Urine TMPRSS2:ERG Plus PCA3 for Individualized Prostate Cancer Risk Assessment

Prostate cancer reshapes the secreted and extracellular vesicle urinary proteomes

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Established and emerging liquid biomarkers for prostate cancer detection: A review

Prostate cancer remains one of the most frequently diagnosed cancers among men in the world today. Since its introduction in 1987 and FDA approval in 1994, prostate specific antigen (PSA) has reduced prostate cancer specific mortality considerably. However, the positive and negative predictive value of PSA is less than ideal and can lead to the over-detection of clinically insignificant prostate cancer. In the search for better screening measures to identify this cohort, liquid biomarkers for prostate cancer have emerged. In this review we will explore the commonly used urine and blood based prostate cancer liquid biomarkers. We detail the mechanism of each test and the validation studies that underscore their efficacy. Additionally, we will examine each test's effect on shared decision making as well as their cost efficacy in clinical practice.

[Prostate cancer screening-current overview]

BACKGROUND

The harm-to-benefit ratio of prostate cancer (PCa) screening remains controversial mainly due to the unfavorable test characteristics of prostate-specific antigen (PSA) as a screening test.

METHODS

In this nonsystematic review, we present a current overview of the body of evidence on prostate cancer screening with a focus on the role of magnetic resonance imaging (MRI) of the prostate.

RESULTS

Evidence generated in large randomized controlled trials showed that PSA-based screening significantly decreases cancer-specific mortality. The main obstacle in developing and implementing PCa screening strategies is the resulting overdiagnosis and as a consequence overtreatment of indolent cancers. Opportunistic screening is characterized by an adverse benefit-to-harm ratio and should, therefore, not be recommended. The German Statutory Early Detection Program for prostate cancer, which consists of a digital rectal examination (DRE) as a stand-alone screening test, is not evidence-based, neither specific nor sensitive enough and results in unnecessary diagnostics. The European Commission recently urged member states to develop population-based and organized risk-adapted PSA-based screening programs, which are currently tested in the ongoing German PROBASE trial. Finetuning of the diagnostic pathway following PSA-testing seems key to improve its positive and negative predictive value and thereby making PCa screening more accurate. Incorporation of prostatic MRI into screening strategies leads to more accurate diagnosis of clinically significant prostate cancer, while diagnosis of indolent cancers is reduced. In the future, molecular liquid-based biomarkers have the potential to complement or even replace PSA in PCa screening and further personalize screening strategies. Active surveillance as an alternative to immediate radical therapy of demographically increasing PCa diagnoses can potentially further improve the benefit-to-harm ratio of organized screening.

CONCLUSION

Early detection of PCa should be organized on a population level into personalized and evidence-based screening strategies. Multiparametric MRI of the prostate may play a key role in this setting.

Development and Validation of an 18-Gene Urine Test for High-Grade Prostate Cancer

Importance

Benefits of prostate cancer (PCa) screening with prostate-specific antigen (PSA) alone are largely offset by excess negative biopsies and overdetection of indolent cancers resulting from the poor specificity of PSA for high-grade PCa (ie, grade group [GG] 2 or greater).

Objective

To develop a multiplex urinary panel for high-grade PCa and validate its external performance relative to current guideline-endorsed biomarkers.

Design, Setting, and Participants

RNA sequencing analysis of 58 724 genes identified 54 markers of PCa, including 17 markers uniquely overexpressed by high-grade cancers. Gene expression and clinical factors were modeled in a new urinary test for high-grade PCa (MyProstateScore 2.0 [MPS2]). Optimal models were developed in parallel without prostate volume (MPS2) and with prostate volume (MPS2+). The locked models underwent blinded external validation in a prospective National Cancer Institute trial cohort. Data were collected from January 2008 to December 2020, and data were analyzed from November 2022 to November 2023.

Exposure

Protocolized blood and urine collection and transrectal ultrasound-guided systematic prostate biopsy.

Main Outcomes and Measures

Multiple biomarker tests were assessed in the validation cohort, including serum PSA alone, the Prostate Cancer Prevention Trial risk calculator, and the Prostate Health Index (PHI) as well as derived multiplex 2-gene and 3-gene models, the original 2-gene MPS test, and the 18-gene MPS2 models. Under a testing approach with 95% sensitivity for PCa of GG 2 or greater, measures of diagnostic accuracy and clinical consequences of testing were calculated. Cancers of GG 3 or greater were assessed secondarily.

Results

Of 761 men included in the development cohort, the median (IQR) age was 63 (58-68) years, and the median (IQR) PSA level was 5.6 (4.6-7.2) ng/mL; of 743 men included in the validation cohort, the median (IQR) age was 62 (57-68) years, and the median (IQR) PSA level was 5.6 (4.1-8.0) ng/mL. In the validation cohort, 151 (20.3%) had high-grade PCa on biopsy. Area under the receiver operating characteristic curve values were 0.60 using PSA alone, 0.66 using the risk calculator, 0.77 using PHI, 0.76 using the derived multiplex 2-gene model, 0.72 using the derived multiplex 3-gene model, and 0.74 using the original MPS model compared with 0.81 using the MPS2 model and 0.82 using the MPS2+ model. At 95% sensitivity, the MPS2 model would have reduced unnecessary biopsies performed in the initial biopsy population (range for other tests, 15% to 30%; range for MPS2, 35% to 42%) and repeat biopsy population (range for other tests, 9% to 21%; range for MPS2, 46% to 51%). Across pertinent subgroups, the MPS2 models had negative predictive values of 95% to 99% for cancers of GG 2 or greater and of 99% for cancers of GG 3 or greater.

Conclusions and Relevance

In this study, a new 18-gene PCa test had higher diagnostic accuracy for high-grade PCa relative to existing biomarker tests. Clinically, use of this test would have meaningfully reduced unnecessary biopsies performed while maintaining highly sensitive detection of high-grade cancers. These data support use of this new PCa biomarker test in patients with elevated PSA levels to reduce the potential harms of PCa screening while preserving its long-term benefits.

Liquid Biomarkers in Prostate Cancer Diagnosis: Current Status and Emerging Prospects

Prostate cancer (PCa) is a major health concern that necessitates appropriate diagnostic approaches for timely intervention. This review critically evaluates the role of liquid biopsy techniques, focusing on blood- and urine-based biomarkers, in overcoming the limitations of conventional diagnostic methods. The 4Kscore test and Prostate Health Index have demonstrated efficacy in distinguishing PCa from benign conditions. Urinary biomarker tests such as PCa antigen 3, MyProstateScore, SelectMDx, and ExoDx Prostate IntelliScore test have revolutionized risk stratification and minimized unnecessary biopsies. Emerging biomarkers, including non-coding RNAs, circulating tumor DNA, and prostate-specific antigen (PSA) glycosylation, offer valuable insights into PCa biology, enabling personalized treatment strategies. Advancements in non-invasive liquid biomarkers for PCa diagnosis may facilitate the stratification of patients and avoid unnecessary biopsies, particularly when PSA is in the gray area of 4 to 10 ng/mL.

Fragmentation patterns of cell-free DNA and somatic mutations in the urine of metastatic breast cancer patients

BACKGROUND

Urinary cell-free deoxyribonucleic acid (DNA) (ucfDNA) holds promise as a biomarker; however, its potential remains largely unexplored. We examined the fragmentation pattern of ucfDNA and identified somatic mutations within urine samples from metastatic breast cancer (MBC) patients.

METHODS

Urine and blood specimens were collected before treatment from 45 MBC patients and posttreatment urine samples from 16 of the 45 patients at the China National Cancer Center. Somatic mutations and tumor mutational burden (TMB) in the urine and plasma of 10 patients were analyzed by next-generation sequencing (NGS). Fragmentation patterns of cfDNA were displayed using electropherograms. Differences in the extracted amount of cfDNA, length of cfDNA fragments, and TMB between urine and plasma were compared using a Wilcoxon test.

RESULTS

The fragmentation patterns of ucfDNA were categorized as follows: (1) profile A (n = 26) containing a short peak (100-200 bp) and a long peak (>1500 bp); (2) profile B (n = 8) containing only a long peak; and (3) profile C (n = 11) containing flat pattern. For profile A patients, the short-peaked ucfDNA circulating in the bloodstream was much shorter compared with plasma cfDNA (149 vs. 171 bp, Wilcoxon test, P = 0.023). The fragmentation patterns in lung metastasis patients exhibited a higher propensity toward profile C ( P = 0.002). After treatment, 87.5% of the patients exhibited consistent fragmentation patterns. The concordance rate for somatic mutations in the plasma and urine was 30%, and the median TMB of urine and plasma was not significantly different.

CONCLUSIONS

This study established a fragmentation pattern for ucfDNA and detected somatic mutations in the urine of MBC patients. These results suggest the potential application of ucfDNA as a biomarker for MBC.

