在一项横断面研究中，患有系统性红斑狼疮的成年人表现出不同的分子表型。
Adults with systemic lupus exhibit distinct molecular phenotypes in a cross-sectional study.
作者信息
Guthridge Joel M, Lu Rufei, Tran Ly Thi-Hai, Arriens Cristina, Aberle Teresa, Kamp Stan, Munroe Melissa E, Dominguez Nicolas, Gross Timothy, DeJager Wade, Macwana Susan R, Bourn Rebecka L, Apel Stephen, Thanou Aikaterini, Chen Hua, Chakravarty Eliza F, Merrill Joan T, James Judith A
机构信息
Arthritis and Clinical Immunology, Oklahoma Medical Research Foundation, 825 NE 13th Street, Oklahoma City, OK 73104, USA.
Departments of Medicine and Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
出版信息
EClinicalMedicine. 2020 Mar 4;20:100291. doi: 10.1016/j.eclinm.2020.100291. eCollection 2020 Mar.
BACKGROUND
The clinical and pathologic diversity of systemic lupus erythematosus (SLE) hinders diagnosis, management, and treatment development. This study addresses heterogeneity in SLE through comprehensive molecular phenotyping and machine learning clustering.
METHODS
Adult SLE patients ( = 198) provided plasma, serum, and RNA. Disease activity was scored by modified SELENA-SLEDAI. Twenty-nine co-expression module scores were calculated from microarray gene-expression data. Plasma soluble mediators ( = 23) and autoantibodies ( = 13) were assessed by multiplex bead-based assays and ELISAs. Patient clusters were identified by machine learning combining K-means clustering and random forest analysis of co-expression module scores and soluble mediators.
FINDINGS
SLEDAI scores correlated with interferon, plasma cell, and select cell cycle modules, and with circulating IFN-α, IP10, and IL-1α levels. Co-expression modules and soluble mediators differentiated seven clusters of SLE patients with unique molecular phenotypes. Inflammation and interferon modules were elevated in Clusters 1 (moderately) and 4 (strongly), with decreased T cell modules in Cluster 4. Monocyte, neutrophil, plasmablast, B cell, and T cell modules distinguished the remaining clusters. Active clinical features were similar across clusters. Clinical SLEDAI trended highest in Clusters 3 and 4, though Cluster 3 lacked strong interferon and inflammation signatures. Renal activity was more frequent in Cluster 4, and rare in Clusters 2, 5, and 7. Serology findings were lowest in Clusters 2 and 5. Musculoskeletal and mucocutaneous activity were common in all clusters.
INTERPRETATION
Molecular profiles distinguish SLE subsets that are not apparent from clinical information. Prospective longitudinal studies of these profiles may help improve prognostic evaluation, clinical trial design, and precision medicine approaches.
FUNDING
US National Institutes of Health.
背景
系统性红斑狼疮（SLE）的临床和病理多样性阻碍了诊断、管理及治疗进展。本研究通过全面的分子表型分析和机器学习聚类来解决SLE的异质性问题。
方法
成年SLE患者（n = 198）提供了血浆、血清和RNA。采用改良的SELENA-SLEDAI对疾病活动进行评分。从微阵列基因表达数据中计算出29个共表达模块得分。通过基于多重微珠的检测方法和酶联免疫吸附测定法评估血浆可溶性介质（n = 23）和自身抗体（n = 13）。通过结合K均值聚类和共表达模块得分及可溶性介质的随机森林分析的机器学习方法识别患者聚类。
研究结果
SLEDAI评分与干扰素、浆细胞和特定细胞周期模块相关，也与循环中的IFN-α、IP10和IL-1α水平相关。共表达模块和可溶性介质区分出了具有独特分子表型的7个SLE患者聚类。炎症和干扰素模块在聚类1（中度）和聚类4（强烈）中升高，聚类4中的T细胞模块减少。单核细胞、中性粒细胞、浆母细胞、B细胞和T细胞模块区分了其余聚类。各聚类的活跃临床特征相似。临床SLEDAI评分在聚类3和聚类4中最高，尽管聚类3缺乏强烈的干扰素和炎症特征。肾脏活动在聚类4中更常见，在聚类2、5和7中罕见。血清学结果在聚类2和聚类5中最低。肌肉骨骼和皮肤黏膜活动在所有聚类中都很常见。
解读
分子特征区分了从临床信息中不明显的SLE亚组。对这些特征进行前瞻性纵向研究可能有助于改善预后评估、临床试验设计和精准医学方法。
资助
美国国立卫生研究院。
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