Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors
| dc.contributor.author | Chaix M-A | |
| dc.contributor.author | Parmar N | |
| dc.contributor.author | Kinnear C | |
| dc.contributor.author | Lafreniere-Roula M | |
| dc.contributor.author | Akinrinade O | |
| dc.contributor.author | Yao R | |
| dc.contributor.author | Miron A | |
| dc.contributor.author | Lam E | |
| dc.contributor.author | Meng G | |
| dc.contributor.author | Christie A | |
| dc.contributor.author | Manickaraj AK | |
| dc.contributor.author | Marjerrison S | |
| dc.contributor.author | Dillenburg R | |
| dc.contributor.author | Bassal M | |
| dc.contributor.author | Lougheed J | |
| dc.contributor.author | Zelcer S | |
| dc.contributor.author | Rosenberg H | |
| dc.contributor.author | Hodgson D | |
| dc.contributor.author | Sender L | |
| dc.contributor.author | Kantor P | |
| dc.contributor.author | Manlhiot C | |
| dc.contributor.author | Ellis J | |
| dc.contributor.author | Mertens L | |
| dc.contributor.author | Nathan PC | |
| dc.contributor.author | Mital S | |
| dc.contributor.department | Pediatrics | |
| dc.date.accessioned | 2021-10-08T13:51:40Z | |
| dc.date.available | 2021-10-08T13:51:40Z | |
| dc.date.issued | 2020-12 | |
| dc.date.updated | 2021-10-08T13:51:33Z | |
| dc.description.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). | |
| dc.identifier.doi | https://doi.org/10.1016/j.jaccao.2020.11.004 | |
| dc.identifier.issn | 2666-0873 | |
| dc.identifier.issn | 2666-0873 | |
| dc.identifier.uri | http://hdl.handle.net/11375/27024 | |
| dc.publisher | Elsevier BV | |
| dc.subject | Science & Technology | |
| dc.subject | Life Sciences & Biomedicine | |
| dc.subject | Oncology | |
| dc.subject | Cardiac & Cardiovascular Systems | |
| dc.subject | Cardiovascular System & Cardiology | |
| dc.subject | anthracycline | |
| dc.subject | cancer survivorship | |
| dc.subject | cardiomyopathy | |
| dc.subject | echocardiography | |
| dc.subject | genomics | |
| dc.subject | machine learning | |
| dc.subject | risk prediction | |
| dc.subject | GENOME-WIDE ASSOCIATION | |
| dc.subject | RISK-FACTORS | |
| dc.subject | DOXORUBICIN | |
| dc.subject | VARIANT | |
| dc.subject | CARDIOMYOPATHY | |
| dc.subject | MECHANISMS | |
| dc.subject | PREDICTION | |
| dc.subject | THERAPY | |
| dc.subject | TUMOR | |
| dc.title | Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors | |
| dc.type | Article |
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