Prospective comparison of restriction spectrum imaging and non-invasive biomarkers to predict upgrading on active surveillance prostate biopsy

BACKGROUND

Protocol-based active surveillance (AS) biopsies have led to poor compliance. To move to risk-based protocols, more accurate imaging biomarkers are needed to predict upgrading on AS prostate biopsy. We compared restriction spectrum imaging (RSI-MRI) generated signal maps as a biomarker to other available non-invasive biomarkers to predict upgrading or reclassification on an AS biopsy.

METHODS

We prospectively enrolled men on prostate cancer AS undergoing repeat biopsy from January 2016 to June 2019 to obtain an MRI and biomarkers to predict upgrading. Subjects underwent a prostate multiparametric MRI and a short duration, diffusion-weighted enhanced MRI called RSI to generate a restricted signal map along with evaluation of 30 biomarkers (14 clinico-epidemiologic features, 9 molecular biomarkers, and 7 radiologic-associated features). Our primary outcome was upgrading or reclassification on subsequent AS prostate biopsy. Statistical analysis included operating characteristic improvement using AUROC and AUPRC.

RESULTS

The individual biomarker with the highest area under the receiver operator characteristic curve (AUC) was RSI-MRI (AUC = 0.84; 95% CI: 0.71-0.96). The best non-imaging biomarker was prostate volume-corrected Prostate Health Index density (PHI, AUC = 0.68; 95% CI: 0.53-0.82). Non-imaging biomarkers had a negligible effect on predicting upgrading at the next biopsy but did improve predictions of overall time to progression in AS.

CONCLUSIONS

RSI-MRI, PIRADS, and PHI could improve the predictive ability to detect upgrading in AS. The strongest predictor of clinically significant prostate cancer on AS biopsy was RSI-MRI signal output.

Non-coding RNAs in disease: from mechanisms to therapeutics

Non-coding RNAs (ncRNAs) are a heterogeneous group of transcripts that, by definition, are not translated into proteins. Since their discovery, ncRNAs have emerged as important regulators of multiple biological functions across a range of cell types and tissues, and their dysregulation has been implicated in disease. Notably, much research has focused on the link between microRNAs (miRNAs) and human cancers, although other ncRNAs, such as long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), are also emerging as relevant contributors to human disease. In this Review, we summarize our current understanding of the roles of miRNAs, lncRNAs and circRNAs in cancer and other major human diseases, notably cardiovascular, neurological and infectious diseases. Further, we discuss the potential use of ncRNAs as biomarkers of disease and as therapeutic targets.

Prostate tumor markers: diagnosis, prognosis and management

Prostate cancer (PCA) is the second most common type of cancer in the world. Nevertheless, diagnosis is still based on nonspecific methods, or invasive methods which makes clinical decision and diagnosis difficult, generating risk of both underdiagnosis and overdiagnosis. Given the high prevalence, morbidity and mortality of PCA, new strategies are needed for its diagnosis. A review of the literature on available biomarkers for PCA was performed, using the following terms: prostate cancer AND marker OR biomarker. The search was carried out in Pubmed, Science Direct, Web of Science and Clinical Trial. A total of 35 articles were used, and PHI (Prostate Health Index) and the 4Kscore tests were identified as the best well-established serum markers. These tests are based on the evaluation of expression levels of several molecules. For analysis of urine samples, Progensa, ExoDXProstate, and Mi Prostate Score Urine Test are available. All these tests have the potential to help diagnosis, avoiding unnecessary biopsies, but they are used only in association with digital rectal examination and PSA level data. The search for biomarkers that can help in the diagnosis and therapeutic management of PCA is still in its initial phase, requiring more efforts for an effective clinical application.

A meta-analysis for the diagnostic accuracy of SelectMDx in prostate cancer

To overview the diagnostic accuracy of SelectMDx for the detection of clinically significant prostate cancer and to review sources of methodologic variability. Four electronic databases, including PubMed, Embase, Web of Science, and Cochrane Library were searched for eligible studies investigating the diagnostic value of SelectMDx compared with the gold standard. The pooled sensitivity, specificity, and positive and negative predictive values were calculated. Included studies were assessed according to the Standards for Quality Assessment of Diagnostic Accuracy Studies 2 tool. The review identified 14 relevant publications with 2579 patients. All reports constituted phase 1 biomarker studies. Pooled analysis of findings found an area under the receiver operating characteristic analysis curve of 70% [95% CI, 66%-74%], a sensitivity of 81% [95% CI, 69%-89%], and a specificity of 52% [95% CI, 41%-63%]. The positive likelihood ratio was 1.68, and the negative predictive value is 0.37. Factors that may influence variability in test results included the breath collection method, the patient's physiologic condition, the test environment, and the method of analysis. Considerable heterogeneity was observed among the studies owing to the difference in the sample size. SelectMDx appears to have moderate to good diagnostic accuracy in differentiating patients with clinically significant prostate cancer from people at high risk of developing prostate cancer. Higher-quality clinical studies assessing the diagnostic accuracy of SelectMDx for clinically significant cancer are still needed.

Robust data integration from multiple external sources for generalized linear models with binary outcomes

We aim to estimate parameters in a generalized linear model (GLM) for a binary outcome when, in addition to the raw data from the internal study, more than 1 external study provides summary information in the form of parameter estimates from fitting GLMs with varying subsets of the internal study covariates. We propose an adaptive penalization method that exploits the external summary information and gains efficiency for estimation, and that is both robust and computationally efficient. The robust property comes from exploiting the relationship between parameters of a GLM and parameters of a GLM with omitted covariates and from downweighting external summary information that is less compatible with the internal data through a penalization. The computational burden associated with searching for the optimal tuning parameter for the penalization is reduced by using adaptive weights and by using an information criterion when searching for the optimal tuning parameter. Simulation studies show that the proposed estimator is robust against various types of population distribution heterogeneity and also gains efficiency compared to direct maximum likelihood estimation. The method is applied to improve a logistic regression model that predicts high-grade prostate cancer making use of parameter estimates from 2 external models.

How to Integrate Prostate Cancer Biomarkers in Urology Clinical Practice: An Update

Nowadays, the management of prostate cancer has become more and more challenging due to the increasing number of available treatment options, therapeutic agents, and our understanding of its carcinogenesis and disease progression. Moreover, currently available risk stratification systems used to facilitate clinical decision-making have limitations, particularly in providing a personalized and patient-centered management strategy. Although prognosis and prostate cancer-specific survival have improved in recent years, the heterogenous behavior of the disease among patients included in the same risk prognostic group negatively impacts not only our clinical decision-making but also oncological outcomes, irrespective of the treatment strategy. Several biomarkers, along with available tests, have been developed to help clinicians in difficult decision-making scenarios and guide management strategies. In this review article, we focus on the scientific evidence that supports the clinical use of several biomarkers considered by professional urological societies (and included in uro-oncological guidelines) in the diagnosis process and specific difficult management strategies for clinically localized or advanced prostate cancer.

High expression of Trop2 is associated with aggressive localized prostate cancer and is a candidate urinary biomarker

Distinguishing indolent from clinically significant localized prostate cancer is a major clinical challenge and influences clinical decision-making between treatment and active surveillance. The development of novel predictive biomarkers will help with risk stratification, and clinical decision-making, leading to a decrease in over or under-treatment of patients with prostate cancer. Here, we report that Trop2 is a prognostic tissue biomarker for clinically significant prostate cancer by utilizing the Canary Prostate Cancer Tissue Microarray (CPCTA) cohort composed of over 1100 patients from a multi-institutional study. We demonstrate that elevated Trop2 expression is correlated with worse clinical features including Gleason score, age, and pre-operative PSA levels. More importantly, we demonstrate that elevated Trop2 expression at radical prostatectomy predicts worse overall survival in men undergoing radical prostatectomy. Additionally, we detect shed Trop2 in urine from men with clinically significant prostate cancer. Our study identifies Trop2 as a novel tissue prognostic biomarker and a candidate non-invasive marker for prostate cancer.

