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Title: | A new molecular classification to drive precision treatment strategies in primary Sjögren’s syndrome |
Author: | Soret, Perrine Le Dantec, Christelle Desvaux, Emiko Foulquier, Nathan Chassagnol, Bastien Hubert, Sandra Jamin, Christophe Barturen, Guillermo Desachy, Guillaume Devauchelle Pensec, Valérie Boudjeniba, Cheïma Cornec, Divi Saraux, Alain Jousse Joulin, Sandrine Barbarroja, Nuria Rodríguez Pintó, Ignasi De Langhe, Ellen Beretta, Lorenzo Chizzolini, Carlo Kovács, László Witte, Torsten PRECISESADS Clinical Consortium PRECISESADS Flow Cytometry Consortium Bettacchioli, Eléonore Buttgereit, Anne Makowska, Zuzanna Lesche, Ralf Borghi, Maria Orietta Martin, Javier Courtade Gaiani, Sophie Xuereb, Laura Guedj, Mickaël Moingeon, Philippe Alarcón Riquelme, Marta Laigle, Laurence Pers, Jacques Olivier |
Keywords: | Malalties autoimmunitàries Assaigs clínics Marcadors bioquímics Epigenètica Autoimmune diseases Clinical trials Biochemical markers Epigenetics |
Issue Date: | 10-Jun-2021 |
Publisher: | Springer Science and Business Media LLC |
Abstract: | There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials. |
Note: | Reproducció del document publicat a: https://doi.org/10.1038/s41467-021-23472-7 |
It is part of: | Nature Communications, 2021, vol. 12, num. 3523 |
URI: | http://hdl.handle.net/2445/179151 |
Related resource: | https://doi.org/10.1038/s41467-021-23472-7 |
ISSN: | 2041-1723 |
Appears in Collections: | Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer) Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) |
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File | Description | Size | Format | |
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s41467-021-23472-7.pdf | 3.41 MB | Adobe PDF | View/Open |
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