MicroRNAs are associated with cardiac biomarkers, cardiac structure and function and incident outcomes in heart failure

AIMS

The association between microRNAs (miRNAs) and established cardiac biomarkers is largely unknown. We aimed to measure the association between plasma miRNAs and N-terminal pro-B-type natriuretic peptide (NT-proBNP), cardiac troponin I, soluble urokinase-type plasminogen activator receptor (suPAR), and galectin-3 with cardiac structure and function and clinical outcomes.

METHODS AND RESULTS

We quantified 32 plasma miRNAs using the FirePlex miRNA assay and measured biomarkers in 139 individuals with symptomatic heart failure (HF). We used principal component (PC) analysis and linear regression to evaluate the association between miRNAs and biomarkers with ventricular size and function by echocardiography and Cox modelling for the incidence of a first composite event of HF hospitalization, heart transplant, left ventricular assist device implant, or death. The mean (standard deviation) age at baseline was 64.3 (12.4) years, 33 (24%) were female, and 122 (88%) were White. A total of 45 events occurred over a median follow-up of 368 (interquartile range 234, 494) days. Baseline NT-proBNP (β = -2.0; P = 0.001) and miRNA PC2 (β = 2.6; P = 0.002) were associated with baseline left ventricular ejection fraction. NT-proBNP (β = 20.6; P = 0.0004), suPAR (β = -39.6; P = 0.005), and PC4 (β = 21.1; P = 0.02) were associated with baseline left ventricular end-diastolic volumes. NT-proBNP [hazard ratio (HR) 1.67, 95% confidence interval (CI) 1.28-2.18, P = 0.0002], galectin-3 (HR 2.02, 95% CI 1.05-3.91, P = 0.036), PC3 (HR 1.75, 95% CI 1.23-2.49, P = 0.002), and PC4 (HR 1.67, 95% CI 1.1-2.52, P = 0.016) were independently associated with incident events.

CONCLUSIONS

Biomarkers and miRNA PCs are associated with cardiac structure and function and incident cardiovascular outcomes. Combining information from miRNAs provides prognostic information beyond biomarkers in HF.

Association between proteomic biomarkers and myocardial fibrosis measured by MRI: the multi-ethnic study of atherosclerosis

BACKGROUND

Cardiac magnetic resonance imaging (CMR) determines the extent of interstitial fibrosis, measured by increased extracellular volume (ECV), and replacement fibrosis with late gadolinium myocardial enhancement (LGE). Despite advances in detection, the pathophysiology of subclinical myocardial fibrosis is incompletely understood. Targeted proteomic discovery technologies enable quantification of low abundance circulating proteins to elucidate cardiac fibrosis mechanisms.

METHODS

Using a cross-sectional design, we selected 92 LGE+ cases and 92 LGE- demographically matched controls from the Multi-Ethnic Study of Atherosclerosis. Similarly, we selected 156 cases from the highest ECV quartile and matched with 156 cases from the lowest quartile. The plasma serum proteome was analyzed using proximity extension assays to determine differential regulation of 92 proteins previously implicated with cardiovascular disease. Results were analyzed using volcano plots of statistical significance vs. magnitude of change and Bayesian additive regression tree (BART) models to determine importance.

FINDINGS

After adjusting for false discovery, higher ECV was significantly associated with 17 proteins. Using BART, Plasminogen activator inhibitor 1, Insulin-like growth factor-binding protein 1, and N-terminal pro-B-type natriuretic peptide were associated with higher ECV after accounting for other proteins and traditional cardiovascular risk factors. In contrast, no circulating proteins were associated with replacement fibrosis.

INTERPRETATIONS

Our results suggest unique circulating proteomic signatures associated with interstitial fibrosis emphasizing its systemic influences. With future validation, protein panels may identify patients who may develop interstitial fibrosis with progression to heart failure.

FUNDING

This research was supported by contracts and grants from NHLBI, NCATS and the Inova Heart and Vascular Institute.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

A Novel Circulating MicroRNA for the Detection of Acute Myocarditis

BACKGROUND

The diagnosis of acute myocarditis typically requires either endomyocardial biopsy (which is invasive) or cardiovascular magnetic resonance imaging (which is not universally available). Additional approaches to diagnosis are desirable. We sought to identify a novel microRNA for the diagnosis of acute myocarditis.

