Discovery and validation of urinary biomarkers for detection of renal cell carcinoma

Microbiota-derived extracellular vesicles: current knowledge, gaps, and challenges in precision nutrition

The gut microbiota has co-evolved with its host, profoundly shaping the development and functioning of the immune system. This co-evolution has led to a dynamic relationship where microbial metabolites and molecular signals influence immune maturation, tolerance, and defense mechanisms, highlighting its essential role in maintaining host health. Recently, bacterial extracellular vesicles (BEVs), membrane nanoparticles produced by bacteria, have emerged as important players in gut balance and as potent immune modulators. These vesicles reflect the characteristics of the bacterial membrane and contain nucleic acids, proteins, lipids, and metabolites. They can regulate immune processes and are involved in neurological and metabolic diseases due to their ability to distribute both locally in the gut and systemically, affecting immune responses at both levels. This review provides a comprehensive overview of the characteristics and functional profile of BEVs, detailing how nutrition influences the production and function of these vesicles, how antibiotics can disrupt or alter their composition, and how these factors collectively impact immunity and disease development. It also highlights the potential of BEVs in the development of precision nutritional strategies through dietary modulation, such as incorporating prebiotic fibers to enhance beneficial BEV production, reducing intake of processed foods that may promote harmful BEVs, and tailoring probiotic interventions to influence specific microbial communities and their vesicular outputs.

Expanding frontiers in liquid biopsy-discovery and validation of circulating biomarkers in renal cell carcinoma and bladder cancer

Renal cell carcinoma (RCC) and Bladder cancer (BC) are two lethal urological cancers that require diagnosis at their earliest stage causing decreasing survival rates in case of aggressive disease. However, there is no reliable circulating marker in blood or urine for their less or non-invasive diagnosis. Our objective was to review the potential circulating biomarkers, namely proteins, micro-RNA (miRNA), long non-coding RNA (lncRNA), and circulating tumour cells (CTCs) for which we performed a PubMed-based literature search of biomolecules (protein, miRNA, lncRNA and CTCs) found as circulating biomarkers in blood and urine for the early detection of RCC and BC. Among the numerous studies, certain biomolecules represent promising early-stage biomarkers such as proteins (NNMT, LCP1, and NM23A; KIM1), mi-RNAs (5-panel: miR-193a-3p, miR-362, miR-572, miR-378, and miR-28-5p; miR-200a) and lncRNAs (5-panel: LET, PVT1, PANDAR, PTENP1 and linc00963; GIHCG) for RCC. Similarly, proteins (APOA1), miRNAs (7-panel: miR-7-5p, miR-22-3p, miR-29a-3p, miR-126-5p, miR- 200a-3p, miR-375, and miR-423-5p; miRNA 181a, miRNA 30c, and miRNA 570) and lncRNAs (3-panel: MALAT1, MEG3, and SNHG16; exosomal derived 3-panel: PCAT-1, UBC1 and SNHG16; H19) were reported in BC subjects. Notably, the majority of the biomarkers presented for early detection in RCC cases were found in blood, while in urine for BC. Our results reveal that though a plethora of circulating biomarkers show early diagnostic ability, all of them are still bench-only biomarkers and require further validation. Adequate clinical trials/studies testing which of these potential markers individually or in combination, will become clinically applicable still remain elusive.

Multiple urinary peptides display distinct sex-specific distribution

Previous studies have established the association of sex with gene and protein expression. This study investigated the association of sex with the abundance of endogenous urinary peptides, using capillary electrophoresis-coupled to mass spectrometry (CE-MS) datasets from 2008 healthy individuals and patients with type II diabetes, divided in one discovery and two validation cohorts. Statistical analysis using the Mann-Whitney test, adjusted for multiple testing, revealed 143 sex-associated peptides in the discovery cohort. Of these, 90 peptides were associated with sex in at least one of the validation cohorts and showed agreement in their regulation trends across all cohorts. The 90 sex-associated peptides were fragments of 29 parental proteins. Comparison with previously published transcriptomics data demonstrated that the genes encoding 16 of these parental proteins had sex-biased expression. The 143 sex-associated peptides were combined into a support vector machine-based classifier that could discriminate males from females in two independent sets of healthy individuals and patients with type II diabetes, with an AUC of 89% and 81%, respectively. Collectively, the urinary peptidome contains multiple sex-associated differences, which may enable a better understanding of sex-biased molecular mechanisms and the development of more accurate diagnostic, prognostic, or predictive classifiers for each individual sex.

Recent progress in mass spectrometry-based urinary proteomics

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

Kidney Function and Risk of Renal Cell Carcinoma

BACKGROUND

We evaluated the temporal association between kidney function, assessed by estimated glomerular filtration rate (eGFR), and the risk of incident renal cell carcinoma (RCC). We also evaluated whether eGFR could improve RCC risk discrimination beyond established risk factors.

METHODS

We analyzed the UK Biobank cohort, including 463,178 participants of whom 1,447 were diagnosed with RCC during 5,696,963 person-years of follow-up. We evaluated the temporal association between eGFR and RCC risk using flexible parametric survival models, adjusted for C-reactive protein and RCC risk factors. eGFR was calculated from creatinine and cystatin C levels.

RESULTS

Lower eGFR, an indication of poor kidney function, was associated with higher RCC risk when measured up to 5 years prior to diagnosis. The RCC HR per SD decrease in eGFR when measured 1 year before diagnosis was 1.26 [95% confidence interval (95% CI), 1.16-1.37], and 1.11 (95% CI, 1.05-1.17) when measured 5 years before diagnosis. Adding eGFR to the RCC risk model provided a small improvement in risk discrimination 1 year before diagnosis with an AUC of 0.73 (95% CI, 0.67-0.84) compared with the published model (0.69; 95% CI, 0.63-0.79).

CONCLUSIONS

This study demonstrated that kidney function markers are associated with RCC risk, but the nature of these associations are consistent with reversed causality. Markers of kidney function provided limited improvements in RCC risk discrimination beyond established risk factors.

IMPACT

eGFR may be of potential use to identify individuals in the extremes of the risk distribution.

Prediction of coronary artery disease using urinary proteomics

AIMS

Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD.

METHODS AND RESULTS

Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78-0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66-0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47-0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26-1.89, P < 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001; 0.64, 95% CI: 0.28-0.98, P = 0.001, correspondingly).

CONCLUSION

A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention.

