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

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, Yang Xie and Guanghua Xiao ()
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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
Yang Xie: University of Texas Southwestern Medical Center
Guanghua Xiao: University of Texas Southwestern Medical Center

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-43172-8

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