Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial.
Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial.
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DOI：
10.1038/s41591-022-01789-0
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发表时间：
2022-06
期刊：
影响因子：
82.9
通讯作者：
Pitzalis, Costantino
中科院分区：
文献类型：
--
作者：
Rivellese, Felice;Surace, Anna E. A.;Goldmann, Katriona;Sciacca, Elisabetta;cubuk, 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
类风湿性关节炎（RA）患者接受高度靶向的生物治疗，而不需要事先了解病变组织中的靶表达水平。大约40%的患者对单个生物疗法无反应，5-20%的患者对所有生物疗法都难治。在一项基于活组织检查、精准医学的RA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）的反应低于对托珠单抗（抗il6r单克隆抗体）的反应，尽管反应/无反应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，以及对所有药物都难治性患者的基质/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珠单抗的不同作用，这与不同的反应/无反应机制有关。通过十乘十的嵌套交叉验证，我们开发了机器学习算法来预测对利妥昔单抗（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）。该研究支持这样一种观点，即病变组织中由不同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，并为难治性患者的新药开发提供信息。4期R4RA试验的生物标志物分析确定了预处理滑膜活检特征与利妥昔单抗或托珠单抗的反应选择性相关，并导致模型的发展，可能预测类风湿关节炎患者的治疗益处
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. Biomarker analysis of the phase 4 R4RA trial identifies pretreatment synovial biopsy features selectively associated with response to rituximab or tocilizumab, and leads to the development of models that might predict treatment benefit in patients with rheumatoid arthritis
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