Project Search

Hypertrophic cardiomyopathy (HCM) is a common form of heart disease, affecting 1 in 200 people. In HCM, the heart muscle thickens and over time this can lead to other health problems, such as shortness of breath or even sudden death. 

There are limited tools to help identify people at risk of poor outcomes related to their disease or to monitor or predict the rate of disease progression. Imaging, using MRI, is currently the best tool to diagnose HCM and to help guide decision making by the clinical care team. Frequent follow-up MRIs would be helpful but are neither feasible nor would they give a complete picture of disease progression. 

The goal of this project was to integrate data from genetics, imaging, and surgical heart specimens in order to develop a blood test that can identify people at risk of heart failure and monitor HCM disease progression which would benefit patients in BC and around the world. 

This blood test has not been developed yet but the research team has catalyzed the formation of several new collaborations and workflows that are expected to endure for years to come and will allow to continue this work. 

By working with the radiology team, cutting-edge imaging protocols for HCM patients have been established, and with data scientists these imaging data will be integrated with other biological data using machine learning techniques. 

The research team's progress was slowed by external factors, most notably an unexpected drop in the number of heart surgeries performed, resulting in a lack of specimens. The researchers identified that clinical data for HCM patients is not well-integrated, and to remedy that they have established a new central database and research registry. 

During this one year project, the single-nucleus RNA sequencing (snRNA-seq) of 9 septal myectomy specimens was successfully completed and these data will serve as a reference for future personalized medicine applications. 

Faced with the unexpected and large increase in the cost of single-cell sequencing reagents, the team will in the future combine spatial transcriptomics with tissue microarrays to allow for a more cost-effective analysis on a per-sample basis, while adding the potential to identify differentially-expressed in a spatially-resolved context.

This collaborative research will continue to work on data integration to address the original goals and additional translational questions, and will be integrated with similar efforts across Canada to build towards better health outcomes.