Prostate Cancer Detection Using Urine Glycoproteins
Overview
In a recent study, researchers investigated the potential of using pre-digital rectal examination (DRE) urine specimens to detect aggressive prostate cancer (AG-PCa) by comparing glycoproteins found in pre- and post-DRE urine samples. The study involved 154 pre-DRE urine specimens from individuals with no cancer at biopsy, those with non-AG-PCa (Gleason score = 6), and those with AG-PCa (Gleason score 7 or above).
The analysis revealed distinct differences in glycopeptides between pre- and post-DRE urine samples. Pre-DRE urine samples displayed an enrichment of humoral immunity-related proteins, while post-DRE urine samples exhibited an enrichment of cell-mediated immune response proteins. Furthermore, the investigation into AG-PCa-associated glycoproteins in pre-DRE urine identified three known urinary glycoproteins associated with AG-PCa: prostate-specific antigen (PSA), prostatic acid phosphatase (ACPP), and CD97 antigen (CD97), which had previously been identified in post-DRE urine samples.
Additionally, the study unveiled three new potential AG-PCa-associated glycoproteins in pre-DRE urine specimens: fibrillin 1 (FBN1), vitronectin (VTN), and hemicentin 2 (HMCN2). These findings suggest that glycoprotein profiles differ between pre- and post-DRE urine samples, and the identified AG-PCa-associated glycoproteins, both known and new, hold promise for further evaluation in a larger cohort of pre-DRE urine specimens to improve the detection of clinically significant prostate cancer.
Introduction
In 2022, prostate cancer (PCa) ranked as the most frequently diagnosed cancer among males, with 268,490 new cases reported annually in the United States. This number represented a substantial increase of approximately 20,000 cases compared to the previous year. Furthermore, PCa stands as the second leading cause of death in the male population in the United States, with 34,500 reported deaths in 2021.
It’s worth noting that around 80% of males diagnosed with PCa have a low risk of cancer progression, posing limited threat to their lives, even without clinical intervention. These individuals are estimated to have a life expectancy of about 10 years. The critical challenge lies in monitoring the development of low-risk PCa, also referred to as indolent or nonaggressive PCa (NAG-PCa), to prevent its progression into aggressive PCa (AG-PCa). Such monitoring is vital for enhancing the quality of life for patients and reducing mortality rates. However, the inherent unpredictability of clinical PCa diagnostic tests has hindered accurate prognosis for many years.
Hence, there is a pressing need to identify biomarkers applicable at the earliest stages of PCa that can predict the trajectory from indolent NAG-PCa to AG-PCa. Cancer-related proteins found in body fluids are valuable for not only determining the disease’s origin but also reflecting disease-specific changes indicative of its state. Additionally, the accessibility of these proteins in body fluids makes them promising candidates for noninvasive cancer diagnostics. Most notably, glycoproteins, which include secreted proteins, transmembrane proteins, and cell surface proteins, constitute a significant portion of extracellular proteins. Consequently, they play a pivotal role in cancer diagnostics, with a majority of proteins approved by the US Food and Drug Administration as tumor markers falling within this category.
To identify novel biomarkers associated with both AG and non-AG-PCa, biomarker candidates were selected or screened based on glycoproteomics data derived from AG and NAG-PCa tissues. This approach led to the discovery of several glycoproteins linked to AG-PCa, representing a significant breakthrough in the analysis of extracellular or secreted glycoproteins.
When compared to blood, urine stands as an ideal biospecimen for noninvasive biomarker discovery in the case of PCa. Blood samples dilute secretory proteins from cancer tissues due to their mixing with proteins from various other tissues and organs, reducing the sensitivity and specificity of PCa-specific protein detection. Moreover, the anatomical location of the prostate gland within the genitourinary system offers advantages for detecting disease-related tumor cells, DNA, RNA, and proteins in urine.
Urine-related proteins, as proxies for the early detection of AG-PCa, present an ideal avenue for biomarker development. This is due to the ease with which urine can be rapidly, inexpensively, and noninvasively obtained. Notably, vesicles secreted by the prostate and other urological organs may be released into urine, further enhancing its suitability for diagnostic purposes.
Preliminary studies involving prostate-specific antigen (PSA) in PCa tissues, sera, and urine samples from AG and NAG-PCa patients indicated that urinary proteins serve as better surrogates for prostate tissues than serum markers. These findings were substantiated by various analyses, demonstrating that urine holds potential as a reliable proxy for proteins originating from PCa.
