Saturated single nucleotide variant library, method for preparing same, and use thereof

Abstract

The present specification discloses a saturated single nucleotide variant library enabling highly reliable analysis utilizing a quasi-single nucleotide variant (quasi-SNV). Also disclosed is a method for creating the saturated single nucleotide variant library in a high-throughput manner using a prime editor. In addition, disclosed is a method for confirming the effect of the single nucleotide variant on gene function and the effect thereof on drug sensitivity by using the saturated single nucleotide variant library.

Description

Saturated single nucleotide mutation library, method for producing the same, and use thereof

[0001] This specification discloses a technology related to saturation genome editing (SGE). Furthermore, it discloses a technology for studying the impact of variants of uncertainty significance (VUS) on gene function or drug sensitivity by applying the saturation genome editing technology.

[0002] Prime editor

[0003] Prime Editor is a platform that improves the CRISPR / Cas system to allow for the editing of genes into a desired sequence. Prime Editor adds reverse transcriptase and a reverse transcription template (RT-Template) to the CRISPR / Cas system. Prime Editor recognizes and cleaves target nucleic acids in the genome. Prime Editor's reverse transcriptase and RT-Template work together at the site of the cut target nucleic acid, editing the gene as intended.

[0004] Prime Editor was developed by David R. Liu et al., and various implementations of Prime Editor have been disclosed in numerous literatures (Anzalone, Andrew V., et al. "Search-and-replace genome editing without double-strand breaks or donor DNA." Nature 576.7785 (2019): 149-157.; Chen, Peter J., et al. "Enhanced prime editing systems by manipulating cellular determinants of editing outcomes." Cell 184.22 (2021): 5635-5652.; and PCT application No. PCT / US2020 / 023712, publication No. WO2020191233A1, etc.).

[0005] Single Nucleotide Variant (SNV)

[0006] The nucleic acid sequence of the reference genome representing the human species has been determined. However, not all human genomes are identical to the reference genome. When examining an individual's genome sequence, it is very common for the nucleic acid sequence of a specific gene to differ from that of the reference gene. A gene whose nucleic acid sequence differs from that of the reference gene is said to contain a genetic variant. A single nucleotide variant (SNV) refers to a mutation or such variant in which one base in the nucleic acid sequence of the reference gene is substituted for another base. SNV encompasses single nucleotide polymorphisms (SNPs) and point mutations.

[0007] Single nucleotide mutations in a gene can significantly impact its function. Understanding the impact of single nucleotide mutations is crucial for biomedical research and clinical applications. However, the vast majority of single nucleotide mutations in nearly all genes remain as variants of uncertain significance (VUS), the effects of which are unknown.

[0008] There is a need to study variants of uncertain significance (VUS) among single nucleotide variants (SNVs).

[0009] Single nucleotide mutations in a gene can significantly impact its function. Therefore, understanding the impact of single nucleotide mutations is crucial for biomedical research and clinical applications. However, the vast majority of single nucleotide mutations in virtually all genes are variants of uncertain significance (VUS), the effects of which are unknown.

[0010] An efficient way to create and study libraries in a high-throughput manner

[0011] The number of single nucleotide mutations that can occur in a gene is three times the number of base pairs in its coding sequence (CDS). A single gene contains at least hundreds or thousands of coding sequence bases. Consequently, the number of single nucleotide mutations associated with a single gene can also reach hundreds or thousands. Evaluating the impact of each single nucleotide mutation individually would be prohibitively time-consuming and labor-intensive. A more efficient approach is to create a library of single nucleotide mutations using a high-throughput method and assess their impacts simultaneously.

[0012] Due to the limitations of high-throughput methods, the impact of sequencing errors is significant, making it difficult to distinguish single nucleotide mutations.

[0013] Studying single nucleotide mutations using a single nucleotide library requires knowledge of the genome sequences of the cells within the library. Because a vast number of single nucleotide mutations must be identified by sequence, accurate reading of each cell's sequence information is crucial. However, current sequencing technologies are not perfect. The probability of misidentifying a single nucleotide during sequencing is known to be approximately 0.1%. While 0.1% is a relatively small number, it is by no means insignificant when attempting to identify sequence information in a high-throughput manner. This will be explained in detail in the examples below.

[0014] Let's say we're studying the effects of a single nucleotide mutation introduced into a 200-bp gene. In this case, there are 600 possible single nucleotide mutations (3 single nucleotide mutations per 200 base pairs). Assume the library contains 6,000 cells, of which 50% have a single nucleotide mutation. Let's say we're trying to identify a specific single nucleotide mutation (let's call it "mutation A"):

[0015] 1) Due to sequencing errors, about 6000 Х 50% (proportion of wild-type cells) Х 0.1% (sequencing error rate) Х 1 / 3 = 1 cell will actually have the wild-type nucleic acid sequence, but will be judged to have 'mutant A'.

[0016] 2) Assuming that all single base mutations are made at equal rates, the number of cells with 'mutation A' will be 6000 X 50% (proportion of mutant cells) X 1 / 600 = 5.

[0017] Therefore, if you perform sequencing, you will read six genomes containing 'variant A'. Of these, approximately 17% (1 out of 6) are false positives due to sequencing errors. This is not a negligible error.

[0018] In reality, it's common to study genes longer than 200 bp. Therefore, the number of cells harboring a specific mutation may be less than 1 / 600 of the total number of cells harboring the mutation. When using Prime Editor, the actual number of cells harboring a single nucleotide mutation is approximately 10% to 30% of the total. Therefore, false positive results due to sequencing errors may be more prevalent. Consequently, the impact of sequencing errors is expected to be greater in real-world experimental settings. When distinguishing hundreds or thousands of single nucleotide mutations, the impact of sequencing errors is difficult to ignore.

[0019] Indirect verification methods are not accurate enough.

[0020] An alternative to directly sequencing the cellular genome is to decipher the vector sequence used for gene editing. This indirect verification method determines the presence of cells with a specific mutation by detecting the vector used to introduce that mutation. Incorporating an appropriate tag into the vector used for gene editing has the advantage of allowing high-precision identification of the vector sequence. However, gene editing technology is not 100% efficient, so there are a significant number of cells that have undergone only vector introduction but no gene editing. This method also treats these cells as having mutations. Therefore, this indirect verification method is also not sufficiently accurate.

[0021] A technology is needed that can directly verify single nucleotide mutations while reducing the impact of sequencing errors.

[0022] When trying to identify cells with single-nucleotide mutations, direct sequencing of the cell genome is ideal. To increase accuracy, direct verification methods should be used while minimizing the impact of errors. Because the absolute rate of read errors occurring at a single base is low, reducing errors themselves is extremely difficult. Therefore, other methods are needed to minimize the impact of sequencing errors.

[0023] Simultaneous introduction of single nucleotide mutations and synonymous mutations

[0024] The inventors of this specification sought to minimize the impact of sequencing errors by simultaneously introducing single nucleotide mutations and synonymous mutations into the cellular genome.

[0025] Synonymous mutations do not affect the translational outcome of a genome. Therefore, genomes containing one or more synonymous mutations in addition to single-nucleotide mutations can be treated identically to genomes containing only single-nucleotide mutations.

[0026] The probability of a sequencing error occurring at two or more bases is approximately 0.1% χ 0.1% = 0.001%. Therefore, adding one or more synonymous mutations to a single-nucleotide variant greatly reduces the probability of false positives when reading the corresponding sequence. Even when creating a library in a high-throughput manner and trying to distinguish each mutant sequence, errors of this probability can be ignored.

[0027] The idea of ​​constructing a library by simultaneously introducing single-nucleotide mutations and synonymous mutations is a groundbreaking solution. It preserves the purpose of studying single-nucleotide mutations and reduces the impact of sequencing errors.

[0028] We implement the above idea using Prime Editor technology and demonstrate the impact of sequencing errors.

[0029] To implement the above idea, the inventors used Prime Editor technology. They designed a method for generating a saturated single-nucleotide mutation library in a high-throughput manner using Prime Editor. Using this method, the inventors successfully produced a saturated single-nucleotide mutation library.

[0030] The inventors examined the impact of sequencing errors when identifying mutations by comparing just one base pair versus two or more base pairs. Their results showed that the impact of sequencing errors was dramatically reduced when single-base mutations and one or more synonymous mutations were simultaneously introduced and sequenced.

[0031] The inventors completed the invention by specifically revealing the characteristics of the saturated single nucleotide mutation library created through the above method and how the library can be used.

[0032] The above saturated single-nucleotide mutation library allows for efficient, high-throughput study of the effects of single-nucleotide mutations. Not only is it efficient, but it also yields highly reliable and accurate results.

[0033] Figure 1 is a schematic diagram illustrating the Prime Editing and Endogenous Region Sequencing (PEER-seq) method for introducing additional synonymous mutations near an intended single nucleotide variant (SNV).

[0034] Figure 2 shows the distribution of DeepPrime-FT scores for 1,674 pegRNAs for use with PEmax and 1,674 pegRNAs for use with NRCH-PEmax. The DeepPrime-FT scores are provided by a deep learning model called DeepPrime-FT and represent the predicted prime editing efficiency in mammalian cells. Boxes represent the 25th, 50th, and 75th percentiles, and whiskers represent the 10th and 90th percentiles. ****P <10-4 (Two-tailed Student's t-test).

[0035] Figure 3 shows the frequencies of 558 SNVs generated using prime editing in PC-9 cells according to MLH1 status. Boxes represent the 25th, 50th, and 75th percentiles, and whiskers represent the 10th and 90th percentiles. ****P <10 -4 (two-tailed paired t-test).

[0036] Figure 4 shows the observed frequency (left graph), odds ratio (middle graph), and P-value (right graph) of 558 SNVs in unedited control cells and prime-edited cells (NRCH-exon20). The dotted lines indicate positions with an odds ratio of 3 (middle graph) and a P-value of 0.05 (right graph).

[0037] Figure 5 is a heatmap showing the odds ratios and / or P-values ​​of 558 (= 186 x 3) single nucleotide variants (SNVs) generated by prime editing in exon 20 of EGFR 10 days after introduction of the Syn-exon20 library. SNVs with a P-value greater than 0.05 in a two-sided Fisher's exact test are indicated by a hash box, in which case the odds ratio is not displayed. SNVs with an odds ratio lower than 3 are indicated with a white background. The numbers at the bottom of each heatmap indicate the position in the EGFR coding sequence. The nucleotide in the reference sequence is indicated at each position. The upper heatmap shows the results based on single nucleotide variants (SNVs) only. The lower heatmap shows the results based on both single nucleotide variants and additional synonymous mutations.

[0038] Figure 6 shows the results of sequencing exon 20 of EGFR in unedited control PC-9 cells. Two significant figures are shown to illustrate ratios.

[0039] Figure 7 schematically illustrates a method for introducing single nucleotide mutations and synonymous mutations into the RPL15 gene in a high-throughput manner and sequencing them to evaluate the single nucleotide mutation frequency.

[0040] Figure 8 shows the frequency of 516 SNVs observed in unedited control cells and cells edited with the Syn-RPL15 library, respectively, after Syn-RPL15 library treatment.

[0041] Figure 9 shows the proportion of sequencing reads containing both the intended single nucleotide mutation and the intended synonymous mutation, and the proportion of sequencing reads containing only the intended single nucleotide mutation without other mutations, in unedited PEmax-expressing PC-9 cells (left, unedited control) and in cells 10 days after introduction of the Syn-RPL15 library (right). Two significant figures are shown to illustrate the proportions.

[0042] Figure 10 shows the odds ratios of 516 SNVs in PEmax-expressing cells 10 days after introduction of the Syn-RPL15 library. The dotted line indicates the position where the odds ratio is 3.

[0043] Figure 11 shows the P-values ​​of 516 SNVs in PEmax-expressing cells 10 days after introduction of Syn-RPL15. The dotted line indicates the position where the P-value is 0.05.

[0044] Figure 12 shows a heatmap showing the odds ratios and / or P-values ​​of 516 (= 172 x 3) single nucleotide variants (SNVs) generated by prime editing in exon 2 of RPL15 10 days after introduction of the Syn-RPL15 library. SNVs with a P-value greater than 0.05 in a two-sided Fisher's exact test are indicated by a hash box, in which case the odds ratio is not displayed. SNVs with an odds ratio less than 3 are indicated by a white background. SNVs for which no pegRNA was designed are indicated by a gray box. The numbers at the bottom of each heatmap indicate the position in the EGFR coding sequence. The nucleotides of the reference sequence are indicated at each position.

[0045] Figure 13 shows the results of estimating the kernel density of adjusted LFCs in RPL15 by SNV category and plotting them. For each category, the number and proportion of SNVs with adjusted LFC values ​​lower than the cutoff value (gray vertical line), which represents the 5th percentile of adjusted LFC values ​​for synonymous mutations, are shown.

[0046] Figure 14 shows the ROC curves for the adjusted LFC of SNVs determined by PEER-seq and pegRNA abundance-based analysis (blue) for a set of nonsense SNVs (n = 27) and synonymous SNVs (n = 117) in exon 2 of RPL15. Area under the curves are indicated.

[0047] Figure 15 shows precision-recall curves determined by PEER-seq and pegRNA abundance-based analysis (blue) for a set of nonsense SNVs (n = 27) and synonymous SNVs (n = 117) in exon 2 of RPL15. Area under the curves are indicated.

[0048] Figure 16 shows a schematic diagram of EGFR. The tyrosine kinase domain is encoded by exons 18-24. The extracellular domain is composed of subdomains I, II, III, and IV.

[0049] Figure 17 schematically illustrates a method for high-throughput analysis of drug sensitivity using pooled pegRNA libraries to generate SNVs in EGFR. One pegRNA library per exon was generated and pooled for use (a total of seven libraries for the seven EGFR exons).

[0050] Figure 18 shows the number of significantly generated and identified SNVs (top graph) and protein variants (bottom graph) in each EGFR exon. SNVs with a frequency ≥0.0005%, an odds ratio ≥3, and a P-value <0.05 were considered significantly generated. The percentages of significantly generated SNVs (top graph) and protein variants (bottom graph) are indicated in parentheses (e.g., 99% of SNVs in exon 18).

[0051] Figure 19 shows the odds ratios of 2,515 SNVs in PEmax-expressing cells 10 days after introduction of Syn-exon18 to Syn-exon24. The dotted line indicates the position with an odds ratio of 3.

[0052] Figure 20 shows the P-values ​​of 2,515 SNVs in PEmax-expressing cells 10 days after introduction of Syn-exon18 to Syn-exon24. The dotted line indicates the position where the P-value is 0.05.

[0053] Figure 21 presents the results of a plot of kernel density estimates of adjusted LFC by SNV category in the region encoding the tyrosine kinase domain of EGFR. For each category, the number and proportion of SNVs with adjusted LFC values ​​lower than the cutoff value (gray dashed vertical line), which represents the 5th percentile of adjusted LFC values ​​for synonymous mutations, are shown.

[0054] Figure 22 shows ROC curves for the adjusted LFC of SNVs determined by PEER-seq and pegRNA abundance-based analysis (blue) for a set of nonsense SNVs (n = 107) and synonymous SNVs (n = 552) in exons 18-24 of EGFR. Area under the curves are indicated.

[0055] Figure 23 shows precision-recall curves for the adjusted LFC of SNVs determined by PEER-seq and pegRNA abundance-based analysis (blue) for a set of nonsense SNVs (n = 107) and synonymous SNVs (n = 552) in exons 18-24 of EGFR. Area under the curves are indicated.

[0056] Figure 24 shows the correlation between LFC values ​​of PEER-seq after afatinib (right) and osimertinib (left) treatment in two biological replicate experiments. The drug sensitivity classification of each SNV is indicated by a dot. r represents the Pearson correlation coefficient. The number of SNVs is 2,476 (osimertinib) and 2,476 (afatinib), respectively.

[0057] Figure 25 is a graph showing the drug sensitivity assessment scores of EGFR single nucleotide polymorphisms (SNVs) for afatinib (x-axis) and osimertinib (y-axis) in PC-9 cells harboring the T790M gatekeeper mutation. The dotted line indicates the lowest resistance score value among the resistance SNVs in each assessment. SNVs conferring resistance to tyrosine kinase inhibitors (TKIs) are indicated by different shapes depending on the drug (afatinib, osimertinib, or both).

[0058] Figure 26 is a graph showing the drug sensitivity assessment scores of osimertinib in PC-9 cells containing the T790M gatekeeper mutation (x-axis) and in PC-9 cells without the T790M gatekeeper mutation (y-axis). The dotted line indicates the lowest resistance score value among the resistance SNVs in each assessment. SNVs that confer resistance to tyrosine kinase inhibitors (TKIs) are indicated by different shapes depending on the drug (afatinib, osimertinib, or both).

[0059] Figure 27 shows a lollipop plot for resistance-conferring SNVs in EGFR exons 18 to 24. The top row represents resistance to afatinib, the middle row represents resistance to osimertinib, and the bottom row represents resistance to osimertinib in the presence of the T790M mutation.

[0060] Figure 28 shows a heatmap of osimertinib resistance scores for 445 protein variants, based on SNVs at each position, in exon 20 of PC-9 cells. Boxes outlined in white and gray represent protein variants conferring resistance and intermediate phenotypes, respectively. The numbers at the bottom of each heatmap indicate the position in the EGFR amino acid sequence. The amino acid sequence of the reference sequence is indicated at the top for each position. Protein variants with a P-value greater than 0.05 or an odds ratio less than 3 are excluded from the analysis and are indicated by a white background.

[0061] Figure 29 shows the position of residue G930 in the crystal structure of the EGFR tyrosine kinase domain bound to osimertinib (Protein Data Bank: 6JXT, EGFR complex bound to osimertinib). The amino acid regions corresponding to the coding sequence of each exon are indicated in different colors.

[0062] Figure 30 is a graph comparing the proportion of sequencing reads containing single nucleotide mutations corresponding to the K754Q, G930R, and E931K mutations, respectively, before and after (days 0 and 11) treatment with a tyrosine kinase inhibitor (TKI) in cells. The upper graphs represent the results after osimertinib administration. The lower graphs represent the results after afatinib administration. The number of biological replicates for each is n = 3.

[0063] Figure 31 is a graph showing the sequencing results before and after osimertinib treatment at 6 nM, 12 nM, and 25 nM. The upper graph shows the proportion of sequencing reads containing a single nucleotide mutation corresponding to G796G, and the lower graph shows the proportion of sequencing reads containing a single nucleotide mutation corresponding to C797S. The number of biological replicates treated with osimertinib is n = 3.

[0064] Figure 32 is a graph showing the concentration-dependent sensitivity of PC-9 cells containing specific mutations (K754Q, G796G, E931K, G796R, G930R, T790M, and C797S) to afatinib. The sensitivity was assessed using the MTT assay after 3 days of afatinib treatment. Error bars represent the standard error of four replicates. The dashed line indicates the half-maximal inhibition of cell survival.

[0065] Figure 33 is a graph showing the concentration-dependent sensitivity of PC-9 cells containing specific mutations (K754Q, G796G, E931K, G796R, G930R, T790M, and C797S) to osimertinib. The sensitivity was assessed using the MTT assay after 3 days of treatment with osimertinib. Error bars represent the standard error of four replicates. The dashed line indicates the half-maximal inhibition of cell survival.

[0066] Figure 34 is a graph depicting the correlation between the afatinib resistance score (y-axis) determined by PEER-seq experiments and the half-maximal inhibitory concentration (IC50) of afatinib (x-axis) for cells containing the indicated mutations as determined by MTT assay for PC-9 cells containing the indicated mutations (K754Q, G796G, E931K, G796R, G930R, T790M, and C797S). R represents Pearson's correlation coefficient, and r represents Spearman's correlation coefficient. The classification of each SNV determined by PEER-seq experiments is indicated.

[0067] Figure 35 is a graph depicting the correlation between the osimertinib resistance score (y-axis) determined by PEER-seq experiments and the half-maximal inhibitory concentration (IC50) (x-axis) of osimertinib for cells containing the indicated mutations as determined by MTT assay for PC-9 cells containing the indicated mutations (K754Q, G796G, E931K, G796R, G930R, T790M, and C797S). R represents Pearson's correlation coefficient, and r represents Spearman's correlation coefficient. The classification of each SNV determined by PEER-seq experiments is indicated.

[0068] Figure 36 shows tumor growth curves when PC-9 cells containing wild-type or EGFR mutants G930R, E931K, or K754Q were treated daily with the indicated doses of afatinib or osimertinib. The horizontal axis of the graph represents the experimental period (in days), and the vertical axis represents tumor volume. The drug and concentration used are indicated below each graph. Data are normalized to the value on day 0 and represent the mean tumor volume ± the standard error of the mean (SEM) from three replicate experiments.

[0069] Figure 37 schematically illustrates the functional evaluation method for SNVs of EGFR exon 20 generated by prime editing in PC-9 cells. A total of 28 pegRNAs were selected based on the PEER-seq evaluation results, and this set included pegRNAs that generated 3 resistance, 7 intermediate, and 18 sensitizing SNVs. NOG, NOD / Shi-scid / IL-2Rγ-null.

[0070] Figure 38 is a graph showing the correlation between the resistance scores assessed using cultured PC-9 cells and the resistance scores assessed using xenografted tumors in mice. The Pearson correlation coefficient r is shown. The number of SNVs compared is n = 28.

[0071] Figure 39 is a schematic diagram of the lentiviral vectors used to express the Prime Editor (PEmax) and pegRNA. (a) shows the PEmax map. bpNLS, bipartite nuclear localization signal; MMLV-RT, human codon-optimized Moloney murine leukemia virus reverse transcriptase; ITR, inverted terminal repeat. (b) shows the epegRNA map. The positions of the PCR primers used to deep sequence a 229-bp region containing the RT template, primer binding site (PBS), pegRNA barcode, and unique molecular identifier (UMI) are indicated.

[0072] Figure 40 shows the results of sequencing exon 20 of EGFR in unedited PC-9 cells expressing NRCH-PEmax (left, unedited control) and in cells transfected with the NRCH-exon20 library 10 days later (right). The percentage of each sequencing read that introduced only one substitution and the percentage of reads that introduced two or more substitutions are shown.

[0073] Figure 41 is a heatmap showing the odds ratios and / or P-values ​​of 558 (= 186 x 3) single nucleotide variants (SNVs) generated by prime editing in EGFR exon 20 10 days after transfection of the NRCH-exon20 library. SNVs with a P-value greater than 0.05 in a two-sided Fisher's exact test are indicated by a hash box, and the odds ratio is not shown in this case. SNVs with an odds ratio less than 3 are indicated with a white background. The numbers at the bottom of each heatmap indicate the position in the EGFR coding sequence. The nucleotide of the reference sequence is indicated at each position.

[0074] Figure 42 is a graph showing the distribution of odds ratios of 558 SNVs in cells transformed with the NRCH-exon20 library. The dotted horizontal line indicates the position where the odds ratio = 3.

[0075] Figure 43 is a graph showing the distribution of P-values ​​of 558 SNVs in cells transformed with the NRCH-exon20 library. The dotted horizontal line indicates the position where the P-value = 0.05.

[0076] Figure 44 shows the distribution of SNV frequencies observed in PC-9 cells expressing PEmax 10 days after transfection with NRCH-exon20. The number of SNVs for each is as follows: n = 524 (Non-significant); n = 34 (Significant). Boxes represent the 25th, 50th, and 75th percentiles, and whiskers represent the 10th and 90th percentiles.

[0077] Figure 45 shows the pre- and post-treatment graphs after correcting for positional bias by applying LOWESS regression using synonymous SNVs assumed to have no functional effect. The LOWESS regression curve is shown as a line. The overall reduction in nonsense SNVs after adjustment was more pronounced.

[0078] Figure 46 presents the results of ROC-AUC analysis to determine the effectiveness of positional bias correction. ROC-AUC before (Pre) and after (Post) adjustment were compared for nonsense SNVs (n = 27) and synonymous SNVs (n = 117) sets in exon 2 of RPL15.

[0079] Figure 47 shows the correlation between PEER-seq LFC values ​​in two biological replicates. Pearson correlation coefficients (r) are shown. The number of SNVs is n = 511.

[0080] Figure 48 is a heatmap showing the adjusted LFC of 516 (= 172 x 3) SNVs generated by prime editing in RPL15 exon 2. Subjects with a P value greater than 0.05 or an odds ratio less than 3 were excluded from the analysis and are indicated by a white background. Subjects without the designed pegRNA are indicated by a gray box. The numbers at the bottom of each heatmap indicate the position in the RPL15 coding sequence. The nucleotide in the reference sequence is indicated at each position.

[0081] Figure 49 is a heatmap showing the adjusted LFCs of 336 protein variants generated by prime editing in RPL15 exon 2. Subjects with a P value greater than 0.05 or an odds ratio less than 3 were excluded from the analysis and are indicated by a white background. Subjects without the designed pegRNA are indicated by a gray box. The numbers at the bottom of each heatmap indicate the position in the RPL15 amino acid sequence. The amino acid in the reference sequence (WT, wild-type sequence) at each position is indicated.

[0082] Figure 50 is a graph depicting the correlation between the adjusted LFC values ​​for each single nucleotide variant calculated by pegRNA abundance-based analysis for two biological replicates (replicates 1 and 2). The Pearson correlation coefficient r is shown. The number of SNVs is n = 511.

[0083] Figure 51 is a graph depicting kernel density estimation plots of adjusted LFCs according to the category (Nonsense, Missense, Synonymous) of each SNV in a saturated single nucleotide variant library for the RPL15 gene. The adjusted LFCs were determined by pegRNA abundance-based analysis. For each category, the number and proportion of SNVs with adjusted LFC values ​​lower than a cutoff value (gray dashed vertical line) are shown. The cutoff value represents the 5th percentile of the adjusted LFC values ​​of synonymous mutations.

[0084] Figure 52 is a graph showing the correlation between the adjusted LFC values ​​of SNVs calculated from the PEER-seq evaluation and those calculated from the pegRNA abundance-based analysis. The Pearson correlation coefficient r is shown. The number of SNVs is n = 511.

[0085] Figure 53 shows the results of cell analysis measured using a flow cytometer. The characteristics utilized to isolate haploid cells are depicted in the graph.

[0086] Figure 54 is a graph showing the correlation between adjusted LFC values ​​measured using the PEER-seq method in each of two biological replicates (replicates 1 and 2). The Pearson correlation coefficient r is shown. The number of SNVs is n = 239.

[0087] Figure 55 is a graph showing the correlation between the HDR function score obtained by Findlay et al. (2018) and PEER-seq LFC values ​​for SNVs in BRCA1 exon 19. Pearson correlation coefficient r is shown. The number of SNVs is n = 239.

[0088] Figure 56 is a graph showing a kernel density plot of adjusted LFCs by single nucleotide variant category in cells from a saturated single nucleotide variant library for the exon 19 region of BRCA1. For each category, the number and proportion of SNVs with adjusted LFC values ​​lower than a cutoff value (gray dashed vertical line) are shown. The cutoff value represents the 5th percentile of adjusted LFC values ​​of synonymous mutations. "Canonical splice" indicates two intronic positions immediately surrounding BRCA1 exon 19, and "splice region" indicates three other intronic positions.

[0089] Figure 57 shows receiver operating characteristic (ROC) curves for adjusted LFCs of single nucleotide variants (SNVs) determined by PEER-seq and HDR functional scores. Specifically, these ROC curves are for the pathogenic / potentially pathogenic (n = 10) and benign / potentially benign (n = 6) SNV sets in BRCA1 exon 19 from ClinVar. Area under the curves are indicated.

[0090] Figure 58 shows receiver operating characteristic (ROC) curves for adjusted LFCs of single nucleotide mutations determined by PEER-seq and HDR feature scores. Specifically, the ROC curves are for missense mutations, canonical splice sites (n = 17), and a set of synonymous mutations (n ​​= 44). The area under the curve is indicated.

[0091] Figure 59 is a heatmap showing the odds ratios and / or P-values ​​of 2,610 (= 870 x 3) single nucleotide variants (SNVs) introduced by prime editing in EGFR exons 18 to 24 10 days after introduction of the Syn-exon18 to Syn-exon24 libraries. SNVs with a P-value greater than 0.05 in a two-sided Fisher-exact test are indicated by hash boxes, and in this case, the odds ratios are not displayed. SNVs with an odds ratio lower than 3 are indicated with a white background. The numbers at the bottom of each heatmap indicate the position in the EGFR coding sequence. The nucleotides in the reference sequence at each position are indicated. The above SNVs were identified based on pseudo-SNVs (single nucleotide variants and synonymous mutations).

[0092] Figure 60 is a graph showing the correlation between drug resistance scores of SNV pairs (SNV 1, SNV 2) encoding the same protein variant. The left column shows resistance to afatinib, the middle column shows resistance to osimertinib, and the right column shows resistance to osimertinib in cells additionally harboring the T790M mutation. Pearson's correlation coefficient r is shown. The classification of each protein variant is indicated by a dot. The number of SNV pairs is n = 218 on the left, n = 218 in the middle, and n = 210 on the right.

[0093] Figure 61 is a graph showing the correlation between drug resistance scores of single amino acid variants (SAAVs) between two biological replicates (Replicates 1 and 2). The left column shows resistance to afatinib, the middle column shows resistance to osimertinib, and the right column shows resistance to osimertinib in cells additionally harboring the T790M mutation. Pearson correlation coefficient r is shown. The classification of each protein variant is indicated by dots. The number of SNV pairs is as follows: left n = 1726, middle n = 1726, and right n = 1671.

[0094] Figure 62 is a graph showing the number of single amino acid mutations (SAAV) that confer sensitivity and resistance to each drug, classified based on the experimental results of this specification. Previously published mutations are indicated as Previously Reported, and novel mutations that have not yet been discovered are indicated as Novel.

[0095] Figure 63 is a heatmap showing the results of evaluating sensitivity to afatinib, based on amino acid sequence, using a saturated single-nucleotide mutation library for EGFR exons 18-24 in PC-9 cells. Protein variants causing resistance and intermediate phenotypes are outlined in bold. The numbers at the bottom of each heatmap indicate the position in the EGFR amino acid sequence. The amino acid in the reference sequence at each position is indicated at the top. Thirty protein variants with a P-value greater than 0.05 or an odds ratio less than 3 were excluded from the analysis and are indicated by a white background. Protein variants without a designed pegRNA are indicated by a gray box.

