Transcriptomic profiling of secukinumab-treated psoriatic arthritis reveals potential novel response-associated pathways

Outcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS)

Transcriptomic profiling of secukinumab-treated psoriatic arthritis reveals potential novel response-associated pathways

Outcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS)

Department of Chemistry

University of Manchester
Manchester University NHS Foundation Trust
University Hospital Sussex NHS Foundation Trust
Cardiff University
Centre for Musculoskeletal Research
Manchester Academic Health Science Centre

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Abstract

Objectives: IL-17A inhibitors are therapeutic options in PsA, but response is not universal. Evidence from other inflammatory arthritides suggests differential gene expression may predict the outcomes. This study aimed to identify transcriptomic predictive biomarkers of response in PsA patients commencing secukinumab. Methods: Participants were recruited to OUtcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS), a prospective observational cohort study of patients with PsA initiating advanced therapeutics. Samples for this analysis were chosen based on extreme phenotype response. Whole-blood RNA sequencing was performed longitudinally in 13 secukinumab-treated patients at baseline (pre-treatment) and at 3 months post-treatment, with response evaluated at 3 months using the DAS28 criteria and the Psoriatic Arthritis Response Criteria. Differential gene expression analysis, Ingenuity Pathway Analysis (IPA), and weighted gene co-expression network analysis (WGCNA) were performed to identify significantly differentially expressed genes (DEGs) (adjusted P < 0.05, |log ₂ fold change| ≥ 1), enriched pathways (P < 0.05), and co-expressed gene modules. Immune cell subset proportions were estimated by deconvolution, and hub genes were identified by integrating DEGs and WGCNA, with overlapping genes defined as potential driver genes. Results: IGHV3-64D and IGHV1-46 were differentially expressed at baseline, 3 months, and sustained over time in the responder group (adjusted P < 0.05). Five overlapping genes (GMPR, CDC34, DMTN, UBXN6 and SLC25A39) were identified as potential drivers. Functional analysis indicated a potential contribution of metabolic pathways to the modulation of therapeutic response. Conclusion: We identified two genes as pre-treatment predictive biomarkers of secukinumab response that persisted over time. Integration with WGCNA revealed five additional candidate genes. These genes are implicated in metabolic pathways, which may modulate the secukinumab response. These findings warrant further validation.

Original language	English
Article number	keag193
Number of pages	11
Journal	Rheumatology (Oxford, England)
Volume	65
Issue number	5
Early online date	15 Apr 2026
Publication status	Published - 31 May 2026

Data Availability Statement

Data are available upon reasonable request.

Acknowledgements

We would like to thank the patients and volunteers for their par
ticipation in this study, and the Genomic Technologies Core
Facility (GTCF) for their assistance in library preparation and
RNA sequencing.

Funding

This research was funded by The Ministry of Higher Education of Malaysia, Versus Arthritis (21754) and the National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (NIHR203308). M.J. is funded by a NIHR Advanced Fellowship (NIHR301413). A.B. is an NIHR Senior Investigator. The views expressed are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care. We would like to thank the patients and volunteers for their participation in this study, and the Genomic Technologies Core Facility (GTCF) for their assistance in library preparation and RNA sequencing. This research was funded by The Ministry of Higher Education of Malaysia, Versus Arthritis (21754) and the National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (NIHR203308). M.J. is funded by a NIHR Advanced Fellowship (NIHR301413). A.B. is an NIHR Senior Investigator. The views expressed are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care.Disclosure statement:J.B. reports a research grant award from Pfizer and in the last 3 years travel/conference fees from Fresenius Kabi and Novartis. H.C. reports speaker fees from UCB and consultancy fees from Pfizer. The remaining authors have declared no conflicts of interest. Disclosure statement: J.B. reports a research grant award from Pfizer and in the last 3 years travel/conference fees from Fresenius Kabi and Novartis. H.C. reports speaker fees from UCB and consultancy fees from Pfizer. The remaining authors have declared no conflicts of interest. Acknowledgements

Funders	Funder number
National Institute for Health and Care Research
Ministry of Higher Education, Malaysia
Pfizer
Versus Arthritis	21754
Manchester Biomedical Research Centre	NIHR203308, NIHR301413

Keywords

Humans
Arthritis, Psoriatic/drug therapy
Antibodies, Monoclonal, Humanized/therapeutic use
Female
Male
Gene Expression Profiling
Antirheumatic Agents/therapeutic use
Middle Aged
Transcriptome
Prospective Studies
Adult
Treatment Outcome
Biomarkers
predictive biomarkers
molecular signatures
transcriptomics
precision medicine
psoriatic arthritis

ASJC Scopus subject areas

Rheumatology
Pharmacology (medical)

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keag193Final published version, 1.66 MBLicence: CC BY

