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<p>Immune checkpoint inhibitors (ICIs) have reshaped the treatment in multiple tumors. However, a substantial proportion of patients exhibit limited responses. The lack of harmonized, large-scale pan-cancer ICI datasets coupled with accessible analysis tools hinders the systematic discovery of response biomarkers and resistance mechanisms. Therefore, we developed a comprehensive resource named ICIsAtlas, which encompassed curated transcriptomic and clinical data from 1,268 ICI-treated patients across eight tumor types, with an accompanying R package implementing a complete workflow for deconvolution and biomarker evaluation. Applying the ICIsAtlas framework, a systematic pan-cancer analysis was performed. We identified the universal signatures that were specific for ICI response, including cooperative interactions among favorable immune cells. In addition, we discovered a competitive cell community and SERPING1 + VEGFA+ Natural Killer (NK) cell-mediated immunosuppressive niche in non-responders, which were further validated with single-cell and multiplex immunohistochemistry data. The ICIsAtlas resource and R package represent a powerful, publicly available platform for hypothesis generation and biomarker discovery, which could be used to develop the next-generation biomarkers and therapeutic targets to improve tumor immunotherapy.</p>

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ICIsAtlas reveals a suppressive NK cell niche in pan-cancer immunotherapy profiles

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

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  • Yumo Xie,
  • Jinxin Lin,
  • Haotian Liu,
  • Ying Xiong,
  • Junyi Han,
  • Ziying Huang,
  • Jingrong Weng,
  • Zixiao Wan,
  • Peisi Li,
  • Puning Wang,
  • Xiaoxia Liu,
  • Linping Wu,
  • Qian Cai,
  • Meijin Huang,
  • Yanxin Luo,
  • Xiaolin Wang,
  • Huichuan Yu

摘要

Immune checkpoint inhibitors (ICIs) have reshaped the treatment in multiple tumors. However, a substantial proportion of patients exhibit limited responses. The lack of harmonized, large-scale pan-cancer ICI datasets coupled with accessible analysis tools hinders the systematic discovery of response biomarkers and resistance mechanisms. Therefore, we developed a comprehensive resource named ICIsAtlas, which encompassed curated transcriptomic and clinical data from 1,268 ICI-treated patients across eight tumor types, with an accompanying R package implementing a complete workflow for deconvolution and biomarker evaluation. Applying the ICIsAtlas framework, a systematic pan-cancer analysis was performed. We identified the universal signatures that were specific for ICI response, including cooperative interactions among favorable immune cells. In addition, we discovered a competitive cell community and SERPING1 + VEGFA+ Natural Killer (NK) cell-mediated immunosuppressive niche in non-responders, which were further validated with single-cell and multiplex immunohistochemistry data. The ICIsAtlas resource and R package represent a powerful, publicly available platform for hypothesis generation and biomarker discovery, which could be used to develop the next-generation biomarkers and therapeutic targets to improve tumor immunotherapy.