Skip to main content

Advertisement

SpringerLink
Log in

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

  • Review Article
  • Published:
Clinical Rheumatology Aims and scope Submit manuscript

Abstract

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by progressive joint erosion. Tumor necrosis factor (TNF) antagonists are the most widely used biological disease-modifying anti-rheumatic drug in RA. However, there continue to be one third of RA patients who have poor or no response to TNF antagonists. Following consideration of the uncertainty of therapeutic effects and the high price of TNF antagonists, it is worthy to predict the treatment responses before anti-TNF therapy. According to the comparisons between the responders and non-responders to TNF antagonists by omic technologies, such as genomics, transcriptomics, proteomics, and metabolomics, rheumatologists are eager to find significant biomarkers to predict the effect of TNF antagonists in order to optimize the personalized treatment in RA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Singh JA, Saag KG, Bridges SL Jr, Akl EA, Bannuru RR, Sullivan MC, Vaysbrot E et al (2016) 2015 American College of Rheumatology Guideline for the treatment of rheumatoid arthritis. Arthritis Rheumatol 68:1–26. doi:10.1002/art.39480

    PubMed  Google Scholar 

  2. Mewar D, Wilson AG (2011) Treatment of rheumatoid arthritis with tumour necrosis factor inhibitors. Br J Pharmacol 162:785–791. doi:10.1111/j.1476-5381.2010.01099.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Cuchacovich M, Bueno D, Carvajal R, Bravo N, Aguillon JC, Catalan D, Soto L (2014) Clinical parameters and biomarkers for anti-TNF treatment prognosis in rheumatoid arthritis patients. Clin Rheumatol 33:1707–1714. doi:10.1007/s10067-014-2756-2

    Article  PubMed  Google Scholar 

  4. Zeng Z, Duan Z, Zhang T, Wang S, Li G, Gao J, Ye D et al (2013) Association between tumor necrosis factor-alpha (TNF-alpha) promoter −308 G/A and response to TNF-alpha blockers in rheumatoid arthritis: a meta-analysis. Mod Rheumatol 23:489–495. doi:10.1007/s10165-012-0699-5

    Article  CAS  PubMed  Google Scholar 

  5. Kang CP, Lee KW, Yoo DH, Kang C, Bae SC (2005) The influence of a polymorphism at position −857 of the tumour necrosis factor alpha gene on clinical response to etanercept therapy in rheumatoid arthritis. Rheumatology (Oxford) 44:547–552. doi:10.1093/rheumatology/keh550

    Article  CAS  Google Scholar 

  6. Miceli-Richard C, Comets E, Verstuyft C, Tamouza R, Loiseau P, Ravaud P, Kupper H et al (2008) A single tumour necrosis factor haplotype influences the response to adalimumab in rheumatoid arthritis. Ann Rheum Dis 67:478–484. doi:10.1136/ard.2007.074104

    Article  CAS  PubMed  Google Scholar 

  7. Criswell LA, Lum RF, Turner KN, Woehl B, Zhu Y, Wang J, Tiwari HK et al (2004) The influence of genetic variation in the HLA-DRB1 and LTA-TNF regions on the response to treatment of early rheumatoid arthritis with methotrexate or etanercept. Arthritis Rheum 50:2750–2756. doi:10.1002/art.20469

    Article  CAS  PubMed  Google Scholar 

  8. Pavy S, Toonen EJ, Miceli-Richard C, Barrera P, van Riel PL, Criswell LA, Mariette X et al (2010) Tumour necrosis factor alpha -308G->A polymorphism is not associated with response to TNFalpha blockers in Caucasian patients with rheumatoid arthritis: systematic review and meta-analysis. Ann Rheum Dis 69:1022–1028. doi:10.1136/ard.2009.117622

    Article  CAS  PubMed  Google Scholar 

  9. Ongaro A, De Mattei M, Pellati A, Caruso A, Ferretti S, Masieri FF, Fotinidi M et al (2008) Can tumor necrosis factor receptor II gene 676T>G polymorphism predict the response grading to anti-TNFalpha therapy in rheumatoid arthritis? Rheumatol Int 28:901–908. doi:10.1007/s00296-008-0552-5

