ArticleLupus science & medicine2017
Identification of biomarkers of response to abatacept in patients with SLE using deconvolution of whole blood transcriptomic data from a phase IIb clinical trial.
Article in Lupus science & medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00119678 (A Phase IIB, Multi-Center, Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Efficacy and Safety of Abatacept vs Placebo on a Background of Oral Glucocorticosteroids in the Treatment of Subjects With Systemic Lupus Erythematosus and the Prevention of Subsequent Lupus Flares), which is not on this map. Cited by 10 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.
A Phase IIB, Multi-Center, Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Efficacy and Safety of Abatacept vs Placebo on a Background of Oral Glucocorticosteroids in the Treatment of Subjects With Systemic Lupus Erythematosus and the Prevention of Subsequent Lupus Flares
Who cites it
10 citing papers in PubMed.
- Molecular profiling of rheumatoid arthritis patients reveals an association between innate and adaptive cell populations and response to anti-tumor necrosis factor.Arthritis research & therapy · 2019Trial
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- Identifying lupus Patient Subsets Through Immune Cell Deconvolution of Gene Expression Data in Two Atacicept Phase II Studies.ACR open rheumatology · 2023Article
- Novel Therapeutic Strategies in the Treatment of Systemic Sclerosis.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Revealing the immune cell subtype reconstitution profile in patients from the CLARITY study using deconvolution algorithms after cladribine tablets treatment.Scientific reports · 2023Article
- Differential Changes in ACPA Fine Specificity and Gene Expression in a Randomized Trial of Abatacept and Adalimumab in Rheumatoid Arthritis.Rheumatology and therapy · 2022Article
- High serum IgA and activated Th17 and Treg predict the efficacy of abatacept in patients with early, seropositive rheumatoid arthritis.Clinical rheumatology · 2021Article
- Quantitative planar array screen of 1000 proteins uncovers novel urinary protein biomarkers of lupus nephritis.Annals of the rheumatic diseases · 2020Article
- Are lupus animal models useful for understanding and developing new therapies for human SLE?Journal of autoimmunity · 2020Review
- Personalized medicine-concepts, technologies, and applications in inflammatory skin diseases.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo characterise patients with active SLE based on pretreatment gene expression-defined peripheral immune cell patterns and identify clusters enriched for potential responders to abatacept treatment.
methodsThis post hoc analysis used baseline peripheral whole blood transcriptomic data from patients in a phase IIb trial of intravenous abatacept (~10 mg/kg/month). Cell-specific genes were used with a published deconvolution algorithm to identify immune cell proportions in patient samples, and unsupervised consensus clustering was generated. Efficacy data were re-analysed.
resultsPatient data (n=144: abatacept: n=98; placebo: n=46) were grouped into four main clusters (C) by predominant characteristic cells: C1-neutrophils; C2-cytotoxic T cells, B-cell receptor-ligated B cells, monocytes, IgG memory B cells, activated T helper cells; C3-plasma cells, activated dendritic cells, activated natural killer cells, neutrophils; C4-activated dendritic cells, cytotoxic T cells. C3 had the highest baseline total British Isles Lupus Assessment Group (BILAG) scores, highest antidouble-stranded DNA autoantibody levels and shortest time to flare (TTF), plus trends in favour of response to abatacept over placebo: adjusted mean difference in BILAG score over 1 year, -4.78 (95% CI -12.49 to 2.92); median TTF, 56 vs 6 days; greater normalisation of complement component 3 and 4 levels. Differential improvements with abatacept were not seen in other clusters, except for median TTF in C1 (201 vs 109 days).
conclusionsImmune cell clustering segmented disease severity and responsiveness to abatacept. Definition of immune response cell types may inform design and interpretation of SLE trials and treatment decisions. TRIAL REGISTRATION NUMBER: NCT00119678; results.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.