DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Choi, Jung Kyoon | - |
dc.contributor.advisor | 최정균 | - |
dc.contributor.author | Kim, Kyeong Hui | - |
dc.date.accessioned | 2023-06-23T19:30:55Z | - |
dc.date.available | 2023-06-23T19:30:55Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=997760&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308745 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2022.2,[v, 47 p. :] | - |
dc.description.abstract | Cancer is one of the main causes of death in Korea and requires early detection and treatment. Accordingly, cancer immunotherapy using individual immune responses is emerging as a new treatment. In this study, we predicted clinical responses to immunotherapy largely based on genetic and epigenetic alterations. First, we constructed a convolutional neural network (CNN) based DeepNeo model to discover neoantigens that not only bind to major histocompatibility complexes (MHCs) but also react with T cells. As a result of predicting clinical responses to immunotherapy in multiple cancer types, it was confirmed that DeepNeo outperforms the previous prediction markers and consequently, we can expect a more accurate prediction with our model. Next, to predict responses based on methylation level, one of epigenetic changes, it was confirmed a high predictive power with selected 10 LINE-1 regions in tumor tissue samples. In addition, there was a substantial correlation between methylation levels of a cell free DNA (cfDNA) and of a tumor tissue. Finally, we suggested the probability that a cfDNA can be a non-invasive and simple prediction marker of cancer immunotherapy as an alternative for a tumor tissue. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.title | Predicting clinical responses to checkpoint immunotherapy based on genetic and epigenetic alterations in cancer | - |
dc.title.alternative | 유전적 및 후성유전적 변이에 기반한 항암면역치료 반응성 예측 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | 김경희 | - |
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