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Prostate cancer (PCa) is the second most common cancer in men. Diagnosis and risk assessment are widely based on serum Prostate Specific Antigen (PSA) and biopsy, which might not represent the exact degree of PCa risk. Towards the discovery of biomarkers for better patient stratification, we performed proteomic analysis of Formalin Fixed Paraffin Embedded (FFPE) prostate tissue specimens using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Comparative analysis of 86 PCa samples among grade groups 1–5 identified 301 significantly altered proteins. Additional analysis based on biochemical recurrence (BCR; BCR+ n = 14, BCR- n = 51) revealed 197 significantly altered proteins that indicate disease persistence. Filtering the overlapping proteins of these analyses, seven proteins (NPM1, UQCRH, HSPA9, MRPL3, VCAN, SERBP1, HSPE1) had increased expression in advanced grades and in BCR+/BCR- and may play a critical role in PCa aggressiveness. Notably, all seven proteins were significantly associated with progression in Prostate Cancer Transcriptome Atles (PCTA) and NPM1NPM1, UQCRH, and VCAN were further validated in The Cancer Genome Atlas (TCGA), where they were upregulated in BCR+/BCR-. UQCRH levels were also associated with poorer 5-year survival. Our study provides valuable insights into the key regulators of PCa progression and aggressiveness. The seven selected proteins could be used for the development of risk assessment tools.

Current diagnostic measures for Chronic Kidney Disease (CKD) include detection of reduced estimated glomerular filtration rate (eGFR) and albuminuria, which have suboptimal accuracies in predicting disease progression. The disease complexity and heterogeneity underscore the need for multiplex quantification of different markers. The goal of this study was to determine the association of six previously reported CKD-associated plasma proteins [B2M (Beta-2-microglobulin), SERPINF1 (Pigment epithelium-derived factor), AMBP (Protein AMBP), LYZ (Lysozyme C), HBB (Hemoglobin subunit beta) and IGHA1 (Immunoglobulin heavy constant alpha 1)], as measured in a multiplex format, with kidney function, and outcome. Antibody-free, multiple reaction monitoring mass spectrometry (MRM) assays were developed, characterized for their analytical performance, and used for the analysis of 72 plasma samples from a patient cohort with longitudinal follow-up. The MRM significantly correlated (Rho = 0.5–0.9) with results from respective ELISA. Five proteins [AMBP, B2M, LYZ, HBB and SERPINF1] were significantly associated with eGFR, with the three former also associated with unfavorable outcome. The combination of these markers provided stronger associations with outcome (p

Background: The absence of efficient inhibitors for diabetic kidney disease (DKD) progression reflects the gaps in our understanding of DKD molecular pathogenesis. Methods: A comprehensive proteomic analysis was performed on the glomeruli and kidney cortex of diabetic mice with the subsequent validation of findings in human biopsies and omics datasets, aiming to better understand the underlying molecular biology of early DKD development and progression. Results: LC–MS/MS was employed to analyze the kidney proteome of 2 DKD models: Ins2Akita (early and late DKD) and db/db mice (late DKD). The abundance of detected proteins was defined. Pathway analysis of differentially expressed proteins in the early and late DKD versus the respective controls predicted dysregulation in DKD hallmarks (peroxisomal lipid metabolism and β-oxidation), supporting the functional relevance of the findings. Comparing the observed protein changes in early and late DKD, the consistent upregulation of 21 and downregulation of 18 proteins was detected. Among these were downregulated peroxisomal and upregulated mitochondrial proteins. Tissue sections from 16 DKD patients were analyzed by IHC confirming our results. Conclusion: Our study shows an extensive differential expression of peroxisomal proteins in the early stages of DKD that persists regardless of the disease severity, providing new perspectives and potential markers of diabetic kidney dysfunction.

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.

The absence of efficient inhibitors for diabetic kidney disease (DKD) progression reflects the gaps in our understanding of DKD molecular pathogenesis.A comprehensive proteomic analysis was performed on the glomeruli and kidney cortex of diabetic mice with the subsequent validation of findings in human biopsies and omics datasets, aiming to better understand the underlying molecular biology of early DKD development and progression.LC-MS/MS was employed to analyze the kidney proteome of 2 DKD models: Ins2Akita (early and late DKD) and db/db mice (late DKD). The abundance of detected proteins was defined. Pathway analysis of differentially expressed proteins in the early and late DKD versus the respective controls predicted dysregulation in DKD hallmarks (peroxisomal lipid metabolism and β-oxidation), supporting the functional relevance of the findings. Comparing the observed protein changes in early and late DKD, the consistent upregulation of 21 and downregulation of 18 proteins was detected. Among these were downregulated peroxisomal and upregulated mitochondrial proteins. Tissue sections from 16 DKD patients were analyzed by IHC confirming our results.Our study shows an extensive differential expression of peroxisomal proteins in the early stages of DKD that persists regardless of the disease severity, providing new perspectives and potential markers of diabetic kidney dysfunction.

