1. Abstract 14410: A Machine Learning Approach to Predicting Risk of Anthracycline Cardiotoxicity in Pediatric Cancer Survivors
- Author
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Jane Lougheed, Paul F. Kantor, Marie A. Chaix, Rejane Dillenburg, Myriam Lafreniere-Roula, Cedric Manlhiot, Leonard S. Sender, Stacey Marjerrison, Guoliang Meng, Roderick Yao, Ashok Kumar Manickaraj, Anne Christie, Seema Mital, Paul C. Nathan, Shayna Zelcer, Luc Mertens, Mylene Bassal, Neha Parmar, David R. W. Hodgson, James Ellis, Emily Lam, Peter Liu, Oyediran Akinrinade, Herschel Rosenberg, and Anastasia Miron
- Subjects
Oncology ,medicine.medical_specialty ,Cardiotoxicity ,Anthracycline ,business.industry ,Physiology (medical) ,Internal medicine ,medicine ,Cardio oncology ,Genetic risk ,Cardiology and Cardiovascular Medicine ,business ,Pediatric cancer - Abstract
Background: Despite known clinical and genetic risk factors, predicting anthracycline cardiotoxicity remains challenging. Objective: To develop a risk prediction model for anthracycline cardiotoxicity in childhood cancer survivors. Methods: We performed exome sequencing in 289 childhood cancer survivors at least 3 years from anthracycline exposure. In a nested case-control design, 183 cases with LV ejection fraction (LVEF) ≤55% despite low-dose doxorubicin (DOX) (≤250 mg/m2), and 106 controls with LVEF >55% despite DOX >250 mg/m2 were selected as extreme phenotypes. Rare/low-frequency variants were collapsed to identify genes differentially enriched for variants between cases and controls. The top-ranked genes were functionally evaluated in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and variant enrichment was confirmed in a replication cohort. Using random forest, a risk prediction model that included genetic and clinical predictors was developed. Results: Thirty-one genes were differentially enriched for variants between cases and controls (p-15 ] suggesting that absence of these variants may predispose to cardiotoxicity. A risk prediction model for cardiotoxicity that included clinical and genetic factors had a higher prediction accuracy and lower misclassification rate compared to the clinical model. In vitro inhibition of gene-associated pathways ( PI3KR2, ZNF827 ) provided protection from cardiotoxicity in CMs. Conclusions: Our study identified variants that protect against cardiotoxicity and informed the development of a prediction model for delayed anthracycline cardiotoxicity, and also provided new targets in autophagy genes for development of cardio-protective drugs.
- Published
- 2020
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