OP0119 THE PRE-TREATMENT GUT MICROBIOME PREDICTS EARLY RESPONSE TO RHEUMATOID ARTHRITIS THERAPY. (June 2019)

• Record Type: Journal Article
• Authors: Isaac, Sandrine; Artacho, Alejandro; Nayak, Renuka; Flor, Alejandra; Abramson, Steven; Rosenthal, Pamela; Puchades, Leonor; Patterson, Andrew; Pineda-Lucena, Antonio; Turnbaugh, Peter; Ubeda, Carles; Scher, Jose
• Is Part Of: Annals of the rheumatic diseases. Volume 78(2019)Supplement 2
• Page Start: 133
• Page End: 134
• Publication Date: 2019-06
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2019-eular.8378
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Early treatment initiation in rheumatoid arthritis (RA) is fundamental to avoid chronic joint destruction and disability. Despite remarkable advances in RA therapeutics, oral methotrexate (MTX) remains the anchor drug and mainstay of treatment worldwide (1, 2). However, MTX bioavailability has a wide inter-individual variability and >50% of patients with moderate or severe RA show no or suboptimal improvement in their symptoms in response to MTX (1, 3). The reasons for these disparities in treatment response remain unclear. Prior studies have shown that the biotransformation of MTX is altered in germ-free and microbiome-depleted mice (4), prompting us to hypothesize that inter-individual differences in the human gut microbiome could impact drug bioavailability and thus clinical efficacy. Objectives: To determine differences in the microbiome of drug-naïve, new onset RA (NORA) patients that could predict response to MTX therapy. Methods: We performed 16S rRNA gene and shotgun metagenomic sequencing on the baseline gut microbiomes of 27 drug-naïve, NORA patients. Results: Our analysis revealed significant associations between the abundance of gut bacterial taxa and genes including gene families related with purine and methotrexate metabolism. Machine learning techniques were applied to this metagenomic data, resulting in a robust predictive model based on bacterial gene abundance that accurately predicted response to MTX therapy in an independent group of patients. Finally, MTX available levels remaining after ex vivo incubation with distal gut samples from pre-treatment RA patients significantly correlated with the magnitude of future clinical response, suggesting a direct effect of the gut microbiome on MTX bioavailability and response to therapy. Conclusion: Together, these results provide the first step towards predicting response to oral MTX in NORA patients and support the utility of the gut microbiome as a prognostic tool and perhaps even as a target for manipulation in the treatment of rheumatic and autoimmune disease. References: [1] Detert, J. et al. Ann Rheum Dis72, 844-850, doi:10.1136/annrheumdis-2012-201612 (2013). [2] Favalli, E. G. et al. Autoimmunity reviews13, 1102-1108, doi:10.1016/j.autrev.2014.08.026 (2014). [3] Emery, P. et al. Lancet372, 375-382, doi:10.1016/S0140-6736(08)61000-4 (2008). [4] Valerino, D. M. et al. Biochem Pharmacol21, 821-831 (1972). Disclosure of Interests: Sandrine Isaac: None declared, Alejandro Artacho: None declared, Renuka Nayak: None declared, Alejandra Flor: None declared, Steven Abramson: None declared, Pamela Rosenthal: None declared, Leonor Puchades: None declared, Andrew Patterson: None declared, Antonio Pineda-Lucena: None declared, Peter Turnbaugh Consultant for: P.J.T is on the scientific advisory board for Kaleido, Seres, SNIPRbiome, uBiome, and WholeBiome; there is no direct overlap between the current study and these consulting duties., Carles Ubeda: None declared, Jose Scher Grant/research support from: Pfizer, Novartis, Consultant for: Janssen, UCB, Novartis, Amgen