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

文献类型：期刊论文

作者Zhao, Lianhe1; Qi, Xiaoning6; Chen, Yang; Qiao, Yixuan2,6; Bu, Dechao5; Wu, Yang5; Luo, Yufan5; Wang, Sheng5; Zhang, Rui4; Zhao, Yi2,3,6
刊名BRIEFINGS IN BIOINFORMATICS
出版日期2023-03-19
卷号24期号:2页码:9
ISSN号1467-5463
关键词immune checkpoints inhibitors deep learning graph neural networks
DOI10.1093/bib/bbad023
英文摘要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.
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
语种英语
出版者OXFORD UNIV PRESS
WOS记录号WOS:001042120200032
源URL[http://119.78.100.204/handle/2XEOYT63/21317]  
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.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Shandong First Med Univ & Shandong Acad Med Sci, Jinan, Peoples R China
4.BGI Beijing, Multiom Joint Ctr, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing, 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.

入库方式： OAI收割

来源：计算技术研究所

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