Urinary peptides provide information about the risk of mortality across a spectrum of diseases and scenarios.

BACKGROUND: There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides. METHODS: Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated. RESULTS: In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17-1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47-1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39-1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I). CONCLUSION: The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death.

Clonal Hematopoiesis of Indeterminate Potential and Diabetic Kidney Disease: A Nested Case-Control Study.

Introduction: The disease trajectory of diabetic kidney disease (DKD) shows a high interindividual variability not sufficiently explained by conventional risk factors. Clonal hematopoiesis of indeterminate potential (CHIP) is a proposed novel cardiovascular risk factor. Increased kidney fibrosis and glomerulosclerosis were described in mouse models of CHIP. Here, we aim to analyze whether CHIP affects the incidence or progression of DKD. Methods: A total of 1419 eligible participants of the PROVALID Study were the basis for a nested case-control (NCC) design. A total of 64 participants who reached a prespecified composite endpoint within the observation period (initiation of kidney replacement therapy, death from kidney failure, sustained 40% decline in estimated glomerular filtration rate or sustained progression to macroalbuminuria) were identified and matched to 4 controls resulting in an NCC sample of 294 individuals. CHIP was assessed via targeted amplicon sequencing of 46 genes in peripheral blood. Furthermore, inflammatory cytokines were analyzed in plasma via a multiplex assay. Results: The estimated prevalence of CHIP was 28.91% (95% CI 22.91%-34.91%). In contrast to other known risk factors (albuminuria, hemoglobin A1c, heart failure, and smoking) and elevated microinflammation, CHIP was not associated with incident or progressive DKD (hazard ratio [HR] 1.06 [95% CI 0.57-1.96]). Conclusions: In this NCC study, common risk factors as well as elevated microinflammation but not CHIP were associated with kidney function decline in type 2 diabetes mellitus.

Coregulation Analysis of Mechanistic Biomarkers in Autosomal Dominant Polycystic Kidney Disease.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder leading to deterioration of kidney function and end stage kidney disease (ESKD). A number of molecular processes are dysregulated in ADPKD but the exact mechanism of disease progression is not fully understood. We measured protein biomarkers being linked to ADPKD-associated molecular processes via ELISA in urine and serum in a cohort of ADPKD patients as well as age, gender and eGFR matched CKD patients and healthy controls. ANOVA and t-tests were used to determine differences between cohorts. Spearman correlation coefficient analysis was performed to assess coregulation patterns of individual biomarkers and renal function. Urinary epidermal growth factor (EGF) and serum apelin (APLN) levels were significantly downregulated in ADPKD patients. Serum vascular endothelial growth factor alpha (VEGFA) and urinary angiotensinogen (AGT) were significantly upregulated in ADPKD patients as compared with healthy controls. Arginine vasopressin (AVP) was significantly upregulated in ADPKD patients as compared with CKD patients. Serum VEGFA and VIM concentrations were positively correlated and urinary EGF levels were negatively correlated with urinary AGT levels. Urinary EGF and AGT levels were furthermore significantly associated with estimated glomerular filtration rate (eGFR) in ADPKD patients. In summary, altered protein concentrations in body fluids of ADPKD patients were found for the mechanistic markers EGF, APLN, VEGFA, AGT, AVP, and VIM. In particular, the connection between EGF and AGT during progression of ADPKD warrants further investigation.

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions.

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of both synthetic lethal interactors leads to loss of cell viability or cell death. The best studied synthetic lethal interaction is between BRCA1/2 and PARP1, with PARP1 inhibitors being used in clinical practice to treat patients with BRCA1/2 mutated tumors. Large genetic screens in model organisms but also in haploid human cell lines have led to the identification of numerous additional synthetic lethal interaction pairs, all being potential targets of interest in the development of novel tumor therapies. One approach is to therapeutically target genes with a synthetic lethal interactor that is mutated or significantly downregulated in the tumor of interest. A second approach is to formulate drug combinations addressing synthetic lethal interactions. In this article, we outline a data integration workflow to evaluate and identify drug combinations targeting synthetic lethal interactions. We make use of available datasets on synthetic lethal interaction pairs, homology mapping resources, drug-target links from dedicated databases, as well as information on drugs being investigated in clinical trials in the disease area of interest. We further highlight key findings of two recent studies of our group on drug combination assessment in the context of ovarian and breast cancer.

