The molecular features of chronic lung allograft dysfunction in lung transplant airway mucosa

Predicting Primary Graft Dysfunction in Lung Transplantation: Machine Learning-Guided Biomarker Discovery

BACKGROUND –

There is an urgent need to better understand the pathophysiology of primary graft dysfunction (PGD) so that point-of-care methods can be developed to predict those at risk. Here we utilize a multiplex multivariable approach to define cytokine, chemokines, and growth factors in patient-matched biospecimens from multiple biological sites to identify factors predictive of PGD.

METHODS –

Biospecimens were collected from patients undergoing bilateral LTx from three distinct sites: donor lung perfusate, post-transplant bronchoalveolar lavage (BAL) fluid (2h), and plasma (2h and 24h). A 71-multiplex panel was performed on each biospecimen. Cross-validated logistic regression (LR) and random forest (RF) machine learning models were used to determine whether analytes in each site or from combination of sites, with or without clinical data, could discriminate between PGD grade 0 (n = 9) and 3 (n = 8).

RESULTS –

Using optimal AUROC, BAL fluid at 2h was the most predictive of PGD (LR, 0.825; RF, 0.919), followed by multi-timepoint plasma (LR, 0.841; RF, 0.653), then perfusate (LR, 0.565; RF, 0.448). Combined clinical, BAL, and plasma data yielded strongest performance (LR, 1.000; RF, 1.000). Using a LASSO of the predictors obtained using LR, we selected IL-1RA, BCA-1, and Fractalkine, as most predictive of severe PGD.

CONCLUSIONS –

BAL samples collected 2h post-transplant were the strongest predictors of severe PGD. Our machine learning approach not only identified novel cytokines not previously associated with PGD, but identified analytes that could be used as a point-of-care cytokine panel aimed at identifying those at risk for developing severe PGD.

Lung transplantation: Current insights and outcomes

Until now, the ability to predict or retard immune-mediated rejection events after lung transplantation is still limited due to the lack of specific biomarkers. The pressing need remains to early diagnose or predict the onset of chronic lung allograft dysfunction (CLAD) and its differential phenotypes that is the leading cause of death. Omics technologies (mainly genomics, epigenomics, and transcriptomics) combined with advanced bioinformatic platforms are clarifying the key immune-related molecular routes that trigger early and late events of lung allograft rejection supporting the biomarker discovery. The most promising biomarkers came from genomics. Both unregistered and NIH-registered clinical trials demonstrated that the increased percentage of donor-derived cell-free DNA in both plasma and bronchoalveolar lavage fluid showed a good diagnostic performance for clinically silent acute rejection events and CLAD differential phenotypes. A further success arose from transcriptomics that led to development of Molecular Microscope® Diagnostic System (MMDx) to interpret the relationship between molecular signatures of lung biopsies and rejection events. Other immune-related biomarkers of rejection events may be exosomes, telomer length, DNA methylation, and histone-mediated neutrophil extracellular traps (NETs) but none of them entered in registered clinical trials. Here, we discuss novel and existing technologies for revealing new immune-mediated mechanisms underlying acute and chronic rejection events, with a particular focus on emerging biomarkers for improving precision medicine of lung transplantation field.

Small airway brush gene expression predicts chronic lung allograft dysfunction and mortality

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.

The diagnosis and management of chronic lung allograft dysfunction

PURPOSE OF REVIEW

Chronic lung allograft dysfunction (CLAD) remains a life-threatening complication following lung transplantation. Different CLAD phenotypes have recently been defined, based on the combination of pulmonary function testing and chest computed tomography (CT) scanning and spurred renewed interests in differential diagnosis, risk factors and management of CLAD.

RECENT FINDINGS

Given their crucial importance in the differential diagnosis, we will discuss the latest development in assessing the pulmonary function and chest CT scan, but also their limitations in proper CLAD phenotyping, especially with regards to patients with baseline allograft dysfunction. Since no definitive treatment exists, it remains important to timely identify clinical risk factors, but also to assess the presence of specific patterns or biomarkers in tissue or in broncho alveolar lavage in relation to CLAD (phenotypes). We will provide a comprehensive overview of the latest advances in risk factors and biomarker research in CLAD. Lastly, we will also review novel preventive and curative treatment strategies for CLAD.