    Article  CAS  PubMed  Google Scholar 

  10. Nishimoto T, Seta N, Anan R, Yamamoto T, Kaneko Y, Takeuchi T, Kuwana M (2014) A single nucleotide polymorphism of TRAF1 predicts the clinical response to anti-TNF treatment in Japanese patients with rheumatoid arthritis. Clin Exp Rheumatol 32:211–217

    CAS  PubMed  Google Scholar 

  11. Chatzikyriakidou A, Georgiou I, Voulgari PV, Venetsanopoulou AI, Drosos AA (2007) Combined tumour necrosis factor-alpha and tumour necrosis factor receptor genotypes could predict rheumatoid arthritis patients’ response to anti-TNF-alpha therapy and explain controversies of studies based on a single polymorphism. Rheumatology (Oxford) 46:1034–1035. doi:10.1093/rheumatology/kem041

    Article  CAS  Google Scholar 

  12. Marotte H, Pallot-Prades B, Grange L, Tebib J, Gaudin P, Alexandre C, Blond JL et al (2006) The shared epitope is a marker of severity associated with selection for, but not with response to, infliximab in a large rheumatoid arthritis population. Ann Rheum Dis 65:342–347. doi:10.1136/ard.2005.037150

    Article  CAS  PubMed  Google Scholar 

  13. Martinez A, Salido M, Bonilla G, Pascual-Salcedo D, Fernandez-Arquero M, de Miguel S, Balsa A et al (2004) Association of the major histocompatibility complex with response to infliximab therapy in rheumatoid arthritis patients. Arthritis Rheum 50:1077–1082. doi:10.1002/art.20154

    Article  CAS  PubMed  Google Scholar 

  14. Schotte H, Schluter B, Drynda S, Willeke P, Tidow N, Assmann G, Domschke W et al (2005) Interleukin 10 promoter microsatellite polymorphisms are associated with response to long term treatment with etanercept in patients with rheumatoid arthritis. Ann Rheum Dis 64:575–581. doi:10.1136/ard.2004.027672

    Article  CAS  PubMed  Google Scholar 

  15. Schotte H, Schluter B, Schmidt H, Gaubitz M, Drynda S, Kekow J, Willeke P (2015) Putative IL-10 low producer genotypes are associated with a favourable etanercept response in patients with rheumatoid arthritis. PLoS One 10:e0130907. doi:10.1371/journal.pone.0130907

    Article  PubMed  PubMed Central  Google Scholar 

  16. Padyukov L, Lampa J, Heimburger M, Ernestam S, Cederholm T, Lundkvist I, Andersson P et al (2003) Genetic markers for the efficacy of tumour necrosis factor blocking therapy in rheumatoid arthritis. Ann Rheum Dis 62:526–529

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Davila-Fajardo CL, Marquez A, Pascual-Salcedo D, Moreno Ramos MJ, Garcia-Portales R, Magro C, Alegre-Sancho JJ et al (2014) Confirmation of -174G/C interleukin-6 gene promoter polymorphism as a genetic marker predicting antitumor necrosis factor treatment outcome. Pharmacogenet Genomics 24:1–5. doi:10.1097/FPC.0000000000000013

    Article  CAS  PubMed  Google Scholar 

  18. Schotte H, Schmidt H, Gaubitz M, Drynda S, Kekow J, Willeke P, Schluter B (2015) Interleukin-6 promoter haplotypes are associated with etanercept response in patients with rheumatoid arthritis. Clin Rheumatol 34:2021–2028. doi:10.1007/s10067-015-3107-7

    Article  PubMed  Google Scholar 

  19. Coulthard LR, Taylor JC, Eyre S, Biologics in Rheumatoid Arthritis G, Genomics, Robinson JI, Wilson AG et al (2011) Genetic variants within the MAP kinase signalling network and anti-TNF treatment response in rheumatoid arthritis patients. Ann Rheum Dis 70:98–103. doi:10.1136/ard.2010.133249

    Article  PubMed  Google Scholar 

  20. Potter C, Cordell HJ, Barton A, Daly AK, Hyrich KL, Mann DA, Morgan AW et al (2010) Association between anti-tumour necrosis factor treatment response and genetic variants within the TLR and NF{kappa}B signalling pathways. Ann Rheum Dis 69:1315–1320. doi:10.1136/ard.2009.117309

