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<p>Epidermal growth factor receptor (EGFR) mutation is a key oncogenic driver in lung adenocarcinoma (LUAD), but its impact on the tumor immune microenvironment (TIME) remains unclear. By integrating single-cell transcriptomes from 153 LUAD samples using machine learning, we generated an atlas of over one million cells that delineates immune heterogeneity. EGFR-mutant tumors exhibited enrichment of <i>TIGIT</i><sup>+</sup>regulatory T cells, neutrophils, and macrophages, whereas wild-type tumors contained abundant <i>ZNF683</i><sup>+</sup><i>CD8</i><sup><i>+</i></sup>tissue-resident memory T cells, diverse memory B cells, and <i>FGFBP2</i><sup><i>+</i></sup><i>CD16</i><sup><i>high</i></sup> natural killer cells, reflecting an immune-active TIME. Non-negative matrix factorization defined five TIME subtypes, with EGFR-mutant patients clustering into immunosuppressive profiles linked to poor prognosis. Flow cytometry and mouse models confirmed the cytotoxic and PD-1 blockade-enhancing functions of <i>FGFBP2</i><sup>+</sup>NK cells. These findings reveal distinct TIME landscapes in EGFR-mutant LUAD and illustrate the potential of machine learning-based immunogenomic analysis to inform precision immunotherapy.</p>

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Machine learning identifies TIME subtypes linking EGFR mutations and immune states in lung adenocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Zetian Gong,
  • Mingjun Du,
  • Ying Li,
  • Bicheng Ye,
  • Yuming Huang,
  • Hui Gong,
  • Wei Wang,
  • Liang Chen,
  • Zongli Ding,
  • Pengpeng Zhang

摘要

Epidermal growth factor receptor (EGFR) mutation is a key oncogenic driver in lung adenocarcinoma (LUAD), but its impact on the tumor immune microenvironment (TIME) remains unclear. By integrating single-cell transcriptomes from 153 LUAD samples using machine learning, we generated an atlas of over one million cells that delineates immune heterogeneity. EGFR-mutant tumors exhibited enrichment of TIGIT+regulatory T cells, neutrophils, and macrophages, whereas wild-type tumors contained abundant ZNF683+CD8+tissue-resident memory T cells, diverse memory B cells, and FGFBP2+CD16high natural killer cells, reflecting an immune-active TIME. Non-negative matrix factorization defined five TIME subtypes, with EGFR-mutant patients clustering into immunosuppressive profiles linked to poor prognosis. Flow cytometry and mouse models confirmed the cytotoxic and PD-1 blockade-enhancing functions of FGFBP2+NK cells. These findings reveal distinct TIME landscapes in EGFR-mutant LUAD and illustrate the potential of machine learning-based immunogenomic analysis to inform precision immunotherapy.