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Supplementary Table S6. Multivariate analysis for investigating the comparative performance of the biomarker classifier and cytology for detecting recurrence. Of the 211 subjects that were included in the validation phase, voided urinary cytology (VUC) results were available for 96 urinary samples.

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.

Biomarker konnen die Diagnostik maligner Erkrankungen erganzen. Im Zuge der modernen individualisierten Krebstherapie haben Risikostratifizierung, individuelle Prognose und nicht-invasives Monitoring an Bedeutung gewonnen. In den letzten Jahren ist eine Vielzahl an Biomarkern und zugehoriger Testverfahren hierfur entwickelt worden. Der folgende Ubersichtsartikel fasst die jungsten Entwicklungen bezuglich der Verwendung von Biomarkern in der Diagnostik urologischer Krebserkrankungen zusammen.

BackgroundProstate cancer (PCa) is the most common cancer and one of the leading causes of death worldwide. However, a significant proportion of PCa are low risk PCa which do not require an active treatment due to its low mortality rates. Thus, one major issue in PCa management is to accurately distinguish between indolent and clinically significant (cs) PCa to reduce overdiagnosis and overtreatment. In this study, we aim to validate the performance of diagnostic nomograms (DN) based on previously published urinary biomarkers for discriminating csPCa.Patients and MethodsCapillary electrophoresis/ mass spectrometry has been employed to validate a published biomarker model based on 19 urinary peptides specific for csPCa. Added value of the 19-biomarker model (19-BM) was assessed in diagnostic nomograms including prostate specific antigen (PSA), PSA density and the risk calculator from The European Randomized Study of Screening for Prostate Cancer (ERSPC). For this purpose, urine samples from 147 PCa patients (including 80 low, 44 intermediate and 17 high risk patients) were collected prior to prostate biopsy. The 19-BM score was calculated via a support vector machine-based software based on the pre-defined cut-off criterion of -0.07. DNs were subsequently developed to assess added value of integrative diagnostics.ResultsIndependent validation of the 19-BM resulted in 87% sensitivity and 65% specificity, with an AUC of 0.81, outperforming PSA (AUCPSA:0.64), PSA density (AUCPSAD: 0.64) and ERSPC-3/4 risk calculator (0.67). Integration of 19-BM with the other clinical variables into distinct DN, resulted in improved (AUC range: 0.82-0.88) but not significantly better performance compared to 19-BM alone.Conclusions19-BM alone or combined with clinical variables into DN, demonstrated value for detecting csPCa, and decreasing the number of biopsies.

Non-invasive urine-based biomarkers can potentially improve current diagnostic and monitoring protocols for bladder cancer (BC). Here we assess the performance of earlier published biomarker panels for BC detection (BC-116) and monitoring of recurrence (BC-106) in combination with cytology, in two prospectively collected patient cohorts.Of the 602 patients screened for BC, 551 were found eligible. For the primary setting, 73 patients diagnosed with primary BC (n = 27) and benign urological disorders, including patients with macroscopic haematuria, cystitis and/or nephrolithiasis (n = 46) were included. In total, 478 patients under surveillance were additionally considered (83 BC recurrences; 395 negative for recurrence). Urine samples were analysed with capillary electrophoresis-mass spectrometry. The biomarker score was estimated via support vector machine-based software.Validation of BC-116 biomarker panel resulted in 89% sensitivity and 67% specificity (AUCBC-116 biomarker panel is a useful test for detecting primary BC. BC-106 classifier integrated with cytology showing95% negative predictive value, might be useful for decreasing the number of cystoscopies during surveillance.

Prostate cancer (PCa) is one of the most frequently diagnosed malignancies worldwide and is associated with high mortality. Broad screening through prostate-specific antigen analysis, along with an...

