Deciphering differential biomarkers for anti-interleukin-6 receptor and anti-tumour necrosis factor-α treatment response in rheumatoid arthritis by multiomics analysis
Objective To identify blood-based predictive and pharmacodynamic biomarkers at different timepoints in patients with active rheumatoid arthritis (RA) treated with anti-interleukin-6 receptor (anti-IL-6R) and anti-tumour necrosis factor-α (anti-TNF-α).Methods This study used blood samples from the MO...
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BMJ Publishing Group
2025-08-01
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| Series: | RMD Open |
| Online Access: | https://rmdopen.bmj.com/content/11/3/e005556.full |
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| author | Emilie Gerard Michel Didier Matthias Herrmann Inoncent Agueusop Daniel Margerie Anne Remaury Raphaël Brard Francesca Frau Gilbert Thill Yaligara Veeranagouda Markus Kohlmann Nadine Biesemann |
| author_facet | Emilie Gerard Michel Didier Matthias Herrmann Inoncent Agueusop Daniel Margerie Anne Remaury Raphaël Brard Francesca Frau Gilbert Thill Yaligara Veeranagouda Markus Kohlmann Nadine Biesemann |
| author_sort | Emilie Gerard |
| collection | DOAJ |
| description | Objective To identify blood-based predictive and pharmacodynamic biomarkers at different timepoints in patients with active rheumatoid arthritis (RA) treated with anti-interleukin-6 receptor (anti-IL-6R) and anti-tumour necrosis factor-α (anti-TNF-α).Methods This study used blood samples from the MONARCH trial (NCT02332590), a randomised, double-blind, phase III trial that compared the safety and efficacy of sarilumab (anti-IL-6R) and adalimumab (anti-TNF-α) monotherapy in patients with RA who were intolerant/inadequate responders to methotrexate. The study evaluated predictive biomarkers to anti-IL-6R and anti-TNF-α treatments at baseline and week 2 and pharmacodynamic biomarkers at week 2 and week 24 using Olink proteomics analysis (n=804 serum samples from 268 patients). Change in gene expression levels (n=522 peripheral blood samples from 261 patients) by both treatments was assessed using RNA sequencing analysis.Results Serum biomarkers most predictive to anti-IL-6R were different from those of anti-TNF-α; predictive biomarkers for anti-IL-6R were correlated with innate immune activation and synovial inflammation, while predictive biomarkers for anti-TNF-α seemed to be more T-cell and neutrophil-related. For baseline predictive biomarkers, we had to focus on relative prediction as the absolute prediction performance of single and combination biomarkers using cross-validation was limited. Additionally, the pharmacodynamic effects of anti-IL-6R and anti-TNF-α on biomarkers as well as pathway signatures were distinct.Conclusion The unbiased analysis of serum proteins identified biomarkers most predictive of anti-IL-6R and anti-TNF-α at different timepoints that could explain the difference in the response rate in patients with RA. Further, both biomarker and pathway results highlighted a differentiated mode of action of both treatments. |
| format | Article |
| id | doaj-art-6a7681035888433fbae41a89af93b683 |
| institution | Kabale University |
| issn | 2056-5933 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | RMD Open |
| spelling | doaj-art-6a7681035888433fbae41a89af93b6832025-08-20T03:59:31ZengBMJ Publishing GroupRMD Open2056-59332025-08-0111310.1136/rmdopen-2025-005556Deciphering differential biomarkers for anti-interleukin-6 receptor and anti-tumour necrosis factor-α treatment response in rheumatoid arthritis by multiomics analysisEmilie Gerard0Michel Didier1Matthias Herrmann2Inoncent Agueusop3Daniel Margerie4Anne Remaury5Raphaël Brard6Francesca Frau7Gilbert Thill8Yaligara Veeranagouda9Markus Kohlmann10Nadine Biesemann11Sanofi, Randomised Clinical Trials and Post Hoc Analyses Team, Gentilly, FranceSanofi R&D, Translational Sciences, Chilly-Mazarin, FranceSanofi R&D, Immunology and Inflammation Therapeutic Area, Type 1/17 Immunology Cluster, Industriepark Hoechst, Frankfurt am Main, GermanySanofi R&D, Biostatistics, Industriepark Hoechst, Frankfurt am Main, GermanySanofi R&D, Digital and Data