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

Abstract

Elexacaftor/tezacaftor/ivacaftor (ETI) has made a substantial positive impact for people living with CF (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 prior to ETI initiation and performed a transcriptome-wide analysis of baseline gene expression to predict changes in FEV1 (∆FEV1), year's best FEV1 (∆ybFEV1), and body mass index (∆BMI). Using the top differentially expressed genes (DEGs), we generated transcriptomic risk scores (TRS) and evaluated their predictive performance. The study included 40 pwCF aged ≥6 years (mean 27.7 [SD=15.1] years; 40% female). After ETI initiation, FEV1 improved ≥5% in 22 (61.1%) participants and ybFEV1 improved ≥5% in 19 (50%). TRS were constructed using top over-expressed and under-expressed genes for each. Adding the ∆FEV1 TRS for to a model with age, sex, and baseline FEV1 increased the AUC from 0.41 to 0.88; the ∆ybFEV1 TRS increased the AUC from 0.51 to 0.88; and the ∆BMI 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 TRS could serve as predictive biomarkers for clinical response to modulator treatment in pwCF.