Protein Biomarkers of New-Onset Heart Failure: Insights From the Heart Omics and Ageing Cohort, the Atherosclerosis Risk in Communities Study, and the Framingham Heart Study.

BACKGROUND: We sought to identify protein biomarkers of new-onset heart failure (HF) in 3 independent cohorts (HOMAGE cohort [Heart Omics and Ageing], ARIC study [Atherosclerosis Risk in Communities], and FHS [Framingham Heart Study]) and assess if and to what extent they improve HF risk prediction compared to clinical risk factors alone. METHODS: A nested case-control design was used with cases (incident HF) and controls (without HF) matched on age and sex within each cohort. Plasma concentrations of 276 proteins were measured at baseline in ARIC (250 cases/250 controls), FHS (191/191), and HOMAGE cohort (562/871). RESULTS: In single protein analysis, after adjusting for matching variables and clinical risk factors (and correcting for multiple testing), 62 proteins were associated with incident HF in ARIC, 16 in FHS, and 116 in HOMAGE cohort. Proteins associated with incident HF in all cohorts were BNP (brain natriuretic peptide), NT-proBNP (N-terminal pro-B-type natriuretic peptide), eukaryotic translation initiation factor 4E-BP1 (4E-binding protein 1), hepatocyte growth factor (HGF), Gal-9 (galectin-9), TGF-alpha (transforming growth factor alpha), THBS2 (thrombospondin-2), and U-PAR (urokinase plasminogen activator surface receptor). The increment in C-index for incident HF based on a multiprotein biomarker approach, in addition to clinical risk factors and NT-proBNP, was 11.1% (7.5%-14.7%) in ARIC, 5.9% (2.6%-9.2%) in FHS, and 7.5% (5.4%-9.5%) in HOMAGE cohort, all P<0.001), each of which was a larger increase than that for NT-proBNP on top of clinical risk factors. Complex network analysis revealed a number of overrepresented pathways related to inflammation (eg, tumor necrosis factor and interleukin) and remodeling (eg, extracellular matrix and apoptosis). CONCLUSIONS: A multiprotein biomarker approach improves prediction of incident HF when added to natriuretic peptides and clinical risk factors.

Identification of sex-specific biomarkers predicting new-onset heart failure.

AIMS: Heart failure (HF) is common in both men and women, yet disease pathophysiology, presentation, and progression differ between sexes. Studies addressing whether biomarkers predict new onset HF sex-specifically are scarce. This study therefore aims to test the sex-specificity of 252 protein biomarkers for new-onset HF. METHODS AND RESULTS: A matched case-control design in patients selected from cohorts within the HOMAGE consortium was used. Cases (new-onset HF, n = 562) and controls (n = 780) were matched for cohort (PREDICTOR, HEALTH-ABC, & PROSPER), follow-up time (defined as time from entry to incident HF), and age. Incident HF was defined as first hospitalization for HF. Targeted plasma proteins (n = 252) were measured using Proximity Extension Assay technology from O-link. To look for sex differences for new onset HF, we adjusted for cohort, age, and baseline clinical parameters. At baseline, women had a biomarker profile reflecting activated metabolism and immune responses. However, none of the biomarkers had a significant interaction with sex in predicting new onset HF, but four biomarkers had a trend towards sex-specificity (P < 0.013). E-selectin and interleukin 1 receptor antagonist were more female-specific, whereas IL17A and CHIT1 tended to be male sex-specific for incident HF. CONCLUSIONS: The majority of biomarkers associated with incident HF did not significantly differ between women and men, despite clear differences in biomarkers at baseline.

Proteomic Bioprofiles and Mechanistic Pathways of Progression to Heart Failure.

Background Identifying the mechanistic pathways potentially associated with incident heart failure (HF) may provide a basis for novel preventive strategies. Methods and Results To identify proteomic biomarkers and the potential underlying mechanistic pathways that may be associated with incident HF defined as the first hospitalization for HF, a nested-matched case-control design was used with cases (incident HF) and controls (without HF) selected from 3 cohorts (>20 000 individuals). Controls were matched on cohort, follow-up time, age, and sex. Two independent sample sets (a discovery set with 286 cases and 591 controls and a replication set with 276 cases and 280 controls) were used to discover and replicate the findings. Two hundred fifty-two circulating proteins in the plasma were studied. Adjusting for the matching variables age, sex, and follow-up time (and correcting for multiplicity of tests), 89 proteins were found to be associated with incident HF in the discovery phase, of which 38 were also associated with incident HF in the replication phase. These 38 proteins pointed to 4 main network clusters underlying incident HF: (1) inflammation and apoptosis, indicated by the expression of the TNF (tumor necrosis factor)-family members; (2) extracellular matrix remodeling, angiogenesis and growth, indicated by the expression of proteins associated with collagen metabolism, endothelial function, and vascular homeostasis; (3) blood pressure regulation, indicated by the expression of natriuretic peptides and proteins related to the renin-angiotensin-aldosterone system; and (4) metabolism, associated with cholesterol and atherosclerosis. Conclusions Clusters of biomarkers associated with mechanistic pathways leading to HF were identified linking inflammation, apoptosis, vascular function, matrix remodeling, blood pressure control, and metabolism. These findings provide important insight on the pathophysiological mechanisms leading to HF. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02556450.

