Whole‐Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib
Objective Patients with rheumatoid arthritis (RA) often fail to respond to therapies, including JAK inhibitors (JAKi), and treatment allocation is made via a trial‐and‐error strategy. A comprehensive analysis of responses to JAKi, including tofacitinib, by RNA sequencing (RNAseq) would allow the dis...
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| Format: | Article |
| Language: | English |
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Wiley
2025-07-01
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| Series: | ACR Open Rheumatology |
| Online Access: | https://doi.org/10.1002/acr2.11761 |
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| author | Chiara Bellocchi Ennio Giulio Favalli Gabriella Maioli Elena Agape Marzia Rossato Matteo Paini Adriana Severino Barbara Vigone Martina Biggioggero Elena Trombetta Roberto Caporali Lorenzo Beretta |
| author_facet | Chiara Bellocchi Ennio Giulio Favalli Gabriella Maioli Elena Agape Marzia Rossato Matteo Paini Adriana Severino Barbara Vigone Martina Biggioggero Elena Trombetta Roberto Caporali Lorenzo Beretta |
| author_sort | Chiara Bellocchi |
| collection | DOAJ |
| description | Objective Patients with rheumatoid arthritis (RA) often fail to respond to therapies, including JAK inhibitors (JAKi), and treatment allocation is made via a trial‐and‐error strategy. A comprehensive analysis of responses to JAKi, including tofacitinib, by RNA sequencing (RNAseq) would allow the discovery of transcriptomic markers with a two‐fold meaning: (1) an improved knowledge about the mechanisms of response to treatment (inference modeling) and (2) the definition of features that may be useful in treatment optimization and assignment (predictive modeling). Methods Thirty‐three patients with active RA were treated with a tofacitinib dose of 5 mg twice a day for 24 weeks and evaluated for EULAR Disease Activity Score in 28 joints using the C‐reactive protein level response. Whole‐blood RNA was collected before and after treatment to perform RNAseq transcriptome analysis. Linear models were used to determine differentially expressed genes (DEGs) (1) at baseline according to clinical responses and (2) in the pre‐post comparison after tofacitinib treatment and in relation to EULAR responses. The capability of DEGs to predict a successful treatment was tested via machine learning modeling after extensive internal validation. Results Of 26 patients who completed the study (per‐protocol analysis), 15 (57.7%) achieved good responses, and 7 (26.9%) and 4 (15.3%) had moderate and no responses, respectively. Overall, 273 baseline genes were significantly associated with the attainment of good responses, contributing to several pathways linked to the immune system or RA pathogenesis (eg, citrullination processes and the negative regulation of natural killer function). The expression of several molecules was reverted by tofacitinib when good responses were reached, including AKT3, GK5, KLF12, FCRL3, BIRC3, TSPOAP1, and P2RY10. Finally, we isolated 14 markers that singularly were capable of predicting the attainment of good responses, including, NKG2D, CD226, CLEC2D, and CD52. Conclusion Whole‐blood transcriptome analysis of patients with RA treated with tofacitinib identified genes whose expression may be relevant in prognostication and understanding the mechanisms of responses to therapy. |
| format | Article |
| id | doaj-art-b70cba6ca6aa43ac82d8537949f3c7a4 |
| institution | DOAJ |
| issn | 2578-5745 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | ACR Open Rheumatology |
| spelling | doaj-art-b70cba6ca6aa43ac82d8537949f3c7a42025-08-20T02:46:13ZengWileyACR Open Rheumatology2578-57452025-07-0177n/an/a10.1002/acr2.11761Whole‐Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With TofacitinibChiara Bellocchi0Ennio Giulio Favalli1Gabriella Maioli2Elena Agape3Marzia Rossato4Matteo Paini5Adriana Severino6Barbara Vigone7Martina Biggioggero8Elena Trombetta9Roberto Caporali10Lorenzo Beretta11Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico and University of Milan Milan ItalyUniversity of Milan and ASST PiniCTO ‐ Presidio Gaetano Pini Milan ItalyUniversity of Milan and ASST PiniCTO ‐ Presidio Gaetano Pini Milan ItalyUniversity of Milan Milan ItalyUniversity of Verona Verona ItalyUniversity of Verona Verona ItalyFondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Milan ItalyFondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Milan ItalyASST PiniCTO ‐ Presidio Gaetano Pini Milan ItalyFondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Milan ItalyUniversity of Milan and ASST PiniCTO ‐ Presidio Gaetano Pini Milan ItalyFondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Milan ItalyObjective Patients with rheumatoid arthritis (RA) often fail to respond to therapies, including JAK inhibitors (JAKi), and treatment allocation is made via a trial‐and‐error strategy. A comprehensive analysis of responses to JAKi, including tofacitinib, by RNA sequencing (RNAseq) would allow the discovery of transcriptomic markers with a two‐fold meaning: (1) an improved knowledge about the mechanisms of response to treatment (inference modeling) and (2) the definition of features that may be useful in treatment optimization and assignment (predictive modeling). Methods Thirty‐three patients with active RA were treated with a tofacitinib dose of 5 mg twice a day for 24 weeks and evaluated for EULAR Disease Activity Score in 28 joints using the C‐reactive protein level response. Whole‐blood RNA was collected before and after treatment to perform RNAseq transcriptome analysis. Linear models were used to determine differentially expressed genes (DEGs) (1) at baseline according to clinical responses and (2) in the pre‐post comparison after tofacitinib treatment and in relation to EULAR responses. The capability of DEGs to predict a successful treatment was tested via machine learning modeling after extensive internal validation. Results Of 26 patients who completed the study (per‐protocol analysis), 15 (57.7%) achieved good responses, and 7 (26.9%) and 4 (15.3%) had moderate and no responses, respectively. Overall, 273 baseline genes were significantly associated with the attainment of good responses, contributing to several pathways linked to the immune system or RA pathogenesis (eg, citrullination processes and the negative regulation of natural killer function). The expression of several molecules was reverted by tofacitinib when good responses were reached, including AKT3, GK5, KLF12, FCRL3, BIRC3, TSPOAP1, and P2RY10. Finally, we isolated 14 markers that singularly were capable of predicting the attainment of good responses, including, NKG2D, CD226, CLEC2D, and CD52. Conclusion Whole‐blood transcriptome analysis of patients with RA treated with tofacitinib identified genes whose expression may be relevant in prognostication and understanding the mechanisms of responses to therapy.https://doi.org/10.1002/acr2.11761 |
| spellingShingle | Chiara Bellocchi Ennio Giulio Favalli Gabriella Maioli Elena Agape Marzia Rossato Matteo Paini Adriana Severino Barbara Vigone Martina Biggioggero Elena Trombetta Roberto Caporali Lorenzo Beretta Whole‐Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib ACR Open Rheumatology |
| title | Whole‐Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib |
| title_full | Whole‐Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib |
| title_fullStr | Whole‐Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib |
| title_full_unstemmed | Whole‐Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib |
| title_short | Whole‐Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib |
| title_sort | whole blood rna sequencing profiling of patients with rheumatoid arthritis treated with tofacitinib |
| url | https://doi.org/10.1002/acr2.11761 |
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