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

Marie A. Chaix, Neha Parmar, Caroline Kinnear, Myriam Lafreniere-Roula, Oyediran Akinrinade, Roderick Yao, Anastasia Miron, Emily Lam, Guoliang Meng, Anne Christie, Ashok Kumar Manickaraj, Stacey Marjerrison, Rejane Dillenburg, Mylène Bassal, Jane Lougheed, Shayna Zelcer, Herschel Rosenberg, David Hodgson, Leonard Sender, Paul KantorCedric Manlhiot, James Ellis, Luc Mertens, Paul C. Nathan, Seema Mital

Research output: Contribution to journalArticlepeer-review

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)

Original languageEnglish (US)
Pages (from-to)690-706
Number of pages17
JournalJACC: CardioOncology
Volume2
Issue number5
DOIs
StatePublished - Dec 2020

Keywords

  • anthracycline
  • cancer survivorship
  • cardiomyopathy
  • echocardiography
  • genomics
  • machine learning
  • risk prediction

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Oncology

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