Dataset Information

Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer.

ABSTRACT: The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin-stained pathology images in lung adenocarcinoma. Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas lung adenocarcinoma dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (P = 0.001), with a HR of 2.23 (1.37-3.65) after adjusting for clinical variables. Furthermore, the image-derived TME features significantly correlated with the gene expression of biological pathways. For example, transcriptional activation of both the T-cell receptor and programmed cell death protein 1 pathways positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway positively correlated with the density of stromal cells. In summary, we demonstrate that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. SIGNIFICANCE: These findings present a deep learning-based analysis tool to study the TME in pathology images and demonstrate that the cell spatial organization is predictive of patient survival and is associated with gene expression.See related commentary by Rodriguez-Antolin, p. 1912.

SUBMITTER: Wang S

PROVIDER: S-EPMC7919065 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer.

Wang Shidan S Rong Ruichen R Yang Donghan M DM Fujimoto Junya J Yan Shirley S Cai Ling L Yang Lin L Luo Danni D Behrens Carmen C Parra Edwin R ER Yao Bo B Xu Lin L Wang Tao T Zhan Xiaowei X Wistuba Ignacio I II Minna John J Xie Yang Y Xiao Guanghua G

Cancer research 20200108 10

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin-stained pathology images in lung adenocarcinoma. Using this too ...[more]

PMID: 31915129

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Project description:Background:Kejinyan decoction, as an experienced formula of Zhou Zhongying (the Master of Traditional Chinese Medicine) has been widely used in clinic for lung cancer treatment in China, while the anti-lung cancer mechanism of it is still remained to be elucidated. Herein, our basic study found that the survival of lung cancer xenograft mice was significantly prolonged after intragastrically administered high dose of Kejinyan decoction (3.8 g per kg BW) for 15 days. More importantly, we found that Kejinyan decoction inhibited the metastasis of lung cancer cells in vivo. Thus in this study, we aim to elucidate the anti-metastasis effects of Kejinyan decoction. Methods:RNA-Seq was used to find out the gene regulation of Kejinyan decoction on the mice, flow cytometry assay was used to detect the immunocytes in the spleen, ELISA assay was used to detect the inflammatory factors in the serum and spleen, and immunofluorescence assay was used to detect the level of immune cells and the expression of glycol-metabolism related enzymes in situ. Also, we established a lung cancer orthotopic xenograft tumor model to assess the influence of Kejinyan decoction on the metastatic ability of lung cancer cells in vivo. Results:GO analysis of gene sequencing of tumor tissue samples showed that Kejinyan decoction regulated immune response. Further flow cytometry analysis of splenic lymphocyte showed that Kejinyan decoction upregulated M1 macrophages and downregulated M2 macrophages, while the total level of macrophages changed little, which was verified by detection of CD68, F4/80, CD206, and CD86 in tumor tissue section. Moreover, detection of inflammatory cytokines showed that Kejinyan decoction downregulated TNF-?, IFN-?, IL-6, as well as IL-4, IL-13 in tumor microenvironment. Further studies also showed that Kejinyan decoction had little effect on tumor hypoxia, but downregulated glycolysis in tumor tissues. More importantly, we found that Kejinyan decoction inhibited the metastasis of lung cancer cells in vivo. Conclusion:Our findings conclude that Kejinyan decoction inhibited lung cancer cell metastasis through affecting macrophage polarization and energy reprogramming.

Dataset Information

Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer.

Publications

Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer.

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