A synthetic data integration framework to leverage external summary-level information from heterogeneous populations

There is a growing need for flexible general frameworks that integrate individual-level data with external summary information for improved statistical inference. External information relevant for a risk prediction model may come in multiple forms, through regression coefficient estimates or predicted values of the outcome variable. Different external models may use different sets of predictors and the algorithm they used to predict the outcome Y given these predictors may or may not be known. The underlying populations corresponding to each external model may be different from each other and from the internal study population. Motivated by a prostate cancer risk prediction problem where novel biomarkers are measured only in the internal study, this paper proposes an imputation-based methodology, where the goal is to fit a target regression model with all available predictors in the internal study while utilizing summary information from external models that may have used only a subset of the predictors. The method allows for heterogeneity of covariate effects across the external populations. The proposed approach generates synthetic outcome data in each external population, uses stacked multiple imputation to create a long dataset with complete covariate information. The final analysis of the stacked imputed data is conducted by weighted regression. This flexible and unified approach can improve statistical efficiency of the estimated coefficients in the internal study, improve predictions by utilizing even partial information available from models that use a subset of the full set of covariates used in the internal study, and provide statistical inference for the external population with potentially different covariate effects from the internal population.

miRNA Expression Patterns in Early- and Late-Stage Prostate Cancer Patients: High-Throughput Analysis

Prostate cancer (PCa) is one of the most common types of cancer among men. To date, there have been no specific markers identified for the diagnosis and prognosis or response to treatment of this disease. Thus, there is an urgent need for promising markers, which may be fulfilled by small non-coding RNAs known as microRNAs (miRNAs). Therefore, the present study aimed to investigate the miRNA profile in tissue samples obtained from patients with PCa using microarrays, followed by reverse transcriptase quantitative PCRs (RT-qPCRs). In the discovery phase, 754 miRNAs were screened in tissues obtained from patients (n = 46) with PCa in early and late stages. Expression levels of miRNA-324-3p, miRNA-429, miRNA-570, and miRNA-616 were found to be downregulated, and miRNA-423-5p expression was upregulated in patients with early-stage cancer compared to the late-stage ones. These five miRNAs were further validated in an independent cohort of samples (n = 39) collected from patients with PCa using RT-qPCR-based assays. MiRNA-324-3p, miRNA-429, miRNA-570, and miRNA-616 expression levels remained significantly downregulated in early-stage cancer tissues compared to late-stage tissues. Remarkably, for a combination of three miRNAs, PSA levels and Gleason scores were able to discriminate between patients with early-stage PCa and late-stage PCa, with an AUC of 95%, a sensitivity of 86%, and a specificity close to 94%. Thus, the data obtained in this study suggest a possible involvement of the identified miRNAs in the pathogenesis of PCa, and they may also have the potential to be developed into diagnostic and prognostic tools for PCa. However, further studies with a larger cohort are needed.

Biomarkers for Prostate Cancer: From Diagnosis to Treatment

Prostate cancer (PCa) is a widespread malignancy with global significance, which substantially affects cancer-related mortality. Its spectrum varies widely, from slow-progressing cases to aggressive or even lethal forms. Effective patient stratification into risk groups is crucial to therapeutic decisions and clinical trials. This review examines a wide range of diagnostic and prognostic biomarkers, several of which are integrated into clinical guidelines, such as the PHI, the 4K score, PCA3, Decipher, and Prolaris. It also explores the emergence of novel biomarkers supported by robust preclinical evidence, including urinary miRNAs and isoprostanes. Genetic alterations frequently identified in PCa, including BRCA1/BRCA2, ETS gene fusions, and AR changes, are also discussed, offering insights into risk assessment and precision treatment strategies. By evaluating the latest developments and applications of PCa biomarkers, this review contributes to an enhanced understanding of their role in disease management.

A urine extracellular vesicle lncRNA classifier for high-grade prostate cancer and increased risk of progression: A multi-center study

To construct a urine extracellular vesicle long non-coding RNA (lncRNA) classifier that can detect high-grade prostate cancer (PCa) of grade group 2 or greater and estimate the risk of progression during active surveillance, we identify high-grade PCa-specific lncRNAs by combined analyses of cohorts from TAHSY, TCGA, and the GEO database. We develop and validate a 3-lncRNA diagnostic model (Clnc, being made of AC015987.1, CTD-2589M5.4, RP11-363E6.3) that can detect high-grade PCa. Clnc shows higher accuracy than prostate cancer antigen 3 (PCA3), multiparametric magnetic resonance imaging (mpMRI), and two risk calculators (Prostate Cancer Prevention Trial [PCPT]-RC 2.0 and European Randomized Study of Screening for Prostate Cancer [ERSPC]-RC) in the training cohort (n = 350), two independent cohorts (n = 232; n = 251), and TCGA cohort (n = 499). In the prospective active surveillance cohort (n = 182), Clnc at diagnosis remains a powerful independent predictor for overall active surveillance progression. Thus, Clnc is a potential biomarker for high-grade PCa and can also serve as a biomarker for improved selection of candidates for active surveillance.

Recent advances and future perspectives in the therapeutics of prostate cancer

Prostate cancer (PC) is one of the most common cancers in males and the fifth leading reason of death. Age, ethnicity, family history, and genetic defects are major factors that determine the aggressiveness and lethality of PC. The African population is at the highest risk of developing high-grade PC. It can be challenging to distinguish between low-risk and high-risk patients due to the slow progression of PC. Prostate-specific antigen (PSA) is a revolutionary discovery for the identification of PC. However, it has led to an increase in over diagnosis and over treatment of PC in the past few decades. Even if modifications are made to the standard PSA testing, the specificity has not been found to be significant. Our understanding of PC genetics and proteomics has improved due to advances in different fields. New serum, urine, and tissue biomarkers, such as PC antigen 3 (PCA3), have led to various new diagnostic tests, such as the prostate health index, 4K score, and PCA3. These tests significantly reduce the number of unnecessary and repeat biopsies performed. Chemotherapy, radiotherapy, and prostatectomy are standard treatment options. However, newer novel hormone therapy drugs with a better response have been identified. Androgen deprivation and hormonal therapy are evolving as new and better options for managing hormone-sensitive and castration-resistant PC. This review aimed to highlight and discuss epidemiology, various risk factors, and developments in PC diagnosis and treatment regimens.

MyProstateScore in men considering repeat biopsy: validation of a simple testing approach

BACKGROUND

Men with persistent risk of Grade Group (GG) ≥ 2 cancer after a negative biopsy present a unique clinical challenge. The validated MyProstateScore test is clinically-available for pre-biopsy risk stratification. In biopsy-naïve patients, we recently validated a straightforward testing approach to rule-out GG ≥ 2 cancer with 98% negative predictive value (NPV) and 97% sensitivity. In the current study, we established a practical MPS-based testing approach in men with a previous negative biopsy being considered for repeat biopsy.

METHODS

Patients provided post-digital rectal examination urine prior to repeat biopsy. MyProstateScore was calculated using the validated, locked model including urinary PCA3 and TMPRSS2:ERG scores with serum PSA. In a clinically-appropriate primary (i.e., training) cohort, we identified a lower (rule-out) threshold approximating 90% sensitivity and an upper (rule-in) threshold approximating 80% specificity for GG ≥ 2 cancer. These thresholds were applied to an external validation cohort, and performance measures and clinical outcomes associated with their use were calculated.

RESULTS

MyProstateScore thresholds of 15 and 40 met pre-defined performance criteria in the primary cohort (422 patients; median PSA 6.4, IQR 4.3-9.1). In the 268-patient validation cohort, 25 men (9.3%) had GG ≥ 2 cancer on repeat biopsy. The rule-out threshold of 15 provided 100% NPV and sensitivity for GG ≥ 2 cancer and would have prevented 23% of unnecessary biopsies. Use of MyProstateScore >40 to rule-in biopsy would have prevented 67% of biopsies while maintaining 95% NPV. In the validation cohort, the prevalence of GG ≥ 2 cancer was 0% for MyProstateScore 0-15, 6.5% for MyProstateScore 15-40, and 19% for MyProstateScore >40.

CONCLUSIONS

In patients who previously underwent a negative prostate biopsy, the MyProstateScore values of 15 and 40 yielded clinically-actionable rule-in and rule-out risk groups. Using this straightforward testing approach, MyProstateScore can meaningfully inform patients and physicians weighing the need for repeat biopsy.

2022 Update on Prostate Cancer Epidemiology and Risk Factors-A Systematic Review

CONTEXT

Prostate cancer (PCa) is one of the most common cancers worldwide. Understanding the epidemiology and risk factors of the disease is paramount to improve primary and secondary prevention strategies.

OBJECTIVE

To systematically review and summarize the current evidence on the descriptive epidemiology, large screening studies, diagnostic techniques, and risk factors of PCa.

EVIDENCE ACQUISITION

PCa incidence and mortality rates for 2020 were obtained from the GLOBOCAN database of the International Agency for Research on Cancer. A systematic search was performed in July 2022 using PubMed/MEDLINE and EMBASE biomedical databases. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines and was registered in PROSPERO (CRD42022359728).

EVIDENCE SYNTHESIS

Globally, PCa is the second most common cancer, with the highest incidence in North and South America, Europe, Australia, and the Caribbean. Risk factors include age, family history, and genetic predisposition. Additional factors may include smoking, diet, physical activity, specific medications, and occupational factors. As PCa screening has become more accepted, newer approaches such as magnetic resonance imaging (MRI) and biomarkers have been implemented to identify patients who are likely to harbor significant tumors. Limitations of this review include the evidence being derived from meta-analyses of mostly retrospective studies.

