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

Ki Jo Kim, Minseung Kim, Iannis Adamopoulos, Ilias Tagkopoulos

Research output: Contribution to journalArticle

Abstract

Rheumatoid arthritis (RA) is therapeutically challenging due to patient heterogeneity and variability. Herein we describe a novel integration of RA synovial genome-scale transcriptomic profiling of different patient cohorts that can be used 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 build and compared with similar datasets derived from OA patients and healthy controls. Differentially expression genes (DEGs) that were identified in three independent methods were fed into functional network analysis, with subsequent grouping of the samples based on a non-negative matrix factorization method. RA-relevant pathway activation scores and four machine learning classification techniques supported the generation of 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. The most informative pathways in achieving this performance were the NFκB-, FcεRI- TCR-, and TNF signaling pathways. Similarly, the expression of the HMMR, PRPF4B, EVI2A, RAB27A, MALT1, SNX6, and IFIH1 genes contributed in predicting the patient outcome. Construction and analysis of normalized synovial transcriptomic compendia can provide useful insights for understanding RA-related pathway involvement and drug responses for individual patients.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalClinical Immunology
Volume202
DOIs
StatePublished - May 1 2019

Fingerprint

Rheumatoid Arthritis
Gene Expression
Pharmaceutical Preparations
Therapeutics
Genes
Area Under Curve
Genome

Keywords

  • Clustering
  • Drug responsiveness
  • Gene expression
  • Machine learning
  • Rheumatoid arthritis

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology

Cite this

Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients. / Kim, Ki Jo; Kim, Minseung; Adamopoulos, Iannis; Tagkopoulos, Ilias.

In: Clinical Immunology, Vol. 202, 01.05.2019, p. 1-10.

Research output: Contribution to journalArticle

@article{b52b2be3a82648428a0e598cd1617915,
title = "Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients",
abstract = "Rheumatoid arthritis (RA) is therapeutically challenging due to patient heterogeneity and variability. Herein we describe a novel integration of RA synovial genome-scale transcriptomic profiling of different patient cohorts that can be used 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 build and compared with similar datasets derived from OA patients and healthy controls. Differentially expression genes (DEGs) that were identified in three independent methods were fed into functional network analysis, with subsequent grouping of the samples based on a non-negative matrix factorization method. RA-relevant pathway activation scores and four machine learning classification techniques supported the generation of 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. The most informative pathways in achieving this performance were the NFκB-, FcεRI- TCR-, and TNF signaling pathways. Similarly, the expression of the HMMR, PRPF4B, EVI2A, RAB27A, MALT1, SNX6, and IFIH1 genes contributed in predicting the patient outcome. Construction and analysis of normalized synovial transcriptomic compendia can provide useful insights for understanding RA-related pathway involvement and drug responses for individual patients.",
keywords = "Clustering, Drug responsiveness, Gene expression, Machine learning, Rheumatoid arthritis",
author = "Kim, {Ki Jo} and Minseung Kim and Iannis Adamopoulos and Ilias Tagkopoulos",
year = "2019",
month = "5",
day = "1",
doi = "10.1016/j.clim.2019.03.002",
language = "English (US)",
volume = "202",
pages = "1--10",
journal = "Clinical Immunology",
issn = "1521-6616",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients

AU - Kim, Ki Jo

AU - Kim, Minseung

AU - Adamopoulos, Iannis

AU - Tagkopoulos, Ilias

PY - 2019/5/1

Y1 - 2019/5/1

N2 - Rheumatoid arthritis (RA) is therapeutically challenging due to patient heterogeneity and variability. Herein we describe a novel integration of RA synovial genome-scale transcriptomic profiling of different patient cohorts that can be used 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 build and compared with similar datasets derived from OA patients and healthy controls. Differentially expression genes (DEGs) that were identified in three independent methods were fed into functional network analysis, with subsequent grouping of the samples based on a non-negative matrix factorization method. RA-relevant pathway activation scores and four machine learning classification techniques supported the generation of 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. The most informative pathways in achieving this performance were the NFκB-, FcεRI- TCR-, and TNF signaling pathways. Similarly, the expression of the HMMR, PRPF4B, EVI2A, RAB27A, MALT1, SNX6, and IFIH1 genes contributed in predicting the patient outcome. Construction and analysis of normalized synovial transcriptomic compendia can provide useful insights for understanding RA-related pathway involvement and drug responses for individual patients.

AB - Rheumatoid arthritis (RA) is therapeutically challenging due to patient heterogeneity and variability. Herein we describe a novel integration of RA synovial genome-scale transcriptomic profiling of different patient cohorts that can be used 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 build and compared with similar datasets derived from OA patients and healthy controls. Differentially expression genes (DEGs) that were identified in three independent methods were fed into functional network analysis, with subsequent grouping of the samples based on a non-negative matrix factorization method. RA-relevant pathway activation scores and four machine learning classification techniques supported the generation of 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. The most informative pathways in achieving this performance were the NFκB-, FcεRI- TCR-, and TNF signaling pathways. Similarly, the expression of the HMMR, PRPF4B, EVI2A, RAB27A, MALT1, SNX6, and IFIH1 genes contributed in predicting the patient outcome. Construction and analysis of normalized synovial transcriptomic compendia can provide useful insights for understanding RA-related pathway involvement and drug responses for individual patients.

KW - Clustering

KW - Drug responsiveness

KW - Gene expression

KW - Machine learning

KW - Rheumatoid arthritis

UR - http://www.scopus.com/inward/record.url?scp=85062697758&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062697758&partnerID=8YFLogxK

U2 - 10.1016/j.clim.2019.03.002

DO - 10.1016/j.clim.2019.03.002

M3 - Article

C2 - 30831253

AN - SCOPUS:85062697758

VL - 202

SP - 1

EP - 10

JO - Clinical Immunology

JF - Clinical Immunology

SN - 1521-6616

ER -