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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Data availability
The deep sequencing data from this study were deposited to the National Center for Biotechnology Information’s Sequence Read Archive under accession number PRJNA1018283. We provided the datasets used in this study as Supplementary Tables 2–5.
Code availability
Screening data were analyzed with in-house custom Python scripts and MAGeCK (version 0.5.9.3). Custom Python scripts (version 3.8.16) were used to generate input files on the basis of grouped UMIs for MAGeCK; they are available from GitHub (https://github.com/oreolic/SGE_EGFR).
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Acknowledgements
We thank J. Hong, J. Lee, B. Park, Y. Kim, G. Baek and S. Park for assisting with the experiments. Schematics in Figs. 2a, 3b and 6b, and Supplementary Figs. 10a and 12a created with BioRender.com. This work was supported, in part, by the National Research Foundation (NRF) of Korea grant funded by the Korean Ministry of Science and ICT (MSIT) (2022R1A3B1078084 and 2018R1A5A2025079 (to H.H.K.)), the Bio and Medical Technology Development Program of the NRF funded by the Korean MSIT (2022M3A9E4017127, 2022M3A9F3017506 and RS-2023-00260968 (to H.H.K.)), the Yonsei Signature Research Cluster Program of 2024-22-0165 (to H.H.K.), the Brain Korea 21 FOUR Project for Medical Science (Yonsei University College of Medicine), the SNUH Kun-hee Lee Child Cancer and Rare Disease Project, Republic of Korea (22B-000-0101 (to H.H.K.)), the Yonsei Fellow Program, funded by Lee Youn Jae (to H.H.K.), the Genome Editing Research Program funded by the Korean MSIT (RS-2023-00263285 (to Y.K.)), the Basic Science Research Program through the NRF of Korea funded by the Ministry of Education (2022R1I1A1A01066096 (to Y.K.)), a faculty research grant of Yonsei University College of Medicine (6-2022-0078 (to Y.K.)), the Catholic Medical Center Research Foundation in the program year of 2023 (5-2023-B0001-00047 (to Y.K.)), the Basic Medical Science Facilitation Program through the Catholic Medical Center of The Catholic University of Korea funded by the Catholic Education Foundation (to Y.K.) and a grant of the MD–PhD/Medical Scientist Training Program (to H.C.O.) through the Korea Health Industry Development Institute, funded by the Korean Ministry of Health and Welfare.
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Y.K., H.C.O., S.L. and H.H.K. conceptualized and designed the research. Y.K., H.C.O. and S.L. performed the experiments and data analysis. Y.K., H.C.O., S.L. and H.H.K. wrote the paper.
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Yonsei University has filed a patent application based on this work, in which Y.K., H.C.O., S.L. and H.H.K. are listed as inventors. H.H.K is the founder of LiquidCRISPR.
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Extended data
Extended Data Fig. 1 Sequencing errors can hinder the accurate identification of SNVs induced by prime editing.
a,b. Maps of lentiviral vectors used for the expression of prime editor (PEmax) (a) and pegRNAs (b). bpNLS, bipartite nuclear localization signal; MMLV-RT, human codon-optimized Moloney murine leukemia virus reverse transcriptase; ITR, inverted termainal repeat. (b) Locations of PCR primers used to deep-sequence a 229-bp region containing the RT template, PBS (primer binding site), pegRNA barcode, and unique molecular identifier (UMI) are shown. c, Proportion of sequencing reads containing substitution(s) in unedited PC-9 cells expressing NRCH-PEmax (left, unedited control) and in those ten days after transduction with the NRCH-exon20 library (right) for editing exon 20 of EGFR. d, Heatmap showing odds ratios and/or P-values of 558 (=186 ×3) SNVs generated by prime editing in exon 20 of EGFR ten days after the transuction of the NRCH-exon20 library. SNVs with P-values greater than 0.05 by the two-sided Fisher’s exact test are indicated in red; in these cases, odds ratios are not shown. SNVs with odds ratios lower than 3 are shown with a white background. The numbers at the bottom of each heatmap represent the ___location in the EGFR coding sequence. At each position, the nucleotide in the reference sequence is shown. e,f, Distribution of odds ratios (e) and P-values (f) of 558 SNVs in cells transduced with the NRCH-exon20 library. The dashed horizontal lines indicate the position at which the odds ratio = 3 (e) and the P-value = 0.05 (f). The P-value was calculated using a two-sided Fisher’s exact test. g, Distribution of observed SNV frequencies in PC-9 cells expresing PEmax ten days after transduction with NRCH-exon20. The number of SNVs n = 524 (Nonsignificant), n = 34 (Significant). Boxes represent the 25th, 50th(median), and 75th percentiles, and whiskers show the 10th and 90th percentiles.
