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<p>Non-small cell lung cancer (NSCLC) shows variable responses to immunotherapy, highlighting the need for biomarkers to guide patient selection. We applied a spatial multi-omics approach to 234 advanced NSCLC patients treated with programmed death 1-based immunotherapy across three cohorts to identify biomarkers associated with outcome. Spatial proteomics (<i>n</i> = 67) and spatial compartment-based transcriptomics (<i>n</i> = 131) enabled profiling of the tumor immune microenvironment (TIME). Using spatial proteomics, we identified a resistance cell-type signature including proliferating tumor cells, granulocytes, vessels (hazard ratio (HR) = 3.8, <i>P</i> = 0.004) and a response signature, including M1/M2 macrophages and CD4 T cells (HR = 0.4, <i>P</i> = 0.019). We then generated a cell-to-gene resistance signature using spatial transcriptomics, which was predictive of poor outcomes (HR = 5.3, 2.2, 1.7 across Yale, University of Queensland and University of Athens cohorts), while a cell-to-gene response signature predicted favorable outcomes (HR = 0.22, 0.38 and 0.56, respectively). This framework enables robust TIME modeling and identifies biomarkers to support precision immunotherapy in NSCLC.</p>

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Spatial signatures for predicting immunotherapy outcomes using multi-omics in non-small cell lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月10日

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  • Thazin N. Aung,
  • James Monkman,
  • Jonathan Warrell,
  • Ioannis Vathiotis,
  • Katherine M. Bates,
  • Niki Gavrielatou,
  • Ioannis P. Trontzas,
  • Chin Wee Tan,
  • Aileen I. Fernandez,
  • Myrto Moutafi,
  • Ken O’ Byrne,
  • Kurt A. Schalper,
  • Konstantinos Syrigos,
  • Roy S. Herbst,
  • Arutha Kulasinghe,
  • David L. Rimm

摘要

Non-small cell lung cancer (NSCLC) shows variable responses to immunotherapy, highlighting the need for biomarkers to guide patient selection. We applied a spatial multi-omics approach to 234 advanced NSCLC patients treated with programmed death 1-based immunotherapy across three cohorts to identify biomarkers associated with outcome. Spatial proteomics (n = 67) and spatial compartment-based transcriptomics (n = 131) enabled profiling of the tumor immune microenvironment (TIME). Using spatial proteomics, we identified a resistance cell-type signature including proliferating tumor cells, granulocytes, vessels (hazard ratio (HR) = 3.8, P = 0.004) and a response signature, including M1/M2 macrophages and CD4 T cells (HR = 0.4, P = 0.019). We then generated a cell-to-gene resistance signature using spatial transcriptomics, which was predictive of poor outcomes (HR = 5.3, 2.2, 1.7 across Yale, University of Queensland and University of Athens cohorts), while a cell-to-gene response signature predicted favorable outcomes (HR = 0.22, 0.38 and 0.56, respectively). This framework enables robust TIME modeling and identifies biomarkers to support precision immunotherapy in NSCLC.