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Stratification of Patients With Sjögren’s Syndrome and Patients With Systemic Lupus Erythematosus According to Two Shared Immune Cell Signatures, With Potential Therapeutic Implications
Author(s) -
MartinGutierrez Lucia,
Peng Junjie,
Thompson Nicolyn L.,
Robinson George A.,
Naja Meena,
Peckham Hannah,
Wu WingHan,
J’bari Hajar,
Ahwireng Nyarko,
Waddington Kirsty E.,
Bradford Claire M.,
Varnier Giulia,
Gandhi Akash,
Radmore Rebecca,
Gupta Vivek,
Isenberg David A.,
Jury Elizabeth C.,
Ciurtin Coziana
Publication year - 2021
Publication title -
arthritis and rheumatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.106
H-Index - 314
eISSN - 2326-5205
pISSN - 2326-5191
DOI - 10.1002/art.41708
Subject(s) - medicine , immune system , logistic regression , immunology , disease
Objective Similarities in the clinical and laboratory features of primary Sjögren’s syndrome (SS) and systemic lupus erythematosus (SLE) have led to attempts to treat patients with primary SS or SLE with similar biologic therapeutics. However, the results of many clinical trials are disappointing, and no biologic treatments are licensed for use in primary SS, while only a few biologic agents are available to treat SLE patients whose disease has remained refractory to other treatments. With the aim of improving treatment selections, this study was undertaken to identify distinct immunologic signatures in patients with primary SS and patients with SLE, using a stratification approach based on immune cell endotypes. Methods Immunophentyping of 29 immune cell subsets was performed using flow cytometry in peripheral blood from patients with primary SS (n = 45), patients with SLE (n = 29), and patients with secondary SS associated with SLE (SLE/SS) (n = 14), all of whom were considered to have low disease activity or be in clinical remission, and sex‐matched healthy controls (n = 31). Data were analyzed using supervised machine learning (balanced random forest, sparse partial least squares discriminant analysis), logistic regression, and multiple t ‐tests. Patients were stratified by K‐means clustering and clinical trajectory analysis. Results Patients with primary SS and patients with SLE had a similar immunologic architecture despite having different clinical presentations and prognoses. Stratification of the combined primary SS, SLE, and SLE/SS patient cohorts by K‐means cluster analysis revealed 2 endotypes, characterized by distinct immune cell profiles spanning the diagnoses. A signature of 8 T cell subsets that distinctly differentiated the 2 endotypes with high accuracy (area under the curve 0.9979) was identified in logistic regression and machine learning models. In clinical trajectory analyses, the change in damage scores and disease activity levels from baseline to 5 years differed between the 2 endotypes. Conclusion These findings identify an immune cell toolkit that may be useful for differentiating, with high accuracy, the immunologic profiles of patients with primary SS and patients with SLE as a way to achieve targeted therapeutic approaches.