Extended Data Fig. 2 Optimization of PEER-seq and comparison with pegRNA-based analysis.
a, Correction of positional biases. To correct positional biases in LFCs, we employed LOWESS regression by using synonymous SNVs, which were assumed to have no functional effect. The LOWESS regression curves are shown as orange lines. The overall depletion of nonsense SNVs was more distinct after the adjustment. b, ROC-AUC analysis to determine the effect of positional bias correction for sets of nonsense (the number of SNVs n = 27) vs. synonymous SNVs (n = 117) in exon 2 of RPL15. Pre, ROC-AUC before adjustment; Post, ROC-AUC after adjustment. c, Correlation between PEER-seq LFC values from two biological replicates. The Pearson correlation coefficient (r) is shown. The number of SNVs n = 511. d,e, Heatmaps showing adjusted LFCs of 516 (=172 × 3) SNVs (d) and 336 protein variants (e) generated by prime editing in exon 2 of RPL15. SNVs (d) and protein variants (e) with P-values of two-sided Fisher’s exact test greater than 0.05 or odds ratios lower than 3 were excluded from the analysis and are shown with a white background. SNVs (d) and protein variants (e) for which no pegRNAs were designed are shown as gray boxes. The numbers at the bottom of each heatmap represent the ___location in the RPL15 coding sequence (d) and in the RPL15 amino acid sequence (e). At each position, the nucleotide (d) and amino acid (e; WT, wild-type; top) in the reference sequences are shown. f, Correlation between adjusted LFC values of SNVs determined by pegRNA abundance-based analysis from two biological replicates. The Pearson correlation coefficient (r) is shown. The number of SNVs n = 511. g, Kernel density estimation plots of adjusted LFCs of SNVs in RPL15 determined by pegRNA abudance-based analysis as a function of the SNV category. For each category, the number and percentage of SNVs with adjusted LFC values lower than a cutoff value (the gray dashed vertical line), representing the 5th percentile of the adjusted LFC values of synonynous mutations, are shown. h, Correlation between adjusted LFC values of SNVs calculated from PEER-seq evaluations and those from pegRNA abundance-based analysis. The Pearson correlation coefficient (r) is shown. The number of SNVs n = 511.
Extended Data Fig. 3 PEER-seq evaluation of SNVs in BRCA1.
a, Flow cytometry gating strategy used to isolate haploid cells. b, Correlation between PEER-seq LFC values of SNVs in BRCA1 from two biological replicates. The Pearson correlation coefficient (r) is shown. The number of SNVs n = 239. c, Correlation between HDR function scores obtained by Findlay et al. (2018) and PEER-seq LFC values for SNVs in exon 19 of BRCA1. The Pearson correlation coefficient (r) is shown. The number of SNVs n = 239. d, Kernel density estimation plots of adjusted LFCs of SNVs in the region encoding exon 19 of BRCA1 as a function of the category of SNV. For each category, the number and percentage of SNVs with adjusted LFC values lower than a cutoff value (the gray dashed vertical line), representing the 5th percentile of adjusted LFC values of synoynous mutations, are shown. ‘Canonical splice’ denotes the two intronic positions immediately flanking exon 19 of BRCA1 and ‘splice region’ denotes three other intronic positions. e,f, ROC curves for adjusted LFCs of SNVs determined by PEER-seq (blue) and HDR function scores (red) for sets of SNVs with pathogenic/likely pathogenic classifications (the number of SNVs n = 10) vs. SNVs with benign/likely benign classifications (n = 6) from ClinVar in exon 19 of BRCA1 (e) or sets of nonsense and canonical splice sites (n = 17) vs. synonymous SNVs (n = 44) (f). Area under curve values are shown.
Extended Data Fig. 4 Identified SNVs.
Heatmap showing odds ratios and/or P-values of 2,610 (=870 × 3) SNVs generated by prime editing in exons 18-24 of EGFR ten days after the transduction of the Syn-exon18, Syn-exon19, …, Syn-exon24 libraries. SNVs with P-values greater than 0.05 by the two-sided Fisher’s exact test are indicated in red; in these cases, odds ratios are not shown. SNVs with odds ratios lower than 3 are shown with a white background. The numbers at the bottom of each heatmap represent the ___location in the EGFR coding sequence. At each position, the nucleotide in the reference sequence is shown. Edited reads were identified based on the the presence of both the intended edit and an additional synonymous edit.
