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ImmunoAIzer: A Deep Learning-Based Computational Framework to Characterize Cell Distribution and Gene Mutation in Tumor Microenvironment
Bian, Chang1,2; Wang, Yu1,2; Lu, Zhihao3; An, Yu1,4; Wang, Hanfan1,5; Kong, Lingxin1,2; Du, Yang1,2; Tian, Jie1,2,4,5
Source PublicationCANCERS
2021-04-01
Volume13Issue:7Pages:21
Abstract

Simple Summary A comprehensive evaluation of immune cell distribution in the tumor microenvironment (TME) and tumor gene mutation status may contribute to therapeutic optimization of cancer patients. In this study, we aimed to demonstrate that deep learning (DL)-based computational frameworks have remarkable potential as a tool to analyze the spatial distribution of immune cells and cancer cells in TME and detect tumor gene mutations. TME analysis can benefit from the computational framework, mainly due to its efficiency and low cost. Cells distribution in TME and tumor gene mutation status can be characterized accurately and efficiently. This may lead to a reduced working load of pathologists and may result in an improved and more standardized workflow. Spatial distribution of tumor infiltrating lymphocytes (TILs) and cancer cells in the tumor microenvironment (TME) along with tumor gene mutation status are of vital importance to the guidance of cancer immunotherapy and prognoses. In this work, we developed a deep learning-based computational framework, termed ImmunoAIzer, which involves: (1) the implementation of a semi-supervised strategy to train a cellular biomarker distribution prediction network (CBDPN) to make predictions of spatial distributions of CD3, CD20, PanCK, and DAPI biomarkers in the tumor microenvironment with an accuracy of 90.4%; (2) using CBDPN to select tumor areas on hematoxylin and eosin (H&E) staining tissue slides and training a multilabel tumor gene mutation detection network (TGMDN), which can detect APC, KRAS, and TP53 mutations with area-under-the-curve (AUC) values of 0.76, 0.77, and 0.79. These findings suggest that ImmunoAIzer could provide comprehensive information of cell distribution and tumor gene mutation status of colon cancer patients efficiently and less costly; hence, it could serve as an effective auxiliary tool for the guidance of immunotherapy and prognoses. The method is also generalizable and has the potential to be extended for application to other types of cancers other than colon cancer.

Keyworddeep learning cell distribution biomarker tumor gene mutation tumor microenvironment (TME) semi-supervised learning hematoxylin and eosin (H& E)
DOI10.3390/cancers13071659
Indexed BySCI
Language英语
Funding ProjectBeijing Natural Science Foundation[7212207] ; Ministry of Science and Technology of China[2017YFA0205] ; National Natural Science Foundation of China[81871514] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81470083] ; National Natural Science Foundation of China[91859119] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61901472] ; National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences[2018PT32003] ; National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences[2017PT32004] ; National Key R&D Program of China[2018YFC0910602] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFA0700401] ; National Key R&D Program of China[2016YFA0100902] ; National Key R&D Program of China[2016YFC0103702] ; National Natural Science Foundation of shaanxi Provience[2019JM-459]
Funding OrganizationBeijing Natural Science Foundation ; Ministry of Science and Technology of China ; National Natural Science Foundation of China ; National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences ; National Key R&D Program of China ; National Natural Science Foundation of shaanxi Provience
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000638365800001
PublisherMDPI
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44270
Collection中国科学院分子影像重点实验室
Corresponding AuthorDu, Yang; Tian, Jie
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Peking Univ Canc Hosp & Inst, Minist Educ, Key Lab Carcinogenesis & Translat Res, Dept Gastrointestinal Oncol, Beijing 100142, Peoples R China
4.Beihang Univ, Sch Med Sci & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
5.Xidian Univ, Sch Life Sci & Technol, Xian 710071, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Bian, Chang,Wang, Yu,Lu, Zhihao,et al. ImmunoAIzer: A Deep Learning-Based Computational Framework to Characterize Cell Distribution and Gene Mutation in Tumor Microenvironment[J]. CANCERS,2021,13(7):21.
APA Bian, Chang.,Wang, Yu.,Lu, Zhihao.,An, Yu.,Wang, Hanfan.,...&Tian, Jie.(2021).ImmunoAIzer: A Deep Learning-Based Computational Framework to Characterize Cell Distribution and Gene Mutation in Tumor Microenvironment.CANCERS,13(7),21.
MLA Bian, Chang,et al."ImmunoAIzer: A Deep Learning-Based Computational Framework to Characterize Cell Distribution and Gene Mutation in Tumor Microenvironment".CANCERS 13.7(2021):21.
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