英国伦敦玛丽女王大学Costantino Pitzalis、Myles J. Lewis等研究人员合作完成利妥昔单抗与托西单抗治疗类风湿性关节炎的临床试验。2022年5月19日，《自然—医学》杂志在线发表了这一最新研究成果。

据研究人员介绍，类风湿性关节炎（RA患者在没有事先了解病变组织中靶点表达水平的情况下，接受高度针对性的生物疗法。大约40%的患者对单个生物疗法没有反应，5-20%的患者对所有的生物疗法都是难治的。在一项基于活检的、精准医疗的RA随机临床试验（R4RA；n=164）中，低/无滑膜B细胞分子特征的患者对利妥昔单抗（抗CD20单克隆抗体）的反应比对托西单抗（抗IL6R单克隆抗体）的反应低，尽管反应/无反应的确切机制仍有待确定。

研究人员对R4RA滑膜活检的深入组织学/分子分析确定了与利妥昔单抗和托西单抗反应相关的体液免疫反应基因特征，以及对所有药物难治的患者的基质/成纤维细胞特征。治疗后滑膜基因表达和细胞浸润的变化突出了利妥昔单抗和托西单抗与不同的反应/非反应机制有关的不同影响。利用十倍嵌套交叉验证，研究人员开发了预测利妥昔单抗（曲线下面积（AUC）=0.74）、托西单抗（AUC=0.68）和值得注意的多药耐药（AUC=0.69）反应的机器学习算法。

这项研究支持这样的观点，即由患病组织中不同的分子病理途径驱动的疾病内型决定了不同的临床和治疗反应表型。它还强调了将分子病理学特征纳入临床算法的重要性，以优化现有药物的未来使用，并为难治性患者的新药开发提供信息。

附：英文原文

Title: Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial

Author: Rivellese, Felice, Surace, Anna E. A., Goldmann, Katriona, Sciacca, Elisabetta, ubuk, Cankut, Giorli, Giovanni, John, Christopher R., Nerviani, Alessandra, Fossati-Jimack, Liliane, Thorborn, Georgina, Ahmed, Manzoor, Prediletto, Edoardo, Church, Sarah E., Hudson, Briana M., Warren, Sarah E., McKeigue, Paul M., Humby, Frances, Bombardieri, Michele, Barnes, Michael R., Lewis, Myles J., Pitzalis, Costantino

Issue&Volume: 2022-05-19

Abstract: Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not respond to individual biologic therapies and 5–20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n=164), patients with low/absent synovial Bcell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC)=0.74), tocilizumab (AUC=0.68) and, notably, multidrug resistance (AUC=0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment–response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients.

DOI: 10.1038/s41591-022-01789-0

Source: https://www.nature.com/articles/s41591-022-01789-0