A diVIsive Shuffling Approach (VIStA) for gene expression analysis to identify subtypes in Chronic Obstructive Pulmonary Disease

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.

COPD comorbidities network

“…Moreover, diseases or behavioural risks present in module 2A like substance abuse, depression and anxiety are highly connected (more than 25 links) providing support for the role of behavioural and social ties in a complex diseases such as COPD [55][56][57][58]. In addition to its practical application, the visualisation of comorbidities into modules or subnetworks provide clues that are hypothesis generating as they suggest the possibility of shared genetics or pathobiological mechanisms within highly correlated comorbidities [59]. When combining disease modules with clinical characteristics we observed that not all comorbidities affect all COPD individuals similarly as is the case with modules 1A, 2A and 3A (figure 3).…”

“…When combining disease modules with clinical characteristics we observed that not all comorbidities affect all COPD individuals similarly as is the case with modules 1A, 2A and 3A (figure 3). Indeed, age or BMI are correlated with particular types of comorbidities, an observation that has implications for clinicians attempting to relate diseases to specific clinical sub-groups [23,59].…”

Relevance of Systems Biology to Respiratory Medicine

“…The latter are often studied by determining the presence of "functional modules" [17][18][19] . The disease module hypothesis proposes that the cellular components (genes, proteins, metabolites) associated with a given disease, segregate (i.e., are located) in the same "neighbourhood" (i.e., area) of the human interactome, the map (network) of biologically relevant molecular interactions (Fig.…”

“…Using this methodology in 140 COPD patients included in the ECLIPSE cohort [40][41][42] , these investigators identified four distinct, biologically and clinically meaningful combinations of clinical, functional, imaging, and biological characteristics that are associated with large gene expression differences (Fig. 6) 17 .…”

“…Menche et al 17 developed a novel unbiased method (diVIsive Shuffling Approach or VIStA) that identifies subgroups (i.e., phenotypes) of COPD patients by maximizing the difference in their gene expression patterns (genetic, biological, and clinical levels in figure 5). Using this methodology in 140 COPD patients included in the ECLIPSE cohort [40][41][42] , these investigators identified four distinct, biologically and clinically meaningful combinations of clinical, functional, imaging, and biological characteristics that are associated with large gene expression differences (Fig.…”