Validation of a 2-gene mRNA urine test for the detection of ≥GG2 prostate cancer in an opportunistic screening population

Risk calculators for the detection of prostate cancer: a systematic review

BACKGROUND

Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm.

METHODS

We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men.

RESULTS

We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93.

CONCLUSIONS

This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.

Increased Density of Growth Differentiation Factor-15+ Immunoreactive M1/M2 Macrophages in Prostate Cancer of Different Gleason Scores Compared with Benign Prostate Hyperplasia

Simple Summary

Prostate cancer (PCa) is the second most diagnosed cancer and cause of death in men worldwide. The main challenge is to discover biomarkers for malignancy to guide the physician towards optimized diagnosis and therapy. There is recent evidence that growth differentiation factor-15 (GDF-15) is elevated in cancer patients. Therefore, we aimed to decipher GDF-15+ cell types and their density in biopsies of human PCa patients with Gleason score (GS)6–9 and benign prostate hyperplasia (BPH). Here we show that the density of GDF-15+ cells, mainly identified as interstitial macrophages (MΦ), was higher in GS6–9 than in BPH, and, thus, GDF-15 is intended to differentiate patients with high GS vs. BPH, as well as GS6 vs. GS7 (or even with higher malignancy). Some GDF-15+ MΦ showed a transepithelial migration into the glandular lumen and, thus, might be used for measurement in urine/semen. Taken together, GDF-15 is proposed as a novel tool to diagnose PCa vs. BPH or malignancy (GS6 vs. higher GS) and as a potential target for anti-tumor therapy. GDF-15 in seminal plasma and/or urine could be utilized as a non-invasive biomarker of PCa as compared to BPH.

Abstract

Although growth differentiation factor-15 (GDF-15) is highly expressed in PCa, its role in the development and progression of PCa is unclear. The present study aims to determine the density of GDF-15+ cells and immune cells (M1-/M2 macrophages [MΦ], lymphocytes) in PCa of different Gleason scores (GS) compared to BPH. Immunohistochemistry and double immunofluorescence were performed on paraffin-embedded human PCa and BPH biopsies with antibodies directed against GDF-15, CD68 (M1 MΦ), CD163 (M2 MΦ), CD4, CD8, CD19 (T /B lymphocytes), or PD-L1. PGP9.5 served as a marker for innervation and neuroendocrine cells. GDF-15+ cell density was higher in all GS than in BPH. CD68+ MΦ density in GS9 and CD163+ MΦ exceeded that in BPH. GDF-15+ cell density correlated significantly positively with CD68+ or CD163+ MΦ density in extratumoral areas. Double immunoreactive GDF-15+/CD68+ cells were found as transepithelial migrating MΦ. Stromal CD68+ MΦ lacked GDF-15+. The area of PGP9.5+ innervation was higher in GS9 than in BPH. PGP9.5+ cells, occasionally copositive for GDF-15+, also occurred in the glandular epithelium. In GS6, but not in BPH, GDF-15+, PD-L1+, and CD68+ cells were found in epithelium within luminal excrescences. The degree of extra-/intra-tumoral GDF-15 increases in M1/M2Φ is proposed to be useful to stratify progredient malignancy of PCa. GDF-15 is a potential target for anti-tumor therapy.

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

BACKGROUND

We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user.

METHODS

Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach.

RESULTS

Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.

CONCLUSION

Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

A Stepwise Algorithm for Linearly Combining Biomarkers under Youden Index Maximization

Combining multiple biomarkers to provide predictive models with a greater discriminatory ability is a discipline that has received attention in recent years. Choosing the probability threshold that corresponds to the highest combined marker accuracy is key in disease diagnosis. The Youden index is a statistical metric that provides an appropriate synthetic index for diagnostic accuracy and a good criterion for choosing a cut-off point to dichotomize a biomarker. In this study, we present a new stepwise algorithm for linearly combining continuous biomarkers to maximize the Youden index. To investigate the performance of our algorithm, we analyzed a wide range of simulated scenarios and compared its performance with that of five other linear combination methods in the literature (a stepwise approach introduced by Yin and Tian, the min-max approach, logistic regression, a parametric approach under multivariate normality and a non-parametric kernel smoothing approach). The obtained results show that our proposed stepwise approach showed similar results to other algorithms in normal simulated scenarios and outperforms all other algorithms in non-normal simulated scenarios. In scenarios of biomarkers with the same means and a different covariance matrix for the diseased and non-diseased population, the min-max approach outperforms the rest. The methods were also applied on two real datasets (to discriminate Duchenne muscular dystrophy and prostate cancer), whose results also showed a higher predictive ability in our algorithm in the prostate cancer database.