Extracellular urinary microRNAs as non-invasive biomarkers of endometrial and ovarian cancer

INTRODUCTION

Gynecological cancers account for a large number of cancer-related deaths in women. Endometrial cancer is the most prevalent, while ovarian cancer is the deadliest gynecological cancer worldwide. To overcome the clinical need for easy and rapid testing, there is a growing interest in cancer detection in non-invasive modalities. With a growing field of liquid biopsy, urine became interesting source of cancer biomarkers.

OBJECTIVES

The aim of this manuscript is to provide an overview on the origin, analysis and the clinical significance of urine microRNAs in gynecological cancers, with a focus on ovarian and endometrial cancer. MicroRNAs, a class of small non-coding nucleic acids, are emerging as a non-invasive biomarkers due to the feasibility and the extreme stability in body fluids. Specific miRNA expression signatures have been previously identified in ovarian and endometrial cancer.

RESULTS

The aim of this manuscript is to provide an overview on the origin, analysis and the clinical significance of urine microRNAs in gynecological cancers, with the focus on ovarian and endometrial cancer.  CONCLUSION: The advantages and limitations of urine microRNA utility and technologies are discussed. Previously detected microRNA from urine of the patients are summarized to evaluate their potential as non-invasive clinical biomarkers in gynecological oncology.

Urinary collagen peptides: Source of markers for bone metabolic processes in kidney transplant recipients

INTRODUCTION

Kidney transplant recipients (KTRs) are at an increased risk of fractures. Total urinary hydroxyproline excretion served as marker for bone resorption (BR) but was replaced by β-CrossLaps (CTX), a C-terminal collagen α-1(I) chain (COL1A1) telopeptide. We investigated the low-molecular-weight urinary proteome for peptides associated with changes in bone metabolism after kidney transplantation.

METHODS

Clinical and laboratory data including serum levels of CTX in 96 KTR from two nephrology centers were correlated with signal intensities of urinary peptides identified by capillary electrophoresis mass spectrometry.

RESULTS

Eighty-two urinary peptides were significantly correlated with serum CTX levels. COL1A1 was the predominant peptide source. Oral bisphosphonates were administered for decreased bone density in an independent group of 11 KTR and their effect was evaluated on the aforementioned peptides. Study of the peptides cleavage sites revealed a signature of Cathepsin K and MMP9. Seventeen of these peptides were significantly associated with bisphosphonate treatment, all showing a marked reduction in their excretion levels compared to baseline.

DISCUSSION

This study provides strong evidence for the presence of collagen peptides in the urine of KTR that are associated with BR and that are sensitive to bisphosphonate treatment. Their assessment might become a valuable tool to monitor bone status in KTR.

Urine biomarkers in cancer detection: A systematic review of preanalytical parameters and applied methods

The aim of this review was to explore the status of urine sampling as a liquid biopsy for noninvasive cancer research by reviewing used preanalytical parameters and protocols. We searched two main health sciences databases, PubMed and Web of Science. From all eligible publications (2010-2022), information was extracted regarding: (a) study population characteristics, (b) cancer type, (c) urine preanalytics, (d) analyte class, (e) isolation method, (f) detection method, (g) comparator used, (h) biomarker type, (i) conclusion and (j) sensitivity and specificity. The search query identified 7835 records, of which 924 unique publications remained after screening the title, abstract and full text. Our analysis demonstrated that many publications did not report information about the preanalytical parameters of their urine samples, even though several other studies have shown the importance of standardization of sample handling. Interestingly, it was noted that urine is used for many cancer types and not just cancers originating from the urogenital tract. Many different types of relevant analytes have been shown to be found in urine. Additionally, future considerations and recommendations are discussed: (a) the heterogeneous nature of urine, (b) the need for standardized practice protocols and (c) the road toward the clinic. Urine is an emerging liquid biopsy with broad applicability in different analytes and several cancer types. However, standard practice protocols for sample handling and processing would help to elaborate the clinical utility of urine in cancer research, detection and disease monitoring.

Human Urinary Volatilome Analysis in Renal Cancer by Electronic Nose

Currently, in clinical practice there are still no useful markers available that are able to diagnose renal cancer in the early stages in the context of population screening. This translates into very high costs for healthcare systems around the world. Analysing urine using an electronic nose (EN) provides volatile organic compounds that can be easily used in the diagnosis of urological diseases. Although no convincing results have been published, some previous studies suggest that dogs trained to sniff urine can recognize different types of tumours (bladder, lung, breast cancer) with different success rates. We therefore hypothesized that urinary volatilome profiling may be able to distinguish patients with renal cancer from healthy controls. A total of 252 individuals, 110 renal patients and 142 healthy controls, were enrolled in this pilot monocentric study. For each participant, we collected, stabilized (at 37 °C) and analysed urine samples using a commercially available electronic nose (Cyranose 320^®). Principal component (PCA) analyses, discriminant analysis (CDA) and ROC curves were performed to provide a complete statistical analysis of the sensor responses. The best discriminating principal component groups were identified with univariable ANOVA analysis. The study correctly identified 79/110 patients and 127/142 healthy controls, respectively (specificity 89.4%, sensitivity 71.8%, positive predictive value 84.04%, negative predictive value 80.37%). In order to test the study efficacy, the Cross Validated Accuracy was calculated (CVA 81.7%, p < 0.001). At ROC analysis, the area under the curve was 0.85. The results suggest that urine volatilome profiling by e-Nose seems a promising, accurate and non-invasive diagnostic tool in discriminating patients from controls. The low costs and ease of execution make this test useful in clinical practice.

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.

Cell-Free DNA, MicroRNAs, Proteins, and Peptides as Liquid Biopsy Biomarkers in Prostate Cancer and Bladder Cancer

Liquid biopsy, as a novel noninvasive tool for biomarker discovery, has gained a lot of attention and represents a significant innovation in precision medicine. Due to its minimally invasive nature, liquid biopsy has fewer complications and can be scheduled more frequently to provide individualized snapshots of the disease at successive time points. This is particularly valuable in providing simultaneous measurements of tumor burden during treatment and early detection of tumor recurrence or drug resistance. Blood-based liquid biopsy is an attractive, minimally invasive alternative, which has shown promise in diagnosis, risk stratification, disease monitoring, and more. Urine has gained popularity due to its less invasive sampling, the ability to easily repeat samples, and the ability to track tumor evolution in real time, making it a powerful tool for diagnosis and treatment monitoring, especially in urologic cancers. In this review, we provide a detailed discussion on the potential clinical applications of prostate cancer (PCa) and bladder cancer (BCa), with cell-free DNA (cfDNA), microRNAs (miRNAs), proteins, and peptides as liquid biopsy biomarkers.

