Purpose of review Systemic sclerosis (SSc) has been difficult to treat due to disease heterogeneity and an incomplete understanding of the molecular mechanisms underlying pathogenesis. Some patients show improvement on existing or experimental therapies while others show no significant response. Due to these challenges, clinical trials often do not meet their primary clinical endpoints. Here we review the data-driven genomewideapproaches that have recently been employed to characterize the molecular changes observed in SSc patients.

Recent findings Understanding an individual patient’s gene expression phenotype or genotype, a cornerstone of personalized medicine, has been proposed to help determine which therapies are most likely to treat their disease. Network and systems biology methods have been applied to the compendium of publicly available SSc data and suggest that there are shared mechanisms driving the disease in different organs. Computational methods have also been applied to meta-analyses of SSc clinical trials. These approaches have led to the prediction of potential combination therapies that target the multiple pathways deregulated in SSc, which can now be tested in the clinic.

Summary Data driven methods to analyze compendia of data are providing additional insights into SSc and related conditions. Modern bioinformatics and systems biology are ecosystems of data and code that are growing exponentially. Data integration allows researchers to combine multiple underpowered studies, a concern in a rare disorder such as SSc, for greater gain.