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<p>Advances in tissue labeling, imaging, and automated cell identification now enable the visualization of immune cell types in human tumors. However, a framework for analyzing spatial patterns within the tumor microenvironment (TME) is still lacking. To address this, we develop <i>Spatiopath</i>, a null-hypothesis framework that distinguishes statistically significant immune cell associations from random distributions. Using embedding functions to map cell contours and tumor regions, <i>Spatiopath</i> extends Ripley’s K function to analyze both cell-cell and cell-tumor interactions. We validate the method with synthetic simulations and apply it to multi-color images of lung tumor sections, revealing significant spatial patterns such as mast cells accumulating near T cells and the tumor epithelium. These patterns highlight differences in spatial organization, with mast cells clustering near the epithelium and T cells positioned farther away. <i>Spatiopath</i> enables a better understanding of immune responses and may help identify biomarkers for patient outcomes.</p>

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Statistical analysis of spatial patterns in tumor microenvironment images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月31日

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  • Mohamed M. Benimam,
  • Vannary Meas-Yedid,
  • Suvadip Mukherjee,
  • Astri Frafjord,
  • Alexandre Corthay,
  • Thibault Lagache,
  • Jean-Christophe Olivo-Marin

摘要

Advances in tissue labeling, imaging, and automated cell identification now enable the visualization of immune cell types in human tumors. However, a framework for analyzing spatial patterns within the tumor microenvironment (TME) is still lacking. To address this, we develop Spatiopath, a null-hypothesis framework that distinguishes statistically significant immune cell associations from random distributions. Using embedding functions to map cell contours and tumor regions, Spatiopath extends Ripley’s K function to analyze both cell-cell and cell-tumor interactions. We validate the method with synthetic simulations and apply it to multi-color images of lung tumor sections, revealing significant spatial patterns such as mast cells accumulating near T cells and the tumor epithelium. These patterns highlight differences in spatial organization, with mast cells clustering near the epithelium and T cells positioned farther away. Spatiopath enables a better understanding of immune responses and may help identify biomarkers for patient outcomes.