Nasal Epithelium Transcriptomics Predict Clinical Response to Elexacaftor/Tezacaftor/Ivacaftor

Elexacaftor/tezacaftor/ivacaftor (ETI) has had a substantial positive impact for people living with cystic fibrosis (pwCF). However, there can be substantial variability in efficacy, and we lack adequate biomarkers to predict individual response. We thus aimed to identify transcriptomic profiles in nasal respiratory epithelium that predict clinical response to ETI treatment. We obtained nasal epithelial samples from pwCF before ETI initiation and performed a transcriptome-wide analysis of baseline gene expression to predict changes in forced expiratory volume in 1 second (ΔFEV₁), year's best FEV₁ (ΔybFEV₁), and body mass index (ΔBMI). Using the top differentially expressed genes, we generated transcriptomic risk scores (TRSs) and evaluated their predictive performance. The study included 40 pwCF ≥6 years of age (mean, 27.7 [SD, 15.1] years; 40% female). After ETI initiation, FEV₁ improved by ≥5% in 22 (61.1%) participants, and ybFEV₁ improved by ≥5% in 19 (50%). TRSs were constructed using top overexpressed and underexpressed genes for each outcome. Adding the ΔFEV₁ TRS to a model with age, sex, and baseline FEV₁ increased the area under the receiver operating characteristic curve (AUC) from 0.41 to 0.88, the ΔybFEV₁ TRS increased the AUC from 0.51 to 0.88, and the ΔBMI TRS increased the AUC from 0.46 to 0.92. Average accuracy was thus ∼85% in predicting the response to the three outcomes. Results were similar in models further adjusted for F508del zygosity and previous CFTR modulator use. In conclusion, we identified nasal epithelial transcriptomic profiles that help accurately predict changes in FEV₁ and BMI with ETI treatment. These novel TRSs could serve as predictive biomarkers for clinical response to modulator treatment in pwCF.