    Article  CAS  PubMed  Google Scholar 

  21. Kim TH, Choi SJ, Lee YH, Song GG, Ji JD (2014) Gene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets. Joint Bone Spine 81:325–330. doi:10.1016/j.jbspin.2014.01.013

    Article  CAS  PubMed  Google Scholar 

  22. Sode J, Vogel U, Bank S, Andersen PS, Hetland ML, Locht H, Heegaard NH et al (2016) Confirmation of an IRAK3 polymorphism as a genetic marker predicting response to anti-TNF treatment in rheumatoid arthritis. Pharmacogenomics J. doi:10.1038/tpj.2016.66

    PubMed  Google Scholar 

  23. Tan RJ, Gibbons LJ, Potter C, Hyrich KL, Morgan AW, Wilson AG, Isaacs JD et al (2010) Investigation of rheumatoid arthritis susceptibility genes identifies association of AFF3 and CD226 variants with response to anti-tumour necrosis factor treatment. Ann Rheum Dis 69:1029–1035. doi:10.1136/ard.2009.118406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cui J, Saevarsdottir S, Thomson B, Padyukov L, van der Helm-van Mil AH, Nititham J, Hughes LB et al (2010) Rheumatoid arthritis risk allele PTPRC is also associated with response to anti-tumor necrosis factor alpha therapy. Arthritis Rheum 62:1849–1861. doi:10.1002/art.27457

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Smith SL, Eyre S, Yarwood A, Hyrich K, Morgan AW, Wilson AG, Isaacs J et al (2015) Investigating CD11c expression as a potential genomic biomarker of response to TNF inhibitor biologics in whole blood rheumatoid arthritis samples. Arthritis Res Ther 17:359. doi:10.1186/s13075-015-0868-y

    Article  PubMed  PubMed Central  Google Scholar 

  26. Liu C, Batliwalla F, Li W, Lee A, Roubenoff R, Beckman E, Khalili H et al (2008) Genome-wide association scan identifies candidate polymorphisms associated with differential response to anti-TNF treatment in rheumatoid arthritis. Mol Med 14:575–581. doi:10.2119/2008-00056.Liu

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cui J, Stahl EA, Saevarsdottir S, Miceli C, Diogo D, Trynka G, Raj T et al (2013) Genome-wide association study and gene expression analysis identifies CD84 as a predictor of response to etanercept therapy in rheumatoid arthritis. PLoS Genet 9:e1003394. doi:10.1371/journal.pgen.1003394

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Umicevic Mirkov M, Cui J, Vermeulen SH, Stahl EA, Toonen EJ, Makkinje RR, Lee AT et al (2013) Genome-wide association analysis of anti-TNF drug response in patients with rheumatoid arthritis. Ann Rheum Dis 72:1375–1381. doi:10.1136/annrheumdis-2012-202405

    Article  PubMed  Google Scholar 

  29. Plant D, Bowes J, Potter C, Hyrich KL, Morgan AW, Wilson AG, Isaacs JD et al (2011) Genome-wide association study of genetic predictors of anti-tumor necrosis factor treatment efficacy in rheumatoid arthritis identifies associations with polymorphisms at seven loci. Arthritis Rheum 63:645–653. doi:10.1002/art.30130

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Krintel SB, Palermo G, Johansen JS, Germer S, Essioux L, Benayed R, Badi L et al (2012) Investigation of single nucleotide polymorphisms and biological pathways associated with response to TNFalpha inhibitors in patients with rheumatoid arthritis. Pharmacogenet Genomics 22:577–589. doi:10.1097/FPC.0b013e3283544043

    Article  CAS  PubMed  Google Scholar 

  31. Marquez A, Ferreiro-Iglesias A, Davila-Fajardo CL, Montes A, Pascual-Salcedo D, Perez-Pampin E, Moreno-Ramos MJ et al (2014) Lack of validation of genetic variants associated with anti-tumor necrosis factor therapy response in rheumatoid arthritis: a genome-wide association study replication and meta-analysis. Arthritis Res Ther 16:R66. doi:10.1186/ar4504

