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<p>Novel preclinical models that better mimic the in vivo tumor microenvironment are essential to advance understanding of tumor biology and resistance/response to therapy. Herein, we report development of a novel ex vivo patient-derived three-dimensional lung tumor model (3D-LTM) for use in evaluating response to therapy. With this model system that maintains cell-cell interactions and tissue architecture, we observed heterogeneity of response to immune checkpoint inhibitors (ICI), as noted in non-small cell lung cancer (NSCLC) patients, and defined gene signatures associated with response. Spatial transcriptomics identified positive correlation of CD8<sup>+</sup> T cell populations, CD4<sup>+</sup> memory T cells, mast cells, NK cells, naive B cells, endothelial cells and non-classical monocytes with response status, and negative correlation of macrophages with response status. Pathway analysis of gene expression showed that chemokine signaling related pathways were activated in responder 3D-LTM tissues, whereas suppression of antigen presentation-related pathways and activation of T<sub>reg</sub> differentiation-related pathways were associated with non-responder 3D-LTM tissues. Additionally, the abundance of dividing T cells and naive CD8<sup>+</sup> T cells differentially correlated with T cell cytotoxicity gene signatures based on response status. Thus, this model may provide utility for rapid testing of therapeutic outcomes and biomarker development.</p>

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Patient-derived three-dimensional lung tumor models to evaluate response to therapy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

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  • Kayla F. Goliwas,
  • Aakash Desai,
  • Kenneth P. Hough,
  • Sruti Sivan,
  • Sierra L. Single,
  • Nithya Puritipati,
  • Aroma G. Tom,
  • Sanad Alhushki,
  • Sameer S. Deshmukh,
  • Joel L. Berry,
  • Maya Khalil,
  • Benjamin Wei,
  • Yanis Boumber,
  • Mohammad Athar,
  • Selvarangan Ponnazhagan,
  • James M. Donahue,
  • Jessy S. Deshane

摘要

Novel preclinical models that better mimic the in vivo tumor microenvironment are essential to advance understanding of tumor biology and resistance/response to therapy. Herein, we report development of a novel ex vivo patient-derived three-dimensional lung tumor model (3D-LTM) for use in evaluating response to therapy. With this model system that maintains cell-cell interactions and tissue architecture, we observed heterogeneity of response to immune checkpoint inhibitors (ICI), as noted in non-small cell lung cancer (NSCLC) patients, and defined gene signatures associated with response. Spatial transcriptomics identified positive correlation of CD8+ T cell populations, CD4+ memory T cells, mast cells, NK cells, naive B cells, endothelial cells and non-classical monocytes with response status, and negative correlation of macrophages with response status. Pathway analysis of gene expression showed that chemokine signaling related pathways were activated in responder 3D-LTM tissues, whereas suppression of antigen presentation-related pathways and activation of Treg differentiation-related pathways were associated with non-responder 3D-LTM tissues. Additionally, the abundance of dividing T cells and naive CD8+ T cells differentially correlated with T cell cytotoxicity gene signatures based on response status. Thus, this model may provide utility for rapid testing of therapeutic outcomes and biomarker development.