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<p>Spatial proteomics technologies have been pivotal in profiling tumor immune microenvironments at single-cell resolution, advancing our understanding of cancer biology, identifying key cell populations in solid tumors, and predicting treatment responses. Although immune checkpoint and molecular inhibitors have revolutionized cancer care, resistance mechanisms remain a major therapeutic challenge that hinder productive responses in a notable fraction of cancer patients. In this review, we outline current spatial proteomics and computational analysis tools for studying the tumor immune microenvironment and discuss how spatial proteomics techniques have helped elucidate cancer resistance mechanisms across multiple tumor types. In this process, we highlight the importance of investigating immunosuppressive cell populations that can mediate cancer resistance, specifically with regard to their localization, protein signatures, and surrounding interactions. Finally, we provide a look ahead at future applications of artificial intelligence/machine learning and multi-omics approaches that will help further propel our understanding of cancer resistance mechanisms through spatial biology research.&#xa0;</p>

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Spatial proteomics for investigating solid tumor resistance mechanisms

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Xin Ming M. Zhou,
  • Anjali J. D’Amiano,
  • Charles Lu,
  • Vrinda Madan,
  • Sara Khoshniyati,
  • Jack Kollings,
  • Noah E. Sunshine,
  • Sachin S. Surwase,
  • Joel C. Sunshine

摘要

Spatial proteomics technologies have been pivotal in profiling tumor immune microenvironments at single-cell resolution, advancing our understanding of cancer biology, identifying key cell populations in solid tumors, and predicting treatment responses. Although immune checkpoint and molecular inhibitors have revolutionized cancer care, resistance mechanisms remain a major therapeutic challenge that hinder productive responses in a notable fraction of cancer patients. In this review, we outline current spatial proteomics and computational analysis tools for studying the tumor immune microenvironment and discuss how spatial proteomics techniques have helped elucidate cancer resistance mechanisms across multiple tumor types. In this process, we highlight the importance of investigating immunosuppressive cell populations that can mediate cancer resistance, specifically with regard to their localization, protein signatures, and surrounding interactions. Finally, we provide a look ahead at future applications of artificial intelligence/machine learning and multi-omics approaches that will help further propel our understanding of cancer resistance mechanisms through spatial biology research.