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%.

Increased Collagen Turnover Is a Feature of Fibromuscular Dysplasia and Associated With Hypertrophic Radial Remodeling: A Pilot, Urine Proteomic Study

Fibromuscular dysplasia (FMD), a nonatherosclerotic, noninflammatory disease of medium-sized arteries, is an underdiagnosed disease. We investigated the urinary proteome and developed a classifier for discrimination of FMD from healthy controls and other diseases. We further hypothesized that urinary proteomics biomarkers may be associated with alterations in medium-sized, but not large artery geometry and mechanics. The study included 33 patients with mostly multifocal, renal FMD who underwent in depth arterial exploration using ultra-high frequency ultrasound. The cohort was separated in a training set of 23 patients with FMD from Belgium and an independent test set of 10 patients with FMD from Italy. For each set, controls matched 2:1 were selected from the Human Urinary Proteome Database. The specificity of the classifier was tested in 700 additional controls from general population studies, patients with chronic kidney disease (n=66) and coronary artery disease (n=31). Three hundred thirty-five urinary peptides, mostly related to collagen turnover, were identified in the training cohort and combined into a classifier. When applying in the test cohort, the area under the receiver operating characteristic curve was 1.00, 100% specificity at 100% sensitivity. The classifier maintained a high specificity in additional controls (98.3%), patients with chronic kidney (90.9%) and coronary artery (96.8%) diseases. Furthermore, in patients with FMD, the proteomic score was positively associated with radial wall thickness and wall cross-sectional area. In conclusion, a proteomic score has the potential to discriminate between patients with FMD and controls. If confirmed in a wider and more diverse cohort, these findings may pave the way for a noninvasive diagnostic test of FMD.

Risk Prediction Models for Kidney Cancer: A Systematic Review

CONTEXT

Early detection of kidney cancer improves survival; however, low prevalence means that population-wide screening may be inefficient. Stratification of the population into risk categories could allow for the introduction of a screening programme tailored to individuals.

OBJECTIVE

This review will identify and compare published models that predict the risk of developing kidney cancer in the general population.

EVIDENCE ACQUISITION

A search identified primary research reporting or validating models predicting the risk of kidney cancer in Medline and EMBASE. After screening identified studies for inclusion, we extracted data onto a standardised form. The risk models were classified using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and evaluated using the PROBAST assessment tool.

EVIDENCE SYNTHESIS

The search identified 15 281 articles. Sixty-two satisfied the inclusion criteria; performance measures were provided for 11 models. Some models predicted the risk of prevalent undiagnosed disease and others future incident disease. Six of the models had been validated, two using external populations. The most commonly included risk factors were age, smoking status, and body mass index. Most of the models had acceptable-to-good discrimination (area under the receiver-operating curve >0.7) in development and validation. Many models also had high specificity; however, several had low sensitivity. The highest performance was seen for the models using only biomarkers to detect kidney cancer; however, these were developed and validated in small case-control studies.

CONCLUSIONS

We identified a small number of risk models that could be used to stratify the population according to the risk of kidney cancer. Most exhibit reasonable discrimination, but a few have been validated externally in population-based studies.

PATIENT SUMMARY

In this review, we looked at mathematical models predicting the likelihood of an individual developing kidney cancer. We found several suitable models, using a range of risk factors (such as age and smoking) to predict the risk for individuals. Most of the models identified require further testing in the general population to confirm their usefulness.

Pathophysiological Implications of Urinary Peptides in Hepatocellular Carcinoma

Simple Summary

In this study, the application of capillary electrophoresis mass spectrometry enabled identification of 31 urinary peptides significantly associated with hepatocellular carcinoma diagnosis and prognosis. Further assessment of these peptides lead to prediction of cellular proteases involved in their development namely Meprin A subunit α and Kallikrein-6. Subsequent identification of the proteases was verified by immunohistochemistry in normal liver, cirrhosis and hepatocellular carcinoma. Histopathological assessment of the proteases revealed numerical gradient staining signifying their involvement in liver fibrosis and hepatocellular carcinoma formation. The discovered urinary peptides offered a potential noninvasive tool for diagnosis and prognosis of hepatocellular carcinoma.

Abstract

Hepatocellular carcinoma (HCC) is known to be associated with protein alterations and extracellular fibrous deposition. We investigated the urinary proteomic profiles of HCC patients in this prospective cross sectional multicentre study. 195 patients were recruited from the UK (Coventry) and Germany (Hannover) between 1 January 2013 and 30 June 2019. Out of these, 57 were HCC patients with a background of liver cirrhosis (LC) and 138 were non-HCC controls; 72 patients with LC, 57 with non-cirrhotic liver disease and 9 with normal liver function. Analysis of the urine samples was performed by capillary electrophoresis (CE) coupled to mass spectrometry (MS). Peptide sequences were obtained and 31 specific peptide markers for HCC were identified and further integrated into a multivariate classification model. The peptide model demonstrated 79.5% sensitivity and 85.1% specificity (95% CI: 0.81–0.93, p < 0.0001) for HCC and 4.1-fold increased risk of death (95% CI: 1.7–9.8, p = 0.0005). Proteases potentially involved in HCC progression were mapped to the N- and C-terminal sequence motifs of the CE-MS peptide markers. In silico protease prediction revealed that kallikrein-6 (KLK6) elicits increased activity, whilst Meprin A subunit α (MEP1A) has reduced activity in HCC compared to the controls. Tissue expression of KLK6 and MEP1A was subsequently verified by immunohistochemistry.