    Article  PubMed  PubMed Central  Google Scholar 

  32. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63. doi:10.1038/nrg2484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lindberg J, Wijbrandts CA, van Baarsen LG, Nader G, Klareskog L, Catrina A, Thurlings R et al (2010) The gene expression profile in the synovium as a predictor of the clinical response to infliximab treatment in rheumatoid arthritis. PLoS One 5:e11310. doi:10.1371/journal.pone.0011310

    Article  PubMed  PubMed Central  Google Scholar 

  34. Pachot A, Arnaud B, Marrote H, Cazalis MA, Diasparra J, Gouraud A, Mougin B et al (2007) Increased tumor necrosis factor-alpha mRNA expression in whole blood from patients with rheumatoid arthritis: reduction after infliximab treatment does not predict response. J Rheumatol 34:2158–2161

    CAS  PubMed  Google Scholar 

  35. Sekiguchi N, Kawauchi S, Furuya T, Inaba N, Matsuda K, Ando S, Ogasawara M et al (2008) Messenger ribonucleic acid expression profile in peripheral blood cells from RA patients following treatment with an anti-TNF-alpha monoclonal antibody, infliximab. Rheumatology (Oxford) 47:780–788. doi:10.1093/rheumatology/ken083

    Article  CAS  Google Scholar 

  36. Wright HL, Thomas HB, Moots RJ, Edwards SW (2015) Interferon gene expression signature in rheumatoid arthritis neutrophils correlates with a good response to TNFi therapy. Rheumatology (Oxford) 54:188–193. doi:10.1093/rheumatology/keu299

    Article  CAS  Google Scholar 

  37. Julia A, Erra A, Palacio C, Tomas C, Sans X, Barcelo P, Marsal S (2009) An eight-gene blood expression profile predicts the response to infliximab in rheumatoid arthritis. PLoS One 4:e7556. doi:10.1371/journal.pone.0007556

    Article  PubMed  PubMed Central  Google Scholar 

  38. Stuhlmuller B, Haupl T, Hernandez MM, Grutzkau A, Kuban RJ, Tandon N, Voss JW et al (2010) CD11c as a transcriptional biomarker to predict response to anti-TNF monotherapy with adalimumab in patients with rheumatoid arthritis. Clin Pharmacol Ther 87:311–321. doi:10.1038/clpt.2009.244

    Article  CAS  PubMed  Google Scholar 

  39. Meugnier E, Coury F, Tebib J, Ferraro-Peyret C, Rome S, Bienvenu J, Vidal H et al (2011) Gene expression profiling in peripheral blood cells of patients with rheumatoid arthritis in response to anti-TNF-alpha treatments. Physiol Genomics 43:365–371. doi:10.1152/physiolgenomics.00127.2010

    Article  CAS  PubMed  Google Scholar 

  40. Drynda S, Ringel B, Kekow M, Kuhne C, Drynda A, Glocker MO, Thiesen HJ et al (2004) Proteome analysis reveals disease-associated marker proteins to differentiate RA patients from other inflammatory joint diseases with the potential to monitor anti-TNFalpha therapy. Pathol Res Pract 200:165–171

    Article  CAS  PubMed  Google Scholar 

  41. Fabre S, Dupuy AM, Dossat N, Guisset C, Cohen JD, Cristol JP, Daures JP et al (2008) Protein biochip array technology for cytokine profiling predicts etanercept responsiveness in rheumatoid arthritis. Clin Exp Immunol 153:188–195. doi:10.1111/j.1365-2249.2008.03691.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hueber W, Tomooka BH, Batliwalla F, Li W, Monach PA, Tibshirani RJ, Van Vollenhoven RF et al (2009) Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis. Arthritis Res Ther 11:R76. doi:10.1186/ar2706

    Article  PubMed  PubMed Central  Google Scholar 

  43. Morozzi G, Fabbroni M, Bellisai F, Cucini S, Simpatico A, Galeazzi M (2007) Low serum level of COMP, a cartilage turnover marker, predicts rapid and high ACR70 response to adalimumab therapy in rheumatoid arthritis. Clin Rheumatol 26:1335–1338. doi:10.1007/s10067-006-0520-y

