한빛사 논문
Kwoneel Kim1,2,3,7, Hong Sook Kim4,7, Jeong Yeon Kim2, Hyunchul Jung2, Jong-Mu Sun4, Jin Seok Ahn4, Myung-Ju Ahn4, Keunchil Park4, Se-Hoon Lee4,5,* & Jung Kyoon Choi2,6,*
1Department of Biology, Kyung Hee University, Seoul 02447, Republic of Korea. 2Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea. 3Clinical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul 138736, Republic of Korea. 4Division of Hematology/ Oncology, Department of Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea. 5Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University, Seoul 06351, Republic of Korea. 6Penta Medix Co., Ltd., Seongnam-si, Gyeongi-do 13449, Republic of Korea. 7These authors contributed equally: Kwoneel Kim, Hong Sook Kim.
*Correspondence to Se-Hoon Lee or Jung Kyoon Choi.
Abstract
Neoantigen burden is regarded as a fundamental determinant of response to immunotherapy. However, its predictive value remains in question because some tumours with high neoantigen load show resistance. Here, we investigate our patient cohort together with a public cohort by our algorithms for the modelling of peptide-MHC binding and inter-cohort genomic prediction of therapeutic resistance. We first attempt to predict MHC-binding peptides at high accuracy with convolutional neural networks. Our prediction outperforms previous methods in > 70% of test cases. We then develop a classifier that can predict resistance from functional mutations. The predictive genes are involved in immune response and EGFR signalling, whereas their mutation patterns reflect positive selection. When integrated with our neoantigen profiling, these anti-immunogenic mutations reveal higher predictive power than known resistance factors. Our results suggest that the clinical benefit of immunotherapy can be determined by neoantigens that induce immunity and functional mutations that facilitate immune evasion.
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