CONCLUSIONS

PCa remains the second most common cancer among men worldwide. PCa screening is gaining acceptance and will likely reduce PCa mortality at the cost of overdiagnosis and overtreatment. Increasing use of MRI and biomarkers for the detection of PCa may mitigate some of the negative consequences of screening.

PATIENT SUMMARY

Prostate cancer (PCa) remains the second most common cancer among men, and screening for PCa is likely to increase in the future. Improved diagnostic techniques can help reduce the number of men who need to be diagnosed and treated to save one life. Avoidable risk factors for PCa may include factors such as smoking, diet, physical activity, specific medications, and certain occupations.

Prostate Cancer Reshapes the Secreted and Extracellular Vesicle Urinary Proteomes

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions, and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome, and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Beyond blood biomarkers: the role of SelectMDX in clinically significant prostate cancer identification

INTRODUCTION

New potential biomarkers to pre-intervention identification of a clinically significant prostate cancer (csPCa) will prevent overdiagnosis and overtreatment and limit quality of life impairment of PCa patients.

AREAS COVERED

We have developed a comprehensive review focusing our research on the increasing knowledge of the role of SelectMDX® in csPCa detection. Areas identified as clinically relevant are the ability of SelectMDX® to predict csPCa in active surveillance setting, its predictive ability when combined with multiparametric MRI and the role of SelectMDX® in the landscape of urinary biomarkers.

EXPERT OPINION

Several PCa biomarkers have been developed either alone or in combination with clinical variables to improve csPCa detection. SelectMDX® score includes genomic markers, age, PSA, prostate volume, and digital rectal examination. Several studies have shown consistency in the ability to improve detection of csPCa, avoidance of unnecessary prostate biopsies, helpful in decision-making for clinical benefit of PCa patients with future well designed, and impactful studies.

Relationship between Proclarix and the Aggressiveness of Prostate Cancer

INTRODUCTION

Proclarix is a CE-marked test that provides the risk of clinically significant prostate cancer (csPCa), ranging from 0% to 100%, based on the serum measurement of Thrombospondin-1, cathepsin D, prostate-specific antigen (PSA), and percentage of free PSA in addition to age. We hypothesize that Proclarix could be correlated with PCa aggressiveness. We analyzed the association of this new biomarker with four surrogates of aggressiveness: grade group (GG) in the biopsy, clinical stage, risk of biochemical recurrence after primary treatment of localized PCa, and pathology in the surgical specimen.

MATERIAL AND METHODS

This is a retrospective study from 606 men with suspicion of PCa [PSA of ≥ 3.0 ng/mL and/or abnormal digital rectal examination (DRE)], in whom Proclarix was assessed (0-100%). The GG was defined by the International Society of Urological Pathology categories. The TNM was used for clinical staging (cT based on DRE, whereas cN and cM were established with computed tomography and 99-technetium bone scintigraphy). The risk of biochemical recurrence of localized PCa after primary treatment was defined by combining PSA, GG, and cT. Finally, an unfavorable pathology in a surgical specimen was defined as GG > 2 or pT ≥ 3.

RESULTS

The median age of the cohort was 67 years old, with a median PSA of 7 ng/mL and a rate of abnormal DRE of 23.3%. CsPCa was detected in 254 men (41.9%), with a median Proclarix of 60.1% compared with 37.3% obtained in patients with insignificant PCa and 20.7% in men without PCa. Among patients with GG > 3, Proclarix was significantly higher (58.2%) than in those with GG of 3 or lower (33.1%, p < 0.001). Men with localized tumors exhibited a Proclarix median of 37.3% compared with those with advanced disease (60.1%, p < 0.001). Proclarix levels among 197 patients with low and intermediate risk of biochemical recurrence were 24.9% and 35.0%, respectively, significantly lower compared with patients with high-risk disease (58.7%, p < 0.001). Unfavorable pathology was observed in 35 patients out of the 79 who underwent radical prostatectomy, with a Proclarix median of 35.7% compared with 23.7% obtained in patients with favorable pathology (p = 0.013). Proclarix and magnetic resonance imaging were independent predictors of the four surrogates of aggressiveness analyzed.

CONCLUSION

There is a correlation between Proclarix and the aggressiveness of PCa.

Clinical performance of magnetic resonance imaging and biomarkers for prostate cancer diagnosis in men at high genetic risk

OBJECTIVES

To evaluate different scenarios for the management of early diagnosis of cancer (PCa) in men at high genetic risk, using recently developed blood and urinary molecular biomarkers in combination with clinical information alongside multiparametric magnetic resonance imaging (mpMRI).

PATIENTS AND METHODS

A total of 322 patients with a high genetic risk (familial or personal history of cancers or a predisposing germline variant) were included in this study. The primary outcome was the detection rates of PCa (positive biopsy) or clinically significant PCa (biopsy with International Society of Urological Pathology [ISUP] grade >1). Clinical parameters included age, body mass index, ancestry, and germline mutational status, mpMRI, prostate-specific antigen density (PSAD), Prostate Health Index and urinary markers (Prostate Cancer Associated 3, SelectMdx™ and T2:ERG score) were assessed. Sensitivity (Se) and specificity (Sp) for each marker at their recommended cut-off for clinical practice were calculated. Comparison between diagnoses accuracy of each procedure and scenario was computed using mutual information based and direct effect contribution using a supervised Bayesian network approach.

RESULTS

A mpMRI Prostate Imaging-Reporting and Data System (PI-RADS) score ≥3 showed higher Se than mpMRI PI-RADS score ≥4 for detection of PCa (82% vs 61%) and for the detection of ISUP grade >1 lesions (96% vs 80%). mpMRI PI-RADS score ≥3 performed better than a PSA level of ≥3 ng/mL (Se 96%, Sp 53% vs Se 91%, Sp 8%) for detection of clinically significant PCa. In case of negative mpMRI results, the supervised Bayesian network approach showed that urinary markers (with the same accuracy for all) and PSAD of ≥0.10 ng/mL/mL were the most useful indicators of decision to biopsy.

CONCLUSIONS

We found that screening men at high genetic risk of PCa must be based on mpMRI without pre-screening based on a PSA level of >3 ng/mL, to avoid missing too many ISUP grade >1 tumours and to significantly reduce the number of unnecessary biopsies. However, urinary markers or a PSAD of ≥0.10 ng/mL/mL when mpMRI was negative increased the detection of ISUP grade >1 cancers. We suggest that a baseline mpMRI be discussed for men at high genetic risk from the age of 40 years.

Integrating Information from Existing Risk Prediction Models with No Model Details

Consider the setting where (i) individual-level data are collected to build a regression model for the association between an event of interest and certain covariates, and (ii) some risk calculators predicting the risk of the event using less detailed covariates are available, possibly as algorithmic black boxes with little information available about how they were built. We propose a general empirical-likelihood-based framework to integrate the rich auxiliary information contained in the calculators into fitting the regression model, to make the estimation of regression parameters more efficient. Two methods are developed, one using working models to extract the calculator information and one making a direct use of calculator predictions without working models. Theoretical and numerical investigations show that the calculator information can substantially reduce the variance of regression parameter estimation. As an application, we study the dependence of the risk of high grade prostate cancer on both conventional risk factors and newly identified molecular biomarkers by integrating information from the Prostate Biopsy Collaborative Group (PBCG) risk calculator, which was built based on conventional risk factors alone.

The performance and limitations of PCA3, TMPRSS2:ERG, HOXC6 and DLX1 urinary markers combined in the improvement of prostate cancer diagnostics

BACKGROUND

Prostate cancer (PCa) is the second most commonly diagnosed cancer in men. To date, the role of the combined application of long non-coding RNAs (PCA3, DLX1, HOXC6, TMPRSS2:ERG) for obtaining the most accurate method of detection of PCa has not yet been comprehensively investigated.

METHODS

In total 240 persons were included in the retrospective study. Among them were 150 patients with confirmed PCa, 30 patients with benign prostatic hyperplasia, 30 patients with active chronic prostatitis and 30 healthy volunteers. In all patients, the urine samples were collected prior to biopsy or treatment. Polymerase chain reaction with reverse transcription was performed to detect the expression level of PCA3, HOXC6, DLX1 and the presence of the TMPRSS2:ERG transcript.

RESULTS

PCA3 was detected in urine samples in all cases. Using a PCA3 score of 56 allowed the differentiation between PCa and all other cases with a sensitivity of 61% and specificity of 96% (p<0.001) while a PCA3 score threshold value of 50 resulted in a differentiation between clinically significant PCa (ISUP grades 2-5) and all other cases with a sensitivity of 93% and specificity of 93% (p<0.001). The TMPRSS2:ERG expression in urine was detected exclusively in the group of patients with PCa and only in 16% of all cases.

