Prediction of chemotherapy-induced nausea and vomiting from patient-reported and genetic risk factors

Purpose Chemotherapy-induced nausea and vomiting (CINV) is common among cancer patients. Early identification of patients at risk for CINV may help to personalize anti-emetic therapies. To date, few studies have examined the combined contributions of patient-reported and genetic risk factors to CINV...
Ausführliche Beschreibung

Purpose Chemotherapy-induced nausea and vomiting (CINV) is common among cancer patients. Early identification of patients at risk for CINV may help to personalize anti-emetic therapies. To date, few studies have examined the combined contributions of patient-reported and genetic risk factors to CINV. The goal of this study was to evaluate these risk factors. Methods Prior to their first chemotherapy infusion, participants completed demographic and risk factor questionnaires and provided a blood sample to measure genetic variants in ABCB1 (rs1045642) and HTR3B (rs45460698) as well as CYP2D6 activity score. The M.D. Anderson Symptom Inventory was completed at 24 h and 5-day post-infusion to assess the severity of acute and delayed CINV, respectively. Results Participants were 88 patients (55% female, M = 60 years). A total of 23% experienced acute nausea and 55% delayed nausea. Younger age, history of pregnancy-related nausea, fewer hours slept the night prior to infusion, and variation in ABCB1 were associated with more severe acute nausea; advanced-stage cancer and receipt of highly emetogenic chemotherapy were associated with more severe delayed nausea (p values < 0.05). In multivariable analyses, ABCB1 added an additional 5% predictive value beyond the 13% variance explained by patient-reported risk factors. Conclusions The current study identified patient-reported and genetic factors that may place patients at risk for acute nausea despite receipt of guideline-consistent anti-emetic prophylaxis. Additional studies examining other genetic variants are needed, as well as the development of risk prediction models including both patient-reported and genetic risk factors. Ausführliche Beschreibung