Validation of diagnostic nomograms based on CE–MS urinary biomarkers to detect clinically significant prostate cancer

Purpose Prostate cancer (PCa) is one of the most common cancers and one of the leading causes of death worldwide. Thus, one major issue in PCa research is to accurately distinguish between indolent and clinically significant (csPCa) to reduce overdiagnosis and overtreatment. In this study, we aim to validate the usefulness of diagnostic nomograms (DN) to detect csPCa, based on previously published urinary biomarkers. Methods Capillary electrophoresis/mass spectrometry was employed to validate a previously published biomarker model based on 19 urinary peptides specific for csPCa. Added value of the 19-biomarker (BM) model was assessed in diagnostic nomograms including prostate-specific antigen (PSA), PSA density and the risk calculator from the European Randomized Study of Screening. For this purpose, urine samples from 147 PCa patients were collected prior to prostate biopsy and before performing digital rectal examination (DRE). The 19-BM score was estimated via a support vector machine-based software based on the pre-defined cutoff criterion of − 0.07. DNs were subsequently developed to assess added value of integrative diagnostics. Results Independent validation of the 19-BM resulted in an 87% sensitivity and 65% specificity, with an AUC of 0.81, outperforming PSA (AUC PSA: 0.64), PSA density (AUC PSAD: 0.64) and ERSPC-3/4 risk calculator (0.67). Integration of 19-BM with the rest clinical variables into distinct DN, resulted in improved (AUC range: 0.82–0.88) but not significantly better performances over 19-BM alone. Conclusion 19-BM alone or upon integration with clinical variables into DN, might be useful for detecting csPCa by decreasing the number of biopsies. Supplementary Information The online version contains supplementary material available at 10.1007/s00345-022-04077-1.


Introduction
Prostate cancer (PCa) ranks as the second most frequent and the fifth leading cause of cancer death among men [1]. In 2020, almost 1.4 million new cases were diagnosed worldwide, and 375 000 deaths were reported due to PCa. Although this malignancy is diagnosed in 15-20% of men, the lifetime risk of death is significantly lower (3%) [2]. For patients presenting with slow growing PCa, defined according to the European Association of Urology (EAU) guidelines as clinically insignificant cancer (insPCa: Gleason score, GS < 7 and PSA < 10 ng/ml and max 2 cores) [3], immediate treatment is not recommended, rather management by active surveillance (AS) including re-biopsies in certain time frames [3].
Screening for PCa is currently based on serum prostatespecific antigen (PSA) testing and digital rectal examination (DRE). However, multiple factors not related to prostate malignancy may affect the level of serum PSA [4]; thus, less than half of patients with elevated PSA are consequently confirmed with PCa [5]. Therefore, intensive PSA screening has led to the identification of numerous insPCa often associated with overtreatment. Definitive diagnosis of PCa is based on the histopathological confirmation of PCa in biopsy cores, following a positive result of DRE and/or high PSA levels [3]. Until recently, the procedure was guided only by transrectal ultrasound (TRUS) [6]. In an effort to improve the accuracy for PCa detection, multiparametric magnetic resonance imaging (mpMRI) has been recently adopted, resulting in good sensitivity for detecting GS ≥ 3 + 4 (sensitivity of 91%, specificity of 37%) [7]. Nevertheless, mpMRI is less sensitive for GS < 3 + 4 (sensitivity of 70%, specificity of 27%) [7]. Moreover, while mpMRI is beneficial particularly for guiding repeated biopsy [8], inter-reader variability among radiologists as well as the limited capacity to perform a high number of MRI-guided procedures remain significant challenges [9].
High-throughput -omics technologies allow for simultaneous acquisition of thousands of features and a better definition of molecular pathophysiology in cancer [10]. In the context of PCa, various candidate biomarkers have been described [11] with single biomarkers frequently lacking diagnostic accuracy for routine clinical application [12]. In this context, high-resolution urinary proteomics profiles from > 800 patients had been previously acquired by capillary electrophoresis coupled to mass spectrometry (CE-MS). Subsequently, proteomics patterns that were developed using machine learning algorithms in a form of a 19-biomarker model  were developed to discriminate csPCa (GS ≥ 7) from slow-progressing PCa in patients with low PSA levels (< 15 ng/ml) [13]. Based on the previously published data [13], the 19-BM resulted in a 90% sensitivity and 59% specificity with an AUC of 0.81, outperforming PSA (AUC PSA : 0.58) and the ERSPC-3/4 risk calculator (AUC ERSPC : 0.69). Moreover, based on a first investigation, integration of the CE-MS biomarkers with other variables such as PSA and age showed an increased performance (AUC: 0.83), demonstrating a level of complementarity between the tests. Considering this evidence, in this study, the aim was to validate the previously established CE-MS-based 19-BM, and additionally investigate whether integrative models can lead to improved non-invasive and more accurate discrimination between insPCa and csPCa.

Materials and methods
This study was performed according to the REMARK Reporting Recommendations [14] and the recommendations for biomarker identification and reporting in clinical proteomics [15], including 148 patients who underwent a transrectal ultrasound (TRUS)-guided prostate biopsy based on suspicion for PCa [16] at the Department of Urology of Medical University of Innsbruck, between 2005 and 2012. Sample collection and processing were approved by the local Ethics Committee at Innsbruck Medical University (reference number: 11438/2017) and informed consent was obtained from all participants. Of the 148 patients for whom biopsy results confirmed presence of adenocarcinoma of prostate, one patient was excluded as the PSA measurement was missing. A schematic representation of the study design is presented in Supplementary Figure. D'Amico classification utilizing Gleason Score (GS), PSA criteria [3,17] and T-stage were applied to classify the PCa patients into risk groups (low, intermediate and high).
At the time of patient enrollment, mpMRI-guided biopsy was not yet implemented in the clinical practice, therefore, all patients underwent TRUS-guided biopsy and 15-biopsy cores were obtained. Full clinical and laboratory data were collected and are presented in the Supplementary Table S1. The European Randomised Study of Screening for Prostate Cancer (ERSPC) estimates for risk stratification were calculated as previously described [18], considering serum PSA levels, the DRE result and information about the previous biopsies (https:// www. prost ateca ncer-riskc alcul ator. com). The patient cohort characteristics are summarized in Table 1.

