期刊
PHARMACOGENOMICS
卷 16, 期 10, 页码 1065-1076出版社
FUTURE MEDICINE LTD
DOI: 10.2217/PGS.15.61
关键词
anthracyclines; association study; cardiotoxicity; childhood cancer; pharmacogenomics
资金
- Canadian Institutes of Health Research
- Canada Foundation for Innovation
- Genome Canada
- Genome British Columbia
- Genome Quebec
- Child & Family Research Institute (Vancouver, BC, Canada)
- Faculty of Pharmaceutical Sciences, University of British Columbia
- Faculty of Medicine, University of British Columbia
- University of Western Ontario
- Canada Gene Cure Foundation
- Canadian Society of Clinical Pharmacology
- C17 Research Network
- Canadian Paediatric Society
- Merck Frosst
- Janssen-Ortho
- Illumina
- Eli Lilly
- Pfizer
- Genome Canada Applied Health Research Program
- Canada Foundation for Innovation/Canadian Institutes of Health Research Regional/National Clinical Research Initiatives
- Genome British Columbia Translational Program for Applied Health
- Michael Smith Foundation for Health Research
- Child & Family Research Institute
- Stichting Kindergeneeskundig Kankeronderzoek (Foundation for Pediatric Cancer Research)
- Childhood Cancer Foundation-Candlelighters Canada
To identify novel variants associated with anthracycline-induced cardiotoxicity and to assess these in a genotype-guided risk prediction model. Patients & methods: Two cohorts treated for childhood cancer (n = 344 and 218, respectively) were genotyped for 4578 SNPs in drug ADME and toxicity genes. Results: Significant associations were identified in SLC22A17 (rs4982753; p = 0.0078) and SLC22A7 (rs4149178; p = 0.0034), with replication in the second cohort (p = 0.0071 and 0.047, respectively). Additional evidence was found for SULT2B1 and several genes related to oxidative stress. Adding the SLC22 variants to the prediction model improved its discriminative ability (AUC 0.78 vs 0.75 [p = 0.029]). Conclusion: Two novel variants in SLC22A17 and SLC22A7 were significantly associated with anthracycline-induced cardiotoxicity and improved a genotype-guided risk prediction model, which could improve patient risk stratification.
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