Mining the Biomarker Potential of the Urine Peptidome: From Amino Acids Properties to Proteases

Native biofluid peptides offer important information about diseases, holding promise as biomarkers. Particularly, the non-invasive nature of urine sampling, and its high peptide concentration, make urine peptidomics a useful strategy to study the pathogenesis of renal conditions. Moreover, the high number of detectable peptides as well as their specificity set the ground for the expansion of urine peptidomics to the identification of surrogate biomarkers for extra-renal diseases. Peptidomics further allows the prediction of proteases (degradomics), frequently dysregulated in disease, providing a complimentary source of information on disease pathogenesis and biomarkers. Then, what does urine peptidomics tell us so far? In this paper, we appraise the value of urine peptidomics in biomarker research through a comprehensive analysis of all datasets available to date. We have mined > 50 papers, addressing > 30 different conditions, comprising > 4700 unique peptides. Bioinformatic tools were used to reanalyze peptide profiles aiming at identifying disease fingerprints, to uncover hidden disease-specific peptides physicochemical properties and to predict the most active proteases associated with their generation. The molecular patterns found in this study may be further validated in the future as disease biomarker not only for kidney diseases but also for extra-renal conditions, as a step forward towards the implementation of a paradigm of predictive, preventive and personalized (3P) medicine.

Bioinformatic Analysis of the Wound Peptidome Reveals Potential Biomarkers and Antimicrobial Peptides

Wound infection is a common and serious medical condition with an unmet need for improved diagnostic tools. A peptidomic approach, aided by mass spectrometry and bioinformatics, could provide novel means of identifying new peptide biomarkers for wound healing and infection assessment. Wound fluid is suitable for peptidomic analysis since it is both intimately tied to the wound environment and is readily available. In this study we investigate the peptidomes of wound fluids derived from surgical drainages following mastectomy and from wound dressings following facial skin grafting. By applying sorting algorithms and open source third party software to peptidomic label free tandem mass spectrometry data we provide an unbiased general methodology for analyzing and differentiating between peptidomes. We show that the wound fluid peptidomes of patients are highly individualized. However, differences emerge when grouping the patients depending on wound type. Furthermore, the abundance of peptides originating from documented antimicrobial regions of hemoglobin in infected wounds may contribute to an antimicrobial wound environment, as determined by in silico analysis. We validate our findings by compiling literature on peptide biomarkers and peptides of physiological significance and cross checking the results against our dataset, demonstrating that well-documented peptides of immunological significance are abundant in infected wounds, and originate from certain distinct regions in proteins such as hemoglobin and fibrinogen. Ultimately, we have demonstrated the power using sorting algorithms and open source software to help yield insights and visualize peptidomic data.

Comparison of the amniotic fluid and fetal urine peptidome for biomarker discovery in renal developmental disease

Production of amniotic fluid (AF) is view as predominately driven by excretion of fetal urine (FU). However, the origin of AF peptides, often considered as potential biomarkers of developmental diseases, has never been investigated. Here, we evaluated the FU origin of AF peptides and if the AF peptide content can be used as a surrogate of FU. The abundance of endogenous peptides was analyzed by capillary electrophoresis coupled to mass spectrometry in 216 AF and 64 FU samples. A total of 2668 and 3257 peptides was found in AF and FU respectively. The AF peptidome largely overlapped with the FU peptidome, ranging from 54% in the second pregnancy trimester to 65% in the third trimester. Examination of a subset of 16 paired AF and FU samples revealed that 67 peptides displayed a significant positively correlated abundance in AF and FU, strongly suggesting that their presence in AF was directly associated to FU excretion. As proof-of-concept we showed that measuring the AF abundance of these 67 peptides of FU origin allowed prediction of postnatal renal survival in fetuses with posterior urethral valves. These results demonstrate that the AF peptidome can be considered as a good surrogate of the FU peptidome.

Current evidence on screening for renal cancer

Renal cell carcinoma (RCC) incidence is increasing worldwide. A high proportion of individuals are asymptomatic at diagnosis, but RCC has a high mortality rate. These facts suggest that RCC meets some of the criteria for screening, and a new analysis shows that screening for RCC could potentially be cost-effective. Targeted screening of high-risk individuals is likely to be the most cost-effective strategy to maximize the benefits and reduce the harms of screening. However, the size of the benefit of earlier initiation of treatment and the overall cost-effectiveness of screening remains uncertain. The optimal screening modality and target population is also unclear, and uncertainties exist regarding the specification and implementation of a screening programme. Before moving to a fully powered trial of screening, future work should focus on the following: developing and validating accurate risk prediction models; developing non-invasive methods of early RCC detection; establishing the feasibility, public acceptability and potential uptake of screening; establishing the prevalence of RCC and stage distribution of RCC detected by screening; and evaluating the potential harms of screening, including the impact on quality of life, overdiagnosis and over-treatment.

Collagen: quantification, biomechanics, and role of minor subtypes in cartilage

Collagen is a ubiquitous biomaterial in vertebrate animals. Although each of its 28 subtypes contributes to the functions of many different tissues in the body, most studies on collagen or collagenous tissues have focussed on only one or two subtypes. With recent developments in analytical chemistry, especially mass spectrometry, significant advances have been made toward quantifying the different collagen subtypes in various tissues; however, high-throughput and low-cost methods for collagen subtype quantification do not yet exist. In this Review, we introduce the roles of collagen subtypes and crosslinks, and describe modern assays that enable a deep understanding of tissue physiology and disease states. Using cartilage as a model tissue, we describe the roles of major and minor collagen subtypes in detail; discuss known and unknown structure-function relationships; and show how tissue engineers may harness the functional characteristics of collagen to engineer robust neotissues.

Proteomic approaches for characterizing renal cell carcinoma

Renal cell carcinoma is among the top 15 most commonly diagnosed cancers worldwide, comprising multiple sub-histologies with distinct genomic, proteomic, and clinicopathological features. Proteomic methodologies enable the detection and quantitation of protein profiles associated with the disease state and have been explored to delineate the dysregulated cellular processes associated with renal cell carcinoma. In this review we highlight the reports that employed proteomic technologies to characterize tissue, blood, and urine samples obtained from renal cell carcinoma patients. We describe the proteomic approaches utilized and relate the results of studies in the larger context of renal cell carcinoma biology. Moreover, we discuss some unmet clinical needs and how emerging proteomic approaches can seek to address them. There has been significant progress to characterize the molecular features of renal cell carcinoma; however, despite the large-scale studies that have characterized the genomic and transcriptomic profiles, curative treatments are still elusive. Proteomics facilitates a direct evaluation of the functional modules that drive pathobiology, and the resulting protein profiles would have applications in diagnostics, patient stratification, and identification of novel therapeutic interventions.

Urinary protein biomarkers for pediatric medulloblastoma

OBJECTIVE

To identify candidate urinary protein biomarkers to distinguish medulloblastoma (MB) patients from healthy patients or benign brain disease control patients.