BackgroundThe absence of efficient inhibitors for DKD progression reflects the gaps in our understanding of DKD molecular pathogenesis. A comprehensive proteomic analysis was performed on glomeruli and kidney cortex of diabetic mice with subsequent validation of findings in human biopsies and - omics datasets aiming to better understand the underlying molecular biology of early DKD development and progression.MethodsLC–MS/MS was employed to analyze the kidney proteome of DKD mouse models: Glomeruli of Ins2Akita mice 2 month and 4 month old, and cortex of db/db mice 6 month old. Following label-free quantification, the abundance of detected proteins were correlated with existing kidney datasets and functionally annotated. Tissue sections from 16 DKD patients were analyzed by IHC.ResultsPathway analysis of differentially expressed proteins in the early and late DKD versus controls predicted dysregulation in DKD hallmarks (such as peroxisomal lipid metabolism, β-oxidation and TCA cycle) supporting the functional relevance of the findings. Comparing the observed protein changes in early and late DKD, consistent upregulation of 21 and downregulation of 18 proteins was detected. Among these were downregulated peroxisomal proteins such as NUDT19, ACOX1, and AMACR and upregulated mitochondrial proteins related to aminoacid metabolism including GLS, GLDC, and GCAT. Several of these changes were also observed in the kidney cortex proteome of db/db mice. IHC of human kidney further confirmed the differential expression of NUDT19, AGPS, AMACR and CAT proteins in DKD.ConclusionsOur study shows an extensive differential expression of peroxisomal proteins in the early stages of DKD that persists regardless of the disease severity. These proteins therefore represent potential markers of early DKD pathogenesis. Collectively, essential pathways associated with peroxisomes such as lipid β-oxidation, plasmalogen synthesis, aminoacid metabolism and response to oxidative stress are downregulated in early DKD, providing new perspectives and potential markers of diabetic kidney dysfunction.

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.

In the search for candidate predictive biomarkers to evaluate response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer, only a few studies report proteomic profiles of tumor tissue before and after nCRT. The aim of our study was to determine differentially expressed proteins between responders and non-responders before and after the therapy in order to identify candidate molecules for prediction and follow-up of response to nCRT.The study has included tissue sections of rectal tumor and non-tumor mucosa from five responders and five non-responders taken before and after nCRT from patients with locally advanced rectal cancer. Extracted proteins were analyzed by LC-MS/MS analysis followed by a set of bioinformatics analyses.Proteomics analysis provided a mean of approximately 1050 protein identifications per sample. A comparison of proteomic profiles between responders and non-responders has identified 18 differentially expressed proteins. Pathway analysis demonstrated high metabolic activity in non-responders' tumors before nCRT, indicating the presence of intrinsic chemoradioresistance in these subjects. Two proteins associated with poor prognosis in colorectal cancer, ADAM10 and CAD, were identified as candidate predictive biomarkers as they were present in non-responders only.Shortlisted proteins from our study should be further validated as candidate biomarkers for response to routinely applied nCRT protocols.

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.

AIMS: Heart failure (HF) is a major public health concern worldwide. The diversity of HF makes it challenging to decipher the underlying complex pathological processes using single biomarkers. We examined the association between urinary peptides and HF with reduced (HFrEF), mid-range (HFmrEF) and preserved (HFpEF) ejection fraction, defined based on the European Society of Cardiology guidelines, and the links between these peptide biomarkers and molecular pathophysiology. METHODS AND RESULTS: Analysable data from 5608 participants were available in the Human Urinary Proteome database. The urinary peptide profiles from participants diagnosed with HFrEF, HFmrEF, HFpEF and controls matched for sex, age, estimated glomerular filtration rate, systolic and diastolic blood pressure, diabetes and hypertension were compared applying the Mann-Whitney test, followed by correction for multiple testing. Unsupervised learning algorithms were applied to investigate groups of similar urinary profiles. A total of 577 urinary peptides significantly associated with HF were sequenced, 447 of which (77%) were collagen fragments. In silico analysis suggested that urinary biomarker abnormalities in HF principally reflect changes in collagen turnover and immune response, both associated with fibrosis. Unsupervised clustering separated study participants into two clusters, with 83% of non-HF controls allocated to cluster 1, while 65% of patients with HF were allocated to cluster 2 (P

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.

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.

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.