Advertisement
Advanced searchCitation search
Skip main navigation
You are prohibited from using or uploading content you accessed through this website into external applications, bots, software, or websites, including those using artificial intelligence technologies and infrastructure, including deep learning, machine learning and large language models and generative AI.
Advertisement
Open AccessJournal of UrologyOriginal Research Articles1 May 2025

Clinical Validation of MyProstateScore 2.0 Testing Using First-Catch, Non–Digital Rectal Examination Urine

View All Author Information
https://doi.org/10.1097/JU.0000000000004421

Abstract

Download PPT

Purpose:

The 18-gene MyProstateScore 2.0 (MPS2) test was previously validated for detection of grade group (GG) ≥ 2 prostate cancer using post–digital rectal examination (DRE) urine. To improve ease of testing, we validated MPS2 using first-catch, non-DRE urine.

Materials and Methods:

Patients provided first-catch urine before biopsy. MPS2 values were calculated using previously validated models differing only by extent of clinical data included biomarkers alone (BA; no clinical data), biomarkers and clinical factors (BA + CF), and biomarkers, clinical factors, and prostate volume (BA + CF + PV). The primary outcome was GG ≥ 2 cancer on biopsy. MPS2 performance and clinical consequences of testing were compared with PSA and the Prostate Cancer Prevention Trial risk calculator (PCPTrc).

Results:

The cohort included 266 men with median PSA 6.6 ng/mL (IQR, 4.9-9.1) of whom 103 (39%) had GG ≥ 2 cancer on biopsy. The AUC for GG ≥ 2 cancer was 57% for PSA, 62% for PCPTrc, and 71%, 74%, and 77% for MPS2 models. Under a testing approach detecting > 90% of GG ≥ 2 cancers, MPS2 testing would have avoided 36% to 42% of unnecessary biopsies, as compared with 13% using the PCPTrc. In patients with a prior negative biopsy, MPS2 testing would have avoided 44% to 53% of repeat biopsies, as compared with only 2.6% using PCPTrc.

Conclusions:

Using first-catch urine, MPS2 meaningfully improved the proportion of biopsies avoided relative to PCPTrc while maintaining highly sensitive detection of GG ≥ 2 cancer. Non-DRE testing provides a convenient, objective, and highly accurate testing option to reduce the need for imaging and biopsy in men with elevated PSA.

JU Insight

  • Study Need and Importance

  • Traditional prostate cancer screening with PSA testing and biopsy leads to unnecessary procedures and overdiagnosis of low-grade cancers. The 18-gene MyProstateScore 2.0 (MPS2) test, previously validated using post–digital rectal examination (DRE) urine, has been shown to preserve detection of clinically significant prostate cancers (grade group [GG] ≥2) while reducing the rate of biopsies. This study validated the MPS2 test using first-catch, non-DRE urine to improve testing convenience and access, including at-home testing in the telehealth setting.

  • What We Found

  • In men considering prostate biopsy, non-DRE MPS2 testing significantly reduced the need for biopsy compared with the Prostate Cancer Prevention Trial risk calculator (PCPTrc) while maintaining reliable detection of GG ≥ 2 cancers. Under a testing approach maintaining 92% sensitivity for GG ≥ 2 cancer in the overall population, MPS2 testing would have avoided 36% to 42% of unnecessary biopsies, as compared with 13% using the PCPTrc. In patients considering repeat biopsy, MPS2 testing would have avoided 44% to 53% of unnecessary biopsies, as compared with only 2.6% using the PCPTrc. As the MPS2 test can be performed with or without inclusion of standard clinical factors (ie, age, race, PSA, DRE findings, family history, and prior negative biopsy) or prostate volume, the current findings demonstrated that all MPS2 models provide substantial improvement relative to the PCPTrc.

  • Limitations

  • A limited proportion of patients underwent prebiopsy MRI, and use of systematic biopsy as the reference standard can lead to undersampling. Consistent with the ProScreen trial, MPS2 was evaluated as a first-line test after PSA to rule out the need for MRI or biopsy. Therefore, this study does not inform the combined use of MPS2 and MRI, which remains an important clinical question. Finally, there were limited data for pertinent subpopulations, such as African American men, warranting further investigation.