CONCLUSIONS

PCA3 score detected in urine demonstrated moderate sensitivity and good specificity in differentiation between PCa and non-PCa and high sensitivity and specificity in differentiation between clinically significant PCa and non-PCa.

Data integration: exploiting ratios of parameter estimates from a reduced external model

We consider the situation of estimating the parameters in a generalized linear prediction model, from an internal dataset, where the outcome variable [Formula: see text] is binary and there are two sets of covariates, [Formula: see text] and [Formula: see text]. We have information from an external study that provides parameter estimates for a generalized linear model of [Formula: see text] on [Formula: see text]. We propose a method that makes limited assumptions about the similarity of the distributions in the two study populations. The method involves orthogonalizing the [Formula: see text] variables and then borrowing information about the ratio of the coefficients from the external model. The method is justified based on a new result relating the parameters in a generalized linear model to the parameters in a generalized linear model with omitted covariates. The method is applicable if the regression coefficients in the [Formula: see text] given [Formula: see text] model are similar in the two populations, up to an unknown scalar constant. This type of transportability between populations is something that can be checked from the available data. The asymptotic variance of the proposed method is derived. The method is evaluated in a simulation study and shown to gain efficiency compared to simple analysis of the internal dataset, and is robust compared to an alternative method of incorporating external information.

Mass Spectrometry-Based Biomarkers to Detect Prostate Cancer: A Multicentric Study Based on Non-Invasive Urine Collection without Prior Digital Rectal Examination

(1) Background: Prostate cancer (PCa) is the most frequently diagnosed cancer in men. Wide application of prostate specific antigen test has historically led to over-treatment, starting from excessive biopsies. Risk calculators based on molecular and clinical variables can be of value to determine the risk of PCa and as such, reduce unnecessary and invasive biopsies. Urinary molecular studies have been mostly focusing on sampling after initial intervention (digital rectal examination and/or prostate massage). (2) Methods: Building on previous proteomics studies, in this manuscript, we aimed at developing a biomarker model for PCa detection based on urine sampling without prior intervention. Capillary electrophoresis coupled to mass spectrometry was applied to acquire proteomics profiles from 970 patients from two different clinical centers. (3) Results: A case-control comparison was performed in a training set of 413 patients and 181 significant peptides were subsequently combined by a support vector machine algorithm. Independent validation was initially performed in 272 negative for PCa and 138 biopsy-confirmed PCa, resulting in an AUC of 0.81, outperforming current standards, while a second validation phase included 147 PCa patients. (4) Conclusions: This multi-dimensional biomarker model holds promise to improve the current diagnosis of PCa, by guiding invasive biopsies.

Urinary PSA and Serum PSA for Aggressive Prostate Cancer Detection

Serum PSA, together with digital rectal examination and imaging of the prostate gland, have remained the gold standard in urological practices for the management of and intervention for prostate cancer. Based on these adopted practices, the limitations of serum PSA in identifying aggressive prostate cancer has led us to evaluate whether urinary PSA levels might have any clinical utility in prostate cancer diagnosis. Utilizing the Access Hybritech PSA assay, we evaluated a total of n = 437 urine specimens from post-DRE prostate cancer patients. In our initial cohort, PSA tests from a total of one hundred and forty-six (n = 146) urine specimens were obtained from patients with aggressive (Gleason Score ≥ 8, n = 76) and non-aggressive (Gleason Score = 6, n = 70) prostate cancer. A second cohort, with a larger set of n = 291 urine samples from patients with aggressive (GS ≥ 7, n = 168) and non-aggressive (GS = 6, n = 123) prostate cancer, was also utilized in our study. Our data demonstrated that patients with aggressive disease had lower levels of urinary PSA compared to the non-aggressive patients, while the serum PSA levels were higher in patients with aggressive prostate disease. The discordance between serum and urine PSA levels was further validated by immuno-histochemistry (IHC) assay in biopsied tumors and in metastatic lesions (n = 62). Our data demonstrated that aggressive prostate cancer was negatively correlated with the PSA in prostate cancer tissues, and, unlike serum PSA, urinary PSA might serve a better surrogate for capitulating tissue milieus to detect aggressive prostate cancer. We further explored the utility of urine PSA as a cancer biomarker, either alone and in combination with serum PSA, and their ratio (serum to urine PSA) to predict disease status. Comparing the AUCs for the urine and serum PSA alone, we found that urinary PSA had a higher predictive power (AUC= 0.732) in detecting aggressive disease. Furthermore, combining the ratios between serum to urine PSA with urine and serum assay enhanced the performance (AUC = 0.811) in predicting aggressive prostate disease. These studies support the role of urinary PSA in combination with serum for detecting aggressive prostate cancer.

Long noncoding RNA as a potential diagnostic tool for prostate cancer: a systematic review and meta-analysis

PURPOSE

To identify consistently expressed lncRNAs and suitable lncRNAs with high sensitivity and specificity from multiple independent studies as potential biomarkers for PCa diagnostics.

METHODS

We searched multiple electronic databases including PubMed, Web of Science, EMBASE, Cochrane Library, CNKI, CQVIP, Wanfang, and CBMdisc for studies published up to July 2022. The quality of the included studies was assessed by two independent reviewers based on the QUADAS-2 tool using Review Manager 5.3. A vote-counting method was used based on the ranking of potential molecular biomarkers. The top-ranked lncRNAs were further assessed for diagnostic value using Meta-disc version 1.4 software.

RESULTS

Among the 26 included studies, 2 circulating lncRNAs (PCA3 and MALAT-1) were reported 3 or more times in PCa patients versus non-PCa patients. In further analysis, the areas under the curve of the summary receiver operating characteristic curves for PCA3 and MALAT-1 distinguishing PCa patients were 0.775 and 0.771, respectively.

CONCLUSIONS

Based on the current evidence, PCA3 and MALAT-1 are reliable lncRNAs for the diagnosis of PCa.

Extracellular vesicles for precision medicine in prostate cancer - Is it ready for clinical translation?

Biofluid-based biomarker tests hold great promise for precision medicine in prostate cancer (PCa) clinical practice. Extracellular vesicles (EV) are established as intercellular messengers in cancer development with EV cargos, including protein and nucleic acids, having the potential to serve as biofluid-based biomarkers. Recent clinical studies have begun to evaluate EV-based biomarkers for PCa diagnosis, prognosis, and disease/therapy resistance monitoring. Promising results have led to PCa EV biomarker validation studies which are currently underway with the next challenge being translation to robust clinical assays. However, EV research studies generally use low throughput EV isolation methods and costly molecular profiling technologies that are not suitable for clinical assays. Here, we consider the technical hurdles in translating EV biomarker research findings into precise and cost-effective clinical biomarker assays. Novel microfluidic devices coupling EV extraction with sensitive antibody-based biomarker detection are already being explored for point-of-care applications for rapid provision in personalised medicine approaches.

Gene-Transcript Expression in Urine Supernatant and Urine Cell-Sediment Are Different but Equally Useful for Detecting Prostate Cancer

There is considerable interest in urine as a non-invasive liquid biopsy to detect prostate cancer (PCa). PCa-specific transcripts such as the TMPRSS2:ERG fusion gene can be found in both urine extracellular vesicles (EVs) and urine cell-sediment (Cell) but the relative usefulness of these and other genes in each fraction in PCa detection has not been fully elucidated. Urine samples from 76 men (PCa n = 40, non-cancer n = 36) were analysed by NanoString for 154 PCa-associated genes-probes, 11 tissue-specific, and six housekeeping. Comparison to qRT-PCR data for four genes (PCA3, OR51E2, FOLH1, and RPLP2) was strong (r = 0.51-0.95, Spearman p < 0.00001). Comparing EV to Cells, differential gene expression analysis found 57 gene-probes significantly more highly expressed in 100 ng of amplified cDNA products from the EV fraction, and 26 in Cells (p < 0.05; edgeR). Expression levels of prostate-specific genes (KLK2, KLK3) measured were ~20× higher in EVs, while PTPRC (white-blood Cells) was ~1000× higher in Cells. Boruta analysis identified 11 gene-probes as useful in detecting PCa: two were useful in both fractions (PCA3, HOXC6), five in EVs alone (GJB1, RPS10, TMPRSS2:ERG, ERG_Exons_4-5, HPN) and four from Cell (ERG_Exons_6-7, OR51E2, SPINK1, IMPDH2), suggesting that it is beneficial to fractionate whole urine prior to analysis. The five housekeeping genes were not significantly differentially expressed between PCa and non-cancer samples. Expression signatures from Cell, EV and combined data did not show evidence for one fraction providing superior information over the other.