METHODS

The tandem mass tag (TMT)-labeled quantitative proteomics approach was used to identify differential proteins in the urinary proteome of 9 pre- and postsurgery MB patients and 9 healthy control patients, respectively. Ingenuity pathway analysis was used for functional annotation of differential proteins. The biomarker candidates were validated by the parallel reaction monitoring (PRM) method in 112 samples (29 pre- and postsurgery MB patients, 26 healthy control patients, and 28 benign brain disease control patients). Receiver operating characteristic (ROC) curves were developed to evaluate candidate biomarkers.

RESULTS

A total of 114 differential proteins were found. Bioinformatic analysis revealed that the urinary proteome could reflect changes in MB. Seventeen candidate biomarkers were validated by PRM. The combination of CADH1, FGFR4 and FIBB could be used to discriminate MB patients from healthy control patients with an area under the curve (AUC) of 0.973, and the combination of CADH1 and FIBB showed good discriminative power for differentiating MB from benign brain disease with an AUC of 0.884.

CONCLUSION

This report describes the first application of a TMT-PRM workflow to identify and validate MB-specific biomarkers in urine. These findings might contribute to the application of urinary proteomics for detecting and monitoring MB.

BIOLOGICAL SIGNIFICANCE

Medulloblastoma (MB) is among the most common pediatric brain malignancies. This tumor has a highly aggressive clinical course with a high tendency for relapses. Magnetic resonance imaging (MRI) is the major means of diagnosis and for radiographic surveillance after surgery. In MRI, sedation is often required in young children, which could expose them to a series of risks, including airway obstruction and even death. Aside from MRI, there is no reliable biomarker for clinical screening or monitoring of the disease. These facts introduce the clinical need of noninvasive biomarkers for early screening or monitoring of MB. This study is focused on the investigation of a marker panel based on urinary proteome, as a tool for the detection of MB in selected patients at risk. Upon evaluation of the marker model in an independent blinded set of 112 samples, the panel (CADH1, FGFR4 and FIBB) could be used to discriminate MB patients from healthy control patients with an area under the curve (AUC) of 0.973, and the combination of CADH1 and FIBB showed good discriminative power for differentiating MB from benign brain disease with an AUC of 0.884.

CE-MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer

Background

Prostate cancer progresses slowly when present in low risk forms but can be lethal when it progresses to metastatic disease. A non-invasive test that can detect significant prostate cancer is needed to guide patient management.

Methods

Capillary electrophoresis/mass spectrometry has been employed to identify urinary peptides that may accurately detect significant prostate cancer. Urine samples from 823 patients with PSA (<15 ng/ml) were collected prior to biopsy. A case–control comparison was performed in a training set of 543 patients (n_Sig = 98; n_non-Sig = 445) and a validation set of 280 patients (n_Sig = 48, n_non-Sig = 232). Totally, 19 significant peptides were subsequently combined by a support vector machine algorithm.

Results

Independent validation of the 19-biomarker model in 280 patients resulted in a 90% sensitivity and 59% specificity, with an AUC of 0.81, outperforming PSA (AUC = 0.58) and the ERSPC-3/4 risk calculator (AUC = 0.69) in the validation set.

Conclusions

This multi-parametric model holds promise to improve the current diagnosis of significant prostate cancer. This test as a guide to biopsy could help to decrease the number of biopsies and guide intervention. Nevertheless, further prospective validation in an external clinical cohort is required to assess the exact performance characteristics.

Development of biomarkers of genitourinary cancer using mass spectrometry-based clinical proteomics

Prostate, bladder and kidney cancer are the three most common types of genitourinary cancer in the world. Of these, prostate and bladder cancers are within the top 10 most common cancers in men. Notably, kidney cancer causes no obvious symptoms in the early stages. To satisfy clinical-management requirements, researchers have developed numerous biomarkers by applying proteomic approaches using clinical serum, urine and tissue specimens, as well as cell and animal models. Through application of biomarker pipeline protocols, including discovery, verification and validation phases, and mass-spectrometric based proteomic platforms coupled with multiplexed quantification assays, these studies have led to recent rapid progress in this area. With improvements in mass-spectrometric based proteomic techniques, numerous promising biomarker candidates and marker panels for various clinical purposes have been proposed. Verification of novel protein biomarker candidates is very resource demanding (e.g. on the clinical and laboratory sides). With the support of national consortia, it is now possible to investigate the future clinical use of such biomarker strategies and assess their cost-effectiveness in personalized medicine.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Urinary proteomic biomarkers in oncology: ready for implementation?

Introduction: Biomarkers are expected to improve the management of cancer patients by enabling early detection and prediction of therapeutic response. Proteins reflect a molecular phenotype, have high potential as biomarkers, and also are key targets for intervention. Given the ease of collection and proximity to certain tumors, the urinary proteome is a rich source of biomarkers and several proteins have been already implemented. Areas covered: We examined the literature on urine proteins and proteome analysis in oncology from reports published during the last 5 years to generate an overview on the status of urine protein and peptide biomarkers, with emphasis on their actual clinical value. Expert commentary: A few studies report on biomarkers that are ready to be implemented in patient management, among others in bladder cancer and cholangiocarcinoma. These reports are based on multi-marker approaches. A high number of biomarkers, though, has been described in studies with low statistical power. In fact, several of them have been consistently reported across different studies. The latter should be the focus of attention and be tested in properly designed confirmatory and ultimately, prospective investigations. It is expected that multi-marker classifiers for a specific context-of-use, will be the preferred path toward clinical implementation.

Urinary Glycopeptide Analysis for the Investigation of Novel Biomarkers

PURPOSE

Urine is a rich source of potential biomarkers, including glycoproteins. Glycoproteomic analysis remains difficult due to the high heterogeneity of glycans. Nevertheless, recent advances in glycoproteomics software solutions facilitate glycopeptide identification and characterization. The aim is to investigate intact glycopeptides in the urinary peptide profiles of normal subjects using a novel PTM-centric software-Byonic.

EXPERIMENTAL DESIGN

The urinary peptide profiles of 238 normal subjects, previously analyzed using CE-MS and CE-MS/MS and/or LC-MS/MS, are subjected to glycopeptide analysis. Additionally, glycopeptide distribution is assessed in a set of 969 patients with five different cancer types: bladder, prostate and pancreatic cancer, cholangiocarcinoma, and renal cell carcinoma.