  • Interpretation for Patient Care

  • Using urine obtained without DRE, the MPS2 test provides a highly accurate, personalized risk score to better identify patients who can confidently forgo additional testing with MRI or biopsy. The non-DRE test maintains exceptional performance while enhancing access and ease of use, enabling at-home urine collection and telehealth care. This noninvasive test offers patients and clinicians a convenient and widely accessible option to reduce the need for more burdensome testing while maintaining high sensitivity for clinically significant cancers.

Screening with serum PSA has been shown to significantly reduce prostate cancer (PCa) mortality.1 Long-term follow-up from the Goteborg randomized screening trial revealed a number needed to invite to screen of 221 patients to prevent 1 PCa death,1 comparing favorably with other prevalent cancers.2 At the same time, the traditional approach to PSA screening—in which patients with an elevated serum PSA are directed to prostate biopsy—is associated with potential harms, including unnecessary biopsies and overdiagnosis of low-grade, indolent cancers.3 As such, contemporary guidelines emphasize a focus on higher-grade, clinically significant PCa (grade group [GG] ≥2) through the use of MRI or biomarker testing before biopsy.4,5

Indeed, prostate MRI followed by targeted biopsy of abnormal regions has proven effective in detecting GG ≥ 2 cancer and reducing overdiagnosis of GG1 disease under some diagnostic pathways.6 However, MRI is resource dependent, and its population-wide use is limited by availability, dependence on expert interpretation, and, in the United States, cost.7-9 As a result, there is great interest in the use of blood-based and urine-based biomarkers in men with elevated PSA to rule out benign and low-grade lesions, preserving the use of MRI and biopsy for higher-risk men most likely to benefit.10 Several blood-based and urine-based biomarker tests are available to that end.11

One such option is the urinary MyProstateScore 2.0 (MPS2) test. Using urine samples obtained after digital rectal examination (DRE), the assay measures expression of 18 cancer-specific and high-grade cancer-specific genes to provide an individualized risk of GG ≥ 2 cancer.12 On external validation, MPS2 testing reduced unnecessary biopsies by 35% to 51% while maintaining detection of 95% of GG ≥ 2 cancers. Yet DRE is uncomfortable for patients and is now considered optional by clinical guidelines.4,5 Moreover, performing DRE is not feasible in the growing population undergoing telehealth consultations. As such, in this study, we validated the MPS2 test in urine specimens obtained without DRE and evaluated its clinical performance for detection of GG ≥ 2 PCa.

MATERIALS AND METHODS

Study Population and Protocol

Approval was obtained from the University of Michigan (U-M) Institutional Review Board (HUM00042749), and all participants provided written informed consent. The study population included patients without PCa who underwent prostate biopsy at U-M from November 2020 to March 2023 for elevated PSA and/or abnormal DRE. Initial biopsy patients with PSA > 20 ng/mL (n = 7) and repeat biopsy patients with PSA > 25 ng/mL (n = 0) were excluded.5 All patients underwent 12-core systematic biopsy through the transrectal or transperineal approach. Based on clinical judgment, a proportion of patients underwent prebiopsy MRI. Patients with suspicious lesions (defined as Prostate Imaging Reporting and Data System ≥ 3 [PI-RADS ≥ 3]) underwent targeted biopsy of the lesions.

Participants provided ≤ 40 mL of first-catch urine without prior DRE. Per institutional protocol, specimens were stored unbuffered at −80 °C until processing. RNA extraction was performed from ≤ 5 mL of urine using an extraction method adapted and optimized for non-DRE urine. RNA was reverse transcribed to cDNA, and biomarker amplification and measurement were performed using the QuantStudio 12K Flex Real-Time PCR System (Thermo Fisher Scientific) as described.12 Expression was quantified by the relative cycle threshold (Crt), defined as the number of amplification cycles required for fluorescence to exceed the background level. All samples were run in triplicate, and mean Crt values were normalized to the housekeeping gene KLK3. Normalized mean Crt values were used to calculate risk scores using the previously validated MPS2 models.12