Biomarkers of Aggressive Prostate Cancer at Diagnosis

In the United States, prostate cancer (CaP) remains the second leading cause of cancer deaths in men. CaP is predominantly indolent at diagnosis, with a small fraction (25-30%) representing an aggressive subtype (Gleason score 7-10) that is prone to metastatic progression. This fact, coupled with the criticism surrounding the role of prostate specific antigen in prostate cancer screening, demonstrates the current need for a biomarker(s) that can identify clinically significant CaP and avoid unnecessary biopsy procedures and psychological implications of being diagnosed with low-risk prostate cancer. Although several diagnostic biomarkers are available to clinicians, very few comparative trials have been performed to assess the clinical effectiveness of these biomarkers. It is of note, however, that a majority of these clinical trials have been over-represented by men of Caucasian origin, despite the fact that African American men have a 1.7 times higher incidence and 2.1 times higher rate of mortality from prostate cancer. Biomarkers for CaP diagnosis based on the tissue of origin include urine-based gene expression assays (PCA3, Select MDx, ExoDx Prostate IntelliScore, Mi-Prostate Score, PCA3-PCGEM1 gene panel), blood-based protein biomarkers (4K, PHI), and tissue-based DNA biomarker (Confirm MDx). Another potential direction that has emerged to aid in the CaP diagnosis include multi-parametric magnetic resonance imaging (mpMRI) and bi-parametric magnetic resonance imaging (bpMRI), which in conjunction with clinically validated biomarkers may provide a better approach to predict clinically significant CaP at diagnosis. In this review, we discuss some of the adjunctive biomarker tests along with newer imaging modalities that are currently available to help clinicians decide which patients are at risk of having high-grade CaP on prostate biopsy with the emphasis on clinical utility of the tests across African American (AA) and Caucasian (CA) men.

METCAM Is a Potential Biomarker for Predicting the Malignant Propensity of and as a Therapeutic Target for Prostate Cancer

Prostate cancer is the second leading cause of cancer-related death worldwide. This is because it is still unknown why indolent prostate cancer becomes an aggressive one, though many risk factors for this type of cancer have been suggested. Currently, many diagnostic markers have been suggested for predicting malignant prostatic carcinoma cancer; however, only a few, such as PSA (prostate-specific antigen), Prostate Health Index (PHI), and PCA3, have been approved by the FDA. However, each biomarker has its merits as well as shortcomings. The serum PSA test is incapable of differentiating prostate cancer from BPH and also has an about 25% false-positive prediction rate for the malignant status of cancer. The PHI test has the potential to replace the PSA test for the discrimination of BPH from prostate cancer and for the prediction of high-grade cancer avoiding unnecessary biopsies; however, the free form of PSA is unstable and expensive. PCA3 is not associated with locally advanced disease and is limited in terms of its prediction of aggressive cancer. Currently, several urine biomarkers have shown high potential in terms of being used to replace circulating biomarkers, which require a more invasive method of sample collection, such as via serum. Currently, the combined multiple tumor biomarkers may turn out to be a major trend in the diagnosis and assessment of the treatment effectiveness of prostate cancer. Thus, there is still a need to search for more novel biomarkers to develop a perfect cocktail, which consists of multiple biomarkers, in order to predict malignant prostate cancer and follow the efficacy of the treatment. We have discovered that METCAM, a cell adhesion molecule in the Ig-like superfamily, has great potential regarding its use as a biomarker for differentiating prostate cancer from BPH, predicting the malignant propensity of prostate cancer at the early premalignant stage, and differentiating indolent prostate cancers from aggressive cancers. Since METCAM has also been shown to be able to initiate the spread of prostate cancer cell lines to multiple organs, we suggest that it may be used as a therapeutic target for the clinical treatment of patients with malignant prostate cancer.

Multifunctional Hybrid Nanozymes for Magnetic Enrichment and Bioelectrocatalytic Sensing of Circulating Tumor RNA during Minimal Residual Disease Monitoring

Iron oxide nanozymes are a form of nanomaterial with both superparamagnetic and enzyme-mimicking properties. Ongoing research efforts have been made to create multifunctional iron oxide hybrid nanozymes with auxiliary properties through biomolecular modifications. Such iron oxide hybrid nanozymes can be useful for rapid and cost-effective analysis of circulating tumor nucleic acids (ctNAs) in patient liquid biopsies during minimal residual disease (MRD) monitoring of cancer recurrence. Herein, the use of streptavidin-modified iron oxide hybrid nanozymes is reported for magnetic enrichment and bioelectrocatalytic sensing of three prostate cancer (PCa) ctRNA biomarkers with high detection specificity and sensitivity (10 copies) over an ultrabroad dynamic range (five orders of magnitude). Furthermore, the feasibility of ctRNA analysis for pre- and post-cancer treatment MRD monitoring is demonstrated using PCa urinary liquid biopsy samples.

Biomarkers for the Detection and Risk Stratification of Aggressive Prostate Cancer

Current strategies for the clinical management of prostate cancer are inadequate for a precise risk stratification between indolent and aggressive tumors. Recently developed tissue-based molecular biomarkers have refined the risk assessment of the disease. The characterization of tissue biopsy components and subsequent identification of relevant tissue-based molecular alterations have the potential to improve the clinical decision making and patient outcomes. However, tissue biopsies are invasive and spatially restricted due to tumor heterogeneity. Therefore, there is an urgent need for complementary diagnostic and prognostic options. Liquid biopsy approaches are minimally invasive with potential utility for the early detection, risk stratification, and monitoring of tumors. In this review, we focus on tissue and liquid biopsy biomarkers for early diagnosis and risk stratification of prostate cancer, including modifications on the genomic, epigenomic, transcriptomic, and proteomic levels. High-risk molecular alterations combined with orthogonal clinical parameters can improve the identification of aggressive tumors and increase patient survival.

Accuracy of novel urinary biomarker tests in the diagnosis of prostate cancer: A systematic review and network meta-analysis

Objective

The purpose of this study was to conduct a network meta-analysis comparing the diagnostic value of different urinary markers for prostate cancer.

Methods

As of June 2022, the literature was retrieved by searching Pubmed, EMBASE, Web of Science databases and other databases. The methodological quality of included studies was assessed using the Cochrane Collaboration’s risk of bias tool, and publication bias was assessed using funnel plots. The surface under the cumulative ranking curve (SUCRA) values ​​was used to determine the most effective diagnostic method and the data were analyzed accordingly using data analysis software.

Results

A total of 16 articles was included including 9952 patients. The ranking results of network meta-analysis showed that the diagnostic performance of the four urine markers Selectmdx, MIPS, PCA3 and EPI was better than that of PSA. Among them, the specificity, positive predictive value and diagnostic accuracy of Selectmdx ranked first in the SUCRA ranking (SUCRA values: 85.2%, 88.3%, 97.1%), and the sensitivity ranked second in the SUCRA ranking (SUCRA value: 54.4%), and the negative predictive value ranked fourth in SUCRA (SUCRA value: 51.6%). The most sensitive screening tool was MIPS (SUCRA value: 67.1%), and it was also the second screening tool ranked higher in specificity, positive predictive value, negative predictive value and diagnostic accuracy (SUCRA value: 56.5%, respectively)., 57.1%, 67.9%, 74.3%). The high negative predictive value SUCRA ranking is EPI (SUCRA value: 68.0%), its sensitivity ranks third (SUCRA value: 45.6%), and its specificity, positive predictive value and diagnostic accuracy are ranked fourth (SUCRA values are: 45%, 38.2%, 35.8%).

Conclusion

According to the network ranking diagram, we finally concluded that Selectmdx and MIPS can be used as the most suitable urine markers for prostate cancer screening and diagnosis. To further explore the diagnostic value of different urinary markers in the screening of PCa patients.

Systematic Review Registration

https://inplasy.com/, identifier INPLASY202290094.

Prostate cancer-associated urinary proteomes differ before and after prostatectomy

BACKGROUND

A wide range of disorders can be detected in the urine. Tumor-modifying proteins in the urine may serve as a diagnostic tool for cancer patients and the alterations in their profiles may indicate efficacies of chemotherapy, radiotherapy, and surgery.

METHODS

We focused on urinary proteomes of patients with prostate cancer and identified tumor-modifying proteins in the samples before and after prostatectomy. Protein array analysis was conducted to evaluate a differential profile of tumor-promoting cytokines, while mass spectrometry-based global proteomics was conducted to identify tumor-suppressing proteins.

