Identification and systemic analyses of immunogenic neoantigens in human cancers면역 원성을 가지는 신생항원 예측 및 암에서의 항암반응성 시스템적인 규명

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Despite advances in predicting physical peptide-major histocompatibility complex I (pMHC I) binding, it remains challenging to identify functionally immunogenic neoepitopes, especially for MHC II. By using the results of >36,000 immunogenicity assay, we developed an algorithm to identify pMHC whose structural alignment facilitates T cell reaction. The algorithm was extensively tested on collected data and our mouse experiment. Our method predicted neoepitopes for MHC I and MHC II that were responsive to checkpoint blockade when applied to >1,200 samples of various tumor types. To investigate selection by spontaneous immunity at the single epitope level, we analyzed the frequency spectrum of >25 million mutations in >9,000 treatment-naive tumors with >100 immune phenotypes. MHC II immunogenicity specifically lowered variant frequencies in tumors under high immune pressure, particularly with high TCR clonality and MHC II expression. A similar trend was shown for MHC I neoepitopes, but only in particular tissue types. In summary, we report immune selection imposed by MHC II-restricted natural or therapeutic T cell reactivity.
Advisors
최정균researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2023
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2023.8,[iii, 81 p. :]

Keywords

신생항원▼a딥러닝▼a선택적 제거; Neoantigen▼aDeep-learning▼aNegative selection

URI
http://hdl.handle.net/10203/320857
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1046629&flag=dissertation
Appears in Collection
BiS-Theses_Ph.D.(박사논문)
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