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 1,339 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.
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来自:
陆彩女
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链接:
https://academic.oup.com/bib/article/25/3/bbae121/7636761?login=true
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期刊:
Briefings in Bioinformatics
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分类:
药物科学
>>
药物设计
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投稿状态:
已在期刊出版
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引用:
ChinaXiv:202405.00110
(或此版本
ChinaXiv:202405.00110V1)
DOI:10.1093/bib/bbae121
CSTR:32003.36.ChinaXiv.202405.00110.V1
- 推荐引用方式:
Yulong Shi,Chongwu Li,Xinben Zhang,Cheng Peng,Peng Sun,Qian Zhang,Leilei Wu,Ying Ding,Dong Xie,Zhijian Xu,Weiliang Zhu.(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.doi:10.1093/bib/bbae121
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