University College London, London, UK.
University College London and University College London Hospitals, London, UK.
Arthritis Rheumatol. 2021 Sep;73(9):1626-1637. doi: 10.1002/art.41708. Epub 2021 Aug 6.
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.
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.
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.
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.
原发性干燥综合征（SS）和系统性红斑狼疮（SLE）的临床和实验室特征相似，这导致人们尝试用类似的生物疗法治疗原发性 SS 或 SLE 患者。然而，许多临床试验的结果令人失望，没有生物疗法被批准用于原发性 SS，而只有少数生物制剂可用于治疗对其他治疗仍有抗药性的 SLE 患者。本研究旨在通过基于免疫细胞表型的分层方法，确定原发性 SS 和 SLE 患者的独特免疫特征，以改善治疗选择。
采用流式细胞术对 29 种免疫细胞亚群进行免疫表型分析，纳入外周血样本，分别来自低疾病活动度或处于临床缓解期的原发性 SS 患者（n=45）、SLE 患者（n=29）、与 SLE 相关的继发性 SS 患者（SLE/SS，n=14），以及性别匹配的健康对照者（n=31）。采用有监督机器学习（平衡随机森林、稀疏偏最小二乘判别分析）、逻辑回归和多重 t 检验进行数据分析。采用 K-means 聚类和临床轨迹分析对患者进行分层。
尽管原发性 SS 和 SLE 患者的临床表现和预后不同，但具有相似的免疫结构。通过 K-means 聚类分析对原发性 SS、SLE 和 SLE/SS 患者队列进行分层，发现 2 个表型，具有跨越不同诊断的独特免疫细胞特征。逻辑回归和机器学习模型鉴定出 8 种 T 细胞亚群的特征性签名，可准确区分这 2 个表型（曲线下面积 0.9979）。在临床轨迹分析中，从基线到 5 年，2 个表型的损伤评分和疾病活动水平的变化不同。
这些发现确定了一个免疫细胞工具包，可用于高度准确地区分原发性 SS 和 SLE 患者的免疫特征，作为实现靶向治疗方法的一种方式。