During the late months of last year, a novel coronavirus was detected in Hubei, China The virus, since then, has spread all across the globe forcing Word Health Organization (WHO) to declare COVID-19 outbreak a pandemic In Spain, the virus started infecting the country slowly until rapid growth of infected people occurred in Madrid, Barcelona and other major cities The government in an attempt to stop the rapssid spread of the virus and ensure that health system will not reach its capacity, implement strict measures by putting the entire country in quarantine The duration of these measures, depends on the evolution of the virus in Spain In this study, a Deep Neural Network approach using Monte Carlo is proposed for generating a database to train networks for estimating the optimal parameters of a SIR epidemiology model The number of total infected people as of April 7 in Spain is considered as input to the Deep Neural Network The adaptability of the model was evaluated using the latest data upon completion of this paper, i e , April 14 The date range for the peak of infected people (i e , active cases) based on the new information is estimated to be within 74 to 109 days after the first recorded case of COVID-19 in Spain In addition, a curve fitting measure based on the squared Euclidean distance indicates that according to the current data the peak might occur before the 86 th day Collectively, Deep Neural Networks have proven accurate and useful tools in handling big epidemiological data and for peak prediction estimates

Background Detection of cholangiocarcinoma (CCA) remains a diagnostic challenge. We established diagnostic peptide biomarkers in bile and urine based on capillary electrophoresis coupled to mass spectrometry (CE-MS) to detect both local and systemic changes during CCA progression. In a prospective cohort study we recently demonstrated that combined bile and urine proteome analysis could further improve diagnostic accuracy of CCA diagnosis in patients with unknown biliary strictures. As a continuation of these investigations, the aim of the present study was to investigate the pathophysiological mechanisms behind the molecular determinants reflected by bile and urine peptide biomarkers. Methods Protease mapping and gene ontology cluster analysis were performed for the previously defined CE-MS based biomarkers in bile and urine. For that purpose, bile and urine peptide profiles (from samples both collected at the date of endoscopy) were investigated from a representative cohort of patients with benign (n = 76) or CCA-associated (n = 52) biliary strictures (verified during clinical follow-up). This was supplemented with a literature search for the association of the individual biomarkers included in the proteomic patterns with CCA or cancer progression. Results For most of the peptide markers, association to CCA has been described in literature. Protease mapping revealed ADAMTS4 activity in cleavage of both bile and urine CCA peptide biomarkers. Furthermore, increased chymase activity in bile points to mast cell activation at the tumor site. Gene ontology cluster analysis indicates cellular response to chemical stimuli and stress response as local and extracellular matrix reorganization by tissue destruction and repair as systemic events. The analysis further supports that the mapped proteases are drivers of local and systemic events. Conclusions The study supports connection of the CCA-associated peptide biomarkers to the molecular pathophysiology and indicates an involvement in epithelial-to-mesenchymal transition, generation of cancer-associated fibroblasts and activation of residual immune cells. Proteases, extracellular matrix components, inflammatory cytokines, proangiogenic, growth and vasoactive factors released from the tumor microenvironment are drivers of systemic early events during CCA progression.

PurposeNon-invasive urine-based biomarkers for bladder cancer (BC) diagnosis and surveillance can potentially improve current diagnostic and monitoring protocols by guiding cystoscopy. Here, we aim to access the diagnostic performance of nomograms based on published biomarker panels for BC detection (BC-116) and monitoring of recurrence (BC-106) in combination with cytology, in two prospectively collected patient cohorts.Experimental Design602 recruited patients were screened for presence of BC, out of which 551 were found eligible for further analysis. For the primary setting, urine samples from 73 eligible patients were analyzed from those diagnosed with primary BC (n=27) and benign urological disorders (n=46). For the surveillance setting, 478 eligible patients were considered (83 BC recurrences; 395 negative for recurrence). Urine samples were analyzed with capillary electrophoresis coupled to mass spectrometry and the biomarker score was estimated via a support vector machine-based software.ResultsValidation of the BC-116 biomarker panel resulted in 89% sensitivity and 67% specificity (AUCBC-116=0.82), similar to the published estimates. The nomogram based on cytology and BC-116 resulted in good (AUCNom116=0.85) but not significantly better performance than the BC-116 alone (P=0.5672). BC-106 biomarker panel showed 89% sensitivity and 32% specificity for surveillance, while improved performance was achieved when a nomogram including BC-106 and cytology was evaluated (AUCNom106=0.82), significantly outperforming both cytology (AUCcyt=0.72;P=0.0022) and BC-106 alone (AUCBC-106=0.67;P=0.0012).ConclusionsBC-116 biomarker panel is a useful test for detecting primary BC. BC-106 classifier integrated with cytology and showing >95% negative predictive value, might be useful for decreasing the number of cystoscopies during surveillance.