To maximize the predictive value for each patient based on the extent of clinical data available, all 3 previously established MPS2 models were validated: (1) a model using biomarkers alone (BA; no clinical data included), (2) a model including biomarkers and clinical factors (BA + CF; age, race, PSA, DRE findings, family history, and prior negative biopsy status), and (3) a model including biomarkers, clinical factors, and prostate volume (BA + CF + PV). Notably, addition of clinical factors did not improve performance in the repeat biopsy population; thus, the MPS2 (BA) model is used when standard clinical factors without volume are provided in this setting. The assay measures RNA expression of 18 genes: 4 high-grade cancer-specific genes (APOC1, B3GNT6, NKAIN1, and SCHLAP1), 13 cancer-specific genes (PCGEM1, SPON2, TRGV9, PCA3, OR51E2, CAMKK2, TFF3, PCAT14, TMSB15A, HOXC6, ERG, TMPRSS2:ERG, and KLK4), and the reference gene KLK3.12 MPS2 results are standardized to represent the percentage likelihood of detecting GG ≥ 2 cancer (0%-100%) on biopsy.

Statistical Analysis

The primary outcome was detection of GG ≥ 2 cancer on biopsy. GG ≥ 3 cancer was evaluated secondarily. Performance of the non-DRE MPS2 assay for GG ≥ 2 cancer was compared with logistic regression models for serum PSA and the Prostate Cancer Prevention Trial risk calculator (PCPTrc), which builds on the PSA-only model by also including age, race, family history of PCa, DRE findings, and previous negative biopsy status.13 Overall discriminative performance was quantified as the AUC, and comparisons were performed using the DeLong method.14 Decision curve analysis (DCA) quantified the net benefit of biomarker testing on the decision for biopsy compared with (1) performing biopsy in all patients and (2) performing biopsy in no patients.15 Because an estimated risk of GG ≥ 2 cancer less than 5% justifies forgoing biopsy and a risk greater than 20% justifies performing biopsy in most patients, DCA included threshold probabilities spanning this range (0%-30%). DCA was performed using dca in the R package dcurves.16 Statistical analyses were performed using R version 4.1.1.

Threshold Analysis

The MPS2 test provides a continuous percentage risk of GG ≥ 2 cancer to best support individualized decision-making. In light of the strong motivation in medicine to provide thresholds to support decision-making,17,18 we secondarily sought to establish a testing threshold (ie, cutoff) to broadly identify patients at the lowest risk of harboring GG ≥ 2 cancer. Threshold identification was based primarily on clinical considerations. Considering the relative harms of false-positive testing (ie, unnecessary MRI or biopsy) and false-negative testing (ie, missed detection of GG ≥2 cancer), and in alignment with the proposed role of biomarkers for rule-out testing, we sought a threshold providing a low (≤10%) false-negative rate.19,20 Candidate values spanning this false negative rate (ie, 0%-15%) were evaluated, and the value providing an optimal balance of biopsy avoidance and reliable rule-out ability was evaluated. Test performance and clinical consequences of testing were calculated for MPS2 and PCPTrc at a projected GG ≥ 2 prevalence of 25%, consistent with prior analyses and published biomarker populations (17%-31%).21-25 To ensure level comparison, the PCPTrc threshold yielding a false negative rate equivalent to MPS2 (BA) for each subgroup was evaluated. Finally, we performed subgroup analysis in patients with PSA < 10 ng/mL and evaluated MPS2 and MRI in patients who underwent prebiopsy MRI.

RESULTS

Study Cohort

The study cohort included 266 men of median age 67 years (IQR, 62-71) and median PSA 6.6 ng/mL (IQR, 4.9-9.1; Table 1). Fifty-four men (20%) had undergone a previous negative biopsy, and 47 (18%) underwent prebiopsy MRI, of whom 25 (53%) had a PI-RADS ≥ 3 lesion and underwent targeted sampling. Overall, GG ≥ 2 cancer was detected in 103 men (39%) of whom 83 had GG2 (31%), 9 had GG3 (3.4%), and 11 had GG4-5 disease (4.1%).

