DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kong, J | - |
dc.contributor.author | Ha, D | - |
dc.contributor.author | Kim, D | - |
dc.contributor.author | Han, SeongKyu | - |
dc.contributor.author | Lee, H | - |
dc.contributor.author | Shin, Kunyoo | - |
dc.contributor.author | KIM, SANGUK | - |
dc.date.accessioned | 2020-12-15T07:50:18Z | - |
dc.date.available | 2020-12-15T07:50:18Z | - |
dc.date.created | 2020-11-19 | - |
dc.date.issued | 2020-10 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/104598 | - |
dc.description.abstract | Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches. Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. Here, the authors present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data. | - |
dc.language | English | - |
dc.publisher | NATURE RESEARCH | - |
dc.relation.isPartOf | NATURE COMMUNICATIONS | - |
dc.title | Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients | - |
dc.type | Article | - |
dc.identifier.doi | 10.1038/s41467-020-19313-8 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | NATURE COMMUNICATIONS, v.11, no.1 | - |
dc.identifier.wosid | 000588063600008 | - |
dc.citation.number | 1 | - |
dc.citation.title | NATURE COMMUNICATIONS | - |
dc.citation.volume | 11 | - |
dc.contributor.affiliatedAuthor | Kong, J | - |
dc.contributor.affiliatedAuthor | Ha, D | - |
dc.contributor.affiliatedAuthor | Kim, D | - |
dc.contributor.affiliatedAuthor | Han, SeongKyu | - |
dc.contributor.affiliatedAuthor | Lee, H | - |
dc.contributor.affiliatedAuthor | Shin, Kunyoo | - |
dc.contributor.affiliatedAuthor | KIM, SANGUK | - |
dc.identifier.scopusid | 2-s2.0-85094679448 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | BH3-ONLY PROTEINS | - |
dc.subject.keywordPlus | CANCER | - |
dc.subject.keywordPlus | SENSITIVITY | - |
dc.subject.keywordPlus | 5-FLUOROURACIL | - |
dc.subject.keywordPlus | MECHANISMS | - |
dc.subject.keywordPlus | SIGNATURES | - |
dc.subject.keywordPlus | GENOMICS | - |
dc.subject.keywordPlus | REVEALS | - |
dc.subject.keywordPlus | BASAL | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
library@postech.ac.kr Tel: 054-279-2548
Copyrights © by 2017 Pohang University of Science ad Technology All right reserved.