Patient-derived cell line models revealed therapeutic targets and molecular mechanisms underlying disease progression of high grade serous ovarian cancer.

High grade serous ovarian cancer (HGSOC) is the most frequent type of ovarian cancer. Most patients have primary response to platinum-based chemotherapy but frequently relapse, which leads to patient death. A lack of well documented and characterized patient-derived HGSOC cell lines is so far a major barrier to define tumor specific therapeutic targets and to study the molecular mechanisms underlying disease progression. We established 34 patient-derived HGSOC cell lines and characterized them at cellular and molecular level. Particularly, we demonstrated that a cancer-testis antigen PRAME and Estrogen Receptor could serve as therapeutic targets. Notably, data from the cell lines did not demonstrate acquired resistance due to tumor recurrence that matched with clinical observations. Finally, we presented that all HGSOC had no or very low CDKN1A (p21) expression due to loss of wild-type TP53, suggesting that loss of cell cycle control is the determinant for tumorigenesis and progression. In conclusion, patient-derived cell lines reveal that PRAME is a potential tumor specific therapeutic target in HGSOC and counteracting the down-regulation of p21 caused by loss of wild-type TP53 might be the key to impede disease progression.

Canagliflozin reduces inflammation and fibrosis biomarkers: a potential mechanism of action for beneficial effects of SGLT2 inhibitors in diabetic kidney disease.

AIMS/HYPOTHESIS: The sodium-glucose cotransporter 2 (SGLT2) inhibitor canagliflozin slows progression of kidney function decline in type 2 diabetes. The aim of this study was to assess the effect of the SGLT2 inhibitor canagliflozin on biomarkers for progression of diabetic kidney disease (DKD). METHODS: A canagliflozin mechanism of action (MoA) network model was constructed based on an in vitro transcriptomics experiment in human proximal tubular cells and molecular features linked to SGLT2 inhibitors from scientific literature. This model was mapped onto an established DKD network model that describes molecular processes associated with DKD. Overlapping areas in both networks were subsequently used to select candidate biomarkers that change with canagliflozin therapy. These biomarkers were measured in 296 stored plasma samples from a previously reported 2 year clinical trial comparing canagliflozin with glimepiride. RESULTS: Forty-four proteins present in the canagliflozin MoA molecular model overlapped with proteins in the DKD network model. These proteins were considered candidates for monitoring impact of canagliflozin on DKD pathophysiology. For ten of these proteins, scientific evidence was available suggesting that they are involved in DKD progression. Of these, compared with glimepiride, canagliflozin 300 mg/day decreased plasma levels of TNF receptor 1 (TNFR1; 9.2%; p < 0.001), IL-6 (26.6%; p = 0.010), matrix metalloproteinase 7 (MMP7; 24.9%; p = 0.011) and fibronectin 1 (FN1; 14.9%; p = 0.055) during 2 years of follow-up. CONCLUSIONS/INTERPRETATION: The observed reduction in TNFR1, IL-6, MMP7 and FN1 suggests that canagliflozin contributes to reversing molecular processes related to inflammation, extracellular matrix turnover and fibrosis. Trial registration ClinicalTrials.gov NCT00968812.

Synthetic lethality guiding selection of drug combinations in ovarian cancer.

BACKGROUND: Synthetic lethality describes a relationship between two genes where single loss of either gene does not trigger significant impact on cell viability, but simultaneous loss of both gene functions results in lethality. Targeting synthetic lethal interactions with drug combinations promises increased efficacy in tumor therapy. MATERIALS AND METHODS: We established a set of synthetic lethal interactions using publicly available data from yeast screens which were mapped to their respective human orthologs using information from orthology databases. This set of experimental synthetic lethal interactions was complemented by a set of predicted synthetic lethal interactions based on a set of protein meta-data like e.g. molecular pathway assignment. Based on the combined set, we evaluated drug combinations used in late stage clinical development (clinical phase III and IV trials) or already in clinical use for ovarian cancer with respect to their effect on synthetic lethal interactions. We furthermore identified a set of drug combinations currently not being tested in late stage ovarian cancer clinical trials that however have impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer. RESULTS: Twelve of the tested drug combinations addressed a synthetic lethal interaction with the anti-VEGF inhibitor bevacizumab in combination with paclitaxel being the most studied drug combination addressing the synthetic lethal pair between VEGFA and BCL2. The set of 84 predicted drug combinations for example holds the combination of the PARP inhibitor olaparib and paclitaxel, which showed efficacy in phase II clinical studies. CONCLUSION: A set of drug combinations currently not tested in late stage ovarian cancer clinical trials was identified having impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.

