Identification of immune susceptibility features to predict response in cancer immunotherapy면역항암치료 반응의 예측을 위한 면역감수성 특징의 동정

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dc.contributor.advisorCho, Kwang Hyun-
dc.contributor.advisor조광현-
dc.contributor.authorKang, Junsoo-
dc.date.accessioned2021-05-13T19:42:15Z-
dc.date.available2021-05-13T19:42:15Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=947961&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/285220-
dc.description학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2020.2,[iii, 29 p. :]-
dc.description.abstractCancer immune checkpoint blockade (ICB) shows durable clinical benefits in treating melanoma, but only limited number of patient responds to such therapy. Combination ICB therapies have shown to increase number of responding patients. A reliable predictor of ICB response is needed to ascertain patients who will respond to ICB prior to treatment and to efficiently seek novel ICB combination drugs. Here I present anti-PD-1 Immunotherapy Signature (aPIMS), a melanoma-intrinsic predictor of anti-PD-1 ICB response. It is an unbiased, machine learning based signature that is able to predict anti-PD-1 ICB response in patient-derived data as well as cell line data. I also use aPIMS on cell line perturbation data to screen for novel anti-PD-1 ICB combination drugs.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectimmune checkpoint blockade-
dc.subjectPD-1-
dc.subjectcombination therapy-
dc.subjectmelanoma-
dc.subjectmachine learning-
dc.subject면역관문억제-
dc.subject병용치료-
dc.subject흑색종-
dc.subject기계학습-
dc.titleIdentification of immune susceptibility features to predict response in cancer immunotherapy-
dc.title.alternative면역항암치료 반응의 예측을 위한 면역감수성 특징의 동정-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :바이오및뇌공학과,-
dc.contributor.alternativeauthor강준수-
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BiS-Theses_Master(석사논문)
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