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.

Keywords: Cardiovascular Disease; Cardiovascular Function or Biology; Diagnostic; Mass Spectrometry; Multiple Reaction Monitoring.

Graphical abstract

Fig. 1.

Experimental design and workflow. Previous published work identified differentially expressed proteotypic peptides in the myocardium of patients with HCM and LVH+ (*). Plasma from another set of LVH+ HCM patients was proteomically profiled to identify differentially expressed candidate peptides (*). A panel of 26 quantatypic peptide biomarkers was created from candidates identified in these profiling experiments. This panel was multiplexed into a 10-min LC-MS/MS assay and first applied to plasma samples from LVH+ HCMs and controls in a training dataset using ML, where six proteolytic peptide biomarkers were found to be differentially expressed. These differences were confirmed in the validation dataset. Correlation analysis with clinical and imaging information and with the five-year HCM sudden cardiac risk score was subsequently performed in LVH+ HCM patients to explore the assay's potential clinical utility. Five of the six biomarkers were also elevated in the plasma of a smaller group of patients with five subclinical HCM compared with controls. SVM, support vector machine.

Fig. 2.

Overlaid chromatogram of the six marker peptides that validated in the multiplexed targeted proteomic assay. Individual chromatograms are provided in Fig. S1.

Fig. 3.

Box and whisker plots showing the six differentially expressed plasma peptides identified in training dataset consisting of LVH+ HCM and controls by the targeted proteomic multiplexed assay (using the Mann-Whitney-Wilcoxon test with p value adjustment for multiple comparisons by the Bonferroni method).

Fig. 4.

Box plots showing performance of prediction scores calculated by a support vector machine supervised ML method in the training (A) and validation (C) datasets made up of LVH+ HCM patients and controls (whiskers indicate variability outside the third and first quartiles [75^th and 25^th percentiles] represented as hinges around the median [bold midline]). Receiver operating characteristics (ROC) curves (B and D) show performance of the ML prediction score in training and validation datasets.