Synthetic lethal combinations of low-toxicity drugs for breast cancer identified in silico by genetic screens in yeast.

In recent years, the concept of synthetic lethality, describing a cellular state where loss of two genes leads to a non-viable phenotype while loss of one gene can be compensated, has emerged as a novel strategy for cancer therapy. Various compounds targeting synthetic lethal pathways are either under clinical investigation or are already routinely used in multiple cancer entities such as breast cancer. Most of them target the well-described synthetic lethal interplay between PARP1 and BRCA1/2. In our study, we investigated, using an in silico methodological approach, clinically utilized drug combinations for breast cancer treatment, by correlating their known molecular targets with known homologous interaction partners that cause synthetic lethality in yeast. Further, by creating a machine-learning algorithm, we were able to suggest novel synthetic lethal drug combinations of low-toxicity drugs in breast cancer and showed their negative effects on cancer cell viability in vitro. Our findings foster the understanding of evolutionarily conserved synthetic lethality in breast cancer cells and might lead to new drug combinations with favorable toxicity profile in this entity.

Systems Biology-Derived Biomarkers to Predict Progression of Renal Function Decline in Type 2 Diabetes.

OBJECTIVE: Chronic kidney disease (CKD) in diabetes has a complex molecular and likely multifaceted pathophysiology. We aimed to validate a panel of biomarkers identified using a systems biology approach to predict the individual decline of estimated glomerular filtration rate (eGFR) in a large group of patients with type 2 diabetes and CKD at various stages. RESEARCH DESIGN AND METHODS: We used publicly available "omics" data to develop a molecular process model of CKD in diabetes and identified a representative parsimonious set of nine molecular biomarkers: chitinase 3-like protein 1, growth hormone 1, hepatocyte growth factor, matrix metalloproteinase (MMP) 2, MMP7, MMP8, MMP13, tyrosine kinase, and tumor necrosis factor receptor-1. These biomarkers were measured in baseline serum samples from 1,765 patients recruited into two large clinical trials. eGFR decline was predicted based on molecular markers, clinical risk factors (including baseline eGFR and albuminuria), and both combined, and these predictions were evaluated using mixed linear regression models for longitudinal data. RESULTS: The variability of annual eGFR loss explained by the biomarkers, indicated by the adjusted R2 value, was 15% and 34% for patients with eGFR ≥60 and <60 mL/min/1.73 m2, respectively; variability explained by clinical predictors was 20% and 31%, respectively. A combination of molecular and clinical predictors increased the adjusted R2 to 35% and 64%, respectively. Calibration analysis of marker models showed significant (all P < 0.0001) but largely irrelevant deviations from optimal calibration (calibration-in-the-large: -1.125 and 0.95; calibration slopes: 1.07 and 1.13 in the two groups, respectively). CONCLUSIONS: A small set of serum protein biomarkers identified using a systems biology approach, combined with clinical variables, enhances the prediction of renal function loss over a wide range of baseline eGFR values in patients with type 2 diabetes and CKD.

Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis.

OBJECTIVE: To evaluate tacrolimus as therapeutic option for diabetic nephropathy (DN) based on molecular profile and network-based molecular model comparisons. MATERIALS AND METHODS: We generated molecular models representing pathophysiological mechanisms of DN and tacrolimus mechanism of action (MoA) based on literature derived data and transcriptomics datasets. Shared enriched molecular pathways were identified based on both model datasets. A newly generated transcriptomics dataset studying the effect of tacrolimus on mesangial cells in vitro was added to identify mechanisms in DN pathophysiology. We searched for features in interference between the DN molecular model and the tacrolimus MoA molecular model already holding annotation evidence as diagnostic or prognostic biomarker in the context of DN. RESULTS: Thirty nine molecular features were shared between the DN molecular model, holding 252 molecular features and the tacrolimus MoA molecular model, holding 209 molecular features, with six additional molecular features affected by tacrolimus in mesangial cells. Significantly affected molecular pathways by both molecular model sets included cytokine-cytokine receptor interactions, adherens junctions, TGF-beta signaling, MAPK signaling, and calcium signaling. Molecular features involved in inflammation and immune response contributing to DN progression were significantly downregulated by tacrolimus (e.g. the tumor necrosis factor alpha (TNF), interleukin 4, or interleukin 10). On the other hand, pro-fibrotic stimuli being detrimental to renal function were induced by tacrolimus like the transforming growth factor beta 1 (TGFB1), endothelin 1 (EDN1), or type IV collagen alpha 1 (COL4A1). CONCLUSION: Patients with DN and elevated TNF levels might benefit from tacrolimus treatment regarding maintaining GFR and reducing inflammation. TGFB1 and EDN1 are proposed as monitoring markers to assess degree of renal damage. Next to this stratification approach, the use of drug combinations consisting of tacrolimus in addition to ACE inhibitors, angiotensin receptor blockers, TGFB1- or EDN1-receptor antagonists might warrant further studies.

