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<p>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 (<i>n</i> = 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 (<i>n</i> = 67-72) as determined by repeated nested cross-validation. Prediction models for tocilizumab and rituximab are validated in an independent cohort (R4RA): AUC 0.713 and 0.786 respectively (<i>n</i> = 65-68). Predictive signatures are converted for use with a custom synovium-specific 524-gene nCounter panel and retested on synovial biopsy RNA from STRAP patients, demonstrating accurate prediction of treatment response (AUC 0.82-0.87). The converted models are combined into a unified clinical decision algorithm that has the potential to transform future clinical practice by assisting the selection of biologic therapies.</p>

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Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Myles J. Lewis,
  • Cankut Çubuk,
  • Anna E. A. Surace,
  • Elisabetta Sciacca,
  • Rachel Lau,
  • Katriona Goldmann,
  • Giovanni Giorli,
  • Liliane Fossati-Jimack,
  • Alessandra Nerviani,
  • Felice Rivellese,
  • Costantino Pitzalis,
  • Louise Warren,
  • Edyta Jaworska,
  • Michele Bombardieri,
  • Frances Humby,
  • Arthur G. Pratt,
  • Andrew Filer,
  • Nagui Gendi,
  • Alberto Cauli,
  • Ernest Choy,
  • Iain McInnes,
  • Patrick Durez,
  • Christopher J. Edwards,
  • Maya H. Buch,
  • Elisa Gremese,
  • Peter C. Taylor,
  • Nora Ng,
  • Juan D. Cañete,
  • Sabrina Raizada,
  • Neil D. McKay,
  • Deepak Jadon,
  • Pier Paolo Sainaghi,
  • Richard Stratton,
  • Michael R. Ehrenstein,
  • Pauline Ho,
  • Joaquim P. Pereira,
  • Bhaskar Dasgupta,
  • Claire Gorman,
  • Ahmed Zayat,
  • Ana Rita Machado,
  • Andrea Cuervo,
  • Arti Mahto,
  • Charlotte Rawlings,
  • Chijioke Mosanya,
  • Christopher D. Buckley,
  • Chris Holroyd,
  • Deborah Maskall,
  • Francesco Carlucci,
  • Georgina Thorburn,
  • Gina Tan,
  • Gloria Lliso-Ribera,
  • Hasan Rizvi,
  • Joanna Peel,
  • João Eurico Fonseca,
  • John D. Isaacs,
  • Julio Ramírez,
  • Laurent Meric de Bellefon,
  • Mary Githinji,
  • Mattia Congia,
  • Neal Millar,
  • Nirupam Purkayastha,
  • Rakhi Seth,
  • Raquel Celis,
  • Rebecca Hands-Greenwood,
  • Robert Landewé,
  • Simone Perniola,
  • Stefano Alivernini,
  • Stefano Marcia,
  • Stefano Marini,
  • Stephen Kelly,
  • Vasco Romão,
  • James Galloway,
  • Hector Chinoy,
  • Désirée van der Heijde,
  • Peter Sasieni,
  • Anne Barton

摘要

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 cross-validation. Prediction models for tocilizumab and rituximab are validated in an independent cohort (R4RA): AUC 0.713 and 0.786 respectively (n = 65-68). Predictive signatures are converted for use with a custom synovium-specific 524-gene nCounter panel and retested on synovial biopsy RNA from STRAP patients, demonstrating accurate prediction of treatment response (AUC 0.82-0.87). The converted models are combined into a unified clinical decision algorithm that has the potential to transform future clinical practice by assisting the selection of biologic therapies.