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Identification of biomarkers of response to abatacept in patients with SLE using deconvolution of whole blood transcriptomic data from a phase IIb clinical trial
OBJECTIVE: To 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. METHODS: This post hoc analysis used baseline peripheral whole blood transcriptomic data f...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BMJ Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704740/ https://www.ncbi.nlm.nih.gov/pubmed/29214034 http://dx.doi.org/10.1136/lupus-2017-000206 |
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author | Bandyopadhyay, Somnath Connolly, Sean E Jabado, Omar Ye, June Kelly, Sheila Maldonado, Michael A Westhovens, Rene Nash, Peter Merrill, Joan T Townsend, Robert M |
author_facet | Bandyopadhyay, Somnath Connolly, Sean E Jabado, Omar Ye, June Kelly, Sheila Maldonado, Michael A Westhovens, Rene Nash, Peter Merrill, Joan T Townsend, Robert M |
author_sort | Bandyopadhyay, Somnath |
collection | PubMed |
description | OBJECTIVE: To 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. METHODS: This 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. RESULTS: Patient 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). CONCLUSIONS: Immune 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. |
format | Online Article Text |
id | pubmed-5704740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-57047402017-12-06 Identification of biomarkers of response to abatacept in patients with SLE using deconvolution of whole blood transcriptomic data from a phase IIb clinical trial Bandyopadhyay, Somnath Connolly, Sean E Jabado, Omar Ye, June Kelly, Sheila Maldonado, Michael A Westhovens, Rene Nash, Peter Merrill, Joan T Townsend, Robert M Lupus Sci Med Biomarker Studies OBJECTIVE: To 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. METHODS: This 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. RESULTS: Patient 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). CONCLUSIONS: Immune 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. BMJ Publishing Group 2017-07-28 /pmc/articles/PMC5704740/ /pubmed/29214034 http://dx.doi.org/10.1136/lupus-2017-000206 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
spellingShingle | Biomarker Studies Bandyopadhyay, Somnath Connolly, Sean E Jabado, Omar Ye, June Kelly, Sheila Maldonado, Michael A Westhovens, Rene Nash, Peter Merrill, Joan T Townsend, Robert M Identification of biomarkers of response to abatacept in patients with SLE using deconvolution of whole blood transcriptomic data from a phase IIb clinical trial |
title | Identification of biomarkers of response to abatacept in patients with SLE using deconvolution of whole blood transcriptomic data from a phase IIb clinical trial |
title_full | Identification of biomarkers of response to abatacept in patients with SLE using deconvolution of whole blood transcriptomic data from a phase IIb clinical trial |
title_fullStr | Identification of biomarkers of response to abatacept in patients with SLE using deconvolution of whole blood transcriptomic data from a phase IIb clinical trial |
title_full_unstemmed | Identification of biomarkers of response to abatacept in patients with SLE using deconvolution of whole blood transcriptomic data from a phase IIb clinical trial |
title_short | Identification of biomarkers of response to abatacept in patients with SLE using deconvolution of whole blood transcriptomic data from a phase IIb clinical trial |
title_sort | identification of biomarkers of response to abatacept in patients with sle using deconvolution of whole blood transcriptomic data from a phase iib clinical trial |
topic | Biomarker Studies |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704740/ https://www.ncbi.nlm.nih.gov/pubmed/29214034 http://dx.doi.org/10.1136/lupus-2017-000206 |
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