Extended Data Fig. 5 Evaluation of resistance profiles of EGFR protein variants.
a, Correlation between resistance scores following treatment with afatinib (left), osimertinib in the absence of T790M (middle), and osimertinib in the presence of T790M (right) in pairs of SNVs encoding the same protein variants. The classification of each protein variant is indicated by the dot color. Pearson correlation coefficients (r) are shown. The number of SNV pairs n = 218 (left), 218 (middle), and 210 (right). b, Correlation between resistance scores of protein variants following treatment with afatinib (left), osimertinib in the absence of T790M (middle), and osimertinib in the presence of T790M (right) in two biological replicates. The classification of each protein variant is indicated by the dot color. Pearson correlation coefficients are shown. The number of protein variants n = 1,726 (left), 1,726 (middle), and 1,671 (right). c, The number of sensitive and resistant protein variants functionally classified in the current study. The percentages of protein variants lacking previously published information about their effect on drug resistance, among all sensitive or resistant variants, are indicated on the blue bars.
Extended Data Fig. 6 Heatmap showing afatinib resistance scores of 1,817 protein variants.
These variants were generated by prime editing in exons 18-24 of EGFR in PC-9 cells. Boxes outlined in yellow and gray indicate protein variants causing resistant and intermediate phenotypes, respectively. The numbers at the bottom of each heatmap represent the ___location in the EGFR amino acid sequence. At each position, the amino acid in the reference sequence is shown at the top. Thirty protein variants with P-values of two-sided Fisher’s exact test greater than 0.05 or odds ratios lower than 3 were excluded from the analysis and are shown with a white background. Protein variants for which no pegRNAs were designed are shown as gray boxes.
Extended Data Fig. 7 Heatmap showing osimertinib resistance scores of 1,817 protein variants.
These variants were generated by prime editing in exons 18-24 of EGFR in PC-9 cells. Boxes outlined in yellow and gray indicate protein variants causing resistant and intermediate phenotypes, respectively. The numbers at the bottom of each heatmap represent the ___location in the EGFR amino acid sequence. At each position, the amino acid in the reference sequence is shown at the top. Thirty protein variants with P-values of two-sided Fisher’s exact test greater than 0.05 or odds ratios lower than 3 were excluded from the analysis and are shown with a white background. Protein variants for which no pegRNAs were designed are shown as gray boxes.
Extended Data Fig. 8 Heatmap showing osimertinib resistance scores of 1,817 protein variants in the presence of a co-occuring T790M mutation.
These variants were generated by prime editing in exons 18-24 of EGFR in PC-9 cells containing the T790M mutation. Boxes outlined in yellow and gray indicate protein variants causing resistant and intermediate phenotypes, respectively. The numbers at the bottom of each heatmap represent the ___location in the EGFR amino acid sequence. At each position, the amino acid in the reference sequence is shown at the top. Ninety-four protein variants with P-values of two-sided Fisher’s exact test greater than 0.05 or odds ratios lower than 3 were excluded from the analysis and are shown with a white background. Protein variants for which no pegRNAs were designed are shown as gray boxes.
Extended Data Fig. 9 Zygosity of prime edits.
a, Distribution of reads for each single cell-derived clone containing the indicated SNVs. ‘SNVs’ (shown in sky blue) indicates reads that contain the indicated SNVs without any other mutations. ‘Wild-type’ (green) indicates reads without the intended SNVs or any other mutations. ‘Other’ (yellow) indicates reads that fall into neither of these categories. The number of analyzed single cell-derived clones n = 51 for G930R, 50 for K754Q, and 38 for C797S in EGFR, 12 for K13* and 17 for A48E in RPL15. The stacked bars shown on the left of each graph represent the reads for the populations from which the clones are derived. b, Zygosity of the prime editing-induced SNVs indicated on the x axis. Given that the PC-9 cells that we used contain six and two copies of EGFR and RPL15, respectively, we classified the intended prime edits as partial gene copy editing if the percentage of reads containing SNVs in a clone ranged from 8.3% (=100/6 × 0.5) to 91.7% (100–8.3%) for EGFR and from 20% to 80% for RPL15.
Extended Data Fig. 10 PEER-seq experiments with higher concentrations of TKIs.
a, Correlation between PEER-seq resistance scores for mutations in exon 20 of EGFR following treatment with 8 nM osimertinib or a higher dose of osimertinib. The classification of each SNV is indicated by the dot color. The Pearson correlation coefficients (r) are shown. b, Comparison between functional classification results from PEER-seq experiments following treatment with 8 nM osimertinib and those with higher doses of osimertinib. The intensity of the color associated with entries in a given column was determined by the relative number of variants (that is, the percentages shown within the parentheses) within each category in that column. The variants are listed when there are clear discrepancies between evaluation results using different concentrations of osimertinib. c, Relative cell counts, compared to the cell count at seeding, are shown for the time point after 5 days of treatment with the specified doses of osimertinib. Error bars represent the mean and standard errors. The number of replicates n = 3.
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Supplementary Notes 1–4, Figs. 1–12 and Tables 2, 6 and 7.
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Kim, Y., Oh, HC., Lee, S. et al. Saturation profiling of drug-resistant genetic variants using prime editing. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02465-z
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DOI: https://doi.org/10.1038/s41587-024-02465-z
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