Circulating tumor cells mirror bone metastatic phenotype in prostate cancer.

Circulating tumor cells (CTCs) are promising biomarkers in prostate cancer (PC) because they derive from primary tumor and metastatic tissues. In this study, we used quantitative real-time PCR (qPCR) to compare the expression profiles of 41 PC-related genes between paired CTC and spinal column metastasis samples from 22 PC patients that underwent surgery for spinal cord compression. We observed good concordance between the gene expression profiles in the CTC and metastasis samples in most of the PC patients. Expression of nine genes (AGR2, AKR1C3, AR, CDH1, FOLH1, HER2, KRT19, MDK, and SPINK1) showed a significant correlation between the CTC and metastasis samples. Hierarchical clustering analysis showed a similar grouping of PC patients based on the expression of these nine genes in both CTC and metastasis samples. Our findings demonstrate that CTCs mirror gene expression patterns in tissue metastasis samples from PC patients. Although low detection frequency of certain genes is a limitation in CTCs, our results indicate the potential for CTC phenotyping as a tool to improve individualized therapy in metastatic prostate cancer.

Methods to determine limit of detection and limit of quantification in quantitative real-time PCR (qPCR).

Quantitative Real-Time Polymerase Chain Reaction, better known as qPCR, is the most sensitive and specific technique we have for the detection of nucleic acids. Even though it has been around for more than 30 years and is preferred in research applications, it has yet to win broad acceptance in routine practice. This requires a means to unambiguously assess the performance of specific qPCR analyses. Here we present methods to determine the limit of detection (LoD) and the limit of quantification (LoQ) as applicable to qPCR. These are based on standard statistical methods as recommended by regulatory bodies adapted to qPCR and complemented with a novel approach to estimate the precision of LoD.

The focus on sample quality: Influence of colon tissue collection on reliability of qPCR data.

Successful molecular analyses of human solid tissues require intact biological material with well-preserved nucleic acids, proteins, and other cell structures. Pre-analytical handling, comprising of the collection of material at the operating theatre, is among the first critical steps that influence sample quality. The aim of this study was to compare the experimental outcomes obtained from samples collected and stored by the conventional means of snap freezing and by PAXgene Tissue System (Qiagen). These approaches were evaluated by measuring rRNA and mRNA integrity of the samples (RNA Quality Indicator and Differential Amplification Method) and by gene expression profiling. The collection procedures of the biological material were implemented in two hospitals during colon cancer surgery in order to identify the impact of the collection method on the experimental outcome. Our study shows that the pre-analytical sample handling has a significant effect on the quality of RNA and on the variability of qPCR data. PAXgene collection mode proved to be more easily implemented in the operating room and moreover the quality of RNA obtained from human colon tissues by this method is superior to the one obtained by snap freezing.

Biomarkers for monitoring pre-analytical quality variation of mRNA in blood samples.

There is an increasing need for proper quality control tools in the pre-analytical phase of the molecular diagnostic workflow. The aim of the present study was to identify biomarkers for monitoring pre-analytical mRNA quality variations in two different types of blood collection tubes, K2EDTA (EDTA) tubes and PAXgene Blood RNA Tubes (PAXgene tubes). These tubes are extensively used both in the diagnostic setting as well as for research biobank samples. Blood specimens collected in the two different blood collection tubes were stored for varying times at different temperatures, and microarray analysis was performed on resultant extracted RNA. A large set of potential mRNA quality biomarkers for monitoring post-phlebotomy gene expression changes and mRNA degradation in blood was identified. qPCR assays for the potential biomarkers and a set of relevant reference genes were generated and used to pre-validate a sub-set of the selected biomarkers. The assay precision of the potential qPCR based biomarkers was determined, and a final validation of the selected quality biomarkers using the developed qPCR assays and blood samples from 60 healthy additional subjects was performed. In total, four mRNA quality biomarkers (USP32, LMNA, FOSB, TNRFSF10C) were successfully validated. We suggest here the use of these blood mRNA quality biomarkers for validating an experimental pre-analytical workflow. These biomarkers were further evaluated in the 2nd ring trial of the SPIDIA-RNA Program which demonstrated that these biomarkers can be used as quality control tools for mRNA analyses from blood samples.