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 &lt, 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.

Cancer diagnostics can be supplemented by disease-related biomarkers. In the course of modern patient-tailored cancer treatment, the importance of correct risk stratification, prognosis and monitoring has significantly increased. In recent years, a multitude of biomarkers and related test procedures have emerged to fulfil this purpose. The following review article summarizes the most recent developments with respect to the use of biomarkers in the diagnostics of urological cancers.Biomarker können die Diagnostik maligner Erkrankungen ergänzen. Im Zuge der modernen individualisierten Krebstherapie haben Risikostratifizierung, individuelle Prognose und nicht-invasives Monitoring an Bedeutung gewonnen. In den letzten Jahren ist eine Vielzahl an Biomarkern und zugehöriger Testverfahren hierfür entwickelt worden. Der folgende Übersichtsartikel fasst die jüngsten Entwicklungen bezüglich der Verwendung von Biomarkern in der Diagnostik urologischer Krebserkrankungen zusammen.

Despite advancements in therapeutics, Bladder Cancer (BLCA) constitutes a major clinical burden, with locally advanced and metastatic cases facing poor survival rates. Aiming at expanding our knowledge of BLCA molecular pathophysiology, we integrated 1,508 publicly available, primary, well-characterized BLCA transcriptomes and investigated alterations in gene expression with stage (T0-Ta-T1-T2-T3-T4). We identified 157 genes and several pathways related prominently with cell cycle, showing a monotonically up- or down-regulated trend with higher disease stage. Genome wide coexpression across stages further revealed intrinsic and microenvironmental gene rewiring programs that shape BLCA evolution. Novel associations between epigenetic factors (CBX7, ZFP2) and BLCA survival were validated in external data. T0 together with advanced stages were heavily infiltrated with immune cells, but of distinct populations. We found AIF1 to be a novel driver of macrophage-based immunosuppression in T4 tumors. Our results suggest a continuum of alterations with increasing malignancy.

DNA/RNA-based classification of bladder cancer (BC) supports the existence of multiple molecular subtypes, while investigations at the protein level are scarce. Here, we aimed to investigate if Nonmuscle Invasive Bladder Cancer (NMIBC) can be stratified to biologically meaningful groups based on the proteome. Tissue specimens from 117 patients at primary diagnosis (98 with NMIBC and 19 with MIBC), were processed for high-resolution proteomics analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The proteomics output was subjected to unsupervised consensus clustering, principal component analysis (PCA) and investigation of subtype-specific features, pathways, and gene sets. NMIBC patients were optimally stratified to three NMIBC proteomic subtypes (NPS), differing in size, clinicopathologic and molecular backgrounds: NPS1 (mostly high stage/grade/risk samples) was the smallest in size (17/98) and overexpressed proteins reflective of an immune/inflammatory phenotype, involved in cell proliferation, unfolded protein response and DNA damage response, whereas NPS2 (mixed stage/grade/risk composition) presented with an infiltrated/mesenchymal profile. NPS3 was rich in luminal/differentiation markers, in line with its pathological composition (mostly low stage/grade/risk samples). PCA revealed a close proximity of NPS1 and conversely, remoteness of NPS3 to the proteome of MIBC. Proteins distinguishing these two extreme subtypes were also found to consistently differ at the mRNA levels between high and low-risk subtypes of the UROMOL and LUND cohorts. Collectively, our study identifies three proteomic NMIBC subtypes and following a cross-omics validation in two independent cohorts, shortlists molecular features meriting further investigation for their biomarker or potentially therapeutic value.

