Two Distinct Immune Cell Signatures Predict the Clinical Outcomes in Patients With Amyopathic Dermatomyositis With Interstitial Lung Disease
Yan Ye, Xueliang Zhang, Teng Li, Jiaqiang Ma, Ran Wang, Chunmei Wu, Runci Wang, Chunde Bao, Shuang Ye, Nan Shen, Qiang Guo, Qiong Fu, Xiaoming Zhang
Abstract
OBJECTIVE: Amyopathic dermatomyositis (ADM) is a heterogeneous and life-threatening autoimmune disease with a high mortality rate. In particular, anti-melanoma differentiation-associated protein 5 antibody-positive patients are at a high risk of developing rapidly progressive interstitial lung disease (RPILD). This study was undertaken to identify immunologic signatures among patients who have ADM with ILD (ADM-ILD) and to discover the biomarkers predicting prognosis. METHODS: The landscape of 42 immune cell phenotypes in the peripheral blood of 82 ADM-ILD patients and 82 age- and sex-matched healthy donors was assessed by multicolor flow cytometry. Patients were stratified using an unsupervised machine learning method (hierarchical clustering analysis) by immune cell subsets. Multiple Wilcoxon's signed rank tests and supervised machine learning methods were performed to identify important immune cell subsets. Kaplan-Meier survival analysis with log rank tests was used to create survival curves. RESULTS: natural killer cell proportions and showed a higher prevalence of RPILD and higher mortality. In contrast, the other subgroup, cluster 2 (the nonactivated T cell-dominant cluster), displayed favorable clinical outcomes with high survival rates. Our data also revealed that immunophenotype was an independent risk factor associated with 1-year survival. CONCLUSION: Peripheral immunologic features may have the potential to stratify patients with ADM-ILD according to different disease severity and clinical outcomes, which may have implications for outcome prediction, pathogenesis study, and therapy selection.