GSE128723
Transcriptomic responses to Ivacaftor and prediction of Ivacaftor clinical responsiveness
Rationale: Ivacaftor is a recently FDA-approved drug for the treatment of cystic fibrosis (CF) patients with at least one copy of the G511D mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. The transcriptomic effect of Ivacaftor in CF patients remains unclear. Objectives: We aim to examine if and how the transcriptome of patients is influenced by Ivacaftor treatment and to determine if these data allow prediction of Ivacaftor responsiveness. Methods: We performed RNA-sequencing (RNA-seq) on PBMCs from CF patients and compared the transcriptomic changes before and after Ivacaftor treatments. Consensus clustering method is employed to stratify patients into sub-groups based on clinical responses post treatment, and determined differences in baseline gene expression. A random forest model is built to predict Ivacaftor responsiveness. Measurement and Main Results: We identified 239 genes that were significantly influenced by Ivacaftor in PBMC. 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” responders based on clinical response to Ivacraftor. 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. Conclusions: For the first time, the present study demonstrates profound transcriptomic impacts of Ivafactor in CF patients PBMCs and successfully built a statistical model for predicting the clinical responsiveness to Ivacaftor prior to treatment. Overall design: before- and after-Ivacaftor treatment RNA-seq samples from CF subjects