Table 1. Demographic and Clinical Characteristics of Study Cohort
Characteristic GG1/benign GG ≥2 P value
No. (%) 163 (61) 103 (39)
Median age (IQR), y 67 (61-71) 67 (64-72) .4
No. Black race (%) 8 (4.9) 7 (6.8) .6
No. positive family history (%) 52 (32) 44 (43) .1
No. previous negative biopsy (%) 39 (24) 15 (15) .09
No. suspicious DRE (%) 5 (3.1) 13 (13) .004
Median PSA (IQR), ng/mL  6.4 (4.7-8.8) 6.8 (5.2-9.9) .060
Median prostate volume (IQR) 52 (40-69) 40 (31-55) < .001
No. underwent MRI (%) 28 (17) 19 (18) > .9
 PI-RADS 1-2 18 (11) 4 (3.9)
 PI-RADS 3 3 (1.8) 2 (1.9)
 PI-RADS 4 4 (2.5) 8 (7.8)
 PI-RADS 5 3 (1.8) 5 (4.9)
Median MPS2 (BA) score (IQR), % 15 (7-28) 31 (18-51) < .001
Median MPS2 (BA + CF) score (IQR), % 15 (5-28) 36 (18-65) < .001
Median MPS2 (BA + CF + PV) score (IQR), % 13 (5-28) 41 (19-72) < .001

Abbreviations: BA, biomarkers alone model; BA + CF, biomarkers plus clinical factors model; BA + CF + PV, biomarkers, clinical factors, and prostate volume model; DRE, digital rectal examination; GG, grade group; MPS2, MyProstateScore 2.0; PI-RADS, Prostate Imaging Reporting and Data System.

P values calculated using the Wilcoxon rank-sum test for medians and the χ2 or Fisher exact test for proportions.

Median PSA did not significantly differ between patients with and without GG ≥ 2 cancer (6.8 vs 6.4 ng/mL, P = .06). Median MPS2 values were significantly higher in patients with vs without GG ≥ 2 cancer across all models: 31% vs 15% using MPS2 (BA), 36% vs 15% using MPS2 (BA + CF), and 41% vs 13% using MPS2 (BA + CF + PV) (all P < .001). Notably, the proportion of patients who underwent MRI did not significantly differ between those with and without GG ≥ 2 cancer (18% vs 17%, P > .9).

MPS2 Comparative Performance

As compared with PSA (AUC, 57%, 95% CI, 50%-64%) and the PCPTrc (AUC, 62%, 95% CI, 55%-69%), all MPS2 models provided improved AUC, spanning 71% (95% CI, 65%-77%) for MPS2 (BA), 74% (95% CI, 68%-80%) for MPS2 (BA + CF), and 77% (95% CI, 71%-83%) for MPS2 (BA + CF + PV). All AUC comparisons were statistically significant except MPS2 (BA) vs the PCPTrc, which did not meet conventional levels of significance (P = .054, Table 2). Similar relationships were observed on subgroup analyses by previous biopsy status (Supplementary Figures 1 and 2, https://www.jurology.com). While MPS2 (BA + CF) provided 3% improvement in AUC relative to MPS2 (BA), this did not meet conventional levels of statistical significance (P = .076). MPS2 (BA + CF + PV) provided statistically significant improvement in AUC relative to MPS2 (BA) and MPS2 (BA + CF) models (Table 2).

Table 2. Discriminative Accuracy of PSA, the Prostate Cancer Prevention Trial Risk Calculator, and MyProstateScore 2.0 Models for Grade Group ≥ 2 Cancer
Model AUC (95% CI), % P value vs PSA P value vs PCPTrc P value vs preceding model
PSA 57 (50-64)
PCPTrc 62 (55-69) .035
MPS2 (BA) 71 (65-77) .003 .054
MPS2 (BA + CF) 74 (68-80) < .001 .004 .076
MPS2 (BA + CF + PV) 77 (71-83) < .001 < .001 .01 (< .001 vs MPS2-BA)

Abbreviations: BA, biomarkers alone model; BA + CF, biomarkers plus clinical factors model; BA + CF + PV, biomarkers, clinical factors, and prostate volume model; MPS2, MyProstateScore 2.0; PCPTrc, Prostate Cancer Prevention Trial risk calculator.

Comparisons performed using the DeLong method.

MPS2-based predicted probabilities of GG ≥ 2 cancer closely approximated observed prevalence, reflecting good calibration in the study population (Figure 1) and on calibration at 25% prevalence (Supplementary Figure 3, https://www.jurology.com).21-25 On DCA, MPS2 models provided higher net clinical benefit and a higher net reduction in biopsies per 100 patients than PSA and the PCPTrc over clinically pertinent threshold probabilities (Figure 2). Similar findings were observed in the biopsy-naïve subpopulation (Supplementary Figure 4, https://www.jurology.com).

