Toward Molecular Stratification and Precision Medicine in Systemic Sclerosis

Area of Science:

• Immunology
• Rheumatology
• Genetics

Background:

• Systemic sclerosis (SSc) is a complex, multi-systemic disease with high mortality, characterized by immune dysregulation, vasculopathy, and fibrosis.
• The pathogenesis of SSc is not fully understood, and its heterogeneous nature poses challenges for effective treatment strategies.
• Current clinical sub-setting systems for SSc have limitations in predicting treatment responses due to varying patient outcomes.

Purpose of the Study:

• To review updates in clinical and molecular stratification of Systemic sclerosis (SSc).
• To explore how molecular stratification can improve patient outcomes and guide personalized therapies.
• To discuss the potential of high-dimensional tools and machine learning in SSc sub-setting.

Main Methods:

• Review of current literature on Systemic sclerosis (SSc) clinical and molecular stratification.
• Discussion of omic-based stratification techniques (transcriptomics, genomics, epigenomics, proteomics, cytomics, microbiomics).
• Exploration of machine learning applications in identifying patient subgroups.

Main Results:

• Molecular stratification promises to improve current clinical sub-setting systems in SSc.
• Identifying patient groups with similar molecular characteristics can guide personalized therapies.
• Granular stratification integrating molecular data with clinical phenotypes is key to advancing precision medicine.

Conclusions:

• Personalized medicine approaches, driven by molecular stratification, are crucial for improving SSc treatment.
• Innovative stratification systems are needed to better tailor therapies to individual patient profiles.
• Advances in high-dimensional omics and machine learning offer powerful tools for SSc sub-setting and improved patient care.