Improving Prediction of Chemotherapy-Induced Nausea: Integrating Genes, Behavior, and the Microbiome

项目总结

PROJECT SUMMARY Moderate to severe nausea is reported by up to 38% of cancer patients receiving moderately- or highly- emetogenic chemotherapy despite international antiemetic guidelines. Chemotherapy-induced nausea is associated with worse quality of life, increased healthcare utilization, and greater costs of care. Previous research has identified clinical and personal risk factors for nausea (e.g., disease stage, emetogenicity of chemotherapy, age, sex, history of motion sickness, anxiety, alcohol use), but risk-prediction algorithms based on these factors demonstrate room for improvement. Recent methodologies for studying the human genome and microbiome enable us for the first time to develop a more comprehensive understanding of the pathophysiology of chemotherapy-induced nausea and develop better algorithms. The goal of the current study is to improve understanding of risk of chemotherapy-induced nausea through integrated examination of genetic and microbiome variables with well-established clinical and personal risk factors. We will conduct the first GWAS of chemotherapy-induced nausea, then use pathway analysis to place results into functional context. Significant variants will be incorporated with clinical and personal factors into a clinical risk prediction algorithm. This study will also be among the first to examine the association of the gut microbiome with chemotherapy-induced nausea. Importantly, this study is one of the first to truly integrate biological, clinical, and patient-reported data to predict risk of treatment toxicity. This groundbreaking study will be a model for similar efforts in other side effects of chemotherapy such as cognitive impairment, fatigue, and peripheral neuropathy. Updated risk algorithms from the current study will be made publicly available and will inform a future randomized trial of risk-based antiemetic cancer care delivery. Positive results will spur integration of the risk algorithms into the electronic medical record with provider alerts to identify patients at risk of chemotherapy-induced nausea. Microbiome analyses conducted in the first two years of the study will provide rapid knowledge about its contributions to chemotherapy-induced nausea. Positive results will generate additional studies to determine whether microbial manipulation can prevent chemotherapy-induced nausea. Microbiome data are particularly exciting because they focus on an entirely new mechanism in chemotherapy-related toxicity. In summary, this rigorous, comprehensive study provides an integrated new approach to chemotherapy-induced nausea that is expected to significantly advance our understanding of pathophysiology and risk of this important clinical problem.