한빛사논문
Younggwang Kim1,2,3,12, Hyeong-Cheol Oh1,12, Seungho Lee1,4,12 & Hyongbum Henry Kim1,5,6,7,8,9,10,11
1Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.
2Department of Pathology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
3Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
4Seoul National University Hospital, Department of Surgery, Seoul, Republic of Korea.
5Graduate School of Medical Science, Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea.
6Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
7Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea.
8Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea.
9Woo Choo Lee Institute for Precision Drug Development, Yonsei University College of Medicine, Seoul, Republic of Korea.
10Institute for Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea.
11Won-Sang Lee Institute for Hearing Loss, Yonsei University College of Medicine, Seoul, Republic of Korea.
12These authors contributed equally: Younggwang Kim, Hyeong-Cheol Oh, Seungho Lee.
Corresponding author
Correspondence to Hyongbum Henry Kim.
Abstract
Methods to characterize the functional effects of genetic variants of uncertain significance (VUSs) have been limited by incomplete coverage of the mutational space. In clinical oncology, drug resistance arising from VUSs can prevent optimal treatment. Here we introduce PEER-seq, a high-throughput method based on prime editing that can evaluate the functional effects of single-nucleotide variants (SNVs). PEER-seq introduces both intended SNVs and synonymous marker mutations using prime editing and deep sequences the endogenous target regions to identify the introduced SNVs. We generate and functionally evaluate 2,476 SNVs in the epidermal growth factor receptor gene (EGFR), including 99% of all possible variants in the canonical tyrosine kinase domain. We determined resistance profiles of 95% of all possible EGFR protein variants encoded in the whole tyrosine kinase domain against the common tyrosine kinase inhibitors afatinib, osimertinib and osimertinib in the presence of the co-occurring substitution T790M, in PC-9 cells. Our study has the potential to substantially improve the precision of therapeutic choices in clinical settings.
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