Recent research efforts applied quantitative global proteomics and glycoproteomics to evaluate urine samples from post-digital rectal examination (DRE) urine specimens. This analysis identified 13 candidate glycoproteins significantly associated with AG-PCa, including prostatic acid phosphatase (ACPP), PSA, clusterin (CLU), and CD97 antigen (CD97).
However, it’s essential to note that the previous studies on AG-PCa-associated glycoprotein biomarkers were conducted using post-DRE urine specimens, which involve an invasive procedure. To facilitate broader applications and the development of a convenient self-test for the urinary detection of AG-PCa, it is imperative to evaluate biomarkers from pre-DRE urine samples.
In the current study, an automated workflow was employed for urinary protein isolation and glycopeptide enrichment in both pre-DRE and post-DRE urine samples. The study aimed to achieve three objectives: (1) ascertain the differences in glycoproteins between pre- and post-DRE urine samples, (2) investigate the clinical utility of candidate glycoproteins identified from post-DRE urine in detecting AG-PCa in pre-DRE urine, and (3) identify AG-PCa-associated glycoproteins specifically in pre-DRE urine samples. This research represents a significant step towards advancing the noninvasive diagnosis and monitoring of AG-PCa.
Method
This study employed two clinical cohorts, each containing distinct sets of urine samples. The first cohort consisted of pre-digital rectal examination (pre-DRE) urine samples, comprising 154 archived first-catch non-DRE urine specimens. Among these, 41 individuals had no evidence of cancer upon biopsy, while 113 individuals were diagnosed with prostate cancer (PCa) and had a Gleason score of 6 or higher. These samples were collected under an institutional review board-approved protocol from patients undergoing diagnostic biopsy. The urine samples were collected from men suspected of having PCa before their diagnostic biopsies. The collected urine was treated with a preservative and processed through centrifugation to eliminate cell debris. The resulting supernatant was divided into 5 ml aliquots and stored at -80°C.
The second cohort, post-DRE urine samples, included 292 specimens from individuals with PCa with a Gleason score of 6 or higher. These samples were collected by the Department of Urology at Johns Hopkins University School of Medicine and had been previously studied.
In addition to these two cohorts, the study also analyzed urine samples from pre-DRE specimens, which were matched with post-DRE samples collected from 14 patients. For the analysis, various chemicals and reagents, including Sep-PAK C18, Oasis MAX resins, Lys-C, trypsin, and PNGase F, were employed. Synthetic heavy peptides were added as spike-in markers.
An automated workflow was used for the digestion of human urine specimens by proteases. This process involved desalting, protease digestion, and intact glycopeptide enrichment. Subsequently, PNGase F was utilized to remove N-linked glycans from glycopeptides, as per a previously published experimental workflow.
The N-linked glycopeptides from pre-DRE urine samples were enriched, and synthetic heavy peptides were spiked into each sample for mass spectrometry analysis. The LC-MS/MS analysis was conducted using DIA-MS on an Orbitrap Exploris 480 MS, coupled with an EASY-nLC 1200 system for peptide separation.
The urinary PSA analysis was carried out at the EDRN Biomarker Reference Laboratory at Johns Hopkins University, using the Beckman Coulter Access 2 Immunoassay Analyzer.
The DIA data analysis involved Spectronaut with a precursor and protein q-value cutoff of 1%. Database searches were performed with specific parameters, including missed cleavages, modifications, and quantification approaches. The database for directDIA searches included an iRT fusion protein and human proteins. The relative abundances of glycopeptides from each urine sample were normalized for comparison.
In summary, this study focused on the analysis of glycoproteins in urine samples from pre- and post-DRE cohorts to identify markers associated with aggressive prostate cancer. The research aimed to improve the noninvasive detection and monitoring of aggressive prostate cancer using urine specimens collected before and after digital rectal examination.
Statistical Analysis
The study employed logistic regression to assess the ability to distinguish between two sample groups, specifically biopsy-negative and prostate cancer (PCa), using urinary markers and multi-urinary marker panels. To evaluate this discrimination, receiver operating characteristic (ROC) analysis was conducted. The data underwent preprocessing, which included log transformation and z-scoring. In cases of missing data, imputation was carried out based on Gleason group or biopsy-negative status.
To ensure the statistical stability of the results, bootstrap resampling was employed, with 500 iterations, on the dataset for constructing and evaluating predictive models involving the presented markers and marker panels. The mean ROC curves were derived from the results of these bootstrap resampling iterations, and the area under the curve (AUC) was computed for the mean ROC curve. All of these analyses were conducted in R (version 3.5). The predictive models were developed using the caret package (version 6.0-85), while ROC curves and AUCs were generated with the pROC package (version 1.13).
