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, 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
Institut de Recherches Internationales Servier, Departments of Translational Medicine and Immuno-Inflammatory Diseases Research and Development, Suresnes, France.
LBAI, UMR1227, Univ Brest, Inserm, Brest, France.
Nat Commun. 2021 Jun 10;12(1):3523. doi: 10.1038/s41467-021-23472-7.
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.
原发性干燥综合征目前尚无获批的治疗方法，该疾病主要影响成年女性。开发有效疗法存在困难，部分原因在于该疾病临床表现和病理生理学的异质性。在患者亚组中找到共同的分子特征可以增进我们对疾病病因的理解，并促进靶向治疗药物的开发。在此，我们在一个横断面队列研究中报告了一种针对干燥综合征患者的分子分类方案，该方案基于对来自欧洲一个超过300名患者的队列以及数量相近的年龄和性别匹配的健康志愿者的全血样本进行的多组学分析。利用转录组学、基因组学、表观遗传学、细胞因子表达和流式细胞术数据，并结合临床参数，我们识别出四组免疫失调模式各异的患者。我们识别出的生物标志物可被机器学习分类器用于将未来的患者分类到亚组中，从而在临床试验中重新评估对治疗的反应。