[0096] Figure 64 is a heatmap showing the results of evaluating sensitivity to osimertinib, based on amino acid sequence, using a saturated single-nucleotide mutation library for EGFR exons 18-24 in PC-9 cells. Protein variants causing resistance and intermediate phenotypes are outlined in bold. The numbers at the bottom of each heatmap indicate the position in the EGFR amino acid sequence. The amino acid in the reference sequence at each position is indicated at the top. Thirty protein variants with a P-value greater than 0.05 or an odds ratio less than 3 were excluded from the analysis and are indicated by a white background. Protein variants without a designed pegRNA are indicated by a gray box.

[0097] Figure 65 is a heatmap showing the results of evaluating sensitivity to osimertinib based on amino acid sequence using a saturated single-nucleotide mutation library for EGFR exons 18-24 in PC-9 cells containing the T790M mutation. Protein variants causing resistance and intermediate phenotypes are outlined in bold. The numbers at the bottom of each heatmap indicate the position in the EGFR amino acid sequence. The amino acid in the reference sequence at each position is indicated at the top. Thirty protein variants with a P-value greater than 0.05 or an odds ratio less than 3 were excluded from the analysis and are indicated with a white background. Protein variants without a designed pegRNA are indicated by a gray box.

[0098] Figure 66 is a graph showing the read distribution of each single-cell-derived clone containing the designated SNV. 'SNVs' represents reads containing the designated SNV without other mutations. 'Wild-type' represents reads without introduced mutations. 'Other' represents reads that do not fall into the previous two categories. The number of single-cell-derived clones analyzed was n = 51 for G930R in EGFR, n = 50 for K754Q, n = 38 for C797S, n = 12 for K13* in RPL15, and n = 17 for A48E. The stacked bars shown on the left of each graph represent the reads in the population from which the clones were derived.

[0099] Figure 67 is a graph depicting the zygosity of single nucleotide variants (SNVs) induced by prime editing. Since the PC-9 cells used for prime editing contained six and two copies of EGFR and RPL15, respectively, the intended prime edits were classified as partial gene copy editing when the proportion of reads containing SNVs in each clone was between 8.3% (= 100 / 6 x 0.5) and 91.7% (100 - 8.3%) for EGFR and between 20% and 80% for RPL15.

[0100] Figure 68 is a graph showing the correlation between the PEER-seq resistance score (x-axis) measured after treatment with 8 nM osimertinib and the PEER-seq resistance score (y-axis) measured after treatment with higher concentrations of osimertinib (25 nM, 50 nM, 100 nM, and 200 nM, respectively) for a saturated single nucleotide mutation library for EGFR exon 20. The classification of each single nucleotide mutation is indicated by a dot. The Pearson correlation coefficient r is indicated.

[0101] Figure 69 shows the PEER-seq resistance scores (x-axis) measured after treatment with 8 nM osimertinib for a saturated single-nucleotide mutation library for EGFR exon 20, compared to the PEER-seq resistance scores (y-axis) measured after treatment with higher concentrations of osimertinib (25 nM, 50 nM, 100 nM, and 200 nM, respectively). The intensity of the color associated with an entry in a given row was determined by the relative number of variants (i.e., the percentages indicated in parentheses) within each category of that row. Where there was a clear difference between the evaluation results using different concentrations of osimertinib, the corresponding mutations were noted.

[0102] Figure 70 shows the relative cell counts at each time point after 5 days of treatment with various concentrations of osimertinib. Error bars represent standard error. The number of replicates is n = 3.

[0103] Figure 71 illustrates specific sequences of intended 1-base pair (bp) substitutions, 1-bp substitution errors generated by sequencing or PCR, and 1-bp synonymous substitutions.

[0104] Figure 72 shows the proportion of reads with one or more substitutions introduced by sequencing unedited PC-9 cells. Each exon is plotted, and exon lengths are indicated.

[0105] Figure 73 is a graph showing the correlation between the length of the analyzed exons and the sum of the false-positive single nucleotide variant (SNV) frequencies. The numbers next to each dot indicate the exon number of the EGFR or RPL15 gene. The number of analyzed exons is n = 8. The Pearson correlation coefficient (r) is shown.

[0106] Figure 74 is a graph showing the frequency of single nucleotide variations (SNVs) observed in the sequencing results of cells treated with the Syn-RPL15-initial library, the unedited control, and the library-treated cells (prime-edited).

[0107] Figure 75 is a graph showing the percentage of sequencing reads containing both the intended substitution and the intended synonymous mutation, as well as the percentage of sequencing reads containing only the intended substitution without other mutations, in unedited PEmax-expressing PC-9 cells (left, Unedited control) and in cells 10 days after Syn-RPL15-initial introduction (right). Values ​​are shown to two significant figures to illustrate the percentages.

[0108] Figure 76 is a graph showing the odds ratio of 500 SNVs in PEmax expressing cells 10 days after introduction of Syn-RPL15-initial. The dotted horizontal line indicates the position where the odds ratio = 3.

[0109] Figure 77 is a graph showing the P-values ​​of 500 SNVs in PEmax-expressing cells 10 days after introduction of Syn-RPL15-initial. The dotted horizontal line indicates the position where the P-value = 0.05.

[0110] Figure 78 is a heatmap showing the odds ratios and / or P-values ​​of 516 (= 172 x 3) SNVs generated by prime editing in RPL15 exon 2 10 days after introduction of the Syn-RPL15-initial library. SNVs with a P-value greater than 0.05 in a two-sided Fisher-exact test are indicated by a hash box, in which case the odds ratio is not shown. SNVs with an odds ratio less than 3 are indicated by a white background. SNVs without a designed pegRNA are indicated by a gray box. The numbers at the bottom of each heatmap indicate the position in the EGFR coding sequence. The nucleotide of the reference sequence is indicated at each position.

[0111] Figure 79 is a graph showing a kernel density plot of adjusted LFCs by single nucleotide variant (SNV) category in a saturated single nucleotide variant library for the RPL15 gene. For each category, the number and proportion of SNVs with adjusted LFC values ​​lower than a cutoff value (gray dashed vertical line) are shown. The cutoff value represents the 5th percentile of adjusted LFC values ​​for synonymous mutations.

[0112] Figure 80 is a graph showing the ROC curves for the adjusted LFC values ​​determined based on the PEER-seq method (PEER-seq) or pegRNA abundance (pegRNA) for sets of missense SNVs (n = 25) and synonymous SNVs (n = 104) in a saturated single nucleotide variant library for exon 2 of the RPL15 gene. The area under the curve is indicated.

[0113] Figure 81 is a graph showing precision-recall curves for adjusted LFC values ​​determined based on the PEER-seq method (PEER-seq) or pegRNA abundance (pegRNA) for sets of missense SNVs (n = 25) and synonymous SNVs (n = 104) in a saturated single nucleotide variant library for exon 2 of the RPL15 gene. The area under the curve is indicated.

[0114] Figure 82 shows the pre- and post-treatment graphs after correcting for positional bias by applying LOWESS regression using synonymous SNVs assumed to have no functional effect. The LOWESS regression curve is shown as a line. The overall reduction in nonsense SNVs after adjustment was more pronounced.

[0115] Figure 83 shows the results of ROC-AUC analysis to determine the effectiveness of positional bias correction. ROC-AUC before (Pre) and after (Post) adjustment were compared for nonsense SNVs (n = 25) and synonymous SNVs (n = 104) sets in exon 2 of RPL15.

[0116] Figure 84 is a graph showing the correlation between PEER-seq LFC values ​​in two biological replicates (Replicates 1 and 2). The Pearson correlation coefficient (r) is shown. The number of SNVs is n = 440.

[0117] Figure 85 shows a kernel density plot of adjusted LFCs in a saturated single nucleotide variant library for the RPL15 gene. For each category, the number and proportion of SNVs with adjusted LFC values ​​lower than a cutoff value (gray dashed vertical line) are shown. The cutoff value represents the 5th percentile of adjusted LFC values ​​for synonymous mutations.

[0118] Figure 86 is a graph showing the ROC curves for the adjusted LFC values ​​determined based on the PEER-seq method (PEER-seq) or pegRNA abundance (pegRNA) for the sets of missense SNVs (n = 25) and synonymous SNVs (n = 104) in a saturated single nucleotide variant library for exon 2 of the RPL15 gene. The area under the curve is indicated.

[0119] Figure 87 is a graph showing precision-recall curves for adjusted LFC values ​​determined based on the PEER-seq method (PEER-seq) or pegRNA abundance (pegRNA) for sets of missense SNVs (n = 25) and synonymous SNVs (n = 104) in a saturated single nucleotide variant library for exon 2 of the RPL15 gene. The area under the curve is indicated.

[0120] Figure 88 is a heatmap showing the adjusted LFC of 516 (= 172 x 3) SNVs generated by prime editing in RPL15 exon 2. Subjects with a P value greater than 0.05 or an odds ratio less than 3 were excluded from the analysis and are indicated by a white background. Subjects without the designed pegRNA are indicated by a gray box. The numbers at the bottom of each heatmap indicate the position in the RPL15 coding sequence. The nucleotide in the reference sequence is indicated at each position.

[0121] Figure 89 is a heatmap showing the adjusted LFCs of 336 protein variants generated by prime editing in RPL15 exon 2. Subjects with a P value greater than 0.05 or an odds ratio less than 3 were excluded from the analysis and are indicated by a white background. Subjects without the designed pegRNA are indicated by a gray box. The numbers at the bottom of each heatmap indicate the position in the RPL15 amino acid sequence. The amino acid in the reference sequence (WT, wild-type sequence) at each position is indicated.

[0122] Figure 90 is a graph depicting the correlation between the adjusted LFC values ​​for each single nucleotide variant calculated by pegRNA abundance-based analysis for two biological replicates (replicates 1 and 2) of a saturated single nucleotide variant library for the RPL15 gene. The Pearson correlation coefficient r is shown. The number of SNVs is n = 440.

[0123] Figure 91 is a graph depicting kernel density plots of adjusted LFCs according to the category (Nonsense, Missense, Synonymous) of each SNV in a saturated single nucleotide variant library for the RPL15 gene. The adjusted LFCs were determined using pegRNA abundance-based analysis. For each category, the number and proportion of SNVs with adjusted LFC values ​​lower than a cutoff value (gray dashed vertical line) are shown. The cutoff value represents the 5th percentile of the adjusted LFC values ​​of synonymous mutations.

[0124] Figure 92 is a graph showing the correlation between the adjusted LFC values ​​of SNVs calculated from the PEER-seq evaluation and those calculated from the pegRNA abundance-based analysis. The Pearson correlation coefficient r is shown. The number of SNVs is n = 440.

[0125] Figure 93 is a graph showing the percentage of sequencing reads containing both the intended single nucleotide mutation and the synonymous mutation, and the percentage of sequencing reads containing only the intended single nucleotide mutation without other mutations, for each exon, after 10 days of treatment with a pegRNA library (Syn-exon18 to Syn-exon20) for EGFR gene exons in PEmax-expressing PC-9 cells (right, Primed-edited), and for the unedited control (Unedited control).

[0126] Figure 94 is a graph showing the proportion of sequencing reads containing both intended single nucleotide mutations and intended synonymous mutations for exons 18 to 24 of the EGFR gene. The unedited control group (Unedited) and the experimental group (Prime-edited) are shown. The boxes represent the 25th, 50th, and 75th percentiles, and the whiskers represent the 10th and 90th percentiles. The number of exons is n = 7.

[0127] Figure 95 is a graph showing a kernel density plot of adjusted LFCs by single nucleotide variant (SNV) category in a saturated single nucleotide variant library for the EGFR gene. For each category, the number and percentage of SNVs with adjusted LFC values ​​lower than a cutoff value (gray dashed vertical line) are shown. The cutoff value represents the 5th percentile of the adjusted LFC values ​​of sham-editing / non-targeting pegRNAs.

[0128] Figure 96 is a graph showing the correlation between the adjusted LFC values ​​of SNVs determined by pegRNA abundance-based analysis in two biological replicates (Replicates 1 and 2) of a saturated single nucleotide variant library for the EGFR gene. Pearson correlation coefficients (r) are shown. The numbers of missense, nonsense, and synonymous SNVs are n = 1,817, 107, and 552, respectively. The number of sham-editing and non-targeting pegRNAs is n = 394.

[0129] Figure 97 is a graph showing the correlation between the adjusted LFC value (x-axis) of each single nucleotide variant (SNV) determined by the PEER-seq method in a saturated single nucleotide variant library for the EGFR gene and the adjusted LFC value (y-axis) of the SNV determined by pegRNA abundance-based analysis. Pearson correlation coefficients (r) are shown. The number of SNVs is n = 2,476 (the numbers of missense, nonsense, and synonymous SNVs are n = 1,817, 107, and 552, respectively).

[0130] Figure 98 is a heatmap showing the results of evaluating sensitivity to afatinib, based on amino acid sequence, using a saturated single-nucleotide mutation library for EGFR exons 18-24 in PC-9 cells. Protein variants causing resistance and intermediate phenotypes are outlined in bold. The numbers at the bottom of each heatmap indicate the position in the EGFR amino acid sequence. The amino acid in the reference sequence at each position is indicated at the top. Forty protein variants with a P-value greater than 0.05 or an odds ratio less than 3 were excluded from the analysis and are indicated with a white background. Protein variants without a designed pegRNA are indicated by a gray box.

[0131] Figure 99 is a heatmap showing the results of evaluating sensitivity to osimertinib, based on amino acid sequence, using a saturated single-nucleotide mutation library for EGFR exons 18-24 in PC-9 cells. Protein variants causing resistance and intermediate phenotypes are outlined in bold. The numbers at the bottom of each heatmap indicate the position in the EGFR amino acid sequence. The amino acid in the reference sequence at each position is indicated at the top. Forty protein variants with a P-value greater than 0.05 or an odds ratio less than 3 were excluded from the analysis and are indicated by a white background. Protein variants without a designed pegRNA are indicated by a gray box.

[0132] Figure 100 presents a schematic for generating PC-9 cells harboring the T790M mutation. MLH1-knockout PC-9 cells expressing PEmax were co-transduced with BE4max, the appropriate sgRNA, and a plasmid vector expressing red fluorescent protein (RFP). Single cells were isolated and expanded using flow cytometry. A portion of the single-cell-derived clones was lysed and subjected to deep sequencing to confirm the base-editing results.

[0133] Figure 101 shows a schematic diagram of the base editing target sequence in EGFR for editing c.2369C>T (p.T790M, bold). Shaded boxes indicate the predicted cytosine base editing window.

[0134] Figure 102 is a graph showing the results of flow cytometry analysis. The attributes used to classify the transduced cells are indicated.

[0135] Figure 103 shows the Sanger sequencing results of the selected base-edited clone. The c.2369C>T edit was confirmed.

[0136] Figure 104 is a graph showing the correlation between LFC values ​​obtained using the PEER-seq method after osimertinib treatment in two biological replicates (Replicates 1 and 2) of an EGFR saturated single nucleotide variant library containing T790M in PC-9 cells. Each SNV is represented by a dot. The Pearson correlation coefficient (r) is indicated. The number of SNVs is n = 2,391.

[0137] Figure 105 is a heatmap showing the results of evaluating sensitivity to osimertinib based on nucleic acid sequences using a saturation single-nucleotide mutation library for EGFR exons 18-24 in PC-9 cells containing the T790M mutation. Protein variants causing resistance and intermediate phenotypes are outlined in bold. The numbers below each heatmap indicate the position in the EGFR encoding nucleic acid sequence. The nucleotide in the reference sequence is indicated at each position. One hundred and twenty-five single nucleotide variants (SNVs) with a P-value greater than 0.05 or an odds ratio less than 3 were excluded from the analysis and are indicated with a white background. SNVs without a designed pegRNA are indicated by a gray box.

[0138] Figure 106 schematically illustrates a conventional method for assessing sensitivity to tyrosine kinase inhibitors (TKIs). Lentiviruses encoding pegRNA designed to induce the generation of SNVs encoding the K754Q, G930R, and E931K mutations, as well as a control containing only an empty vector, were individually transfected into MLH1-knockout PC-9 cells expressing PEmax. Ten or six days after transfection, the two cell populations (i.e., cells transfected with pegRNA and cells transfected with the empty vector) were mixed and divided into untreated and drug-treated conditions. The frequency of SNVs was measured using targeted deep sequencing 10 or 6 days after the start of drug treatment.

[0139] Figure 107 shows the distribution of PEER-seq adjusted log fold changes (LFCs) obtained by comparing the allele frequency on day 10 to the allele frequency on day 0 in the untreated cell population for two biological replicate experiments (Replicate 1, 2). The single nucleotide mutations corresponding to the synonymous mutation and G724S are indicated.

[0140] Figure 108 shows the ratio of leads containing G724S on day 0 and day 10 of an experiment in which only excipients were administered without drugs to PC-9 cells.

[0141] Figure 109 shows the structure of ABL1 after BCR-ABL1 fusion. The tyrosine kinase domain is encoded by exons 4-9. The shaded region represents the protein-coding sequence.

[0142] Figure 110 schematically illustrates the process for producing K562-PE4K cells. Here, MSH6 was deleted using cytosine base editing.

[0143] Figure 111 shows the average prime editing efficiency measured by transfecting 160 pairs of pegRNA encoding and target sequence libraries (Library-Small) in three cell lines expressing different prime editing systems. The horizontal axis represents the elapsed time after transfection with Library-Small, and the vertical axis represents the average prime editing efficiency (%).

[0144] Figure 112 shows the experimentally measured prime editing efficiency 20 days after lentiviral transduction of 44 sensor pairs of pegRNA encoding and target sequences in K562-PE4K cells.

[0145] Figure 113 schematically illustrates a method for producing and evaluating ABL1 kinase variants using a high-throughput method. A single nucleotide variant (SNV) cell library was generated using Prime Editor in K562-PE4K cells.

[0146] Figure 114 is a graph depicting the abundance changes of epegRNAs targeting ABL1 kinase (Missense, Nonsense, Synonymous), epegRNAs targeting essential genes (Essential genes), and negative control epegRNAs (Sham-editing, Non-targeting). The upper, middle, and lower lines within the boxes represent the 25th, 50th, and 75th percentiles, respectively, and the whiskers represent the 5th and 95th percentiles. Outliers are indicated. The numbers of epegRNAs generating missense, nonsense, and synonymous mutations, epegRNAs targeting essential genes, sgRNAs, sham-editing epegRNAs, and non-targeting epegRNAs are n = 2,119, 136, 637, 190, 81, 270, and 200, respectively.

[0147] Figure 115 is a graph showing the log-2 fold change and adjusted P-value (Robust Rank Aggregation (RRA) score) of epegRNAs generating SNVs in ABL1 following nilotinib treatment. Each dot represents the results of 2-3 epegRNAs inducing a single SNV (previously reported resistance SNVs and VUS), a negative control epegRNA (or sgRNA), or 10 epegRNAs targeting essential genes.

[0148] Figure 116 is a graph showing the log-2 fold change and adjusted P values ​​(Robust Rank Aggregation (RRA) scores) of epegRNAs that generate SNVs in ABL1 following dasatinib treatment. Each dot represents the results of 2-3 epegRNAs inducing a single SNV (previously reported resistance SNVs and VUS), a negative control epegRNA (or sgRNA), or 10 epegRNAs targeting essential genes.

[0149] Figure 117 shows the average prime editing efficiency measured by transfecting three cell lines expressing different prime editing systems with a pegRNA library introducing pseudo-single nucleotide mutations into ABL1 exon 8. The horizontal axis represents the time since transfection, and the vertical axis represents the average prime editing efficiency (%).

[0150] Figure 118 is a graph plotting the frequency of SNVs observed in exons in unedited negative control cells and primed edited cells 30 days after transduction with libraries E4, E5, and E9, for cells not treated with a tyrosine kinase inhibitor (TKI). The number of SNVs analyzed was n = 5,420.

[0151] Figure 119 shows the number of reads from sequencing results for unedited K562-PE4K cells (left, Unedited control) and cells into which similar single nucleotide mutations (including SNVs and synonymous substitutions) from exon 4 to exon 9 were introduced through a library into K562-PE4K cells (right, Prime-edited).

[0152] Figure 120 is a graph showing the frequency of reads containing both intended substitutions (SNVs) and synonymous substitutions in unedited K562 cells, unedited K562-PE4K cells, and prime-edited K562-PE4K cells. The number of SNVs is n = 3,853. Boxes represent the 25th, 50th, and 75th percentiles, and whiskers represent the 10th and 90th percentiles. P values ​​were calculated using analysis of variance followed by Tukey's multiple comparisons.

[0153] Figure 121 is a graph showing the proportion of reads containing the wild-type sequence from ABL1 exon 5 to exon 9, including SNVs, two, three, or four or more substitutions, and reads containing insertions, deletions, or other complex mutations, for unedited K562 cells (left, Unedited K562) and unedited K562-PE4K cells (Unedited K562-PE4K). The color code is the same as that shown in Figure 120.

[0154] Figure 122 is a graph showing the odds ratios of 5,420 epegRNAs from ABL1 exons 4 to 9 in K562-PE4K cells after transduction with libraries E4, E5, and E9 for two biological replicate experiments (Replicate 1 and Replicate 2, respectively). The horizontal dashed line indicates the position of odds ratio = 2. The proportion of epegRNAs with odds ratios greater than 2 is indicated in parentheses.

[0155] Figure 123 is a graph showing the Bonferroni-adjusted P values ​​for 5,420 epegRNAs from ABL1 exons 4 to 9 in K562-PE4K cells after transduction with libraries E4, E5, and E9 for two biological replicate experiments (Replicate 1 and Replicate 2, respectively). The horizontal dashed line indicates the position of the adjusted P value = 0.05. The proportion of epegRNAs with an adjusted P value < 0.05 is indicated in parentheses.

[0156] Figure 124 is a graph showing the number and range of significantly generated and identified SNVs (top) and SAAVs (bottom) among all possible SNVs and SAAVs in each ABL1 exon. Each bar is numbered and the corresponding range value is indicated at the top of each bar.

[0157] Figure 125 is a graph showing the ROC curve for each normalization method to determine the effect of the normalization method on the sets of nonsense vs. synonymous SNVs in ABL1 exons 5 to 9 (left, the number of SNVs is 133 nonsense and 598 synonymous, respectively) and SAAV (right, the number of SNVs is 106 nonsense and 290 synonymous, respectively).

[0158] Figure 126 is a graph depicting the number of previously reported or novel resistant SAAVs identified by epegRNA abundance-based analysis.

[0159] Figure 127 shows the correlation between the adjusted LFC of SNV (top right) and the resistance score of SAAV (bottom left) for six tyrosine kinase inhibitors (TKIs). Pearson correlation coefficient r is shown.

[0160] Figure 128 is a graph showing the SAAV resistance score of ABL1 to imatinib and nilotinib in K562-PE4K cells. Each dot represents a different SAAV, circles indicate missense, squares indicate nonsense, and diamonds indicate synonymous.

[0161] Figure 129 is a graph showing the SAAV resistance score of ABL1 to imatinib and asciminib in K562-PE4K cells. Each dot represents a different SAAV, circles indicate missense, squares indicate nonsense, and diamonds indicate synonymous.

[0162] Figure 130 is a diagram showing the three-dimensional structure of the ABL1 core (PDB: 5MO4) showing the binding sites and important domains of six tyrosine kinase inhibitors (TKIs).

[0163] Figure 131 shows a lollipop plot of SAAVs encoded in exons 4 to 9 of ABL1 with resistance scores to imatinib, nilotinib, bosutinib, dasatinib, ponatinib, and asciminib determined for K562-PE4K cells. Only SAAVs classified as resistant are shown. The marks below the x-axis indicate subdomains of the ABL1 kinase domain. The key domain segments are shown in the legend.

[0164] Figure 132 is a graph showing the number of resistant SAAVs identified per amino acid in each subdomain of the ABL1 kinase domain. To calculate the number of each resistance mutation, the number of resistant SAAVs was divided by the length of the amino acid sequence constituting each subdomain.

[0165] Figure 133 is a heatmap showing the SAAV resistance scores and functional classification of M244, L248, G250, Q252, L273, and T315 against imatinib, nilotinib, bosutinib, dasatinib, ponatinib, and asciminib. Boxes marked 'R' and 'I' represent SAAVs exhibiting resistant and intermediate phenotypes, respectively.

[0166] Figure 134 is a schematic diagram illustrating a method for functionally evaluating variants using mouse Ba / F3 cells. Each cell was individually transduced with a retroviral vector expressing sequences containing 12 BCR-ABL1 SNVs and treated with six different tyrosine kinase inhibitors (TKIs), after which cell viability was measured.

[0167] Figure 135 is a graph showing the correlation between the resistance score measured using prime-edited K562-PE4K cells and the half-maximal growth inhibitory concentration (GI50) determined using transformed Ba / F3 cells expressing mutant ABL1. Spearman's correlation coefficient (r) is shown. Based on the results using cultured K562-PE4K cells, the classification of each SAAV is indicated by dots (circles: sensitive, diamonds: medium, squares: resistant). The number of SAAVs is n = 12.

[0168] Figure 136 is a schematic representation of a method for evaluating the function of SAAV introduced into ABL1 exon 4 in cells transplanted into mice. K562-PE4K cells were edited using a pegRNA library called small-vivo designed to induce 31 SAAVs, and then transplanted into mice.

[0169] Figure 137 is a graph depicting the correlation between resistance scores measured in cultured K562-PE4K cells and tumors formed by transplanting K562-PE4K cells into NOG mice. Pearson's correlation coefficient (r) is shown. Based on the results from cultured K562-PE4K cells, the classification of each SAAV is indicated by dots (circles: sensitive, diamonds: intermediate, squares: resistant). The number of SAAVs is n = 31.

[0170] Figure 138 is a graph showing the genomic organization of the ABL1 and BCR gene fusion in K562 (left) and KCL22 (right) cell lines. "e" indicates exon.

[0171] Figure 139 is a graph showing the correlation between resistance scores in cultured K562-PE4K and KCL22-PE4K cells. Each drug is plotted separately, and the Pearson correlation coefficient (r) is indicated. Based on the results from cultured K562-PE4K cells, the classification of each SAAV is indicated by dots (circles: sensitive, diamonds: intermediate, squares: resistant). The number of SAAVs is n = 179.

[0172] Figure 140 shows the results of drug sensitivity assessments for each tyrosine kinase inhibitor (imatinib, nilotinib, bosutinib, dacitinib, and ponatinib) using saturated single-nucleotide mutant library cells, ranked based on adjusted LFC values. The light gray graph represents the results from direct sequencing of the endogenous gene, while the dark gray graph represents the results from sequencing the epegRNA. The arrows indicate the drug resistance ranking for T315I.

[0173] Figure 141 is a graph showing the results of drug sensitivity evaluation for each tyrosine kinase inhibitor (imatinib, nilotinib, bosutinib, dacitinib, and ponatinib) tested with saturated single nucleotide mutation library cells, plotted against the results of direct endogenous gene sequencing (y-axis) and epegRNA sequencing (x-axis) based on adjusted LFC values, showing the correlation. The shape of the dots in the scatter plot represents the mutation result (X: missense, Y: nonsense, Z: synonymous mutation). r represents the Pearson correlation coefficient. The number of SNVs is n = 2,802.

[0174] Figure 142 shows the specific sequences used to introduce a premature stop codon into MSH6 in K562-PE4K cells using cytosine base editing.

[0175] Figure 143 is a diagram showing the gene copy number of chromosome 9 in K562-PE4K cells determined using exome sequencing. The positions of probes corresponding to exons 4 to 9 of ABL1 are indicated by bold vertical lines.

[0176] Figure 144 is a graph showing the distribution of DeepPrime-FT scores for epegRNAs designed to generate SNVs in exons encoding ABL1 kinase.

[0177] Figure 145 is a photograph visualizing K562-PE4K cells using a fluorescence microscope. hMLH1dn contained in the cells fluoresces by red fluorescent protein (RFP), and epegRNA fluoresces by green fluorescent protein (GFP).

[0178] Figure 146 is a graph depicting a Volcano plot showing the log2-fold change and adjusted P values ​​for epegRNAs designed to generate single nucleotide variants (SNVs) following imatinib treatment. Each dot represents the results of two to three epegRNAs designed to induce a single SNV (previously reported resistance-conferring SNVs and VUSs), a negative control epegRNA (or sgRNA), or ten epegRNAs targeting essential genes.

[0179] Figure 147 is a graph depicting a Volcano plot showing the log2-fold change and adjusted P values ​​of epegRNAs designed to generate SNVs after bosutinib treatment. Each dot represents the results of 2-3 epegRNAs designed to induce a single SNV (previously reported resistance-conferring SNVs and VUSs), a negative control epegRNA (or sgRNA), or 10 epegRNAs targeting essential genes.

[0180] Figure 148 is a graph depicting a Volcano plot showing the log2-fold change and adjusted P values ​​for epegRNAs designed to generate single nucleotide variants (SNVs) following ponatinib treatment. Each dot represents the results of two to three epegRNAs designed to induce a single SNV (previously reported resistance-conferring SNVs and VUSs), a negative control epegRNA (or sgRNA), or ten epegRNAs targeting essential genes.

[0181] Figure 149 is a graph showing the number of SAAV resistant strains (single amino acid mutations) identified by epegRNA abundance-based analysis. Previously reported SAAV resistant strains and newly discovered drug-resistant SAAV strains are shown.

[0182] Figure 150 is a graph showing the patterns of prime editing that occurred after transfection of K562-PE4K cells with epegRNAs designed to introduce SAAVs Y253H, E255V, V299L, and T315I, and deep sequencing. Each pattern is described in the chart legend.

