Transcriptional insights into pathogenesis of cutaneous systemic sclerosis using pathway driven meta-analysis assisted by machine learning methods

Abstract

Pathophysiology of systemic sclerosis (SSc, Scleroderma), an autoimmune rheumatic disease, comprises of mechanisms that drive vasculopathy, inflammation and fibrosis. Understanding of the disease and associated clinical heterogeneity has advanced considerably in the past decade, highlighting the necessity of more specific targeted therapy. While many of the recent trials in SSc failed to meet the primary end points that predominantly relied on changes in modified Rodnan skin scores (MRSS), sub-group analysis, especially those focused on the basal skin transcriptomic data have provided insights into patient subsets that respond to therapies. These findings suggest that deeper understanding of the molecular changes in pathways is very important to define disease drivers in various patient subgroups. In view of these challenges, we performed meta-analysis on 9 public available SSc microarray studies using a novel pathway pivoted approach combining consensus clustering and machine learning assisted feature selection. Selected pathway modules were further explored through cluster specific topological network analysis in search of novel therapeutic concepts. In addition, we went beyond previously described SSc class divisions of 3 clusters (e.g. inflammation, fibro-proliferative, normal-like) and expanded into a much finer stratification in order to profile SSc patients more accurately. Our analysis unveiled an important 80 pathway signatures that differentiated SSc patients into 8 unique subtypes. The 5 pathway modules derived from such signature successfully defined the 8 SSc subsets and were validated by in-silico cellular deconvolution analysis. Myeloid cells and fibroblasts involvement in different clusters were confirmed and linked to corresponding pathway activities. Collectively, our findings revealed more complex disease subtypes in SSc; Key gene mediators such as IL6, FGFR1, TLR7, PLCG2, IRK2 identified by network analysis underscored the scientific rationale for exploring additional targets in treatment of SSc.