干旱区生态环境知识资源中心(Arid): The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning

Arid

DOI	10.3389/fimmu.2021.685992
	The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning
	Chen, Yan; Sun, Zepang; Chen, Wanlan; Liu, Changyan; Chai, Ruoyang; Ding, Jingjing; Liu, Wen; Feng, Xianzhen; Zhou, Jun; Shen, Xiaoyi; Huang, Shan; Xu, Zhongqing
通讯作者	Xu, ZQ (corresponding author), Shanghai Jiao Tong Univ, Tongren Hosp, Sch Med, Dept Gen Practice, Shanghai, Peoples R China. ; Huang, S (corresponding author), Shanghai Jiao Tong Univ, Tongren Hosp, Sch Med, Dept Endocrinol, Shanghai, Peoples R China.
来源期刊	FRONTIERS IN IMMUNOLOGY
ISSN	1664-3224
出版年	2021
卷号	12
英文摘要	Background Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies. Methods Based on the abundance of tumor-infiltrating immune cells in GC patients from The Cancer Genome Atlas, we used unsupervised consensus clustering algorithm to identify robust clusters of patients, and assessed their reproducibility in an independent cohort from Gene Expression Omnibus. We further confirmed the feasibility of our immune subtypes in five independent pan-cancer cohorts. Finally, functional enrichment analyses were provided, and a deep learning model studying the pathological images was constructed to identify the immune subtypes. Results We identified and validated three reproducible immune subtypes presented with diverse components of tumor-infiltrating immune cells, molecular features, and clinical characteristics. An immune-inflamed subtype 3, with better prognosis and the highest immune score, had the highest abundance of CD8+ T cells, CD4+ T-activated cells, follicular helper T cells, M1 macrophages, and NK cells among three subtypes. By contrast, an immune-excluded subtype 1, with the worst prognosis and the highest stromal score, demonstrated the highest infiltration of CD4+ T resting cells, regulatory T cells, B cells, and dendritic cells, while an immune-desert subtype 2, with an intermediate prognosis and the lowest immune score, demonstrated the highest infiltration of M2 macrophages and mast cells, and the lowest infiltration of M1 macrophages. Besides, higher proportion of EVB and MSI of TCGA molecular subtyping, over expression of CTLA4, PD1, PDL1, and TP53, and low expression of JAK1 were observed in immune subtype 3, which consisted with the results from Gene Set Enrichment Analysis. These subtypes may suggest different immunotherapy strategies. Finally, deep learning can predict the immune subtypes well. Conclusion This study offers a conceptual frame to better understand the tumor immune microenvironment of GC. Future work is required to estimate its reference value for the design of immune-related studies and immunotherapy selection.
英文关键词	tumor-infiltrating immune cells immune subtypes immunotherapy deep learning gastric cancer
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000671900700001
WOS关键词	T-CELLS ; NIVOLUMAB ; SURVIVAL ; MICROENVIRONMENT ; CHEMOTHERAPY ; BLOCKADE ; ANTIBODY ; SAFETY
WOS类目	Immunology
WOS研究方向	Immunology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350303
作者单位	[Chen, Yan; Liu, Changyan; Chai, Ruoyang; Ding, Jingjing; Liu, Wen; Feng, Xianzhen; Zhou, Jun; Shen, Xiaoyi; Xu, Zhongqing] Shanghai Jiao Tong Univ, Tongren Hosp, Sch Med, Dept Gen Practice, Shanghai, Peoples R China; [Chen, Yan; Liu, Wen; Huang, Shan] Shanghai Jiao Tong Univ, Tongren Hosp, Sch Med, Dept Endocrinol, Shanghai, Peoples R China; [Sun, Zepang] Southern Med Univ, Nanfang Hosp, Dept Gen Surg, Guangzhou, Peoples R China; [Sun, Zepang] Guangdong Prov Key Lab Precis Med Gastrointestina, Guangzhou, Guangdong, Peoples R China; [Chen, Wanlan] Shanghai Jiao Tong Univ, Tongren Hosp, Sch Med, Dept Cardiol, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Chen, Yan,Sun, Zepang,Chen, Wanlan,et al. The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning[J],2021,12.
APA	Chen, Yan.,Sun, Zepang.,Chen, Wanlan.,Liu, Changyan.,Chai, Ruoyang.,...&Xu, Zhongqing.(2021).The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning.FRONTIERS IN IMMUNOLOGY,12.
MLA	Chen, Yan,et al."The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning".FRONTIERS IN IMMUNOLOGY 12(2021).