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Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

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.
AuthorsLiuchao Zhang, Lei Cao, Shuang Li, Liuying Wang, Yongzhen Song, Yue Huang, Zhenyi Xu, Jia He, Meng Wang, Kang Li
JournalJournal of immunotherapy (Hagerstown, Md. : 1997) (J Immunother) 2023 Jul-Aug 01 Vol. 46 Issue 6 Pg. 221-231 ISSN: 1537-4513 [Electronic] United States
PMID37220017 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
CopyrightCopyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
Chemical References
  • B7-H1 Antigen
Topics
  • Humans
  • DNA Copy Number Variations
  • Deep Learning
  • Melanoma (therapy, drug therapy)
  • Immunotherapy
  • Mutation
  • B7-H1 Antigen (genetics)

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