Urine collection and mass spectrometry analysis
All urine samples were collected prior to prostate biopsy according to clinical guidelines and without performing DRE before biopsy. Voided urine samples were collected in sterile containers and immediately stored at -20 ℃ until further processing. Sample preparation and mass spectrometry analysis was performed as described in detail in Supplementary Text.

Statistical evaluation of model predictivity
The proportion, mean, standard deviation, median and interquartile range (25-75th percentiles) estimates were calculated to describe the distribution of the different variables in the patient cohort (summarized in Table 1). The biomarker model's scores were calculated via the support vector machine (SVM)-based software, namely MosaCluster (version1.7.0), as previously described [19]. A detailed description is given in Supplementary Text and the list of scoring data is presented in Supplementary Table S2.

Comparison of 19-BM with the ERSPC clinical risk calculator
In order to investigate if the 19-BM can improve on the current state-of-the-art clinical prognosticators, the SVM-based scores from 19-BM were further compared with the estimates of ERSPC risk calculator for detecting high risk PCa (ERSPC-3/4), as presented in Fig. 2C. Based on the available clinical data for the PSA levels, the DRE result and accounting also for the previous biopsies, ERSPC-3/4 estimates for

Discussion
Accurate and frequent monitoring is required to detect clinically relevant disease in a timely fashion. Routine monitoring is commonly based on either PSA levels in addition to regular mpMRI and invasive re-biopsies. To delay or even avoid unnecessary biopsies, overdiagnosis and overtreatment, biomarkers to guide biopsy are of value. -omics based studies have been published reporting on discriminatory features of PCa biopsy outcome to guide prostate biopsy [20,21] Using CE-MS proteomics, a biomarker model based on 19 urinary peptides (19BM) was established with the aim to accurately detect csPCa and validated in 823 patients suspicious for PCa (reporting an AUC of 0.81, outperforming PSA and ERPSC) [13]. To investigate if improved performance is reported upon combination of 19-BM with current state-of-the-art risk calculators, several integrative diagnostics strategies were employed to develop DN including different combinations of 19-BM with the significant clinical variables such as PSA, PSAd, age, ERSPC. In all above comparisons, the integrative DNs were demonstrating improved performance, an observation which is in line with previous evidence for a level of complementarity of the diagnostics assays. Yet, in all comparisons, the performance of the multimodal DNs was not significantly better than the 19-BM alone.
Considering the above scientific evidence, the very high NPV (> 90%), as well as the fact that for the 19-BM test, there is no need for performing a DRE before urine collection, the specific clinical impact of such a non-invasive test like 19-BM or a DN based on 19-BM, would primarily be to guide biopsy and eventually reduce the number of invasive biopsies in primary PCa diagnosis PATHWAY?. The required high sensitivity for accurate detection of csPCa was achieved in this study. Upon potential application of such a test and in view of a positive test, urologists are alerted to perform a more thorough investigation, improving the overall accuracy in detection of csPCa. Lower specificity would likely result in more misclassifications of an insPCa form as a csPCa. As a result, a positive result based on 19-BM or a DN based on 19-BM should be complemented by MRI to rule out csPCa.
Considering the literature, several biomarkers have been tested in order to discriminate csPCa, such as 4 K score test, PHI, PCA3, SelectMDx) [22,23]. The PCA3 urinary assay demonstrated 67% sensitivity and 83% specificity for detecting PCa [24]. In comparison to the PHI in guiding initial and repeated biopsy, the PCA3 assay performed slightly, but not significantly inferiorly in both the initial (AUC PCA3 : 0.57; AUC PHI : 0.69) and the repeated biopsy setting (AUC PCA3 : 0.63; AUC PHI : 0.72) [25]. SelectMDx assay demonstrated an AUC of 0.73 [26], while Mi-Prostate Score resulted in an AUC of 0.76 for detection of PCa [27]. The validation results shown in this study with an AUC > 0.80 and > 0.90 for the integrative nomogram are higher than the range 0.57-0.73 which is shown by other biomarkers and clearly justify implementation of this approach in a future investigative setting. Moreover, for many urinary biomarkers, performing a DRE is crucial as it increases the excretion of fluid from the prostate. Yet, for the 19-BM test, a DRE is not a prerequisite for the analysis. Yet, the study also presents with certain limitations. First, a direct comparison with the above biomarkers reported in the literature was unfortunately not possible, as paired data were not available. Moreover, this study was performed retrospectively, however, on samples that were prospectively collected. Nevertheless, based on the data presented, implementation in an investigative setting seems to be highly justified. As another limitation, also pMRI data were not available for this patient cohort. 43 out of 104 patients in our study had previous biopsies. However, the samples were collected between 2005 and 2012, at the time when mpMRI was not an established standard in primary or repeated biopsies. To facilitate comparisons and inclusion of multiparametric MRI, validation in a future prospective setting is planned.
Collectively, the data presented in this study could demonstrate the utility of a multimodal approach for improved non-invasive detection of significant PCa. Considering the high NPV, the clinical utility of the presented nomogram could also be potentially investigated in the context of guiding mpMRI.