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D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer

文献类型：期刊论文

作者Shi, Yulong9; Li, Chongwu8; Zhang, Xinben; Peng, Cheng; Sun, Peng7; Zhang, Qian6; Wu, Leilei8; Ding, Ying4,5; Xie, Dong3,8; Xu, Zhijian1,2
刊名BRIEFINGS IN BIOINFORMATICS
出版日期2024-03-27
卷号25期号:3页码:10
关键词lung cancer EGFR mutation drug sensitivity prediction patient case database deep learning
ISSN号1467-5463
DOI10.1093/bib/bbae121
通讯作者Ding, Ying(dingying@njmu.edu.cn) ; Xie, Dong(kongduxd@163.com) ; Xu, Zhijian(zjxu@simm.ac.cn) ; Zhu, Weiliang(wlzhu@simm.ac.cn)
英文摘要As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.
WOS关键词GROWTH-FACTOR-RECEPTOR ; EVALUATION CRITERIA ; 1ST-LINE TREATMENT ; OPEN-LABEL ; GEFITINIB ; NSCLC ; GENE ; ERLOTINIB ; SURVIVAL ; THERAPY
资助项目National Key Research and Development Program of China[2022YFA1004304] ; National Natural Science Foundation of China[82322067] ; National Natural Science Foundation of China[82172991] ; Natural Science Research Program for Higher Education in Jiangsu Province[21KJB320015] ; Shanghai Health Commission[2019SY072] ; Shanghai Pulmonary Hospital Research Fund[FK18001] ; Shanghai Pulmonary Hospital Research Fund[FKGG1805]
WOS研究方向Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:001193845100008
出版者OXFORD UNIV PRESS
源URL[http://119.78.100.183/handle/2S10ELR8/310562]  
专题新药研究国家重点实验室
通讯作者Ding, Ying; Xie, Dong; Xu, Zhijian; Zhu, Weiliang
作者单位1.Univ Chinese Acad Sci, Sch Pharm, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China
3.Tongji Univ, Shanghai Pulm Hosp, Sch Med, Dept Thorac Surg, Shanghai 200433, Peoples R China
4.Nanjing Med Univ, Affiliated Hosp 1, Dept Pathol, Nanjing 210029, Peoples R China
5.Nanjing Med Univ, Affiliated Hosp 1, Nanjing, Peoples R China
6.East China Normal Univ, Shanghai, Peoples R China
7.Nanjing Med Univ, Nanjing, Peoples R China
8.Shanghai Pulm Hosp, Shanghai, Peoples R China
9.Shanghai Inst Mat Med, Shanghai, Peoples R China
推荐引用方式
GB/T 7714		 Shi, Yulong,Li, Chongwu,Zhang, Xinben,et al. D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer[J]. BRIEFINGS IN BIOINFORMATICS,2024,25(3):10.
APA Shi, Yulong.,Li, Chongwu.,Zhang, Xinben.,Peng, Cheng.,Sun, Peng.,...&Zhu, Weiliang.(2024).D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.BRIEFINGS IN BIOINFORMATICS,25(3),10.
MLA Shi, Yulong,et al."D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer".BRIEFINGS IN BIOINFORMATICS 25.3(2024):10.

入库方式： OAI收割

来源：上海药物研究所

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