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Blood Lymphocyte Subsets for Early Identification of Non-Remission to TNF Inhibitors in Rheumatoid Arthritis.

ABSTRACT: Background: TNF inhibitors (TNFis) are widely used for the treatment of rheumatoid arthritis (RA), although the response rates to this therapy in patients with RA remains heterogeneous and < 50% achieve remission (REM). Objective: To analyze baseline peripheral blood leukocytes profiles in order to search for biomarkers identifying patients who will most likely not achieve REM under TNFi treatment. Methods: A prospective bi-center pilot study including 98 RA patients treated with TNFis and followed-up during 6 months. Patients were classified according to DAS28 as follows: those who achieved REM (DAS28 ? 2.6) and those who did not (DAS28 > 2.6) at 6 months after starting TNFis. These rates were also assessed by simplified disease activity index (SDAI ? 3.3 and SDAI > 3.3, respectively). Peripheral blood immune cells were studied by flow cytometry before treatment initiation. Results: At 6 months, 61 or 80% of patients did not achieve REM by DAS28 or SDAI, respectively. Basal leukocyte profiles differed between REM vs. non-REM patients. Non-REM patients showed lower percentages of total and naïve B cells at baseline than REM subjects. A B lymphocyte/CD4+ lymphocyte ratio (BL/CD4 ratio) <0.2 clearly associated with a higher probability of non-REM status based on DAS28 at 6 months (OR = 9.2, p = 0.006). These data were confirmed when patient response was evaluated by SDAI index. Conclusion: Our results strongly suggest that BL/CD4 ratio could be considered as a useful biomarker for the early identification of non-remitters to TNFi in clinical practice.

SUBMITTER: Rodriguez-Martin E

PROVIDER: S-EPMC7481468 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Blood Lymphocyte Subsets for Early Identification of Non-Remission to TNF Inhibitors in Rheumatoid Arthritis.

Rodríguez-Martín Eulalia E Nieto-Gañán Israel I Hernández-Breijo Borja B Sobrino Cristina C García-Hoz Carlota C Bachiller Javier J Martínez-Feito Ana A Navarro-Compán Victoria V Lapuente-Suanzes Paloma P Bonilla Gema G Pascual-Salcedo Dora D Roy Garbiñe G Jurado Teresa T Nozal Pilar P Vázquez-Díaz Mónica M Balsa Alejandro A Villar Luisa M LM Plasencia-Rodríguez Chamaida C

Frontiers in immunology 20200827

Background: TNF inhibitors (TNFis) are widely used for the treatment of rheumatoid arthritis (RA), although the response rates to this therapy in patients with RA remains heterogeneous and < 50% achieve remission (REM). Objective: To analyze baseline peripheral blood leukocytes profiles in order to search for biomarkers identifying patients who will most likely not achieve REM under TNFi treatment. Methods: A prospective bi-center pilot study including 98 RA patients treated ...[more]

PMID: 32973793

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Project description:BackgroundFaced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease.MethodsWe identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n = 116 = 44 + 15 + 30 + 27) were treated with infliximab, and their response assessed 14-16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression.ResultsThe classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment.ConclusionsWe were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use.

Dataset Information

Blood Lymphocyte Subsets for Early Identification of Non-Remission to TNF Inhibitors in Rheumatoid Arthritis.

Publications

Blood Lymphocyte Subsets for Early Identification of Non-Remission to TNF Inhibitors in Rheumatoid Arthritis.

Similar Datasets

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