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TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy

文献类型：期刊论文

作者Wang, Kun3; Shi, Jiangshan2; Tong, Xiaochu2; Qu, Ning2; Kong, Xiangtai2; Ni, Shengkun2; Xing, Jing1; Li, Xutong2; Zheng, Mingyue3
刊名BRIEFINGS IN BIOINFORMATICS
出版日期2024-01-22
卷号25期号:2页码:15
关键词immunotherapy clinical response graph convolutional network biomarker
ISSN号1467-5463
DOI10.1093/bib/bbae017
通讯作者Li, Xutong(lixutong@simm.ac.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn)
英文摘要Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy. Various single-gene-based biomarkers and tumor mutational burden (TMB) have been proposed for predicting clinical responses to ICI; however, their predictive ability is limited. We propose the utilization of the Text Graph Convolutional Network (GCN) method to comprehensively assess the impact of multiple genes, aiming to improve the predictive capability for ICI response. We developed TG468, a Text GCN model framing drug response prediction as a text classification task. By combining natural language processing (NLP) and graph neural network techniques, TG468 effectively handles sparse and high-dimensional exome sequencing data. As a result, TG468 can distinguish survival time for patients who received ICI therapy and outperforms single gene biomarkers, TMB and some classical machine learning models. Additionally, TG468's prediction results facilitate the identification of immune status differences among specific patient types in the Cancer Genome Atlas dataset, providing a rationale for the model's predictions. Our approach represents a pioneering use of a GCN model to analyze exome data in patients undergoing ICI therapy and offers inspiration for future research using NLP technology to analyze exome sequencing data.
WOS关键词CTLA-4 BLOCKADE ; PD-1 BLOCKADE ; CANCER ; LANDSCAPE ; BURDEN
资助项目National Natural Science Foundation of China[T2225002] ; National Natural Science Foundation of China[82273855] ; National Natural Science Foundation of China[82204278] ; SIMM-SHUTCM Traditional Chinese Medicine Innovation Joint Research Program[E2G805H] ; Shanghai Municipal Science and Technology Major Project ; National Key Research and Development Program of China[2022YFC3400504]
WOS研究方向Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:001253138400005
出版者OXFORD UNIV PRESS
源URL[http://119.78.100.183/handle/2S10ELR8/312131]  
专题新药研究国家重点实验室
通讯作者Li, Xutong; Zheng, Mingyue
作者单位1.Lingang Lab, Shanghai 200031, Peoples R China
2.Chinese Acad Sci, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai Inst Mat Med, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
3.Univ Sci & Technol China, Sch Life Sci, Div Life Sci & Med, Hefei 230026, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Kun,Shi, Jiangshan,Tong, Xiaochu,et al. TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy[J]. BRIEFINGS IN BIOINFORMATICS,2024,25(2):15.
APA Wang, Kun.,Shi, Jiangshan.,Tong, Xiaochu.,Qu, Ning.,Kong, Xiangtai.,...&Zheng, Mingyue.(2024).TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy.BRIEFINGS IN BIOINFORMATICS,25(2),15.
MLA Wang, Kun,et al."TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy".BRIEFINGS IN BIOINFORMATICS 25.2(2024):15.

入库方式： OAI收割

来源：上海药物研究所

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