Neutrophil-enriched gene signature correlates with teplizumab therapy resistance in different stages of type 1 diabetes

基因签名 免疫学 免疫系统 基因表达谱 生物 抗体 CD8型 获得性免疫系统 医学 基因表达 基因 遗传学
作者
Gabriele Sassi,Pierre Lemaître,Laura Martínez-Calvo,Francesca Lodi,Álvaro Cortés Calabuig,Samal Bissenova,Amber Wouters,Laure Degroote,Marijke Viaene,Niels Vandamme,Lauren E. Higdon,Peter S. Linsley,S. Alice Long,Chantal Mathieu,Conny Gysemans
出处
期刊:Journal of Clinical Investigation [American Society for Clinical Investigation]
标识
DOI:10.1172/jci176403
摘要

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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