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Network-based machine learning approach to predict immunotherapy response in cancer patients
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kong, JungHo | - |
dc.contributor.author | Ha, Doyeon | - |
dc.contributor.author | Lee, Juhun | - |
dc.contributor.author | Kim, Inhae | - |
dc.contributor.author | Park, Minhyuk | - |
dc.contributor.author | Im, Sin-Hyeog | - |
dc.contributor.author | Shin, Kunyoo | - |
dc.contributor.author | Kim, Sanguk | - |
dc.date.accessioned | 2022-10-11T00:25:55Z | - |
dc.date.available | 2022-10-11T00:25:55Z | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.issued | 2022-06 | - |
dc.identifier.citation | Nature Communications, Vol.13, p. 3703 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | https://hdl.handle.net/10371/185644 | - |
dc.description.abstract | Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (similar to 30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types-melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology. | - |
dc.language | 영어 | - |
dc.publisher | Nature Publishing Group | - |
dc.title | Network-based machine learning approach to predict immunotherapy response in cancer patients | - |
dc.type | Article | - |
dc.identifier.doi | 10.1038/s41467-022-31535-6 | - |
dc.citation.journaltitle | Nature Communications | - |
dc.identifier.wosid | 000820251300008 | - |
dc.identifier.scopusid | 2-s2.0-85133007975 | - |
dc.citation.startpage | 3703 | - |
dc.citation.volume | 13 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Shin, Kunyoo | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | GENE-EXPRESSION | - |
dc.subject.keywordPlus | TUMOR | - |
dc.subject.keywordPlus | BLOCKADE | - |
dc.subject.keywordPlus | METAANALYSIS | - |
dc.subject.keywordPlus | NIVOLUMAB | - |
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