Abstract 4370456: Multiparameter Assessment of TNNI3 Cardiomyopathy Variants for Precision Medicine

Abstract

Introduction: Genetic testing for cardiomyopathies has become routine in clinical practice, yet current testing strategies primarily determine the presence of a probable pathogenic variant but relatively little information regarding prognosis, severity, or potential treatment. This limitation is primarily due to insufficient knowledge of the physiological and clinical impact of cardiomyopathy variants. Methods: We developed a high-throughput, multi-parameter platform for assessing variants in the important cardiomyopathy gene TNNI3. This platform combined both high-throughput measurement of contractility and calcium handling in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) expressing variants in TNNI3, as well as single-cell RNA sequencing-based assessment of gene expression signatures. We applied our platform to a library of variants found in cardiomyopathy patients. We then tested whether our in vitro measurements were able to predict clinical outcomes in cardiomyopathy patients. Results: Physiologic measurement of the relationship between calcium and contractility in hiPSC-CMs was able to accurately separate pathogenic variants from synonymous and benign variants. Unbiased clustering of variants by both functional measurements and single-cell gene expression analysis revealed multiple subgroups that correlated physiological function with structural properties of TNNI3. Finally, comparison with patient outcomes revealed that diastolic tension measured in vitro could predict age of onset (R^2 = 0.5) and pulmonary capillary wedge pressures (R^2 = 0.72), providing an in vitro prediction of disease severity. Conclusions: Multiparameter assessment of cardiomyopathy variants using our scalable platform provides a powerful approach for predicting pathogenicity and severity of disease in patients. Future work on the subgroups identified by physiology and gene expression has the potential to reveal variant-specific targets for personalized management of genetic cardiomyopathies.