Prospective study of diagnostic accuracy in the detection of high-grade prostate cancer in biopsy-naïve patients with clinical suspicion of prostate cancer who underwent the Select MDx test

OBJECTIVES

This study aimed to externally validate the diagnostic accuracy of the Select MDx test for Significant prostate cancer (Sig PCa) (ISUP > 1), in a contemporaneous, prospective, multicenter cohort with a prostate-specific antigen (PSA) between 3 and 10 ng/ml and a non-suspicious digital rectal examination.

METHODS AND PARTICIPANTS

For all enrolled patients, the Select Mdx test, the risk calculator ERSPC3 + DRE, and a prostatic magnetic resonance imaging (MRI) were carried out. Subsequently, a systematic 12-core trans-rectal biopsy and a targeted biopsy, in the case of a prostate imaging-reporting and data system (PIRADS) > 2 lesion (max three lesions), were performed. To assess the accuracy of the Select MDx test in the detection of clinically Sig PCa, the test sensitivity was evaluated. Secondary objectives were specificity, negative predictive value (NPV), positive predictive value (PPV), and area under the curve (AUC). A direct comparison with the ERSPC + DRE risk calculator and MRI were also performed. We also studied the predictive ability to diagnose Sig PCa from the combination of the Select MDx test with MRI using clinical decision-curve analysis.

RESULTS

There were 163 patients enrolled after meeting the inclusion criteria and study protocol. The Select MDx test showed a sensitivity of 76.9% (95% CI, 63.2-87.5), 49.6% specificity (95% CI, 39.9-59.2), 82.09% (95% CI, 70.8-90.4) NPV, and 41.67% (95% CI, 31.7-52.2) PPV for the diagnosis of Sig PCa. COR analysis was also performed, which showed an AUC of 0.63 (95% CI, 0.56-0.71). There were no differences in the accuracy of Select MDx, ERSPC + DRE, or MRI. The combination of Select MDX + MRI showed the highest impact in the decision-curve analysis, with an NPV of 93%.

CONCLUSION

Our study showed a worse performance for the SelectMdx test than previously reported, within a cohort of patients with a PSA 3-10 ng/ml and a normal DRE, with results similar to those from ERSPC + DRE RC and MRI, but with an improvement in the usual PSA pathway. A combination of the Select Mdx test and MRI could improve accuracy, but studies specifically evaluating this scenario with a cost-effective analysis are needed.

Role of PCA3 and SelectMDx in the optimization of active surveillance in prostate cancer

INTRODUCTION & OBJECTIVES

A not negligible percentage of patients included in active surveillance (AS) for low and very low risk prostate cancer (PCa) are reclassified in the confirmatory biopsy or have disease progression during follow-up. Our aim is to evaluate the role of PCA3 and SelectMDx, in an individual and combined way, in the prediction of pathological progression (PP) in a standard AS program.

MATERIALS & METHODS

Prospective and observational study comprised of 86 patients enrolled in an AS program from 2009 to 2019, with results for PCA3 and SelectMDx previous to PCa diagnosis or during their confirmatory period. Univariate and multivariate analysis were performed to correlate PCA3 and SelectMDx scores as well as clinical and pathological variables with PP-free survival (PPFS). The most reliable cut-offs for both biomarkers in the context of AS were defined.

RESULTS

SelectMDx showed statistically significant differences related to PPFS (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. In our series, the most reliable cut-off point for SelectMDx was 5, with a sensitivity and specificity for PP of 69.8% and 67.4%, respectively. Same figure for PCA3 was 65, with a sensitivity and specificity for PP of 51.16% and 74.42%, respectively. The combination of both biomarkers did not improve the prediction of PP, C-index 0.630 (95%CI: 0.455-0.805).

