| Institution: | 1.Center for Complex Networks Research and Department of Physics,Northeastern University,Boston,USA;2.Center for Cancer Systems Biology,Dana-Farber Cancer Institute,Boston,USA;3.Department of Theoretical Physics,Budapest University of Technology and Economics,Budapest,Hungary;4.Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Department of Medicine,Brigham and Women's Hospital, Harvard Medical School,Boston,USA;5.GlaxoSmithKline,King of Prussia,USA;6.University of Nebraska Medical Center,Omaha,USA;7.Pulmonary and Critical Care Division,Brigham and Women's Hospital, Harvard Medical School,Boston,USA;8.GlaxoSmithKline,USA;9.GlaxoSmithKline,Stevenage,UK;10.Hospital Clinic, IDIBAPS, Thorax Institute, University of Barcelona,Barcelona;11.Center for Network Science,Central European University,Budapest,Hungary;12.Department of Medicine,Brigham and Women's Hospital, Harvard Medical School,Boston,USA;13.FISIB, CIBERES,Mallorca,Spain |
| Abstract: | BackgroundAn important step toward understanding the biological mechanisms underlying a complex disease is a refined understanding of its clinical heterogeneity. Relating clinical and molecular differences may allow us to define more specific subtypes of patients that respond differently to therapeutic interventions.ResultsWe developed a novel unbiased method called diVIsive Shuffling Approach (VIStA) that identifies subgroups of patients by maximizing the difference in their gene expression patterns. We tested our algorithm on 140 subjects with Chronic Obstructive Pulmonary Disease (COPD) and found four distinct, biologically and clinically meaningful combinations of clinical characteristics that are associated with large gene expression differences. The dominant characteristic in these combinations was the severity of airflow limitation. Other frequently identified measures included emphysema, fibrinogen levels, phlegm, BMI and age. A pathway analysis of the differentially expressed genes in the identified subtypes suggests that VIStA is capable of capturing specific molecular signatures within in each group.ConclusionsThe introduced methodology allowed us to identify combinations of clinical characteristics that correspond to clear gene expression differences. The resulting subtypes for COPD contribute to a better understanding of its heterogeneity. |