METHODS

To identify a microRNA specific for myocarditis, we performed microRNA microarray analyses and quantitative polymerase-chain-reaction (qPCR) assays in sorted CD4+ T cells and type 17 helper T (Th17) cells after inducing experimental autoimmune myocarditis or myocardial infarction in mice. We also performed qPCR in samples from coxsackievirus-induced myocarditis in mice. We then identified the human homologue for this microRNA and compared its expression in plasma obtained from patients with acute myocarditis with the expression in various controls.

RESULTS

We confirmed that Th17 cells, which are characterized by the production of interleukin-17, are a characteristic feature of myocardial injury in the acute phase of myocarditis. The microRNA mmu-miR-721 was synthesized by Th17 cells and was present in the plasma of mice with acute autoimmune or viral myocarditis but not in those with acute myocardial infarction. The human homologue, designated hsa-miR-Chr8:96, was identified in four independent cohorts of patients with myocarditis. The area under the receiver-operating-characteristic curve for this novel microRNA for distinguishing patients with acute myocarditis from those with myocardial infarction was 0.927 (95% confidence interval, 0.879 to 0.975). The microRNA retained its diagnostic value in models after adjustment for age, sex, ejection fraction, and serum troponin level.

CONCLUSIONS

After identifying a novel microRNA in mice and humans with myocarditis, we found that the human homologue (hsa-miR-Chr8:96) could be used to distinguish patients with myocarditis from those with myocardial infarction. (Funded by the Spanish Ministry of Science and Innovation and others.).

Finding a Needle in a Haystack: Proteomics in Heart Failure

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Proteomics has aided HF biomarker discovery, which allows for greater disease insights.

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Experiment design can be tailored to HF research to discover novel biomarkers.

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Primary methods include MS, protein microarray, aptamer, and PEA-based technologies.

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Proteomics can detect unique low abundance proteins and detect protein modifications.

Circulating protein biomarkers provide information regarding pathways in heart failure (HF) and can add important value to clinicians. Advancements in proteomics allow researchers to measure a multitude of proteins simultaneously with excellent sensitivity and selectivity to detect low abundance proteins. This helps identify previously unrecognized pathways in HF and discover biomarkers and potential targets for HF therapies. Although several proteomic methods exist, including mass spectrometry, protein microarray, aptamer, and proximity extension assay−based techniques, each have their unique advantages. This paper provides an overview of the various proteomic methods, with examples of how each has contributed to understanding the pathways in HF.

Derivation and External Validation of a High-Sensitivity Cardiac Troponin-Based Proteomic Model to Predict the Presence of Obstructive Coronary Artery Disease

Background

Current noninvasive modalities to diagnose coronary artery disease (CAD) have several limitations. We sought to derive and externally validate a hs‐cTn (high‐sensitivity cardiac troponin)–based proteomic model to diagnose obstructive coronary artery disease.

Methods and Results

In a derivation cohort of 636 patients referred for coronary angiography, predictors of ≥70% coronary stenosis were identified from 6 clinical variables and 109 biomarkers. The final model was first internally validated on a separate cohort (n=275) and then externally validated on a cohort of 241 patients presenting to the ED with suspected acute myocardial infarction where ≥50% coronary stenosis was considered significant. The resulting model consisted of 3 clinical variables (male sex, age, and previous percutaneous coronary intervention) and 3 biomarkers (hs‐cTnI [high‐sensitivity cardiac troponin I], adiponectin, and kidney injury molecule‐1). In the internal validation cohort, the model yielded an area under the receiver operating characteristic curve of 0.85 for coronary stenosis ≥70% (P<0.001). At the optimal cutoff, we observed 80% sensitivity, 71% specificity, a positive predictive value of 83%, and negative predictive value of 66% for ≥70% stenosis. Partitioning the score result into 5 levels resulted in a positive predictive value of 97% and a negative predictive value of 89% at the highest and lowest levels, respectively. In the external validation cohort, the score performed similarly well. Notably, in patients who had myocardial infarction neither ruled in nor ruled out via hs‐cTnI testing (“indeterminate zone,” n=65), the score had an area under the receiver operating characteristic curve of 0.88 (P<0.001).

Conclusions

A model including hs‐cTnI can predict the presence of obstructive coronary artery disease with high accuracy including in those with indeterminate hs‐cTnI concentrations.

Proteomic Signatures During Treatment in Different Stages of Heart Failure

Background:

Proteomics have already provided novel insights into the pathophysiology of heart failure (HF) with reduced ejection fraction. Previous studies have evaluated cross-sectional protein signatures of HF, but few have characterized proteomic changes following HF with reduced ejection fraction treatment with ARNI (angiotensin receptor/neprilysin inhibitor) therapy or left ventricular assist devices.

