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<p>Unraveling the spatial configuration of the tumor microenvironment (TME) is crucial for elucidating tumor-immune interactions based on immuno-oncology. We present STopover, a novel approach utilizing spatially resolved transcriptomics (SRT) data and topological analysis to investigate the TME. By gradually lowering the feature threshold, connected components (CCs) are extracted based on spatial distance and persistence, with Jaccard indices quantifying their spatial overlap, and transcriptomic profiles are permutated to assess statistical significance. Applied to lung and breast cancer SRT, STopover revealed immune and stromal cell infiltration patterns, predicted key cell–cell communication, and identified relevant regions, shedding light on cancer pathophysiology (URL: <a href="https://github.com/bsungwoo/STopover">https://github.com/bsungwoo/STopover</a>).</p>

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STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月1日

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  • Sungwoo Bae,
  • Hyekyoung Lee,
  • Kwon Joong Na,
  • Dong Soo Lee,
  • Hongyoon Choi,
  • Young Tae Kim

摘要

Unraveling the spatial configuration of the tumor microenvironment (TME) is crucial for elucidating tumor-immune interactions based on immuno-oncology. We present STopover, a novel approach utilizing spatially resolved transcriptomics (SRT) data and topological analysis to investigate the TME. By gradually lowering the feature threshold, connected components (CCs) are extracted based on spatial distance and persistence, with Jaccard indices quantifying their spatial overlap, and transcriptomic profiles are permutated to assess statistical significance. Applied to lung and breast cancer SRT, STopover revealed immune and stromal cell infiltration patterns, predicted key cell–cell communication, and identified relevant regions, shedding light on cancer pathophysiology (URL: https://github.com/bsungwoo/STopover).