A genomic meta-analysis of clinical variables and their association with intrinsic molecular subsets in systemic sclerosis

Abstract

Objectives Four intrinsic molecular subsets (Inflammatory, Fibroproliferative, Limited, Normal-like) have previously been identified in systemic sclerosis (SSc) and are characterized by unique gene expression signatures and pathways. The intrinsic subsets have been linked to improvement with specific therapies. Here, we investigated associations between baseline demographics and intrinsic molecular subsets in a meta-analysis of published datasets. Methods Publicly available gene expression data from skin biopsies of 311 SSc patients measured by DNA microarray were classified into the intrinsic molecular subsets. RNA-sequencing data from 84 participants from the ASSET trial were used as a validation cohort. Baseline clinical demographics and intrinsic molecular subsets were tested for statistically significant associations. Results Males were more likely to be classified in the fibroproliferative subset (p= 0.0046). SSc patients who identified as African American/Black were 2.5x more likely to be classified as fibroproliferative compared with White/Caucasian patients(p= 0.0378). ASSET participants sera positive for anti-RNA pol I and RNA pol III autoantibodies were enriched in the inflammatory subset (p= 5.8E-5, p= 9.3E-5), while anti-Scl-70 was enriched in the fibroproliferative subset. Mean modified Rodnan Skin Score (mRSS) was statistically higher in the inflammatory and fibroproliferative subsets compared with normal-like(p= 0.0027). The average disease duration for inflammatory subset was less than fibroproliferative and normal-like intrinsic subsets (p= 8.8E-4). Conclusions We identified multiple statistically significant differences in baseline demographics between the intrinsic subsets which may represent underlying features of disease pathogenesis (e.g. chronological stages of fibrosis) and have implications for treatments that are more likely to work in certain SSc populations.

Single-cell analysis reveals key differences between early-stage and late-stage systemic sclerosis skin across autoantibody subgroups

“…However, over longer periods of time, there are changes in gene expression, moving the inflammatory intrinsic subsets towards fibroproliferative or normal-like phenotype 8 19 20. Molecular stratification of SSc patients and relationship to clinical phenotype and therapeutic response have previously been explored in these intrinsic subsets 21–24…”

“…8 19 20 Molecular stratification of SSc patients and relationship to clinical phenotype and therapeutic response have previously been explored in these intrinsic subsets. [21][22][23][24] Single-cell analysis has allowed for an increased understanding of the heterogeneity within fibroblast populations. 17 25 26 It is already appreciated that there are age-related loss of fibroblast priming in healthy skin.…”

Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation

“…This and our previously published analyses suggest that clinical trialists should acknowledge molecular heterogeneity in early SSc and should enrich for pharmacologic target specific subset (such as inflammatory intrinsic subset for abatacept, mycophenolate mofetil, etc) or stratify randomization based on these subsets. In addition, our recent published meta-analysis highlights the role of scleroderma autoantibodies as an enrichment criterion and the overlap with the intrinsic gene expression sets (12,13).…”

“…or stratify randomization based on these subsets. In addition, our recently published meta-analysis highlights the role of scleroderma autoantibodies as an enrichment criterion and the overlap with the intrinsic gene expression sets ( 12 , 13 ).…”

Skin Gene Expression Profiles in Systemic Sclerosis: From Clinical Stratification to Precision Medicine

“…The development of high-throughput sequencing technology and microarrays has been applied to systemic autoimmune rheumatic disease and, in particular, to a large cohort of SSc patients in order to provide a good opportunity to further understand this complex disease and overcome issues of clinical heterogeneity, increasing diagnostic accuracy and develop more effective treatments [ 15 , 17 ].…”