Cite this

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abstract = "Objectives: IL-17A inhibitors are therapeutic options in PsA, but response is not universal. Evidence from other inflammatory arthritides suggests differential gene expression may predict the outcomes. This study aimed to identify transcriptomic predictive biomarkers of response in PsA patients commencing secukinumab. Methods: Participants were recruited to OUtcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS), a prospective observational cohort study of patients with PsA initiating advanced therapeutics. Samples for this analysis were chosen based on extreme phenotype response. Whole-blood RNA sequencing was performed longitudinally in 13 secukinumab-treated patients at baseline (pre-treatment) and at 3 months post-treatment, with response evaluated at 3 months using the DAS28 criteria and the Psoriatic Arthritis Response Criteria. Differential gene expression analysis, Ingenuity Pathway Analysis (IPA), and weighted gene co-expression network analysis (WGCNA) were performed to identify significantly differentially expressed genes (DEGs) (adjusted P < 0.05, |log 2 fold change| ≥ 1), enriched pathways (P < 0.05), and co-expressed gene modules. Immune cell subset proportions were estimated by deconvolution, and hub genes were identified by integrating DEGs and WGCNA, with overlapping genes defined as potential driver genes. Results: IGHV3-64D and IGHV1-46 were differentially expressed at baseline, 3 months, and sustained over time in the responder group (adjusted P < 0.05). Five overlapping genes (GMPR, CDC34, DMTN, UBXN6 and SLC25A39) were identified as potential drivers. Functional analysis indicated a potential contribution of metabolic pathways to the modulation of therapeutic response. Conclusion: We identified two genes as pre-treatment predictive biomarkers of secukinumab response that persisted over time. Integration with WGCNA revealed five additional candidate genes. These genes are implicated in metabolic pathways, which may modulate the secukinumab response. These findings warrant further validation.",

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author = "\{Outcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS)\} and \{Abdul Jalil\}, \{Nahdia Afiifah\} and Hector Chinoy and Meghna Jani and Anne Barton and Charlotte Thompson and Paul Martin and Nisha Nair and James Bluett",

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AU - Outcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS)

AU - Abdul Jalil, Nahdia Afiifah

AU - Chinoy, Hector

AU - Jani, Meghna

AU - Barton, Anne

AU - Thompson, Charlotte

AU - Martin, Paul

AU - Nair, Nisha

AU - Bluett, James

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N2 - Objectives: IL-17A inhibitors are therapeutic options in PsA, but response is not universal. Evidence from other inflammatory arthritides suggests differential gene expression may predict the outcomes. This study aimed to identify transcriptomic predictive biomarkers of response in PsA patients commencing secukinumab. Methods: Participants were recruited to OUtcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS), a prospective observational cohort study of patients with PsA initiating advanced therapeutics. Samples for this analysis were chosen based on extreme phenotype response. Whole-blood RNA sequencing was performed longitudinally in 13 secukinumab-treated patients at baseline (pre-treatment) and at 3 months post-treatment, with response evaluated at 3 months using the DAS28 criteria and the Psoriatic Arthritis Response Criteria. Differential gene expression analysis, Ingenuity Pathway Analysis (IPA), and weighted gene co-expression network analysis (WGCNA) were performed to identify significantly differentially expressed genes (DEGs) (adjusted P < 0.05, |log 2 fold change| ≥ 1), enriched pathways (P < 0.05), and co-expressed gene modules. Immune cell subset proportions were estimated by deconvolution, and hub genes were identified by integrating DEGs and WGCNA, with overlapping genes defined as potential driver genes. Results: IGHV3-64D and IGHV1-46 were differentially expressed at baseline, 3 months, and sustained over time in the responder group (adjusted P < 0.05). Five overlapping genes (GMPR, CDC34, DMTN, UBXN6 and SLC25A39) were identified as potential drivers. Functional analysis indicated a potential contribution of metabolic pathways to the modulation of therapeutic response. Conclusion: We identified two genes as pre-treatment predictive biomarkers of secukinumab response that persisted over time. Integration with WGCNA revealed five additional candidate genes. These genes are implicated in metabolic pathways, which may modulate the secukinumab response. These findings warrant further validation.

AB - Objectives: IL-17A inhibitors are therapeutic options in PsA, but response is not universal. Evidence from other inflammatory arthritides suggests differential gene expression may predict the outcomes. This study aimed to identify transcriptomic predictive biomarkers of response in PsA patients commencing secukinumab. Methods: Participants were recruited to OUtcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS), a prospective observational cohort study of patients with PsA initiating advanced therapeutics. Samples for this analysis were chosen based on extreme phenotype response. Whole-blood RNA sequencing was performed longitudinally in 13 secukinumab-treated patients at baseline (pre-treatment) and at 3 months post-treatment, with response evaluated at 3 months using the DAS28 criteria and the Psoriatic Arthritis Response Criteria. Differential gene expression analysis, Ingenuity Pathway Analysis (IPA), and weighted gene co-expression network analysis (WGCNA) were performed to identify significantly differentially expressed genes (DEGs) (adjusted P < 0.05, |log 2 fold change| ≥ 1), enriched pathways (P < 0.05), and co-expressed gene modules. Immune cell subset proportions were estimated by deconvolution, and hub genes were identified by integrating DEGs and WGCNA, with overlapping genes defined as potential driver genes. Results: IGHV3-64D and IGHV1-46 were differentially expressed at baseline, 3 months, and sustained over time in the responder group (adjusted P < 0.05). Five overlapping genes (GMPR, CDC34, DMTN, UBXN6 and SLC25A39) were identified as potential drivers. Functional analysis indicated a potential contribution of metabolic pathways to the modulation of therapeutic response. Conclusion: We identified two genes as pre-treatment predictive biomarkers of secukinumab response that persisted over time. Integration with WGCNA revealed five additional candidate genes. These genes are implicated in metabolic pathways, which may modulate the secukinumab response. These findings warrant further validation.

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KW - Antirheumatic Agents/therapeutic use

KW - Middle Aged

KW - Transcriptome

KW - Prospective Studies

KW - Adult

KW - Treatment Outcome

KW - Biomarkers

KW - predictive biomarkers

KW - molecular signatures

KW - transcriptomics

KW - precision medicine

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JO - Rheumatology (Oxford, England)

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M1 - keag193

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Transcriptomic profiling of secukinumab-treated psoriatic arthritis reveals potential novel response-associated pathways

Abstract

Data Availability Statement

Acknowledgements

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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