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 Kantor; Cedric Manlhiot; James Ellis; Luc Mertens; Paul C Nathan; Seema Mital

doi:10.1016/j.jaccao.2020.11.004

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

JACC CardioOncol. 2020 Dec 15;2(5):690-706. doi: 10.1016/j.jaccao.2020.11.004. eCollection 2020 Dec.

Authors

Marie-A Chaix^{1

2}, Neha Parmar¹, Caroline Kinnear¹, Myriam Lafreniere-Roula³, Oyediran Akinrinade¹, Roderick Yao¹, Anastasia Miron¹, Emily Lam¹, Guoliang Meng⁴, Anne Christie¹, Ashok Kumar Manickaraj^{5

1}, Stacey Marjerrison⁶, Rejane Dillenburg⁶, Mylène Bassal⁷, Jane Lougheed⁷, Shayna Zelcer⁸, Herschel Rosenberg⁸, David Hodgson⁹, Leonard Sender¹⁰, Paul Kantor¹¹, Cedric Manlhiot¹², James Ellis^{4

5}, Luc Mertens¹³, Paul C Nathan¹³, Seema Mital^{1

13}

Affiliations

¹ Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.
² Adult Congenital Centre, Montréal Heart Institute, Université de Montréal, Montréal, Canada.
³ Ted Rogers Computational Medicine Program, University Health Network, Toronto, Ontario, Canada.
⁴ Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.
⁵ Department of Molecular Genetics, University of Toronto, Ontario, Canada.
⁶ Department of Pediatrics, McMaster University Children's Hospital, Hamilton, Ontario, Canada.
⁷ Department of Pediatrics, Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, Ontario, Canada.
⁸ Department of Pediatrics, Children's Hospital, London Health Sciences Centre, London, Ontario, Canada.
⁹ Radiation Medicine Program, Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
¹⁰ Department of Pediatrics, Children's Hospital of Orange County, Orange, California, USA.
¹¹ Department of Pediatrics, Children's Hospital of Los Angeles, Los Angeles, California, USA.
¹² Department of Pediatrics, Johns Hopkins Medical Center, Baltimore, Maryland, USA.
¹³ Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.

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).

Keywords: AUC, area under the curve; CI, confidence interval; DMSO, dimethyl sulfoxide; DOX, doxorubicin; GSEA, gene set enrichment analysis; H2AX, H2A family member X; IC50, half-maximal inhibitory concentration; LV, left ventricular; LVEF, left ventricular ejection fraction; MAF, minor allele frequency; OR, odds ratio; PGP, Personal Genome Project; RF, random forest; SKAT, sequence kernel association test; SNV, single-nucleotide variant; anthracycline; cancer survivorship; cardiomyopathy; echocardiography; genomics; hiPSC-CM, human induced pluripotent stem cell–derived cardiomyocyte; mRNA, messenger RNA; machine learning; risk prediction.

Associated data

ClinicalTrials.gov/NCT01805778