中性粒细胞富集基因特征与1型糖尿病不同阶段的替普利单抗耐药相关

Gabriele Sassi,Pierre Lemaitre,Laia Fernández Calvo,Francesca Lodi,Álvaro Cortés Calabuig,Samal Bissenova,Amber Wouters,Laure Degroote,Marijke Viaene,Niels Vandamme,Lauren Higdon,Peter S Linsley,S Alice Long,Chantal Mathieu,Conny Gysemans
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引用次数: 0

摘要

Teplizumab是一种人源化抗cd3单克隆抗体,通过延迟2期临床发作和减缓3期早期进展,代表了自身免疫性1型糖尿病(T1D)治疗的突破。然而,治疗反应是不均匀的。为了更好地理解这种可变性,我们将单细胞转录组学应用于抗小鼠cd3治疗的非肥胖糖尿病(NOD)小鼠的配对外周血和胰腺样本,并确定了与治疗结果相关的不同基因特征,并在各区室之间具有一致的模式。成功相关的信号在NK/CD8 + T细胞和其他免疫细胞类型中丰富,而耐药信号主要由中性粒细胞表达。AbATE研究6个月时的人类全血测序数据证实了这些反应特征背后的免疫群落,该研究评估了替普利单抗治疗3期T1D。此外,人类TN10(2期)和AbATE(3期)队列的基线表达谱确定了预测治疗反应的免疫特征,应答者中T细胞富集的特征和无应答者中中性粒细胞富集的特征,强调了适应性和先天免疫在决定teplizumab结果中的作用。使用弹性网络逻辑回归模型,我们建立了一个26个基因的血液特征预测teplizumab反应(AUC = 0.97)。这些发现证明了免疫基因特征的预测潜力,以及转录组分析在指导T1D患者使用teplizumab的个体化治疗策略方面的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neutrophil-enriched gene signature correlates with teplizumab therapy resistance in different stages of type 1 diabetes.
Teplizumab, a humanized anti-CD3 monoclonal antibody, represents a breakthrough in autoimmune type 1 diabetes (T1D) treatment, by delaying clinical onset in stage 2 and slowing progression in early stage 3. However, therapeutic responses are heterogeneous. To better understand this variability, we applied single-cell transcriptomics to paired peripheral blood and pancreas samples from anti-mouse CD3-treated non-obese diabetic (NOD) mice and identified distinct gene signatures associated with therapy outcome, with consistent patterns across compartments. Success-associated signatures were enriched in NK/CD8⁺ T cells and other immune cell types, whereas resistance signatures were predominantly expressed by neutrophils. The immune communities underlying these response signatures were confirmed in human whole-blood sequencing data from the AbATE study at 6 months, which assessed teplizumab therapy in stage 3 T1D. Furthermore, baseline expression profiling in the human TN10 (stage 2) and AbATE (stage 3) cohorts identified immune signatures predictive of therapy response, T cell-enriched signatures in responders and neutrophil-enriched signatures in non-responders, highlighting the roles of both adaptive and innate immunity in determining teplizumab outcome. Using an elastic-net logistic regression model, we developed a 26-gene blood-based signature predicting teplizumab response (AUC = 0.97). These findings demonstrate the predictive potential of immune gene signatures and the value of transcriptomic profiling in guiding individualized treatment strategies with teplizumab in T1D.
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