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SummaryCOV50, a urinary proteomic classifier, predicts disease progression and death from SARS-CoV-2 at early stage, suggesting it might predict pre-established vulnerability. This study investigated the value of COV50 in predicting non-COVID-19 associated death. Urinary proteomic data were extracted from the Human Urinary Proteome Database. In the ICU group (n=1719), an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death (adj. HR 1·2 [95% CI 1·17-1·24]). The same increase in COV50 in non-ICU patients (n=7474) resulted in a higher relative risk of 61% (adj. HR 1·61 [95% CI 1·47-1·76]), in line with adjusted meta-analytic HR estimate of 1·55. A higher COV50 scoring was observed in frail patients (p

(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.

Despite advancements in molecular classification, tumor stage and grade still remain the most relevant prognosticators used by clinicians to decide on patient management. Here, we leverage publicly available data to characterize bladder cancer (BLCA)’s stage biology based on increased sample sizes, identify potential therapeutic targets, and extract putative biomarkers. A total of 1135 primary BLCA transcriptomes from 12 microarray studies were compiled in a meta-cohort and analyzed for monotonal alterations in pathway activities, gene expression, and co-expression patterns with increasing stage (Ta–T1–T2–T3–T4), starting from the non-malignant tumor-adjacent urothelium. The TCGA-2017 and IMvigor-210 RNA-Seq data were used to validate our findings. Wnt, MTORC1 signaling, and MYC activity were monotonically increased with increasing stage, while an opposite trend was detected for the catabolism of fatty acids, circadian clock genes, and the metabolism of heme. Co-expression network analysis highlighted stage- and cell-type-specific genes of potentially synergistic therapeutic value. An eight-gene signature, consisting of the genes AKAP7, ANLN, CBX7, CDC14B, ENO1, GTPBP4, MED19, and ZFP2, had independent prognostic value in both the discovery and validation sets. This novel eight-gene signature may increase the granularity of current risk-to-progression estimators.

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.

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.

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.

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.

Introduction: Although multiple efforts have been initiated to shed light into the molecular mechanisms underlying cardiovascular disease, it still remains one of the major causes of death worldwide. Proteomic approaches are unequivocally powerful tools that may provide deeper understanding into the molecular mechanisms associated with cardiovascular disease and improve its management.\ud \ud Areas covered: Cardiovascular proteomics is an emerging field and significant progress has been made during the past few years with the aim of defining novel candidate biomarkers and obtaining insight into molecular pathophysiology. To summarize the recent progress in the field, a literature search was conducted in PubMed and Web of Science. As a result, 704 studies from PubMed and 320 studies from Web of Science were retrieved. Findings from original research articles using proteomics technologies for the discovery of biomarkers for cardiovascular disease in human are summarized in this review.\ud \ud Expert commentary: Proteins associated with cardiovascular disease represent pathways in inflammation, wound healing and coagulation, proteolysis and extracellular matrix organization, handling of cholesterol and LDL. Future research in the field should target to increase proteome coverage as well as integrate proteomics with other omics data to facilitate both drug development as well as clinical implementation of findings.

Background The increased prevalence of cardiovascular disease (CVD) indicates a demand for novel therapeutic approaches. Proteome analysis of vascular tissues from animal models and humans with CVD could lead to the identification of novel druggable targets. Methods LC-MS/MS analysis of thoracic aortas from three mouse models of non-diabetic and diabetic (streptozotocin (STZ)-induced) atherosclerosis followed by bioinformatics/pathway analysis was performed. Selected findings were confirmed by proteomics analysis of human vessels from patients with CVD as well as in vitro studies (migration, proliferation, angiogenesis assays) using endothelial (HUVEC) cells. Findings Comparative tissue proteomics of low density lipoprotein receptor deficient (Ldlr−/−) and diabetic Ldlr−/− (Ldlr−/−STZ) with wild type (WT) animals led to the identification of 284 differentially expressed proteins in both models. Among them, 177 proteins were also differentially expressed in diabetic apolipoprotein E deficient (ApoE−/−STZ) mice, suggesting expression changes associated with atherosclerosis independent of the model used. These proteins recapitulated the hallmarks of atherosclerosis. Comparison of these findings with differentially expressed proteins in human vessels with CVD enabled shortlisting of six commonly dysregulated proteins. Among them, lysine-specific demethylase 5D (KDM5D) exhibited pronounced overexpression accompanied by a reduction in the protein levels of its substrate, the trimethylated lysine 4 of histone H3 (H3K4me3), in patients with CVD. Functional interference studies applying a KDM5 inhibitor on HUVEC reduced cell proliferation, migration and tube-forming ability in vitro. Interpretation This high-throughput proteomics strategy identified KDM5 histone demethylases being potentially involved in CVD, possibly by affecting H3K4 methylation. Fund [SysVasc, HEALTH-2013 603288], [ERA-CVD PROACT: ANR-17-ECVD-0006, 01KL1805], [FRM, DEQ20170336759].

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.

Background:\ud Bladder Cancer (BC) has two clearly distinct phenotypes. Non-muscle invasive BC has good prognosis and is treated with tumor resection and intravesical therapy whereas muscle invasive BC has poor prognosis and requires usually systemic cisplatin based chemotherapy either prior to or after radical cystectomy. Neoadjuvant chemotherapy is not often used for patients undergoing cystectomy. High-throughput analytical omics techniques are now available that allow the identification of individual molecular signatures to characterize the invasive phenotype. However, a large amount of data produced by omics experiments is not easily accessible since it is often scattered over many publications or stored in supplementary files.\ud Objective:\ud To develop a novel open-source database, BcCluster (http://www.bccluster.org/), dedicated to the comprehensive molecular characterization of muscle invasive bladder carcinoma.\ud Materials:\ud A database was created containing all reported molecular features significant in invasive BC. The query interface was developed in Ruby programming language (version 1.9.3) using the web-framework Rails (version 4.1.5) (http://rubyonrails.org/).\ud Results:\ud BcCluster contains the data from 112 published references, providing 1,559 statistically significant features relative to BC invasion. The database also holds 435 protein-protein interaction data and 92 molecular pathways significant in BC invasion. The database can be used to retrieve binding partners and pathways for any protein of interest. We illustrate this possibility using survivin, a known BC biomarker.\ud Conclusions:\ud BcCluster is an online database for retrieving molecular signatures relative to BC invasion. This application offers a comprehensive view of BC invasiveness at the molecular level and allows formulation of research hypotheses relevant to this phenotype.

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.