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Title: | Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors |
Authors: | Chaix M-A Parmar N Kinnear C Lafreniere-Roula M Akinrinade O Yao R Miron A Lam E Meng G Christie A Manickaraj AK Marjerrison S Dillenburg R Bassal M Lougheed J Zelcer S Rosenberg H Hodgson D Sender L Kantor P Manlhiot C Ellis J Mertens L Nathan PC Mital S |
Department: | Pediatrics |
Keywords: | Science & Technology;Life Sciences & Biomedicine;Oncology;Cardiac & Cardiovascular Systems;Cardiovascular System & Cardiology;anthracycline;cancer survivorship;cardiomyopathy;echocardiography;genomics;machine learning;risk prediction;GENOME-WIDE ASSOCIATION;RISK-FACTORS;DOXORUBICIN;VARIANT;CARDIOMYOPATHY;MECHANISMS;PREDICTION;THERAPY;TUMOR |
Publication Date: | Dec-2020 |
Publisher: | Elsevier BV |
Abstract: | <h4>Background</h4>Despite known clinical risk factors, predicting anthracycline cardiotoxicity remains challenging.<h4>Objectives</h4>This study sought to develop a clinical and genetic risk prediction model for anthracycline cardiotoxicity in childhood cancer survivors.<h4>Methods</h4>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/m<sup>2</sup>), and 106 control patients with preserved left ventricular ejection fraction despite doxorubicin >250 mg/m<sup>2</sup> 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.<h4>Results</h4>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<sup>-15</sup>). 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 (<i>PI3KR2</i>, <i>ZNF827</i>) provided protection from cardiotoxicity in cardiomyocytes.<h4>Conclusions</h4>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). |
URI: | http://hdl.handle.net/11375/27024 |
metadata.dc.identifier.doi: | https://doi.org/10.1016/j.jaccao.2020.11.004 |
ISSN: | 2666-0873 2666-0873 |
Appears in Collections: | Pediatrics Publications |
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File | Description | Size | Format | |
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Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Su.pdf | 4.28 MB | Adobe PDF | View/Open |
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