Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients.

Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients.
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DOI:
10.1016/j.clim.2019.03.002
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发表时间:
2019-05
期刊:
Clinical immunology (Orlando, Fla.)
影响因子:
--
通讯作者:
Tagkopoulos I
Tagkopoulos I
中科院分区:
其他
文献类型:
--
作者:
Kim KJ;Kim M;Adamopoulos IE;Tagkopoulos I

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We describe a novel integration method for RA synovial transcriptional profiling to provide predictive insights on drug responses. A normalized compendium consisting of 256 RA synovial samples that cover an intersection of 11,769 genes from 11 datasets was compared with similar datasets derived from OA patients and healthy controls. RA-relevant pathway activation scores and four machine learning classification techniques led to a predictive model of patient treatment response. We identified 876 up-regulated DEGs including 24 known genetic risk factors and 8 drug targets. DEG-based subgrouping revealed 3 distinct RA patient clusters with distinct activity signatures for RA-relevant pathways. In the case of infliximab, we constructed a classifier of drug response that was highly accurate with an AUC/AUPR of 0.92/0.86. Our work argues that the construction and analysis of normalized synovial transcriptomic compendia can provide useful insights for understanding RA-related pathway involvement and drug responses for individual patients.
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