SynthesisThe Journal of investigative dermatology2017
A Functional Genomic Meta-Analysis of Clinical Trials in Systemic Sclerosis: Toward Precision Medicine and Combination Therapy.
Synthesis in The Journal of investigative dermatology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 28 citations in OpenAlex.
- Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation.JCI insight · 2022Trial
- Harnessing artificial intelligence to advance insights in systemic sclerosis skin and lung disease.Current opinion in rheumatology · 2025Review
- Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives.Frontiers in immunology · 2024Review
- Perspective to precision medicine in scleroderma.Frontiers in immunology · 2023Article
- The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review.Journal of personalized medicine · 2022Review
- Toward Molecular Stratification and Precision Medicine in Systemic Sclerosis.Frontiers in medicine · 2022Review
- Systematic Analysis of the Literature in Search of Defining Systemic Sclerosis Subsets.The Journal of rheumatology · 2021Article
- Insights Into Systemic Sclerosis from Gene Expression Profiling.Current treatment options in rheumatology · 2021Article
- Machine learning for precision dermatology: Advances, opportunities, and outlook.Journal of the American Academy of Dermatology · 2021Review
- Machine Learning in Rheumatic Diseases.Clinical reviews in allergy & immunology · 2021Review
- Big data in systemic sclerosis: Great potential for the future.Journal of scleroderma and related disorders · 2020Review
- Molecular "omic" signatures in systemic sclerosis.European journal of rheumatology · 2020Review
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Recent advances steer the future of systemic sclerosis toward precision medicine.Clinical rheumatology · 2020Article
- TGFB1-Mediated Gliosis in Multiple Sclerosis Spinal Cords Is Favored by the Regionalized Expression of HOXA5 and the Age-Dependent Decline in Androgen Receptor Ligands.International journal of molecular sciences · 2019Article
- Evolving insights into the cellular and molecular pathogenesis of fibrosis in systemic sclerosis.Translational research : the journal of laboratory and clinical medicine · 2019Review
- Existing and novel biomarkers for precision medicine in systemic sclerosis.Nature reviews. Rheumatology · 2018Review
- The mechanistic implications of gene expression studies in SSc: Insights from Systems Biology.Current treatment options in rheumatology · 2017Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Systemic sclerosis 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 patients with systemic sclerosis treated with five therapies: mycophenolate mofetil, rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% or -5 modified Rodnan skin score was applied to each study. We applied a machine learning approach that captured features beyond differential expression and 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 nonimprovers were downregulated in mycophenolate mofetil 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.