Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning

doi:10.1016/j.ajpath.2023.08.015

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning
	Sun, Caixia 1,2,3; Luo, Tao 4,5; Liu, Zhenyu 3,6; Ge, Jia 4; Shao, Lizhi 3; Liu, Xiangyu 3; Li, Bao 3; Zhang, Song 3; Qiu, Qi 3; Wei, Wei 3
刊名	AMERICAN JOURNAL OF PATHOLOGY
	2023-12-01
卷号	193 期号:12 页码:2111-2121
ISSN号	0002-9440
DOI	10.1016/j.ajpath.2023.08.015
通讯作者	Bian, Xiu-Wu(bianxiuwu@263.net) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Tumor mutation burden (TMB) is a potential biomarker for evaluating the prognosis and response to immune checkpoint inhibitors, but its costly and time-consuming method of measurement limits its widespread application. This study aimed to identify the TMB-related histopathologic features from hematoxylin and eosin slides and explore their prognostic value in gliomas. TMB-related features were detected using a graph convolutional neural network from whole-slide images of patients from The Cancer Genome Atlas data set (619 patients), and the correlation between features and TMB was evaluated in an external validation set (237 patients). TMB-related features were used for predicting overall survival (OS) of patients to investigate whether these features have potential for prognostic prediction. Moreover, biological pathways underlying the prognostic value of the features were further explored. Histopathologic features derived from whole-slide images were significantly associated with patient TMB (P = 0.007 in the external validation set). TMB-related features showed excellent per-formance for OS prediction, and patients with lower-grade gliomas could be further stratified into different risk groups according to the features (P = 0.00013; hazard ratio, 4.004). Pathways involved in the cell cycle and execution of immune response were enriched in patients with higher OS risk. The TMB-related features could be used to estimate TMB and aid in prognostic risk stratification of patients with glioma with dysregulated biological pathways. (Am J Pathol 2023, 193: 2111-2121; https:// doi.org/10.1016/j.ajpath.2023.08.015)
资助项目	National Key R&D Program of China[2021YFF1201003] ; National Key R&D Program of China[2021YYF1201002] ; National Natural Science Foundation of China[92059103] ; National Natural Science Foundation of China[62333022] ; National Natural Science Foundation of China[92259301] ; National Natural Science Foundation of China[82371936] ; Beijing Natural Science Foundation[JQ23034] ; Natural Science Basic Research Pro-gram of Shaanxi[2023-JC-YB-682]
WOS关键词	CENTRAL-NERVOUS-SYSTEM ; WHOLE SLIDE IMAGES ; CLASSIFICATION ; CANCER ; IMMUNOTHERAPY ; ORGANIZATION
WOS研究方向	Pathology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:001124361300001
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Natural Science Basic Research Pro-gram of Shaanxi
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/55027]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Bian, Xiu-Wu; Tian, Jie
作者单位	1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China 2.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Chinese Acad Sci Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, Beijing, Peoples R China 4.Third Mil Med Univ, Southwest Hosp, Army Med Univ, Chongqing, Peoples R China 5.Minist Educ China, Key Lab Tumor Immunopathol, Chongqing 400038, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 7.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian, Peoples R China
推荐引用方式 GB/T 7714	Sun, Caixia,Luo, Tao,Liu, Zhenyu,et al. Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning[J]. AMERICAN JOURNAL OF PATHOLOGY,2023,193(12):2111-2121.
APA	Sun, Caixia.,Luo, Tao.,Liu, Zhenyu.,Ge, Jia.,Shao, Lizhi.,...&Tian, Jie.(2023).Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning.AMERICAN JOURNAL OF PATHOLOGY,193(12),2111-2121.
MLA	Sun, Caixia,et al."Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning".AMERICAN JOURNAL OF PATHOLOGY 193.12(2023):2111-2121.