Oral Abstract Session
SCMR 22nd Annual Scientific Sessions
Gabriella Captur, MD, PhD, MSc
Senior Clinical Lecturer
University College London
Wendy Heywood, PhD
Senior Scientist
ICH
Caroline Coats, PhD
Consultant Cardiologist
UCL
Stefania Rosmini, MD, PhD
Cardiology Registrar
Barts Heart Centre
Vimal Patel, MD, PhD
Consultant Cardiologist
UCL
Richard Collis, MD
Cardiology Registrar
UCL
Nina Patel, PhD
Senior Scientist
UCL ICH
Petros Syrris, PhD
PRINCIPAL RESEARCH FELLOW
University College London
Ben O'Brien, PhD
Director of Perioperative Medicine, Professor
Barts and QMUL
James Moon, MD
Clinical Director, Imaging
Barts Heart Centre and UCL
Perry M Elliott, MD
Professor in Inherited Cardiovascular Disease
University College London and Barts Heart Centre
Kevin Mills, PhD
Lead UCL Biological Mass Spec Laboratory
ICH
Background:
We need better biomarkers for hypertrophic cardiomyopathy (HCM). Liquid chromatography-tandem/mass spectrometry (LC-MS/MS)-based assays can analyze multiple candidate biomarkers in a single plasma sample. We hypothesised that interrogation of the plasma proteome in patients with HCM using such an assay, could identify novel staging biomarkers and link to the CMR phenotype.
Methods: Myocardial tissue and plasma samples from patients with HCM and healthy volunteers (controls) were screened using a combined gel- and nano-LC quadrupole time of flight MS approach. Twenty-six potential biomarkers were identified from the proteomics screens and developed into a 10-minute high-throughput, multiplex, and targeted proteomic plasma assay. The association of candidate biomarkers with clinical and CMR phenotypes was tested in plasma from 207 prospectively recruited participants: 110 HCM patients (50.1±15.0 years, 70% male) and 97 controls (49.6±13.4 years, 58% male), randomly split into training (80 HCM, 67 controls) and validation datasets (30 HCM, 30 controls; Figure 1).
Results: Six markers were significantly increased (P<0.006) in HCM plasma compared to controls in the training dataset. These markers correlated with left ventricular (LV) wall thickness, LV mass, native T1 values, and % myocardial scar by CMR. Using supervised machine learning (ML) this panel differentiated HCM from controls with an area under the curve of 0.89 in the training dataset (sensitivity 96%, 95% confidence interval [CI] 77–93; specificity 87%, 95%CI 77–94) and 0.87 in the validation dataset (sensitivity 97%, 95%CI 83–100; specificity 77%, 95%CI 58–90; Figure 2). Four of the biomarkers, as well as the composite ML score of the plasma proteome, correlated with the presence of nonsustained ventricular tachycardia and the estimated 5-year risk of sudden cardiac death.
We assembled a human protein interaction network for our 6 HCM biomarkers (Figure 3) and show how they cluster with biological pathways underpinning hypertrophy, fibrosis and inflammation, all of which are trackable by CMR.
Conclusion: Our proteomic plasma assay identified 6 novel staging HCM biomarkers. Using 10 µl of plasma and this proteomic assay, we can now identify patients with overt HCM particularly those with myocardial fibrosis and high clinical risk scores for sudden cardiac death.