Scientific Challenge: No reliable biomarker exists to predict the risk of radiation-induced heart disease before symptoms develop in cancer patients undergoing radiotherapy.
Study Type: Metabolomics and Lipidomics
Our Contribution: Nelson Scientific Labs conducted integrated metabolomic and lipidomic profiling of patient and animal models, developing machine learning models to identify predictive metabolic signatures.
Key Findings: Shared metabolic disruptions across species revealed conserved pathways linked to cardiotoxicity, including lipid metabolism and oxidative stress-related processes.
Research Impact: This work established a foundation for blood-based biomarker development for radiation-induced cardiac injury and contributed to ongoing translational diagnostic efforts.
Publication: https://doi.org/10.1016/j.radonc.2020.04.018