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Application of Proteomics Profiling for Biomarker Discovery in Hypertrophic Cardiomyopathy.

ABSTRACT: High-throughput proteomics profiling has never been applied to discover biomarkers in patients with hypertrophic cardiomyopathy (HCM). The objective was to identify plasma protein biomarkers that can distinguish HCM from controls. We performed a case-control study of patients with HCM (n?=?15) and controls (n?=?22). We carried out plasma proteomics profiling of 1129 proteins using the SOMAscan assay. We used the sparse partial least squares discriminant analysis to identify 50 most discriminant proteins. We also determined the area under the curve (AUC) of the receiver operating characteristic curve using the Monte Carlo cross validation with balanced subsampling. The average AUC was 0.94 (95% confidence interval, 0.82-1.00) and the discriminative accuracy was 89%. In HCM, 13 out of the 50 proteins correlated with troponin I and 12 with New York Heart Association class. Proteomics profiling can be used to elucidate protein biomarkers that distinguish HCM from controls.

SUBMITTER: Shimada YJ

PROVIDER: S-EPMC7102897 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Application of Proteomics Profiling for Biomarker Discovery in Hypertrophic Cardiomyopathy.

Shimada Yuichi J YJ Hasegawa Kohei K Kochav Stephanie M SM Mohajer Pouya P Jung Jeeyoun J Maurer Mathew S MS Reilly Muredach P MP Fifer Michael A MA

Journal of cardiovascular translational research 20190705 6

High-throughput proteomics profiling has never been applied to discover biomarkers in patients with hypertrophic cardiomyopathy (HCM). The objective was to identify plasma protein biomarkers that can distinguish HCM from controls. We performed a case-control study of patients with HCM (n = 15) and controls (n = 22). We carried out plasma proteomics profiling of 1129 proteins using the SOMAscan assay. We used the sparse partial least squares discriminant analysis to identify 50 most discriminant ...[more]

PMID: 31278493

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Project description:BackgroundHypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE.MethodsIn this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001.ResultsThe study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.ConclusionsThis study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in patients who subsequently experience MACE.

Project description:Hypertrophic cardiomyopathy (HCM) is defined by pathological left ventricular hypertrophy (LVH). It is the commonest inherited cardiac condition and a significant number of high risk cases still go undetected until a sudden cardiac death (SCD) event. Plasma biomarkers do not currently feature in the assessment of HCM disease progression, which is tracked by serial imaging, or in SCD risk stratification, which is based on imaging parameters and patient/family history. There is a need for new HCM plasma biomarkers to refine disease monitoring and improve patient risk stratification. To identify new plasma biomarkers for patients with HCM, we performed exploratory myocardial and plasma proteomics screens and subsequently developed a multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay to validate the 26 peptide biomarkers that were identified. The association of discovered biomarkers with clinical phenotypes was prospectively tested in plasma from 110 HCM patients with LVH (LVH+ HCM), 97 controls, and 16 HCM sarcomere gene mutation carriers before the development of LVH (subclinical HCM). Six peptides (aldolase fructose-bisphosphate A, complement C3, glutathione S-transferase omega 1, Ras suppressor protein 1, talin 1, and thrombospondin 1) were increased significantly in the plasma of LVH+ HCM compared with controls and correlated with imaging markers of phenotype severity: LV wall thickness, mass, and percentage myocardial scar on cardiovascular magnetic resonance imaging. Using supervised machine learning (ML), this six-biomarker panel differentiated between LVH+ HCM and controls, with an area under the curve of ≥ 0.87. Five of these peptides were also significantly increased in subclinical HCM compared with controls. In LVH+ HCM, the six-marker panel correlated with the presence of nonsustained ventricular tachycardia and the estimated five-year risk of sudden cardiac death. Using quantitative proteomic approaches, we have discovered six potentially useful circulating plasma biomarkers related to myocardial substrate changes in HCM, which correlate with the estimated sudden cardiac death risk.

Project description:Hypertrophic cardiomyopathy (HCM) is one of the most common hereditary heart diseases and can be classified into an obstructive (HOCM) and non-obstructive (HNCM) form. Major characteristics for HCM are the hypertrophy of cardiomyocytes and development of cardiac fibrosis. Patients with HCM have a higher risk for sudden cardiac death compared to a healthy population. In the present study, we investigated the abundancy of selected proteins as potential biomarkers in patients with HCM. We included 60 patients with HCM and 28 healthy controls and quantitatively measured the rate of a set of 92 proteins already known to be associated with cardiometabolic processes via protein screening using the proximity extension assay technology in a subgroup of these patients (20 HCM and 10 healthy controls). After validation of four hits in the whole cohort of patients consisting of 88 individuals (60 HCM patients, 28 healthy controls) we found only one candidate, c-KIT, which was regulated significantly different between HCM patients and healthy controls and thus was chosen for further analyses. c-KIT is a tyrosine-protein kinase acting as receptor for the stem cell factor and activating several pathways essential for cell proliferation and survival, hematopoiesis, gametogenesis and melanogenesis. Serum protein levels of c-KIT were significantly lower in patients with HCM than in healthy controls, even after adjusting for confounding factors age and sex. In addition, c-KIT levels in human cardiac tissue of patients with HOCM were significant higher compared to controls indicating high levels of c-KIT in fibrotic myocardium. Furthermore, c-KIT concentration in serum significantly correlated with left ventricular end-diastolic diameter in HOCM, but not HCM patients. The present data suggest c-KIT as a novel biomarker differentiating between patients with HCM and healthy population and might provide further functional insights into fibrosis-related processes of HOCM.

Dataset Information

Application of Proteomics Profiling for Biomarker Discovery in Hypertrophic Cardiomyopathy.

Publications

Application of Proteomics Profiling for Biomarker Discovery in Hypertrophic Cardiomyopathy.

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