Prealbumin, platelet factor 4 and S100A12 combination at baseline predicts good response to TNF alpha inhibitors in rheumatoid arthritis - 21/07/18

Doi : 10.1016/j.jbspin.2018.05.006

Minh Vu Chuong Nguyen ^a,^k, Athan Baillet ^a,^b,^⁎ , Xavier Romand ^a,^b, Candice Trocmé ^c, Anaïs Courtier ^k, Hubert Marotte ^d,^e, Thierry Thomas ^f,^g, Martin Soubrier ^h, Pierre Miossec ⁱ, Jacques Tébib ^j, Laurent Grange ^b, Bertrand Toussaint ^c,ⁱ, Thierry Lequerré ^j, Olivier Vittecoq ^j, Philippe Gaudin ^a,^b
^a EA 7408, University Grenoble Alpes, GREPI, 38400 Saint-Martin-d’Hères, France
^b Rheumatology Department, centre hospitalier universitaire Grenoble Alpes, hôpital Sud Echirolles, 38130 Echirolles, France
^c Pôle biologie, hôpital Michallon, centre hospitalier universitaire Grenoble Alpes, 38700 La Tronche, France
^d Department of Rheumatology, Saint-Etienne University Hospital, 42270 Saint-Etienne, France
^e Inserm 1059, SAINBIOSE University of Saint-Etienne, 42270 Saint-Etienne, France
^f Service de rhumatologie, hôpital G.-Montpied, centre hospitalier universitaire Clermont-Ferrand, 63003 Clermont-Ferrand, France
^g Department of Immunology and Rheumatology, Immunogenomics and Inflammation Research Unit EA 4130, University of Lyon, Edouard Herriot Hospital, 69003 Lyon, France
^h Rheumatology Department, centre hospitalier Lyon-Sud, 69310 Pierre-Bénite, France
ⁱ TIMC-IMAG Laboratory UMR 5525, CNRS, université Grenoble Alpes, 38041 Grenoble, France
^j Inserm CIC/CRB1404, Normandie Univ, UNIROUEN, Inserm U 1234, Rouen University Hospital, Department of Rheumatology, 76031 Rouen cedex, France
^k Sinnovial, 38000 Grenoble, France

^⁎Corresponding author. Rheumatology Department, Hôpital Sud, CHU Grenoble Alpes, 19, avenue de Kimberley, 38130 Echirolles, France.Rheumatology Department, Hôpital Sud, CHU Grenoble Alpes19, avenue de KimberleyEchirolles38130France

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Highlights

•	Prealbumin, PF4 and S100A12 were identified as relevant biomarkers to predict response to a class of bDMARDs such as TNFalpha inhibitors in RA patients.
•	Low levels of prealbumin and S100A12 and high level of PF4 at baseline in RA patients are good predictors for response to TNFalpha inhibitors.
•	Generation of a multivariate model combining prealbumin, platelet factor 4 and S100A12 that accurately predicts response to TNFalpha inhibitors in RA patients.
•	The predictive model showed similar predictive performance to IFX, ADA and ETN taken individually.

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Abstract

Objectives

Tumour necrosis factor-alpha inhibitors (TNFi) are effective treatments for Rheumatoid Arthritis (RA). Responses to treatment are barely predictable. As these treatments are costly and may induce a number of side effects, we aimed at identifying a panel of protein biomarkers that could be used to predict clinical response to TNFi for RA patients.

Methods

Baseline blood levels of C-reactive protein, platelet factor 4, apolipoprotein A1, prealbumin, α1-antitrypsin, haptoglobin, S100A8/A9 and S100A12 proteins in bDMARD naive patients at the time of TNFi treatment initiation were assessed in a multicentric prospective French cohort. Patients fulfilling good EULAR response at 6 months were considered as responders. Logistic regression was used to determine best biomarker set that could predict good clinical response to TNFi.

Results

A combination of biomarkers (prealbumin, platelet factor 4 and S100A12) was identified and could predict response to TNFi in RA with sensitivity of 78%, specificity of 77%, positive predictive values (PPV) of 72%, negative predictive values (NPV) of 82%, positive likelihood ratio (LR+) of 3.35 and negative likelihood ratio (LR−) of 0.28. Lower levels of prealbumin and S100A12 and higher level of platelet factor 4 than the determined cutoff at baseline in RA patients are good predictors for response to TNFi treatment globally as well as to Infliximab, Etanercept and Adalimumab individually.

Conclusion

A multivariate model combining 3 biomarkers (prealbumin, platelet factor 4 and S100A12) accurately predicted response of RA patients to TNFi and has potential in a daily practice personalized treatment.

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Keywords : Biomarkers, Rheumatoid arthritis, TNFα inhibitor, Etanercept, Adalimumab, Infliximab, Prediction, Prealbumin, Platelet factor 4, S100A12