黑色素瘤
免疫疗法
小桶
医学
肿瘤科
突变
基因
计算生物学
内科学
癌症研究
癌症
生物
转录组
基因表达
遗传学
作者
Liuchao Zhang,Lei Cao,Shuang Li,Liuying Wang,Yongzhen Song,Yue Huang,Zhenyi Xu,Jia He,Meng Wang,Kang Li
出处
期刊:Journal of Immunotherapy
[Ovid Technologies (Wolters Kluwer)]
日期:2023-05-24
卷期号:46 (6): 221-231
标识
DOI:10.1097/cji.0000000000000475
摘要
Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the response of patients to immunotherapy pre-clinically. Here, we develop KP-NET, a deep learning model that is sparse on KEGG pathways, and combine it with transfer- learning to accurately predict the response of advanced melanomas to immunotherapy using KEGG pathway-level information enriched from gene mutation and copy number variation data. The KP-NET demonstrates best performance with AUROC of 0.886 on testing set and 0.803 on an unseen evaluation set when predicting responders (CR/PR/SD with PFS ≥6 mo) versus non-responders (PD/SD with PFS <6 mo) in anti-CTLA-4 treated melanoma patients. The model also achieves an AUROC of 0.917 and 0.833 in predicting CR/PR versus PD, respectively. Meanwhile, the AUROC is 0.913 when predicting responders versus non-responders in anti-PD-1/PD-L1 melanomas. Moreover, the KP-NET reveals some genes and pathways associated with response to anti-CTLA-4 treatment, such as genes PIK3CA, AOX1 and CBLB, and ErbB signaling pathway, T cell receptor signaling pathway, et al. In conclusion, the KP-NET can accurately predict the response of melanomas to immunotherapy and screen related biomarkers pre-clinically, which can contribute to precision medicine of melanoma.
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