Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2025年6月27日

查看全文
  • Bing Song,
  • Kaiwen Wang,
  • Saiyang Na,
  • Jia Yao,
  • Farjana J. Fattah,
  • Alexandra L. Martin,
  • Mitchell S. von Itzstein,
  • Donghan M. Yang,
  • Jialiang Liu,
  • Yaming Xue,
  • Chaoying Liang,
  • Yuzhi Guo,
  • Indu Raman,
  • Chengsong Zhu,
  • Jonathan E. Dowell,
  • Jade Homsi,
  • Sawsan Rashdan,
  • Shengjie Yang,
  • Mary E. Gwin,
  • Tuoqi Wu,
  • David Hsiehchen,
  • Yvonne Gloria-McCutchen,
  • Catherine Pei-ju Lu,
  • Prithvi Raj,
  • Xiao-chen Bai,
  • Jun Wang,
  • Jose Conejo-Garcia,
  • Yang Xie,
  • Junzhou Huang,
  • David E. Gerber,
  • Tao Wang

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.