[0183] Figure 151 is a graph depicting the median relative number of surviving K562-PE4K cells after treatment with imatinib (left), nilotinib (middle), and bosutinib in K562-PE4K cells harboring ABL1WT (wild type), ABL1Y253H, ABL1E255V, and ABL1T315I SAAVs, respectively. The relative number of surviving cells was measured by CCK-8 assay and normalized to the number of cells harboring ABL1WT treated with the solvent control (DMSO) (n = 3 independent transduction numbers).

[0184] Figure 152 is a graph showing the read frequency of the prime-edited gene in each single-cell-derived clone containing the Y253H mutation in ABL1. "Intended edit" refers to reads containing only the designated SNV without any other mutations. "Others" refers to reads with other patterns. K562-PE4K cells contain 13 copies of ABL1. Considering this, the number of prime-edited ABL1 gene copies was calculated and displayed on the y-axis.

[0185] Figure 153 is a graph showing the relative viability of K562-PE4K cells harboring ABL1 gene with different copy numbers of single nucleotide mutations, treated with six tyrosine kinase inhibitors at varying concentrations for three days, using the CellTiterGlo assay. EC represents the number of edited gene copies.

[0186] Figure 154 shows the prime editing efficiency at on-target and off-target sites in K562-PE4K cells transduced with a lentiviral vector expressing pegRNA. Prime editing efficiency was evaluated at 13 potential off-target sites (Off) and the corresponding on-target sites (On). The numbers at the top indicate the positions of the protospacer, protospacer adjacent motif (PAM), and reverse transcription template. Intended edit sites, synonymous edit sites, and base pair mismatches at on-target and off-target sites are highlighted. The red vertical line indicates the site cleaved by the prime editor protein. For each potential off-target site, cells edited with pegRNA (Edit) and unedited negative control cells (UE) were evaluated. Deep sequencing was used to measure the frequency of wild-type sequences (WT), sequences containing both intended and synonymous edits (double edits, Dbl. edit), sequences containing only intended edits (Intended edit only, Inten. Only), sequences containing only synonymous edits (Syn. Only), and other sequences (Other).

[0187] Figure 155 is a heatmap showing the resistance score of K562-PE4K cells to imitanib according to SAAV mutations in the ABL1 kinase (encoded by exons 4-9). The resistance scores were determined by directly sequencing the endogenous region of the cells (representing the edited genomic region). Black boxes in white text indicate SAAVs that induce resistance, while gray boxes indicate SAAVs that induce an intermediate phenotype. The numbers at the top of each heatmap indicate the position within the ABL1 amino acid sequence, and the reference amino acid sequence is indicated above. Forty-four protein variants with an adjusted P value greater than 0.05, an odds ratio less than 2, or fewer than 10 reads per million were excluded from the analysis and are indicated by hatched boxes.

[0188] Figure 156 is a heatmap showing the resistance score of K562-PE4K cells to nilotinib according to SAAV mutations in the ABL1 kinase (encoded by exons 4-9). The resistance scores were determined by directly sequencing the endogenous region of the cells (representing the edited genomic region). SAAV mutations indicated by black boxes in white text are those causing resistance, while those outlined in gray are those causing an intermediate phenotype. The numbers above each heatmap indicate the position within the ABL1 amino acid sequence, and the reference amino acid sequence is indicated above. Forty-four protein variants with an adjusted P value greater than 0.05, an odds ratio less than 2, or fewer than 10 reads per million were excluded from the analysis and are indicated by hatched boxes.

[0189] Figure 157 is a heatmap showing the resistance score of K562-PE4K cells to bosutanib according to SAAV mutations in the ABL1 kinase (encoded by exons 4-9). The resistance scores were determined by directly sequencing the endogenous region of the cells (representing the edited genomic region). Black boxes in white text indicate SAAVs that induce resistance, while gray boxes indicate SAAVs that induce an intermediate phenotype. The numbers at the top of each heatmap indicate the position within the ABL1 amino acid sequence, and the reference amino acid sequence is indicated above. Forty-four protein variants with an adjusted P value greater than 0.05, an odds ratio less than 2, or fewer than 10 reads per million were excluded from the analysis and are indicated by hatched boxes.

[0190] Figure 158 is a heatmap showing the resistance score of K562-PE4K cells to dasatinib according to SAAV mutations in the ABL1 kinase (encoded by exons 4-9). The resistance scores were determined by directly sequencing the endogenous region of the cells (representing the edited genomic region). Black boxes in white text indicate SAAVs that induce resistance, while gray boxes indicate SAAVs that induce an intermediate phenotype. The numbers at the top of each heatmap indicate the position within the ABL1 amino acid sequence, and the reference amino acid sequence is indicated above. Forty-four protein variants with an adjusted P value greater than 0.05, an odds ratio less than 2, or fewer than 10 reads per million were excluded from the analysis and are indicated by hatched boxes.

[0191] Figure 159 is a heatmap showing the resistance score of K562-PE4K cells to ponatinib according to SAAV mutations in the ABL1 kinase (encoded by exons 4-9). The resistance scores were determined by directly sequencing the endogenous region of the cells (representing the edited genomic region). Black boxes in white text indicate SAAVs that induce resistance, while gray boxes indicate SAAVs that induce an intermediate phenotype. The numbers at the top of each heatmap indicate the position within the ABL1 amino acid sequence, and the reference amino acid sequence is indicated above. Forty-four protein variants with an adjusted P value greater than 0.05, an odds ratio less than 2, or fewer than 10 reads per million were excluded from the analysis and are indicated by hatched boxes.

[0192] Figure 160 is a heatmap showing the resistance score of K562-PE4K cells to asimiminib according to SAAV mutations in the ABL1 kinase (encoded by exons 4-9). The resistance scores were determined by directly sequencing the endogenous region of the cells (representing the edited genomic region). Black boxes in white text indicate SAAVs that induce resistance, while gray boxes indicate SAAVs that induce an intermediate phenotype. The numbers above each heatmap indicate the position within the ABL1 amino acid sequence, and the reference amino acid sequence is indicated above. Forty-four protein variants with an adjusted P value greater than 0.05, an odds ratio less than 2, or fewer than 10 reads per million were excluded from the analysis and are indicated by hatched boxes.

[0193] Figure 161 is a graph showing the frequency of sequencing reads containing both intended and synonymous substitutions in unedited K562-PE4K and K562-PEmax cells and prime-edited K562-PE4K and K562-PEmax cells. Prime editing was performed by transducing library E5. Boxes represent the 25th, 50th, and 75th percentiles, and whiskers represent the 10th and 90th percentiles. The number of SNVs is n = 496. P values ​​were calculated using analysis of variance followed by Tukey's multiple comparisons.

[0194] Figure 162 shows the sequencing read ratios for each editing pattern in K562-PE4K (left) and K562-PEmax (right) cells transduced with library E5 30 days later. Each editing pattern is indicated in the legend.

[0195] Figure 163 is a graph depicting the correlation between resistance scores determined in cultured K562-PE4K cells (y-axis) and K562-PEmax cells (x-axis). Pearson's correlation coefficient (r) is shown. Based on the results using cultured K562-PE4K cells, the functional classification of each SAAV is indicated by dots (circles: sensitive, diamonds: medium, squares: resistant). The number of SAAVs is n = 175.

[0196] Figure 164 is a graph measuring the viability of K562-PE4K cells containing wild-type ABL1 treated with various concentrations of a tyrosine kinase inhibitor (TKI). Cell viability was assessed using the CCK-8 assay and normalized to the viability of cells treated with the solvent control (DMSO). The vertical line indicates the concentration used in the high-throughput assay. The error bars represent the standard error of three replicates.

[0197] Figure 165 is a graph showing cell viability over time after treatment with imatinib at the indicated doses (10 nM, 400 nM, and 1600 nM) in K562-PE4K cells containing wild-type ABL1. The relative cell counts were normalized to the number of cells treated with the solvent control (DMSO). Error bars represent the standard error. The number of replicates is n = 2.

[0198] Figure 166 is a graph showing cell viability over time after treatment with the indicated doses (10 nM, 400 nM, and 1600 nM) of asimiminib in K562-PE4K cells containing wild-type ABL1. The relative cell counts were normalized to the number of cells treated with the solvent control (DMSO). Error bars represent the standard error. The number of replicates is n = 2.

[0199] Figure 167 is a graph depicting the correlation between the drug resistance score (y-axis) for each single amino acid variant (SAAV) when imatinib was treated at various concentrations in a cell library and the drug resistance score (x-axis) for each SAAV measured by treatment with 100 nM imatinib. The Pearson correlation coefficient is represented as r. The classification of each SAAV is represented by a dot shape (circle: sensitive, diamond: medium, square: resistant). The number of SAAVs is n = 205.

[0200] Figure 168 is a graph showing the correlation between the drug resistance score (y-axis) for each single amino acid variant (SAAV) when the cell library was treated with various concentrations of asiminib and the drug resistance score (x-axis) for each SAAV measured by treating with 5 nM asiminib. The Pearson correlation coefficient is represented as r. The classification of each SAAV is represented by a dot shape (circle: sensitive, diamond: medium, square: resistant). The number of SAAVs is n = 205.

[0201] Figure 169 is a graph depicting the correlation between the normalized LFCs for single nucleotide variants in each of two biological replicates independently treated with each tyrosine kinase inhibitor. The left (a) was measured based on epegRNA abundance. The right (b) was measured by direct sequencing of the endogenous region. The Pearson correlation coefficient is represented as r. The classification of each SNV is indicated by a dot (circle: sensitive, diamond: medium, square: resistant). The number of SNVs is n = 2,892 (a) and 2,802 (b).

[0202] Figure 170 is a graph depicting the correlation between the adjusted LFCs of two single nucleotide variant (SNV) pairs encoding the same SAAV after treatment with each tyrosine kinase inhibitor. The left (a) was measured based on epegRNA abundance. The right (b) was measured by direct sequencing of the endogenous region. The Pearson correlation coefficient is represented as r. The classification of each SNV is indicated by the dot color (light: missense, dark: nonsense). The number of SNVs is n = 2,892 (a) and 2,802 (b).

[0203] Figure 171 shows a heatmap comparing the classification of single amino acid variants (SAAVs) into Sensitive, Intermediate, and Resistance categories based on their resistance scores for each tyrosine kinase inhibitor (imatinib, nilotinib, bosutinib, dasatinib, and ponatinib). The horizontal axis represents the results of epegRNA abundance-based analysis, and the vertical axis represents the results of direct sequencing of the intrinsic region.

[0204] Figure 172 presents a heatmap comparing the classification of single amino acid variants (SAAVs) into Sensitive, Intermediate, and Resistance categories based on their resistance scores to tyrosine kinase inhibitors. The horizontal axis represents the results of epegRNA abundance-based analysis, and the vertical axis represents the results of direct sequencing of the endogenous region.

[0205] Hereinafter, the best mode for carrying out the invention is exemplified. This includes some, but not all, implementations of the invention disclosed herein. The embodiments described in this paragraph are merely exemplary, and the implementations described in this paragraph should not be construed as the "best mode for carrying out the invention." Those skilled in the art will likely envision numerous variations and more desirable implementations of the examples described in this paragraph, and such variations should also be considered to be included within the best mode for carrying out the invention.

[0206] This specification discloses an engineered cell population for studying single nucleotide mutations in a genetic region of interest,

[0207] Hereinafter, the terms used in this claim are defined as follows:

[0208] GOI is a Genetic Region of Interest containing a coding sequence;

[0209] CDS is a coding sequence included in the above GOI;

[0210] l is the total number of base pairs contained in the CDS;

[0211] x is an integer greater than or equal to 1 and less than or equal to l;

[0212] The xth position of the CDS refers to the xth nucleotide from the 5' end of the CDS, based on a particular strand;

[0213] Nx refers to the type of nucleotide at the x-th position of the CDS, for example, A, T, C, or G;

[0214] f(.) is a function that maps one nucleotide type to another, defined as:

[0215] f(A) = T; f(T) = A; f(C) = G; and f(G) = C;

[0216] g(.) is a function that maps one nucleotide type to another, defined as:

[0217] g(A) = C; g(T) = G; g(C) = A; and g(G) = T;

[0218] h(.) is a function that maps one nucleotide type to another, defined as:

[0219] h(A) = G; h(T) = C; h(C) = T; and h(G) = A;

[0220] wherein the engineered cell population comprises at least (2.4 Х l) different types of cells,

[0221] All cells in the engineered cell population above belong to one species selected from:

[0222] (i) as a reference type, containing the wild type GOI;

[0223] (ii) a mutated GOI comprising a first substitution and a synonymous substitution, of type (3x-2),

[0224] The above first substitution means that the x-th position of CDS is substituted from Nx to f(Nx);

[0225] (iii) a mutated GOI comprising a second substitution and a synonymous substitution, of type (3x-1),

[0226] The above second substitution means that the x-th position of CDS is substituted from Nx to g(Nx);

[0227] (vi) (3x) type, including a mutated GOI including a third substitution and a synonymous substitution,

[0228] The above third substitution means that the x-th position of CDS is substituted from Nx to h(Nx);

[0229] (v) Other types other than (i), (ii), (iii), and (iv).

[0230] In one embodiment, there may be at least one integer y greater than or equal to 1 and less than or equal to l, wherein at least one mutated GOI of the (3y-2) type, the (3y-1) type, or the (3y) type is mutated such that the 5'-NGG-3' of the wild-type GOI sequence is not changed.

[0231] In one embodiment, each engineered cell comprises a nucleic acid encoding a prime editor protein;

[0232] The above prime editor protein recognizes PAM,

[0233] There may be at least one integer y greater than or equal to 1 and less than or equal to l, which is mutated such that at least one mutated GOI of the (3y-2) type, the (3y-1) type, or the (3y) type does not change the PAM contained in the wild-type GOI sequence.

[0234] This specification describes a method for preparing a saturated single nucleotide mutation library for a genetic region of interest,

[0235] The cell types included in the above library cover more than 80% of all possible single nucleotide mutation cell types.

[0236] A method is disclosed comprising:

[0237] (a) the process of preparing a cell population,

[0238] Here, each cell contains a nucleic acid encoding a prime editor protein;

[0239] (b) a process of treating the pegRNA library to the cell population;

[0240] Here, the pegRNA library comprises pegRNAs targeting the coding sequence of the genetic region of interest, or comprises nucleic acids encoding the pegRNAs,

[0241] Each pegRNA contained in the above pegRNA can work with the prime editor protein to:

[0242] (i) changing one nucleotide in the coding sequence of the genetic region of interest to another nucleotide; and

[0243] (ii) introducing one synonymous mutation into the coding sequence of the genetic region of interest;

[0244] Here, the pegRNA library comprises pegRNAs capable of inducing more than 90% of all single nucleotide mutations that can occur in the genetic region of interest;

[0245] (c) (b) After that, the process of collecting the first sample;

[0246] (d) a process of obtaining sequence information of a genetic region of interest from the first sample;

[0247] (e) A process of verifying whether the cell population that performed process (b) is a saturated single nucleotide mutation library using the sequence information obtained in (d);

[0248] Here, among the above sequence information, only the nucleic acid sequences that satisfy the following criteria are treated as nucleic acid sequences in which a single nucleotide mutation has occurred in the genetic region of interest:

[0249] (i) one nucleotide in the genetic region of interest of the wild type is changed to a different nucleotide; and

[0250] (ii) One synonymous mutation was introduced into the genetic region of interest of the wild type, which is distinct from the mutation in (i).

[0251] This specification describes a method for preparing a saturated single nucleotide mutation library for a genetic region of interest,

[0252] The cell types included in the above library cover more than 80% of all possible single nucleotide mutation cell types.

[0253] A method is disclosed comprising:

[0254] (a) the process of preparing a cell population,

[0255] (b) a process of treating the cell population with a prime editor protein or a nucleic acid encoding the prime editor protein; and a pegRNA library;

[0256] Here, the pegRNA library comprises pegRNAs targeting the coding sequence of the genetic region of interest, or comprises nucleic acids encoding the pegRNAs,

[0257] Each pegRNA contained in the above pegRNA can work with the prime editor protein to:

[0258] (i) changing one nucleotide in the coding sequence of the genetic region of interest to another nucleotide; and

[0259] (ii) introducing one synonymous mutation into the coding sequence of the genetic region of interest;

[0260] Here, the pegRNA library comprises pegRNAs capable of inducing more than 90% of all single nucleotide mutations that can occur in the genetic region of interest;

[0261] (c) (b) After that, the process of collecting the first sample;

[0262] (d) a process of obtaining sequence information of a genetic region of interest from the first sample;

[0263] (e) A process of verifying whether the cell population that performed process (b) is a saturated single nucleotide mutation library using the sequence information obtained in (d);

[0264] Here, among the above sequence information, only the nucleic acid sequences that satisfy the following criteria are treated as nucleic acid sequences in which a single nucleotide mutation has occurred in the genetic region of interest:

[0265] (i) one nucleotide in the genetic region of interest of the wild type is changed to a different nucleotide; and

[0266] (ii) One synonymous mutation was introduced into the genetic region of interest of the wild type, which is distinct from the mutation in (i).

[0267] In one embodiment, the process (b) may include one of the following processes:

[0268] (i) treating the prime editor protein or a nucleic acid encoding the prime editor protein prior to the pegRNA library;

[0269] (ii) treating said pegRNA library before said prime editor protein or a nucleic acid encoding said prime editor protein; or

[0270] (iii) Simultaneously processing the above prime editor protein or the nucleic acid encoding the above prime editor protein and the above pegRNA library.

[0271] This specification describes a method for evaluating the impact of single nucleotide mutations in a genetic region of interest on cellular function.

[0272] A method is disclosed comprising:

[0273] (a) the process of preparing a cell population,

[0274] Here, each cell contains a nucleic acid encoding a prime editor protein;

[0275] (b) a process of treating the pegRNA library to the cell population;

[0276] Here, the pegRNA library comprises pegRNAs targeting the coding sequence of the genetic region of interest, or comprises nucleic acids encoding the pegRNAs,

[0277] Each pegRNA contained in the above pegRNA can work with the prime editor protein to:

[0278] (i) changing one nucleotide in the coding sequence of the genetic region of interest to another nucleotide; and

[0279] (ii) introducing one synonymous mutation into the coding sequence of the genetic region of interest;

[0280] Here, the pegRNA library comprises pegRNAs capable of inducing more than 90% of all single nucleotide mutations that can occur in the genetic region of interest;

[0281] (c) at a first time point, a process of obtaining first sequence information from the cell population;

[0282] (d) at a second time point, a process of obtaining second sequence information from the cell population; and

[0283] (e) a process of evaluating the impact of a single nucleotide mutation occurring in a genetic region of interest on cell function using the first sequence information and the second sequence information;

[0284] Here, when utilizing the first sequence information and the second sequence information, only nucleic acid sequences satisfying the following criteria are treated as nucleic acid sequences in which a single nucleotide mutation has occurred in the genetic region of interest:

[0285] (i) one nucleotide in the genetic region of interest of the wild type is changed to a different nucleotide; and

[0286] (ii) One synonymous mutation was introduced into the genetic region of interest of the wild type, which is distinct from the mutation in (i).

[0287] This specification describes a method for assessing the impact of single nucleotide mutations in a genetic region of interest on drug sensitivity.

[0288] A method is disclosed comprising:

[0289] (a) the process of preparing a cell population,

[0290] Here, each cell contains a nucleic acid encoding a prime editor protein;

[0291] (b) a process of treating the pegRNA library to the cell population;

[0292] Here, the pegRNA library comprises pegRNAs targeting the coding sequence of the genetic region of interest, or comprises nucleic acids encoding the pegRNAs,

[0293] Each pegRNA contained in the above pegRNA can work with the prime editor protein to:

[0294] (i) changing one nucleotide in the coding sequence of the genetic region of interest to another nucleotide; and

[0295] (ii) introducing one synonymous mutation into the coding sequence of the genetic region of interest;

[0296] Here, the pegRNA library comprises pegRNAs capable of inducing more than 90% of all single nucleotide mutations that can occur in the genetic region of interest;

[0297] (c) a process of dividing the cell population into at least two groups including group 1 and group 2;

[0298] (d) a process of obtaining first sequence information from the first group;

[0299] (e) the process of administering the above drug to the second group;

[0300] (f) a process of obtaining second sequence information from the second group after the above process (e); and

[0301] (g) a process of evaluating the effect of a single nucleotide mutation occurring in a genetic region of interest on the sensitivity of the drug using the first sequence information and the second sequence information;

[0302] Here, when utilizing the first sequence information and the second sequence information, only nucleic acid sequences satisfying the following criteria are treated as nucleic acid sequences in which a single nucleotide mutation has occurred in the genetic region of interest:

[0303] (i) one nucleotide in the genetic region of interest of the wild type is changed to a different nucleotide; and

[0304] (ii) One synonymous mutation was introduced into the genetic region of interest of the wild type, which is distinct from the mutation in (i).

[0305] In one embodiment, the pegRNA library comprises a first pegRNA or DNA encoding the first pegRNA,

[0306] The synonymous mutation introduced by the first pegRNA and the prime editor protein may not destroy the PAM of the prime editor protein.

[0307] This specification discloses a method for providing information on a Tyrosine Kinase Inhibitor (TKI) suitable for treatment using Epidermal Growth Factor Receptor (EGFR) gene sequence information of a cancer patient's tumor, the method comprising:

[0308] (a) A process for obtaining EGFR gene sequence information of a patient's tumor cells;

[0309] (b) a process for obtaining sensitivity to one or more tyrosine kinase inhibitors corresponding to the EGFR gene sequence obtained in (a), with reference to [Table 1] of this specification; and

[0310] (c) A process of comparing the drug sensitivity information obtained in (b) with a predetermined standard to provide information on a tyrosine kinase inhibitor suitable for treating the above patient.

[0311] A method for providing information on a tyrosine kinase inhibitor (TKI) suitable for treatment using ABL1 gene sequence information of a cancer patient's tumor is disclosed, comprising:

[0312] (a) A process of obtaining ABL1 gene sequence information of a patient's tumor cells;

[0313] (b) a process for obtaining sensitivity to one or more tyrosine kinase inhibitors corresponding to the EGFR gene sequence obtained in (a), with reference to [Table 2] and [Table 3] of this specification; and

[0314] (c) A process of comparing the drug sensitivity information obtained in (b) with a predetermined standard to provide information on a tyrosine kinase inhibitor suitable for treating the above patient.

[0315] Hereinafter, the invention will be described in more detail through embodiments and examples with reference to the attached drawings. The attached drawings include only some embodiments of the invention. The attached drawings do not include all embodiments. The invention disclosed in this specification can be implemented in various ways and is not limited to the embodiments described herein. The embodiments have been prepared to satisfy the legal requirements applicable to this specification. Those skilled in the art related to this specification will be able to think of various modifications of the above embodiments and other embodiments not shown. Therefore, the invention disclosed in this specification is not limited to the specific embodiments described herein. The claims of this specification include modifications of the embodiments described herein and other embodiments not shown.

[0316] Definition of terms

[0317] approximately

[0318] The term "about" as used in this specification means an amount, level, value, number, frequency, percentage, dimension, size, amount, weight, or length that varies by about 30, 25, 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1%, or 0% with respect to a reference amount, level, value, number, frequency, percentage, dimension, size, amount, weight, or length.

[0319] Nucleic acid sequence notation

[0320] The symbols A, T, C, G, and U used in this specification are to be interpreted as meanings understood by a person of ordinary skill in the art. They may be appropriately interpreted as bases, nucleosides, or nucleotides in DNA or RNA, depending on the context and technology. For example, when referring to a base, they may be interpreted as adenine (A), thymine (T), cytosine (C), guanine (G), or uracil (U) themselves, respectively; when referring to a nucleoside, they may be interpreted as adenosine (A), thymidine (T), cytidine (C), guanosine (G), or uridine (U), respectively; and when referring to a nucleotide in a sequence, they should be interpreted to mean a nucleotide containing each of the above nucleosides.

[0321] target nucleic acid or target gene

[0322] As used herein, "target gene" or "target nucleic acid" refers to a cellular gene or nucleic acid that is the target of gene editing. The above target gene or target nucleic acid may be used interchangeably and may refer to the same subject. The above target gene or target nucleic acid may refer to the genome of the target cell, a chromosome-specific gene, or nucleic acid, or any gene or nucleic acid of external origin. There are no particular limitations on this, as long as it can be the target of gene editing. The above target gene or target nucleic acid may be single-stranded DNA, double-stranded DNA, RNA, or a combination thereof. Furthermore, the above terms include all other meanings that can be recognized by those skilled in the art. The meaning of the above terms should be appropriately interpreted according to the context.

[0323] Genetic Region

[0324] As used herein, a genetic region refers to a region within a cellular genome that can define a nucleic acid sequence. The genetic region can be specified by its location within the genome and its nucleic acid sequence. The location within the genome can be specified by a specific chromosome, a gene name, a domain, or other means. The nucleic acid sequence can be specified by a sequence of nucleotides (A, T, C, or G). The genetic region can be defined in any way as long as the location and nucleic acid sequence are clearly specified. The genetic region is not limited to a contiguous nucleic acid sequence. The genetic region can also be specified by two or more non-contiguous sequences at two or more locations. The term "genetic region" includes all other meanings recognized by a person skilled in the art.

[0325] Background - Single Nucleotide Variant (SNV)

[0326] Meaning of single nucleotide mutations

[0327] The nucleic acid sequence of the reference genome representing the human species has been determined. However, not all human genomes are identical to the reference genome. When examining an individual's genome sequence, it is very common for the nucleic acid sequence of a specific gene to differ from that of the reference gene. A gene whose nucleic acid sequence differs from that of the reference gene is said to contain a genetic variant. A single nucleotide variant (SNV) refers to a mutation or such variant in which one base in the nucleic acid sequence of the reference gene is substituted for another base. SNV encompasses single nucleotide polymorphisms (SNPs) and point mutations.

[0328] The Importance of Single Nucleotide Mutation Research

[0329] Single nucleotide mutations in a gene can significantly impact its function. Understanding the impact of single nucleotide mutations is crucial for biomedical research and clinical applications. However, the vast majority of single nucleotide mutations in nearly all genes remain as variants of uncertain significance (VUS), the effects of which are unknown.

[0330] Background - Synonymous mutation

[0331] There are two or more codons that code for one amino acid

[0332] The coding sequence (CDS) of a gene encodes one amino acid per three bases. A group of three bases that encodes an amino acid is called a codon. Here, the term "codon" used in this specification can mean DNA triplet code or anti-codon depending on the context. Since there are four types of bases in DNA, there are a total of 64 types of codons (4 3 ) exist. On the other hand, there are a total of 20 amino acids encoded by DNA. Therefore, two or more codons can encode the same amino acid.

[0333] Meaning of consent mutation

[0334] Synonymous mutations are also a type of mutation. A synonymous mutation occurs when the nucleic acid sequence of a gene differs from that of a reference gene, but the protein expressed by translating that gene is identical to the corresponding protein in the reference gene. A synonymous mutation occurs when a codon in the nucleic acid sequence of a gene is replaced with another codon that encodes the same amino acid. A synonymous mutation occurs when a point mutation occurs within a reference gene, but the translation product (protein) of the mutant gene is identical to that of the reference gene.

[0335] Background - Prime Editor

[0336] Prime Editor Overview

[0337] Prime Editor is a platform that improves the CRISPR / Cas system to allow for the editing of genes into a desired sequence. Prime Editor adds reverse transcriptase and a reverse transcription template (RT-Template) to the CRISPR / Cas system. Prime Editor recognizes and cleaves target nucleic acids in the genome. Prime Editor's reverse transcriptase and RT-Template work together at the site of the cut target nucleic acid, editing the gene as intended.

[0338] Prime Editor was developed by David R. Liu et al., and various implementations of Prime Editor have been disclosed in numerous literatures (Anzalone, Andrew V., et al. "Search-and-replace genome editing without double-strand breaks or donor DNA." Nature 576.7785 (2019): 149-157.; Chen, Peter J., et al. "Enhanced prime editing systems by manipulating cellular determinants of editing outcomes." Cell 184.22 (2021): 5635-5652.; and PCT application No. PCT / US2020 / 023712, publication No. WO2020191233A1, etc.).

[0339] Below, the core components and functions of the Prime Editor are explained.

[0340] Prime Editor Configuration

[0341] The prime editor comprises a prime editing protein and pegRNA. The prime editing protein is a protein that adds a reverse transcriptase to the Cas protein. The pegRNA is a nucleic acid that includes a prime editing domain in the guide RNA of the Cas protein. Compared to the CRISPR / Cas system, the Cas protein has a reverse transcriptase enzyme, and the guide RNA has a prime editing domain. The prime editing protein and pegRNA form a protein-RNA complex that functions as a prime editor.

[0342] Prime Editor Protein

[0343] The above prime editor comprises a prime editor protein. The above prime editor protein comprises a Cas protein and a reverse transcriptase. The Cas protein recognizes the protospacer adjacent motif (PAM) of the target nucleic acid and cleaves the target nucleic acid. The above reverse transcriptase reverse transcribes DNA at the site of the cleaved target nucleic acid using the prime editing domain of pegRNA as a template.

[0344] pegRNA

[0345] The above prime editor comprises a prime editing guide RNA (pegRNA). The pegRNA comprises the guide RNA of the above Cas protein and the prime editing domain. The above guide RNA comprises a guide domain and a scaffold. The guide domain is the portion that hybridizes to the target sequence near the PAM sequence. The scaffold allows the prime editor protein and pegRNA to form a protein-RNA complex. The above prime editing domain comprises a primer binding site (PBS) and a reverse transcription template (RT-Template). The primer binding site can bind to the nucleic acid cleaved by the above prime editor protein. The RT-Template serves as a template for the reverse transcriptase of the above prime editor protein. The RT-Template is designed to edit the gene as intended.

[0346] Prime Editor Protein Gene Editing Principles #1 - Target Nucleic Acid Recognition and Cleavage

[0347] The prime editor comprises a prime editor protein and pegRNA. The prime editor protein comprises a Cas protein, and the pegRNA comprises a guide RNA. The prime editor can be viewed as comprising the CRISPR / Cas system. Accordingly, the prime editor can recognize and cleave the target nucleic acid.

