Patients with common variable immunodeficiency (CVID) can develop immune dysregulation complications such as autoimmunity, lymphoproliferation, enteritis, and malignancy, which cause significant morbidity and mortality. We aimed to (i) assess the potential of serum proteomics in stratifying patients with immune dysregulation using two independent cohorts and (ii) identify cytokine and chemokine signaling pathways that underlie immune dysregulation in CVID. A panel of 180 markers was measured in two multicenter CVID cohorts using Olink Protein Extension Assay technology. A classification algorithm was trained to distinguish CVID with immune dysregulation (CVIDid, n = 14) from CVID with infections only (CVIDio, n = 16) in the training cohort, and validated on a second testing cohort (CVIDid n = 23, CVIDio n = 24). Differential expression in both cohorts was used to determine relevant signaling pathways. An elastic net classifier using MILR1, LILRB4, IL10, IL12RB1, and CD83 could discriminate between CVIDid and CVIDio patients with a sensitivity of 0.83, specificity of 0.75, and area under the curve of 0.73 in an independent testing cohort. Activated pathways (fold change > 1.5, FDR-adjusted p < 0.05) in CVIDid included Th1 and Th17-associated signaling, as well as IL10 and other immune regulatory markers (LAG3, TNFRSF9, CD83). Targeted serum proteomics provided an accurate and reproducible tool to discriminate between patients with CVIDid and CVIDio. Cytokine profiles provided insight into activation of Th1 and Th17 pathways and indicate a possible role for chronic inflammation and exhaustion in immune dysregulation. These findings serve as a first step towards the development of biomarkers for immune dysregulation in CVID.

患有常见变异型免疫缺陷 (CVID) 的患者可能会出现免疫失调并发症，例如自身免疫、淋巴组织增生、肠炎和恶性肿瘤，这些并发症会导致显着的发病率和死亡率。我们旨在 (i) 使用两个独立的队列评估血清蛋白质组学在对免疫失调患者进行分层方面的潜力，以及 (ii) 确定构成 CVID 免疫失调的细胞因子和趋化因子信号通路。使用 Olink 蛋白质延伸分析技术在两个多中心 CVID 队列中测量了一组 180 个标记物。训练分类算法以区分具有免疫失调的 CVID (CVIDid, n  = 14) 和仅具有感染的 CVID (CVIDio, n = 16）在训练队列中，并在第二个测试队列中验证（CVIDid n  = 23，CVIDio n  = 24）。两个队列中的差异表达用于确定相关的信号通路。在独立测试队列中，使用 MILR1、LILRB4、IL10、IL12RB1 和 CD83 的弹性网络分类器可以区分 CVIDid 和 CVIDio 患者，灵敏度为 0.83，特异性为 0.75，曲线下面积为 0.73。激活的通路（倍数变化 > 1.5，FDR 调整的p < 0.05) 在 CVIDid 中包括 Th1 和 Th17 相关信号，以及 IL10 和其他免疫调节标志物（LAG3、TNFRSF9、CD83）。靶向血清蛋白质组学提供了一种准确且可重复的工具来区分 CVIDid 和 CVIDio 患者。细胞因子谱提供了对 Th1 和 Th17 通路激活的深入了解，并表明慢性炎症和衰竭在免疫失调中的可能作用。这些发现是开发 CVID 免疫失调生物标志物的第一步。