RESULTS

The result revealed striking differences by prostatectomy. Notably, the urine from the post-prostatectomy significantly decreased the tumorigenic behaviors of prostate tumor cells as well as breast cancer cells. We observed that angiogenin, a stimulator of blood vessel formation, was reduced in the post-prostatectomy urine. By contrast, the levels of three cell-membrane proteins such as prostasin (PRSS8), nectin 2 (PVRL2), and nidogen 1 (NID1) were elevated and they acted as extracellular tumor-suppressing proteins. These three proteins, given extracellularly, downregulated tumorigenic genes such as Runx2, Snail, and transforming growth factor beta and induced apoptosis of tumor cells. However, the role of NID1 differed depending on the location, and intracellular NID1 was tumorigenic and reduced the percent survival.

CONCLUSIONS

This study demonstrated that prostatectomy remarkably altered the profile of urinary proteomes, and the post-prostatectomy urine provided tumor-suppressive proteomes. The result sheds novel light on the dynamic nature of the urinary proteomes and a unique strategy for predicting tumor suppressors.

Ubiquitin-related lncRNAs: The new tool for prognosis prediction in prostate cancer

Objective

To establish a ubiquitin-related long noncoding ribonucleic acids (lncRNAs) prognosis prediction model for prostate cancer (Pca).

Methods

Data were acquired through The Cancer Genome Atlas (TCGA) database. Ubiquitin-related differentially expressed genes (DEGs) and lncRNAs in Pca were filtered out. UBE2S was selected as the representative gene and validated in vitro. Progression-free survival (PFS) predictive signature was established with ubiquitin-related lncRNAs screened by Cox regression analyses and internally validated. A nomogram was constructed to assess the prognosis of Pca patients. Gene enrichment analysis was performed to explore functional differences based on risk stratification. Between different risk groups, immune status and drug sensitivity were contrasted.

Results

A total of 254 ubiquitin-related genes were screened. UBE2S was shown to promote the proliferation of Pca cells in vitro. The predictive signature was established based on six ubiquitin-related lncRNAs and validated. The prognosis of Pca patients was worse with an increasing risk score. The area under the curve (AUC) of the signature was higher than that of clinicopathological variables (0.806 vs 0.504–0.701). The AUC was 0.811 for 1-year PFS, 0.807 for 3-year PFS, and 0.790 for 5-year PFS. The calibration curves of risk score-based nomogram demonstrated high consistency. By contrasting the expression of immune function, cells, and checkpoints, we found that the signature was closely related to immunity. The high-risk patients were more sensitive to gemcitabine, cisplatin, bortezomib, etc. and resistant to bicalutamide.

Conclusion

The ubiquitin-related lncRNAs can effectively predict the prognosis of Pca and may provide new treatment options for Pca.

Volatilomics: An Emerging and Promising Avenue for the Detection of Potential Prostate Cancer Biomarkers

Simple Summary

The lack of highly specific and sensitive biomarkers for the early detection of prostate cancer (PCa) is a major barrier to its management. Volatilomics emerged as a non-invasive, simple, inexpensive, and easy-to-use approach for cancer screening, characterization of disease progression, and follow-up of the treatment’s success. We provide a brief overview of the potential of volatile organic metabolites (VOMs) for the establishment of PCa biomarkers from non-invasive matrices. Endogenous VOMs have been investigated as potential biomarkers since changes in these VOMs can be characteristic of specific disease processes. Recent studies have shown that the conjugation of the prostate-specific antigen (PSA) screening with other methodologies, such as risk calculators, biomarkers, and imaging tests, can attenuate overdiagnosis and under-detection issues. This means that the combination of volatilomics with other methodologies could be extremely valuable for the differentiation of clinical phenotypes in a group of patients, providing more personalized treatments.

Abstract

Despite the spectacular advances in molecular medicine, including genomics, proteomics, transcriptomics, lipidomics, and personalized medicine, supported by the discovery of the human genome, prostate cancer (PCa) remains the most frequent malignant tumor and a leading cause of oncological death in men. New methods for prognostic, diagnostic, and therapy evaluation are mainly based on the combination of imaging techniques with other methodologies, such as gene or protein profiling, aimed at improving PCa management and surveillance. However, the lack of highly specific and sensitive biomarkers for its early detection is a major hurdle to this goal. Apart from classical biomarkers, the study of endogenous volatile organic metabolites (VOMs) biosynthesized by different metabolic pathways and found in several biofluids is emerging as an innovative, efficient, accessible, and non-invasive approach to establish the volatilomic biosignature of PCa patients, unravelling potential biomarkers. This review provides a brief overview of the challenges of PCa screening methods and emergent biomarkers. We also focus on the potential of volatilomics for the establishment of PCa biomarkers from non-invasive matrices.

Development of a Whole-urine, Multiplexed, Next-generation RNA-sequencing Assay for Early Detection of Aggressive Prostate Cancer

BACKGROUND

Despite biomarker development advances, early detection of aggressive prostate cancer (PCa) remains challenging. We previously developed a clinical-grade urine test (Michigan Prostate Score [MiPS]) for individualized aggressive PCa risk prediction. MiPS combines serum prostate-specific antigen (PSA), the TMPRSS2:ERG (T2:ERG) gene fusion, and PCA3 lncRNA in whole urine after digital rectal examination (DRE).

OBJECTIVE

To improve on MiPS with a novel next-generation sequencing (NGS) multibiomarker urine assay for early detection of aggressive PCa.

DESIGN, SETTING, AND PARTICIPANTS

Preclinical development and validation of a post-DRE urine RNA NGS assay (Urine Prostate Seq [UPSeq]) assessing 84 PCa transcriptomic biomarkers, including T2:ERG, PCA3, additional PCa fusions/isoforms, mRNAs, lncRNAs, and expressed mutations. Our UPSeq model was trained on 73 patients and validated on a held-out set of 36 patients representing the spectrum of disease (benign to grade group [GG] 5 PCa).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The area under the receiver operating characteristic curve (AUC) of UPSeq was compared with PSA, MiPS, and other existing models/biomarkers for predicting GG ≥3 PCa.

RESULTS AND LIMITATIONS

UPSeq demonstrated high analytical accuracy and concordance with MiPS, and was able to detect expressed germline HOXB13 and somatic SPOP mutations. In an extreme design cohort (n = 109; benign/GG 1 vs GG ≥3 PCa, stratified to exclude GG 2 cancer in order to capture signal difference between extreme ends of disease), UPSeq showed differential expression for T2:ERG.T1E4 (1.2 vs 78.8 median normalized reads, p < 0.00001) and PCA3 (1024 vs 2521, p = 0.02), additional T2:ERG splice isoforms, and other candidate biomarkers. Using machine learning, we developed a 15-transcript model on the training set (n = 73) that outperformed serum PSA and sequencing-derived MiPS in predicting GG ≥3 PCa in the held-out validation set (n = 36; AUC 0.82 vs 0.69 and 0.69, respectively).

CONCLUSIONS

These results support the potential utility of our novel urine-based RNA NGS assay to supplement PSA for improved early detection of aggressive PCa.

PATIENT SUMMARY

We have developed a new urine-based test for the detection of aggressive prostate cancer, which promises improvement upon current biomarker tests.

Urinary MyProstateScore (MPS) to Rule out Clinically-Significant Cancer in Men with Equivocal (PI-RADS 3) Multiparametric MRI: Addressing an Unmet Clinical Need

OBJECTIVE

To evaluate the complementary value of urinary MyProstateScore (MPS) testing and multiparametric MRI (mpMRI) and assess outcomes in patients with equivocal mpMRI.

MATERIALS AND METHODS

Included patients underwent mpMRI followed by urine collection and prostate biopsy at the University of Michigan between 2015 -2019. MPS values were calculated from urine specimens using the validated model based on serum PSA, urinary PCA3, and urinary TMPRSS2:ERG. In the PI-RADS 3 population, the discriminative accuracy of PSA, PSAD, and MPS for GG≥2 cancer was quantified by the AUC curve. Decision curve analysis was used to assess net benefit of MPS relative to PSAD.

RESULTS

There were 540 patients that underwent mpMRI and biopsy with MPS available. The prevalence of GG≥2 cancer was 13% for PI-RADS 3, 56% for PI-RADS 4, and 87% for PI-RADS 5. MPS was significantly higher in men with GG≥2 cancer [median 44.9, IQR (29.4 -57.5)] than those with negative or GG1 biopsy [median 29.2, IQR (14.8 -44.2); P <.001] in the overall population and when stratified by PI-RADS score. In the PI-RADS 3 population (n = 121), the AUC for predicting GG≥2 cancer was 0.55 for PSA, 0.62 for PSAD, and 0.73 for MPS. MPS provided the highest net clinical benefit across all pertinent threshold probabilities.

CONCLUSION

In patients that underwent mpMRI and biopsy, MPS was significantly associated with GG≥2 cancer across all PI-RADS scores. In the PI-RADS 3 population, MPS significantly outperformed PSAD in ruling out GG≥2 cancer. These findings suggest a complementary role of MPS testing in patients that have undergone mpMRI.

Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer

Background

TMPRSS2-ERG gene rearrangement, the most common E26 transformation specific (ETS) gene fusion within prostate cancer, is known to contribute to the pathogenesis of this disease and carries diagnostic annotations for prostate cancer patients clinically. The ERG rearrangement status in prostatic adenocarcinoma currently cannot be reliably identified from histologic features on H&E-stained slides alone and hence requires ancillary studies such as immunohistochemistry (IHC), fluorescent in situ hybridization (FISH) or next generation sequencing (NGS) for identification.

Methods

Objective

We accordingly sought to develop a deep learning-based algorithm to identify ERG rearrangement status in prostatic adenocarcinoma based on digitized slides of H&E morphology alone.

Design

Setting, and Participants: Whole slide images from 392 in-house and TCGA cases were employed and annotated using QuPath. Image patches of 224 × 224 pixel were exported at 10 ×, 20 ×, and 40 × for input into a deep learning model based on MobileNetV2 convolutional neural network architecture pre-trained on ImageNet. A separate model was trained for each magnification. Training and test datasets consisted of 261 cases and 131 cases, respectively. The output of the model included a prediction of ERG-positive (ERG rearranged) or ERG-negative (ERG not rearranged) status for each input patch.

Outcome measurements and statistical analysis: Various accuracy measurements including area under the curve (AUC) of the receiver operating characteristic (ROC) curves were used to evaluate the deep learning model.

Results and Limitations

All models showed similar ROC curves with AUC results ranging between 0.82 and 0.85. The sensitivity and specificity of these models were 75.0% and 83.1% (20 × model), respectively.

Conclusions

A deep learning-based model can successfully predict ERG rearrangement status in the majority of prostatic adenocarcinomas utilizing only H&E-stained digital slides. Such an artificial intelligence-based model can eliminate the need for using extra tumor tissue to perform ancillary studies in order to assess for ERG gene rearrangement in prostatic adenocarcinoma.

Microbiomes of Urine and the Prostate Are Linked to Human Prostate Cancer Risk Groups

BACKGROUND

Bacteria play a suspected role in the development of several cancer types, and associations between the presence of particular bacteria and prostate cancer have been reported.

OBJECTIVE

To provide improved characterisation of the prostate and urine microbiome and to investigate the prognostic potential of the bacteria present.

DESIGN, SETTING, AND PARTICIPANTS

Microbiome profiles were interrogated in sample collections of patient urine (sediment microscopy: n = 318, 16S ribosomal amplicon sequencing: n = 46; and extracellular vesicle RNA-seq: n = 40) and cancer tissue (n = 204).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Microbiomes were assessed using anaerobic culture, population-level 16S analysis, RNA-seq, and whole genome DNA sequencing.

RESULTS AND LIMITATIONS

We demonstrate an association between the presence of bacteria in urine sediments and higher D'Amico risk prostate cancer (discovery, n = 215 patients, p < 0.001; validation, n = 103, p < 0.001, χ² test for trend). Characterisation of the bacterial community led to the (1) identification of four novel bacteria (Porphyromonas sp. nov., Varibaculum sp. nov., Peptoniphilus sp. nov., and Fenollaria sp. nov.) that were frequently found in patient urine, and (2) definition of a patient subgroup associated with metastasis development (p = 0.015, log-rank test). The presence of five specific anaerobic genera, which includes three of the novel isolates, was associated with cancer risk group, in urine sediment (p = 0.045, log-rank test), urine extracellular vesicles (p = 0.039), and cancer tissue (p = 0.035), with a meta-analysis hazard ratio for disease progression of 2.60 (95% confidence interval: 1.39-4.85; p = 0.003; Cox regression). A limitation is that functional links to cancer development are not yet established.

CONCLUSIONS

This study characterises prostate and urine microbiomes, and indicates that specific anaerobic bacteria genera have prognostic potential.

PATIENT SUMMARY

In this study, we investigated the presence of bacteria in patient urine and the prostate. We identified four novel bacteria and suggest a potential prognostic utility for the microbiome in prostate cancer.

A Model to Detect Significant Prostate Cancer Integrating Urinary Peptide and Extracellular Vesicle RNA Data

There is a clinical need to improve assessment of biopsy-naïve patients for the presence of clinically significant prostate cancer (PCa). In this study, we investigated whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples collected within the Movember GAP1 Urine Biomarker study (n = 192) were analysed by both mass spectrometry-based urine-proteomics and NanoString gene-expression analysis (167 gene-probes). Cross-validated LASSO penalised regression and Random Forests identified a combination of clinical and urinary biomarkers for predictive modelling of significant disease (Gleason Score (Gs) ≥ 3 + 4). Four predictive models were developed: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, and ‘SoC’ (standard of care) clinical data models, alongside a fully integrated omics-model, deemed ‘ExoSpec’. ExoSpec (incorporating four gene transcripts, six peptides, and two clinical variables) is the best model for predicting Gs ≥ 3 + 4 at initial biopsy (AUC = 0.83, 95% CI: 0.77−0.88) and is superior to a standard of care (SoC) model utilising clinical data alone (AUC = 0.71, p < 0.001, 1000 resamples). As the ExoSpec Risk Score increases, the likelihood of higher-grade PCa on biopsy is significantly greater (OR = 2.8, 95% CI: 2.1−3.7). The decision curve analyses reveals that ExoSpec provides a net benefit over SoC and could reduce unnecessary biopsies by 30%.

Plasma Polyamine Biomarker Panels: Agmatine in Support of Prostate Cancer Diagnosis

Prostate cancer is the most frequent malignant tumour among males (19%), often clinically silent and of difficult prognosis. Although several studies have highlighted the diagnostic and prognostic role of circulating biomarkers, such as PSA, their measurement does not necessarily allow the detection of the disease. Within this context, many authors suggest that the evaluation of circulating polyamines could represent a valuable tool, although several analytical problems still counteract their clinical practice. In particular, agmatine seems particularly intriguing, being a potential inhibitor of polyamines commonly derived from arginine. The aim of the present work was to evaluate the potential role of agmatine as a suitable biomarker for the identification of different classes of patients with prostate cancer (PC). For this reason, three groups of human patients—benign prostatic hyperplasia (BPH), precancerous lesion (PL), and prostate cancer (PC)—were recruited from a cohort of patients with suspected prostate cancer (n = 170), and obtained plasma was tested using the LC-HRMS method. Statistics on the receiver operating characteristics curve (ROC), and multivariate analysis were used to examine the predictive value of markers for discrimination among the three patient groups. Statistical analysis models revealed good discrimination using polyamine levels to distinguish the three classes of patients. AUC above 0.8, sensitivity ranging from 67% to 89%, specificity ranging from 74% to 89% and accuracy from 73% to 86%, considering the validation set, were achieved. Agmatine plasma levels were measured in PC (39.9 ± 12.06 ng/mL), BPH (77.62 ± 15.05 ng/mL), and PL (53.31 ± 15.27 ng/mL) patients. ROC analysis of the agmatine panel showed an AUC of 0.959 and p ≤ 0.001. These results could represent a future tool able to discriminate patients belonging to the three different clinical groups.

Molecular locker probe enrichment of gene fusion variants from matched patient liquid biopsy specimens for magneto-bioelectrocatalytic nanosensing

The accurate and sensitive analysis of recurrent gene fusion mutant variants in circulating tumor nucleic acids (NAs) of patient liquid biopsy samples is crucial for realizing clinical potential for cancer screening, diagnostics, and therapeutics. Gene fusion analysis is especially challenging in patient liquid biopsy samples because of trace biotarget levels in high non-target background of highly similar native and variant NA sequences. Herein, we describe accurate analysis of three prostate cancer gene fusion mutant variants in matched plasma and urine specimens from real cancer patients and healthy controls (n = 80) by (i) direct locker probe enrichment of multiple gene fusion mutant variants without tedious upstream sample processing; (ii) magneto-bioelectrocatalytic cycling readout using both NA-intercalating and freely diffusive redox probes for superior signal enhancement. For each mutant variant, an ultrabroad dynamic range (10-10⁵ copies) was achieved with enhanced 10 copies (zmol) detection limit. With the combination of locker probe enrichment and magneto-bioelectrocatalytic cycling readout for NA mutant variant analysis, the potential of non-invasive liquid biopsies may be exploited for the benefit of cancer patients.

Urothelial carcinoembryonic antigen 1 score for early detection of prostate cancer and risk prediction

UCA1 score appears useful in detecting nonhigh-risk (including very low-, low-, or intermediate-risk) prostate cancer. Combination of the PSA level and the UCA1 score may significantly reduce the burden of prostate biopsy.