Novel DNA methylome biomarkers associated with adalimumab response in rheumatoid arthritis patients

Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatm...

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Vydáno v:	Frontiers in immunology Ročník 14; s. 1303231
Hlavní autoři:	Hageman, Ishtu, Mol, Femke, Atiqi, Sadaf, Joustra, Vincent, Sengul, Hilal, Henneman, Peter, Visman, Ingrid, Hakvoort, Theodorus, Nurmohamed, Mike, Wolbink, Gertjan, Levin, Evgeni, Li Yim, Andrew Y.F., D’Haens, Geert, de Jonge, Wouter J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Media S.A 22.12.2023
Témata:	adalimumab Adalimumab - therapeutic use Arthritis, Rheumatoid - diagnosis Arthritis, Rheumatoid - drug therapy Arthritis, Rheumatoid - genetics Biomarkers DNA methylation Epigenome Humans Immunology machine learning rheumatoid arthritis therapy response Tumor Necrosis Factor-alpha DNA methylation machine learning rheumatoid arthritis therapy response adalimumab
ISSN:	1664-3224, 1664-3224
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Abstract	Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL). DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers. Of the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis. Our findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction.
AbstractList	Background and aimsRheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL).MethodsDNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers.ResultsOf the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis.ConclusionOur findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction. Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL).Background and aimsRheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL).DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers.MethodsDNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers.Of the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis.ResultsOf the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis.Our findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction.ConclusionOur findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction. Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL). DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers. Of the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis. Our findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction.
Author	Atiqi, Sadaf D’Haens, Geert Visman, Ingrid de Jonge, Wouter J. Henneman, Peter Mol, Femke Joustra, Vincent Li Yim, Andrew Y.F. Levin, Evgeni Wolbink, Gertjan Sengul, Hilal Hageman, Ishtu Hakvoort, Theodorus Nurmohamed, Mike
AuthorAffiliation	7 Department of Surgery, University of Bonn , Bonn , Germany 2 Tytgat Institute for Liver and Intestinal Research, Amsterdam University Medical Centers (UMC), University of Amsterdam , Amsterdam , Netherlands 4 Genome Diagnostics Laboratory, Department of Human Genetics, Amsterdam University Medical Centers (UMC), University of Amsterdam , Amsterdam , Netherlands 1 Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers (UMC), University of Amsterdam , Amsterdam , Netherlands 3 Department of Rheumatology, Amsterdam Rheumatology and Immunology Center, Vrije Universiteit (VU) University Medical Center , Amsterdam , Netherlands 5 Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam , Amsterdam , Netherlands 6 Horaizon BV , Delft , Netherlands
AuthorAffiliation_xml	– name: 5 Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam , Amsterdam , Netherlands – name: 3 Department of Rheumatology, Amsterdam Rheumatology and Immunology Center, Vrije Universiteit (VU) University Medical Center , Amsterdam , Netherlands – name: 2 Tytgat Institute for Liver and Intestinal Research, Amsterdam University Medical Centers (UMC), University of Amsterdam , Amsterdam , Netherlands – name: 1 Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers (UMC), University of Amsterdam , Amsterdam , Netherlands – name: 6 Horaizon BV , Delft , Netherlands – name: 4 Genome Diagnostics Laboratory, Department of Human Genetics, Amsterdam University Medical Centers (UMC), University of Amsterdam , Amsterdam , Netherlands – name: 7 Department of Surgery, University of Bonn , Bonn , Germany
Author_xml	– sequence: 1 givenname: Ishtu surname: Hageman fullname: Hageman, Ishtu – sequence: 2 givenname: Femke surname: Mol fullname: Mol, Femke – sequence: 3 givenname: Sadaf surname: Atiqi fullname: Atiqi, Sadaf – sequence: 4 givenname: Vincent surname: Joustra fullname: Joustra, Vincent – sequence: 5 givenname: Hilal surname: Sengul fullname: Sengul, Hilal – sequence: 6 givenname: Peter surname: Henneman fullname: Henneman, Peter – sequence: 7 givenname: Ingrid surname: Visman fullname: Visman, Ingrid – sequence: 8 givenname: Theodorus surname: Hakvoort fullname: Hakvoort, Theodorus – sequence: 9 givenname: Mike surname: Nurmohamed fullname: Nurmohamed, Mike – sequence: 10 givenname: Gertjan surname: Wolbink fullname: Wolbink, Gertjan – sequence: 11 givenname: Evgeni surname: Levin fullname: Levin, Evgeni – sequence: 12 givenname: Andrew Y.F. surname: Li Yim fullname: Li Yim, Andrew Y.F. – sequence: 13 givenname: Geert surname: D’Haens fullname: D’Haens, Geert – sequence: 14 givenname: Wouter J. surname: de Jonge fullname: de Jonge, Wouter J.
