COPD is a disease that kills 3.4 mill people around the world. Currently, there are no molecular diagnostic tools for the COPD disease, and diagnose relies on the doctor's experience, based on clinical data such as respiratory capability. The main challenge is to differentiate the COPD from other respiratory symptoms such as Asthma, especially at early stages of the disease. The main purpose of the COPD project is to understand the molecular patterns arise at different stages of the disease, and to use these to develop molecular diagnostic tools and to identify potential biomarkers based on large data patients. We aim to use these tools in clinics to help distinguish between COPD and healthy patients.
Currently, we managed to narrow down 25.000 human genes and identified 10 to 37 key genes that are persistent in COPD patients and can be used as biomarkers from lung cells.
Chronic obstructive pulmonary disease (COPD) claimed 3.2 million lives in 2015, making it the third cause of death worldwide. It is predicted to increase in coming years due to aging populations and thus constitutes an enormous socio-economic burden. Existing assessment strategies neglect the complex, multi-component, and heterogeneous pathophysiology, as well as manifold comorbidities (cardiovascular, metabolic etc.). Therefore, improved COPD diagnosis and classification constitutes an urgent medical need for improved and personalized prevention measures and treatments strategies.
The main aim of our project is to develop a tool that will enable effective preventive measures and personalize treatment strategies for COPD by means of systems medicine. This transnational and interdisciplinary project combines clinical scientists, experimentalists, computational and systems biology researchers, as well as a medium sized company. We will develop a systems medicine model of COPD constructed on (i) machine learning clustering of two comprehensive patient cohorts (COSYCONET, CIRO) providing long-term clinical observations, systematic outcome evaluation, biomaterial collections, multiple laboratory measurements, and extensive imaging data of more than 6,000 patients, complemented by (ii) an iterative systems biology framework of modeling and experimental analysis. Based on this multi-scale systems medicine model, we will generate a novel Clinical Decision Support (CDS) software that we will evaluate for patient
care in the existing IT infrastructure of hospitals and private practices.
As a prototypic demonstrator of applied systems medicine modeling, our tool will enable
i) individual and comprehensive treatment and prevention measures for COPD patients
ii) significant reductions of socio-economic costs due to less mortality and disability
iii) novel insights in the dysregulation of metabolism, immunology and aging in COPD from the underlying model.