Pharmacogenetic and clinical predictors of ondansetron failure in a diverse pediatric oncology population

Supportive Care in Cancer(2022)

引用 2|浏览1
暂无评分
摘要
Purpose Chemotherapy-induced nausea and vomiting (CINV) is a frequently seen burdensome adverse event of cancer therapy. The 5-HT3 receptor antagonist ondansetron has improved the rates of CINV but, unfortunately, up to 30% of patients do not obtain satisfactory control. This study examined whether genetic variations in a relevant drug-metabolizing enzyme (CYP2D6), transporter (ABCB1), or receptor (5-HT3) were associated with ondansetron failure. Methods DNA was extracted from blood and used to genotype: ABCB1 (3435C > T (rs1045642) and G2677A/T (rs2032582)), 5-HT3RB (rs3758987 T > C and rs45460698 (delAAG/dupAAG)), and CYP2D6 variants. Ondansetron failure was determined by review of the medical records and by patient-reported outcomes (PROs). Results One hundred twenty-nine patients were approached; 103 consented. Participants were less than 1 to 33 years (mean 6.85). A total of 39.8% was female, 58.3% was White (22.3% Black, 19.4% other), and 24.3% was Hispanic. A majority had leukemia or lymphoma, and 41 (39.8%) met the definition of ondansetron failure. Of variants tested, rs45460698 independently showed a significant difference in risk of ondansetron failure between a mutant (any deletion) and normal allele ( p = 0.0281, OR 2.67). Age and BMI were both predictive of ondansetron failure (age > 12 ( OR 1.12, p = 0.0012) and higher BMI ( OR 1.13, p = 0.0119)). In multivariate analysis, age > 12 was highly predictive of ondansetron failure ( OR 7.108, p = 0.0008). rs45460698 was predictive when combined with an increased nausea phenotype variant of rs1045642 ( OR 3.45, p = 0.0426). Conclusion Select phenotypes of 5-HT3RB and ABCB1, age, and potentially BMI can help predict increased risk for CINV in a diverse pediatric oncology population.
更多
查看译文
关键词
Pediatric cancer, Chemotherapy-induced nausea, Pharmacogenomics, Ondansetron
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要