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Cites_doi	10.1093/rheumatology/kez411 10.1002/1529-0131(199912)42:12<2593::AID-ANR12>3.0.CO;2-G 10.3389/fimmu.2021.713611 10.1189/jlb.2VMR1215-582R 10.2217/epi.09.14 10.1002/art.41056 10.1186/s13059-014-0503-2 10.1186/1471-2105-11-587 10.3390/cells8090953 10.12688/f1000research.4680.2 10.1007/978-0-387-98141-3 10.1007/s11926-018-0769-6 10.1186/s13075-019-1936-5 10.1186/s13073-018-0575-9 10.1002/art.41516 10.1038/sj.gene.6364142 10.3390/ijms19072012 10.1093/bioinformatics/btu049 10.1002/art.10697 10.1080/03009740600904284 10.3389/fimmu.2020.00194 10.1136/annrheumdis-2012-201526 10.1093/rheumatology/keaa649 10.3389/fimmu.2022.840935 10.1016/j.ebiom.2020.103079 10.1097/01.bor.0000155361.83990.5b 10.1038/tpj.2017.26 10.1186/gb-2014-15-2-r31 10.1002/art.20217 10.1371/journal.pone.0247709 10.2217/epi-2016-0042 10.1002/art.39590 10.1002/acr2.11506 10.1159/000323874 10.1038/tpj.2015.30 10.1038/npp.2012.112 10.1186/gb-2012-13-8-r77 10.1002/acr2.11231 10.1016/j.ebiom.2022.104053 10.1016/j.rdc.2009.10.001 10.1136/annrheumdis-2018-213970 10.1038/sj.gene.6363858 10.1093/bioinformatics/btt684 10.1093/rheumatology/kev421 10.1186/s13059-018-1448-7 10.1093/nar/gkv007 10.1186/s12864-015-2202-0 10.1136/annrheumdis-2020-219796 10.1136/annrheumdis-2016-210711 10.1186/s13075-015-0748-5 10.1007/s10067-014-2756-2 10.1186/ar2678 10.3389/fimmu.2021.651475 10.3390/ijms20246152 10.1007/s10456-015-9477-2 10.1038/73152 10.1038/nbt.2487 10.1038/ncomms11849 10.1371/journal.pone.0213073 10.1186/s13072-016-0107-z 10.3390/biomedicines11071987 10.1186/s13041-017-0324-9
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Copyright	Copyright © 2023 Hageman, Mol, Atiqi, Joustra, Sengul, Henneman, Visman, Hakvoort, Nurmohamed, Wolbink, Levin, Li Yim, D’Haens and de Jonge. Copyright © 2023 Hageman, Mol, Atiqi, Joustra, Sengul, Henneman, Visman, Hakvoort, Nurmohamed, Wolbink, Levin, Li Yim, D’Haens and de Jonge 2023 Hageman, Mol, Atiqi, Joustra, Sengul, Henneman, Visman, Hakvoort, Nurmohamed, Wolbink, Levin, Li Yim, D’Haens and de Jonge
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Keywords	DNA methylation machine learning rheumatoid arthritis therapy response adalimumab
Language	English
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Commentary-3 content type line 23 These authors share last authorship Edited by: Maria I. Bokarewa, University of Gothenburg, Sweden Juliana Imgenberg-Kreuz, Uppsala University, Sweden Reviewed by: Nisha Nair, The University of Manchester, United Kingdom These authors have contributed equally to this work and share first authorship
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Snippet	Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα).... Background and aimsRheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis...
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SubjectTerms	adalimumab Adalimumab - therapeutic use Arthritis, Rheumatoid - diagnosis Arthritis, Rheumatoid - drug therapy Arthritis, Rheumatoid - genetics Biomarkers DNA methylation Epigenome Humans Immunology machine learning rheumatoid arthritis therapy response Tumor Necrosis Factor-alpha
Title	Novel DNA methylome biomarkers associated with adalimumab response in rheumatoid arthritis patients
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