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<p>Extracellular vesicles (EVs) is promising in predicting the efficacy of immune checkpoint inhibitor (ICI) therapies. But it is challenging to determine the level of circulating EVs due to their variations in spatial and temporal distribution. To address this, we developed an in situ EV detection platform integrating multiplex EV capture with microfluidic-generated immune-tumor spheroids. This platform enables in situ monitoring of EV secretion dynamics under ICI and chemotherapeutic treatments, capturing localized and temporal changes in EV release. Using predictive models, we identified EVs carrying programmed cell death ligand 1 (PD-L1) as the most robust predictors of spheroid viability during treatment. RNA sequencing further revealed that dynamic EV expression changes are driven by gene transcription, providing a temporal understanding of EV regulation. Our platform overcomes the limitations of traditional methods by offering a physiologically relevant system to study EV-mediated immune responses. By addressing the spatial and temporal heterogeneity of EVs, this work advances EV-based biomarker discovery and provides a foundation for optimizing personalized immunotherapies.</p> Graphic abstract <p></p>

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In situ and dynamic screening of extracellular vesicles as predictive biomarkers in immune-checkpoint inhibitor therapies

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月3日

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  • Yihe Wang,
  • Yue Sun,
  • Mengqi Liu,
  • Chao Wang,
  • Miao Huang,
  • Jiaoyan Qiu,
  • Ningkai Yang,
  • Yu Zhang,
  • Hong Liu,
  • Lin Han

摘要

Extracellular vesicles (EVs) is promising in predicting the efficacy of immune checkpoint inhibitor (ICI) therapies. But it is challenging to determine the level of circulating EVs due to their variations in spatial and temporal distribution. To address this, we developed an in situ EV detection platform integrating multiplex EV capture with microfluidic-generated immune-tumor spheroids. This platform enables in situ monitoring of EV secretion dynamics under ICI and chemotherapeutic treatments, capturing localized and temporal changes in EV release. Using predictive models, we identified EVs carrying programmed cell death ligand 1 (PD-L1) as the most robust predictors of spheroid viability during treatment. RNA sequencing further revealed that dynamic EV expression changes are driven by gene transcription, providing a temporal understanding of EV regulation. Our platform overcomes the limitations of traditional methods by offering a physiologically relevant system to study EV-mediated immune responses. By addressing the spatial and temporal heterogeneity of EVs, this work advances EV-based biomarker discovery and provides a foundation for optimizing personalized immunotherapies.

Graphic abstract