Toward Molecular Stratification and Precision Medicine in Systemic Sclerosis

Systemic sclerosis (SSc), a complex multi-systemic disease characterized by immune dysregulation, vasculopathy and fibrosis, is associated with high mortality. Its pathogenesis is only partially understood. The heterogenous pathological processes that define SSc and its stages present a challenge to targeting appropriate treatment, with differing treatment outcomes of SSc patients despite similar initial clinical presentations. Timing of the appropriate treatments targeted at the underlying disease process is critical. For example, immunomodulatory treatments may be used for patients in a predominantly inflammatory phase, anti-fibrotic treatments for those in the fibrotic phase, or combination therapies for those in the fibro-inflammatory phase. In advancing personalized care through precision medicine, groups of patients with similar disease characteristics and shared pathological processes may be identified through molecular stratification. This would improve current clinical sub-setting systems and guide personalization of therapies. In this review, we will provide updates in SSc clinical and molecular stratification in relation to patient outcomes and treatment responses. Promises of molecular stratification through advances in high-dimensional tools, including omic-based stratification (transcriptomics, genomics, epigenomics, proteomics, cytomics, microbiomics) and machine learning will be discussed. Innovative and more granular stratification systems that integrate molecular characteristics to clinical phenotypes would potentially improve therapeutic approaches through personalized medicine and lead to better patient outcomes.

Automated summary

Systemic sclerosis (SSc) is a complex multi-systemic disease with high mortality. Timing of appropriate treatments targeted at the underlying disease process is critical for improved outcomes. Molecular stratification can guide personalized therapies and potentially lead to better patient outcomes by integrating molecular characteristics and clinical phenotypes.