[0348] To explain the process of recognizing and cleaving a target nucleic acid, we must first understand its structure. Since the goal is to correct the genomic nucleic acid sequence, the target nucleic acid is double-stranded DNA. The strand to which the pegRNA complementarily binds is called the target strand, and the other strand is called the non-target strand. The target strand contains the target sequence. The non-target strand contains a protospacer adjacent motif (PAM). The prime editor above will recognize the PAM and the target sequence and cleave the target nucleic acid.

[0349] For a target nucleic acid to be cleaved, 1) the prime editor protein must recognize the PAM of the non-target strand, and 2) the pegRNA must complementarily bind to the target sequence on the target strand adjacent to the PAM. If these conditions are met, the prime editor cleaves the target nucleic acid. The location and method of cleavage of the target nucleic acid can vary depending on the prime editor protein used, and this is well known. The prime editor uses a reverse transcription template and reverse transcriptase to edit the cleaved target nucleic acid.

[0350] Prime Editor Protein Gene Editing Principle #2 - Gene Editing via Reverse Transcription

[0351] The reverse transcriptase of the prime editor protein and the prime editing domain of pegRNA function at the cleaved site of the target nucleic acid to edit the gene. This is a process in which the nucleic acid sequence of the prime editing domain is transferred to endogenous DNA via the reverse transcriptase. To this end, a portion of the nucleic acid sequence upstream of the cleaved site of the target nucleic acid, i.e., in the 5' direction, is used as a primer. The primer binding site of pegRNA is a domain that binds complementarily to the primer. When pegRNA binds to the cleaved nucleic acid site through the primer binding site, the reverse transcriptase reverse transcribes DNA from the cleaved nucleic acid site. At this time, the reverse-transcribed DNA uses the reverse transcription template (RT-Template) of pegRNA as a template. The reverse transcription template can be intentionally designed. Therefore, the nucleic acid sequence of the target nucleic acid site can be edited as intended.

[0352] Types of Prime Editor

[0353] Since David R. Liu and others developed the Prime Editor, it has been refined by numerous researchers. While these Prime Editors come in various versions, their core principles of gene editing remain the same. In this specification, the term "Prime Editor" encompasses all versions of Prime Editor developed to date, as well as those to be developed in the future. This is because the invention described in this specification can be implemented as long as genes are edited according to the principles described above.

[0354] For example, the Prime Editor can be a selected version of:

[0355] PE1 [Anzalone, Andrew V., et al. "Search-and-replace genome editing without double-strand breaks or donor DNA." Nature 576.7785 (2019): 149-157.]; PE2 [Anzalone, Andrew V., et al. "Search-and-replace genome editing without double-strand breaks or donor DNA." Nature 576.7785 (2019): 149-157.; 및 Chen, Peter J., et al. "Enhanced prime editing systems by manipulating cellular determinants of editing outcomes." Cell 184.22 (2021): 5635-5652.]; PE3 [Anzalone, Andrew V., et al. "Search-and-replace genome editing without double-strand breaks or donor DNA." Nature 576.7785 (2019): 149-157.]; PE4 [Chen, Peter J., et al. "Enhanced prime editing systems by manipulating cellular determinants of editing outcomes." Cell 184.22 (2021): 5635-5652.]; PE5 [Chen, Peter J., et al. "Enhanced prime editing systems by manipulating cellular determinants of editing outcomes." Cell 184.22 (2021): 5635-5652.]; PE6 [Doman, Jordan L., et al."Phage-assisted evolution and protein engineering yield compact, efficient prime editors." Cell 186.18 (2023): 3983-4002.]; PE7 [Yan, Jun et al., "Improving prime editing with an endogenous small RNA-binding protein." Nature 628 (2024): 639-647]; a configuration in which PEmax [Chen, Peter J., et al., "Enhanced prime editing systems by manipulating cellular determinants of editing outcomes." Cell 184.22 (2021): 5635-5652.] was applied to any of the above prime editors; and other prime editors.

[0356] Invention related to saturated single nucleotide variant library

[0357] Overview of inventions related to saturated single-nucleotide mutation libraries

[0358] This specification discloses an invention related to a "saturated single nucleotide variation library." The invention related to the saturated single nucleotide variation library includes 1) a saturated single nucleotide variation library, 2) a method for producing the same, 3) components required for production (prime editor and pegRNA library), 4) a method for using the library for drug resistance evaluation, 5) a method for using the library for gene function evaluation, and 6) other techniques required for using the invention.

[0359] Key Concept #1 - Genetic Region of Interest

[0360] As described above, a "genetic region" refers to a region within a cell genome that can define a nucleic acid sequence. The nucleic acid sequence of a genetic region is typically specified by the nucleic acid sequence of the corresponding region in a reference genome. The genetic region of interest is a "genetic region" determined based on the purpose of the saturation single nucleotide mutation library. The scope of the genetic region of interest is not limited, and the method of specifying the region of interest is not limited as long as it can be clearly specified. The genetic region of interest does not necessarily mean a contiguous nucleic acid sequence. The genetic region of interest may encompass two or more nucleic acid sequences that are separated from each other. For example, the genetic region of interest may be specified as follows: two or more genes; one specific gene; one or more exons within a specific gene; a coding region sequence within a specific gene; or a specific range of nucleic acid sequences within a specific gene. The genetic region of interest of cells included in the saturation single nucleotide mutation library has diverse nucleic acid sequences. Unless otherwise stated, references to a reference nucleic acid sequence or mutation of a gene in this specification should be interpreted as meaning within the genetic region of interest.

[0361] Key Concept #2 - Reference Nucleic Acid Sequence

[0362] To determine what constitutes a variant in a genetic region of interest, a reference must first be established. The nucleic acid sequence of the reference genetic region of interest is called the reference nucleic acid sequence. The reference nucleic acid sequence is typically defined as the nucleic acid sequence of the human reference genome, also known as the wild-type sequence. However, if necessary, the reference nucleic acid sequence may be defined differently from the nucleic acid sequence of the reference genome.

[0363] Key Concept #3 - Quasi-Single Nucleotide Variants

[0364] A quasi-single nucleotide mutation is a mutation consisting of one single nucleotide mutation and one or more synonymous mutations. Specifically, it is a mutation in which two or more bases in the wild-type nucleic acid sequence are substituted with different bases. Because synonymous mutations do not alter the translational output of a gene, they have no significant effect on its function. Therefore, quasi-single nucleotide mutations have nearly the same effects as the single nucleotide mutations they contain. Details are provided in the [Pseudo-Single Nucleotide Mutations] section.

[0365] Key Concept #4 - Reference Cell

[0366] In this specification, a cell in which the nucleic acid sequence of the genetic region of interest is identical to the reference nucleic acid sequence is referred to as a reference cell. The reference cell is appropriately selected depending on the research purpose.

[0367] Key Concept #5 - Quasi-SNV Cells

[0368] Cells that have introduced a quasi-single nucleotide mutation into the genetic region of interest are referred to as quasi-single nucleotide mutation cells. As explained above, a quasi-single nucleotide mutation is equivalent to the corresponding single nucleotide mutation. That is, quasi-single nucleotide mutation cells exhibit nearly identical characteristics to cells that only have the single nucleotide mutation (i.e., single nucleotide mutation cells). Therefore, quasi-single nucleotide mutation cells can be treated as cells in which a single nucleotide mutation has occurred in the genetic region of interest of a reference cell.

[0369] Saturated single nucleotide mutation library

[0370] This specification discloses a saturated single nucleotide mutation library. The saturated single nucleotide mutation library disclosed herein comprises cells with virtually all types of pseudo-single nucleotide mutations that can occur in a genetic region of interest. Each pseudo-single nucleotide mutation cell can be treated as a cell in which the associated single nucleotide mutation has occurred. Therefore, the saturated single nucleotide mutation library disclosed herein can be used to study the effects of single nucleotide mutations. Details are described in the [Saturated Single Nucleotide Mutation Library] section.

[0371] Method for preparing a saturated single nucleotide mutation library

[0372] This specification discloses a method for preparing a saturated single nucleotide mutation library. The method comprises the steps of: a) preparing a cell population suitable for research purposes; b) treating a composition for preparing a saturated single nucleotide mutation library; and c) verifying whether the desired saturated single nucleotide mutation library has been prepared. The method is characterized by introducing pseudo-single nucleotide mutations into cells using a prime editor, verifying whether the pseudo-single nucleotide mutations have been successfully introduced through sequencing, and performing the above process using a high-throughput method. Specific details are described in the [Method for Preparing a Saturated Single Nucleotide Mutation Library] section.

[0373] Composition for preparing a saturated single nucleotide mutation library

[0374] This specification discloses a composition for preparing a saturated single nucleotide mutation library (hereinafter referred to as a "composition for preparing a library"). The composition for preparing the library is a composition for preparing a saturated single nucleotide mutation library using a high-throughput method. To prepare the saturated single nucleotide mutation library, the composition is treated to a cell population. The purpose of treating the composition is to deliver a prime editor capable of introducing pseudo-single nucleotide mutations into the nuclei of each cell. To the extent that the above purpose can be achieved, the composition for preparing the library can be implemented in various ways. Specific details are described in the [Composition for Preparing a Saturated Single Nucleotide Mutation Library] section.

[0375] Prime Editor capable of introducing pseudo-single nucleotide mutations

[0376] This specification discloses a prime editor capable of introducing a quasi-single nucleotide mutation into a genetic region of interest within a cellular genome. The prime editor comprises a prime editor protein and a prime editing guide RNA (pegRNA). The prime editor protein comprises a Cas protein and a reverse transcriptase. The pegRNA comprises a guide domain, a scaffold, and a prime editing domain. The prime editor recognizes a specific target sequence within the cellular genome, cleaves the target site, and corrects a nucleic acid sequence within the cellular genome as designed by the prime editing domain of the pegRNA. The prime editing domain is designed to introduce a specific quasi-single nucleotide mutation into the genetic region of interest within the cellular genome. Specific details are described in the [Prime Editor Capable of Introducing a Pseudo-Single Nucleotide Mutation] section.

[0377] Uses of saturated single nucleotide mutation libraries

[0378] This saturated single-nucleotide mutation library can be used for various purposes. For example, it can be used to assess drug sensitivity. By treating this saturated single-nucleotide mutation library with a drug and determining which pseudo-single-nucleotide mutation cells survive, one can study which single-nucleotide mutations are drug-resistant and which are drug-sensitive. Another example is that this saturated single-nucleotide mutation library can be used to study the function of a genetic region of interest. By generating this saturated single-nucleotide mutation library and determining which pseudo-single-nucleotide mutation cells die over time, one can identify single-nucleotide mutations that affect cell survival. Details are described in the sections [Method for Assessing Drug Sensitivity Using a Saturated Single-Nucleotide Mutation Library] and [Method for Assessing Gene Function Using a Saturated Single-Nucleotide Mutation Library].

[0379] Quasi-Single Nucleotide Variant (Quasi-SNV)

[0380] Overview of pseudo-single nucleotide mutations

[0381] A quasi-single nucleotide mutation is a mutation in which two or more bases in a reference nucleic acid sequence are substituted with different bases. All but one of these mutations are synonymous mutations. In other words, a quasi-single nucleotide mutation consists of one single nucleotide mutation and one or more synonymous mutations. Pseudo-single nucleotide mutations are distinguished by the location and content of the single nucleotide mutations contained within them. Two quasi-single nucleotide mutations with identical single nucleotide mutation locations and content but different synonymous mutation locations or content are considered the same type. Synonymous mutations do not significantly affect gene function and do not alter gene translation output. If a quasi-single nucleotide mutation does affect gene function, it is likely caused by the single nucleotide mutation contained within it. A quasi-single nucleotide mutation is a genetic mutation that exhibits an effect nearly identical to that of the single nucleotide mutation contained within it. Therefore, quasi-single nucleotide mutations can be used to study the effects of single nucleotide mutations.

[0382] Pseudo-Single Nucleotide Mutation Structure #1 - Single Nucleotide Mutation

[0383] A pseudo-single nucleotide mutation involves a single nucleotide mutation. As explained above, a single nucleotide mutation refers to a mutation in which one base in a reference nucleic acid sequence is replaced by another base. This single nucleotide mutation serves as a criterion for distinguishing between different types of pseudo-single nucleotide mutations.

[0384] Pseudo-Single Nucleotide Mutation Structure #2 - Synonymous Mutations

[0385] A synonymous single-nucleotide mutation contains one or more synonymous mutations. A synonymous mutation means that a single base is substituted for another base. Each of these synonymous mutations does not change the amino acid encoded by that base. These synonymous mutations do not affect the amino acid sequence of the genetic region of interest when translated.

[0386] Positional relationship between single nucleotide mutations and synonymous mutations

[0387] Within a nucleic acid sequence corresponding to a synonymous single nucleotide mutation, the positions of the single nucleotide mutation and the synonymous mutation are independent. However, it is desirable that the single nucleotide mutation and the synonymous mutation are not too far apart within the genetic region of interest. This is to ensure that both single nucleotide mutations and synonymous mutations can be introduced using a single prime editor.

[0388] For example, single nucleotide mutations and synonymous mutations may exist on different codons.

[0389] For another example, a single-nucleotide mutation and a synonymous mutation can exist on the same codon. In this case, the synonymous mutation does not change the amino acid encoded by the codon containing the single-nucleotide mutation to a different amino acid. In other words, the amino acid encoded when a single-nucleotide mutation alone is present in the codon is identical to the amino acid encoded when both a single-nucleotide mutation and a synonymous mutation are present in the codon.

[0390] Examples of pseudo-single nucleotide mutations

[0391] For example, let's say the reference nucleic acid sequence is 5'-ATG AGT-3'. The above reference nucleic acid sequence consists of two codons, which encode two amino acids, one for methionine (ATG) and one for serine (AGT).

[0392] Consider a mutation in which the first base of the reference nucleic acid sequence above is replaced with a different base. For example, 5'-GTG AGT-3', in which the first base A is changed to G, is a single-base mutation.

[0393] Meanwhile, let's consider a mutation that changes the sixth base, T, to C in the above reference nucleic acid sequence. This mutation has the sequence 5'-ATG AGC-3'. Here, the second codon (AGT) in the reference nucleic acid sequence and the second codon (AGC) in the mutant sequence both encode serine. Therefore, the translation products of the reference nucleic acid sequence and the mutant sequence are identical. A mutation that changes the sixth base, T, to C in the above reference nucleic acid sequence is a synonymous mutation.

[0394] Consider a mutation that contains both the single-nucleotide mutation described above and the synonymous mutation described above. This mutation has the nucleic acid sequence 5'-GTG AGC-3'. It differs by two bases from the reference nucleic acid sequence 5'-ATG AGT-3'. The mutation where the first base, A, is changed to G is a single-nucleotide mutation. The mutation where the sixth base, T, is changed to C is a synonymous mutation. Therefore, a mutation with the nucleic acid sequence 5'-GTG AGC-3' is a pseudo-single-nucleotide mutation that contains one single-nucleotide mutation and one synonymous mutation. The pseudo-single-nucleotide mutation described above can be treated equivalently to a single-nucleotide mutation with the nucleic acid sequence 5'-GTG AGT-3'.

[0395] Distinguishing between pseudo-single nucleotide mutations

[0396] A quasi-single nucleotide mutation corresponding to a single nucleotide mutation can be implemented in various nucleic acid sequences. This is because there may be multiple locations and contents where synonymous mutations can occur. However, when classifying the quasi-single nucleotide mutations by type in this specification, the location and content of the synonymous mutation are not considered important. In other words, two quasi-single nucleotide mutations that have the same location and content as the single nucleotide mutation, but different synonymous mutation locations or contents, are treated as the same type. Therefore, the total number of quasi-single nucleotide mutations that can occur in the genetic region of interest is equal to the total number of single nucleotide mutations. This is three times the number of coding sequence base pairs contained in the genetic region of interest.

[0397] Equivalence of pseudo-single nucleotide mutations and single nucleotide mutations

[0398] Synonymous mutations have no significant effect on gene function and do not alter the protein produced by translation. If synonymous single nucleotide mutations do affect gene function, the single nucleotide mutations contained within them are likely the cause. Therefore, synonymous single nucleotide mutations are equivalent to the single nucleotide mutations contained within them. Consequently, when studying single nucleotide mutations, the same results can be obtained using synonymous single nucleotide mutations.

[0399] Description of pseudo-single nucleotide mutation types

[0400] Overview of pseudo-single nucleotide mutation types

[0401] The saturated single nucleotide mutation library disclosed in this invention includes cells with virtually all types of pseudo-single nucleotide mutations. As described above, the pseudo-single nucleotide mutations are classified based on the position at which the single nucleotide mutation occurs and the type of base substituted. Therefore, when comparing two pseudo-single nucleotide mutations, if the position at which the single nucleotide mutation is inserted and the type of base substituted are the same, they are classified as the same type even if the position at which the synonymous mutation is inserted and the content are different. Below, appropriate variables and functions are defined to refer to all possible types of pseudo-single nucleotide mutations. The classification of pseudo-single nucleotide mutations below is merely a device for easy explanation and has no other meaning.

[0402] Variable and function definitions to describe the types of pseudo-single nucleotide variant cells.

[0403] Let l be the number of bases contained in the coding sequence of the genetic region of interest, and there are 3 Х l types of pseudo-single nucleotide variants. To name each of the 3 Х l pseudo-single nucleotide variants, we define the following variables and functions:

[0404] l represents the number of bases contained in the coding sequence of the genetic region of interest.

[0405] x is any integer greater than or equal to 1 and less than or equal to l.

[0406] “xth position” means the xth position when the bases included in the coding sequence of the genetic region of interest are listed in order from the 5' end to the 3' end.

[0407] N x refers to the base (A, T, C, or G) at the xth position of the reference cell. Here, the genetic region of interest of the reference cell is identical to the wild-type sequence.

[0408] N at a specific location x To describe the case where is substituted with another base, the functions f(.), g(.), and h(.) are defined as follows.

[0409] f(.) is a function that maps a specific base to another base, and is defined as follows:

[0410] f(A) = T; f(T) = A; f(C) = G; and f(G) = C.

[0411] g(.) is a function that maps a specific base to another base, and is defined as follows:

[0412] g(A) = C; g(T) = G; g(C) = A; and g(G) = T.

[0413] h(.) is a function that maps a specific base to another base, and is defined as follows:

[0414] h(A) = G; h(T) = C; h(C) = T; and h(G) = A.

[0415] The above functions are arbitrarily set for explanation purposes and can be defined differently.

[0416] Pseudo-single nucleotide mutation type #1 - (3x-2) type

[0417] This type is called "Type 3x-2" or "Type (3x-2)". For example, it could be described as "Type 1", "Type 4", etc.

[0418] This type of quasi-single nucleotide variant is 1) in the genetic region of interest, N x f(N x ) and 2) a mutation in which one or more synonymous mutations are introduced at a location other than the x-th position of the genetic region of interest.

[0419] In other words, this type of synonymous single nucleotide mutation is 1) a single nucleotide mutation is introduced at the xth position from the 5' end of the coding sequence of the genetic region of interest, and 2) one or more synonymous mutations are introduced at other positions. Here, the single nucleotide mutation means a mutation in which the base of the wild-type sequence at the corresponding position is substituted with T if A, with A if T, with G if C, and with C if G.

[0420] In other words, this type of quasi-single nucleotide mutation is c.xN within the genetic region of interest. x >f(N x ) and one or more synonymous mutations. For example, a quasi-single nucleotide mutation of type 1 may include a single nucleotide mutation of c.1A>T and one or more synonymous mutations.

[0421] Pseudo-single nucleotide mutation type #2 - (3x-1) type

[0422] This type is called "Type 3x-1" or "Type (3x-1)". For example, it could be described as "Type 2", "Type 5", etc.

[0423] This type of quasi-single nucleotide variant is 1) in the genetic region of interest, N x g(N x ) and 2) a mutation in which one or more synonymous mutations are introduced at a location other than the x-th position of the genetic region of interest.

[0424] In other words, this type of synonymous single nucleotide mutation is 1) a single nucleotide mutation is introduced at the xth position from the 5' end of the coding sequence of the genetic region of interest, and 2) one or more synonymous mutations are introduced at other positions. Here, the single nucleotide mutation means a mutation in which the base of the wild-type sequence at the corresponding position is substituted for C if A, for G if T, for A if C, and for T if G.

[0425] In other words, this type of quasi-single nucleotide mutation is c.xN within the genetic region of interest. x >g(N x ) and one or more synonymous mutations. For example, a second type of pseudo-single nucleotide mutation may include a single nucleotide mutation of c.1A>C and one or more synonymous mutations.

[0426] Pseudo-single nucleotide mutation type #3 - (3x) type

[0427] This type is called "Type 3x" or "Type 3x". For example, it could be described as "Type 3", "Type 6", etc.

[0428] This type of quasi-single nucleotide variant is 1) in the genetic region of interest, N x h(N x ) and 2) a mutation in which one or more synonymous mutations are introduced at a location other than the x-th position of the genetic region of interest.

[0429] In other words, this type of synonymous single nucleotide mutation is 1) a single nucleotide mutation is introduced at the xth position from the 5' end of the coding sequence of the genetic region of interest, and 2) one or more synonymous mutations are introduced at other positions. Here, the single nucleotide mutation means a mutation in which the base of the wild-type sequence at the corresponding position is substituted for G if A, for C if T, for T if C, and for A if G.

[0430] In other words, this type of quasi-single nucleotide mutation is c.xN within the genetic region of interest. x >h(N x ) and one or more synonymous mutations. For example, a quasi-single nucleotide mutation of type 3 may include a single nucleotide mutation of c.1A>G and one or more synonymous mutations.

[0431] Examples of classification of quasi-single nucleotide mutation types

[0432] Considering the coding sequence of a genetic region of interest as 5'-AAANNNNNN-3', the following pseudo-single nucleotide mutations can be considered. For convenience, it is assumed that in all pseudo-single nucleotide mutations, the synonymous mutation is inserted within the 4th to 9th base of the coding sequence. Here, N is any base selected from A, T, C, or G:

[0433] 5'-TAANNNNNN-3' has a single nucleotide mutation at the 1st position, so x is 1 in the above classification, and since A is replaced with T, it corresponds to type 3x-2. In other words, the pseudo-single nucleotide mutation of 5'-TAANNNNNN-3' is type 1.

[0434] 5'-ACANNNNNN-3' has a single nucleotide mutation at the second position, so x is 2 in the above classification, and since A is replaced with C, it corresponds to type 3x-1. In other words, the pseudo-single nucleotide mutation of 5'-ACANNNNNN-3' is type 5.

[0435] 5'-AAGNNNNNN-3' has a single nucleotide mutation at the 3rd position, so x is 3 in the above classification, and since A is replaced with G, it corresponds to type 3x. In other words, the pseudo-single nucleotide mutation of 5'-AAGNNNNNN-3' is type 9.

[0436] Expansion of the pseudo-single nucleotide mutation type

[0437] In a saturated single nucleotide mutation library, each cell type is distinguished by the type of pseudo-single nucleotide mutation it contains. Therefore, a cell containing a specific type of pseudo-single nucleotide mutation can be referred to as that type of cell. For example, a "type 1 cell" refers to a cell containing a type 1 pseudo-single nucleotide mutation.

[0438] A prime editor that introduces a specific type of pseudo-single nucleotide mutation may be referred to as a prime editor of that type. For example, a "type 2 prime editor" refers to a prime editor capable of introducing type 2 pseudo-single nucleotide mutations. The above also applies equally to each implementation form of the above prime editor, i.e., protein-RNA complex, vector, and prime editing composition.

[0439] In the above Prime Editor, the structure that determines the content of the gene editing is the pegRNA. Therefore, a pegRNA that introduces a specific type of pseudo-single nucleotide mutation can be referred to as that type of pegRNA. For example, "type 3 pegRNA" refers to a pegRNA capable of introducing type 3 pseudo-single nucleotide mutations.

[0440] Saturated single nucleotide mutation library

[0441] Overview of saturated single-nucleotide mutation libraries

[0442] This specification discloses a saturated single nucleotide mutation library. The saturated single nucleotide mutation library comprises reference cells and pseudo-single nucleotide mutation cells. The reference cells are reference cells for comparing the effects of single nucleotide mutations. The reference cells have a nucleic acid sequence identical to the nucleic acid sequence of a corresponding genetic region in a reference genome. The library comprises cells with substantially all types of pseudo-single nucleotide mutations that can occur within the genetic region of interest. Each of the pseudo-single nucleotide mutation cells comprises a pseudo-single nucleotide mutation in the genetic region of interest.

[0443] Saturated Single-Nucleotide Mutation Library Construction #1 - Containing Homogeneous Cells

[0444] The above saturated single nucleotide mutation library is prepared for the purpose of studying the effects of single nucleotide mutations in a genetic region of interest. The cells constituting the library are homogeneous. Preferably, the cells are identical except for the nucleic acid sequence of the genetic region of interest. The term "fully identical" encompasses both cases where the cells are identical in composition and cases where the cells are not completely identical but still function the same.

[0445] Saturated Single-Nucleotide Mutation Library Construction #2 - Cell Characteristics

[0446] The above saturation single nucleotide mutation library is constructed for the purpose of studying the effects of single nucleotide mutations occurring in a genetic region of interest. Therefore, the cells have characteristics suitable for studying the effects of single nucleotide mutations. For example, the cells included in the above saturation single nucleotide mutation library may be haploid, diploid, or non-haploid. In another example, the above cells may be cells that mimic the environment in which a specific disease occurs. Specifically, the cells may be cells in which a specific gene has been mutated or deleted. Here, the specific gene is determined independently of the genetic region of interest.

[0447] Saturated Single-Nucleotide Mutation Library Construction #3 - Reference Cells

[0448] The above saturation single nucleotide mutation library includes a reference cell. The nucleic acid sequence of the genetic region of interest in the reference cell is identical to the nucleic acid sequence of the reference genome, i.e., the wild-type nucleic acid sequence. However, the entire genome of the reference cell does not need to be identical to the reference genome. This is because there are situations where the effects of a single nucleotide mutation occurring in a "cell that already carries a specific mutation" are studied. The reference cell can be selected in various ways depending on the purpose. The nucleic acid sequence of the genetic region of interest in the reference cell serves as a standard for determining single nucleotide mutations.

[0449] Saturated Single-Nucleotide Mutation Library Construction #4 - Pseudo-Single-Nucleotide Mutation Cells

[0450] The above saturated single nucleotide mutation library contains quasi-SNV cells. These quasi-SNV cells introduce quasi-SNV mutations within the genetic region of interest. These quasi-SNV cells are categorized based on the type of quasi-SNV introduced. The types of quasi-SNV cells are described in the [Description of Pseudo-SNV Types] section.

[0451] Saturated Single-Nucleotide Mutation Library Construction #5 - Other Cells

[0452] The above saturated single-nucleotide library also includes cells other than the reference cell or the pseudo-single-nucleotide mutant cell. These other cells may be cells generated during the library preparation process or may be introduced for specific purposes. For example, these other cells may be cells that have mutations other than the pseudo-single-nucleotide mutant within the genetic region of interest, such as indels.

[0453] Prime Editor may contain protein expression nucleic acids

[0454] As described above, this specification discloses a method for producing a saturated single-nucleotide library using a prime editor. Here, there are various methods for treating cells with the prime editor. Among these, a library can be produced by introducing a nucleic acid expressing the prime editor protein into the cells and separately treating them with pegRNA. In this case, the cells included in the saturated single-nucleotide library may contain nucleic acids expressing the prime editor protein within them.

[0455] Introduced pseudo-single nucleotide mutation features - their relationship to the PAM sequence in Prime Editor.

[0456] The above pseudo-single nucleotide mutation cells are produced using a prime editor. The prime editor recognizes the PAM sequence and the target sequence and operates. Here, the prime editor can be designed so that the PAM sequence above is changed to a different sequence as a result of gene editing. By destroying the PAM sequence through gene editing, the prime editor cannot repeatedly recognize the target. By preventing the prime editor from repeatedly functioning at the target site after gene editing, the gene can be edited more efficiently and accurately. However, not all pseudo-single nucleotide mutations can be introduced by destroying the PAM sequence of the prime editor. Therefore, when designing pseudo-single nucleotide mutations, methods to destroy the PAM sequence of the prime editor are selectively considered. That is, some pseudo-single nucleotide mutations are designed without considering the PAM sequence of the prime editor.

[0457] Some pseudo-single nucleotide mutations are designed to destroy the PAM of the prime editor that introduces them, and some pseudo-single nucleotide mutations are designed not to destroy the PAM of the prime editor that introduces them. Here, the PAM being destroyed refers to a situation in which, after a prime editor capable of introducing the pseudo-single nucleotide mutation is delivered to a cell and the gene is corrected, the PAM sequence of the prime editor is changed to a different sequence, so that the prime editor can no longer recognize the target sequence. The PAM not being destroyed refers to a situation in which, after a prime editor capable of introducing the pseudo-single nucleotide mutation is delivered to a cell and the gene is corrected, the PAM sequence of the prime editor remains intact, so that the prime editor can again recognize the target sequence.

[0458] Advantage #1 of Saturated Single-Nucleotide Mutation Libraries: Minimizing the Impact of Sequencing Errors

[0459] The saturated single nucleotide mutation library disclosed in this specification includes cells harboring pseudo-single nucleotide mutations. The genes of these pseudo-single nucleotide mutation cells differ from the nucleic acid sequence of a reference gene by at least two bases. Therefore, when sequencing to determine which pseudo-single nucleotide mutation cells are contained in the library, false positives, i.e., false information resulting from sequencing errors, do not significantly affect accuracy. In other words, using the saturated single nucleotide mutation library, it is possible to directly sequence the cell genome to verify with high accuracy which cells harboring pseudo-single nucleotide mutations are contained in the library. As described above, each pseudo-single nucleotide mutation can be treated as a single nucleotide mutation associated with it. Consequently, using the saturated single nucleotide mutation library, it is possible to determine with high accuracy the impact of each single nucleotide mutation within a genetic region of interest.

