Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission

Sustained clinical remission (CR) without drug treatment has not been achieved in patients with rheumatoid arthritis (RA). This implies a substantial difference between CR and the healthy state, but it has yet to be quantified. We report a longitudinal monitoring of the drug response at multi-omics levels in the peripheral blood of patients with RA. Our data reveal that drug treatments alter the molecular profile closer to that of HCs at the transcriptome, serum proteome, and immunophenotype level. Patient follow-up suggests that the molecular profile after drug treatments is associated with long-term stable CR. In addition, we identify molecular signatures that are resistant to drug treatments. These signatures are associated with RA independently of known disease severity indexes and are largely explained by the imbalance of neutrophils, monocytes, and lymphocytes. This high-dimensional phenotyping provides a quantitative measure of molecular remission and illustrates a multi-omics approach to understanding drug response.Little information is available on molecular changes in response to treatment of rheumatoid arthritis (RA). Here the authors report a multi-omics study collecting patients' transcriptome, proteome, and immunophenotype data to help understand the impact of drug treatments on RA molecular phenotypes.

[1]  G. Nolan,et al.  Mass cytometry as a platform for the discovery of cellular biomarkers to guide effective rheumatic disease therapy , 2015, Arthritis Research & Therapy.

[2]  F. Tubach,et al.  Body mass index and response to infliximab in rheumatoid arthritis. , 2015, Clinical and experimental rheumatology.

[3]  G. Nagy,et al.  Sustained biologic-free and drug-free remission in rheumatoid arthritis, where are we now? , 2015, Arthritis Research & Therapy.

[4]  R. Morita,et al.  Serum proteomic analysis identifies interleukin 16 as a biomarker for clinical response during early treatment of rheumatoid arthritis. , 2016, Cytokine.

[5]  Qiang Feng,et al.  The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment , 2015, Nature Medicine.

[6]  P. V. van Riel,et al.  The Disease Activity Score and the EULAR response criteria , 2009 .

[7]  S. Shen-Orr,et al.  Social network architecture of human immune cells unveiled by quantitative proteomics , 2017, Nature Immunology.

[8]  M. Zimmerman,et al.  Monocytosis and a Low Lymphocyte to Monocyte Ratio Are Effective Biomarkers of Ulcerative Colitis Disease Activity , 2015, Inflammatory bowel diseases.

[9]  S. Soraci,et al.  Assessment of patient satisfaction in activities of daily living using a modified Stanford Health Assessment Questionnaire. , 1983, Arthritis and rheumatism.

[10]  Erich Barke,et al.  Hierarchical partitioning , 1996, Proceedings of International Conference on Computer Aided Design.

[11]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[12]  angesichts der Corona-Pandemie,et al.  UPDATE , 1973, The Lancet.

[13]  P. Taylor,et al.  A structured literature review of the burden of illness and unmet needs in patients with rheumatoid arthritis: a current perspective , 2016, Rheumatology International.

[14]  E. Sasso,et al.  Association of a multibiomarker disease activity score at multiple time-points with radiographic progression in rheumatoid arthritis: results from the SWEFOT trial , 2016, RMD Open.

[15]  G. Cavet,et al.  Performance of a multi-biomarker score measuring rheumatoid arthritis disease activity in the CAMERA tight control study , 2012, Annals of the rheumatic diseases.

[16]  T. Vyse,et al.  C-reactive protein in rheumatology: biology and genetics , 2011, Nature Reviews Rheumatology.

[17]  P. Gregersen,et al.  A genetic study on C5-TRAF1 and progression of joint damage in rheumatoid arthritis , 2015, Arthritis Research & Therapy.

[18]  C. Meisel,et al.  Standardization of whole blood immune phenotype monitoring for clinical trials: panels and methods from the ONE study , 2013, Transplantation research.

[19]  M. Ronaghi,et al.  Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data , 2010, PloS one.

[20]  Gary D Bader,et al.  Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation , 2010, PloS one.

[21]  Michaela Oswald,et al.  Modular Analysis of Peripheral Blood Gene Expression in Rheumatoid Arthritis Captures Reproducible Gene Expression Changes in Tumor Necrosis Factor Responders , 2015, Arthritis & rheumatology.

[22]  J. Mesirov,et al.  The Molecular Signatures Database Hallmark Gene Set Collection , 2015 .

[23]  R. Nussenblatt,et al.  Standardizing immunophenotyping for the Human Immunology Project , 2012, Nature Reviews Immunology.

