Search

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

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.