Predicting clinical benefit of immunotherapy by antigenic or functional mutations affecting tumour immunogenicity

Kwoneel Kim, Hong Sook Kim, Jeong Yeon Kim, Hyunchul Jung, Jong-Mu Sun, Jin Seok Ahn, Myung-Ju Ahn, Keunchil Park, Se-Hoon Lee () and Jung Kyoon Choi ()
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Kwoneel Kim: Kyung Hee University
Hong Sook Kim: Samsung Medical Center
Jeong Yeon Kim: Department of Bio and Brain Engineering, KAIST
Hyunchul Jung: Department of Bio and Brain Engineering, KAIST
Jong-Mu Sun: Samsung Medical Center
Jin Seok Ahn: Samsung Medical Center
Myung-Ju Ahn: Samsung Medical Center
Keunchil Park: Samsung Medical Center
Se-Hoon Lee: Samsung Medical Center
Jung Kyoon Choi: Department of Bio and Brain Engineering, KAIST

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-14562-z

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