Predicting treatable traits for long-acting bronchodilators in patients with stable COPD

Purpose There is currently no measure to predict a treatability of long-acting β-2 agonist (LABA) or long-acting muscarinic antagonist (LAMA) in patients with chronic obstructive pulmonary disease (COPD). We aimed to build prediction models for the treatment response to these bronchodilators, in order to determine the most responsive medication for patients with COPD. Methods We performed a prospective open-label crossover study, in which each long-acting bronchodilator was given in a random order to 65 patients with stable COPD for 4 weeks, with a 4-week washout period in between. We analyzed 14 baseline clinical traits, expression profiles of 31,426 gene transcripts, and damaged-gene scores of 6,464 genes acquired from leukocytes. The gene expression profiles were measured by RNA microarray and the damaged-gene scores were obtained after DNA exome sequencing. Linear regression analyses were performed to build prediction models after using factor and correlation analyses. Results Using a prediction model for a LABA, traits found associated with the treatment response were post-bronchodilator forced expiratory volume in 1 second, bronchodilator reversibility (BDR) to salbutamol, expression of three genes (CLN8, PCSK5, and SKP2), and damage scores of four genes (EPG5, FNBP4, SCN10A, and SPTBN5) (R2=0.512, p<0.001). Traits associated with the treatment response to a LAMA were COPD assessment test score, BDR, expression of four genes (C1orf115, KIAA1618, PRKX, and RHOQ) and damage scores of three genes (FBN3, FDFT1, and ZBED6) (R2=0.575, p<0.001). The prediction models consisting only of clinical traits appeared too weak to predict the treatment response, with R2=0.231 for the LABA model and R2=0.121 for the LAMA model. Conclusion Adding the expressions of genes and damaged-gene scores to the clinical traits may improve the predictability of treatment response to long-acting bronchodilators.