Therapeutic targeting of MYBPC3 mutation-specific hypertrophic cardiomyopathy guided by network modeling

Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death with genotype positive cases most commonly associated with mutations of cardiac myosin binding protein-C (MYBPC3). Recently approved drugs for HCM target the myofilaments rather than the aberrant molecular signaling pathways that drive long-term remodeling. Here, we identified a patient with familial HCM that was associated with a MYBPC3 W1078 truncation mutation. A CRISPR knock-in mouse model of the orthologous mutation MYBPC3 W1082 exhibited marked cardiac hypertrophy including wall thickening, reduced ejection fraction, and decreased survival. To identify pathways, mechanisms, and potential candidate therapeutics, we integrated the MYBPC3 mutation into a computational network model of the signaling underlying familial cardiomyopathy. The network model predicted that the MYBPC3 mutation drove hypertrophy through mTOR/PI3K pathways, consistent with the results of RNA sequencing of cardiomyocytes of MYBPC3W1082*/W1082* mice. A virtual drug screen using FDA-approved drugs predicted that the mTOR inhibitor, Rapamycin, could mitigate mutation-induced hypertrophy. We then experimentally validated the effects of Rapamycin on hypertrophic responses using cultured cardiomyocytes. Further, Rapamycin attenuated cardiac hypertrophy and fibrosis of MYBPC3W1082*/W1082* mice in vivo. mTOR inhibitors (rapamycin and everolimus) were associated with a decreased incidence of cardiac hypertrophy associated diagnostic codes in patients in the FDA Adverse Events Reporting System. Query of electronic health records and echocardiograms from a University of Virginia cohort of patients treated with mTOR or calcineurin inhibitors showed that patients prescribed everolimus or tacrolimus had reduced LV wall thicknesses. Together, these studies suggest that targeting mTOR as a translationally relevant target for a mutation-induced hypertrophic cardiomyopathy, as well as demonstrating the utility of guiding precision therapies by iterating between network models and experimental validation.