[0460] Advantage #2 of Saturated Single-Nucleotide Mutation Libraries: Cell Types Can Be Selected for Purpose

[0461] In previous studies, cells constituting saturated single-nucleotide mutation libraries were haploid, or at least haploidized in the genetic region of interest. This was because constructing libraries using non-haploid cells made it difficult to distinguish individual single-nucleotide mutation sequences. Non-haploid cells contain more than two sets of genomes. When introducing single-nucleotide mutations into non-haploid cells, more unmutated regions of interest are detected compared to when using haploid cells. This increases false positives, which are identified as single-nucleotide mutations due to sequencing errors, making it more difficult to distinguish individual single-nucleotide mutation sequences. Therefore, even if libraries were constructed using non-haploid cells using conventional techniques, their use in research was limited. However, since most cells in the body are non-haploid, it is preferable to utilize libraries containing non-haploid cells for research.

[0462] The saturated single-nucleotide mutation library disclosed in this specification allows for the free selection of cells constituting it, depending on the research purpose. Utilizing the invention disclosed in this specification allows for the introduction of pseudo-single-nucleotide mutations using Prime Editor and accurate verification of their introduction without the influence of sequencing errors. This is a significant advantage, enabling more accurate research.

[0463] Advantage #3 of Saturated Single-Nucleotide Mutation Libraries: Ease of Manufacturing

[0464] This specification describes the use of a Prime Editor to produce a saturated single-nucleotide mutation library. Prime Editor allows for editing of nucleotide sequences at any desired location without significant limitations. This is because target nucleic acids can be flexibly selected, and the pegRNA prime editing domain can be freely designed accordingly. Therefore, the method for producing a saturated single-nucleotide mutation library disclosed in this specification has the advantage of being easily implemented by those skilled in the art.

[0465] Method for preparing a saturated single-nucleotide mutation library - Overview

[0466] This paragraph describes a method for preparing a saturated single nucleotide mutation library. The method for preparing a saturated single nucleotide mutation library disclosed in this specification is a method for simultaneously preparing various types of pseudo-single nucleotide mutation cells in a high-throughput manner. The method includes the steps of a) preparing a cell population suitable for research purposes; b) treating a composition for library preparation; and c) verifying whether the desired saturated single nucleotide mutation library has been prepared. The method is characterized by introducing pseudo-single nucleotide mutations into cells using a prime editor, verifying whether the pseudo-single nucleotide mutations have been successfully introduced through sequencing, and performing the above process in a high-throughput manner.

[0467] Saturated Single-Nucleotide Mutation Library Preparation Method #1 - Cell Population Preparation Process

[0468] Overview of cell population preparation process

[0469] The method for preparing the above saturated single nucleotide mutation library involves preparing a cell population suitable for the research purpose. The above cell population includes cells suitable for the research purpose (reference cells). The cells within the above cell population are homogeneous. Any known technique can be used to prepare the above cell population. For example, cells can be cultured, expanded, and pretreated using known methods, or any suitable combination of the above processes.

[0470] Includes reference cells

[0471] The above study examines the impact of single nucleotide mutations in a genetic region of interest. Such studies require reference cells for comparison. Furthermore, this specification presents a method for constructing a library by introducing a prime editor into the reference cells to produce cells with individual pseudo-single nucleotide mutations. Therefore, the above preparation method requires reference cells. Accordingly, the cell population prepared in the above process includes reference cells.

[0472] Contains homogeneous cells

[0473] The above saturated single-nucleotide mutation library is a tool for studying genes by comparing them with wild-type genes and genes with single-nucleotide mutations. Therefore, the fewer differences between cells outside of the genetic region of interest, the better. Accordingly, the cell population prepared in the above process contains homogeneous cells. While not all cells within the above cell population need to be completely identical, it is sufficient for them to be treated identically for research purposes.

[0474] Cells can be prepared using known techniques

[0475] A sufficient number of cells must be prepared for library preparation. Once the cells constituting the cell population are appropriately selected, the cell population can be prepared using any known technique appropriate to the selected cells. For example, cells can be cultured, expanded, and pretreated using known methods, or any suitable combination of the above processes.

[0476] Saturated Single-Nucleotide Mutation Library Preparation Method #2 - Processing of Composition for Library Preparation

[0477] The method for producing a saturated single nucleotide mutation library comprises treating a prepared cell population with a composition for preparing a library. Through this process, a type of prime editor is delivered to each cell in the cell population. Each prime editor can introduce a type of pseudo-single nucleotide mutation. To introduce a pseudo-single nucleotide mutation, the prime editor must be brought into contact with the genome of each cell. There are various methods for bringing the prime editor into contact with the genome of each cell. Therefore, the process for preparing the composition for preparing the library can be implemented in various ways. For example, the process for preparing the composition for preparing the library can be a process for treating protein-RNA complexes to the prepared cell population. As another example, the process for preparing the composition for preparing the library can be a process for treating a pegRNA library to a cell population expressing the prime editor protein. The specific composition of the composition for preparing the library is described in the [Composition for Preparing a Saturated Single Nucleotide Mutation Library] section.

[0478] Saturated Single-Nucleotide Mutation Library Preparation Method #3 - Library Validation Process

[0479] Overview of the Library Verification Process

[0480] A saturated single nucleotide mutation library is prepared by preparing a cell population and treating it with a composition for library preparation. However, to utilize it for research, it must be confirmed whether cells with each pseudo-single nucleotide mutation are included in the library. Therefore, the method for preparing a saturated single nucleotide mutation library includes a process for verifying whether the desired saturated single nucleotide library has been prepared. Specifically, a sample is collected, sequence information of the genetic region of interest of the cells in the collected sample is obtained, and whether the pseudo-single nucleotide mutation is included is verified.

[0481] Take a sample

[0482] Because obtaining cellular genome sequence information requires cell destruction, the entire cell cannot be used for verification during the above library validation. Therefore, the above library validation process is performed by collecting samples. These samples are of a sufficient size to represent the entire cell population of the above library. The method for collecting these samples is not limited, and any known method can be used.

[0483] Obtain sequence information of genetic regions of interest in cells within a sample.

[0484] To validate the above library, the sequence information of the genetic region of interest in the cells within the sample must be analyzed. To obtain the sequence information to be analyzed, the genomes of the cells within the sample are sequenced. The sequencing method is not limited, and any known method can be used.

[0485] Verification of whether a quasi-single nucleotide mutation is included

[0486] As described above, the above manufacturing method introduces quasi-single nucleotide mutations into some cells. Therefore, verification must also be performed based on whether quasi-single nucleotide mutations have been introduced. Among the sequence information of the genetic region of interest obtained from the sample, a sequence containing one single nucleotide mutation and one or more synonymous mutations is considered a sequence in which quasi-single nucleotide mutations have occurred. The sequence information above is checked to determine which quasi-single nucleotide mutations are included and how many types of quasi-single nucleotide mutations are included. Based on the verification results, it can be determined whether the intended saturated single nucleotide mutation library has been successfully manufactured.

[0487] Composition for preparing a saturated single nucleotide mutation library

[0488] Overview of Compositions for Preparation of Saturated Single-Nucleotide Mutation Libraries

[0489] This paragraph describes a composition for preparing a saturated single nucleotide mutation library. The composition for preparing a saturated single nucleotide mutation library refers to a composition that is treated to a cell population when preparing a saturated single nucleotide mutation library in a high-throughput manner. The composition is treated to deliver a prime editor capable of introducing pseudo-single nucleotide mutations to each cell. Various methods exist for delivering a prime editor to cells. Therefore, the composition for preparing a saturated single nucleotide mutation library can be implemented in various forms. For example, the composition may include a pegRNA library. As another example, the composition may include protein-RNA complexes. As another example, the composition may include a prime editor protein or a nucleic acid encoding the prime editor protein, and a pegRNA library. The mechanism by which the prime editor is delivered to each cell may vary depending on the form of the composition. However, the result is the same: the prime editor is delivered to each cell, and each pseudo-single nucleotide mutation is introduced.

[0490] pegRNA library

[0491] The composition for preparing the above saturated single nucleotide mutation library may comprise pegRNAs capable of introducing pseudo-single nucleotide mutations, or DNAs encoding the pegRNAs, together with an appropriate prime editor protein. The reason why the "pegRNAs" are expressed in plural is because the composition for preparing the above library includes almost all pegRNAs that can be designed. In other words, the composition for preparing the above library includes types of pegRNAs that are close to three times the number of bases of the coding sequence of the genetic region of interest (e.g., more than 90%). As described above, each pegRNA can be classified according to the type of pseudo-single nucleotide mutation introduced. For example, a type 1 pegRNA is a pegRNA capable of introducing a type 1 pseudo-single nucleotide mutation. The specific composition of each of the above pegRNAs is described in the [Prime Editors Capable of Introducing Pseudo-Single Nucleotide Mutations] section.

[0492] protein-RNA complexes

[0493] The composition for preparing the above saturated single nucleotide mutation library may include protein-RNA complexes. The protein-RNA complexes are prime editors, comprising a prime editor protein and a pegRNA. The prime editors can introduce pseudo-single nucleotide mutations into a genetic region of interest. The specific composition of each protein-RNA complex is described in the section [Prime Editors Capable of Introducing Pseudo-Single Nucleotide Mutations]. The reason for expressing the protein-RNA complexes in plural is the same as the reason for expressing the pegRNAs in plural. As described above, each protein-RNA complex can be classified according to the type of pseudo-single nucleotide mutation introduced. For example, a type 2 protein-RNA complex is a protein-RNA complex capable of introducing a type 2 pseudo-single nucleotide mutation. The specific composition of each protein-RNA complex is described in the section [Prime Editors Capable of Introducing Pseudo-Single Nucleotide Mutations].

[0494] Prime Editor Compositions

[0495] The composition for preparing the above saturated single nucleotide mutation library may include prime editor compositions capable of introducing pseudo-single nucleotide mutations. Specifically, each composition includes a prime editor protein or a nucleic acid encoding the same; and a pegRNA or a nucleic acid encoding the same. The specific composition of each prime editor composition is described in the [Prime Editor Capable of Introducing Pseudo-Single Nucleotide Mutations] section. The reason why the prime editor compositions are expressed in plural is the same reason why the pegRNAs are expressed in plural. As described above, each prime editor composition can be classified according to the type of pseudo-single nucleotide mutation introduced. For example, a third type of prime editor composition is a prime editor composition capable of introducing a type 3 pseudo-single nucleotide mutation. The specific composition of each of the above prime editor compositions is described in the [Prime editor capable of introducing pseudo-single nucleotide mutations] section.

[0496] Prime Editor capable of introducing pseudo-single nucleotide mutations

[0497] Overview of Prime Editor, which can introduce pseudo-single nucleotide mutations.

[0498] This section describes a single-unit prime editor capable of introducing quasi-single-nucleotide mutations into the cellular genome. The prime editor comprises a prime editor protein and a prime editing guide RNA (pegRNA). The prime editor protein comprises a Cas protein and a reverse transcriptase. The pegRNA comprises a guide domain, a scaffold, and a prime editing domain. The prime editor protein and pegRNA work together to introduce quasi-single-nucleotide mutations into the cellular genome. The components of the prime editor are described in more detail below.

[0499] Prime Editor Protein

[0500] The prime editor capable of introducing the above-described single-nucleotide mutations comprises a prime editor protein. The prime editor protein comprises a Cas protein and a reverse transcriptase. The Cas protein is guided by the guide domain of pegRNA to cleave the target nucleic acid sequence or its vicinity. The reverse transcriptase, in conjunction with the prime editing domain sequence of pegRNA, edits the gene.

[0501] Prime Editing Guide RNA (pegRNA)

[0502] The above pegRNA comprises a guide domain, a scaffold, and a prime editing domain. The above pegRNA forms a complex with the above prime editor protein. It also functions together with the prime editor protein to edit genes. The above guide domain can guide the above prime editor protein to a specific location within the cellular genome. Here, the specific location is also referred to as a target sequence. The above scaffold can interact with the above prime editor protein to form a complex. The above prime editing domain is the domain involved in gene editing. The above prime editing domain comprises a primer binding site (PBS) and a reverse transcription template (RT-Template). The above primer binding site binds to the genome cut by the above prime editor protein. The above reverse transcription template functions as a template for the reverse transcriptase contained in the above prime editor protein.

[0503] Structure of pegRNA #1 - Guide domain

[0504] The above pegRNA contains a guide domain. This guide domain guides the above prime editor protein to the target nucleic acid. As described above, the target nucleic acid selected from the cellular genome is a double-stranded nucleic acid. In this case, the guide domain has a sequence complementary to one strand of the target nucleic acid (the target strand). Additionally, the guide domain has a sequence equivalent to the other strand of the target nucleic acid (the non-target strand).

[0505] pegRNA Structure #2 - Scaffold

[0506] The above pegRNA contains a scaffold. The scaffold interacts with the prime editor protein, allowing the pegRNA and the prime editor protein to form a complex. The scaffold's nucleic acid sequence may vary depending on the prime editor protein. For some prime editor proteins, the scaffold structure that interacts with them is well known. Those skilled in the art will be able to select an appropriate scaffold for the desired prime editor protein.

[0507] pegRNA Structure #3 - Prime Editing Domain

[0508] The above pegRNA contains a prime editing domain. The prime editing domain includes a primer binding site and a reverse transcription template. The prime editing domain has a structure in which the reverse transcription template and the primer binding site are sequentially connected from the 5' end to the 3' end. The primer binding site can complementarily bind to one strand of the target nucleic acid cleaved by the prime editor protein. The reverse transcription template functions as a template when the reverse transcriptase contained in the prime editor protein reverse transcribes DNA.

[0509] pegRNA Structure #4 - Other Domains

[0510] The above pegRNA may include additional components (other domains) that enhance its function. These additional components can be freely selected as long as they do not impair the essential function of the pegRNA. The composition and location of these other domains are appropriately determined based on their function. For example, the above pegRNA may include a tmpknot sequence at the 3' end.

[0511] Structure of pegRNA

[0512] The structure of the above pegRNA can vary depending on the prime editor protein. A pegRNA that works with a prime editing protein, including the Cas9 protein, orthologs of the Cas9 protein, or variants thereof, has a structure in which a guide domain, a scaffold, and a prime editing domain are sequentially linked from the 5' end to the 3' end.

[0513] pegRNA design

[0514] PegRNAs can be designed in various ways by changing the guide domain and the prime editing domain. In other words, multiple different pegRNAs can be designed to correct genes with the same content. By changing the target nucleic acid position, the guide domain, primer binding site, and reverse transcription template sequence can be varied accordingly. Even using the same guide domain, the length of the primer binding site and the reverse transcription template can be varied. Even a change in just one or two base sequences can significantly affect gene editing efficiency. Therefore, the design of the pegRNA sequence is crucial.

[0515] pegRNA design using machine learning models

[0516] There are many different ways to design pegRNAs. For example, researchers can design pegRNAs based on the findings on the relationship between pegRNA structure and gene editing efficiency. In this paper, pegRNAs are designed using a machine-learned model that predicts the relationship between pegRNA structure and gene editing efficiency. For example, the DeepPrime model (Yu, G. et al. "Prediction of efficiencies for diverse prime editing systems in multiple cell types." Cell 186 (2023), 2256-2272.e2223 (2023)) can be used to design desired pegRNAs.

[0517] protein-RNA complex

[0518] The above prime editor may be in the form of a protein-RNA complex. The above protein-RNA complex is a complex of the above prime editor protein and the above pegRNA, and is also referred to as a ribonucleoprotein (RNP) or prime editor complex.

[0519] A vector representing each component of the Prime Editor

[0520] The above Prime Editor may be in the form of a vector expressing each component. Specifically, the above vector comprises a nucleic acid encoding the above Prime Editor protein and a nucleic acid encoding the above pegRNA. The above vector may be a single nucleic acid molecule or two or more nucleic acid molecules. Various vector technologies for delivering the Prime Editor have been studied and disclosed. The vector expressing each component of the above Prime Editor encompasses all vector technologies for Prime Editor disclosed to date.

[0521] Prime Editor Composition

[0522] The above prime editor may be in the form of a prime editor composition. The above prime editor composition comprises 1) a prime editor protein or a nucleic acid encoding the above prime editor protein, and 2) a pegRNA or a nucleic acid encoding the above pegRNA. The above prime editor protein and the above pegRNA are as described above.

[0523] Features of pseudo-single nucleotide mutation design

[0524] The above Prime Editor can introduce pre-designed pseudo-single nucleotide mutations into the cell genome. When designing pseudo-single nucleotide mutations, the position at which the synonymous mutation is inserted can be any position that can be introduced by the Prime Editor. In other words, the position of the above synonymous mutation is not limited to the PAM position recognized by the Prime Editor. Therefore, in some pseudo-single nucleotide mutation cells, the synonymous mutation is introduced at a position that is not part of the PAM recognized by the Prime Editor. In other words, in some pseudo-single nucleotide mutation cells, the synonymous mutation is inserted at a position that does not disrupt the PAM sequence. By designing the above to introduce synonymous mutations regardless of the PAM, it is possible to design a Prime Editor that can introduce almost any type of pseudo-single nucleotide mutation.

[0525] Drug sensitivity assessment method using a saturated single nucleotide mutation library

[0526] Overview of Drug Sensitivity Assessment Methods

[0527] This section describes a method for assessing drug sensitivity using a saturated single-nucleotide mutation library. The saturated single-nucleotide mutation library can be used to assess drug sensitivity. When a drug is treated with the saturated single-nucleotide mutation library, cells will either survive or die depending on the influence of the pseudo-single-nucleotide mutations. By identifying which pseudo-single-nucleotide mutations kill more cells and which survive more, the drug sensitivity of each pseudo-single-nucleotide mutation can be assessed. Specifically, the method may include: (a) dividing the library into two or more groups; (b) treating only one group with the drug for which sensitivity is to be assessed; (c) obtaining sequence information for both the untreated and drug-treated groups; and (d) comparing the two sequence information to assess drug sensitivity for each pseudo-single-nucleotide mutation. Using the above drug sensitivity assessment method, it is possible to identify which single-nucleotide mutations affect drug sensitivity in a high-throughput manner.

[0528] Cells and drugs to be evaluated in a saturated single nucleotide mutation library

[0529] Depending on the drug being evaluated, the saturated single-nucleotide mutation library to use is determined. Specifically, the genetic region of interest and the cell type comprising the library are appropriately selected. Furthermore, multiple drugs under evaluation with similar therapeutic effects can be used simultaneously.

[0530] Divide the library into two or more groups

[0531] To accurately assess drug sensitivity, it is necessary to compare the same cells before and after drug treatment. However, because obtaining sequence information requires cell destruction, comparing the same cells before and after drug treatment is technically difficult. The above method circumvents this problem by dividing the library into two or more groups and comparing them. Each group is randomly divided, and there are no restrictions on the method of division. Since the library contains a sufficient number of cells and the groups are randomly divided, each group can be expected to be homogeneous. That is, each group will contain cells with nearly all possible quasi-single nucleotide mutations. The method evaluates the drug's effect by treating the two homogeneous groups differently and comparing their respective sequence information.

[0532] At least one group is treated with the drug, and at least one group is not treated with the drug.

[0533] As described above, the library is divided into two or more groups. At least one group is treated with a drug, and at least one group is left untreated. When evaluating multiple drugs with similar effects simultaneously, the library is divided into groups exceeding the number of drugs to be evaluated, and each drug is treated in at least one group. Similarly, at least one group is left untreated. This process allows for comparison of sequence information between drug-treated and untreated groups.

[0534] Drug sensitivity was assessed by comparing sequence information between drug-treated and drug-untreated groups.

[0535] Among the groups divided above, some are treated with drugs, while others are not. The sequence information of each group is compared to determine the extent to which a quasi-single nucleotide mutation is killed, and based on this, drug sensitivity is assessed. To obtain this sequence information, samples can be collected from each group. The sequence information of the genetic region of interest in the cells included in each group is acquired and classified by quasi-single nucleotide mutation type. Only sequences containing both one single nucleotide mutation and one or more synonymous mutations will be classified as quasi-single nucleotide mutation sequences. The sequence information of the drug-treated and drug-untreated groups is compared by quasi-single nucleotide mutation type. Based on the results of this comparison, it is possible to determine whether a specific quasi-single nucleotide mutation is sensitive or resistant to the drug.

[0536] A method for functional evaluation of genetic regions of interest using a saturated single nucleotide variant library.

[0537] Overview of Gene Function Assessment Methods

[0538] This section describes a method for assessing the function of a genetic region of interest using a saturated single nucleotide mutation library. The saturated single nucleotide mutation library can be used to assess the function of a genetic region of interest. If the genetic region of interest encompasses a region essential for cell survival, quasi-single nucleotide mutations occurring in the region can affect cell survival. To assess whether quasi-single nucleotide mutations affect cell survival, quasi-single nucleotide mutations can be introduced and, after a certain period of time, whether the cells die can be determined. Specifically, the method may include the steps of (a) obtaining library sequence information at a specific time point; (b) obtaining library sequence information again at a different time point; and (c) comparing the sequence information obtained at the two time points to assess gene function for each quasi-single nucleotide mutation.

[0539] Obtain library sequence information from two time points with a time difference.

[0540] When assessing the impact of quasi-single nucleotide mutations on the function of a genetic region of interest, it is crucial to identify changes over time. Therefore, library sequence information must be obtained at two different time points. Sequence information can be obtained by collecting samples at two different time points. The earlier time point is selected before quasi-single nucleotide mutations significantly affect cell viability. The later time point is selected after quasi-single nucleotide mutations have sufficiently affected cell viability. The difference between the two time points can be appropriately determined based on the research objectives.

[0541] Determine which pseudo-single nucleotide mutation cells died over time.

[0542] The sequence information obtained at each time point is categorized by quasi-single nucleotide mutation type. Only sequences containing both a single nucleotide mutation and one or more synonymous mutations will be classified as quasi-single nucleotide mutation sequences. If a quasi-single nucleotide mutation affects cell survival, cells with such quasi-single nucleotide mutations will die over time, resulting in fewer occurrences at later time points than at earlier time points. Therefore, comparing sequence information at earlier and later time points by quasi-single nucleotide mutation type allows us to assess whether and to what extent a specific quasi-single nucleotide mutation affects the essential function of the genetic region of interest.

[0543] Possible embodiments of the invention

[0544] Variables and functions used in the examples

[0545] Definition 1

[0546] When the nucleic acid sequence of the genetic region of interest and the coding sequence (CDS) of the genetic region of interest are specified,

[0547] Based on this definition, we define the following constants, variables, and functions:

[0548] l is the number of bases contained in the above coding sequence,

[0549] Here, if the genetic region of interest is a double-stranded nucleic acid, l may mean the number of base pairs (bp), or the number of nucleotides contained in one strand of the nucleic acid (nt), etc., and has a meaning appropriate for a person skilled in the art to understand l as “the length of a specific nucleic acid”;

[0550] x is a random integer chosen between 1 and l;

[0551] s is a random integer chosen between 1 and l, and is different from x;

[0552] N x is one of A, T, G, or C, which is the same base as the xth base from the 5' end of the coding sequence of the genetic region of interest;

[0553] N x c is one of A, T, G, or C, and is a base different from the xth base from the 5' end of the coding sequence of the genetic region of interest;

[0554] f(.), g(.), and h(.) are functions that map specific bases to other bases,

[0555] Here, f(N x ) is one of A, T, G, or C, but N x It's different from that,

[0556] g(N x ) is one of A, T, G, or C, but f(N x ) and N x It's different from that,

[0557] h(N x ) is one of A, T, G, or C, but f(N x ), g(N x ) and N x different from;

[0558] Definition 2

[0559] In Definition 1, f(.), g(.), and h(.) are specifically defined as follows:

[0560] f(A) = T; f(T) = A; f(C) = G; and f(G) = C;

[0561] g(A) = C; g(T) = G; g(C) = A; and g(G) = T; and

[0562] h(A) = G; h(T) = C; h(C) = T; and h(G) = A.

[0563] Definition 3

[0564] In either Definition 1 or Definition 2, the following is further defined:

[0565] t is a random integer chosen between 1 and l, and is different from x and s;

[0566] u is a random integer chosen between 1 and l, and is different from x, s, and t.

[0567] Examples of application of the above definition

[0568] The nucleic acid sequence of the genetic region of interest is 5'-ATG CAT GCA TGC-3',

[0569] When the coding sequence of the genetic region of interest is 5'-ATG CAT-3',

[0570] By definition 1, the following is defined:

[0571] l is 6;

[0572] x is a random integer chosen between 1 and 6;

[0573] s is a random integer chosen between 1 and 6, and is different from x;

[0574] N1 is A, N2 is T, N3 is G, N4 is C, N5 is A, and N6 is T;

[0575] N1 c is T, G, or C, N2 c is A, G, or C, N3 c is A, T, or C, N4 c is A, T, or G, N5 c is T, C, or G, and N6 c is A, C, or G;

[0576] A, f(A), g(A), h(A) are different bases;

[0577] T, f(T), g(T), h(T) are different bases;

[0578] G, f(G), g(G), h(G) are different bases; and,

[0579] C, f(C), g(C), h(C) are different bases.

[0580] By definition 2, the following is defined:

[0581] Variables and functions defined by definition 1 above; and,

[0582] f(A) = T, g(A) = C, and h(A) = G;

[0583] f(T) = A, g(T) = G, and h(T) = C;

[0584] f(G) = C, g(G) = T, and h(G) = A; and,

[0585] f(C) = G, g(C) = A, and h(C) = T.

[0586] By definition 3, the following is defined:

[0587] Variables and functions defined by definition 1 or definition 2 above;

[0588] pseudo-single nucleotide mutations

[0589] Example 1, pseudo-single nucleotide mutations

[0590] As a pseudo-single nucleotide mutation for a specific coding sequence,

[0591] When defining constants, variables, and functions according to either Definition 1 or Definition 2,

[0592] The above pseudo-single nucleotide mutations consist of:

[0593] 1 single nucleotide mutation,

[0594] Here, the above single nucleotide mutation is a mutation in which one base in the above specific coding sequence is substituted with another base; and

[0595] One or more synonymous mutations,

[0596] Each of the above synonymous mutations does not change the amino acid sequence encoded by the above specific coding sequence.

[0597] Example 2, HGVS expression

[0598] In Example 1,

[0599] The above pseudo-single nucleotide mutation is 1 c.xN x >N x c Including mutations,

[0600] Above c.xN x >N x c Mutations are called single nucleotide mutations,

[0601] One or more c.sN s >N s c Includes mutations,

[0602] Here, each c.sN s >N s c Mutations do not change the amino acid sequence encoded by the specific coding sequence above,

[0603] One or more of the above c.sN s >N s c Each mutation is referred to as a synonymous mutation.

[0604] Example 3: Distinguishing between pseudo-single nucleotide mutation types when expressing HGVS

[0605] In Example 2,

[0606] When there are two or more pseudo-single nucleotide mutations,

[0607] c.xN contained in each pseudo-single nucleotide mutation x >N x c If the mutations are identical,

[0608] The above two pseudo-single nucleotide mutations are treated as the same type.

[0609] Example 4, Alternative Expressions

[0610] In Example 1,

[0611] In the above single base mutation, one of the 1st to 1st bases is replaced with a different base,

[0612] In each of the above mutations, one of the 1st to 1st bases is replaced with a different base,

[0613] The above single nucleotide mutations and the above synonymous mutations occur at different locations,

[0614] Each of the above synonymous mutations does not change the amino acid sequence encoded by the above specific coding sequence.

[0615] Example 5: Distinguishing between pseudo-single nucleotide mutation types in alternative expressions

[0616] In Example 4,

[0617] The above pseudo-single base mutations are classified by the location of the above single base mutation and the type of base to be substituted.

[0618] When there are two pseudo-single nucleotide mutations, each pseudo-single nucleotide mutation

[0619] a) Replace the base at the same position with a different base,

[0620] b) If the types of bases being substituted are the same,

[0621] The above two pseudo-single nucleotide mutations are defined as the same type.

[0622] Example 6, pseudo-single nucleotide mutations, including identical codon positions

[0623] As a pseudo-single nucleotide mutation for a specific coding sequence,

[0624] When defining constants, variables, and functions according to either Definition 1 or Definition 2,

[0625] The above pseudo-single nucleotide mutations consist of:

[0626] single nucleotide mutation,

[0627] Here, the above single nucleotide mutation is a mutation in which one base in the above specific coding sequence is substituted with another base; and

[0628] One or more synonymous mutations,

[0629] Here, the translation result when only the single base mutation is introduced into the above specific coding sequence,

[0630] The translation results are identical when the above pseudo-single nucleotide mutation is introduced into the specific coding sequence above.

[0631] Nucleic acid sequence of the genetic region of interest where a quasi-single nucleotide mutation occurs

[0632] Example 7, nucleic acid sequence in which a synonymous single nucleotide mutation occurred, one synonymous mutation

[0633] pseudo-single nucleotide variant nucleic acid sequence,

[0634] Here, the above pseudo-single nucleotide mutation nucleic acid sequence is a nucleic acid sequence in which a pseudo-single nucleotide mutation of any one of Examples 1 to 6 is introduced into the genetic region of interest,

[0635] When defining constants, variables, and functions according to either Definition 1 or Definition 2,

[0636] The coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence satisfies the following conditions:

[0637] Identical to the coding sequence of the genetic region of interest except for the xth base and the sth base;

[0638] The xth base is f(N x ), g(N x ), or h(N x ) is a selected base;

[0639] The sth base is N s is not a base (or N s c is one of); and

[0640] The translation product of the coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence is f(N x ), g(N x ), or h(N x ) is identical to the translation result of the nucleic acid sequence substituted with the selected base.

