背景:重症肌无力(MG)的临床异质性,由针对突触后膜的抗体(Ab)定义的自身免疫性疾病,对患者分层和治疗决策构成了挑战。需要新的策略来根据患者的生物学表型对患者进行分类,以改善患者选择和治疗结果。
方法:为此,我们评估了140例抗乙酰胆碱受体-Ab阳性MG患者的血清蛋白质组,并利用共识聚类作为无监督工具将患者分配至生物学特征.为了深入分析,我们使用免疫基因组测序来研究患者亚组的B细胞库,并使用原代人肌细胞进行体外分析以询问血清诱导的补体形成.
结果:该策略根据其血清中的蛋白质组模式鉴定了四种不同的患者表型。值得注意的是,一个患者表型,在这里被命名为PS3,其特征在于高疾病严重程度和补体激活作为定义特征.评估患者亚组,与其他患者相比,PS3组的B细胞库中存在过度扩增的抗体克隆,并有效激活了补体。根据他们的疾病表型,PS3患者更有可能从补体抑制疗法中获益。使用基于细胞的测定在18名患者的前瞻性队列中验证了这些发现。
结论:总的来说,这项研究表明,基于蛋白质组学的聚类是将患者分配给可能受益于补体抑制的生物学特征的门户,并为临床实践提供了分层策略.
背景:CN和CBS得到了杜塞尔多夫海涅大学医学院的Forschungskommission的支持。CN得到了ElseKröner-Fresenius-Stiftung(EKEA.38)的支持。CBS得到了德国经济研究基金会(DFG-德国研究基金会)的支持,并获得了WalterBenjamin奖学金(项目539363086)。该项目得到了北莱茵-威斯特法伦州文化和科学部(MODS,“Profilbildung2020”[批准号。PROFILNRW-2020-107-A])。
BACKGROUND: The clinical heterogeneity of myasthenia gravis (MG), an autoimmune disease defined by antibodies (Ab) directed against the postsynaptic membrane, constitutes a challenge for patient stratification and treatment decision making. Novel strategies are needed to classify patients based on their biological phenotypes aiming to improve patient selection and treatment outcomes.
METHODS: For this purpose, we assessed the serum proteome of a cohort of 140 patients with anti-acetylcholine receptor-Ab-positive MG and utilised consensus clustering as an unsupervised tool to assign patients to biological profiles. For in-depth analysis, we used immunogenomic sequencing to study the B cell repertoire of a subgroup of patients and an in vitro assay using primary human muscle cells to interrogate serum-induced
complement formation.
RESULTS: This strategy identified four distinct patient phenotypes based on their proteomic patterns in their serum. Notably, one patient phenotype, here named PS3, was characterised by high disease severity and
complement activation as defining features. Assessing a subgroup of patients, hyperexpanded antibody clones were present in the B cell repertoire of the PS3 group and effectively activated
complement as compared to other patients. In line with their disease phenotype, PS3 patients were more likely to benefit from
complement-inhibiting therapies. These findings were validated in a prospective cohort of 18 patients using a cell-based assay.
CONCLUSIONS: Collectively, this study suggests proteomics-based clustering as a gateway to assign patients to a biological signature likely to benefit from
complement inhibition and provides a stratification strategy for clinical practice.
BACKGROUND: CN and CBS were supported by the Forschungskommission of the Medical Faculty of the Heinrich Heine University Düsseldorf. CN was supported by the Else Kröner-Fresenius-Stiftung (EKEA.38). CBS was supported by the Deutsche Forschungsgemeinschaft (DFG-German Research Foundation) with a Walter Benjamin fellowship (project 539363086). The project was supported by the Ministry of Culture and Science of North Rhine-Westphalia (MODS, \"Profilbildung 2020\" [grant no. PROFILNRW-2020-107-A]).