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Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images

Author

Listed:
  • Shidan Wang

    (University of Texas Southwestern Medical Center)

  • Ruichen Rong

    (University of Texas Southwestern Medical Center)

  • Qin Zhou

    (University of Texas Southwestern Medical Center)

  • Donghan M. Yang

    (University of Texas Southwestern Medical Center)

  • Xinyi Zhang

    (University of Texas Southwestern Medical Center)

  • Xiaowei Zhan

    (University of Texas Southwestern Medical Center)

  • Justin Bishop

    (University of Texas Southwestern Medical Center)

  • Zhikai Chi

    (University of Texas Southwestern Medical Center)

  • Clare J. Wilhelm

    (Memorial Sloan Kettering Cancer Center)

  • Siyuan Zhang

    (University of Texas Southwestern Medical Center)

  • Curtis R. Pickering

    (Yale School of Medicine)

  • Mark G. Kris

    (Memorial Sloan Kettering Cancer Center)

  • John Minna

    (UT Southwestern Medical Center
    University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

  • Yang Xie

    (University of Texas Southwestern Medical Center
    UT Southwestern Medical Center
    UT Southwestern Medical Center)

  • Guanghua Xiao

    (University of Texas Southwestern Medical Center
    UT Southwestern Medical Center
    UT Southwestern Medical Center)

Abstract

Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (for example,. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.

Suggested Citation

  • Shidan Wang & Ruichen Rong & Qin Zhou & Donghan M. Yang & Xinyi Zhang & Xiaowei Zhan & Justin Bishop & Zhikai Chi & Clare J. Wilhelm & Siyuan Zhang & Curtis R. Pickering & Mark G. Kris & John Minna & , 2023. "Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43172-8
    DOI: 10.1038/s41467-023-43172-8
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    References listed on IDEAS

    as
    1. Kun-Hsing Yu & Ce Zhang & Gerald J. Berry & Russ B. Altman & Christopher Ré & Daniel L. Rubin & Michael Snyder, 2016. "Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features," Nature Communications, Nature, vol. 7(1), pages 1-10, November.
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