In essence, the study utilized logistic regression and ROC analysis to assess the performance of urinary markers and marker panels in differentiating between biopsy-negative and PCa sample groups. Bootstrap resampling was used to enhance the statistical reliability of the results, and the AUC served as a metric to quantify the discriminative power of the models.
Result
This study aimed to investigate the differences in urinary glycoprotein profiles between pre- and post-digital rectal examination (DRE) urine samples for the detection of aggressive prostate cancer (AG-PCa). Two sample sets were analyzed, each consisting of more than 40 urine specimens from AG-PCa patients (Gleason score ≥7) and non-aggressive PCa (NAG-PCa) patients (Gleason score ≤6). The glycoproteomic analysis revealed variations in the detected glycopeptides between pre- and post-DRE urine samples.
In pre-DRE samples, glycoproteins associated with humoral immunity-related processes were prevalent, while post-DRE samples were characterized by cell-mediated immune response-related enzymes. The differences in glycopeptide abundances between the two sets were notable. Additionally, matched pre- and post-DRE urine samples from the same patients were analyzed, further confirming these distinctions. Urinary prostate-specific antigen (PSA) and prostatic acid phosphatase (ACPP) levels were examined and showed higher intensities in post-DRE samples compared to pre-DRE samples.
The clinical utility of post-DRE urinary glycoprotein biomarkers, such as ACPP and CD97, for detecting AG-PCa in pre-DRE samples was evaluated. ACPP, CD97, and urinary PSA were found to be useful for discriminating AG-PCa from biopsy-negative and Gleason score 6 groups in pre-DRE urine samples, albeit with relatively small fold-changes.
Additionally, new AG-PCa-associated glycoproteins were identified in pre-DRE urine samples. Three glycopeptides, from fibrillin 1 (FBN1), hemicentin 2 (HMCN2), and vitronectin (VTN), were found to be associated with AG-PCa in pre-DRE urine samples, each with varying levels of abundance.
Furthermore, the study explored multi-urinary marker panels for AG-PCa detection in pre-DRE urine, aiming to develop a potential home kit for future clinical use. A panel comprising glycopeptides from FBN1, VTN, CD97, and urinary PSA showed a promising AUC of 0.77 for distinguishing AG-PCa from biopsy-negative and Gleason score 6 cases. Another panel incorporating glycopeptides from VTN, HMCN2, CD97, and urinary PSA also demonstrated improved performance with an AUC of 0.78.
In conclusion, this study highlighted the differences in glycoprotein profiles between pre- and post-DRE urine samples and suggested the potential of urinary glycoproteins, both individual markers and multi-marker panels, for non-invasive AG-PCa detection in pre-DRE urine samples, holding promise for future clinical applications.
Conclusion
This study aimed to investigate the glycoprotein profiles in pre- and post-digital rectal examination (DRE) urine samples to assess their utility for the detection of aggressive prostate cancer (AG-PCa) and potential home-based testing. The analysis of 154 pre-DRE and 292 post-DRE urine specimens revealed distinct protein profiles. Pre-DRE samples contained humoral immunity-related proteins, which may be linked to the urinary tract’s innate immune system, offering protection against infections. Post-DRE samples were enriched with prostatic secretions, including immune cell response proteins.
Previously identified AG-PCa-associated glycoproteins from post-DRE samples, such as PSA, ACPP, and CD97, retained their discriminatory potential in pre-DRE urine samples. However, immune cell-related glycoproteins displayed weaker performance in the pre-DRE setting. The study also identified novel AG-PCa-associated glycoproteins specific to pre-DRE urine samples, including FBN1, CD97, VTN, and HMCN2. These glycoproteins showed good performance in AG-PCa detection and may have diagnostic potential.
The research underscores the differences in glycoprotein profiles between pre- and post-DRE urine samples and the feasibility of identifying AG-PCa-associated glycoproteins in pre-DRE samples. The study suggests the use of multi-marker panels for AG-PCa detection, with promising results, though the limited sample size is a constraint. Future research plans include validating these findings in larger patient cohorts, conducting multicenter validation, and exploring home-based testing using pre-DRE urinary glycoproteins.
In conclusion, this study represents an initial exploration of protein differences between pre- and post-DRE urine samples and the discovery of urinary glycoprotein changes associated with AG-PCa. Further validation and systematic evaluation of pre-DRE urinary glycoproteins are essential for the development of home-based testing and early-stage AG-PCa detection.
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