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

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Cancer 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.
Advisors
Cho, Kwang Hyunresearcher조광현researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2020.2,[iii, 29 p. :]

Keywords

immune checkpoint blockade; PD-1; combination therapy; melanoma; machine learning; 면역관문억제; 병용치료; 흑색종; 기계학습

URI
http://hdl.handle.net/10203/285220
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=947961&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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