Validation of a protein panel for the noninvasive detection of recurrent non-muscle invasive bladder cancer.

CONTEXT: About 50-70% of patients with non-muscle invasive bladder cancer (NMIBC) experience relapse of disease. OBJECTIVE: To establish a panel of protein biomarkers incorporated in a multiplexed microarray (BCa chip) and a classifier for diagnosing recurrent NMIBC. MATERIALS AND METHODS: Urine samples from 45 patients were tested. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: A multi biomarker panel (ECadh, IL8, MMP9, EN2, VEGF, past recurrences, BCG therapies and stage at diagnosis) was identified yielding an area under the curve of 0.96. DISCUSSION AND CONCLUSION: This biomarker panel represents a potential diagnostic tool for noninvasive diagnosis of recurrent NMIBC.

Vascular endothelial growth factor A as predictive marker for mTOR inhibition in relapsing high-grade serous ovarian cancer.

BACKGROUND: Development of resistance against first line drug therapy including cisplatin and paclitaxel in high-grade serous ovarian cancer (HGSOC) presents a major challenge. Identifying drug candidates breaking resistance, ideally combined with predictive biomarkers allowing precision use are needed for prolonging progression free survival of ovarian cancer patients. Modeling of molecular processes driving drug resistance in tumor tissue further combined with mechanism of action of drugs provides a strategy for identification of candidate drugs and associated predictive biomarkers. RESULTS: Consolidation of transcriptomics profiles and biomedical literature mining results provides 1242 proteins linked with ovarian cancer drug resistance. Integrating this set on a protein interaction network followed by graph segmentation results in a molecular process model representation of drug resistant HGSOC embedding 409 proteins in 24 molecular processes. Utilizing independent transcriptomics profiles with follow-up data on progression free survival allows deriving molecular biomarker-based classifiers for predicting recurrence under first line therapy. Biomarkers of specific relevance are identified in a molecular process encapsulating TGF-beta, mTOR, Jak-STAT and Neurotrophin signaling. Mechanism of action molecular model representations of cisplatin and paclitaxel embed the very same signaling components, and specifically proteins afflicted with the activation status of the mTOR pathway become evident, including VEGFA. Analyzing mechanism of action interference of the mTOR inhibitor sirolimus shows specific impact on the drug resistance signature imposed by cisplatin and paclitaxel, further holding evidence for a synthetic lethal interaction to paclitaxel mechanism of action involving cyclin D1. CONCLUSIONS: Stratifying drug resistant high grade serous ovarian cancer via VEGFA, and specifically treating with mTOR inhibitors in case of activation of the pathway may allow adding precision for overcoming resistance to first line therapy.

Renal microRNA- and RNA-profiles in progressive chronic kidney disease.