[0641] Example 8, nucleic acid sequences in which quasi-single nucleotide mutations occur, two synonymous mutations

[0642] pseudo-single nucleotide variant nucleic acid sequence,

[0643] Here, the above pseudo-single nucleotide mutation nucleic acid sequence is a nucleic acid sequence in which a pseudo-single nucleotide mutation of any one of Examples 1 to 6 is introduced into the genetic region of interest,

[0644] When defining constants, variables, and functions according to any one of Definitions 1 to 3,

[0645] The coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence satisfies the following conditions:

[0646] The coding sequence of the genetic region of interest is identical except for the xth base, the sth base, and the tth base;

[0647] The xth base is f(N x ), g(N x ), or h(N x ) is a selected base;

[0648] The sth base is N s is not a base (or N s cis one of);

[0649] The tth base is N t is not a base (or N t c is one of); and

[0650] The translation product of the coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence is f(N x ), g(N x ), or h(N x ) is identical to the translation result of the nucleic acid sequence substituted with the selected base.

[0651] Example 9, nucleic acid sequences in which quasi-single nucleotide mutations occurred, three synonymous mutations

[0652] pseudo-single nucleotide variant nucleic acid sequence,

[0653] Here, the above pseudo-single nucleotide mutation nucleic acid sequence is a nucleic acid sequence in which a pseudo-single nucleotide mutation of any one of Examples 1 to 6 is introduced into the genetic region of interest,

[0654] When defining constants, variables, and functions according to any one of Definitions 1 to 3,

[0655] The coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence satisfies the following conditions:

[0656] The coding sequence of the genetic region of interest is identical except for the xth base, the sth base, the tth base, and the uth base;

[0657] The xth base is f(N x ), g(N x ), or h(N x ) is a selected base;

[0658] The sth base is N s is not a base (or N s c is one of);

[0659] The tth base is N t is not a base (or Nt c is one of);

[0660] The uth base is N u is not a base (or N u c is one of); and

[0661] The translation product of the coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence is f(N x ), g(N x ), or h(N x ) is identical to the translation result of the nucleic acid sequence substituted with the selected base.

[0662] Example 10, nucleic acid sequence in which a synonymous single nucleotide mutation has occurred, one or more synonymous mutations

[0663] pseudo-single nucleotide variant nucleic acid sequence,

[0664] Here, the above pseudo-single nucleotide mutation nucleic acid sequence is a nucleic acid sequence in which a pseudo-single nucleotide mutation of any one of Examples 1 to 6 is introduced into the genetic region of interest,

[0665] When defining constants, variables, and functions according to any one of Definitions 1 to 3,

[0666] The coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence satisfies the following conditions:

[0667] Identical to the coding sequence of the genetic region of interest except for the xth base and the sth base;

[0668] The xth base is f(N x ), g(N x ), or h(N x ) is a selected base;

[0669] The sth base is N s is not a base (or N s c is one of);

[0670] Optionally, the coding sequence of the genetic region of interest differs from the xth base and one or more bases other than the sth base; and

[0671] The translation product of the coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence is f(N x ), g(N x ), or h(N x ) is identical to the translation result of the nucleic acid sequence substituted with the selected base.

[0672] Example 11, f mutation

[0673] In any one of Examples 7 to 10, the coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence further satisfies the following conditions:

[0674] The xth base is f(N x )lim.

[0675] Example 12, g mutation

[0676] In any one of Examples 7 to 10, the coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence further satisfies the following conditions:

[0677] The xth base is g(N x )lim.

[0678] Example 13, h mutation

[0679] In any one of Examples 7 to 10, the coding sequence of the above pseudo-single nucleotide variant nucleic acid sequence further satisfies the following conditions:

[0680] The xth base is h(N x )lim.

[0681] Example 14, PAM sequence not changed

[0682] In any one of Examples 7 to 10, or any one of Examples 11 to 13, the coding sequence of the pseudo-single nucleotide variant nucleic acid sequence further satisfies one or more conditions selected from the following:

[0683] (i) the sth base is not G;

[0684] (ii) 5'-N s-2 N s-1 N s -3', 5'-N s-1 N s N s+1 -3', 5'-N s N s+1 N s+2 -3' differs from 5'-RCH-3', where R is G or A and H is A, C, or T;

[0685] (iii) 5'-N s-2 N s-1 N s -3', 5'-N s-1 N s N s+1 -3', 5'-N s N s+1 N s+2 -3' is different from 5'-RRH-3', where R is G or A and H is A, C, or T; and

[0686] (iv) 5'-N s-2 N s-1 N s -3', 5'-N s-1 N s N s+1 -3', 5'-N s N s+1 N s+2 -3' is different from 5'-RTH-3', where R is G or A and H is A, C, or T.

[0687] Saturated single nucleotide mutation library

[0688] Example 15, saturated single nucleotide mutation library

[0689] A saturated single nucleotide variant library for studying single nucleotide variants in genetic regions of interest.

[0690] Here, when defining constants, variables, and functions according to any one of the definitions 1 to 3,

[0691] The above saturated single nucleotide mutation library contains:

[0692] reference cells; and,

[0693] N or more types of quasi-SNV cells,

[0694] Here, N is an integer greater than or equal to 1 and less than or equal to 3l,

[0695] The coding sequence of the genetic region of interest in each of the pseudo-single nucleotide variant cells is

[0696] A sequence in which a pseudo-single nucleotide mutation of any one of Examples 1 to 6 is introduced into the coding sequence of the genetic region of interest of the reference cell.

[0697] Example 16, Number of types of pseudo-single nucleotide mutation cells

[0698] In Example 15, the above N is greater than a real number selected from the following and less than or equal to 3l:

[0699] About 2.10l; About 2.11l; About 2.11l; About 2.12l; About 2.13l; About 2.14l; About 2.15l; About 2.16l; About 2.17l; About 2.18l; About 2.19l; About 2.20l; About 2.21l; About 2.22l; About 2.23l; About 2.24l; About 2.25l; About 2.26l; About 2.27l; About 2.28l; About 2.29l; About 2.30l; About 2.31l; About 2.32l; About 2.33l; About 2.34l; About 2.35l; About 2.36l; About 2.37l; About 2.38l; About 2.39l; About 2.40l; About 2.50l; About 2.51l; About 2.52l; About 2.53l; About 2.54l; About 2.55l; About 2.56l; About 2.57l; About 2.58l; About 2.59l; About 2.60l; About 2.61l; About 2.62l; About 2.63l; About 2.64l; About 2.65l; About 2.66l; About 2.67l; About 2.68l; About 2.69l; About 2.70l; About 2.71l; About 2.72l; About 2.73l; About 2.74l; About 2.75l; About 2.76l; About 2.77l; About 2.78l; About 2.79l; About 2.80l; About 2.81l; About 2.82l; About 2.83l; About 2.84l; About 2.85l; About 2.86l; About 2.87l; About 2.88l; About 2.89l; About 2.90l; About 2.91l; About 2.92l; About 2.93l; About 2.94l; About 2.95l; About 2.96l; About 2.97l; About 2.98l; About 2.99l; and About 3.00l.

[0700] Example 17, Quasi-single nucleotide mutation nucleic acid sequence limitation

[0701] In any one of Examples 15 to 16,

[0702] The nucleic acid sequence of the genetic region of interest of each of the above pseudo-single nucleotide mutation cells is independently a nucleic acid sequence in which any one of the pseudo-single nucleotide mutations selected from Examples 7 to 10 is introduced into the genetic region of interest of the above reference cell.

[0703] Example 18, Distinguishing Cell Types with Pseudo-Single Nucleotide Mutations, HGVS Expression #1

[0704] In any one of Examples 15 to 17, the type of each of the pseudo-single nucleotide mutant cells is one selected from the following:

[0705] (i) (3x-2) type,

[0706] The coding sequence of the genetic region of interest in this type of pseudo-single nucleotide variant cell is

[0707] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >f(N x ) mutations are introduced,

[0708] One or more c.sN in the coding sequence of the genetic region of interest in the reference cell above s >N s c Mutations are introduced,

[0709] Here, each c.sN s >N s c Mutations do not change the amino acid sequence encoded by the coding sequence of the genetic region of interest in the reference cell above;

[0710] (ii) (3x-1) type,

[0711] The coding sequence of the genetic region of interest in this type of pseudo-single nucleotide variant cell is

[0712] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >g(N x ) mutations are introduced,

[0713] One or more c.sN in the coding sequence of the genetic region of interest in the reference cell above s >N s c Mutations are introduced,

[0714] Here, each c.sN s >Ns c The mutations do not alter the amino acid sequence encoded by the coding sequence of the genetic region of interest of the reference cell; and

[0715] (iii) (3x) type,

[0716] The coding sequence of the genetic region of interest in this type of pseudo-single nucleotide variant cell is

[0717] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >h(N x ) mutations are introduced,

[0718] One or more c.sN in the coding sequence of the genetic region of interest in the reference cell above s >N s c Mutations are introduced,

[0719] Here, each c.sN s >N s c Mutations do not change the amino acid sequence encoded by the coding sequence of the genetic region of interest in the reference cell.

[0720] Example 19, Distinguishing Cell Types with Pseudo-Single Nucleotide Mutations, HGVS Expression #2

[0721] In any one of Examples 15 to 18, the type of each of the pseudo-single nucleotide mutant cells is one selected from the following:

[0722] (i) (3x-2) type,

[0723] The coding sequence of the genetic region of interest in this type of pseudo-single nucleotide variant cell is

[0724] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >f(N x ) mutations are introduced,

[0725] One or more c.sN in the coding sequence of the genetic region of interest in the reference cell above s >Ns c Mutations are introduced,

[0726] Here, the translation result of the coding sequence of the genetic region of interest of the above-mentioned pseudo-single nucleotide variant cell is,

[0727] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >f(N x ) is identical to the translation result of the nucleic acid sequence into which only the mutation has been introduced;

[0728] (ii) (3x-1) type,

[0729] The coding sequence of the genetic region of interest in this type of pseudo-single nucleotide variant cell is

[0730] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >g(N x ) mutations are introduced,

[0731] One or more c.sN in the coding sequence of the genetic region of interest in the reference cell above s >N s c Mutations are introduced,

[0732] Here, the translation result of the coding sequence of the genetic region of interest of the above-mentioned pseudo-single nucleotide variant cell is,

[0733] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >g(N x ) is identical to the translation result of the nucleic acid sequence into which only the mutation has been introduced; and

[0734] (iii) (3x) type,

[0735] The coding sequence of the genetic region of interest in this type of pseudo-single nucleotide variant cell is

[0736] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >h(N x ) mutations are introduced,

[0737] One or more c.sN in the coding sequence of the genetic region of interest in the reference cell above s >N s c Mutations are introduced,

[0738] Here, the translation result of the coding sequence of the genetic region of interest of the above-mentioned pseudo-single nucleotide variant cell is,

[0739] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >h(N x ) is identical to the translation result of the nucleic acid sequence with only the mutation introduced.

[0740] Example 20, Distinguishing between pseudo-single nucleotide variant cell types, alternative expression

[0741] In any one of Examples 15 to 19, the type of each of the pseudo-single nucleotide mutant cells is one selected from the following:

[0742] (i) (3x-2) type,

[0743] The coding sequence of the genetic region of interest in this type of pseudo-single nucleotide variant cell is

[0744] The xth base of the coding sequence of the genetic region of interest in the above reference cell is N x In f(N x ) is changed to,

[0745] One or more synonymous mutations are introduced into the coding sequence of the genetic region of interest of the reference cell above;

[0746] (ii) (3x-1) type,

[0747] The coding sequence of the genetic region of interest in this type of pseudo-single nucleotide variant cell is

[0748] The xth base of the coding sequence of the genetic region of interest in the above reference cell is N x In g(N x ) is changed to,

[0749] One or more synonymous mutations are introduced into the coding sequence of the genetic region of interest of the reference cell above; and

[0750] (ii) (3x) type,

[0751] The coding sequence of the genetic region of interest in this type of pseudo-single nucleotide variant cell is

[0752] The xth base of the coding sequence of the genetic region of interest in the above reference cell is N x In h(N x ) is changed to,

[0753] One or more synonymous mutations are introduced into the coding sequence of the genetic region of interest of the reference cell above.

[0754] Example 21, Distinguishing cell types with pseudo-single nucleotide mutations, sequence expression

[0755] In any one of Examples 15 to 20, the type of each of the pseudo-single nucleotide mutant cells is one selected from the following:

[0756] (i) (3x-2) type,

[0757] The genetic region of interest of this type of pseudo-single nucleotide variant cell is the pseudo-single nucleotide variant nucleic acid sequence of Example 11,

[0758] Here, the above nucleic acid sequence is a nucleic acid sequence in which a pseudo-single nucleotide mutation is introduced into the genetic region of interest of the reference cell;

[0759] (ii) (3x-1) type,

[0760] The genetic region of interest of this type of pseudo-single nucleotide variant cell is the pseudo-single nucleotide variant nucleic acid sequence of Example 12,

[0761] Here, the above nucleic acid sequence is a nucleic acid sequence in which a pseudo-single nucleotide mutation is introduced into the genetic region of interest of the reference cell;

[0762] (iii) 3x type,

[0763] The genetic region of interest of this type of pseudo-single nucleotide variant cell is the pseudo-single nucleotide variant nucleic acid sequence of Example 13,

[0764] Here, the above nucleic acid sequence is a nucleic acid sequence in which a pseudo-single nucleotide mutation is introduced into the genetic region of interest of the reference cell;

[0765] Example 22, including other cells

[0766] In any one of Examples 15 to 21,

[0767] The above saturated single nucleotide mutation library further includes other cells,

[0768] The above other cells are different from the reference cells and the pseudo-single nucleotide variant cells.

[0769] Example 23, Cell Type Restriction - Chromosome Number Distinction

[0770] In any one of Examples 15 to 22,

[0771] The cells included in the above saturated single nucleotide mutation library are selected from the following:

[0772] Haploid cell; non-haploid cell; monoploid cell; diploid cell; triploid cell; tetraploid cell; and cell with more than one set of chromosomes, pentaploid.

[0773] Example 24, Cell Type Restriction - Restricting Genetic Regions of Interest

[0774] In any one of Examples 15 to 23,

[0775] Cells included in the above saturated single nucleotide mutation library have the following characteristics selected from:

[0776] Contains a haploid region of interest; Contains a doubling region of interest; Contains a triploid region of interest; Contains a tetraploid region of interest; and Contains a pentaploid or more of the genetic region of interest.

[0777] Example 25, PAM Non-Disruptive Mutation #1 - HGVS Expression, Sequence-Limited

[0778] In any one of Examples 15 to 24,

[0779] The above saturated single nucleotide mutation library contains one or more types of pseudo-single nucleotide mutation cells:

[0780] The coding sequence of the genetic region of interest,

[0781] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >N x c Mutations are introduced,

[0782] One or more c.sN in the coding sequence of the genetic region of interest in the reference cell above s >N s c Mutations are introduced,

[0783] Here, each c.sN s >N s c Mutations do not change the amino acid sequence encoded by the coding sequence of the genetic region of interest in the reference cell above,

[0784] At least one c.sN s >N s c A mutation does not change one or more of the following sequences into another sequence:

[0785] 5'-NGG-3'; 5'-NG-3'; 5'-NRCH-3'; 5'-NRRH-3'; and 5'-NRTH-3'.

[0786] Example 26, including mutations that do not destroy PAM #2 - cells containing the prime editor

[0787] In any one of Examples 15 to 25,

[0788] The cells included in the above saturated single nucleotide mutation library contain nucleic acids capable of expressing the prime editor protein,

[0789] Contains one or more types of cells with the following pseudo-single nucleotide mutations:

[0790] 1 c.xN in the coding sequence of the genetic region of interest in the above reference cell x >N x c Mutations are introduced,

[0791] One or more c.sN in the coding sequence of the genetic region of interest in the reference cell above s >N s c Mutations are introduced,

[0792] Here, each c.sN s >N s c Mutations do not change the amino acid sequence encoded by the coding sequence of the genetic region of interest in the reference cell above,

[0793] At least one c.sN s >N s c The mutation does not change the PAM sequence of the above prime editor protein to a different sequence.

[0794] Example 27, mutations that do not disrupt PAM #3 - including specific sequences

[0795] In any one of Examples 15 to 26,

[0796] The above saturated single nucleotide mutation library contains one or more types of pseudo-single nucleotide mutation cells:

[0797] The nucleic acid sequence of the genetic region of interest is a pseudo-single nucleotide variant nucleic acid sequence of Example 14.

[0798] Prime Editor capable of introducing pseudo-single nucleotide mutations

[0799] Example 28, pegRNA

[0800] Prime editing guide RNA (pegRNA) containing:

[0801] Guide domain;

[0802] scaffold; and

[0803] Prime Editing Domain,

[0804] Here, the above prime editing domain includes a primer binding site (PBS) and a reverse transcription template (RT-Template);

[0805] Here, the above guide domain targets a specific target nucleic acid sequence,

[0806] The above scaffold can interact with the prime editor protein to form a complex,

[0807] The above prime editing domain can act together with the reverse transcriptase of the above prime editor protein to introduce a pseudo-single nucleotide mutation of any one of Examples 1 to 6 into the above coding sequence.

[0808] Example 28-1, including additional domains

[0809] In Example 28, the above pegRNA additionally includes a 3'-terminal engineering sequence.

[0810] Example 28-2, Sequence limitation

[0811] In any one of Examples 28 to 28-1, the scaffold comprises the sequence of SEQ ID NO: 5, and the 3'-terminal engineering sequence (if present) comprises the sequence of SEQ ID NO: 6.

[0812] Example 29, pegRNA linkage structure

[0813] In any one of Examples 28 to 28-2, the pegRNA has a structure in which a guide domain, a scaffold, a prime editing domain, and, optionally, a 3'-terminal engineering sequence are sequentially connected from the 5'-terminal to the 3'-terminal.

[0814] Example 30, Prime Editor Protein

[0815] Prime Editor proteins including:

[0816] Cas9 protein; and

[0817] Reverse transcriptase.

[0818] Example 31, Cas9 protein restriction

[0819] In Example 30, the above Cas9 protein is a Cas9 protein derived from Streptococcus pyogenes, a variant of the Cas9 protein derived from Streptococcus pyogenes, or a Cas9 protein exhibiting nickase activity among the variants of the Cas9 protein derived from Streptococcus pyogenes.

[0820] Example 31-1, Cas9 protein sequence limitation

[0821] In Example 31, the above Cas9 protein comprises an amino acid sequence selected from SEQ ID NO: 1 to SEQ ID NO: 3.

[0822] Example 32, PAM limited

[0823] In any one of Examples 30 to 31-1, the Cas9 protein recognizes a protospacer adjacent motif (PAM) selected from the following:

[0824] 5'-NG-3'; 5'-NGG-3'; 5'-NRCH-3'; 5'-NRTH-3'; and 5'-NRRH-3'.

[0825] Example 33, Specific PE type

[0826] In any one of Examples 30 to 32, the above prime editor protein is a prime editor of a selected version of:

[0827] PE or PEmax; PE2 or PE2max; PE3 or PE3max; PE4 or PE4max; PE5 or PE5max; PE6 or PE6max; and PE7 or PE7max.

[0828] Example 34, including NLS

[0829] In any one of Examples 30 to 33, the prime editor protein further comprises one or more nuclear localization signals (NLS).

[0830] Example 35, RNA-protein complex

[0831] RNA-protein complexes for introducing pseudo-single nucleotide mutations into a genetic region of interest, including:

[0832] A prime editor protein of any one of Examples 30 to 34; and

[0833] The pegRNA of any one of Examples 28 to 29;

[0834] Here, the scaffold of the above pegRNA interacts with the above prime editor protein to form a complex,

[0835] The guide domain of the above pegRNA targets the target nucleic acid sequence contained in the above genetic region of interest,

[0836] The prime editing domain of the above pegRNA and the reverse transcriptase of the above prime editor can work together to introduce a pseudo-single nucleotide mutation of any one of Examples 1 to 6 into the coding sequence of the above genetic region of interest.

[0837] Example 36, vector

[0838] A vector expressing each component of the Prime Editor for introducing pseudo-single nucleotide mutations into a genetic region of interest, including:

[0839] A nucleic acid encoding a prime editor protein of any one of Examples 30 to 34; and

[0840] A nucleic acid encoding the pegRNA of any one of Examples 28 to 29;

[0841] Here, the scaffold of the above pegRNA interacts with the above prime editor protein to form a complex,

[0842] The guide domain of the above pegRNA targets the target nucleic acid sequence contained in the above genetic region of interest,

[0843] The prime editing domain of the above pegRNA and the reverse transcriptase of the above prime editor can work together to introduce a pseudo-single nucleotide mutation of any one of Examples 1 to 6 into the coding sequence of the above genetic region of interest.

[0844] Example 37, one molecule nucleic acid

[0845] In Example 36, the nucleic acid encoding the above prime editor protein and the nucleic acid encoding the above pegRNA are contained in the same nucleic acid molecule.

[0846] Example 38, two molecules of nucleic acid

[0847] In Example 36, the nucleic acid encoding the above prime editor protein and the nucleic acid encoding the above pegRNA are contained in different nucleic acid molecules.

[0848] Example 39, nucleic acid limitation

[0849] In any one of Examples 36 to 38, the nucleic acid encoding the above prime editor protein and the nucleic acid encoding the above pegRNA are each independently RNA or DNA.

[0850] Example 40, Prime Editor Composition

[0851] A prime editor composition for introducing a pseudo-single nucleotide mutation into a genetic region of interest, comprising:

[0852] A prime editor protein of any one of Examples 30 to 34, or a nucleic acid encoding said prime editor protein; and

[0853] Any one of the pegRNAs of Examples 28 to 29, or a nucleic acid encoding the pegRNA,

[0854] Here, the scaffold of the above pegRNA interacts with the above prime editor protein to form a complex,

[0855] The guide domain of the above pegRNA targets the target nucleic acid sequence contained in the above genetic region of interest,

[0856] The prime editing domain of the above pegRNA and the reverse transcriptase of the above prime editor can work together to introduce a pseudo-single nucleotide mutation of any one of Examples 1 to 6 into the coding sequence of the above genetic region of interest.

[0857] Example 41, Cas9 mRNA delivery

[0858] In Example 40, the above prime editor composition comprises RNA encoding the above prime editor protein and the above pegRNA.

[0859] Composition for preparing a saturated single nucleotide mutation library

[0860] Example 42, pegRNA library

[0861] A composition for preparing a saturated single nucleotide mutation library comprising:

[0862] N or more types of pegRNAs, or nucleic acids encoding each of the above pegRNAs,

[0863] Here, each of the above pegRNAs is independently a pegRNA selected from Examples 28 to 29,

[0864] Each of the above pegRNAs can introduce a pseudo-single nucleotide mutation of any one of Examples 1 to 6 into the coding sequence of the genetic region of interest together with the prime editor protein,

[0865] The types of the above pegRNAs are distinguished based on one single nucleotide mutation included in each pseudo-single nucleotide mutation.

[0866] Example 43, RNA-protein complex

[0867] A composition for preparing a saturated single nucleotide mutation library comprising:

[0868] N or more types of RNA-protein complexes,

[0869] Here, each of the above RNA-protein complexes is one selected from Example 35,

[0870] Each of the above RNA-protein complexes can introduce a pseudo-single nucleotide mutation of any one of Examples 1 to 6 into the coding sequence of the genetic region of interest,

[0871] The types of the above RNA-protein complexes are distinguished based on one single nucleotide mutation included in each of the above pseudo-single nucleotide mutations.

[0872] Example 44, vector

[0873] A composition for preparing a saturated single nucleotide mutation library comprising:

[0874] N or more types of vectors,

[0875] Here, each of the above vectors is one selected from Examples 36 to 39,

[0876] Each of the above vectors can introduce a pseudo-single nucleotide mutation of any one of Examples 1 to 6 into the coding sequence of the genetic region of interest,

[0877] The types of the above vectors are distinguished based on one single nucleotide mutation included in each of the above pseudo-single nucleotide mutations.

[0878] Example 45, Prime Editor Composition

[0879] A composition for preparing a saturated single nucleotide mutation library comprising:

[0880] N or more types of pegRNAs, or nucleic acids encoding each of the above pegRNAs,

[0881] Here, each of the above pegRNAs is independently a pegRNA selected from Examples 28 to 29,

[0882] The types of the above pegRNAs are distinguished based on one single nucleotide mutation included in each pseudo-single nucleotide mutation; and

[0883] Prime editor proteins or nucleic acids encoding the above prime editor proteins,

[0884] Here, each of the above pegRNAs interacts with each of the above prime editor proteins to form a complex,

[0885] Each of the above complexes can introduce a pseudo-single nucleotide mutation of any one of Examples 1 to 6 into the coding sequence of the genetic region of interest.

[0886] Example 46, Number of pegRNA types included in the pegRNA library #1 - Number expression

[0887] In any one of Examples 42 to 45,

[0888] Let the number of bases contained in the coding sequence of the genetic region of interest above be l,

[0889] N is an integer greater than or equal to 3l and less than or equal to one of the following real numbers:

[0890] About 2.10l; About 2.11l; About 2.11l; About 2.12l; About 2.13l; About 2.14l; About 2.15l; About 2.16l; About 2.17l; About 2.18l; About 2.19l; About 2.20l; About 2.21l; About 2.22l; About 2.23l; About 2.24l; About 2.25l; About 2.26l; About 2.27l; About 2.28l; About 2.29l; About 2.30l; About 2.31l; About 2.32l; About 2.33l; About 2.34l; About 2.35l; About 2.36l; About 2.37l; About 2.38l; About 2.39l; About 2.40l; About 2.50l; About 2.51l; About 2.52l; About 2.53l; About 2.54l; About 2.55l; About 2.56l; About 2.57l; About 2.58l; About 2.59l; About 2.60l; About 2.61l; About 2.62l; About 2.63l; About 2.64l; About 2.65l; About 2.66l; About 2.67l; About 2.68l; About 2.69l; About 2.70l; About 2.71l; About 2.72l; About 2.73l; About 2.74l; About 2.75l; About 2.76l; About 2.77l; About 2.78l; About 2.79l; About 2.80l; About 2.81l; About 2.82l; About 2.83l; About 2.84l; About 2.85l; About 2.86l; About 2.87l; About 2.88l; About 2.89l; About 2.90l; About 2.91l; About 2.92l; About 2.93l; About 2.94l; About 2.95l; About 2.96l; About 2.97l; About 2.98l; About 2.99l; and About 3.00l.

[0891] Example 47, Number of pegRNA types included in the pegRNA library #2 - Number expression

[0892] In any one of Examples 42 to 46,

[0893] Let M be the number of single nucleotide mutations that can occur in the coding sequence of the genetic region of interest above.

[0894] N is an integer greater than or equal to one of the following real numbers, or an integer within the numerical range of two of the following real numbers:

[0895] M 70%; M 71%; M 72%; M 73%; M 74%; M 75%; M 76%; M 77%; M 78%; M 79%; M 80%; M 81%; M 82%; M 83%; M 84%; M 85%; M 86%; M 87%; M 88%; M 89%; M 90%; M 91%; M 92%; M 93%; M 94%; M 95%; M 96%; M 97%; M 98%; M 99%; And M Х 100%,

[0896] For example, N may be an integer greater than or equal to 80% of M; N may be an integer greater than or equal to 80% of M and less than or equal to 100% of M.

[0897] Saturated Single-Nucleotide Mutation Library Preparation Method #1 - Cell Population Preparation Process

[0898] Example 48, Process for preparing a cell population

[0899] The process of preparing a population of cells.

[0900] Example 49, alternative expressions for the preparation process

[0901] In Example 48, the verb "prepare" in the above process is replaced with an expression selected from the following:

[0902] Set up; obtain; and bring.

[0903] Example 50, homogeneous cells

[0904] In any one of Examples 48 to 49, all cells included in the above cell population are homogeneous cells.

[0905] Example 51, Homogeneous Alternative Expressions

[0906] In Example 50, the expression “homogeneous cells” is replaced with an expression selected from the following:

[0907] Identical cells; uniform cells; similar cells; equivalent cells; analogous cells; and isogenic cells.

[0908] Example 52, Meaning of Homogeneous Cells

[0909] In any one of Examples 48 to 51, the cells included in the above cell population are cells cloned from a single cell.

[0910] Example 53, Number of chromosome sets

[0911] In any one of Examples 48 to 52,

[0912] The cells included in the above cell population are selected from the following:

[0913] Haploid cell; non-haploid cell; monoploid cell; diploid cell; triploid cell; tetraploid cell; and cell with more than one set of chromosomes (pentaploid).

[0914] Example 54, Limiting the genetic region of interest

[0915] In any one of Examples 48 to 53,

[0916] Cells included in the above cell population have selected characteristics from the following:

[0917] Contains a haploid region of interest; Contains a doubling region of interest; Contains a triploid region of interest; Contains a tetraploid region of interest; and Contains a pentaploid or more of the genetic region of interest.

[0918] Example 55, including the Prime Editor protein

[0919] In any one of Examples 48 to 54,

[0920] The cells included in the above cell population contain a nucleic acid encoding a prime editor protein selected from Examples 30 to 34.

[0921] Saturated Single-Nucleotide Mutation Library Preparation Method #2 - Composition Processing

[0922] Example 56, Process of processing the composition

[0923] A process of treating a cell population with a composition for preparing a saturated single nucleotide mutation library selected from Examples 42 to 47.

[0924] Example 57, pegRNA library

[0925] In any one of Example 56, the composition for preparing the above saturated single nucleotide mutation library is a composition selected from Example 42, Example 46, or Example 47.

[0926] Example 58, RNA-protein complexes

[0927] In any one of Examples 56 to 57, the composition for preparing the above saturated single nucleotide mutation library is a composition selected from Example 43, Example 46, or Example 47.

[0928] Example 59, Vectors

[0929] In any one of Examples 56 to 58, the composition for preparing the above saturated single nucleotide mutation library is a composition selected from Example 44, Example 46, or Example 47.

[0930] Example 60, Compositions

[0931] In any one of Examples 56 to 59, the composition for preparing the above saturated single nucleotide mutation library is a composition selected from Examples 45 to 47.

