Stratification of Patients With Sjögren’s Syndrome and Patients With Systemic Lupus Erythematosus According to Two Shared Immune Cell Signatures, With Potential Therapeutic Implications

• Published in: Arthritis & rheumatology (Hoboken, N.J.) N.J.), 2021-09, Vol.73 (9), p.1626-1637
• Main Authors: Martin‐Gutierrez, 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
• Format: Article
• Language: English
• Subjects: Autoimmune diseases; Cell differentiation; Chronic conditions; Clinical trials; Cluster analysis; Clustering; Discriminant analysis; Flow cytometry; Immune system; Learning algorithms; Lupus; Lymphocytes; Lymphocytes T; Machine learning; Patients; Peripheral blood; Regression analysis; Remission; Signatures; Sjogren's syndrome; Systemic lupus erythematosus; Trajectory analysis
• ISSN: 2326-5191; 2326-5205
• DOI: 10.1002/art.41708

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.