BACKGROUND: MicroRNAs (miRNAs) contribute to chronic kidney disease (CKD) progression via regulating mRNAs involved in renal homeostasis. However, their association with clinical outcome remains poorly understood. MATERIALS AND METHODS: We performed miRNA and mRNA expression profiling on renal biopsy sections by qPCR (miRNA) and microarrays (mRNA) in a discovery (n = 43) and in a validation (n = 29) cohort. miRNAs differentiating stable and progressive cases were inversely correlated with putative target mRNAs, which were further characterized by pathway analysis using KEGG pathways. RESULTS: miR-30d, miR-140-3p, miR-532-3p, miR-194, miR-190, miR-204 and miR-206 were downregulated in progressive cases. These seven miRNAs correlated with upregulated 29 target mRNAs involved in inflammatory response, cell-cell interaction, apoptosis and intra-cellular signalling. In particular, miR-206 and miR-532-3p were associated with distinct biological processes via the expression of their target mRNAs: Reduced expression of miR-206 in progressive disease correlated with the upregulation of target mRNAs participating in inflammatory pathways (CCL19, CXCL1, IFNAR2, NCK2, PTK2B, PTPRC, RASGRP1 and TNFRSF25). Progressive cases also showed a lower expression of miR-532-3p and an increased expression of target transcripts involved in apoptosis pathways (MAP3K14, TNFRSF10B/TRAIL-R2, TRADD and TRAF2). In the validation cohort, we confirmed the decreased expression of miR-206 and miR-532-3p, and the inverse correlation of these miRNAs with the expression of nine of the 12 target genes. The levels of the identified miRNAs and the target mRNAs correlated with clinical parameters and histological damage indices. CONCLUSIONS: These results suggest the involvement of specific miRNAs and mRNAs in biological pathways associated with the progression of CKD.

Disease map-based biomarker selection and pre-validation for bladder cancer diagnostic.

CONTEXT: Urinary biomarkers are promising as simple alternatives to cystoscopy for the diagnosis of de novo and recurrent bladder cancer. OBJECTIVE: To identify a highly sensitive and specific biomarker candidate set with potential clinical utility in bladder cancer. MATERIALS AND METHODS: Urinary biomarker concentrations were determined by ELISA. The performance of individual markers and marker combinations was assessed using ROC analysis. RESULTS: A five-biomarker panel (IL8, MMP9, VEGFA, PTGS2 and EN2) was defined from the candidate set. DISCUSSION AND CONCLUSION: This panel showed a better overall performance than the best individual marker. Further validation studies are needed to evaluate its clinical utility in bladder cancer.

A panel of novel biomarkers representing different disease pathways improves prediction of renal function decline in type 2 diabetes.

OBJECTIVE: We aimed to identify a novel panel of biomarkers predicting renal function decline in type 2 diabetes, using biomarkers representing different disease pathways speculated to contribute to the progression of diabetic nephropathy. RESEARCH DESIGN AND METHODS: A systematic data integration approach was used to select biomarkers representing different disease pathways. Twenty-eight biomarkers were measured in 82 patients seen at an outpatient diabetes center in The Netherlands. Median follow-up was 4.0 years. We compared the cross-validated explained variation (R2) of two models to predict eGFR decline, one including only established risk markers, the other adding a novel panel of biomarkers. Least absolute shrinkage and selection operator (LASSO) was used for model estimation. The C-index was calculated to assess improvement in prediction of accelerated eGFR decline defined as <-3.0 mL/min/1.73m2/year. RESULTS: Patients' average age was 63.5 years and baseline eGFR was 77.9 mL/min/1.73m2. The average rate of eGFR decline was -2.0 ± 4.7 mL/min/1.73m2/year. When modeled on top of established risk markers, the biomarker panel including matrix metallopeptidases, tyrosine kinase, podocin, CTGF, TNF-receptor-1, sclerostin, CCL2, YKL-40, and NT-proCNP improved the explained variability of eGFR decline (R2 increase from 37.7% to 54.6%; p=0.018) and improved prediction of accelerated eGFR decline (C-index increase from 0.835 to 0.896; p=0.008). CONCLUSIONS: A novel panel of biomarkers representing different pathways of renal disease progression including inflammation, fibrosis, angiogenesis, and endothelial function improved prediction of eGFR decline on top of established risk markers in type 2 diabetes. These results need to be confirmed in a large prospective cohort.

Protein interactome of muscle invasive bladder cancer.

Muscle invasive bladder carcinoma is a complex, multifactorial disease caused by disruptions and alterations of several molecular pathways that result in heterogeneous phenotypes and variable disease outcome. Combining this disparate knowledge may offer insights for deciphering relevant molecular processes regarding targeted therapeutic approaches guided by molecular signatures allowing improved phenotype profiling. The aim of the study is to characterize muscle invasive bladder carcinoma on a molecular level by incorporating scientific literature screening and signatures from omics profiling. Public domain omics signatures together with molecular features associated with muscle invasive bladder cancer were derived from literature mining to provide 286 unique protein-coding genes. These were integrated in a protein-interaction network to obtain a molecular functional map of the phenotype. This feature map educated on three novel disease-associated pathways with plausible involvement in bladder cancer, namely Regulation of actin cytoskeleton, Neurotrophin signalling pathway and Endocytosis. Systematic integration approaches allow to study the molecular context of individual features reported as associated with a clinical phenotype and could potentially help to improve the molecular mechanistic description of the disorder.