Methods:

In this retrospective omics study, we performed targeted proteomics (N=625) of whole blood sera from patients with American College of Cardiology/American Heart Association stage D (N=29) and stage C (N=12) HF using proximity extension assays. Samples were obtained before and after (median=82 days) left ventricular assist device implantation (stage D; primary analysis) and ARNI therapy initiation (stage C; matched reference). Oblique principal component analysis and point biserial correlations were used for feature extraction and selection; standardized mean differences were used to assess within and between-group differences; and enrichment analysis was used to generate and cluster Gene Ontology terms.

Results:

Core sets of proteins were identified for stage C (N=9 proteins) and stage D (N=18) HF; additionally, a core set of 5 shared HF proteins (NT-proBNP [N-terminal pro-B type natriuretic peptide], ESM [endothelial cell-specific molecule]-1, cathepsin L1, osteopontin, and MCSF-1) was also identified. For patients with stage D HF, moderate (δ, 0.40–0.60) and moderate-to-large (δ, 0.60–0.80) sized differences were observed in 8 of their 18 core proteins after left ventricular assist devices implantation. Additionally, specific protein groups reached concentration levels equivalent ( g <0.10) to stage C HF after initiation on ARNI therapy.

Conclusions:

HF with reduced ejection fraction severity associates with distinct proteomic signatures that reflect underlying disease attributes; these core signatures may be useful for monitoring changes in cardiac function following initiation on ARNI or left ventricular assist device implantation.

Abstract 4398: In vitro Tumor Treating Fields (TTFields) alter proliferation and morphology of patient-derived high-grade meningioma cell lines

Introduction: Meningiomas are the most common neoplasm of the central nervous system (CNS), accounting for 37% of primary CNS tumors. Though derived from the arachnoid cap cells within the meninges, high-grade meningiomas (WHO grades II and III) invade into the normal brain tissue, and have a high rate of recurrence. As there are no efficacious chemotherapies for these high-grade meningiomas, standard of care is currently limited to maximal surgical resection followed by radiotherapy, with a median overall survival of only 2.5-3 years. Clinical trials with TTFields therapy are under way for patients with high-grade meningiomas. In this study, in vitro experiments comparing TTFields to untreated controls were performed on two patient-derived anaplastic (WHO grade III) meningioma cell lines to determine the effects of TTFields on cell proliferation and clonogenicity.

Methods: Studies utilizing patient tumor specimens were approved by the Wayne State University Institutional Review Board and written informed consent was obtained from participants. Meningioma tumor specimens were collected immediately following microsurgical resection. Single-cell suspensions from the tumor tissues were prepared by enzymatic and mechanical disruption. Cells were plated on plastic coverslips (10,000 each) in DMEM/F12 media supplemented with 10% fetal bovine serum. TTFields were applied at ~1.6 V/cm at 100, 200, 300, or 400 kHz (groups n=4-5) with one control group. Culture media were replaced every day. After 12 days of treatment, cell proliferation was assessed with the XTT assay and cells were harvested and replated for clonogenic assays (500 cells/well), which were stained with crystal violet and counted with an automated colony counter. Harvested cells were also fixed and immunostained with the diagnostic meningioma cytoplasmic marker vimentin to observe the cell morphology in control vs. TTFields-treated cells. Control vs. TTFields-treated groups were compared by one- or two-tailed t-tests.

Results: The 12-day TTFields treatment significantly reduced cell proliferation, with the greatest decrease found in the 200 kHz group (0.508±0.21; mean±SEM) compared to the control (1.515±0.08; two-tailed t test p=0.0019). Similar results were found in the clonogenic assay, with control cell counts of 4219±793 compared to the 200kHz treated cells 1793±444 (one-tailed t test p=0.0279). In the vimentin-stained cells, the control cells displayed a typical meningioma spindle morphology, while the TTFields-treated cells demonstrated pleomorphic and irregular morphologies.

Conclusions: Application of TTFields in vitro reduced proliferation and clonogenicity and altered morphology of patient-derived anaplastic meningioma cell lines. This is the first report on the in vitro effects of TTFields on meningioma.

Citation Format: Sharon K. Michelhaugh, Sam Kiousis, Sam A. Michelhaugh, Neil V. Klinger, Sandeep Mittal. In vitro Tumor Treating Fields (TTFields) alter proliferation and morphology of patient-derived high-grade meningioma cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4398.