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A Functional Genomic Meta-Analysis of Clinical Trials in Systemic Sclerosis: Toward Precision Medicine and

Jaclyn N Taroni1, Viktor Martyanov1, J Matthew Mahoney2

  • 1Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.

The Journal of Investigative Dermatology
|December 25, 2016
PubMed

Insights

Systemic sclerosis treatment shows promise with a new machine learning approach analyzing gene expression. This method identifies potential therapies for patients who did not respond to initial treatments, improving disease management.

Area of Science:

  • Immunology
  • Genomics
  • Computational Biology

Background:

  • Systemic sclerosis is a rare autoimmune disease lacking FDA-approved treatments.
  • Disease heterogeneity and small clinical trials complicate molecular data analysis.
  • Current treatment strategies for systemic sclerosis are limited.

Purpose of the Study:

  • To perform a meta-analysis of gene expression data from systemic sclerosis patients undergoing various treatments.
  • To apply a machine learning approach to identify therapy targets beyond traditional differential expression analysis.
  • To predict patient response to different therapies based on molecular profiles.

Main Methods:

  • Meta-analysis of gene expression data from skin biopsies of systemic sclerosis patients.
  • Application of a machine learning framework to analyze molecular data.
  • Utilized a common clinical improvement criterion (-20% or -5 modified Rodnan skin score).

Main Results:

  • Abrogation of inflammatory pathways correlated with clinical improvement across multiple therapies.
  • High expression of immune-related genes indicates active and targetable disease.
  • Identified potential benefit from immunomodulatory or combination therapy for patients non-responsive to fresolimumab.

Conclusions:

  • Machine learning enhances the identification of therapy targets in systemic sclerosis compared to differential expression alone.
  • Gene expression patterns can predict treatment response and guide therapy selection.
  • The developed framework offers a sensitive and specific approach for analyzing differential gene expression in complex diseases.

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