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基于深度学习的肿瘤B细胞受体库抗原结合亲和力分析可预测免疫检查点抑制剂治疗结局

Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes

原文发布日期：2025-06-27

英文摘要：

摘要翻译：

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文章：

基于深度学习的肿瘤B细胞受体库抗原结合亲和力分析可预测免疫检查点抑制剂治疗结局

Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes

原文发布日期：2025-06-27

英文摘要：

The capability to profile the landscape of antigen-binding affinities of a vast number of antibodies (B cell receptors, BCRs) will provide a powerful tool to reveal biological insights. However, experimental approaches for detecting antibody–antigen interactions are costly and time-consuming and can only achieve low-to-mid throughput. In this work, we developed Cmai (contrastive modeling for antigen–antibody interactions) to address the prediction of binding between antibodies and antigens that can be scaled to high-throughput sequencing data. We devised a biomarker based on the output from Cmai to map the antigen-binding affinities of BCR repertoires. We found that the abundance of tumor antigen-targeting antibodies is predictive of immune-checkpoint inhibitor (ICI) treatment response. We also found that, during immune-related adverse events (irAEs) caused by ICI, humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. We used Cmai to construct a BCR-based irAE risk score, which predicted the timing of the occurrence of irAEs.

摘要翻译：

大规模抗体（B细胞受体，BCR）抗原结合亲和力图谱的解析能力，将为揭示生物学机制提供强大工具。然而，现有检测抗体-抗原相互作用的实验方法成本高昂、耗时且仅能达到中低通量。本研究开发了Cmai（抗原-抗体相互作用对比建模算法），用于预测可适配高通量测序数据的抗体-抗原结合特性。基于Cmai输出结果，我们设计了一种生物标志物来绘制BCR谱系的抗原结合亲和力图谱。研究发现：靶向肿瘤抗原的抗体丰度可预测免疫检查点抑制剂（ICI）治疗反应；ICI引发的免疫相关不良事件（irAE）期间，体液免疫会优先针对irAE受累器官的细胞内抗原产生应答；利用Cmai构建的BCR-irAE风险评分模型可预测irAE发生时间。