SysCLAD – EISBM

SysCLAD – Systems prediction of Chronic Lung Allograft Dysfunction

Project reference: 305457
Funded under: FP7-HEALTH and managed by HLA-MED
From 2012-11-01 to 2014-10-31, closed project
More information: SysCLAD Website

Lung transplantation (LT) is the standard of care for selected patients with chronic respiratory failure. Chronic lung allograft dysfunction (CLAD) represents a major health risk for LT recipients. Observed in almost 50% of patients after 5 years post LT, it is currently impossible to predict the appearance of CLAD before its onset. The SysCLAD model integrating biological, clinical, environmental and multi-omics data holds further promises in the context of other chronic bronchial inflammatory diseases of major incidence such as severe asthma and Chronic Obstructive Pulmonary Disease (COPD) to predict decline in lung function.

Objectives

The aim is to identify and validate the signature of CLAD both at the clinical and molecular levels to allow for an early recognition and specific interventions in patients at risk of CLAD. The implementation of the model is expected to significantly improve the cost-effectiveness of post-LT treatments, limit the risk of graft rejection in LT recipients and, ultimately lead to an improved quality of life and a prolonged life expectancy of patients following LT.

EISBM contribution

Through an integrative systems biology research strategy, our aim was to perform omics data integration using exploratory data analysis methods in order to predict a future CLAD before any decline in lung function.
Whole exome, transcriptome, proteome datasets collected in the French Cohort Of Lung Transplantation (COLT) and the Swiss Transplant Cohort Study (STCS) were incorporated together with biological, clinical and public data into a knowledge management platform. The sampling for each patient was made at month 6 and 12 post-LT. Patient phenotypes were defined after 3 years of follow-up. Data from ninety-five patients from COLT and STCS cohorts were integrated in our multi-omics analysis.

The first results of the Systems prediction of CLAD handprint analysis were presented during the European Respiratory Society conference in Amsterdam, September 2015. Through the integration of several large experimental datasets, we identified potential biomarkers associated with the prediction of CLAD development.