Ivacaftor is a drug that was recently approved by the U.S. Food and Drug Administration for the treatment of patients with cystic fibrosis (CF) and at least one copy of the G511D mutation in the CFTR (CF transmembrane conductance regulator) gene. The transcriptomic effect of ivacaftor in patients with CF remains unclear. Here, we sought to examine whether and how the transcriptome of patients is influenced by ivacaftor treatment, and to determine whether these data allow prediction of ivacaftor responsiveness. Our data originated from the G551D Observational Study (GOAL). We performed RNA sequencing (RNA-seq) on peripheral blood mononuclear cells (PBMCs) from 56 patients and compared the transcriptomic changes that occurred before and after ivacaftor treatment. We used consensus clustering to stratify patients into subgroups based on their clinical responses after treatment, and we determined differences between subgroups in baseline gene expression. A random forest model was built to predict ivacaftor responsiveness. We identified 239 genes (false discovery rate < 0.1) that were significantly influenced by ivacaftor in PBMCs. The functions of these genes relate to cell differentiation, microbial infection, inflammation, Toll-like receptor signaling, and metabolism. We classified patients into "good" and "moderate" responder groups based on their clinical response to ivacaftor. We identified a panel of signature genes and built a statistical model for predicting CFTR modulator responsiveness. Despite a limited sample size, adequate prediction performance was achieved with an accuracy of 0.92. In conclusion, for the first time, the present study demonstrates profound transcriptomic impacts of ivacaftor in PBMCs from patients with CF, and provides a pilot statistical model for predicting clinical responsiveness to ivacaftor before treatment.
Keywords: cystic fibrosis; drug responsiveness; gene expression; ivacaftor; prediction.