SUMMARY

Although this knowledge has significantly advanced the field of lung transplantation, more research is warranted because CLAD remains a life-threatening complication for all lung transplant recipients.

Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas

The pathogenesis of allograft (dys)function has been increasingly studied using 'omics'-based technologies, but the focus on individual organs has created knowledge gaps that neither unify nor distinguish pathological mechanisms across allografts. Here we present a comprehensive study of human pan-organ allograft dysfunction, analyzing 150 datasets with more than 12,000 samples across four commonly transplanted solid organs (heart, lung, liver and kidney, n = 1,160, 1,241, 1,216 and 8,853 samples, respectively) that we leveraged to explore transcriptomic differences among allograft dysfunction (delayed graft function, acute rejection and fibrosis), tolerance and stable graft function. We identified genes that correlated robustly with allograft dysfunction across heart, lung, liver and kidney transplantation. Furthermore, we developed a transfer learning omics prediction framework that, by borrowing information across organs, demonstrated superior classifications compared to models trained on single organs. These findings were validated using a single-center prospective kidney transplant cohort study (a collective 329 samples across two timepoints), providing insights supporting the potential clinical utility of our approach. Our study establishes the capacity for machine learning models to learn across organs and presents a transcriptomic transplant resource that can be employed to develop pan-organ biomarkers of allograft dysfunction.

Molecular monitoring of lung allograft health: is it ready for routine clinical use?

Maintenance of long-term lung allograft health in lung transplant recipients (LTRs) requires a fine balancing act between providing sufficient immunosuppression to reduce the risk of rejection whilst at the same time not over-immunosuppressing individuals and exposing them to the myriad of immunosuppressant drug side-effects that can cause morbidity and mortality. At present, lung transplant physicians only have limited and rather blunt tools available to assist them with this task. Although therapeutic drug monitoring provides clinically useful information about single time point and longitudinal exposure of LTRs to immunosuppressants, it lacks precision in determining the functional level of immunosuppression that an individual is experiencing. There is a significant gap in our ability to monitor lung allograft health and therefore tailor optimal personalised immunosuppression regimens. Molecular diagnostics performed on blood, bronchoalveolar lavage or lung tissue that can detect early signs of subclinical allograft injury, differentiate rejection from infection or distinguish cellular from humoral rejection could offer clinicians powerful tools in protecting lung allograft health. In this review, we look at the current evidence behind molecular monitoring in lung transplantation and ask if it is ready for routine clinical use. Although donor-derived cell-free DNA and tissue transcriptomics appear to be the techniques with the most immediate clinical potential, more robust data are required on their performance and additional clinical value beyond standard of care.

Defining a natural killer cell-enriched molecular rejection-like state in lung transplant transbronchial biopsies

In lung transplantation, antibody-mediated rejection (AMR) diagnosed using the International Society for Heart and Lung Transplantation criteria is uncommon compared with other organs, and previous studies failed to find molecular AMR (ABMR) in lung biopsies. However, understanding of ABMR has changed with the recognition that ABMR in kidney transplants is often donor-specific antibody (DSA)-negative and associated with natural killer (NK) cell transcripts. We therefore searched for a similar molecular ABMR-like state in transbronchial biopsies using gene expression microarray results from the INTERLUNG study (#NCT02812290). After optimizing rejection-selective transcript sets in a training set (N = 488), the resulting algorithms separated an NK cell-enriched molecular rejection-like state (NKRL) from T cell-mediated rejection (TCMR)/Mixed in a test set (N = 488). Applying this approach to all 896 transbronchial biopsies distinguished 3 groups: no rejection, TCMR/Mixed, and NKRL. Like TCMR/Mixed, NKRL had increased expression of all-rejection transcripts, but NKRL had increased expression of NK cell transcripts, whereas TCMR/Mixed had increased effector T cell and activated macrophage transcripts. NKRL was usually DSA-negative and not recognized as AMR clinically. TCMR/Mixed was associated with chronic lung allograft dysfunction, reduced one-second forced expiratory volume at the time of biopsy, and short-term graft failure, but NKRL was not. Thus, some lung transplants manifest a molecular state similar to DSA-negative ABMR in kidney and heart transplants, but its clinical significance must be established.