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

Area of Science:

• Immunology
• Rheumatology
• Genomics

Background:

• Systemic sclerosis is a complex autoimmune disease with distinct molecular subtypes.
• Identifying effective treatments for early diffuse systemic sclerosis (dcSSc) remains a challenge.

Purpose of the Study:

• To evaluate the efficacy of abatacept in dcSSc patients.
• To test if the inflammatory molecular subset shows the greatest clinical improvement with abatacept.
• To explore gene expression changes and pathway alterations in response to treatment.

Main Methods:

• A 12-month randomized, placebo-controlled trial of 84 dcSSc patients.
• RNA sequencing of skin biopsies at baseline, 3, and 6 months.
• Classification of samples into inflammatory, fibroproliferative, or normal-like gene expression subsets.

Main Results:

• Abatacept demonstrated the most significant improvement in modified Rodnan skin score (mRSS) in the inflammatory and normal-like subsets compared to placebo.
• Abatacept treatment shifted gene expression in the inflammatory subset towards a normal-like profile.
• Decreased Costimulation of the CD28 Family pathway correlated with improved mRSS in the inflammatory subset.

Conclusions:

• Abatacept shows promise for treating dcSSc, especially in patients with an inflammatory molecular profile.
• The Costimulation of the CD28 Family pathway is a potential therapeutic target in systemic sclerosis.
• This study exemplifies precision medicine strategies in systemic sclerosis clinical trials.