RESULTS

A total of 37 intact O-glycopeptides and 23 intact N-glycopeptides are identified in the urinary profiles of 238 normal subjects. Among the most commonly identified O-glycoproteins are Apolipoprotein C-III and insulin-like growth factor II, while titin among the N-glycoproteins. Further statistical analysis reveals that three O-glycopeptides and five N-glycopeptides differed significantly in their abundance among the different cancer types, comparing to normal subjects.

CONCLUSIONS AND CLINICAL RELEVANCE

Through the established glycoproteomics workflow, intact O- and N-glycopeptides in human urine are identified and characterized, providing novel insights for further exploration of the glycoproteome with respect to specific diseases.

Epidemiology and screening for renal cancer

PURPOSE

The widespread use of abdominal imaging has affected the epidemiology of renal cell carcinoma (RCC). Despite this, over 25% of individuals with RCC have evidence of metastases at presentation. Screening for RCC has the potential to downstage the disease.

METHODS

We performed a literature review on the epidemiology of RCC and evidence base regarding screening. Furthermore, contemporary RCC epidemiology data was obtained for the United Kingdom and trends in age-standardised rates of incidence and mortality were analysed by annual percentage change statistics and joinpoint regression.

RESULTS

The incidence of RCC in the UK increased by 3.1% annually from 1993 through 2014. Urinary dipstick is an inadequate screening tool due to low sensitivity and specificity. It is unlikely that CT would be recommended for population screening due to cost, radiation dose and increased potential for other incidental findings. Screening ultrasound has a sensitivity and specificity of 82-83% and 98-99%, respectively; however, accuracy is dependent on tumour size. No clinically validated urinary nor serum biomarkers have been identified. Major barriers to population screening include the relatively low prevalence of the disease, the potential for false positives and over-diagnosis of slow-growing RCCs. Individual patient risk-stratification based on a combination of risk factors may improve screening efficiency and minimise harms by identifying a group at high risk of RCC.

CONCLUSION

The incidence of RCC is increasing. The optimal screening modality and target population remain to be elucidated. An analysis of the benefits and harms of screening for patients and society is warranted.

Promise and Implementation of Proteomic Prostate Cancer Biomarkers

Prostate cancer is one of the most commonly diagnosed malignancy and the fifth leading cause of cancer mortality in men. Despite the broad use of prostate-specific antigen test that resulted in an increase in number of diagnosed cases, disease management needs to be improved. Proteomic biomarkers alone and or in combination with clinical and pathological risk calculators are expected to improve on decreasing the unnecessary biopsies, stratify low risk patients, and predict response to treatment. To this end, significant efforts have been undertaken to identify novel biomarkers that can accurately discriminate between indolent and aggressive cancer forms and indicate those men at high risk for developing prostate cancer that require immediate treatment. In the era of “big data” and “personalized medicine” proteomics-based biomarkers hold great promise to provide clinically applicable tools, as proteins regulate all biological functions, and integrate genomic information with the environmental impact. In this review article, we aim to provide a critical assessment of the current proteomics-based biomarkers for prostate cancer and their actual clinical applicability. For that purpose, a systematic review of the literature published within the last 10 years was performed using the Web of Science Database. We specifically discuss the potential and prospects of use for diagnostic, prognostic and predictive proteomics-based biomarkers, including both body fluid- and tissue-based markers.

Urinary CE-MS peptide marker pattern for detection of solid tumors

Urinary profiling datasets, previously acquired by capillary electrophoresis coupled to mass-spectrometry were investigated to identify a general urinary marker pattern for detection of solid tumors by targeting common systemic events associated with tumor-related inflammation. A total of 2,055 urinary profiles were analyzed, derived from a) a cancer group of patients (n = 969) with bladder, prostate, and pancreatic cancers, renal cell carcinoma, and cholangiocarcinoma and b) a control group of patients with benign diseases (n = 556), inflammatory diseases (n = 199) and healthy individuals (n = 331). Statistical analysis was conducted in a discovery set of 676 cancer cases and 744 controls. 193 peptides differing at statistically significant levels between cases and controls were selected and combined to a multi-dimensional marker pattern using support vector machine algorithms. Independent validation in a set of 635 patients (293 cancer cases and 342 controls) showed an AUC of 0.82. Inclusion of age as independent variable, significantly increased the AUC value to 0.85. Among the identified peptides were mucins, fibrinogen and collagen fragments. Further studies are planned to assess the pattern value to monitor patients for tumor recurrence. In this proof-of-concept study, a general tumor marker pattern was developed to detect cancer based on shared biomarkers, likely indicative of cancer-related features.

Meta-analysis of a 10-plex urine-based biomarker assay for the detection of bladder cancer

A 10-plex urine-based bladder cancer (BCa) diagnostic signature has the potential to non-invasively predict the presence of BCa in at-risk patients, as reported in various case-control studies. The present meta-analysis was performed to re-evaluate and demonstrate the robustness and consistency of the diagnostic utility of the 10-plex urine-based diagnostic assay. We re-analyzed primary data collected in five previously published case-control studies on the 10-plex diagnostic assay. Studies reported the sensitivity and specificity of ten urinary protein biomarkers for the detection of BCa, including interleukin 8, matrix metalloproteinases 9 and 10, angiogenin, apolipoprotein E, syndecan 1, alpha-1 antitrypsin, plasminogen activator inhibitor-1, carbonic anhydrase 9, and vascular endothelial growth factor A. Data were extracted and reviewed independently by two investigators. Log odds ratios (ORs) were calculated to determine how strongly the 10-plex biomarker panel and individual biomarkers are associated with the presence of BCa. Data pooled from 1,173 patients were analyzed. The log OR for each biomarker was improved by 1.5 or greater with smaller 95% CI in our meta-analysis of the overall cohort compared with each analysis of an individual cohort. The combination of the ten biomarkers showed a higher log OR (log OR: 3.46, 95% CI: 2.60–4.31) than did any single biomarker irrespective of histological grade or disease stage of tumors. We concluded that the 10-plex BCa-associated diagnostic signature demonstrated a higher potential to identify BCa when compared to any single biomarker. Our results justify further advancement of the 10-plex protein-based diagnostic signature toward clinical application.