CONCLUSIONS

In the context of low or very low risk PCa, SelectMDx > 5 predicted 5 years PP free survival with a moderate discrimination ability outperforming PCA3. The combination of both tests did not improved outcomes.

Role of PCA3 and SelectMDx in the optimization of active surveillance in prostate cancer

INTRODUCTION AND OBJECTIVES

A not negligible percentage of patients included in active surveillance (AS) for low and very low risk prostate cancer (PCa) are reclassified in the confirmatory biopsy or have disease progression during follow-up. Our aim is to evaluate the role of PCA3 and SelectMDx, in an individual and combined way, in the prediction of pathological progression (PP) in a standard AS program.

MATERIALS AND METHODS

Prospective and observational study comprised of 86 patients enrolled in an AS program from 2009 to 2019, with results for PCA3 and SelectMDx previous to PCa diagnosis or during their confirmatory period. Univariate and multivariate analysis were performed to correlate PCA3 and SelectMDx scores as well as clinical and pathological variables with PP-free survival (PPFS). The most reliable cut-offs for both biomarkers in the context of AS were defined.

RESULTS

SelectMDx showed statistically significant differences related to PPFS (HR: 1.035; 95%CI: 1.012-1.057) (P=.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 5years. In our series, the most reliable cut-off point for SelectMDx was 5, with a sensitivity and specificity for PP of 69.8% and 67.4%, respectively. Same figure for PCA3 was 65, with a sensitivity and specificity for PP of 51.16% and 74.42%, respectively. The combination of both biomarkers did not improve the prediction of PP, C-index 0.630 (95%CI: 0.455-0.805).

CONCLUSIONS

In the context of low or very low risk PCa, SelectMDx >5 predicted 5years PP free survival with a moderate discrimination ability outperforming PCA3. The combination of both tests did not improved outcomes.

SelectMDx and Multiparametric Magnetic Resonance Imaging of the Prostate for Men Undergoing Primary Prostate Biopsy: A Prospective Assessment in a Multi-Institutional Study

Prostate-specific antigen (PSA) testing as the sole indication for prostate biopsy lacks specificity, resulting in overdiagnosis of indolent prostate cancer (PCa) and missing clinically significant PCa (csPCa). SelectMDx is a biomarker-based risk score to assess urinary HOXC6 and DLX1 mRNA expression combined with traditional clinical risk factors. The aim of this prospective multi-institutional study was to evaluate the diagnostic accuracy of SelectMDx and its association with multiparametric magnetic resonance (mpMRI) when predicting PCa in prostate biopsies. Overall, 310 consecutive subjects were included. All patients underwent mpMRI and SelectMDx prior to prostate biopsy. SelectMDx and mpMRI showed sensitivity and specificity of 86.5% vs. 51.9%, and 73.8% vs. 88.3%, respectively, in predicting PCa at biopsy, and 87.1% vs. 61.3%, and 63.7% vs. 83.9%, respectively, in predicting csPCa at biopsy. SelectMDx was revealed to be a good predictor of PCa, while with regards to csPCa detection, it was demonstrated to be less effective, showing results similar to mpMRI. With analysis of strategies assessed to define the best diagnostic strategy to avoid unnecessary biopsy, SelectMDx appeared to be a reliable pathway after an initial negative mpMRI. Thus, biopsy could be proposed for all cases of mpMRI PI-RADS 4-5 score, and to those with Prostate Imaging-Reporting and Data System (PI-RADS) 1-3 score followed by a positive SelectMDx.

Urinary microRNA and mRNA in Tumors

Liquid biopsy is gaining importance in the context of analysis of circulating subcellular components, such as exosomes and nucleic acids, and the investigation of biological fluids is increasing because they express features common to the tissue of origin. Particularly, urine has become one of the most attractive biofluids in clinical practice due to its easy collection approach, its availability of large quantities, and its noninvasiveness. Furthermore, a peculiarity is that, compared to serum or plasma, urine is characterized by a simpler composition that improves isolation and identification of biomarkers. Recent studies have been associated with the investigation of mRNAs and microRNAs as potential noninvasive cancer biomarkers in urine, and to date, several approaches for isolating and measuring urinary nucleic acids have been established, despite still developing. This chapter aims at giving some main published evidences on urinary microRNAs and mRNAs, with the intent to consider their potential translational use in clinical practice.