Predicting chronic lung allograft dysfunction (CLAD) with a systems biology approach

Biomax provides the knowledge management platform for the European-wide SysCLAD study (Systems prediction of Chronic Lung Allograft Dysfunction) to elucidate CLAD mechanisms, including the role of environmental factors, and establish biomarkers predictive of CLAD post lung transplantation (LT). Using a systems medicine approach, the project’s aim is to identify and validate biomarker signatures that predict whether a lung transplant recipient will develop CLAD and deliver specific personalized medical interventions that reduce or delay the onset of dysfunction.

Lung transplantation is the standard of care for selected patients with chronic respiratory failure. Chronic lung allograft dysfunction (CLAD) represents a major health risk for lung transplant recipients. Almost half of patients show CLAD after 5 years post transplantation and it is currently not possible to predict CLAD before the onset of its first symptoms. SysCLAD aims to develop a predictive model which will identify (within the first year after surgery) lung transplant recipients at risk of developing CLAD. By improving the early prediction of CLAD, the SysCLAD model and biomarker signature are expected to improve patient outcomes and cost-effectiveness of lung transplants by enabling effective, personalized medical interventions.

Using clinical and biological data collected on a cohort of 1,000 lung transplant recipients in the first year after their transplants, a mathematical model will be developed following systems medicine approach to establish the CLAD signature. A wide variety of donor/recipient (D/R) characteristics are included: post LT clinical events, exposure to outdoor air pollutants, D/R HLA, recipient exome sequences, peripheral blood monocyte cell (PBMC) subpopulations, blood transcriptomics and proteomics, bronchoalveolar lavage (BAL) with macrophage functional polarization, proteomics, microbiote, and blood miRNA.

The multi-scale mathematical disease models designed in the project provide a powerful link to bridge systems biology and clinical application. The main objective of the SysCLAD project is to deliver personalized medicine and drug effectiveness prediction over a specifically characterized target population (lung transplant recipients). Using 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. The SysCLAD model holds further promise 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.

For its part, Biomax built the SysCLAD Information System, a knowledge database on CLAD after lung transplant, using extensive data mining of published literature linked to the dynamic model of irreversible decline in lung function after LT. Biomax has provided the BioXM™ Knowledge Management Environment to the project partners as a secure, federated software environment to enable the data flow between partners and potential external collaborations. Security issues have been highly important, including access control, privacy protection, auditing and adherence to legal requirements for clinical data exchange and data transfer protocols or storage reliability. The BioXM system has allowed the different SysCLAD data types, information from public repositories and knowledge extracted from the literature to be semantically mapped into a coherent knowledge base. This systems-biology-driven modeling approach includes integrating all biological components that are thought to play a role in the course of CLAD with experimental data generated by the project.

The BioXM system has provided the capacity to merge, integrate and analyze large amounts of data, perform omics data integration and network modeling. In the project, the BioXM system maps participant-related clinical data with environmental information and digital omics measurements, thus integrating federated data sources into a coherent information resource. Together with the information extracted from un-structured literature, the system forms the initial knowledge base required for the CLAD model generation and validation.

University Hospital Zurich, Switzerland
Centre Universitaire Vaudois, Switzerland
Université Joseph Fourier Grenoble, France
Unité INSERM UJF, France
Centre Hospitalier Universitaire de Grenoble, France
Centre Hospitalier Universitaire de Nantes, France
Novadiscovery, France
Finovatis, France
GATC Biotech, Germany

SysCLAD is funded by the European Union under the FP7 framework (Grant # 305457 FP7-HEALTH-2012).

  • The SysCLAD- Systems Prediction of Chronic Lung Allograft Dysfunction Study: Aims, Strategy and First Data
    Pison C, Tissot A, Magnan A, et al (2013) J Heart Lung Transplantation 32: S220
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