The potential of liquid biopsies for the early detection of cancer

Precision medicine refers to the choosing of targeted therapies based on genetic data. Due to the increasing availability of data from large-scale tumor genome sequencing projects, genome-driven oncology may have enormous potential to change the clinical management of patients with cancer. To this end, components of tumors, which are shed into the circulation, i.e., circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), or extracellular vesicles, are increasingly being used for monitoring tumor genomes. A growing number of publications have documented that these "liquid biopsies" are informative regarding response to given therapies, are capable of detecting relapse with lead time compared to standard measures, and reveal mechanisms of resistance. However, the majority of published studies relate to advanced tumor stages and the use of liquid biopsies for detection of very early malignant disease stages is less well documented. In early disease stages, strategies for analysis are in principle relatively similar to advanced stages. However, at these early stages, several factors pose particular difficulties and challenges, including the lower frequency and volume of aberrations, potentially confounding phenomena such as clonal expansions of non-tumorous tissues or the accumulation of cancer-associated mutations with age, and the incomplete insight into driver alterations. Here we discuss biology, technical complexities and clinical significance for early cancer detection and their impact on precision oncology.

Liquid biopsy: a step forward towards precision medicine in urologic malignancies

There is a growing trend towards exploring the use of a minimally invasive "liquid biopsy" to identify biomarkers in a number of cancers, including urologic malignancies. Multiple aspects can be assessed in circulating cell-free DNA, including cell-free DNA levels, integrity, methylation and mutations. Other prospective liquid biopsy markers include circulating tumor cells, circulating RNAs (miRNA, lncRNAs and mRNAs), cell-free proteins, peptides and exosomes have also emerged as non-invasive cancer biomarkers. These circulating molecules can be detected in various biological fluids, including blood, urine, saliva and seminal plasma. Liquid biopsies hold great promise for personalized medicine due to their ability to provide multiple non-invasive global snapshots of the primary and metastatic tumors. Molecular profiling of circulating molecules has been a stepping-stone to the successful introduction of several non-invasive multi-marker tests into the clinic. In this review, we provide an overview of the current state of cell-free DNA-based kidney, prostate and bladder cancer biomarker research and discuss the potential utility other circulating molecules. We will also discuss the challenges and limitations facing non-invasive cancer biomarker discovery and the benefits of this growing area of translational research.

An integrated proteomic and peptidomic assessment of the normal human urinome

BACKGROUND

Urine represents an ideal source of clinically relevant biomarkers as it contains a large number of proteins and low molecular weight peptides. The comprehensive characterization of the normal urinary proteome and peptidome can serve as a reference for future biomarker discovery. Proteomic and peptidomic analysis of urine can also provide insight into normal physiology and disease pathology, especially for urogenital diseases.

METHODS

We developed an integrated proteomic and peptidomic analytical protocol in normal urine. We employed ultrafiltration to separate protein and peptide fractions, which were analyzed separately using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) on the Q-Exactive mass spectrometer.

RESULTS

By analyzing six urines from healthy individuals with advanced age, we identified 1754 proteins by proteomic analysis and 4543 endogenous peptides, arising from 566 proteins by peptidomic analysis. Overall, we identified 2091 non-redundant proteins by this integrated approach. In silico protease activity analysis indicated that metalloproteases are predominantly involved in the generation of the endogenous peptide signature. In addition, a number of proteins that were detected in normal urine have previously been implicated in various urological malignancies, including bladder cancer and renal cell carcinoma (RCC).

CONCLUSIONS

We utilized a highly sensitive proteomics approach that enabled us to identify one of the largest sets of protein identifications documented in normal human urine. The raw proteomics and peptidomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD003595.

Prediction of Proteases Involved in Peptide Generation

Clinical proteomics has led to the identification of a substantial number of disease-associated peptides and protein fragments in several conditions such as cancer, kidney, or cardiovascular diseases. In silico prediction tools that can facilitate linking of identified peptide biomarkers to predicted protease activity might therefore significantly contribute to the understanding of pathophysiological mechanisms of these diseases. Proteasix is an open-source, peptide-centric tool that can be used to predict in silico the proteases involved in naturally occurring peptide generation. From an input peptide list, Proteasix allows for automatic cleavage site reconstruction and protease associations.

The proteomic landscape of renal tumors

INTRODUCTION

Renal cell carcinoma (RCC) is the most fatal of the common urologic cancers, with approximately 35% of patients dying within 5 years following diagnosis. Therefore, there is a need for non-invasive markers that are capable of detecting and determining the severity of small renal masses at an early stage in order to tailor treatment and follow-up. Proteomic studies have proved to be very useful in the study of tumors. Areas covered: In this review, we will detail the current knowledge obtained by the different proteomic approaches, focusing on MS-based strategies, used to investigate RCC biology in order to identify diagnostic, prognostic and predictive biomarkers on tissue, cultured cells and biological fluids. Expert commentary: Currently, no reliable biomarkers or targets for RCC have been translated into the clinical setting. Moreover, despite the efforts of proteomics and other -omics disciplines, only a small number of them have been observed as shared targets between the different analytical platforms and biological specimens. The difficulty to define a specific molecular pattern for RCC and its subtypes highlights a peculiar profile and a heterogeneity that must be taken into account in future studies.

Proteomics and peptidomics: moving toward precision medicine in urological malignancies

Urological malignancies are a major cause of morbidity and mortality worldwide. Advances in early detection, diagnosis, prognosis and prediction of treatment response can significantly improve patient care. Proteomic and peptidomic profiling studies are at the center of kidney, prostate and bladder cancer biomarker discovery and have shown great promise for improved clinical assessment. Mass spectrometry (MS) is the most widely employed method for proteomic and peptidomic analyses. A number of MS platforms have been developed to facilitate accurate identification of clinically relevant markers in various complex biological samples including tissue, urine and blood. Furthermore, protein profiling studies have been instrumental in the successful introduction of several diagnostic multimarker tests into the clinic. In this review, we will provide a brief overview of high-throughput technologies for protein and peptide based biomarker discovery. We will also examine the current state of kidney, prostate and bladder cancer biomarker research as well as review the journey toward successful clinical implementation.

Urinary Peptide Analysis Differentiates Pancreatic Cancer From Chronic Pancreatitis

OBJECTIVES

Differentiation of pancreatic cancer (PCA) from chronic pancreatitis (CP) is challenging. We searched for peptide markers in urine to develop a diagnostic peptide marker model.

METHODS

Capillary electrophoresis-mass spectrometry was used to search for peptides in urine of patients with PCA (n = 39) or CP (n = 41). Statistical different peptides were included in a peptide multimarker model. Peptide markers were sequence identified and validated by immunoassay and immunohistochemistry (IHC).

