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A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy

View ORCID ProfileJaclyn N. Taroni, Viktor Martyanov, J. Matthew Mahoney, View ORCID ProfileMichael L. Whitfield
doi: https://doi.org/10.1101/087361
Jaclyn N. Taroni
1Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America
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Viktor Martyanov
1Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America
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J. Matthew Mahoney
2Department of Neurological Sciences, College of Medicine, University of Vermont, Burlington, Vermont, United States of America
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Michael L. Whitfield
1Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America
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ABSTRACT

Systemic sclerosis (SSc) is an orphan, systemic autoimmune disease with no FDA-approved treatments. Its heterogeneity and rarity often result in underpowered clinical trials making the analysis and interpretation of associated molecular data challenging. We performed a meta-analysis of gene expression data from skin biopsies of SSc patients treated with five therapies: mycophenolate mofetil (MMF), rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% OR -5 modified Rodnan Skin Score was applied to each study. We developed a machine learning approach that captured features beyond differential expression that was better at identifying targets of therapies than the differential expression alone. Regardless of treatment mechanism, abrogation of inflammatory pathways accompanied clinical improvement in multiple studies suggesting that high expression of immune-related genes indicates active and targetable disease. Our framework allowed us to compare different trials and ask if patients who failed one therapy would likely improve on a different therapy, based on changes in gene expression. Genes with high expression at baseline in fresolimumab non-improvers were downregulated in MMF improvers, suggesting that immunomodulatory or combination therapy may have benefitted these patients. This approach can be broadly applied to increase tissue-specificity and sensitivity of differential expression results.

Footnotes

  • Abbreviations used
    SSc
    systemic sclerosis
    mRSS
    modified Rodnan skin score
    MMF
    mycophenolate mofetil
    GSEA
    Gene Set Enrichment Analysis
    DEGs
    differentially expressed genes
    GIANT
    Genome-scale Integrated Analysis of gene Networks in Tissues
    HPRD
    Human Protein Reference Database
    TKI
    tyrosine kinase inhibitor

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted November 13, 2016.
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A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy
Jaclyn N. Taroni, Viktor Martyanov, J. Matthew Mahoney, Michael L. Whitfield
bioRxiv 087361; doi: https://doi.org/10.1101/087361
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A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy
Jaclyn N. Taroni, Viktor Martyanov, J. Matthew Mahoney, Michael L. Whitfield
bioRxiv 087361; doi: https://doi.org/10.1101/087361

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