PT  - JOURNAL ARTICLE
AU  - Johann Pellet
AU  - Diane Lefaudeux
AU  - Pierre-Joseph Royer
AU  - Angela Koutsokera
AU  - Sandrine Bourgoin-Voillard
AU  - Markus Schmitt
AU  - Candice Trocme
AU  - Johanna Claustre
AU  - Andreas Fritz
AU  - Michel Seve
AU  - Antoine Magnan
AU  - Christophe Pison
AU  - Laurent Nicod
AU  - Charles Auffray
TI  - A multi-omics data integration approach to identify a predictive molecular signature of CLAD
AID  - 10.1183/13993003.congress-2015.OA3271
DP  - 2015 Sep 01
TA  - European Respiratory Journal
PG  - OA3271
VI  - 46
IP  - suppl 59
4099  - http://erj.ersjournals.com/content/46/suppl_59/OA3271.short
4100  - http://erj.ersjournals.com/content/46/suppl_59/OA3271.full
SO  - Eur Respir J2015 Sep 01; 46
AB  - Chronic lung allograft dysfunction (CLAD) is the major long-term complication of lung transplantation (LT), occurring in up to 50% of cases within 5 years post-LT and rising to 75% after 10 years. The two most common phenotypes are a bronchiolitis obliterans syndrome (BOS) and less frequently a restrictive allograft syndrome (RAS).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, as shown in the table below. CLAD (46)BOS (27)RAS (19)Stable (49)Age, years median (range)49 (16-68)39 (16-66)58 (20-68)38 (17-63)% Male54436544% Alive at 3 years post-LT59644596Demographics summary of patient groupsFor each platform, we compared CLAD to stable phenotypes and identified 14 biomarkers that could be used to predict the development of CLAD.We present the first results of the Systems prediction of CLAD (SysCLAD) handprint analysis. Through the integration of several large experimental datasets, we identified potential biomarkers associated with the prediction of CLAD development.Supported by SysCLAD Consortium Grant FP7-Health n°30545.