi-Frias, D.; Cooperberg, M.R.; Brown, M.D.; Brooks, J.D.; Dash, A.; Fabrizio, M.D.; Gleave, M.E.; et al. Performance of PCA3 and TMPRSS2:ERG urinary biomarkers in prediction of biopsy outcome in the Canary Prostate Active Surveillance Study (PASS). Prostate Cancer Prostatic Dis. 2019, 22, 438–445. [Google Scholar] [CrossRef] [PubMed]
  75. Tomlins, S.A.; Day, J.R.; Lonigro, R.J.; Hovelson, D.H.; Siddiqui, J.; Kunju, L.P.; Dunn, R.L.; Meyer, S.; Hodge, P.; Groskopf, J.; et al. Urine TMPRSS2:ERG Plus PCA3 for Individualized Prostate Cancer Risk Assessment. Eur. Urol. 2016, 70, 45–53. [Google Scholar] [CrossRef] [Green Version]
  76. MPS (MyProstateScore) MLabs. Available online: https://mlabs.umich.edu/tests/mps-myprostatescore (accessed on 25 March 2021).
  77. SelectMDx Urine Test Prostate Cancer Early Detection. Available online: https://mdxhealth.com/selectmdx-physician (accessed on 24 March 2021).
  78. Leyten, G.H.J.M.; Hessels, D.; Smit, F.P.; Jannink, S.A.; De Jong, H.; Melchers, W.J.G.; Cornel, E.B.; De Reijke, T.M.; Vergunst, H.; Kil, P.; et al. Identification of a candidate gene panel for the early diagnosis of prostate cancer. Clin. Cancer Res. 2015, 21, 3061–3070. [Google Scholar] [CrossRef] [Green Version]
  79. Van Neste, L.; Hendriks, R.J.; Dijkstra, S.; Trooskens, G.; Cornel, E.B.; Jannink, S.A.; de Jong, H.; Hessels, D.; Smit, F.P.; Melchers, W.J.G.; et al. Detection of High-grade Prostate Cancer Using a Urinary Molecular Biomarker–Based Risk Score. Eur. Urol. 2016, 70, 740–748. [Google Scholar] [CrossRef]
  80. Fiorella, D.; Marenco, J.L.; Mascarós, J.M.; Borque-fernando, A.; Esteban, L.M.; Calatrava, A.; Pastor, B.; López-Guerrero, J.A.; Rubio-Briones, J. Papel de PCA3 y SelectMDx en la optimización de la vigilancia activa en el cáncer de próstata. Actas Urológicas Españolas 2021. [Google Scholar] [CrossRef]
  81. Pepe, P.; Dibenedetto, G.; Pepe, L.; Pennisi, M. Multiparametric MRI versus selecTMDX accuracy in the diagnosis of clinically significant PCA in men enrolled in active surveillance. Vivo 2020, 34, 393–396. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Hendriks, R.J.; van der Leest, M.M.G.; Dijkstra, S.; Barentsz, J.O.; Van Criekinge, W.; Hulsbergen-van de Kaa, C.A.; Schalken, J.A.; Mulders, P.F.A.; van Oort, I.M. A urinary biomarker-based risk score correlates with multiparametric MRI for prostate cancer detection. Prostate 2017, 77, 1401–1407. [Google Scholar] [CrossRef]
  83. Govers, T.M.; Hessels, D.; Vlaeminck-Guillem, V.; Schmitz-Dräger, B.J.; Stief, C.G.; Martinez-Ballesteros, C.; Ferro, M.; Borque-Fernando, A.; Rubio-Briones, J.; Sedelaar, J.P.M.; et al. Cost-effectiveness of SelectMDx for prostate cancer in four European countries: A comparative modeling study. Prostate Cancer Prostatic Dis. 2019, 22, 101–109. [Google Scholar] [CrossRef] [PubMed]
  84. Dijkstra, S.; Govers, T.M.; Hendriks, R.J.; Schalken, J.A.; Van Criekinge, W.; Van Neste, L.; Grutters, J.P.C.; Sedelaar, J.P.M.; van Oort, I.M. Cost-effectiveness of a new urinary biomarker-based risk score compared to standard of care in prostate cancer diagnostics—A decision analytical model. BJU Int. 2017, 120, 659–665. [Google Scholar] [CrossRef]
  85. Moore, C.M.; Giganti, F.; Albertsen, P.; Allen, C.; Bangma, C.; Briganti, A.; Carroll, P.; Haider, M.; Kasivisvanathan, V.; Kirkham, A.; et al. Reporting Magnetic Resonance Imaging in Men on Active Surveillance for Prostate Cancer: The PRECISE Recommendations—A Report of a European School of Oncology Task Force. Eur. Urol. 2017, 71, 648–655. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Ijms 22 06266 g001 550
Figure 1. Workflow diagram of the references’ selection process. Bibliography was selected considering different sources of biomarkers including tissue, blood and urine.
Figure 1. Workflow diagram of the references’ selection process. Bibliography was selected considering different sources of biomarkers including tissue, blood and urine.
Ijms 22 06266 g001
Table
Table 1. Summary of clinically available biomarkers and tests with AS application.
Table 1. Summary of clinically available biomarkers and tests with AS application.
Biomarker Source Characteristics AS Application References