Prostate cancer (PCa) carries a growing burden on society. Lack of curative treatment and poor prognosis among patients with advanced PCa imply an urgent need for novel and improved drug identification. This is hampered by the disease's high molecular heterogeneity and complex molecular pathophysiology, resulting in drugs being efficient in a few patients and cancer developing resistance to treatment. De novo drug discovery has proven to be complex and challenging. Along with technological advancements (mainly linked to –omics approaches) that allow for comprehensive characterization of the molecular changes underlying disease, and considering respective developments in bioinformatics, computational drug repurposing has emerged as a promising approach to shorten the way from discovery to clinical application and address the disease molecular complexity. With this article, we aimed at reviewing recent studies in which drugs/ compounds for PCa were defined through the investigation of molecular profiling (-omics) data and the application of drug repurposing strategies. A brief overview of the technical requirements and associated challenges with the latter are also provided. For that purpose, a literature search was conducted using the PubMed database. Numerous drugs/ compounds have been proposed as potential PCa therapeutics, mostly based on the investigation of genomics and transcriptomics data. In most cases, further assessment in disease models is required. Since ultimately proteins are targeted by drugs, expanding on the use of proteomics profiling data (alone or in combination with other –omics) is expected to advance further defining new/repurposed drugs for PCa.

International audience; (1) Background: Prostate cancer (PCa) is characterized by high heterogeneity. The aim of this study was to investigate molecular alterations underlying PCa development based on proteomics data. (2) Methods: Liquid chromatography coupled to tandem mass spectrometry was conducted for 22 fresh-frozen tissue specimens from patients with benign prostatic hyperplasia (BPH, n = 5) and PCa (n = 17). Mann Whitney test was used to define significant differences between the two groups. Association of protein abundance with PCa progression was evaluated using Spearman correlation, followed by verification through investigating the Prostate Cancer Transcriptome Atlas. Functional enrichment and interactome analysis were carried out using Metascape and String. (3) Results: Proteomics analysis identified 1433 proteins, including 145 proteins as differentially abundant between patients with PCa and BPH. In silico analysis revealed alterations in several pathways and hallmarks implicated in metabolism and signalling, represented by 67 proteins. Among the latter, 21 proteins were correlated with PCa progression at both the protein and mRNA levels. Interactome analysis of these 21 proteins predicted interactions between Myc proto-oncogene (MYC) targets, protein processing in the endoplasmic reticulum, and oxidative phosphorylation, with MYC targets having a central role. (4) Conclusions: Tissue proteomics allowed for characterization of proteins and pathways consistently affected during PCa development. Further validation of these findings is required.

Molecular studies of the proteome and metabolome in readily available body fluids such as urine and blood performed in a comprehensive qualitative and quantitative way are a valuable source of information for kidney disease research. They provide potential biomarkers of disease progression, markers of efficacy of interventions, as well as information on the underlying pathophysiology. Identified proteins and metabolites may point to dysregulated biological pathways and this knowledge may be useful in the identification of new treatment targets.Many studies, focusing on chronic kidney disease as well as diabetic nephropathy, demonstrate that peptidome and metabolome analysis can substantially contribute to early detection and prediction of disease progression, but also stratification of kidney disease in clinical practice. An innovative, well-explored application of urinary peptidome analysis is the back-translation of results obtained in humans to animals, for animal model validation and improvement of the preclinical readouts. In this chapter, we provide an overview of urinary proteomic analysis with the CE-MS analytical platform, a strategy that has been successfully employed in several studies for the identification and validation of biomarkers in kidney diseases. We describe how to obtain the orthology between the animal model and humans. We also deliver an overview of the analysis of the metabolome with the GC×GC-TOF-MS and UHPLC-Q-TOF-MS analytical platforms for blood and serum as new methods being applied in kidney disease.It is expected that a systems medicine approach to kidney disease including multiple omics methods will provide us with the best way to understand and treat diabetic kidney disease in the future.

Molecular studies of the proteome and metabolome in readily available body fluids such as urine and blood performed in a comprehensive qualitative and quantitative way are a valuable source of information for kidney disease research. They provide potential biomarkers of disease progression, markers of efficacy of interventions, as well as information on the underlying pathophysiology. Identified proteins and metabolites may point to dysregulated biological pathways and this knowledge may be useful in the identification of new treatment targets.Many studies, focusing on chronic kidney disease as well as diabetic nephropathy, demonstrate that peptidome and metabolome analysis can substantially contribute to early detection and prediction of disease progression, but also stratification of kidney disease in clinical practice. An innovative, well-explored application of urinary peptidome analysis is the back-translation of results obtained in humans to animals, for animal model validation and improvement of the preclinical readouts. In this chapter, we provide an overview of urinary proteomic analysis with the CE-MS analytical platform, a strategy that has been successfully employed in several studies for the identification and validation of biomarkers in kidney diseases. We describe how to obtain the orthology between the animal model and humans. We also deliver an overview of the analysis of the metabolome with the GC×GC-TOF-MS and UHPLC-Q-TOF-MS analytical platforms for blood and serum as new methods being applied in kidney disease.It is expected that a systems medicine approach to kidney disease including multiple omics methods will provide us with the best way to understand and treat diabetic kidney disease in the future.