[0932] Example 61, Composition processing sequence

[0933] In Example 60, the process of treating the cell population with a composition selected from Examples 45 to 47 is performed in a selected order from the following:

[0934] After processing the above pegRNAs or nucleic acids encoding each of the above pegRNAs,

[0935] Processing the above prime editor proteins or nucleic acids encoding the above prime editor proteins;

[0936] After processing the above Prime Editor proteins or nucleic acids encoding the above Prime Editor proteins,

[0937] Processing the above pegRNAs or nucleic acids encoding each of the above pegRNAs; and

[0938] Simultaneously processing the above prime editor proteins or nucleic acids encoding the above prime editor proteins and the above pegRNAs or nucleic acids encoding each of the above pegRNAs.

[0939] Example 62, Alternative Representation of Process

[0940] In any one of Examples 56 to 61, the verb "process" in the above process is replaced with an expression selected from the following:

[0941] add; apply; expose; mix; react; interact; put in; contact; deliver; inject; infuse; introduce; administer; and transfect.

[0942] Saturated Single-Nucleotide Mutation Library Preparation Method #3 - Obtaining Sequence Information

[0943] Example 63, Process of Obtaining Sequence Information

[0944] The process of obtaining sequence information of a specific cell population;

[0945] Here, the above sequence information is obtained from all or part of the cells of the above specific cell population.

[0946] Example 64, Meaning of Sequence Information

[0947] In Example 63, the above sequence information is selected from the following:

[0948] A list of nucleic acid sequences of the genetic region of interest comprised by the above specific cell population;

[0949] A list of nucleic acid sequences of genetic regions of interest extracted from the above specified cell population;

[0950] A list of nucleic acid sequences of genetic regions of interest detected in the above specific cell population;

[0951] A list of nucleic acid sequences of genetic regions of interest measured in a specific cell population above;

[0952] A list of nucleic acid sequences of genetic regions of interest read from a specific cell population above;

[0953] A list of nucleic acid sequences of genetic regions of interest confirmed in the above specific cell population; and

[0954] A list of nucleic acid sequences of the genetic region of interest obtained from the above specific cell population.

[0955] Example 65, Method for Obtaining Sequence Information

[0956] In any one of Examples 63 to 64, the above process is performed by sequencing a sample of the above specific cell population.

[0957] Saturated Single-Nucleotide Mutation Library Preparation Method #4 - Verification Process for Saturated Single-Nucleotide Library

[0958] Example 66, Saturated Single Nucleotide Mutation Library Verification Process

[0959] A process for verifying whether a specific cell population is a saturated single nucleotide mutation library selected from Examples 15 to 27 using specific sequence information;

[0960] Here, the above specific sequence information is obtained from the above specific cell population.

[0961] Example 67, Method for Determining Pseudo-Single Nucleotide Mutations

[0962] In Example 66, if the sequence information satisfies the following criteria selected from among the following, it is treated as a pseudo-single nucleotide variant nucleic acid sequence:

[0963] In the case of a nucleic acid sequence in which a pseudo-single nucleotide mutation selected from Examples 1 to 6 is introduced into the coding sequence of the genetic region of interest; or

[0964] A nucleic acid sequence in which a pseudo-single nucleotide mutation of any one of Examples 1 to 6 is introduced into the coding sequence of the genetic region of interest, and is any one selected from Examples 7 to 10.

[0965] Example 68: Verification of a saturated single nucleotide mutation library

[0966] In any one of Examples 66 to 67, when the number of types of similar single nucleotide variant nucleic acid sequences included in the above specific sequence information is greater than a predetermined standard, the above specific cell population is determined to be a saturated single nucleotide variant library.

[0967] Example 69, Predetermined Criteria

[0968] In Example 68, the predetermined criterion is a selected value from among the following:

[0969] When the number of bases included in the coding sequence of the genetic region of interest above is l, about 2.10l; about 2.11l; about 2.11l; about 2.12l; about 2.13l; about 2.14l; about 2.15l; about 2.16l; about 2.17l; about 2.18l; about 2.19l; about 2.20l; about 2.21l; about 2.22l; about 2.23l; about 2.24l; about 2.25l; about 2.26l; about 2.27l; about 2.28l; about 2.29l; about 2.30l; about 2.31l; about 2.32l; about 2.33l; about 2.34l; about 2.35l; about 2.36l; About 2.37l; About 2.38l; About 2.39l; About 2.40l; About 2.50l; About 2.51l; About 2.52l; About 2.53l; About 2.54l; About 2.55l; About 2.56l; About 2.57l; About 2.58l; About 2.59l; About 2.60l; About 2.61l; About 2.62l; About 2.63l; About 2.64l; About 2.65l; About 2.66l; About 2.67l; About 2.68l; About 2.69l; About 2.70l; About 2.71l; About 2.72l; About 2.73l; About 2.74l; About 2.75l; About 2.76l; About 2.77l; About 2.78l; About 2.79l; About 2.80l; About 2.81l; About 2.82l; About 2.83l; About 2.84l; About 2.85l; About 2.86l; About 2.87l; About 2.88l; About 2.89l; About 2.90l; About 2.91l; About 2.92l; About 2.93l; About 2.94l; About 2.95l; About 2.96l; About 2.97l; About 2.98l; About 2.99l; Or About 3.00l; And

[0970] Let M be the number of all possible single nucleotide mutations in the coding sequence of the genetic region of interest above, M Х 70%; M Х 71%; M Х 72%; M Х 73%; M Х 74%; M Х 75%; M Х 76%; M Х 77%; M Х 78%; M Х 79%; M Х 80%; M Х 81%; M Х 82%; M Х 83%; M Х 84%; M Х 85%; M Х 86%; M Х 87%; M Х 88%; M Х 89%; M Х 90%; M Х 91%; M Х 92%; M Х 93%; M Х 94%; M Х 95%; M Х 96%; M Х 97%; M Х 98%; M Х 99%; and M Х 100%.

[0971] Example 70, Verification Alternative Expression

[0972] In any one of Examples 66 to 69, the verb "verify" in the above process is replaced with an expression selected from the following:

[0973] Confirm; validate; certify; substanitiate; prove; investigate; measure; determine; evaluate; and identify.

[0974] Method for preparing a saturated single nucleotide mutation library

[0975] Example 71, Method for preparing a saturated single nucleotide mutation library

[0976] A method for preparing a saturated single nucleotide mutation library for a genetic region of interest,

[0977] The above saturated single nucleotide mutation library is selected from Examples 15 to 27,

[0978] The above method includes:

[0979] (a) A process for preparing a cell population, any one selected from Examples 48 to 55;

[0980] (b) a process for treating a cell population of the above (a) with a composition for producing a saturated single nucleotide mutation library selected from Examples 42 to 47, wherein any one selected from Examples 56 to 62;

[0981] (c) A process for obtaining sequence information of a cell population that has performed the process (b) above, any one selected from Examples 63 to 65;

[0982] (d) A process for verifying whether the cell population that performed the above (b) process using the sequence information obtained in the above (c) process is a saturated single nucleotide mutation library selected from Examples 15 to 27, any one selected from Examples 66 to 70.

[0983] Example 72, Method for Preparing a Saturated Single Nucleotide Mutation Library Example #1 - Processing a pegRNA Library

[0984] In Example 71,

[0985] In the above (a) process, the cells included in the above cell population contain a nucleic acid encoding a prime editor protein selected from Examples 30 to 34,

[0986] In the above process (b), the composition for preparing the above saturated single nucleotide mutation library is a composition according to Example 42,

[0987] The above N is about 2.10l, about 2.11l, about 2.11l, about 2.12l, about 2.13l, about 2.14l, about 2.15l, about 2.16l, about 2.17l, about 2.18l, about 2.19l, about 2.20l, about 2.21l, about 2.22l, about 2.23l, about 2.24l, about 2.25l, about 2.26l, about 2.27l, about 2.28l, about 2.29l, about 2.30l, about 2.31l, about 2.32l, about 2.33l, about 2.34l, about 2.35l, about 2.36l, about 2.37l, about 2.38l, About 2.39l, about 2.40l, about 2.50l, about 2.51l, about 2.52l, about 2.53l, about 2.54l, about 2.55l, about 2.56l, about 2.57l, about 2.58l, about 2.59l, about 2.60l, about 2.61l, about 2.62l, about 2.63l, about 2.64l, about 2.65l, about 2.66l, about 2.67l, about 2.68l, about 2.69l, about 2.70l, about 2.71l, about 2.72l, about 2.73l, about 2.74l, about 2.75l, about 2.76l, about 2.77l, about An integer greater than or equal to 2.78l, about 2.79l, about 2.80l, about 2.81l, about 2.82l, about 2.83l, about 2.84l, about 2.85l, about 2.86l, about 2.87l, about 2.88l, about 2.89l, about 2.90l, about 2.91l, about 2.92l, about 2.93l, about 2.94l, about 2.95l, about 2.96l, about 2.97l, about 2.98l, about 2.99l, or about 3.00l.

[0988] Example 73, Method for Preparing a Saturated Single Nucleotide Mutation Library Example #2 - Treatment of RNA-Protein Complexes

[0989] In Example 71,

[0990] In the above (b) process, the composition for preparing the above saturated single nucleotide mutation library is a composition according to Example 43,

[0991] The above N is about 2.10l, about 2.11l, about 2.11l, about 2.12l, about 2.13l, about 2.14l, about 2.15l, about 2.16l, about 2.17l, about 2.18l, about 2.19l, about 2.20l, about 2.21l, about 2.22l, about 2.23l, about 2.24l, about 2.25l, about 2.26l, about 2.27l, about 2.28l, about 2.29l, about 2.30l, about 2.31l, about 2.32l, about 2.33l, about 2.34l, about 2.35l, about 2.36l, about 2.37l, about 2.38l, About 2.39l, about 2.40l, about 2.50l, about 2.51l, about 2.52l, about 2.53l, about 2.54l, about 2.55l, about 2.56l, about 2.57l, about 2.58l, about 2.59l, about 2.60l, about 2.61l, about 2.62l, about 2.63l, about 2.64l, about 2.65l, about 2.66l, about 2.67l, about 2.68l, about 2.69l, about 2.70l, about 2.71l, about 2.72l, about 2.73l, about 2.74l, about 2.75l, about 2.76l, about 2.77l, about An integer greater than or equal to 2.78l, about 2.79l, about 2.80l, about 2.81l, about 2.82l, about 2.83l, about 2.84l, about 2.85l, about 2.86l, about 2.87l, about 2.88l, about 2.89l, about 2.90l, about 2.91l, about 2.92l, about 2.93l, about 2.94l, about 2.95l, about 2.96l, about 2.97l, about 2.98l, about 2.99l, or about 3.00l.

[0992] Example 74, Saturated Single Nucleotide Mutation Library #3 - Prime Editor Composition Treatment

[0993] In Example 71,

[0994] In the above (b) process, the composition for preparing the above saturated single nucleotide mutation library is a composition according to Example 45,

[0995] The above N is about 2.10l, about 2.11l, about 2.11l, about 2.12l, about 2.13l, about 2.14l, about 2.15l, about 2.16l, about 2.17l, about 2.18l, about 2.19l, about 2.20l, about 2.21l, about 2.22l, about 2.23l, about 2.24l, about 2.25l, about 2.26l, about 2.27l, about 2.28l, about 2.29l, about 2.30l, about 2.31l, about 2.32l, about 2.33l, about 2.34l, about 2.35l, about 2.36l, about 2.37l, about 2.38l, About 2.39l, about 2.40l, about 2.50l, about 2.51l, about 2.52l, about 2.53l, about 2.54l, about 2.55l, about 2.56l, about 2.57l, about 2.58l, about 2.59l, about 2.60l, about 2.61l, about 2.62l, about 2.63l, about 2.64l, about 2.65l, about 2.66l, about 2.67l, about 2.68l, about 2.69l, about 2.70l, about 2.71l, about 2.72l, about 2.73l, about 2.74l, about 2.75l, about 2.76l, about 2.77l, about An integer greater than or equal to 2.78l, about 2.79l, about 2.80l, about 2.81l, about 2.82l, about 2.83l, about 2.84l, about 2.85l, about 2.86l, about 2.87l, about 2.88l, about 2.89l, about 2.90l, about 2.91l, about 2.92l, about 2.93l, about 2.94l, about 2.95l, about 2.96l, about 2.97l, about 2.98l, about 2.99l, or about 3.00l.

[0996] Example 75, PAM not destroyed

[0997] In any one of Examples 71 to 74,

[0998] A composition for preparing the above saturated single nucleotide mutation library comprises at least a kind of pegRNA or a nucleic acid encoding the above pegRNA,

[0999] The pseudo-single nucleotide mutations introduced by each of the above pegRNAs and their corresponding prime editors do not destroy the PAM of the above prime editors.

[1000] Example 76, a limited

[1001] In Example 75, the above a is an integer greater than or equal to 1 and less than or equal to 3l.

[1002] Drug Sensitivity Assessment Method Using a Saturated Single-Nucleotide Mutation Library #1 - Library Partitioning Process

[1003] Example 77, Library Splitting

[1004] The process of dividing a specific cell population or library into two or more groups.

[1005] Example 78, Number of drugs evaluated +1

[1006] In Example 77, a process of dividing a specific cell population or library into groups that is at least one more than the number of types of drugs to be evaluated.

[1007] Example 79, homogeneous group

[1008] In any one of Examples 77 to 78, each group is homogeneous.

[1009] Example 80, Homogeneous Alternative Expressions

[1010] In Example 79, the expression that each group is homogeneous is replaced with an expression selected from the following:

[1011] Each group is identical to the others (identical); each group is divided uniformly; each group is similar to the others (similar); and each group is equivalent to the others (equivalent).

[1012] Drug Sensitivity Assessment Method Using a Saturated Single-Nucleotide Mutation Library #2 - Drug Processing

[1013] Example 81, Drug Treatment Process

[1014] A process of treating one or more types of drugs to a group of cells or a library of cells.

[1015] Treat one type of drug per group,

[1016] At least one group was not treated with drugs.

[1017] Example 82, Alternative Representation of Process

[1018] In Example 81, the verb "process" is replaced with an expression selected from the following:

[1019] add; mix; react; put in; contact; deliver; inject; introduce; administer; and transfect.

[1020] Drug Sensitivity Assessment Method Using a Saturated Single-Nucleotide Mutation Library #4 - A Process of Comparing Two or More Sequence Information

[1021] Example 83, Process of comparing sequence information of each group

[1022] A process of comparing two or more sequence information.

[1023] Each sequence information is selected from the following:

[1024] A list of nucleic acid sequences of the genetic region of interest comprised by the above specific cell population;

[1025] A list of nucleic acid sequences of genetic regions of interest extracted from the above specified cell population;

[1026] A list of nucleic acid sequences of genetic regions of interest detected in the above specific cell population;

[1027] A list of nucleic acid sequences of genetic regions of interest measured in a specific cell population above;

[1028] A list of nucleic acid sequences of genetic regions of interest read from a specific cell population above;

[1029] A list of nucleic acid sequences of genetic regions of interest confirmed in the above specific cell population; and

[1030] A list of nucleic acid sequences of the genetic region of interest obtained from the above specific cell population.

[1031] Example 84, Comparison of pseudo-single nucleotide mutations

[1032] In Example 83, among the nucleic acid sequences included in each of the above sequence information, a nucleic acid sequence satisfying the following criteria is treated as a pseudo-single nucleotide variant nucleic acid sequence:

[1033] A nucleic acid sequence in which a pseudo-single nucleotide mutation selected from Examples 1 to 6 is introduced into the coding sequence of the genetic region of interest; or

[1034] A nucleic acid sequence in which a pseudo-single nucleotide mutation of any one of Examples 1 to 6 is introduced into the coding sequence of a genetic region of interest, wherein any one of the nucleic acid sequences selected from Examples 7 to 10;

[1035] Each sequence information is compared by type of pseudo-single nucleotide mutation.

[1036] Example 85, Comparative Method

[1037] In any one of Examples 83 to 84

[1038] Comparing two or more sequence information by type of similar single nucleotide mutation,

[1039] Based on the number of each similar single nucleotide variant nucleic acid sequence contained in one sequence information,

[1040] This is a process of comparing the number of each similar single nucleotide variant nucleic acid sequence included in other sequence information by converting it into a relative value with respect to the above reference sequence information.

[1041] Drug sensitivity assessment method using a saturated single nucleotide mutation library

[1042] Example 86, Drug Sensitivity Assessment Method #1 - Comparison after Library Preparation

[1043] A method for assessing drug sensitivity using a saturated single nucleotide variant library for a genetic region of interest.

[1044] The above saturated single nucleotide mutation library is selected from Examples 15 to 27,

[1045] More than one type of drug is being evaluated,

[1046] The above method includes:

[1047] (a) A process for preparing a cell population, any one selected from Examples 48 to 55;

[1048] (b) a process for treating a cell population of the above (a) with a composition for producing a saturated single nucleotide mutation library selected from Examples 42 to 47, wherein any one selected from Examples 56 to 62;

[1049] (c) a process of dividing a cell population that has performed the above (b) process into two or more groups, any one selected from Examples 77 to 80;

[1050] (d) A process for treating one or more types of drugs for two or more groups divided through the process (c) above, any one selected from Examples 81 to 82;

[1051] (e) A process of independently performing one of the processes of Examples 63 to 65 for each group that performed the process (d) above to obtain sequence information;

[1052] (f) a process of comparing the sequence information of each group obtained by performing the above (e) process, any one selected from Examples 83 to 85; and

[1053] (g) A process for evaluating drug sensitivity based on the comparative results of the process (f) above.

[1054] Example 87, Drug Sensitivity Assessment Method #2 - Comparison with a Specific Library

[1055] A method for assessing drug sensitivity using a saturated single nucleotide variant library for a genetic region of interest.

[1056] More than one type of drug is being evaluated,

[1057] The above method includes:

[1058] (a) A process for preparing a saturated single nucleotide mutation library selected from any one of Examples 15 to 27;

[1059] (b) a process for dividing the above saturated single nucleotide mutation library into two or more groups, any one selected from Examples 77 to 80;

[1060] (c) A process of treating one or more types of drugs into two or more groups divided through the process (b) above, any one selected from Examples 81 to 82;

[1061] (d) A process for obtaining sequence information of each group that performed the process (c) above, selected from any one of Examples 83 to 85;

[1062] (e) A process of comparing the sequence information of each group obtained in the process (d) above, and selecting one of the No entry found;

[1063] (f) A process for evaluating drug sensitivity based on the comparative results of the process (e) above.

[1064] Gene Function Assessment Method #1 Using Saturated Single-Nucleotide Mutation Libraries - Obtaining Sequence Information at Specific Time Points

[1065] Example 88: Obtaining sequence information at a specific point in time

[1066] The process of obtaining sequence information of a specific cell population or library at a specific point in time.

[1067] Obtain sequence information of a genetic region of interest from all or part of the cells contained in the above cell population or library.

[1068] Example 89: Obtaining sequence information through sequencing

[1069] In Example 88, the process of obtaining the above sequence information is performed through sequencing.

[1070] Example 90: Obtaining sequence information through a sample

[1071] In any one of Examples 88 to 89,

[1072] The process of obtaining the above sequence information is performed by sequencing a sample of the above cell population or library at a specific point in time.

[1073] Method for evaluating gene function using a saturated single nucleotide mutation library

[1074] Example 91, Gene Function Evaluation Method #1 - Post-library Preparation Evaluation

[1075] A method for evaluating gene function using a saturated single nucleotide variant library for a genetic region of interest.

[1076] The above saturated single nucleotide mutation library is selected from Examples 15 to 27,

[1077] The above method includes:

[1078] (a) A process for preparing a cell population, any one selected from Examples 48 to 55;

[1079] (b) a process for treating a cell population of the above (a) with a composition for producing a saturated single nucleotide mutation library selected from Examples 42 to 47, wherein any one selected from Examples 56 to 62;

[1080] (c) At the first time point, a process of obtaining first sequence information by performing any one of the processes of Examples 63 to 65 on a cell population that has performed the process (b) above;

[1081] (d) At the second time point, a process of obtaining second sequence information by performing any one of the processes of Examples 63 to 65 on a cell population that has performed the process (b) above;

[1082] (e) a process of comparing the sequence information of the first sequence information and the second sequence information, any one selected from Examples 83 to 85; and

[1083] (f) A process of evaluating the function of the genetic region of interest based on the comparison results of the process (e) above.

[1084] Example 92, Gene Function Evaluation Method #2 - Evaluation with Manufactured Library

[1085] A method for evaluating the function of a genetic region of interest using a saturated single nucleotide mutation library for the genetic region of interest.

[1086] The above method includes:

[1087] (a) A process for preparing a saturated single nucleotide mutation library selected from any one of Examples 15 to 27;

[1088] (b) At the first point in time, a process of obtaining first sequence information by performing any one of the processes of Examples 63 to 65 on the above library;

[1089] (c) At the second time point, a process of obtaining second sequence information by performing any one of the processes of Examples 63 to 65 on the above library,

[1090] Here, the second point in time is a point in time later than the first point in time;

[1091] (d) a process of comparing the sequence information of the first sequence information and the second sequence information, any one selected from Examples 83 to 85; and

[1092] (e) A process of evaluating the function of the genetic region of interest based on the comparison results of the process (d) above.

[1093] Example 93: Meaning of Gene Function Evaluation

[1094] In any one of Examples 91 to 92, the process of evaluating the function of the genetic region of interest is a process of evaluating which single nucleotide mutation affects cell survival.

[1095] Example 94, Difference between Time 1 and Time 2

[1096] In any one of Examples 91 to 93,

[1097] The time difference between time points 1 and 2 is selected from the following:

[1098] T1 hours (hours), where T1 is a real number greater than or equal to 0.5 and less than or equal to 24;

[1099] T2 days, where T2 is a real number selected from 1 to 30;

[1100] T3 weeks, where T3 is a real number selected from 1 to 52; and

[1101] T4 months, where T4 is a real number greater than or equal to 1 and less than or equal to 12.

[1102] Example 95, Time 1

[1103] In any one of Examples 91 to 94,

[1104] The first time point is the time selected from the following after the time at which the above saturated single nucleotide mutation library was prepared:

[1105] W1 hours (hours), where W1 is a real number greater than or equal to 0.5 and less than or equal to 24;

[1106] W2 days, where W2 is a real number selected from 1 to 30;

[1107] W3 weeks, where W3 is a real number greater than or equal to 1 and less than or equal to 52; and

[1108] W4 months, where W4 is a real number greater than or equal to 1 and less than or equal to 12.

[1109] pegRNA library example

[1110] Example 96, Editing Area Limitation

[1111] A pegRNA library comprising a set of nucleic acids encoding a pegRNA or a pegRNA satisfying the following conditions:

[1112] 1) having the structure of any one of Examples 28 to 29;

[1113] 2) Configured to edit one or more selected areas:

[1114] Exon 2 of the RPL gene; Exon 18 of the EGFR gene; Exon 19 of the EGFR gene; Exon 20 of the EGFR gene; Exon 21 of the EGFR gene; Exon 22 of the EGFR gene; Exon 23 of the EGFR gene; Exon 24 of the EGFR gene; Exon 4 of the ABL1 gene; Exon 5 of the ABL1 gene; Exon 6 of the ABL1 gene; Exon 7 of the ABL1 gene; Exon 8 of the ABL1 gene; Exon 9 of the ABL1 gene;

[1115] 3) Introducing a pseudo-single nucleotide mutation of Examples 1 to 14 into one or more regions selected from 2); and

[1116] 4) The above pegRNA library includes pegRNAs that introduce pseudo-single nucleotide mutations corresponding to at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or 100% of the single nucleotide mutations that can be introduced into one or more regions selected in 2).

[1117] Example 97, EGFR-related, sequence-limited

[1118] In Example 96, the pegRNA library comprises a set of one or more pegRNAs selected from the following, or a set of nucleic acids encoding the pegRNAs:

[1119] 1) Sequence number 1499 to sequence number 2661;

[1120] 2) Sequence number 2662 to sequence number 3448;

[1121] 3) Sequence number 3449 to sequence number 5207;

[1122] 4) Sequence number 5208 to sequence number 6671;

[1123] 5) Sequence number 6672 to sequence number 7363;

[1124] 6) Sequence number 7364 to sequence number 8570;

[1125] 7) Sequence number 8571 to sequence number 9357; and

[1126] 8) Sequence number 9358 to sequence number 11202.

[1127] Example 98, ABL1 related, sequence limited

[1128] In Example 96, the pegRNA library comprises a set of one or more pegRNAs selected from the following, or a set of nucleic acids encoding the pegRNAs:

[1129] 1) Sequence number 11203 to sequence number 13748;

[1130] 2) Sequence number 13749 to sequence number 14603;

[1131] 3) Sequence number 14604 to sequence number 16295;

[1132] 4) Sequence number 16296 to sequence number 18049;

[1133] 5) Sequence number 18050 to sequence number 19510; and

[1134] 6) Sequence number 19511 to sequence number 20411.

[1135] Example 99, RPL15 related, sequence limited

[1136] In Example 96, the pegRNA library comprises a set of one or more pegRNAs selected from the following, or a set of nucleic acids encoding the pegRNAs:

[1137] 1) Sequence number 7 to sequence number 1498.

[1138] Example 100, one pegRNA per site

[1139] In Example 96, the pegRNA library is a portion of any one of the pegRNA libraries selected from Examples 97 to 99 that satisfies the following conditions:

[1140] If there are multiple pegRNAs that introduce a single nucleotide mutation at a specific location, only one selected one is included.

[1141] Example 100-1. epegRNA

[1142] In Example 96, the pegRNA library is a pegRNA library composed of pegRNAs in which the 3'-terminal engineering sequence of SEQ ID NO: 6 is linked to the 3'-terminal of each pegRNA of any one of the pegRNA libraries selected from Examples 97 to 99.

[1143] How to use drug sensitivity assessment results

[1144] Example 101, Tyrosine Kinase Inhibitor Selection Method #1 - EGFR

[1145] A method for selecting a tyrosine kinase inhibitor (TKI) suitable for treatment using epidermal growth factor receptor (EGFR) gene sequence information of a cancer patient's tumor, including:

[1146] (a) A process for obtaining EGFR gene sequence information of a patient's tumor cells;

[1147] (b) a process of obtaining sensitivity to one or more tyrosine kinase inhibitors corresponding to the EGFR gene sequence obtained in (a), with reference to [Table 1] of this specification or Supplementary Table 4 of the literature (Kim et al., Saturation resistance profiling of EGFR variants against tyrosine kinase inhibitors using prime editing); and

[1148] (c) A process of selecting a tyrosine kinase inhibitor suitable for treating the above patient by comparing the drug sensitivity information obtained in (b) with predetermined criteria.

[1149] Example 102, Treatment Method #1 - EGFR

[1150] A cancer treatment method using Epidermal Growth Factor Receptor (EGFR) gene sequence information of a cancer patient's tumor, including:

[1151] (a) A process for obtaining EGFR gene sequence information of a patient's tumor cells;

[1152] (b) a process of obtaining sensitivity to one or more tyrosine kinase inhibitors corresponding to the EGFR gene sequence obtained in (a), with reference to [Table 1] of this specification or Supplementary Table 4 of the literature (Kim et al., Saturation resistance profiling of EGFR variants against tyrosine kinase inhibitors using prime editing); and

[1153] (c) a process of selecting a tyrosine kinase inhibitor suitable for treating the patient by comparing the drug sensitivity information obtained in (b) with predetermined criteria; and

[1154] (d) A process of administering a tyrosine kinase inhibitor selected from (c) to the above patient.

[1155] Example 103, cancer-specific

[1156] In any one of Examples 101 to 102, the patient is a lung cancer patient.

[1157] Example 104, TKI-only

[1158] In any one of Examples 101 to 103, the tyrosine kinase inhibitor is Afatinib or Osimertinib.

[1159] Example 105, Tyrosine Kinase Inhibitor Selection Method #2 - ABL1

[1160] A method for selecting a tyrosine kinase inhibitor suitable for treatment using ABL1 gene sequence information of a cancer patient's tumor, comprising:

[1161] (a) A process of obtaining ABL1 gene sequence information of a patient's tumor cells;

[1162] (b) a process of obtaining sensitivity to one or more tyrosine kinase inhibitors corresponding to the ABL1 gene sequence obtained in (a), with reference to [Table 2] and [Table 3] of this specification, or Supplementary Table 3 and Supplementary Table 4 of the literature (Jung et al., Comprehensive resistance profiling of ABL1 variants against six kinase inhibitors for CML treatment); and

[1163] (c) A process of selecting a tyrosine kinase inhibitor suitable for treating the above patient by comparing the drug sensitivity information obtained in (b) with predetermined criteria.

[1164] Example 106, Treatment Method #2 - ABL1

[1165] A cancer treatment method using ABL1 gene sequence information of a cancer patient's tumor, including:

[1166] (a) A process of obtaining ABL1 gene sequence information of a patient's tumor cells;

[1167] (b) a process of obtaining sensitivity to one or more tyrosine kinase inhibitors corresponding to the ABL1 gene sequence obtained in (a), with reference to [Table 2] and [Table 3] of this specification, or Supplementary Table 3 and Supplementary Table 4 of the literature (Jung et al., Comprehensive resistance profiling of ABL1 variants against six kinase inhibitors for CML treatment); and

[1168] (c) a process of selecting a tyrosine kinase inhibitor suitable for treating the patient by comparing the drug sensitivity information obtained in (b) with predetermined criteria; and

[1169] (d) A process of administering a tyrosine kinase inhibitor selected from (c) to the above patient.

[1170] Example 107, cancer-specific

[1171] In any one of Examples 105 to 106, the patient is a chronic myeloid leukemia (CML) patient.

[1172] Example 108, TKI limited

[1173] In any one of Examples 105 to 107, the tyrosine kinase inhibitor is imatinib, nilotinib, bosutinib, dasatinib, ponatinib, or asciminib.

[1174] Experimental example

[1175] Hereinafter, the invention provided by this specification will be described in more detail through experimental examples and examples. These examples are intended solely to illustrate the subject matter disclosed by this specification, and it will be apparent to those skilled in the art that the scope of the subject matter disclosed by this specification is not limited by these examples.