Figure 1.Calibration curves for grade group ≥ 2 cancer for MyProstateScore 2.0 (MPS2) models in the validation cohort. BA indicates biomarkers alone model; BA + CF, biomarkers plus clinical factors model; BA + CF + PV, biomarkers, clinical factors, and prostate volume model.

Figure 1. Calibration curves for grade group ≥ 2 cancer for MyProstateScore 2.0 (MPS2) models in the validation cohort. BA indicates biomarkers alone model; BA + CF, biomarkers plus clinical factors model; BA + CF + PV, biomarkers, clinical factors, and prostate volume model.

Download PPT
Figure 2.Decision curve analysis plots for the outcome of grade group ≥ 2 cancer based on prebiopsy testing with PSA, the Prostate Cancer Prevention Trial (PCPT) risk calculator, and MyProstateScore 2.0 (MPS2) models compared with baseline approaches of biopsying all patients and biopsying no patients. A, Net clinical benefit in which the unit of net benefit (y-axis) is true positives. A net benefit of 0.1 is equivalent to an approach in which an additional 10 patients per 100 are directed to biopsy, and all 10 patients are found to have grade group ≥ 2 cancer. B, Net reduction in biopsies in which the y-axis represents the net reduction in biopsies performed per 100 patients without missing a single diagnosis of grade group ≥ 2 cancer. BA indicates biomarkers alone model; BA + CF, biomarkers plus clinical factors model; BA + CF + PV, biomarkers, clinical factors, and prostate volume model.

Figure 2. Decision curve analysis plots for the outcome of grade group ≥ 2 cancer based on prebiopsy testing with PSA, the Prostate Cancer Prevention Trial (PCPT) risk calculator, and MyProstateScore 2.0 (MPS2) models compared with baseline approaches of biopsying all patients and biopsying no patients. A, Net clinical benefit in which the unit of net benefit (y-axis) is true positives. A net benefit of 0.1 is equivalent to an approach in which an additional 10 patients per 100 are directed to biopsy, and all 10 patients are found to have grade group ≥ 2 cancer. B, Net reduction in biopsies in which the y-axis represents the net reduction in biopsies performed per 100 patients without missing a single diagnosis of grade group ≥ 2 cancer. BA indicates biomarkers alone model; BA + CF, biomarkers plus clinical factors model; BA + CF + PV, biomarkers, clinical factors, and prostate volume model.

Download PPT

MPS2 Threshold Performance and Clinical Outcomes

Performance measures were calculated for MPS2 thresholds spanning 5% to 15% (Supplementary Tables 1 and 2, https://www.jurology.com), and the threshold of 11.5% provided an optimal balance of biopsy avoidance and preserved GG ≥ 2 cancer detection. Overall performance and clinical consequences of prebiopsy testing with MPS2 and the PCPTrc were calculated per 1000 patients (Table 3). In the overall population, under a testing approach preserving detection of ≥ 92% of GG ≥ 2 cancers, use of MPS2 (BA) would have avoided 274 (36%) unnecessary biopsies and MPS2 (BA + CF + PV) would have avoided 315 (42%) unnecessary biopsies, as compared with only 94 (13%) using the PCPTrc. In the repeat biopsy population, with both MPS2 and PCPTrc detecting 93% of GG ≥ 2 cancers, MPS2 testing would have avoided 327 to 395 (44%-53%) unnecessary biopsies as compared with only 20 (2.6%) unnecessary biopsies avoided using the PCPTrc. Similar findings were observed when limited to patients with PSA < 10 ng/mL (Supplementary Table 3, https://www.jurology.com). Notably, only a single patient with GG ≥ 3 cancer had a false negative MPS2 result. For GG ≥ 3 cancer, the MPS2 models provided 98% to 100% negative predictive value (NPV) and 94% to 100% sensitivity across subgroups (Supplementary Table 4, https://www.jurology.com).