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.

Introduction Molecular signatures may present sources of druggable targets not adequately explored yet. The objective of this study was the identification of known de-risked potential therapeutic compounds (drug repurposing) for the treatment of high-risk NMIBC and MIBC, based on high resolution proteomics profiles. Methods The next generation Connectivity Map (CMap) was employed for drug repurposing using as input proteomic signatures from patients with MIBC, and NMIBC of low (NPS3) and high aggressive molecular profile (NPS1) from our recently published datasets (Stroggilos et al. Int J Cancer.2020;146(1):281). The retrieved candidate drugs were ranked according to the disease–drug connectivity score. The impact of selected drugs was investigated in vitro in a panel of multi-origin BC cell lines including benign (HBLAK), non–muscle invasive (BFTC-905, SW1710) and invasive (T24, T24M, VmCub1, 253J, HT-1376) cells. The impact on cell proliferation (MTS), colony formation (matrigel), and apoptosis (Annexin V) was assessed. Results Among the top ranked compounds reversing the molecular signature of NMIBC from high to low risk subtype were mTOR inhibitors, tubulin inhibitors, caspase activators and RAF inhibitors. WYE-354, an ATP-competitive inhibitor of mTOR, had the highest connectivity score. WYE-354 was also ranked among the top candidate drugs when using as input proteins with consistent changes at the mRNA level in NMIBC progressors versus non-progressors from earlier published studies or proteomics changes in MIBC versus NMIBC. In vitro administration of WYE-354 resulted in a significant reduction in the proliferation rate of all the tested BC cell lines in a concentration-dependent manner and also impaired colony growth by significantly decreasing the size of the colonies. However, no significant effect on BC cell apoptosis could be observed. Conclusions In this study, we describe a promising pipeline for identifying repurposed drugs of potential therapeutic impact for aggressive NMIBC, based on patients’ proteomic signatures. Deciphering the molecular mechanism of the impact of WYE-354 based on the molecular characteristics of the tested cell lines is ongoing. Evaluation of further compounds or combinations predicted by the CMap analysis is also planned.

Patients with advanced bladder cancer have poor outcomes, indicating a need for more efficient therapeutic approaches. This study characterizes proteomic changes underlying bladder cancer invasion aiming for the better understanding of disease pathophysiology and identification of drug targets. High resolution liquid chromatography coupled to tandem mass spectrometry analysis of tissue specimens from patients with non-muscle invasive (NMIBC, stage pTa) and muscle invasive bladder cancer (MIBC, stages pT2+) was conducted. Comparative analysis identified 144 differentially expressed proteins between analyzed groups. These included proteins previously associated with bladder cancer and also additional novel such as PGRMC1, FUCA1, BROX and PSMD12, which were further confirmed by immunohistochemistry. Pathway and interactome analysis predicted strong activation in muscle invasive bladder cancer of pathways associated with protein synthesis e.g. eIF2 and mTOR signaling. Knock-down of eukaryotic translation initiation factor 3 subunit D (EIF3D) (overexpressed in muscle invasive disease) in metastatic T24M bladder cancer cells inhibited cell proliferation, migration, and colony formation in vitro and decreased tumor growth in xenograft models. By contrast, knocking down GTP-binding protein Rheb (which is upstream of EIF3D) recapitulated the effects of EIF3D knockdown in vitro, but not in vivo. Collectively, this study represents a comprehensive analysis of NMIBC and MIBC providing a resource for future studies. The results highlight EIF3D as a potential therapeutic target.

