Op-brhe190339 860..868

Judith A. James,Joel M. Guthridge, Hua Chen,Rufei Lu,Rebecka L. Bourn,Krista Bean,Melissa E. Munroe, Miles Smith,Eliza Chakravarty, Alan N. Baer, Ghaith Noaiseh, Ann Parke, Karen Boyle, Lynette Keyes-Elstein,Andreea Coca,Tammy Utset, Mark C. Genovese, Virginia Pascual,Paul J. Utz, V. Michael Holers, Kevin D. Deane,Kathy L. Sivils,Teresa Aberle,Daniel J. Wallace,James McNamara,Nathalie Franchimont,E. William St. Clair

semanticscholar(2020)

引用 0|浏览6
暂无评分
摘要
Objective. To address heterogeneity complicating primary SS (pSS) clinical trials, research and care by characterizing and clustering patients by their molecular phenotypes. Methods. pSS patients met American European Consensus Group classification criteria and had at least one systemic manifestation and stimulated salivary flow of 50.1 ml/min. Correlated transcriptional modules were derived from gene expression microarray data from blood (n = 47 with appropriate samples). Patients were clustered based on this molecular information using an unbiased random forest modelling approach. In addition, multiplex, bead-based assays and ELISAs were used to assess 30 serum cytokines, chemokines and soluble receptors. Eleven autoantibodies, including anti-Ro/SSA and anti-La/SSB, were measured by Bio-Rad Bioplex 2200. Results. Transcriptional modules distinguished three clusters of pSS patients. Cluster 1 showed no significant elevation of IFN or inflammation modules. Cluster 2 showed strong IFN and inflammation modular network signatures, as well as high plasma protein levels of IP-10/CXCL10, MIG/CXCL9, BLyS (BAFF) and LIGHT. Cluster 3 samples exhibited moderately elevated IFN modules, but with suppressed inflammatory modules, increased IP-10/CXCL10 and B cell attracting chemokine 1/CXCL13 and trends toward increased MIG/CXCL9, IL-1a, and IL-21. Anti-Ro/SSA and anti-La/SSB were present in all three clusters. Conclusion. Molecular profiles encompassing IFN, inflammation and other signatures can be used to separate patients with pSS into distinct clusters. In the future, such profiles may inform patient selection for clinical trials and guide treatment decisions.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要