Table 3. Performance and Clinical Consequences of MyProstateScore 2.0 and Prostate Cancer Prevention Trial Risk Calculator Testing to Select for Biopsy per 1000 Patients
Model Population Threshold (%) GG ≥2 detected (sensitivity), No. (%) GG ≥2 missed, No. (%) Bx avoided, No. (%) Unnec Bx avoided (specificity), No. (%) NPV (%) PPV (%)
MPS2 (BA) Overall 11.5 231 (92) 19 (7.6) 293 (29) 274 (36) 93 33
MPS2 (BA + CF) 11.5 228 (91) 22 (8.8) 294 (29) 272 (36) 93 32
MPS2 (BA + CF + PV) 11.5 235 (94) 15 (6.0) 330 (33) 315 (42) 95 35
PCPTrc 5.6 230 (92) 20 (8.0) 114 (11) 94 (13) 83 26
MPS2 (BA) Initial Bx 11.5 230 (92) 20 (8.0) 280 (28) 260 (35) 93 32
MPS2 (BA + CF) 11.5 227 (91) 23 (9.2) 281 (28) 258 (34) 92 32
MPS2 (BA + CF + PV) 11.5 235 (94) 15 (6.0) 310 (31) 295 (39) 95 34
PCPTrc 6.3 230 (92) 20 (8.0) 174 (20) 154 (20) 88 28
MPS2 (BA) Repeat Bx 11.5 233 (93) 17 (6.8) 344 (34) 327 (44) 95 36
MPS2 (BA + CF) 11.5 233 (93) 17 (6.8) 344 (34) 327 (44) 95 36
MPS2 (BA + CF + PV) 11.5 233 (93) 17 (6.8) 411 (41) 395 (53) 96 40
PCPTrc 4.5 233 (93) 17 (6.8) 36 (3.6) 20 (2.6) 50 27

Abbreviations: BA, biomarkers alone model; BA + CF, biomarkers plus clinical factors model; BA + CF + PV, biomarkers, clinical factors, and prostate volume model; Bx, biopsy; GG, grade group; MPS2, MyProstateScore 2.0; NPV, negative predictive value; PCPTrc, Prostate Cancer Prevention Trial risk calculator; PPV, positive predictive value; Unnec, unnecessary.

Subgroup Analysis: MPS2 + MRI

There were 47 patients who underwent prebiopsy MRI (Supplementary Table 5, https://www.jurology.com). Among 19 patients (40%) with GG ≥ 2 cancer, MPS2 (BA) was positive in 18 (95%), while MRI was positive (PI-RADS ≥ 3) in 15 (79%). Among 7 cases where both MPS2 (BA) and MRI were negative, 0 (0%) had GG ≥ 2 cancer. When both MPS2 (BA) and MRI were positive (n = 21), 14 (67%) had GG ≥ 2 cancer. There were 19 patients with discordant MPS2 (BA) and MRI findings of whom 5 (26%) had GG ≥ 2. Full performance characteristics of MPS2 and MRI in this subgroup are provided in Supplementary Table 6 (https://www.jurology.com).

DISCUSSION

The MyProstateScore 2.0 test measures 18 cancer-associated and high-grade cancer-associated genes in urine to provide a percentage likelihood of detecting GG ≥ 2 cancer on biopsy. In post-DRE urine, MPS2 was previously shown to improve detection of GG ≥ 2 cancer relative to PSA, the PCPTrc, and 2 currently available biomarker tests. While a practical strength of biomarker tests is their ease of use (eg, tests can be readily sent from clinic), the need for DRE before urine collection precluded remote and at-home sample collection—an option increasingly sought in the expanding era of telehealth. Thus, this study validated MPS2 using first-catch, non-DRE urine.

To provide flexibility for ordering clinicians, MPS2 can be performed without requiring any clinical data (MPS2-BA), with standard clinical data (MPS2-BA + CF), or with standard clinical data and prostate volume (MPS2-BA + CF + PV). Clinicians simply provide available data, and readily interpretable results are provided based on the most informative model (Supplementary Figure 5, https://www.jurology.com). Importantly, the current analysis reveals that all MPS2 models provide substantial clinical improvement relative to the PCPTrc. In selecting patients for biopsy, under a clinical approach detecting 92% of GG ≥ 2 cancers, use of MPS2 would have avoided 36% to 42% of unnecessary biopsies, as compared with 13% using the PCPTrc. Consistent with validation of the post-DRE assay,12 MPS2 outperformed the PCPTrc by a remarkable margin in the repeat biopsy setting. While both tests detected 93% of GG ≥ 2 cancers, MPS2 allowed for avoidance of 44% to 53% of unnecessary biopsies as compared with only 2.6% using the PCPTrc. The drastic improvements observed in the repeat biopsy setting are unsurprising given the dependence of the PCPTrc on PSA, and the well-documented limitations of PSA in patients with a previous negative biopsy.5 By capturing 17 non-PSA, cancer-associated markers, the MPS2 test appears to provide uniquely strong clinical advantages in the repeat biopsy setting.