Research efforts targeting the identification of bladder cancer biomarkers have been extensive during the past decade. Investigations have been performed at the genome, transcriptome, proteome, and metabolome levels and outputs have started appearing including the sketching of disease molecular subtypes. Proteins are directly linked to cell phenotype hence they accumulate special interest as both biomarkers and therapeutic targets. Multiple technical challenges exist, of the main, being the protein concentration vast dynamic range and presence of proteins in modified forms. The scope of this review is to summarize the contribution of proteomics research in this quest of bladder cancer biomarkers. To obtain an unbiased and comprehensive overview, the scientific literature was searched for manuscripts describing proteomic studies on urothelial cancer from the last ten years and those including independent verification studies in urine, tissue and blood are briefly presented. General observations include: a) in most cases, suboptimal experimental design including healthy controls in biomarker discovery and frequently biomarker verification, is followed; b) variability in protein findings between studies can be observed, to some extent reflecting complexity of experimental approaches and proteome itself; c) consistently reported biomarkers include mainly plasma proteins and d) compilation of protein markers into diagnostic panels appears the most promising way forward. Two main avenues of research can now be foreseen: targeting integration of the existing disparate data with proteomic findings being placed in the context of existing knowledge on bladder cancer subtypes and in parallel, accumulation of clinical samples to support proper validation studies of promising marker combinations.

This article is published as partofthe special issue of Proteomics Clinical Applications Journal,focused on the implementation of clinical proteomics. In this special issue the most prominent examples of proteomics on the road towards clinical application are presented.

Capillary electrophoresis combined with mass spectrometry (CE-MS) has been used for several years for the investigation of proteins and peptides as biomarkers for diagnosis and prognosis of diseases. In addition, the technology has recently been introduced to support the stratification of patients in clinical trials and in large clinical studies. In this review, we aim at presenting the development of CE-MS over the last 20 years, by focusing on the clinical potential of proteome and peptidome analysis and highlighting some of the key technical issues and advancements that have been made in this context towards implementation. Based on the reviewed literature, it has become evident that CE-MS is now an accepted tool in clinical application in several disease areas. Apart from a critical overview on the current state-of-the-art in CE-MS, we also indicate the expected developments for potential future use.

Scientific reports 9(1), 12864 (2019). doi:10.1038/s41598-019-49304-9, Published by Macmillan Publishers Limited, part of Springer Nature, [London]

Mass spectrometry offers the potential of acquiring high resolution data depicting the functional status of a group of healthy or diseased individuals, according to different conditions. As most of the drugs are currently targeting proteins, proteomics has a dual value, both in the discovery of new molecules as therapeutic targets, but also as a methodology to perform high throughput drug profiling. As there is an evident need for drugs to be improved in terms of efficacy, a mechanistic insight for downstream effectors can be valuable in order to predict side effects and resistance mechanisms. Recently developed assays, like thermal proteome profiling enables comprehensive drug target profiling and is, therefore, of high value in drug discovery. In this review, a systematic literature search is conducted and the most prominent proteomics studies as implicated in assisting drug discovery and development is presented. Focus is placed on investigations that are closer to implementation, therefore particular emphasis is given in studies conducted in human diseased population and further verified in vitro or in vivo.

Multi-omics signatures of patients with bladder cancer (BC) can guide the identification of known de-risked therapeutic compounds through drug repurposing, an approach not extensively explored yet. In this study, we target drug repurposing in the context of BC, driven by tissue omics signatures. To identify compounds that can reverse aggressive high-risk Non-Muscle Invasive BC (NMIBC) to less aggressive low-risk molecular subtypes, the next generation Connectivity Map (CMap) was employed using as input previously published proteomics and transcriptomics respective signatures. Among the identified compounds, the ATP-competitive inhibitor of mTOR, WYE-354, showed a consistently very high score for reversing the aggressive BC molecular signatures. WYE-354 impact was assessed in a panel of eight multi-origin BC cell lines and included impaired colony growth and proliferation rate without any impact on apoptosis. Overall, with this study we introduce a promising pipeline for the repurposing of drugs for BC treatment, based on patients&rsquo, omics signatures.

