MiR-21 in Lung Transplant Recipients With Chronic Lung Allograft Dysfunction

Background: Micro-RNA-21 (miR-21) is a post-translational regulator involved in epithelial-to-mesenchymal transition (EMT). Since EMT is thought to contribute to chronic lung allograft dysfunction (CLAD), we aimed to characterize miR-21 expression and distinct EMT markers in CLAD.Methods: Expression of miR-21, vimentin, Notch intracellular domain (NICD) and SMAD 2/3 was investigated in explanted CLAD lungs of patients who underwent retransplantation. Circulating miR-21 was determined in collected serum samples of CLAD and matched stable recipients.Results: The frequency of miR-21 expression was higher in restrictive allograft syndrome (RAS) than in bronchiolitis obliterans syndrome (BOS) specimens (86 vs 30%, p = 0.01); Vimentin, NICD and p-SMAD 2/3 were positive in 17 (100%), 12 (71%), and 7 (42%) BOS patients and in 7 (100%), 4 (57%) and 4 (57%) RAS cases, respectively. All four markers were negative in control tissue from donor lungs. RAS patients showed a significant increase in serum concentration of miR-21 over time as compared to stable recipients (p = 0.040).Conclusion: To the best of our knowledge this is the first study highlighting the role miR-21 in CLAD. Further studies are necessary to investigate the involvement of miR-21 in the pathogenesis of CLAD and its potential as a therapeutic target.

Disease progression in patients with the restrictive and mixed phenotype of Chronic Lung Allograft dysfunction—A retrospective analysis in five European centers to assess the feasibility of a therapeutic trial

Background Chronic Lung Allograft Dysfunction (CLAD) is a major obstacle for long term survival after lung transplantation (LTx). Besides Bronchiolitis Obliterans Syndrome, two other phenotypes of CLAD, restrictive allograft syndrome (RAS) and mixed phenotype, have been described. Trials to test in these conditions are desperately needed and analyzing natural outcome to plan such trials is essential. Methods We performed a retrospective analysis of functional outcome in bilateral LTx recipients with RAS and mixed phenotype, transplanted between 2009 and 2018 in five large European centers with follow- up spirometry up to 12 months after diagnosis. Based on these data, sample size and power calculations for randomized therapeutic trial was estimated using two imputation methods for missing values. Results Seventy patients were included (39 RAS and 31 mixed phenotype), median 3.1 years after LTx when CLAD was diagnosed. Eight, 13 and 25 patients died within 6, 9 and 12 months after diagnosis and a two patients underwent re-transplantation within 12 months leading to a graft survival of 89, 79 and 61% six, nine and 12 months after diagnosis, respectively. Observed FEV1 decline was 451 ml at 6 months and stabilized at 9 and 12 months, while FVC showed continuous decline. Using two methods of imputation, a progressive further decline after 6 months for FEV1 was noted. Conclusion The poor outcome of these two specific CLAD phenotypes suggests the urgent need for future therapeutic randomized trials. The number of missing values in a potential trial seems to be high and most frequently attributed to death. Survival may be used as an endpoint in clinical trials in these distinct phenotypes and imputation techniques are relevant if graft function is used as a surrogate of disease progression in future trials.

Chronic lung allograft dysfunction phenotype and prognosis by machine learning CT analysis

BackgroundChronic lung allograft dysfunction (CLAD) is the principal cause of graft failure in lung transplant recipients and prognosis depends on CLAD phenotype. We used machine learning computed tomography (CT) lung texture analysis tool at CLAD diagnosis for phenotyping and prognostication compared to radiologists’ scoring.MethodsThis retrospective study included all adult first double-lung transplant patients (01/2010–12/2015) with CLAD (censored 12/2019) and inspiratory CT near CLAD diagnosis. The machine learning tool quantified ground-glass opacity, reticulation, hyperlucent lung, and pulmonary vessel volume (PVV). Two radiologists scored for ground-glass opacity, reticulation, consolidation, pleural effusion, air trapping and bronchiectasis. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of machine learning and radiologist for CLAD phenotype. Multivariable Cox proportional-hazards regression analysis for allograft survival controlled for age, sex, native lung disease, cytomegalovirus serostatus, and CLAD phenotype (bronchiolitis obliterans syndrome [BOS] and restrictive allograft syndrome [RAS]/mixed).Results88 patients were included (57 BOS, 20 RAS/mixed, and 11 unclassified/undefined) with CT a median 9.5 days from CLAD onset. Radiologist and machine learning parameters phenotyped RAS/mixed with PVV as the strongest indicator (AUC 0.85). Machine learning hyperlucent lung phenotyped BOS using only inspiratory CT (AUC=0.76). Radiologist and machine learning parameters predicted graft failure in the multivariable analysis, best with PVV (HR=1.23, 95%CI 1.05–1.44, p=0.01).ConclusionsMachine learning discriminated between CLAD phenotypes on CT. Both radiologist and machine learning scoring were associated with graft failure, independent of CLAD phenotype. PVV, unique to machine learning, was the strongest in phenotyping and prognostication.