Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network

doi:10.1093/bib/bbad023

	Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network
	Zhao, Lianhe 1; Qi, Xiaoning 2; Chen, Yang ; Qiao, Yixuan 2,3; Bu, Dechao 4; Wu, Yang 4; Luo, Yufan 4; Wang, Sheng 4; Zhang, Rui 5; Zhao, Yi 2,3,6
	2023-03-19
发表期刊	BRIEFINGS IN BIOINFORMATICS
ISSN	1467-5463
卷号	24 期号:2 页码:9
摘要	The determination of transcriptome profiles that mediate immune therapy in cancer remains a major clinical and biological challenge. Despite responses induced by immune-check points inhibitors (ICIs) in diverse tumor types and all the big breakthroughs in cancer immunotherapy, most patients with solid tumors do not respond to ICI therapies. It still remains a big challenge to predict the ICI treatment response. Here, we propose a framework with multiple prior knowledge networks guided for immune checkpoints inhibitors prediction-DeepOmix-ICI (or ICInet for short). ICInet can predict the immune therapy response by leveraging geometric deep learning and prior biological knowledge graphs of gene-gene interactions. Here, we demonstrate more than 600 ICI-treated patients with ICI response data and gene expression profile to apply on ICInet. ICInet was used for ICI therapy responses prediciton across different cancer types-melanoma, gastric cancer and bladder cancer, which includes 7 cohorts from different data sources. ICInet is able to robustly generalize into multiple cancer types. Moreover, the performance of ICInet in those cancer types can outperform other ICI biomarkers in the clinic. Our model [area under the curve (AUC=0.85)] generally outperformed other measures, including tumor mutational burden (AUC=0.62) and programmed cell death ligand-1 score (AUC=0.74). Therefore, our study presents a prior-knowledge guided deep learning method to effectively select immunotherapy-response-associated biomarkers, thereby improving the prediction of immunotherapy response for precision oncology.
关键词	immune checkpoints inhibitors deep learning graph neural networks
DOI	10.1093/bib/bbad023
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2021YFC2500200] ; National Key R&D Program of China[2022YFF1203303] ; National Key R&D Program of China[2021YFC2500203] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA16021400] ; National Natural Science Foundation of China[32070670] ; Innovation Project for Institute of Computing Technology, CAS[E161080] ; Zhejiang Provincial Natural Science Foundation of China[LY21C060003] ; Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology[JBZX-202003]
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS记录号	WOS:001042120200032
出版者	OXFORD UNIV PRESS
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21318
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Lianhe; Zhao, Yi
作者单位	1.Univ Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 5.BGI Beijing, Multiom Joint Ctr, Beijing, Peoples R China 6.Shandong First Med Univ & Shandong Acad Med Sci, Jinan, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Lianhe,Qi, Xiaoning,Chen, Yang,et al. Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network[J]. BRIEFINGS IN BIOINFORMATICS,2023,24(2):9.
APA	Zhao, Lianhe.,Qi, Xiaoning.,Chen, Yang.,Qiao, Yixuan.,Bu, Dechao.,...&Zhao, Yi.(2023).Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network.BRIEFINGS IN BIOINFORMATICS,24(2),9.
MLA	Zhao, Lianhe,et al."Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network".BRIEFINGS IN BIOINFORMATICS 24.2(2023):9.