A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome.

There is currently no approved treatment for primary Sjögren’s syndrome, a disease thatprimarily 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 under-standing 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 syn-drome patients based on the multi-omic profiling of whole blood samples from a Europeancohort of over 300 patients, and a similar number of age and gender-matched healthyvolunteers. Using transcriptomic, genomic, epigenetic, cytokine expression andflow cyto-metry data, combined with clinical parameters, we identify four groups of patients withdistinct patterns of immune dysregulation. The biomarkers we identify can be used bymachine learning classifiers to sort future patients into subgroups, allowing the re-evaluationof response to treatments in clinical trials.The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under the Grant Agreement Number 115565 (PRE- CISESADS project), resources of which are composed of financial contribution from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA compa- nies’in-kind contribution. LBAI was supported by the Agence Nationale de la Recherche under the “Investissement d’Avenir”program with the Reference ANR-11-LABX-0016- 001 (Labex IGO) and the Région Bretagne. The authors would like to particularly express their gratitude to the patients, nurses, technicians and many others who helped directly or indirectly in the consecution of this study. They are grateful to the Institut Français de Bioinformatique (ANR-11-INBS-0013), the Roscoff Bioinformatics platform ABiMS (http://abims.sb-roscoff.fr) for providing computing and storage resources and the Hypérion platform at LBAI (Brest, France) for flow cytometry facilities. Finally, this work is now supported by ELIXIR Luxembourg via its data hosting servic