Details

Autor(en) / Beteiligte

• Captur, Gabriella
• Heywood, Wendy E.
• Coats, Caroline
• Rosmini, Stefania
• Patel, Vimal
• Lopes, Luis R.
• Collis, Richard
• Patel, Nina
• Syrris, Petros
• Bassett, Paul
• O’Brien, Ben
• Moon, James C.
• Elliott, Perry M.
• Mills, Kevin

Titel

Identification of a Multiplex Biomarker Panel for Hypertrophic Cardiomyopathy Using Quantitative Proteomics and Machine Learning

Ist Teil von

• Molecular & cellular proteomics, 2020-01, Vol.19 (1), p.114-127

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2020

Quelle

MEDLINE

Beschreibungen/Notizen

• Exploratory myocardial and plasma proteomics screens in patients with hypertrophic cardiomyopathy identified new biomarkers that were developed into a multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay for validation across a larger cohort of patients with this disease. Using quantitative proteomics and supervised machine learning, this six-biomarker panel shows a relationship with myocardial substrate changes in patients with hypertrophic cardiomyopathy and with their estimated sudden cardiac death risk. [Display omitted] Highlights •Quantitative proteomics and machine learning to study plasma biomarkers in HCM.•Six peptides are increased in plasma of LVH+ HCM compared to controls.•Peptide biomarkers correlate with imaging markers of phenotype severity.•Peptide biomarkers correlate with the estimated sudden cardiac death risk. 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.