Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors

Authors

Marie A. Chaix, Hospital for Sick Children University of Toronto
Neha Parmar, Hospital for Sick Children University of Toronto
Caroline Kinnear, Hospital for Sick Children University of Toronto
Myriam Lafreniere-Roula, University Health Network University of Toronto
Oyediran Akinrinade, Hospital for Sick Children University of Toronto
Roderick Yao, Hospital for Sick Children University of Toronto
Anastasia Miron, Hospital for Sick Children University of Toronto
Emily Lam, Hospital for Sick Children University of Toronto
Guoliang Meng, Hospital for Sick Children University of Toronto
Anne Christie, Hospital for Sick Children University of Toronto
Ashok Kumar Manickaraj, Hospital for Sick Children University of Toronto
Stacey Marjerrison, McMaster University
Rejane Dillenburg, McMaster University
Mylène Bassal, Children's Hospital of Eastern Ontario, Ottawa
Jane Lougheed, Children's Hospital of Eastern Ontario, Ottawa
Shayna Zelcer, London Health Sciences CentreFollow
Herschel Rosenberg, London Health Sciences Centre
David Hodgson, University of Toronto
Leonard Sender, CHOC Children‘s UC Irvine School of Medicine
Paul Kantor, Children's Hospital Los Angeles
Cedric Manlhiot, Johns Hopkins University
James Ellis, Hospital for Sick Children University of Toronto
Luc Mertens, Hospital for Sick Children University of Toronto
Paul C. Nathan, Hospital for Sick Children University of Toronto
Seema Mital, Hospital for Sick Children University of Toronto

Document Type

Article

Publication Date

12-1-2020

Journal

JACC: CardioOncology

Volume

2

Issue

5

First Page

690

Last Page

706

URL with Digital Object Identifier

10.1016/j.jaccao.2020.11.004

Abstract

Background: Despite known clinical risk factors, predicting anthracycline cardiotoxicity remains challenging. Objectives: This study sought to develop a clinical and genetic 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 case patients with reduced left ventricular ejection fraction despite low-dose doxorubicin (≤250 mg/m2), and 106 control patients with preserved left ventricular ejection fraction despite doxorubicin >250 mg/m2 were selected as extreme phenotypes. Rare/low-frequency variants were collapsed to identify genes differentially enriched for variants between case patients and control patients. The expression levels of 5 top-ranked genes were evaluated in human induced pluripotent stem cell–derived cardiomyocytes, 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 case patients and control patients (p < 0.001). Only 42.6% case patients harbored a variant in these genes compared to 89.6% control patients (odds ratio: 0.09; 95% confidence interval: 0.04 to 0.17; p = 3.98 × 10–15). 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-only model. In vitro inhibition of gene-associated pathways (PI3KR2, ZNF827) provided protection from cardiotoxicity in cardiomyocytes. Conclusions: Our study identified variants in cardiac injury pathway genes that protect against cardiotoxicity and informed the development of a prediction model for delayed anthracycline cardiotoxicity, and it also provided new targets in autophagy genes for the development of cardio-protective drugs. (Preventing Cardiac Sequelae in Pediatric Cancer Survivors [PCS2]; NCT01805778)