{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lewis MJ"],"funding":["Medical Research Council","National Institute for Health Research (NIHR)","DH | NIHR | Efficacy and Mechanism Evaluation Programme (NIHR Efficacy and Mechanism Evaluation Programme)"],"pagination":["5374"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12223067"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(1)"],"pubmed_abstract":["Approximately 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, while biomarkers predictive of treatment response are lacking. Here we analyse RNA-sequencing (RNA-Seq) of pre-treatment synovial tissue from the biopsy-based, precision-medicine STRAP trial (n = 208), to identify gene response signatures to the randomised therapies: etanercept (TNF-inhibitor), tocilizumab (interleukin-6 receptor inhibitor) and rituximab (anti-CD20 B-cell depleting antibody). Machine learning models applied to RNA-Seq predict clinical response to etanercept, tocilizumab and rituximab at the 16-week primary endpoint with area under receiver operating characteristic curve (AUC) values of 0.763, 0.748 and 0.754 respectively (n = 67-72) as determined by repeated nested cros"],"journal":["Nature communications"],"pubmed_title":["Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis."],"pmcid":["PMC12223067"],"funding_grant_id":["Barts Biomedical Research Centre","131575","MR/V012509/1","MR/K015346/1"],"pubmed_authors":["Fossati-Jimack L","Sciacca E","Carlucci F","Buch MH","Humby F","Giorli G","Maskall D","Fonseca JE","Sainaghi PP","Gremese E","Sasieni P","Marcia S","Chinoy H","Dasgupta B","Stratton R","Thorburn G","Ramirez J","Rivellese F","McKay ND","Pratt AG","Machado AR","Mahto A","Tan G","Cuervo A","van der Heijde D","Canete JD","Buckley CD","Githinji M","Lewis MJ","Gorman C","Bombardieri M","Nerviani A","Rawlings C","Filer A","Ho P","de Bellefon LM","Pereira JP","Celis R","Alivernini S","Landewe R","Durez P","Edwards CJ","Romao V","Kelly S","Taylor PC","Rizvi H","Purkayastha N","Millar N","Barton A","Cubuk C","Holroyd C","Gendi N","Isaacs JD","Galloway J","Choy E","Ehrenstein MR","Jaworska E","Congia M","STRAP collaborative group","Ng N","Marini S","Peel J","McInnes I","Zayat A","Hands-Greenwood R","Lau R","Raizada S","Surace AEA","Jadon D","Mosanya C","Pitzalis C","Goldmann K","Lliso-Ribera G","Perniola S","Warren L","Cauli A","Seth R"],"additional_accession":[]},"is_claimable":false,"name":"Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis.","description":"Approximately 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, while biomarkers predictive of treatment response are lacking. Here we analyse RNA-sequencing (RNA-Seq) of pre-treatment synovial tissue from the biopsy-based, precision-medicine STRAP trial (n = 208), to identify gene response signatures to the randomised therapies: etanercept (TNF-inhibitor), tocilizumab (interleukin-6 receptor inhibitor) and rituximab (anti-CD20 B-cell depleting antibody). Machine learning models applied to RNA-Seq predict clinical response to etanercept, tocilizumab and rituximab at the 16-week primary endpoint with area under receiver operating characteristic curve (AUC) values of 0.763, 0.748 and 0.754 respectively (n = 67-72) as determined by repeated nested cros","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-06-02T08:46:04.551Z","creation":"2026-04-16T03:12:56.156Z"},"accession":"S-EPMC12223067","cross_references":{"pubmed":["40603860"],"doi":["10.1038/s41467-025-60987-9"]}}