    Article  CAS  PubMed  Google Scholar 

  44. Gonzalez-Alvaro I, Ortiz AM, Tomero EG, Balsa A, Orte J, Laffon A, Garcia-Vicuna R (2007) Baseline serum RANKL levels may serve to predict remission in rheumatoid arthritis patients treated with TNF antagonists. Ann Rheum Dis 66:1675–1678. doi:10.1136/ard.2007.071910

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Choi IY, Gerlag DM, Herenius MJ, Thurlings RM, Wijbrandts CA, Foell D, Vogl T et al (2015) MRP8/14 serum levels as a strong predictor of response to biological treatments in patients with rheumatoid arthritis. Ann Rheum Dis 74:499–505. doi:10.1136/annrheumdis-2013-203923

    Article  CAS  PubMed  Google Scholar 

  46. Trocme C, Marotte H, Baillet A, Pallot-Prades B, Garin J, Grange L, Miossec P et al (2009) Apolipoprotein A-I and platelet factor 4 are biomarkers for infliximab response in rheumatoid arthritis. Ann Rheum Dis 68:1328–1333. doi:10.1136/ard.2008.093153

    Article  CAS  PubMed  Google Scholar 

  47. Sekigawa I, Yanagida M, Iwabuchi K, Kaneda K, Kaneko H, Takasaki Y, Jung G et al (2008) Protein biomarker analysis by mass spectrometry in patients with rheumatoid arthritis receiving anti-tumor necrosis factor-alpha antibody therapy. Clin Exp Rheumatol 26:261–267

    CAS  PubMed  Google Scholar 

  48. Visvanathan S, Rahman MU, Keystone E, Genovese M, Klareskog L, Hsia E, Mack M et al (2010) Association of serum markers with improvement in clinical response measures after treatment with golimumab in patients with active rheumatoid arthritis despite receiving methotrexate: results from the GO-FORWARD study. Arthritis Res Ther 12:R211. doi:10.1186/ar3188

    Article  PubMed  PubMed Central  Google Scholar 

  49. Dwivedi RC, Dhindsa N, Krokhin OV, Cortens J, Wilkins JA, El-Gabalawy HS (2009) The effects of infliximab therapy on the serum proteome of rheumatoid arthritis patients. Arthritis Res Ther 11:R32. doi:10.1186/ar2637

    Article  PubMed  PubMed Central  Google Scholar 

  50. Serada S, Fujimoto M, Ogata A, Terabe F, Hirano T, Iijima H, Shinzaki S et al (2010) iTRAQ-based proteomic identification of leucine-rich alpha-2 glycoprotein as a novel inflammatory biomarker in autoimmune diseases. Ann Rheum Dis 69:770–774. doi:10.1136/ard.2009.118919

    Article  CAS  PubMed  Google Scholar 

  51. Bobbio-Pallavicini F, Caporali R, Alpini C, Avalle S, Epis OM, Klersy C, Montecucco C (2007) High IgA rheumatoid factor levels are associated with poor clinical response to tumour necrosis factor alpha inhibitors in rheumatoid arthritis. Ann Rheum Dis 66:302–307. doi:10.1136/ard.2006.060608

    Article  CAS  PubMed  Google Scholar 

  52. Sakthiswary R, Shaharir SS, Mohd Said MS, Asrul AW, Shahril NS (2014) IgA rheumatoid factor as a serological predictor of poor response to tumour necrosis factor alpha inhibitors in rheumatoid arthritis. Int J Rheum Dis 17:872–877. doi:10.1111/1756-185X.12443

    Article  CAS  PubMed  Google Scholar 

  53. Ortea I, Roschitzki B, Ovalles JG, Longo JL, de la Torre I, Gonzalez I, Gomez-Reino JJ et al (2012) Discovery of serum proteomic biomarkers for prediction of response to infliximab (a monoclonal anti-TNF antibody) treatment in rheumatoid arthritis: an exploratory analysis. J Proteomics 77:372–382. doi:10.1016/j.jprot.2012.09.011

    Article  CAS  PubMed  Google Scholar 

  54. Ortea I, Roschitzki B, Lopez-Rodriguez R, Tomero EG, Ovalles JG, Lopez-Longo J, de la Torre I et al (2016) Independent candidate serum protein biomarkers of response to adalimumab and to infliximab in rheumatoid arthritis: an exploratory study. PLoS One 11:e0153140. doi:10.1371/journal.pone.0153140