Based on the current analysis, the MPS2 non-DRE test appears to provide advantages relative to existing non-DRE urine tests. In the initial biopsy population, the MPS2 biomarker-only test (ie, no clinical factors included) provided an AUC of 70%, in line with the 67% to 71% reported for the ExoDx Prostate Intelliscore test,24,26,27 while the ability to include clinical data further improved the AUC of MPS2 to 76%. Acknowledging a limited sample size, the clinical improvements offered by MPS2 were particularly notable in patients with a previous negative biopsy. While MPS2 (BA) allowed for avoidance of 44% of unnecessary biopsies (93% sensitivity, 44% specificity, 95% NPV), it is notable that prostate volume is available in nearly all men considering repeat biopsy because volume is measured at the time of initial biopsy or by MRI, which is guideline-recommended before repeat biopsy.5 As such, the MPS2 model including prostate volume allowed for avoidance of 53% of unnecessary biopsies while missing only 6.8% of GG ≥ 2 cancers (94% sensitivity, 53% specificity, 96% NPV). These findings compare very favorably with published ExoDx Prostate Intelliscore data using the validated threshold of 15.6, which allowed for avoidance of 27% of unnecessary biopsies while missing 18% of GG ≥ 2 cancers (82% sensitivity, 27% specificity, 92% NPV), and findings using an adjusted threshold of 20 (82% sensitivity, 37% specificity, 94% NPV).28 While cross-study comparisons must be made with caution, and predictive values depend on cohort-specific prevalence, sensitivity and specificity are not cohort-dependent metrics.11 Finally, in contrast to the unitless number value returned by existing assays, MPS2 provides each patient’s individualized probability of detecting GG ≥ 2 cancer. This allows for meaningful interpretation and discussion of risk, particularly in patients with results near the threshold value.

Biomarker tests and MRI have both been shown to increase specificity for higher-grade, clinically significant cancers, and the optimal use of these tools has been explored in light of both performance and practicality. Several groups have described the merits of a diagnostic algorithm using biomarkers first—to effectively triage the number of men referred for MRI—followed by MRI to enhance the yield of biopsy. In addition to practical advantages, such an approach leverages the strengths of each test: the high rule-out ability of biomarkers, and the strong positive predictive value (PPV) and lesion-targeting of MRI. Initial data from the ProScreen trial, which used a 4-kallikrein biomarker test to select patients for MRI and possible biopsy, appear to support the proposed advantages of a biomarker-first testing approach.10 Compared with initial findings from the European Randomized Study of Screening for Prostate Cancer,29 in which all men with elevated PSA underwent biopsy, ProScreen reduced the proportion of patients undergoing biopsy (3% vs 22%) while achieving comparable detection of GG ≥ 2 disease (1.7% vs 1.8%). Promisingly, findings from our limited subgroup of patients with MPS2 and MRI suggest that combining these modalities provides strong rule-out and rule-in performance.

There are real and perceived limitations of this study. First, only a limited proportion of patients underwent prebiopsy MRI. As a result, the reference standard in most cases was systematic biopsy, which could result in undersampling and increased NPV relative to surgical pathology.30 At the same time, undersampling would reduce PPV, meaning that this limitation would have a balanced overall impact on test performance. Consistent with the ProScreen trial, the current analysis aimed to evaluate MPS2 as a first-line test after PSA to avoid the use of MRI and biopsy. Thus, data on the combined use of MPS2 and MRI were limited and may not reflect the overall at-risk population. Moreover, there were limited data describing the repeat biopsy population, African American men, and patients with a suspicious DRE, and each of these subgroups merits further evaluation. These limitations notwithstanding the current analysis demonstrates high clinical accuracy of the MPS2 urinary assay performed without DRE.

