Systems Medicine: from molecular features and models to the clinic in COPD

Journal of Translational Medicine

Research

Mercedes Huertas-Migueláñez¹  Dieter Maier²  Jörg Menche³  Albert-László Barabási⁴  Kelly Burrowes⁵  Josep Roca⁶  Isaac Cano⁶  Akos Tenyi⁷  Vitaly Selivanov⁷  Marta Cascante⁷  Igor Marin de Mas⁷  Francesco Falciani⁸  Peter Wagner⁹  Jesper Tegnér^1,10  David Gomez-Cabrero^1,10  Narsis A Kiani^1,10  Francesco Marabita^1,10  Imad Abugessaisa^1,10 

[1] Barcelona Digital Technology Centre Carrer Roc Boronat, 117 08018, Barcelona;Biomax Informatics AG, Munich, Germany;Center for Complex Network Research, Northeastern University Physics Department, 02115, Boston, MA, USA;Department of Theoretical Physics, Budapest University of Technology and Economics, H-1111 Budafoki út. 8., Budapest, Hungary;Center for Complex Network Research, Northeastern University Physics Department, 02115, Boston, MA, USA;Department of Theoretical Physics, Budapest University of Technology and Economics, H-1111 Budafoki út. 8., Budapest, Hungary;Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Smith Bldg., Rm. 858A, 450 Brookline Ave, 02215, Boston, MA, USA;Center for Network Science, Central European University, Nadoru. 9, 1051, Budapest, Hungary;Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, 02115, Boston, MA, USA;Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, OX1 3QD, Oxford, UK;Hospital Clinic, IDIBAPS, CIBERES, Universitat de Barcelona, Barcelona, Catalunya, Spain;Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain;Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool, UK;School of Medicine, University of California, 92093-0623A, San Diego, San Diego, CA, USA;Unit of computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden;

关键词: Chronic diseases;    COPD;    Disease heterogeneity;    Systems Medicine;    Predictive Modeling;    Co-morbidity;   

DOI  :  10.1186/1479-5876-12-S2-S4

来源: Springer

【 摘 要 】

Background and hypothesisChronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice.Objective and methodOur overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework.ResultsIn the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.ConclusionsThe results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.

【 授权许可】

Unknown   
© Gomez-Cabrero et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.