Transcriptomic network analysis reveals key drivers of response to anti-TNF biologics in patients with rheumatoid arthritis
© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissionsoup.com..
OBJECTIVE: Anti-TNF biologics have been widely used to ameliorate disease activity in patients with rheumatoid arthritis (RA). However, a large fraction of patients show a poor response to these agents. Moreover, no clinically applicable predictive biomarkers have been established. This study aimed to identify response-associated biomarkers using longitudinal transcriptomic data in two independent RA cohorts.
METHODS: RNA sequencing data from peripheral blood cell samples of Korean and Caucasian RA cohorts before and after initial treatment with anti-TNF biologics were analyzed to assess treatment-induced expression changes that differed between highly reliable excellent and null responders. Weighted correlation network, immune cell composition, and key driver analyses were performed to understand response-associated transcriptomic networks and cell types and their correlation with disease activity indices.
RESULTS: In total, 305 response-associated genes showed significantly different treatment-induced expression changes between excellent and null responders. Co-expression network construction and subsequent key driver analysis revealed that 41 response-associated genes played a crucial role as key drivers of transcriptomic alteration in four response-associated networks involved in various immune pathways: type I interferon signalling, myeloid leucocyte activation, B cell activation, and NK cell/lymphocyte-mediated cytotoxicity. Transcriptomic response scores that we developed to estimate the individual-level degree of expression changes in the response-associated key driver genes were significantly correlated with the changes in clinical indices in independent patients with moderate or ambiguous response outcomes.
CONCLUSIONS: This study provides response-specific treatment-induced transcriptomic signatures by comparing the transcriptomic landscape between patients with excellent and null responses to anti-TNF drugs at both gene and network levels.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
Rheumatology (Oxford, England) - (2023) vom: 12. Aug. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yu, Chae-Yeon [VerfasserIn] |
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| Links: |
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| Themen: |
Bioinformatics |
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| Anmerkungen: |
Date Revised 12.08.2023 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1093/rheumatology/kead403 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM360717926 |
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| 100 | 1 | |a Yu, Chae-Yeon |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Transcriptomic network analysis reveals key drivers of response to anti-TNF biologics in patients with rheumatoid arthritis |
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| 520 | |a © The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissionsoup.com. | ||
| 520 | |a OBJECTIVE: Anti-TNF biologics have been widely used to ameliorate disease activity in patients with rheumatoid arthritis (RA). However, a large fraction of patients show a poor response to these agents. Moreover, no clinically applicable predictive biomarkers have been established. This study aimed to identify response-associated biomarkers using longitudinal transcriptomic data in two independent RA cohorts | ||
| 520 | |a METHODS: RNA sequencing data from peripheral blood cell samples of Korean and Caucasian RA cohorts before and after initial treatment with anti-TNF biologics were analyzed to assess treatment-induced expression changes that differed between highly reliable excellent and null responders. Weighted correlation network, immune cell composition, and key driver analyses were performed to understand response-associated transcriptomic networks and cell types and their correlation with disease activity indices | ||
| 520 | |a RESULTS: In total, 305 response-associated genes showed significantly different treatment-induced expression changes between excellent and null responders. Co-expression network construction and subsequent key driver analysis revealed that 41 response-associated genes played a crucial role as key drivers of transcriptomic alteration in four response-associated networks involved in various immune pathways: type I interferon signalling, myeloid leucocyte activation, B cell activation, and NK cell/lymphocyte-mediated cytotoxicity. Transcriptomic response scores that we developed to estimate the individual-level degree of expression changes in the response-associated key driver genes were significantly correlated with the changes in clinical indices in independent patients with moderate or ambiguous response outcomes | ||
| 520 | |a CONCLUSIONS: This study provides response-specific treatment-induced transcriptomic signatures by comparing the transcriptomic landscape between patients with excellent and null responses to anti-TNF drugs at both gene and network levels | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Rheumatoid arthritis | |
| 650 | 4 | |a bioinformatics | |
| 650 | 4 | |a biological therapy | |
| 650 | 4 | |a statistics | |
| 650 | 4 | |a transcriptome | |
| 700 | 1 | |a Lee, Hye-Soon |e verfasserin |4 aut | |
| 700 | 1 | |a Joo, Young Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Cho, Soo-Kyung |e verfasserin |4 aut | |
| 700 | 1 | |a Choi, Chan-Bum |e verfasserin |4 aut | |
| 700 | 1 | |a Sung, Yoon-Kyoung |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Tae-Hwan |e verfasserin |4 aut | |
| 700 | 1 | |a Jun, Jae-Bum |e verfasserin |4 aut | |
| 700 | 1 | |a Yoo, Dae Hyun |e verfasserin |4 aut | |
| 700 | 1 | |a Bae, Sang-Cheol |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Kwangwoo |e verfasserin |4 aut | |
| 700 | 1 | |a Bang, So-Young |e verfasserin |4 aut | |
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