Abstract |
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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Authors | Liuchao Zhang, Lei Cao, Shuang Li, Liuying Wang, Yongzhen Song, Yue Huang, Zhenyi Xu, Jia He, Meng Wang, Kang Li |
Journal | Journal of immunotherapy (Hagerstown, Md. : 1997)
(J Immunother)
2023 Jul-Aug 01
Vol. 46
Issue 6
Pg. 221-231
ISSN: 1537-4513 [Electronic] United States |
PMID | 37220017
(Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
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Copyright | Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved. |
Chemical References |
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Topics |
- Humans
- DNA Copy Number Variations
- Deep Learning
- Melanoma
(therapy, drug therapy)
- Immunotherapy
- Mutation
- B7-H1 Antigen
(genetics)
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