The potential evasion of immune surveillance in mucosa associated lymphoid tissue lymphoma by DcR2-mediated up-regulation of nuclear factor-κB.

This study investigated expression profiles of tumor necrosis factor (TNF)-related apoptosis inducing ligand (TRAIL) pathway components and mechanisms underlying TRAIL-induced apoptosis in mucosa associated lymphoid tissue (MALT) lymphoma. Genetic aberrations including translocations and trisomies were assessed by reverse transcription polymerase chain reaction and fluorescence in situ hybridization. Expression of TRAIL, death receptors 4 and 5, decoy receptors 1 and 2, and FADD-like interleukin-1ß-converting enzyme (FLICE) inhibitory protein was analyzed by immunohistochemistry. All 32 patients under study showed some alterations in TRAIL pathway mainly involving loss of death receptors (37.5%), gain of decoy receptors (3.1%) or both (59.4%). Decoy receptor 2 (DcR2) was highly expressed in patients with normal cytogenetic status as compared to those with cytogenetic aberrations (p = 0.005). Moreover, DcR2 expression correlated significantly with nuclear factor-κB (NF-κB) expression (R = 0.372, p = 0.047). High expression of DcR2 in patients with normal cytogenetic status and its significant correlation with NF-κB expression provides a potential clue to evasion of immune surveillance in cytogenetically normal MALT lymphomas.

Mass spectrometric/bioinformatic identification of a protein subset that characterizes the cellular activity of anticancer peptides.

The preclinical study of the mechanism of action of anticancer small molecules is challenging due to the complexity of cancer biology and the fragmentary nature of available data. With the aim of identifying a protein subset characterizing the cellular activity of anticancer peptides, we used differential mass spectrometry to identify proteomic changes induced by two peptides, LR and [d-Gln(4)]LR, that inhibit cell growth and compared them with the changes induced by a known drug, pemetrexed, targeting the same enzyme, thymidylate synthase. The quantification of the proteome of an ovarian cancer cell model treated with LR yielded a differentially expressed protein data set with respect to untreated cells. This core set was expanded by bioinformatic data interpretation, the biologically relevant proteins were selected, and their differential expression was validated on three cis-platinum sensitive and resistant ovarian cancer cell lines. Via clustering of the protein network features, a broader view of the peptides' cellular activity was obtained. Differences from the mechanism of action of pemetrexed were inferred from different modulation of the selected proteins. The protein subset identification represents a method of general applicability to characterize the cellular activity of preclinical compounds and a tool for monitoring the cellular activity of novel drug candidates.

Molecular pathogenesis of post-transplant acute kidney injury: assessment of whole-genome mRNA and miRNA profiles.

Acute kidney injury (AKI) affects roughly 25% of all recipients of deceased donor organs. The prevention of post-transplant AKI is still an unmet clinical need. We prospectively collected zero-hour, indication as well as protocol kidney biopsies from 166 allografts between 2011 and 2013. In this cohort eight cases with AKI and ten matched allografts without pathology serving as control group were identified with a follow-up biopsy within the first twelve days after engraftment. For this set the zero-hour and follow-up biopsies were subjected to genome wide microRNA and mRNA profiling and analysis, followed by validation in independent expression profiles of 42 AKI and 21 protocol biopsies for strictly controlling the false discovery rate. Follow-up biopsies of AKI allografts compared to time-matched protocol biopsies, further baseline adjustment for zero-hour biopsy expression level and validation in independent datasets, revealed a molecular AKI signature holding 20 mRNAs and two miRNAs (miR-182-5p and miR-21-3p). Next to several established biomarkers such as lipocalin-2 also novel candidates of interest were identified in the signature. In further experimental evaluation the elevated transcript expression level of the secretory leukocyte peptidase inhibitor (SLPI) in AKI allografts was confirmed in plasma and urine on the protein level (p<0.001 and p = 0.003, respectively). miR-182-5p was identified as a molecular regulator of post-transplant AKI, strongly correlated with global gene expression changes during AKI. In summary, we identified an AKI-specific molecular signature providing the ground for novel biomarkers and target candidates such as SLPI and miR-182-5p in addressing AKI.