    Article  PubMed  PubMed Central  Google Scholar 

  55. Kobayashi T, Yokoyama T, Ito S, Kobayashi D, Yamagata A, Okada M, Oofusa K et al (2014) Periodontal and serum protein profiles in patients with rheumatoid arthritis treated with tumor necrosis factor inhibitor adalimumab. J Periodontol 85:1480–1488. doi:10.1902/jop.2014.140194

    Article  PubMed  Google Scholar 

  56. Blaschke S, Rinke K, Maring M, Flad T, Patschan S, Jahn O, Mueller CA et al (2015) Haptoglobin-alpha1, -alpha2, vitamin D-binding protein and apolipoprotein C-III as predictors of etanercept drug response in rheumatoid arthritis. Arthritis Res Ther 17:45. doi:10.1186/s13075-015-0553-1

    Article  PubMed  PubMed Central  Google Scholar 

  57. Mavragani CP, La DT, Stohl W, Crow MK (2010) Association of the response to tumor necrosis factor antagonists with plasma type I interferon activity and interferon-beta/alpha ratios in rheumatoid arthritis patients: a post hoc analysis of a predominantly Hispanic cohort. Arthritis Rheum 62:392–401. doi:10.1002/art.27226

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wampler Muskardin T, Vashisht P, Dorschner JM, Jensen MA, Chrabot BS, Kern M, Curtis JR et al (2016) Increased pretreatment serum IFN-beta/alpha ratio predicts non-response to tumour necrosis factor alpha inhibition in rheumatoid arthritis. Ann Rheum Dis 75:1757–1762. doi:10.1136/annrheumdis-2015-208001

    Article  PubMed  Google Scholar 

  59. Jiang M, Chen T, Feng H, Zhang Y, Li L, Zhao A, Niu X et al (2013) Serum metabolic signatures of four types of human arthritis. J Proteome Res 12:3769–3779. doi:10.1021/pr400415a

    Article  CAS  PubMed  Google Scholar 

  60. Young SP, Kapoor SR, Viant MR, Byrne JJ, Filer A, Buckley CD, Kitas GD et al (2013) The impact of inflammation on metabolomic profiles in patients with arthritis. Arthritis Rheum 65:2015–2023. doi:10.1002/art.38021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Wang Z, Chen Z, Yang S, Wang Y, Yu L, Zhang B, Rao Z et al (2012) (1)H NMR-based metabolomic analysis for identifying serum biomarkers to evaluate methotrexate treatment in patients with early rheumatoid arthritis. Exp Ther Med 4:165–171. doi:10.3892/etm.2012.567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Kapoor SR, Filer A, Fitzpatrick MA, Fisher BA, Taylor PC, Buckley CD, McInnes IB et al (2013) Metabolic profiling predicts response to anti-tumor necrosis factor alpha therapy in patients with rheumatoid arthritis. Arthritis Rheum 65:1448–1456. doi:10.1002/art.37921

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Priori R, Casadei L, Valerio M, Scrivo R, Valesini G, Manetti C (2015) (1)H-NMR-based metabolomic study for identifying serum profiles associated with the response to etanercept in patients with rheumatoid arthritis. PLoS One 10:e0138537. doi:10.1371/journal.pone.0138537

    Article  PubMed  PubMed Central  Google Scholar 

  64. Zabek A, Swierkot J, Malak A, Zawadzka I, Deja S, Bogunia-Kubik K, Mlynarz P (2016) Application of (1)H NMR-based serum metabolomic studies for monitoring female patients with rheumatoid arthritis. J Pharm Biomed Anal 117:544–550. doi:10.1016/j.jpba.2015.10.007

    Article  CAS  PubMed  Google Scholar 

  65. Tatar Z, Migne C, Petera M, Gaudin P, Lequerre T, Marotte H, Tebib J et al (2016) Variations in the metabolome in response to disease activity of rheumatoid arthritis. BMC Musculoskelet Disord 17:353. doi:10.1186/s12891-016-1214-5

