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http://hdl.handle.net/11375/27024
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DC Field | Value | Language |
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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.date.accessioned | 2021-10-08T13:51:40Z | - |
dc.date.available | 2021-10-08T13:51:40Z | - |
dc.date.issued | 2020-12 | - |
dc.identifier.issn | 2666-0873 | - |
dc.identifier.issn | 2666-0873 | - |
dc.identifier.uri | http://hdl.handle.net/11375/27024 | - |
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.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 | - |
dc.date.updated | 2021-10-08T13:51:33Z | - |
dc.contributor.department | Pediatrics | - |
dc.identifier.doi | https://doi.org/10.1016/j.jaccao.2020.11.004 | - |
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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