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

Halloran, K.; Macková, Martina; Parkes, Michael D.; Hirji, A.; Weinkauf, J.; Timofte, I.; Snell, Greg; Westall, Glen P.; Lischke, Robert; Zajacova, Andrea; Havlín, Jan; Hachem, Ramsey R.; Kreisel, Daniel; Levine, Deborah J.; Kubisa, Bartosz; Piotrowska, Maria; Juvet, S.; Keshavjee, S.; Jaksch, Péter; Klepetko, Walter; Halloran, Philip F.

doi:10.1016/j.healun.2022.08.014

Cited by 8 publications

(4 citation statements)

References 43 publications

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“…These genes did not overlap with those identified in TBBx. Their ability to predict CLAD was good, with an AUC of 0.7, but lower than the AUC for time (0.83) [59]. These results have no clinical application to date [58].…”

Section: Tissue Transcriptomics and Infectionmentioning

confidence: 73%

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

Pradère,

Zajacova,

Bos

et al. 2023

Eur Respir Rev

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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.

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Section: Tissue Transcriptomics and Infectionmentioning

confidence: 73%

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

Pradère,

Zajacova,

Bos

et al. 2023

Eur Respir Rev

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“…While most biomarker discovery studies use only statistical association tests or univariable prediction models to identify candidates, a growing number use multivariable models, often internally cross-validated (Halloran et al 2022). For example, Wolf et al (2011) put a panel of 7 BOS-discriminating proteins obtained from mass spectrometry of BAL fluid into a RF classifier that successfully risk-stratified patients both alone and in combination with FEV1, and Berra et al (2021) built several ML CLAD prediction models of varying performance from the Western blot–measured concentrations of several peptides in BAL fluid.…”

Section: Discussionmentioning

confidence: 99%

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

Nord,

Brunson,

Langerude

et al. 2024

Preprint

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BACKGROUNDThere 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.METHODSBiospecimens 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).RESULTSUsing 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.CONCLUSIONSBAL 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.

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“…Only a T-cell mediated rejection/mixed state was associated with CLAD, while NK cell-enriched molecular state was not [19]. Along these lines, a gene expression microarray comparison between mucosal biopsies at the third generation of airway branching and transbronchial biopsies was performed, which revealed that there is a diffuse molecular injury signature that impacts prognosis indicating that both have their value in assessing CLAD risk in LTx [20].…”

Section: Andandmentioning

confidence: 99%

The diagnosis and management of chronic lung allograft dysfunction

Verleden,

Hendriks,

Verleden

2024

Current Opinion in Pulmonary Medicine

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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.

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

Cited by 8 publications

References 43 publications

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

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

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

The diagnosis and management of chronic lung allograft dysfunction

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