Grant Number:	5R01CA261750-02 Interpret this number
Primary Investigator:	Howell, Rebecca
Organization:	University Of Tx Md Anderson Can Ctr
Project Title:	Personalized Risk Prediction to Reduce Cardiovascular Disease in Childhood Cancer Survivors
Fiscal Year:	2023

PROJECT SUMMARY/ABSTRACT Among the half a million childhood cancer survivors alive in the US today, the most commonly reported non- cancer severe, life-threatening, or fatal chronic condition is cardiovascular disease (CVD) . It is the leading non- cancer cause of premature death in this population. Heart radiation and anthracycline exposure have been associated with a variety of CVD outcomes including cardiomyopathy, coronary artery disease (CAD), and heart valve disease. Investigations of radiation therapy (RT)-related CVD have typically established associations based solely on whole heart dose metrics; thus, overlooking the heterogeneity of the organ and its substructures. Our team was the first to report data demonstrating substructure-level dose response of CVD risk in childhood cancer survivors. Despite establishing distinct radiosentivities, cardiac substructure dose constraints are not commonly incorporated into RT treatment planning due to the lack o f validated risk prediction models, thus, missing opportunities to prospectively optimize RT planning and retrospectively personalize risk-counseling and long-term cardiovascular surveillance in current and future cancer survivors. The goal of the proposed project is to develop and validate novel CVD risk prediction models that incorporate cardiac substructure doses. Further, we propose to develop tools to clinically translate these models into effective personalized treatment paradigms with prospective and retrospective applications for care providers to reduce CVD risk. We will: (1) develop and validate risk prediction models for cardiomyopathy, CAD, and heart valve disease incorporating cardiac RT substructure doses, adjusting for demographics and chemotherapy exposures; and (2) integrate CVD risk prediction models into commercial RT treatment planning systems and web-based applications, and establish their use via in-silico studies of contemporary patients treated with RT. This will be the first investigation to use the unique radiosensitivity of different cardiac substructures as the foundation for models that can predict the risk of specific types of CVD in children newly diagnosed with cancer as well as among long-term survivors. Incorporating the substructure doses into prediction models will significantly advance clinical care for both prospective RT treatment planning and retrospect ive risk assessments. Prospectively, late CVD risk could be decreased in future survivors by optimizing delivery of chest-directed RT with cardiac substructure dose constraints and selecting the plan that confers the lowest risk, while maintaining optimal clinical target volume coverage. Retrospectively post treatment, the clinical team can provide evidence-based personalized risk mitigation counseling, based on individualized risk profiles determined from delivered cardiac substructure doses adjusted for chemotherapy exposures and demographics. Successful execution of the proposed project has the potential to transform clinical practice for treatment of childhood and adolescent patients with cancer.





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