CONCLUSIONS

This study validated the 18-gene MPS2 assay in urine specimens obtained without DRE. In a clinically pertinent testing population, MPS2 provided meaningful improvements in detection of GG ≥ 2 cancer relative to PSA-based testing approaches. Importantly, improved diagnostic performance was observed in the baseline MPS2 model based on biomarker expression only, with further improvements observed with inclusion of optional clinical data. As such, MPS2 seems to provide a convenient, versatile, and highly accurate testing option to inform the need for MRI or biopsy in patients with elevated PSA.

REFERENCES

Recusal: Drs Tosoian and Salami are on the reviewing board of The Journal of Urology® and were recused from the editorial and peer review processes.

Funding/Support: This study was funded by the Michigan-Vanderbilt Early Detection Research Network Biomarker Characterization Center (grant U2C CA271854) and a Prostate Cancer Foundation Young Investigator Award (20YOUN11, Dr Tosoian). Other sources of funding not directly involved in the conduct of this study included the Michigan Prostate Specialized Program of Research Excellence (grant P50 CA186786), an NIA training grant to the Population Studies Center at the University of Michigan (T32AG000221, Dr Chopra), National Cancer Institute Outstanding Investigator Award (Dr Chinnaiyan; grant R35 CA231996), Prostate Cancer Foundation Young Investigator Award (Dr Xiao), Prostate Cancer Foundation, Howard Hughes Medical Institute (Dr Chinnaiyan), and the American Cancer Society (Dr Chinnaiyan). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Conflict of Interest Disclosures: Lynx Dx has obtained an exclusive license from the University of Michigan to commercialize MPS2 and the TMPRSS2-ERG gene fusion. Drs Zhang, Siddiqui, and Xiao reported receiving consulting fees from Lynx Dx. Drs Meyers and Heaton reported being employees of Lynx Dx. Dr Barocas reported being on the advisory board for Pfizer, Lantheus, Lynx Dx, and Pacific Edge. Dr Ross reported being a consultant or speaker for Astellas, Blue Earth, Bayer, Janssen, Pfizer, BilliontoOne, Lantheus, Veracyte, and Boston Scientific. Dr Salami reported being on the advisory committee for Bayer Pharma. Dr Morgan reported being on the advisory board for Cleveland Diagnostics. Dr Palapattu reported being on the advisory board for Continuity Biosciences, SynDevRx, Taurus Diagnostics, and Lynx Dx, and investment in Continuity Biosciences and ImmunityBio. Dr Chinnaiyan reported serving on the advisory boards of Tempus, Ascentage Pharmaceuticals, Medsyn therapeutics, Esanik, and RAAPTA therapeutics. Drs Tosoian and Chinnaiyan reported being equity holders and scientific advisors to Lynx Dx. No other conflicts of interest were reported.

Ethics Statement: This study received Institutional Review Board approval (IRB No. HUM00042749). All human subjects provided written informed consent with guarantees of confidentiality.

Author Contributions:

Conception and design: Chinnaiyan, Meyers, Tosoian, Salami, Heaton.

Data analysis and interpretation: Chinnaiyan, Ross, Barocas, Palapattu, Meyers, Tosoian, Assani, Xiao, Salami, Heaton, Morgan, Singhal, Zhang.

Data acquisition: Herron, Edelson, Meyers, Tosoian, Wei, Xiao, Graham, Salami, Chopra.

Drafting the manuscript: Meyers, Siddiqui, Tosoian, Heaton.

Critical revision of the manuscript for scientific and factual content: Chinnaiyan, Ross, Barocas, Palapattu, Herron, Edelson, Meyers, Siddiqui, Tosoian, Wei, Assani, Xiao, Graham, Salami, Heaton, Morgan, Singhal, Zhang, Chopra.

Supervision: Chinnaiyan, Ross, Barocas, Palapattu, Herron, Edelson, Meyers, Tosoian, Wei, Assani, Xiao, Salami, Heaton, Morgan, Chopra.

Statistical analysis: Meyers, Tosoian, Zhang.

Samples procurement, data acquisition: Siddiqui.

Data management and integrity: Singhal, Graham, Chopra.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
© 2025 The Author(s). Published on behalf of the American Urological Association, Education and Research, Inc.