Statistical analysis of spatial patterns in tumor microenvironment images

Mohamed M. Benimam, Vannary Meas-Yedid, Suvadip Mukherjee, Astri Frafjord, Alexandre Corthay, Thibault Lagache () and Jean-Christophe Olivo-Marin ()
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Mohamed M. Benimam: Université de Paris Cité, CNRS UMR 3691, BioImage Analysis Unit
Vannary Meas-Yedid: Université de Paris Cité, CNRS UMR 3691, BioImage Analysis Unit
Suvadip Mukherjee: Université de Paris Cité, CNRS UMR 3691, BioImage Analysis Unit
Astri Frafjord: Oslo University Hospital and University of Oslo
Alexandre Corthay: Oslo University Hospital and University of Oslo
Thibault Lagache: Université de Paris Cité, CNRS UMR 3691, BioImage Analysis Unit
Jean-Christophe Olivo-Marin: Université de Paris Cité, CNRS UMR 3691, BioImage Analysis Unit

Nature Communications, 2025, vol. 16, issue 1, 1-20

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-57943-y

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