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

Abstract

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.