Background: Chronic lung allograft dysfunction (CLAD) limits survival following lung transplant, but substantial lung damage occurs before diagnosis by traditional methods. We hypothesized that small airway gene expression patterns could identify CLAD risk before spirometric diagnosis and predict subsequent graft failure.
Methods: Candidate genes from 4 rejection-associated transcript sets were assessed for associations with CLAD or graft failure in a derivation cohort of 156 small airway brushes from 45 CLAD cases and 37 time-matched controls with >1-year stable lung function. Candidate genes not associated with CLAD and time to graft failure were excluded, yielding the Airway Inflammation 2 (AI2) gene set. Area under the receiver operating curve (AUC) for CLAD and competing risks of death or graft failure were assessed in an independent validation cohort of 37 CLAD cases and 37 controls.
Results: Thirty-two candidate genes were associated with CLAD and graft failure, comprising the AI2 score, which clustered into 3 subcomponents. The AI2 score identified CLAD before its onset, in early and late post-CLAD brushes, as well as in the validation cohort (AUC 0.69-0.88). The AI2 score association with CLAD was independent of positive microbiology, CLAD stage, or CLAD subtype. However, transcripts most associated with CLAD evolved over time from CLAD onset. The AI2 score predicted time to graft failure and retransplant-free survival in both cohorts (p ≤ 0.03).
Conclusions: This airway inflammation gene score is associated with CLAD development, graft failure, and death. Future studies defining the molecular heterogeneity of airway inflammation could lead to endotype-targeted therapies.
Keywords: biomarker; chronic lung allograft dysfunction; gene expression; lung transplant; molecular diagnostic; transcriptome.
Published by Elsevier Inc.