Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis

Xie, Xiaoqing; Li, Fen; Li, Shu; Tian, Jing; Chen, Jinwei; Du, Jinfeng; Mao, Ni; Chen, Jian

doi:10.1007/s10067-017-3639-0

Cited by 14 publications

(8 citation statements)

References 73 publications

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“…Several genome-wide association studies (GWAS) have so far attempted to survey susceptible genomic regions for TNFi responses ( 6 ). However, there is still no consensus about a single gene locus with a strong effect that may be replicated across multiple studies ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

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Dynamics of Type I and Type II Interferon Signature Determines Responsiveness to Anti-TNF Therapy in Rheumatoid Arthritis

et al. 2022

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The factors influencing long-term responses to a tumor necrosis factor inhibitor (TNFi) in rheumatoid arthritis (RA) patients currently remain unknown. Therefore, we herein conducted a multi-omics analysis of TNFi responses in a Japanese RA cohort. Blood samples were collected from 27 biological disease-modifying antirheumatic drug (DMARD)-naive RA patients at the initiation of and after three months of treatment with TNFi. Treatment responses were evaluated at one year. Differences in gene expression levels in peripheral blood mononuclear cells (PBMCs), plasma protein levels, drug concentrations, and the presence/absence of anti-drug antibodies were investigated, and a cell phenotypic analysis of PBMCs was performed using flow cytometry. After one year of treatment, thirteen patients achieved clinical remission (responders), while the others did not or switched to other biologics (non-responders). Differentially expressed genes related to treatment responses were enriched for the interferon (IFN) pathway. The expression of type I IFN signaling-related genes was higher in non-responders than in responders before and after treatment (P = 0.03, 0.005, respectively). The expression of type II IFN signaling-related genes did not significantly differ before treatment; however, it increased in non-responders and decreased in responders, with a significant difference being observed after three months of treatment (P = 1.2×10-3). The total number of lymphocytes and C-X-C Motif Chemokine Ligand 10 (CXCL10) protein levels were associated with the type I IFN signature (P = 6.7×10-7, 6.4×10-3, respectively). Hepatocyte growth factor (HGF) protein levels before treatment predicted fold increases in type II IFN (P = 0.03). These IFN signature-related indices (the number of lymphocytes, CXCL10, and HGF) significantly differed between responders and non-responders (P = 0.01, 0.01, and 0.04, respectively). A single-cell analysis revealed that the type I IFN signature was more highly enriched in monocytes than in other cell types. A deconvolution analysis of bulk-RNA sequence data identified CD4+ and CD8+ T cells as the main sources of the type II IFN signature in non-responders. Collectively, the present results demonstrated that the dynamics of the type I and II IFN pathways affected long-term responses to TNFi, providing information on its biological background and potential for clinical applications.

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Section: Introductionmentioning

confidence: 99%

“…To address this issue, dozens of omics studies, including transcriptomics, proteomics, metabolomics, and single-cell omics studies, have been conducted ( 6 , 9 , 10 ). However, those results have not achieved a strong consensus.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Type I and Type II Interferon Signature Determines Responsiveness to Anti-TNF Therapy in Rheumatoid Arthritis

et al. 2022

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“…Transcriptomic biomarkers have also been investigated in RA, with some limited results [55]. In this respect, the combined analysis of gene expression and genetic data in large patient cohorts may be a more promising approach.…”

Section: Transcriptomic Biomarkersmentioning

confidence: 99%

Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers

Mezghiche

Yahia-Cherbal

Rogge

et al. 2021

Expert Review of Clinical Immunology

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transcriptional, protein, cellular) may be necessary to capture the biological complexity of response to treatment.• New bioinformatic tools, including machine learning approaches, are necessary to handle the complexity of the large data sets being currently explored. * This paper explores the ability of random forest-based algorithms to model gene expression and DNA methylation data in order to predict therapeutic responses in RA.

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“… 5 Considering the uncertainty of treatment effect and high cost, it is necessary to identify biomarkers that can predict the treatment effect of bDMARDs. 6 Simultaneously, the resistance mechanism of non-responders to IFX remains unclear except for the influence of drug concentrations and anti-drug antibodies. 7 …”

Section: Introductionmentioning

confidence: 99%

iTRAQ and PRM-Based Proteomic Analysis Provides New Insights into Mechanisms of Response to Triple Therapy in Patients with Rheumatoid Arthritis

Chen¹,

Li²,

Ge³

et al. 2021

JIR

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Background Approximately 30% of patients with rheumatoid arthritis (RA) respond poorly to combination therapy of multiple drugs. The molecular mechanisms of different responses to methotrexate + leflunomide + infliximab therapy in patients with RA were explored in this study. Methods Infliximab was administered to patients with RA whose disease activity score was higher than 5.1 after 1 month of combination therapy with methotrexate and leflunomide. After 14 weeks of undergoing triple therapy, patients with RA were classified as responders and non-responders. Protein profiles at baseline and 14th week were investigated via isobaric tags for relative and absolute quantification (iTRAQ), and proteins with significant differences ≥1.2 folds change or ≤0.8 folds change were defined as differentially expressed proteins (DEPs). Overlapping DEPs between responders and non-responders were confirmed by parallel reaction monitoring (PRM). Bioinformatic analyses were performed for DEPs. Results The results revealed 5 non-responders (NRs) and 15 responders (Rs). iTRAQ analysis indicated 13 overlapping DEPs and included 6 opposite change DEPs such as testicular tissue protein Li 70, cofilin 1, fibrinogen beta chain, galectin-10, serotransferrin (TF) and albumin. The difference in serotransferrin between responders and non-responders confirmed by PRM was significant. Verification by PRM indicated that TF was elevated in the Rs group and was reduced in the NRs group. Bioinformatic analysis indicated that serotransferrin was involved in the hypoxia-inducible factor-1 pathway and ferroptosis. Conclusion Serotransferrin-related molecular mechanism may be a new direction to study refractory RA.

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Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis

Cited by 14 publications

References 73 publications

Dynamics of Type I and Type II Interferon Signature Determines Responsiveness to Anti-TNF Therapy in Rheumatoid Arthritis

Dynamics of Type I and Type II Interferon Signature Determines Responsiveness to Anti-TNF Therapy in Rheumatoid Arthritis

Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers

iTRAQ and PRM-Based Proteomic Analysis Provides New Insights into Mechanisms of Response to Triple Therapy in Patients with Rheumatoid Arthritis

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