    Article  PubMed  PubMed Central  Google Scholar 

  66. Chen YC, Allen SG, Ingram PN, Buckanovich R, Merajver SD, Yoon E (2015) Single-cell migration chip for chemotaxis-based microfluidic selection of heterogeneous cell populations. Sci Rep 5:9980. doi:10.1038/srep09980

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Schmidt F, Efferth T (2016) Tumor heterogeneity, single-cell sequencing, and drug resistance. Pharmaceuticals 9. doi:10.3390/ph9020033

  68. Phetsouphanh C, Zaunders JJ, Kelleher AD (2015) Detecting antigen-specific T cell responses: from bulk populations to single cells. Int J Mol Sci 16:18878–18893. doi:10.3390/ijms160818878

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Mannello F (2012) Single-cell analysis: from innovative omics to target therapy. J Pharmacogenom Pharmacoproteomics 3:3

    Google Scholar 

  70. Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, Schwartz S et al (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498:236–240. doi:10.1038/nature12172

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Cesano A, Spellmeyer D (2014) Immune system functional pathway analysis using single cell network profiling (SCNP): a novel tool in cancer immunotherapy. Methods Mol Biol 1102:583–599. doi:10.1007/978-1-62703-727-3_31

    Article  CAS  PubMed  Google Scholar 

  72. Hawtin RE, Cesano A (2015) Immune monitoring technology primer: single cell network profiling (SCNP). J Immunother Cancer 3. doi:10.1186/s40425-015-0075-z

  73. Pineda AL, Ogoe HA, Balasubramanian JB, Rangel Escareno C, Visweswaran S, Herman JG, Gopalakrishnan V (2016) On predicting lung cancer subtypes using ‘omic’ data from tumor and tumor-adjacent histologically-normal tissue. BMC Cancer 16:184. doi:10.1186/s12885-016-2223-3

    Article  PubMed  PubMed Central  Google Scholar 

  74. Hadi NI, Jamal Q (2015) “OMIC” tumor markers for breast cancer: a review. Pak J Med Sci 31:1256–1262. doi:10.12669/pjms.315.7627

    Article  PubMed  PubMed Central  Google Scholar 

  75. Bao S, Wu Q, McLendon RE, Hao Y, Shi Q, Hjelmeland AB, Dewhirst MW et al (2006) Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 444:756–760. doi:10.1038/nature05236

    Article  CAS  PubMed  Google Scholar 

  76. Chen DY, Chen YM, Chen HH, Hsieh CW, Lin CC, Lan JL (2011) Increasing levels of circulating Th17 cells and interleukin-17 in rheumatoid arthritis patients with an inadequate response to anti-TNF-alpha therapy. Arthritis Res Ther 13:R126. doi:10.1186/ar3431

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Ceeraz S, Hall C, Choy EH, Spencer J, Corrigall VM (2013) Defective CD8+CD28+ regulatory T cell suppressor function in rheumatoid arthritis is restored by tumour necrosis factor inhibitor therapy. Clin Exp Immunol 174:18–26. doi:10.1111/cei.12161

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Tanaka Y, Matsumoto I, Iwanami K, Inoue A, Umeda N, Tanaka Y, Sugihara M et al (2012) Six-transmembrane epithelial antigen of prostate 4 (STEAP4) is expressed on monocytes/neutrophils, and is regulated by TNF antagonist in patients with rheumatoid arthritis. Clin Exp Rheumatol 30:99–102

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Christina Xie (University of Notre Dame, Notre Dame, IN 99354 USA) for English editing support.

Author information

Authors and Affiliations

  1. Department of Rheumatology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China

    Xi Xie, Fen Li, Shu Li, Jing Tian, Jin-wei Chen, Jin-feng Du, Ni Mao & Jian Chen

Authors
  1. Xi Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jing Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jin-wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jin-feng Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ni Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fen Li.

Ethics declarations

Disclosures

None.

Funding

This study was supported by the National Natural Science Foundation of China (81571599) and National Natural Science Foundation of China for Young Scholar (81302567).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, X., Li, F., Li, S. et al. Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis. Clin Rheumatol 37, 13–23 (2018). https://doi.org/10.1007/s10067-017-3639-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10067-017-3639-0

Keywords

Search

Navigation