Bladder cancer (BC) is the second most common malignancy of the genitourinary system, characterized by the highest recurrence rate of all cancers. Treatment options are limited; thus a thorough understanding of the underlying molecular mechanisms is needed to guide the discovery of novel therapeutic targets. Profilins are actin binding proteins with attributed pleiotropic functions to cytoskeletal remodeling, cell adhesion, motility, even transcriptional regulation, not fully characterized yet. Earlier studies from our laboratory revealed that decreased tissue levels of Profilin-1 (PFN1) are correlated with BC progression to muscle invasive disease. Herein, we describe a comprehensive analysis of PFN1 silencing via shRNA, in vitro (by employing T24M cells) and in vivo [(with T24M xenografts in non-obese diabetic severe combined immunodeficient mice (NOD/SCID) mice]. A combination of phenotypic and molecular assays, including migration, proliferation, adhesion assays, flow cytometry and total mRNA sequencing, as well as immunohistochemistry for investigation of selected findings in human specimens were applied. A decrease in BC cell adhesion and tumor growth in vivo following PFN downregulation are observed, likely associated with the concomitant downregulation of Fibronectin receptor, Endothelin-1, and Actin polymerization. A decrease in the levels of multiple key members of the non-canonical Wnt/Ca2+ signaling pathway is also detected following PFN1 suppression, providing the groundwork for future studies, addressing the specific role of PFN1 in Ca2+ signaling, particularly in the muscle invasive disease.

Recent investigations, mainly at the DNA/RNA levels, of muscle-invasive (MIBC) and non-muscle invasive bladder cancer (NMIBC) highlighted the existence of molecular subtypes exhibiting diverse levels of aggressiveness. Studies at the protein level are scarce but are needed to enhance present findings and fill existing gaps. The objective of our work is to subtype NMIBC at the proteomic level and place it in the context of the existing NMIBC classification schemes, targeting to ultimately define optimal patient management. One hundred seventeen fresh-frozen tissue specimens from primary untreated BC cases (98 NMIBC and 19 MIBC samples) were processed for high-resolution LC-MS/MS analysis. By utilizing only the high-confidence spectrometric data (FDR < 0.01), as well as the mean abundance of the top 3 most abundant peptides per protein for protein quantification, more than 5,000 protein IDs in total were identified. The proteomics output was subjected to unsupervised consensus clustering, principal component analysis (PCA), and investigation of subtype-specific features, pathways, and genesets. Based on protein abundance, NMIBC patients were optimally stratified to 3 subtypes (classes), differing at size, clinicopathologic, and molecular backgrounds: Class 1 (mostly high stage/grade/risk samples) was the smallest in size (17/98) and expressed predominantly an immune/inflammatory phenotype, features involved in cell proliferation, unfolded protein response, and DNA damage response; class 2 (mixed stage/grade/risk composition) presented with an infiltrated/mesenchymal profile; and class 3 tumors were rich in luminal/differentiation markers, in line with their pathologic classification (mostly low stage/grade/risk samples). PCA revealed proximity of class 1 and conversely, remoteness of class 3 to the proteome of MIBC. Using GSEA and a Random Forest-based classifier on UROMOL's data (Hedegaard J et al., Cell 2016), we identified strong associations between our proteomics class 1 and the progressed UROMOL subtype and likewise between our proteomics class 3 and the two nonprogressed UROMOL subtypes. Several proteins distinguishing these two extreme proteomic subtypes (class 1 vs. 3) were found to also consistently differ at the mRNA levels between NMIBC “progressor” and “nonprogressor” groups of the UROMOL and LUND cohorts. Collectively, protein-based classification of NMIBC resulted in the definition of 3 classes with distinct pathologic and molecular profiles, sharing analogies to the existing transcriptomic NMIBC subtypes. The presented results facilitate shortlisting potential NMIBC prognosticators for further validation in clinical trials. Citation Format: Rafael S. Stroggilos, Marika Mokou, Agnieszka Latosinska, Manousos Makridakis, Vasiliki Lygirou, Emmanouil Mavrogeorgis, Dimitris Drekolias, Maria Frantzi, William Mullen, Charalampos Fragkoulis, Konstantinos Stasinopoulos, Georgios Papadopoulos, Georgios Stathouros, Konstantinos Ntoumas, Harald Mischak, Jerome Zoidakis, Antonia Vlahou. Molecular classification of NMIBC based on protein abundance [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr B18.

International audience; Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.

International audience; 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.