Integrated, multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity

Gene annotation bias impedes biomedical research

“…Using a multi-cohort analysis framework, 19, 20 we have repeatedly demonstrated that it can identify novel disease-gene relationships that lay outside the “halo of the streetlight”, and which have diagnostic, prognostic, and therapeutic utility across diverse diseases including cancer, 21–23 organ transplantation, 19 infectious diseases, 24–27 and autoimmunity. 28…”

A Unified Molecular Signature of Systemic Lupus Erythematosus Revealed by Integrated, Multi-Cohort Transcriptomic Analysis

“…Previous gene expression meta-analyses in SLE have been limited to few experiments, lacked external validation, or did not investigate the signature's specificity to SLE (43,44). Our method leverages biological and technical heterogeneity to identify a robust disease signature, and has been successful in diverse diseases that range from cancer to autoimmunity and infection (16)(17)(18)(19)(20)(21)(22)(23)(24). We performed a multi-cohort gene expression analysis of over 7,000 samples from 40 datasets representing real-world biological heterogeneity (including genetic background, age, sex, treatment, tissue, cell type, and disease duration) and technical heterogeneity (including RNA isolation, microarray platform, sample preparation, and experimental protocol) to identify a persistent SLE MetaSignature.…”

“…Following the precise molecular definition of SLE, "personalized medicine" could help to identify which patients will respond to which of these drugs, given they have molecularly defined SLE. Previously, we have shown utility of such an approach across a number of disease domains that conserved signatures of disease can be vital to identifying novel mechanisms, and treatments of a disease (13,16,24,118).…”

“…By computing effect sizes for each dataset independently, MetaIntegrator embraces heterogeneity and avoids the limitations of batch effect correction. We have demonstrated application of this framework across a broad spectrum of diseases including cancer (16,17), solid organ transplant (18), sepsis (19), viral infection (20), tuberculosis (21), neurodegenerative diseases (22), vaccination (23), and systemic sclerosis (24). Here, we applied the framework to analyze 40 publicly-available whole transcriptome profile datasets containing 7,471 samples from SLE patients, individuals with other autoimmune diseases or infections, and healthy volunteers.…”

The Mechanistic Implications of Gene Expression Studies in SSc: Insights From Systems Biology

“…Lofgren, et al took a different approach to the integration of data in SSc and identified an SSc skin severity score (4S) that was significantly correlated with mRSS [32]. This study analyzed seven different SSc datasets from six independent clinical centers.…”