Science, Industriepark Hoechst, Frankfurt am Main, GermanySanofi R&D, Translational Sciences, Chilly-Mazarin, FranceSanofi R&D, Biostatistics, Industriepark Hoechst, Frankfurt am Main, GermanySanofi R&D, Digital and Data Science, Industriepark Hoechst, Frankfurt am Main, GermanySanofi R&D, Translational Sciences, Chilly-Mazarin, FranceSanofi R&D, Translational Sciences, Chilly-Mazarin, FranceSanofi R&D, Early Clinical Development Therapeutic Area Immunology and Inflammation, Industriepark Hoechst, Frankfurt am Main, GermanySanofi R&D, Immunology and Inflammation Therapeutic Area, Type 1/17 Immunology Cluster, Industriepark Hoechst, Frankfurt am Main, GermanyObjective To identify blood-based predictive and pharmacodynamic biomarkers at different timepoints in patients with active rheumatoid arthritis (RA) treated with anti-interleukin-6 receptor (anti-IL-6R) and anti-tumour necrosis factor-α (anti-TNF-α).Methods This study used blood samples from the MONARCH trial (NCT02332590), a randomised, double-blind, phase III trial that compared the safety and efficacy of sarilumab (anti-IL-6R) and adalimumab (anti-TNF-α) monotherapy in patients with RA who were intolerant/inadequate responders to methotrexate. The study evaluated predictive biomarkers to anti-IL-6R and anti-TNF-α treatments at baseline and week 2 and pharmacodynamic biomarkers at week 2 and week 24 using Olink proteomics analysis (n=804 serum samples from 268 patients). Change in gene expression levels (n=522 peripheral blood samples from 261 patients) by both treatments was assessed using RNA sequencing analysis.Results Serum biomarkers most predictive to anti-IL-6R were different from those of anti-TNF-α; predictive biomarkers for anti-IL-6R were correlated with innate immune activation and synovial inflammation, while predictive biomarkers for anti-TNF-α seemed to be more T-cell and neutrophil-related. For baseline predictive biomarkers, we had to focus on relative prediction as the absolute prediction performance of single and combination biomarkers using cross-validation was limited. Additionally, the pharmacodynamic effects of anti-IL-6R and anti-TNF-α on biomarkers as well as pathway signatures were distinct.Conclusion The unbiased analysis of serum proteins identified biomarkers most predictive of anti-IL-6R and anti-TNF-α at different timepoints that could explain the difference in the response rate in patients with RA. Further, both biomarker and pathway results highlighted a differentiated mode of action of both treatments.https://rmdopen.bmj.com/content/11/3/e005556.full |
| spellingShingle | Emilie Gerard Michel Didier Matthias Herrmann Inoncent Agueusop Daniel Margerie Anne Remaury Raphaël Brard Francesca Frau Gilbert Thill Yaligara Veeranagouda Markus Kohlmann Nadine Biesemann Deciphering differential biomarkers for anti-interleukin-6 receptor and anti-tumour necrosis factor-α treatment response in rheumatoid arthritis by multiomics analysis RMD Open |
| title | Deciphering differential biomarkers for anti-interleukin-6 receptor and anti-tumour necrosis factor-α treatment response in rheumatoid arthritis by multiomics analysis |
| title_full | Deciphering differential biomarkers for anti-interleukin-6 receptor and anti-tumour necrosis factor-α treatment response in rheumatoid arthritis by multiomics analysis |
| title_fullStr | Deciphering differential biomarkers for anti-interleukin-6 receptor and anti-tumour necrosis factor-α treatment response in rheumatoid arthritis by multiomics analysis |
| title_full_unstemmed | Deciphering differential biomarkers for anti-interleukin-6 receptor and anti-tumour necrosis factor-α treatment response in rheumatoid arthritis by multiomics analysis |
| title_short | Deciphering differential biomarkers for anti-interleukin-6 receptor and anti-tumour necrosis factor-α treatment response in rheumatoid arthritis by multiomics analysis |
| title_sort | deciphering differential biomarkers for anti interleukin 6 receptor and anti tumour necrosis factor α treatment response in rheumatoid arthritis by multiomics analysis |
| url | https://rmdopen.bmj.com/content/11/3/e005556.full |
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