[24]  Georg Schett,et al.  The pathogenesis of rheumatoid arthritis. , 2011, The New England journal of medicine.

[25]  Tsutomu Takeuchi,et al.  EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2016 update , 2010, Annals of the rheumatic diseases.

[26]  M. Prevoo,et al.  Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. , 1995, Arthritis and rheumatism.

[27]  Justin Guinney,et al.  GSVA: gene set variation analysis for microarray and RNA-Seq data , 2013, BMC Bioinformatics.

[28]  Bjørn-Helge Mevik,et al.  Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR) , 2004 .

[29]  S. Ramamoorthy,et al.  Obesity in IBD: epidemiology, pathogenesis, disease course and treatment outcomes , 2017, Nature Reviews Gastroenterology &Hepatology.

[30]  P. J. van der Spek,et al.  Gene Expression Analysis of Peripheral Cells for Subclassification of Pediatric Inflammatory Bowel Disease in Remission , 2013, PloS one.

[31]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[32]  Ori Rogowski,et al.  Leukocytosis in obese individuals: possible link in patients with unexplained persistent neutrophilia , 2006, European journal of haematology.

[33]  Andreas Radbruch,et al.  Monocyte alterations in rheumatoid arthritis are dominated by preterm release from bone marrow and prominent triggering in the joint , 2017, Annals of the rheumatic diseases.

[34]  F. Iannone,et al.  Obesity reduces the drug survival of second line biological drugs following a first TNF-α inhibitor in rheumatoid arthritis patients. , 2015, Joint, bone, spine : revue du rhumatisme.

[35]  R N Maini,et al.  Infliximab and methotrexate in the treatment of rheumatoid arthritis. Anti-Tumor Necrosis Factor Trial in Rheumatoid Arthritis with Concomitant Therapy Study Group. , 2000, The New England journal of medicine.

[36]  R. Pinals,et al.  Leukocytosis in Rheumatoid Arthritis , 1996, Journal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases.

[37]  P. Tugwell,et al.  A simplified disease activity index for rheumatoid arthritis for use in clinical practice. , 2003, Rheumatology.

[38]  F. Houssiau,et al.  Global Molecular Effects of Tocilizumab Therapy in Rheumatoid Arthritis Synovium , 2014, Arthritis & rheumatology.

[39]  F. Guillemin,et al.  Clinical and epidemiological research , 2011 .

[40]  John Wong,et al.  EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs , 2010, Annals of the rheumatic diseases.

[41]  R. Morita,et al.  Multiomic disease signatures converge to cytotoxic CD8 T cells in primary Sjögren’s syndrome , 2017, Annals of the rheumatic diseases.

[42]  Ben S. Wittner,et al.  Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1 , 2009, Nature.

[43]  Chia-Yen Chen,et al.  Being overweight or obese and risk of developing rheumatoid arthritis among women: a prospective cohort study , 2014, Annals of the rheumatic diseases.

[44]  J. Mesirov,et al.  The Molecular Signatures Database (MSigDB) hallmark gene set collection. , 2015, Cell systems.

[45]  M. Uffmann,et al.  Acute phase reactants add little to composite disease activity indices for rheumatoid arthritis: validation of a clinical activity score , 2005, Arthritis research & therapy.

[46]  Hierarchical partitioning , 1996, ICCAD 1996.

[47]  S. Tulgar,et al.  How obesity affects the neutrophil/lymphocyte and platelet/lymphocyte ratio, systemic immune-inflammatory index and platelet indices: a retrospective study. , 2016, European review for medical and pharmacological sciences.

[48]  John D. Storey,et al.  Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.

[49]  P. Emery,et al.  Remission in rheumatoid arthritis: is it all the same? , 2015, Expert review of clinical pharmacology.

[50]  J. Casanova,et al.  Human CD14dim Monocytes Patrol and Sense Nucleic Acids and Viruses via TLR7 and TLR8 Receptors , 2010, Immunity.

[51]  A. Blom,et al.  The complement system as a potential therapeutic target in rheumatic disease , 2017, Nature Reviews Rheumatology.

[52]  P. V. van Riel,et al.  The Disease Activity Score and the EULAR response criteria. , 2005, Clinical and experimental rheumatology.

[53]  R. Morita,et al.  Identification of definitive serum biomarkers associated with disease activity in primary Sjögren’s syndrome , 2016, Arthritis Research & Therapy.

[54]  Yuri Nikolsky,et al.  Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib , 2015, PloS one.