PSABloodPSA is a glycoprotein secreted by prostatic epithelial cells that lyses the clotted ejaculate to enhance sperm motility. It is the most important commonly used biomarker, but it has been shown not to be a cancer-specific marker, as some prostate diseases could also produce PSA elevated levelsIts role as biomarker in AS has been questioned due to its variability and has been mostly studied related to its changes with time, prostate volume or other isoforms[19]
PSA kineticsBloodRate of PSA change over time: PSA doubling time (PSADT) is the number of years over which a certain level of PSA increases by a factor of two and is calculated as DT = ln(2); PSA velocity (PSAV) represents a change in PSA level over time/m Differentiates between PCa with more and less aggressive natural history[20,21,22,23,24,25]
PSA density (PSAD)BloodDividing preoperative PSA by prostate weight without seminal vesiclesPredicts upgrading and reclassification in men with low-risk PCa enrolled in AS, by improving the inclusion criteria and follow-up of PCa patients[26,27,28,29]
Prostate Health Index (PHI)BloodIndex calculated as [-2]proPSA/fPSA x √PSAPHI measurement could be clinically useful in discriminating the presence of insignificant PCa in active surveillance candidates[30,31,32,33,34,35]
4Kscore®BloodA panel of four kallikreins (tPSA, fPSA, intact PSA [iPSA], and human kallikrein 2 [hK2]) combined with clinical data available before cancer diagnosisIdentifies patients most likely to benefit from biopsy because of a high risk of having a clinically significant tumour that would require active treatment[36,37,38,39,40,41]
Oncotype DX® Genomic Prostate Score (GPS)TissueRNA based expression assay of 12 PCa related normalized to 5 housekeeping genesAssociated with an increased risk of AP and BR in the initial test of early-stage PCa[42,43,44,45,46,47,48,49,50]
Genome DX Decipher® Genomic ClassifierTissueIncludes 22 coding and non-coding genes, which covers seven cancer pathways, such as angiogenesis, invasion and metastasis, or growth and differentiationHelps to predict metastasis risk after RP and exclude patients of following AS programs[51,52,53,54,55,56]
Prolaris® (Cell Cycle Progression Score)TissueRNA expression signature based on measuring the expression levels of 31 genes that participate in cell cycle progression and 15 housekeeping genesHelps to identify patients who may warrant increased intervention intensity due to their predicted risk of metastatic disease[57,58,59,60,61]
Prostate Cancer Antigen 3 (PCA3)UrineNon-coding mRNA only expressed in human prostate tissue but overexpressed in PCa tissuePatients who reclassified had significantly higher PCA3 scores at both initial and subsequent measures, indicating an association with grade reclassification[62,63,64,65,66,67]
TMPRSS2:ERG fusion geneUrineT2E fusion gene is present in approximately 50% of prostatic tumours and is detected both in FFPE and urine samplesNon-invasive T2E measurement in urine may refine patient acceptance into AS programs[68,69,70,71,72,73,74,75,76,77]
SelectMDxUrineCombines serum PSA, PSAD and clinical factors such as age and prior negative biopsy with mRNA signatures: urinary homeobox C6 (HOXC6) and distal-less homeobox 1 (DLX1)Can detect high-grade csPCa accurately and could therefore be used in AS decision making, reducing the number of unnecessary prostate biopsies and potential overtreatment[78,79,80,81,82,83,84,85]
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Pastor-Navarro, B.; Rubio-Briones, J.; Borque-Fernando, Á.; Esteban, L.M.; Dominguez-Escrig, J.L.; López-Guerrero, J.A. Active Surveillance in Prostate Cancer: Role of Available Biomarkers in Daily Practice. Int. J. Mol. Sci. 2021, 22, 6266. https://doi.org/10.3390/ijms22126266

AMA Style

Pastor-Navarro B, Rubio-Briones J, Borque-Fernando Á, Esteban LM, Dominguez-Escrig JL, López-Guerrero JA. Active Surveillance in Prostate Cancer: Role of Available Biomarkers in Daily Practice. International Journal of Molecular Sciences. 2021; 22(12):6266. https://doi.org/10.3390/ijms22126266

Chicago/Turabian Style

Pastor-Navarro, Belén, José Rubio-Briones, Ángel Borque-Fernando, Luis M. Esteban, Jose Luis Dominguez-Escrig, and José Antonio López-Guerrero. 2021. "Active Surveillance in Prostate Cancer: Role of Available Biomarkers in Daily Practice" International Journal of Molecular Sciences 22, no. 12: 6266. https://doi.org/10.3390/ijms22126266

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