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

38 Pages Posted: 23 May 2019

See all articles by Shidan Wang

Shidan Wang

University of Texas at Dallas - Quantitative Biomedical Research Center

Ruichen Rong

University of Texas at Dallas - Quantitative Biomedical Research Center

Donghan M. Yang

University of Texas at Dallas - Quantitative Biomedical Research Center

Ling Cai

University of Texas at Dallas - Quantitative Biomedical Research Center

Lin Yang

University of Texas at Dallas - Quantitative Biomedical Research Center; Chinese Academy of Medical Sciences - Cancer Hospital

Danni Luo

University of Texas at Dallas - Quantitative Biomedical Research Center

Bo Yao

University of Texas at Dallas - Quantitative Biomedical Research Center

Lin Xu

University of Texas at Dallas - Quantitative Biomedical Research Center

Tao Wang

University of Texas at Dallas - Quantitative Biomedical Research Center; University of Texas at Dallas - Kidney Cancer Program

Xiaowei Zhan

University of Texas at Dallas - Quantitative Biomedical Research Center

Yang Xie

University of Texas at Dallas - Quantitative Biomedical Research Center; University of Texas at Dallas - Department of Bioinformatics; University of Texas at Dallas - Simmons Comprehensive Cancer Center

John Minna

University of Texas at Dallas - Simmons Comprehensive Cancer Center; University of Texas at Dallas - Southwestern Medical Center; University of Texas at Dallas - Department of Internal Medicine

Guanghua Xiao

University of Texas at Dallas - Southwestern Medical Center

More...

Abstract

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). In order to facilitate the study of cellular spatial organization and interactions, we developed a comprehensive nuclei segmentation and classification tool to characterize the TME from standard Hematoxylin and Eosin (H&E)-stained pathology images. This tool can computationally "stain" different types of cell nuclei in H&E pathology images to facilitate pathologists in analyzing the TME.

A Mask Regional-Convolutional Neural Network (Mask-RCNN) model was developed to segment the nuclei of tumor, stromal, lymphocyte, macrophage, karyorrhexis and red blood cells in lung adenocarcinoma (ADC). 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 (TCGA) lung ADC dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (pv= 0.001), with a hazard ratio of 2.23 [1.37-3.65] after adjusting for clinical variables. Furthermore, the image-derived TME features were significantly correlated with the gene expression of biological pathways. For example, transcription activation of both the T-cell receptor (TCR) and Programmed cell death protein 1 (PD1) pathways was positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway was positively correlated with the density of stromal cells.

This study developed a deep learning-based analysis tool to dissect the TME from tumor tissue images. Using this tool, we demonstrated that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. Although developed from the pathology images of lung ADC, this model can be adapted into other types of cancers.

Funding Statement: This work was partially supported by the National Institutes of Health [5R01CA152301, P50CA70907, 1R01GM115473, and 1R01CA172211], and the Cancer Prevention and Research Institute of Texas [RP190107 and RP180805].

Declaration of Interests: The authors declare that they have no competing interests.

Ethics Approval Statement:Not required; date is available online.

Suggested Citation

Wang, Shidan and Rong, Ruichen and Yang, Donghan M. and Cai, Ling and Yang, Lin and Luo, Danni and Yao, Bo and Xu, Lin and Wang, Tao and Zhan, Xiaowei and Xie, Yang and Minna, John and Xiao, Guanghua, Computational Staining of Pathology Images to Study Tumor Microenvironment in Lung Cancer (May 20, 2019). Available at SSRN: https://ssrn.com/abstract=3391381 or http://dx.doi.org/10.2139/ssrn.3391381

Shidan Wang

University of Texas at Dallas - Quantitative Biomedical Research Center

2601 North Floyd Road
Richardson, TX 75083
United States

Ruichen Rong

University of Texas at Dallas - Quantitative Biomedical Research Center

2601 North Floyd Road
Richardson, TX 75083
United States

Donghan M. Yang

University of Texas at Dallas - Quantitative Biomedical Research Center

2601 North Floyd Road
Richardson, TX 75083
United States

Ling Cai

University of Texas at Dallas - Quantitative Biomedical Research Center

2601 North Floyd Road
Richardson, TX 75083
United States

Lin Yang

University of Texas at Dallas - Quantitative Biomedical Research Center

2601 North Floyd Road
Richardson, TX 75083
United States

Chinese Academy of Medical Sciences - Cancer Hospital

China

Danni Luo

University of Texas at Dallas - Quantitative Biomedical Research Center

2601 North Floyd Road
Richardson, TX 75083
United States

Bo Yao

University of Texas at Dallas - Quantitative Biomedical Research Center

2601 North Floyd Road
Richardson, TX 75083
United States

Lin Xu

University of Texas at Dallas - Quantitative Biomedical Research Center

2601 North Floyd Road
Richardson, TX 75083
United States

Tao Wang

University of Texas at Dallas - Quantitative Biomedical Research Center ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

University of Texas at Dallas - Kidney Cancer Program ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Xiaowei Zhan

University of Texas at Dallas - Quantitative Biomedical Research Center

2601 North Floyd Road
Richardson, TX 75083
United States

Yang Xie

University of Texas at Dallas - Quantitative Biomedical Research Center

2601 North Floyd Road
Richardson, TX 75083
United States

University of Texas at Dallas - Department of Bioinformatics

TX
United States

University of Texas at Dallas - Simmons Comprehensive Cancer Center

2601 North Floyd Road
Richardson, TX 75083
United States

John Minna

University of Texas at Dallas - Simmons Comprehensive Cancer Center

2601 North Floyd Road
Richardson, TX 75083
United States

University of Texas at Dallas - Southwestern Medical Center

2601 North Floyd Road
Richardson, TX 75083
United States

University of Texas at Dallas - Department of Internal Medicine

2601 North Floyd Road
Richardson, TX 75083
United States

Guanghua Xiao (Contact Author)

University of Texas at Dallas - Southwestern Medical Center ( email )

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