RESULTS

Applied to a validation cohort of 54 patients with PCA and 52 patients with CP, the peptide model correctly classified 47 patients with PCA and 44 patients with CP (area under the curve, 0.93; 87% sensitivity; 85% specificity). All 5 patients with PCA with concomitant CP were classified positive. Urine proteome analysis outperformed carbohydrate antigen 19-9 (area under the curve, 0.84) by a 15% increase in sensitivity at the same specificity. From 99 healthy subjects, only four were misclassified. Fetuin-A was the most prominent peptide marker source for PCA as verified by immunoassay and IHC. In silico protease mapping of the peptide markers' terminal sequences pointed to increased meprin-A activity in PCA, which in IHC was associated with neoangiogenesis.

CONCLUSIONS

Urinary proteome analysis differentiates PCA from CP and may serve as PCA screening tool.

Development and Validation of Urine-based Peptide Biomarker Panels for Detecting Bladder Cancer in a Multi-center Study

PURPOSE

Urothelial bladder cancer presents high recurrence rates, mandating continuous monitoring via invasive cystoscopy. The development of noninvasive tests for disease diagnosis and surveillance remains an unmet clinical need. In this study, validation of two urine-based biomarker panels for detecting primary and recurrent urothelial bladder cancer was conducted.

EXPERIMENTAL DESIGN

Two studies (total n = 1,357) were performed for detecting primary (n = 721) and relapsed urothelial bladder cancer (n = 636). Cystoscopy was applied for detecting urothelial bladder cancer, while patients negative for recurrence had follow-up for at least one year to exclude presence of an undetected tumor at the time of sampling. Capillary electrophoresis coupled to mass spectrometry (CE-MS) was employed for the identification of urinary peptide biomarkers. The candidate urine-based peptide biomarker panels were derived from nested cross-sectional studies in primary (n = 451) and recurrent (n = 425) urothelial bladder cancer.

RESULTS

Two biomarker panels were developed on the basis of 116 and 106 peptide biomarkers using support vector machine algorithms. Validation of the urine-based biomarker panels in independent validation sets, resulted in AUC values of 0.87 and 0.75 for detecting primary (n = 270) and recurrent urothelial bladder cancer (n = 211), respectively. At the optimal threshold, the classifier for detecting primary urothelial bladder cancer exhibited 91% sensitivity and 68% specificity, while the classifier for recurrence demonstrated 87% sensitivity and 51% specificity. Particularly for patients undergoing surveillance, improved performance was achieved when combining the urine-based panel with cytology (AUC = 0.87).

CONCLUSIONS

The developed urine-based peptide biomarker panel for detecting primary urothelial bladder cancer exhibits good performance. Combination of the urine-based panel and cytology resulted in improved performance for detecting disease recurrence. Clin Cancer Res; 22(16); 4077-86. ©2016 AACR.

Implementation of CE-MS-identified proteome-based biomarker panels in drug development and patient management

The recent advancements in clinical proteomics enabled identification of biomarker panels for a large range of diseases. A number of CE-MS-identified biomarker panels were verified and implemented in clinical studies. Despite multiple challenges, accumulating evidence supports the value and the need for proteome-based biomarker panels. In this perspective, we provide an overview of clinical studies indicating the added value of CE-MS biomarker panels over traditional diagnostics and monitoring methods. We outline apparent advantages of applying novel proteomic biomarker panels for disease diagnosis, prognosis, staging, drug development and patient management. Facing the plethora of benefits associated with the use of CE-MS biomarker panels, we envision their implementation into the medical practice in the near future.

Deciphering the complexity of the cancer proteome for diagnostic applications

The proteome is the most functional component encoded in the genome, yet most features of the proteome that are deregulated in cancer cannot be predicted from genomic analysis alone. These include post-translational modifications (PTMs), sub-cellular localization, networks and circuitry, formation of complexes, and functional activity, all of which could play a role or be affected as part of tumorigenesis. Thus, there is a substantial opportunity to elucidate protein alterations in cancer and to translate knowledge into diagnostics and therapeutics. The progress made in mining the cancer proteome for diagnostic applications and the path forward are herein reviewed.

Tumor size, stage and grade alterations of urinary peptidome in RCC

Background

Several promising biomarkers have been found for RCC, but none of them has been used in clinical practice for predicting tumour progression. The most widely used features for predicting tumour aggressiveness still remain the cancer stage, size and grade. Therefore, the aim of our study is to investigate the urinary peptidome to search and identify peptides whose concentrations in urine are linked to tumour growth measure and clinical data.

Methods

A proteomic approach applied to ccRCC urinary peptidome (n = 117) based on prefractionation with activated magnetic beads followed by MALDI-TOF profiling was used. A systematic correlation study was performed on urinary peptide profiles obtained from MS analysis. Peptide identity was obtained by LC–ESI–MS/MS.

Results

Fifteen, twenty-six and five peptides showed a statistically significant alteration of their urinary concentration according to tumour size, pT and grade, respectively. Furthermore, 15 and 9 signals were observed to have urinary levels statistically modified in patients at different pT or grade values, even at very early stages. Among them, C1RL, A1AGx, ZAG2G, PGBM, MMP23, GP162, ADA19, G3P, RSPH3, DREB, NOTC2 SAFB2 and CC168 were identified.

Conclusions

We identified several peptides whose urinary abundance varied according to tumour size, stage and grade. Among them, several play a possible role in tumorigenesis, progression and aggressiveness. These results could be a useful starting point for future studies aimed at verifying their possible use in the managements of RCC patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0693-8) contains supplementary material, which is available to authorized users.

Potential Approaches and Recent Advances in Biomarker Discovery in Clear-Cell Renal Cell Carcinoma

The early diagnosis and monitoring of clear-cell Renal Cell Carcinoma (ccRCC), which is the most common renal malignancy, remains challenging. The late diagnosis and lack of tools that can be used to assess the progression of the disease and metastasis significantly influence the chance of survival of ccRCC patients. Molecular biomarkers have been shown to aid the diagnosis and disease monitoring for other cancers, but such markers are not currently available for ccRCC. Recently, plasma and serum circulating nucleic acids, nucleic acids present in urine, and plasma and urine proteins gained interest in the field of cancer biomarker discovery. Here, we describe the applicability of plasma and urine nucleic acids as cancer biomarkers with a particular focus on DNA, small RNA, and protein markers for ccRCC.