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 B cell 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.

类风湿性关节炎 (RA) 患者在事先不了解患病组织中靶标表达水平的情况下接受高度针对性的生物治疗。大约 40% 的患者对个别生物疗法没有反应，5-20% 的患者对所有疗法均无效。在一项基于活检的 RA 精准医学随机临床试验（R4RA； n = 164）中，滑膜 B 细胞分子特征低/缺失的患者对利妥昔单抗（抗 CD20 单克隆抗体）的反应低于托珠单抗（抗 IL6R 单克隆抗体），但应答/无应答的确切机制仍有待确定。在这里，对 R4RA 滑膜活检进行深入的组织学/分子分析，确定了与利妥昔单抗和托珠单抗反应相关的体液免疫反应基因特征，以及对所有药物均无效的患者的基质/成纤维细胞特征。治疗后滑膜基因表达和细胞浸润的变化凸显了利妥昔单抗和托珠单抗与不同反应/无反应机制相关的不同作用。使用十乘十倍嵌套交叉验证，我们开发了机器学习算法，可预测对利妥昔单抗（曲线下面积 (AUC) = 0.74）、托珠单抗 (AUC = 0.68) 的反应，尤其是多药耐药性 (AUC = 0.69) 。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学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整合到临床算法中的重要性，以优化现有药物的未来使用，并为难治性患者开发新药提供信息。