Genetic variants risk assessment for Long QT Syndrome through machine learning and multielectrode array recordings
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Background
Long QT syndrome (LQTS) is a life-threatening genetic disorder characterized by prolonged QT intervals on electrocardiograms. Congenital forms are mostly associated with variants in the KCNQ1 and KCNH2 genes. Among pathogenic or likely pathogenic (P/LP) variants, some are associated with a significantly higher incidence of cardiac events compared to others. While therapies have significantly reduced mortality, some patients are unresponsive or intolerant to therapy, perpetuating their arrhythmic risk, including sudden cardiac death. Current approaches for risk stratification are insufficient, highlighting the critical need for more accurate identification and management of patients carrying high risk genetic variants.
Objectives
To develop a refined risk stratification model for P/LP variants by applying machine learning classification to electrophysiological data measured in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs).
Methods
Eleven patient-specific hiPSC lines carrying six P/LP variants in KCNQ1 or KCNH2 were differentiated to cardiomyocytes (hiPSC-CMs). Electrophysiological responses from multielectrode array recordings at baseline and after application of selective ion channel blockers or pro-arrhythmic compounds were used to train a machine learning model to classify variant-specific risk levels based on in vitro electrophysiological readouts.
Results
Our findings revealed a correlation between variant risk level, hiPSC-CM electrophysiological profiles, and drug responses. The machine learning classifier, trained on multielectrode array recordings, achieved 89% accuracy in classification of P/LP genetic variants according to the associated risk levels.
Conclusions
This study demonstrates that integrating hiPSC-CM electrophysiological profiling with machine learning provides a robust method to improve variant-specific risk stratification for LQTS patients.
Clinical Perspectives
Clinical Aspects
Understanding which patients may be at risk of cardiac events or sudden cardiac death is crucial to implement appropriate preventive measures. This study leverages patient-specific in vitro models and machine learning to improve the risk stratification of pathogenic/likely pathogenic variants associated with LQTS, better supporting clinical decisions related to risk assessment and management of LQTS patients. This scalable approach can be implemented across multiple centres, enhancing the risk stratification of LQTS variants beyond what is currently possible when clinical data are limited.
Translational Outlook
Machine learning-based variant risk stratification is a novel approach for integrating hiPSC-CM-derived electrophysiological data into clinical workflows. While this study demonstrates the feasibility of our approach, further research is required to validate these findings across larger and more diverse patient cohorts. Additionally, efforts to standardize the pipeline and adapt it for multicentric implementation are necessary.
Graphical Abstract
Highlights
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Discriminating LQTS patients at high or low risk for sudden death is a clinical challenge.
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Improved stratification of pathogenic/likely pathogenic variants is achievable through machine learning classification on in vitro electrophysiological data.
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Integration of the clinical workflow with data from patient-specific in vitro models will enhance risk stratification.
