Lewis Myles J, Çubuk Cankut, Surace Anna E A, Sciacca Elisabetta, Lau Rachel, Goldmann Katriona, Giorli Giovanni, Fossati-Jimack Liliane, Nerviani Alessandra, Rivellese Felice, Pitzalis Costantino
Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Barts Health NHS Trust and National Institute for Health and Care Research (NIHR) Barts Biomedical Research Centre (BRC), London, UK.
Nat Commun. 2025 Jul 2;16(1):5374. doi: 10.1038/s41467-025-60987-9.
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.
约40%的类风湿性关节炎患者对单一生物疗法无反应，且缺乏预测治疗反应的生物标志物。在此，我们分析了基于活检的精准医学STRAP试验（n = 208）中治疗前滑膜组织的RNA测序（RNA-Seq），以确定对随机疗法的基因反应特征：依那西普（肿瘤坏死因子抑制剂）、托珠单抗（白细胞介素-6受体抑制剂）和利妥昔单抗（抗CD20 B细胞清除抗体）。应用于RNA-Seq的机器学习模型预测了在16周主要终点时对依那西普、托珠单抗和利妥昔单抗的临床反应，通过重复嵌套交叉验证确定，受试者工作特征曲线下面积（AUC）值分别为0.763、0.748和0.754（n = 67 - 72）。托珠单抗和利妥昔单抗的预测模型在独立队列（R4RA）中得到验证：AUC分别为0.713和0.786（n = 65 - 68）。将预测特征转换后用于定制的滑膜特异性524基因nCounter检测板，并在STRAP患者的滑膜活检RNA上重新测试，证明对治疗反应的预测准确（AUC为0.82 - 0.87）。将转换后的模型整合为一个统一的临床决策算法，该算法有可能通过辅助生物疗法的选择来改变未来的临床实践。
