Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/179151
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 E.
Laigle, Laurence
Pers, Jacques Olivier
Keywords: Malalties autoimmunitàries
Assaigs clinics
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 (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)

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