[1176] Experimental Example 1. Single-nucleotide mutation study in the EGFR gene

[1177] References for Experimental Example 1

[1178] Experimental Example 1 of this specification shares the contents and data of a paper (Kim et al., Saturation resistance profiling of EGFR variants against tyrosine kinase inhibitors using prime editing) in which the inventors of this specification participated. All descriptions, data, and drawings of the above paper are to be understood as being incorporated into this specification.

[1179] For reference, the term "SynPrime" (Synonymous Prime Editing), disclosed in Korean Patent Application No. 10-2023-0131911, Korean Patent Application No. 10-2023-0164825, and Korean Patent Application No. 10-2024-0101453, from which the above paper and this application claim priority, has been replaced in this specification with "PEER-seq" (Prime Editing and Endogenous Region Sequencing). Therefore, the terms "SynPrime" and "PEER-seq" can be used interchangeably and should be interpreted appropriately depending on the context. The same applies hereinafter.

[1180] Experimental Example 1.1. Experimental Method and Materials

[1181] Experimental Example 1.1.1. Prime Editing Guide RNA (pegRNA) Library Design

[1182] Using the human genome (hg38) as a reference sequence, we constructed a library of pegRNA sequences designed to generate all possible nucleotide changes in the coding DNA sequence (CDS) of the EGFR gene from exon 18 to exon 24 (Syn-exon18 to Syn-exon24, see sequence listing). Because PC-9 cells harbor a 15-bp in-frame deletion in exon 19 of EGFR, pegRNAs targeting this region were designed based on the deleted sequence. To achieve a high frequency of prime editing, we calculated the DeepPrime-FT score (DeepPrime-FT is a fine-tuned model to predict the activity of PE4max using an optimized scaffold in HEK293T cells) for pegRNAs designed to generate all possible 1-bp substitutions in the target region. Afterwards, we selected the three pegRNAs with the highest DeepPrime-FT scores for each SNV, excluding all pegRNAs with a left homology arm (LHA) shorter than 5 bp. In addition, we added a 1-bp synonymous substitution to the LHA of the reverse transcriptase (RT) template of the pegRNAs at codons that do not contain the intended substitution. This manipulation was performed to maintain the minimum length of the right homology arm (RHA) of the RT template, which is important for efficient prime editing. The synonymous substitutions were performed for the following purposes: (1) filtering sequencing reads based on these additional substitutions allows us to distinguish between reads containing sequencing errors or partial introduction of the RT template and truly edited sequence reads; (2) introducing additional mismatches near the intended substitutions can inhibit double-stranded DNA repair and bypass the MMR mechanism, thereby increasing the efficiency of prime editing.

[1183] Of the 2,610 possible SNVs, 95 could only be designed with pegRNAs that exceeded the maximum RT template length specified by the DeepPrime model, and thus were excluded from the library, resulting in a total of 2,515 SNVs. Thirty-six (1.4%) of the 2,515 SNVs were targeted by pegRNAs that had no synonymous substitution sites available, so no additional synonymous substitutions were introduced. As negative controls, we included 198 pegRNAs with the same spacer sequence used in the SNV-derived pegRNAs and "sham-editing" pegRNAs containing RT templates matching the reference sequence. We also included 198 pegRNAs that did not target any sequence in the human genome. These were designated "nontargeting" pegRNAs and had RT templates of randomly annotated context sequences. Consequently, a total of 7,884 pegRNAs were designed, including all of the above (see sequence listing).

[1184] For the Syn-RPL15 and Syn-RPL15-initial libraries, we calculated DeepPrime-FT scores for pegRNAs designed to generate all possible 1-bp substitutions within the CDS of exon 2 of RPL15. For each SNV, we selected the three pegRNAs with the highest DeepPrime-FT scores, as we did for Syn-Exon18 through Syn-Exon24. We then introduced additional 1-bp synonymous substitutions into the LHA of the RT templates of Syn-RPL15 and Syn-RPL15-initial. If a 1-bp synonymous substitution could not be introduced into the LHA, i) for Syn-RPL15, we added it to the RHA and extended the length of the RHA by at least 5 nucleotides, based on our previous characterization of pegRNA activity. ii) for Syn-RPL15-initial, we did not design pegRNAs for these SNVs. Additionally, for the construction of Syn-RPL15 described in the 'Construction of the pegRNA Library and Cloning' section below, we increased the number of oligonucleotides per pegRNA to two or three when the sum of the DeepPrime scores of the three pegRNAs designed to generate the intended SNVs was between 5 and 15 or lower than 5, respectively. When the sum of the DeepPrime scores of the three pegRNAs for the intended SNV was lower than 2, we added one additional pegRNA predicted to be the most efficient by PRIDICT 2.070. Further details are provided in the sequence listing. Syn-RPL15 and Syn-RPL15-initial contained 1,555 pegRNA sequences (2,242 oligonucleotides) to induce 516 SNVs in RPL15 and 1,492 pegRNA sequences (1,492 oligonucleotides) to induce 500 SNVs, respectively.

[1185] For Syn-BRCA1-exon19, we calculated DeepPrime-FT scores for pegRNAs designed to generate all possible 1-bp substitutions and an additional 1-bp synonymous substitution within the 5-bp intronic region flanking the exon 19 CDS of BRCA1. As with Syn-RPL15, we selected the three pegRNAs with the highest DeepPrime-FT scores for each SNV, and introduced an additional 1-bp synonymous substitution into the LHA of the RT template. Syn-BRCA1-exon19 contained 846 pegRNA sequences (846 oligonucleotides) to induce 282 SNVs in exon 19 of BRCA1.

[1186] To design a pegRNA library designed to generate all possible nucleotide changes in the exon 20 CDS of EGFR for NRCH-PEmax (NRCH-exon20), we calculated the DeepPrime-FT scores of the pilot version of the original model (specifically designed for NRCH-PE2max in HEK293T cells) for pegRNAs designed to generate all possible 1-bp substitutions in the target region. For each SNV, we selected the three pegRNAs with the highest DeepPrime-FT scores. As negative controls, we included 86 mock-edited pegRNAs and 85 off-target pegRNAs. A total of 1,845 pegRNAs were designed (see sequence listing).

[1187] Experimental Example 1.1.2. Construction and Cloning of a pegRNA Library

[1188] The pegRNA library targeting EGFR was divided into exon-specific subsets of the library (Syn-exon18 to Syn-exon24, see sequence listing). Each subset contained the same number of non-targeting pegRNA sequences as the mock-editing pegRNA sequences with the spacer sequences used in that subset.

[1189] The 228-bp oligonucleotides for plasmid construction were sequence-synthesized by Twist Bioscience. For Syn-exon18 through Syn-exon24, Syn-RPL15, and Syn-BRCA1, the oligonucleotides were designed to contain the following elements (Figure 39): (1) 19-bp homology arms with the 3' terminus at the U6 promoter; (2) a 20-bp sequence with a G at the 5' terminus, followed by a 19-bp spacer sequence; (3) a randomized 18-bp sequence flanked by BsmBI cleavage sites (11 bp each); (4) an RT template and primer binding site (PBS); (5) an 8-bp linker sequence obtained from the pegLIT tool, followed by a 37-bp tevopreQ1 and a 7-bp poly-T sequence; (6) an 18-bp unique barcode sequence corresponding to the unique pegRNA sequence (sequences containing different barcode sequences and the "GTCAG" constant sequence with at least three edit distances were used for subsequent analysis); (7) random "buffer" sequences of varying lengths added to the pegRNA with the short RT template and PBS to ensure that all oligonucleotides were of equal length; (8) a 20-bp unique homology arm for sublibrary amplification.

[1190] The unique homology arm sequences for EGFR were as follows:

[1191] EGFR exon 18: 5'-TGCGACCGTAATCAAACCAA-3' (SEQ ID NO: 21388)

[1192] EGFR exon 19: 5'-ATGCAATCGGCCTGGTATTT-3' (SEQ ID NO: 21389)

[1193] EGFR exon 20: 5'-AAATGGATGCCTTGTGCGAA-3' (SEQ ID NO: 21390)

[1194] EGFR exon 21: 5'-AAACGGAGCCATGAGTTTGT-3' (SEQ ID NO: 21391)

[1195] EGFR exon 22: 5'-AAACTGGAGGCGGCAAATTA-3' (SEQ ID NO: 21392)

[1196] EGFR exon 23: 5'-ACCGCGCTCGAAGAATTTAA-3' (SEQ ID NO: 21393)

[1197] EGFR exon 24: 5'-TCCTCAGCCGATGAATTCC-3' (SEQ ID NO: 21394)

[1198] Oligonucleotides (~4 ng) were amplified using primer set 1 / 2 and Q5 High-Fidelity DNA polymerase (NEB). The unique reverse primer contained an 8-bp random unique molecular identifier (UMI) sequence for pegRNA-based analysis. PCR cycle conditions were as follows: 98°C for 30 s, 68°C for 30 s, and 72°C for 2 min, for a total of 14 cycles. The resulting amplicons were size-selected by 2% agarose gel electrophoresis and assembled into BsmBI-linearized Lenti-gRNA-Puro73 (Addgene, 84752) using NEBuilder HiFi DNA Assembly Master Mix (NEB). After the assembly reaction, the products were isopropanol precipitated and electroporated into Endura electrocompetent cells (Lucigen) to generate a scaffold-free plasmid library.

[1199] An improved version of the SpCas9 sgRNA scaffold sequence and BsmBI cleavage site was synthesized, amplified by PCR, and ligated into a TOPO vector (Invitrogen, Zero Blunt TOPO PCR Cloning Kit). The ligation reaction was performed using the scaffold-free plasmid library generated above and the sgRNA scaffold sequence with sticky ends cleaved with BsmBI. The reaction mixture was pooled, precipitated with isopropanol, and electroporated into Endura electrocompetent cells to construct the final plasmid library.

[1200] Experimental Example 1.1.3. Cell Lines and Culture

[1201] PC-9 (RRID: CVCL_B260) cells were purchased from Riken Cell Bank. PC-9 cells were cultured in RPMI 1640 (Gibco, 2.05 mM L-glutamine) supplemented with 10 mM HEPES, 1 mM sodium pyruvate, 10% fetal bovine serum (FBS) (RDT), and 1% penicillin-streptomycin (Gibco). Cells were passaged every 3 days and maintained in a 37°C, 5% CO2 incubator. The concentrations of polybrene, puromycin, and blasticidin were 8 μg / ml, 0.2 μg / ml, and 0.3 μg / ml, respectively. HAP1-MSH6 knockout (CVCL-SZ01) cells were purchased from Horizon Discovery. HAP1-MSH6 knockout cells were cultured in IMDM (Gibco) supplemented with 10% FBS (RDT) and 1% penicillin-streptomycin (Gibco). HEK293T cells (ATCC) were cultured in DMEM (Gibco) containing 10% FBS (RDT) at 37°C in 5% CO2.

[1202] Experimental Example 1.1.4. Plasmid Vector Construction

[1203] All primers used for cloning are listed in the sequence listing. To generate a lentiviral vector expressing PEmax, three linear DNA fragments were assembled: (1) the linearized lentiviral backbone (XbaI and EcoRI (NEB)) from Lenti-Cas9 Blast (Addgene #52962)75; (2) the sequence encoding Cas9 containing the R221K, N394K, and H840A mutations amplified from pCMV-PEmax-p2A-BSD32 (Addgene #174821) using primer pairs 4 / 5; (3) the optimized MMLV-RT and nuclear localization signal (NLS) sequences and the blasticidin resistance gene amplified from pCMV-PEmax-p2A-BSD using primer pairs 6 / 7. The assembled vector was named pLenti-PEmax-p2A-BSD.

[1204] To generate lentiviruses expressing NRCH-PEmax, five linear DNA fragments were assembled: (1) lentiviral backbone linearized from Lenti-Cas9 Blast (Addgene #52962) digested with XbaI and EcoRI (NEB); (2) sequence encoding the N-terminal region of Cas9 containing the R221K and N394K mutations amplified from pCMV-PEmax-p2A-BSD32 (Addgene #174821) using primer pair 8 / 9; (3 and 4) sequence encoding the C-terminal region of Cas9-NRCH amplified with primers containing the H840 sequence amplified from pCMV-Cas9-NRCH76 (Addgene #136926) using primer pairs 10 / 11 and 12 / 13; (5) Optimized MMLV-RT and NLS sequences amplified from pCMV-PEmax-p2A-BSD32 (Addgene #174821) using primer pair 14 / 15. The assembled vector was named pLenti-Cas9-NRCH-PEmax-p2A-BSD.

[1205] To generate the lentiviral vector plenti-gRNA-puromycin-p2A-luciferase, four linear DNA fragments were assembled: (1) the lentiviral backbone linearized from Lenti-gRNA-puro73 (Addgene #84752) digested with FseI-NcoI; (2) the sequence encoding EF-1a and puromycin resistance genes amplified from Lenti-gRNA-Puro using primer pair 16 / 17; (3) the sequence encoding luciferase amplified from pLenti HRE-Luc PGK Hygro77 (Addgene #118706) using primer pair 18 / 19; and (4) the WPRE and LTR segments amplified from Lenti-gRNA-Puro using primer pair 20 / 21.

[1206] Experimental Example 1.1.5. Lentivirus Production

[1207] HEK293T cells were seeded at a density of 1 Х10^7 cells in 150-mm culture dishes 18 hours before transfection. Transfection was performed using lipofectamine 3000 (Invitrogen) transfection reagent according to the manufacturer's protocol. Briefly, transfer plasmids containing the genes of interest, psPAX2, and pMD2.G were mixed in a molar ratio of 4.3:3.4:1.9 pmol and diluted in 1 ml of Opti-MEM (Life Technology). P3000 reagent was added to the diluted plasmid mixture to obtain a 1:2 μg DNA: μL P3000 reagent ratio. Lipofectamine 3000 reagent was diluted in 1 ml of Opti-MEM to obtain a 1:2.25 μg DNA: μL lipofectamine 3000 reagent ratio, and the resulting solution was added to the DNA mixture. The mixture was incubated for 20 minutes and then added to the cells. Six hours after transfection, 20 ml of culture medium was added to refresh the cell culture.

[1208] Culture media were harvested 24 and 52 hours after transfection. The harvested media were centrifuged at 2,000 g for 10 min to precipitate cell debris. The supernatant was filtered through a Millex-HV 0.45-μm low-protein binding membrane (Millipore), and stored at -80°C for 10 min.

[1209] Experimental Example 1.1.6. Cell Line Production

[1210] To generate cells expressing the prime editor, wild-type PC-9 cells were transfected with a mixture of a plasmid expressing SpCas9 (pRGEN-Cas9-CMV / T7-Puro-RFP; purchased from ToolGen, Korea) and plasmids expressing two sgRNAs (pRG2; Addgene #104174).

[1211] The sgRNA sequences used are as follows:

[1212] MLH1-sgRNA1: 5'-TGGAGGGGGATACAACAAAG(GGG)-3' (SEQ ID NO: 21395)

[1213] MLH1-sgRNA2: 5'-GCCTGGGGCTGACTGGACAG(GGG)-3' (SEQ ID NO: 21396)

[1214] PC-9 cells were electroporated using a NEPA21 electroporator (NepaGene) according to the manufacturer's protocol. Specifically, 5 μg of the plasmid expressing SpCas9 and 2.5 μg each of the plasmids expressing two sgRNAs were mixed and electroporated at 125 V with a pulse length of 7.5 ms. Cells expressing the transgene were sorted using FACS 5 days after transfection and deep-sequenced using primer pair 22 / 23 (see sequence listing).

[1215] The generated PC-9-MLH1-knockout cell lines were transfected with PEmax or NRCH-PEmax and a lentiviral vector expressing the blasticidin resistance gene, as described above. The day after transfection, the cells were replaced with culture medium containing 3 μg / ml blasticidin (Invivogen). After 2 weeks of selection, the resulting PEmax-expressing MLH1-knockout PC-9 cells were subdivided and used for further experiments.

[1216] To generate PC-9 cells expressing T790M, MLH1-knockout PC-9 cells expressing PEmax were transfected with a mixture of a plasmid expressing BE4max (pCMV-BE4max; Addgene #112093)78 and a plasmid expressing sgRNA (pLKO5.sgRNA.EFS.tRFP; Addgene #57823).

[1217] The sgRNA sequences used are as follows:

[1218] T790M-sgRNA: 5'-ATCACGCAGCTCATGCCCTT(CGG)-3' (SEQ ID NO: 21397)

[1219] MLH1-knockout PC-9 cells expressing PEmax were electroporated with 7.5 μg of a plasmid expressing BE4max and 2.5 μg of a plasmid expressing sgRNA as previously described. Cells expressing the transgene were sorted using FACS 7 days after transfection (Fig. 102).

[1220] To generate cells expressing the prime editor, HAP1-MSH6 knockout cells were transfected with lentiviral vectors expressing PEmax and blasticidin resistance genes and selected for 2 weeks by blasticidin (Invivogen) as described above.

[1221] Experimental Example 1.1.7. PEER-seq Pooled Assay

[1222] In all experiments, each exon was transfected in two biological replicates. Cells were seeded 24 hours before lentiviral library transfection, resulting in approximately 10,000 cells per pegRNA. Transfection was performed at a multiplicity of infection (MOI) of 0.5, resulting in approximately 5,000 cells per pegRNA. Twenty-four hours after infection, the medium was replaced with puromycin-containing medium, and the cells were cultured for an additional 9 days. After puromycin removal (day 0), cells were harvested in an amount sufficient to ensure 5,000-fold coverage of the pegRNA library. The remaining cells were divided into drug-treated and untreated groups and maintained in an amount sufficient to maintain 10,000-fold coverage of the pegRNA library. Drugs were used at the following concentrations: afatinib (Santa-Cruz Biotechnology, sc-364398) at 3 nM and osimertinib (SelleckChem, S7297) at 8 nM. Cells were passaged every 3 days for an additional 10 days and harvested for genomic DNA extraction. For PEER-seq experiments using high concentrations of osimertinib (Figures 68–70), cells were treated with osimertinib at concentrations of 25, 50, 100, and 200 nM for an additional 5 days.

[1223] For RPL15 evaluation, cells were transfected as previously described. Twenty-four hours after infection, the medium was replaced with medium containing 0.3 μg / ml puromycin, and cells were cultured for an additional 4 days. After puromycin removal (day 0), cells were harvested in an amount sufficient to achieve 5,000-fold coverage of the pegRNA library. The remaining cells were maintained in an amount sufficient to maintain 10,000-fold coverage of the pegRNA library. Cells were passaged every 3 days for an additional 10 days and harvested for genomic DNA extraction.

[1224] To evaluate BRCA1, HAP1-MSH6 knockout cells expressing PEmax were stained with 5 μg / ml Hoechst 34580 (BD Bioscience) for 1 hour to determine DNA content. Cells with the lowest Hoechst peak, corresponding to 1n ploidy, were isolated using FACS. These cell cultures were expanded for 7 days and seeded at 6 million cells per well, representing approximately 10,000 cells, 24 hours before pegRNA library transfection. Transfection was performed at an MOI of 0.5. Twenty-four hours after transfection, the medium was replaced with medium containing 0.5 μg / ml puromycin, and the cells were cultured for an additional 4 days. After puromycin removal (day 0), cells were harvested in an amount sufficient to ensure 10,000-fold coverage of the pegRNA library. The remaining cells were maintained in an amount sufficient to maintain 10,000-fold coverage of the pegRNA library. Cells were passaged for an additional 9 days and then harvested for genomic DNA extraction.

[1225] Experimental Example 1.1.8. Genomic DNA Preparation and Deep Sequencing

[1226] Genomic DNA was extracted using the Wizard Genomic DNA Purification kit (Promega) according to the manufacturer's protocol. Using the isolated genomic DNA as a template, endogenous genomic target regions or pegRNA-specific barcodes and UMIs (Figure 39) were amplified in two PCR rounds using 2X Pfu PCR Smart Mix (Solgent) and prepared for deep sequencing. In the first step, genomic DNA was divided into multiple 50-μL reactions containing 3 μg of genomic DNA, 20 pmol of forward primer, 20 pmol of reverse primer (primers 24 to 41 for amplification of the endogenous genomic target region and primers 42 to 47 for amplification of the RT template and pegRNA-specific barcodes and UMIs), and 25 μL of PCR premix. PCR cycling parameters were as follows: an initial 2-min cycle at 95°C, followed by 20 cycles of 30 s at 95°C, 30 s at 60°C, and 40 s at 72°C, and a final 5-min extension at 72°C. The total amount of genomic DNA in each experiment was assumed to be 6.6 μg of genomic DNA per 10^6 cells, representing a 10,000-fold library coverage. Amplicons from each experiment were pooled, concentrated using the MEGAquick-Spin Total Fragment DNA Purification kit (iNtRON Biotechnology), and size-selected by agarose gel electrophoresis.

[1227] In the second PCR step, 20 ng of the purified PCR product from the first step was used in two separate 50-μL reactions, each containing 20 pmol of Illumina indexing primers (primer pair 48 / 49). The PCR cycling parameters were the same as for the first PCR, but the number of PCR cycles was different; the second PCR was performed for 5 cycles. Amplicons from each experiment were size-selected by agarose gel electrophoresis and sequenced using a NovaSeq 6000 (Illumina).

[1228] Experimental Example 1.1.9. Data Analysis and Variant Filtering

[1229] To identify SNVs introduced in cells transfected with Syn-RPL15, Syn-BRCA1, Syn-exon18, or Syn-exon24 libraries, we generated an SNV reference sequence library. This library was generated from the CDS and the five nucleotides of the adjacent intron sequence (NM_005228.5). The sequences included in this reference library contained only the intended SNVs and additional synonymous substitutions, and no other mismatches or indels. For the NRCH-exon20 library, we generated an SNV reference sequence library containing only the intended SNVs without any other mismatches or indels. Reads processed by targeted deep sequencing were then aligned to this SNV reference sequence library, and next-generation sequencing (NGS) reads were counted only when they perfectly matched the SNV sequence library. Unedited wild-type reads were also identified when the NGS read perfectly matched the reference sequence (NM_005228.5).

[1230] To distinguish true prime-edited reads from errors generated by partial RT template insertion or errors during NGS or library preparation, we calculated odds ratios and p-values ​​using Fisher's exact test between NGS reads obtained from day 0 cells and NGS reads obtained from unedited cells. The formula for calculating the odds ratio is as follows:

[1231]

[1232] After calculating the odds ratio and P-value of each SNV, we identified SNVs with a P-value less than 0.05 and an odds ratio greater than 3 as actual edited SNVs. Additionally, SNVs with less than 0.5 reads per million in cells on day 0 were removed. SNVs with a difference of at least 2 in the adjusted LFC score between replicates in Syn-BRCA1 and a difference of at least 3 in the resistance score between replicates in Syn-exon 20 using high-dose osimertinib were excluded (Figures 68 to 70). SNVs identified according to the above criteria are statistically significant and can be considered to have a sufficient level of representation in the corresponding sequencing data.

[1233] Experimental Example 1.1.10. Adjusted LFC Calculation

[1234] For Syn-RPL15 and Syn-BRCA1 data, the LFC of each SNV was calculated by comparing the allele frequency in cells on day 10 and day 9, respectively, with that in cells on day 0. For Syn-exon20 data, the LFC of each SNV was calculated by comparing the allele frequency in untreated cells on day 10 with that in cells on day 0 for EGFR dependence analysis (Figures 22-24). Additionally, to generate drug resistance profiles, the LFC of each SNV was calculated by comparing the allele frequency in drug-treated cells on day 10 with that in untreated cells on day 10. These LFC values ​​of each SNV were normalized using Locally Weighted Scatterplot Smoothing (LOWESS) regression.

[1235] Experimental Example 1.1.11. Modeling Positional Bias in Data Integration and Inter-Exon Normalization

[1236] To account for the influence of sequence context on editing efficiency, the LFC of each SNV was normalized using the LFC of a synonymous SNV, which is considered to have a neutral LFC because it does not cause an amino acid change. The regressed synonymous SNV LFC was calculated using LOWESS regression at each position in each exon. The normalized LFC value was then obtained by subtracting the synonymous SNV LFC (LOWESS regression) at each position and dividing by the standard deviation of the synonymous SNVs in each exon (Figure 46). Next, the normalized LFC of each replicate was averaged to obtain the adjusted LFC.

[1237]

[1238] To calculate the adjusted LFC based on protein variants, we averaged the adjusted LFC values ​​of SNVs that induce the same protein variant.

[1239] Experimental Example 1.1.12. SNV Drug Resistance Classification

[1240] To analyze drug resistance induced by the Syn-exon18 to Syn-exon24 library, we used cutoff values ​​determined by the resistance score distribution of synonymous SNVs within each exon. Functional classification was performed as follows:

[1241] Resistance: SNV resistance score exceeding the 99.7th percentile compared to the scores of synonymous SNVs in both replicates.

[1242] Sensitivity: SNV resistance score below the 95th percentile compared to the scores of synonymous SNVs in both replicates.

[1243] Intermediate: SNVs that do not fall into the 'resistant' or 'sensitive' categories.

[1244] To obtain classifications based on protein variants, we calculated the LFC of protein variants induced by multiple SNVs, as previously described. The classification criteria used in each dataset were identical to those used in the SNV-based classification.

[1245] Experimental Example 1.1.13. pegRNA Barcode Count and Library-Based Screening Analysis

[1246] The entire method of counting pegRNA barcodes and calculating the change in SNV frequency between treatment and control groups was performed using our own Python code used in a previous study (see Experimental Example 1.1.X). In the deep sequencing analysis of pegRNAs, we identified barcode motifs that tolerate single-nucleotide mismatches and 20-bp unique homologous sequences that tolerate single-nucleotide mismatches. When analyzing barcodes without allowing single-nucleotide mismatches (i.e., using only perfect-match barcodes), the increase in sequencing reads used in the analysis using barcode collapsing was minimal (median 1.7% to 2.2% of total reads, depending on the exon analyzed), and barcode collapsing resulted in a very small increase in the area under the curve (AUROC) (from 0.740 to 0.743) and area under the curve (AUPRC) (from 0.898 to 0.900). We then defined UMI sequences as motifs located between barcodes and 20-bp unique homologous sequences. UMIs were collapsed if the change in read count between two UMIs with single-nucleotide mismatches was ≥3. When analyzing without allowing single-nucleotide mismatches in UMIs (i.e., using only perfectly matching UMIs), the AUROC and AUPRC levels were barely affected (AUROC: 0.743 and 0.743, respectively; AUPRC: 0.898 and 0.900, respectively, with and without UMI collapse). When neither barcodes nor UMIs were collapsed, the AUROC and AUPRC decreased only slightly, from 0.743 to 0.741 and from 0.900 to 0.897, respectively. These collapsed UMIs were classified into four distinct groups for each pegRNA. Considering that a single SNV can be induced by two or three pegRNAs, we performed MAGeCK tests using eight to twelve pegRNA-UMI group pairs. In this analysis, pegRNAs were treated as sgRNAs, and SNVs were considered as gene names. Changes in SNV frequencies between treatment and control groups were calculated using MAGeCK version 0.5.9.342.Then, the LFC of each SNV was normalized using the LFC of the negative control pegRNA (off-target and pseudo-edited pegRNA, EGFR). Since the off-target and pseudo-edited pegRNAs were not included in Syn-RPL15-initial or Syn-RPL15, the synonymous edited pegRNAs were used as negative controls in the RPL15-related analyses. In the pegRNA-based screening assay, SNVs were filtered according to the criteria described in Experimental Example 1.1.9. The remaining pegRNAs were classified according to the normalized LFCs in both replicates using cutoff values ​​determined by the LFC distributions of the off-target and pseudo-edited pegRNAs in each exon. The classification criteria were the same as those described in Experimental Example 1.1.12.

[1247] Experimental Example 1.1.14. Determining the Frequency of EGFR Mutations in Clinical Samples

[1248] To analyze the number and frequency of EGFR mutations among NSCLC patients in clinical databases, we queried all non-duplicate studies of NSCLC patients in the cBioPortal database86. This study included 11,553 samples from 9,104 patients across 22 clinical studies (query URL: https: / / bit.ly / 49E4iUD).

[1249] Experimental Example 1.1.15. Resistance Profile of EGFR Mutants Described in Previous Literature

[1250] We curated single-nucleotide variants (SNVs) conferring resistance from the Catalog of Somatic Mutations in Cancer (COSMIC) Drug Resistance Database (COSMIC v99), which relies on expert curation. We identified SNVs reported in two or more samples in the database. Furthermore, SNVs were considered resistance-contributing only if experimentally validated by cell-based assays reported in the PubMed database. We identified a total of six resistance-associated variants (L718V / Q, T790M, L792H / F, C797S). SNVs associated with sensitivity to afatinib or osimertinib were curated from the National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines in Oncology. We identified 10 susceptibility variants (G719S, G719C, G719R, G719D, G719V, G719A, S768I, T790M, L858R, L861Q). Additionally, for SNVs that were potentially associated with resistance or response to afatinib, or with resistance or response to osimertinib in the absence of T790M, or with resistance or response to osimertinib in the presence of T790M, we manually checked whether they had been previously reported in the PubMed database through at least one published case report or cell-based assay in the literature, and classified the resistance profile as likely or unlikely to be associated with TKI sensitivity or resistance (see Figure 62, Table 1).

[1251] Experimental Example 1.1.16. Resistance Evaluation in a General Way

[1252] PegRNA sequences designed to generate G724S, K754Q, G796G, C797S, G930R, and E931K SNVs were each cloned into Lenti gRNA Puro (Addgene #87245) linearized with BsmBI. The following four fragments were assembled into the linearized vector by Golden Gate cloning: (1) annealed spacer oligonucleotides with overhangs for cloning; (2) an improved SpCas9 sgRNA scaffold sequence (primer 3) with overhangs for cloning; (3) annealed engineered pegRNA RT template / PBS 3' extension with overhangs for cloning; and (4) tevopreQ1 and poly T sequences with overhangs for cloning.

[1253] A total of 3 million Pemax-expressing MLH1-knockout PC-9 cells were seeded in 150-mm culture dishes 24 h prior to infection at a 3-fold multiplication rate per PegRNA. Cells were infected with lentivirus containing the PegRNA sequence at a high MOI (~1). As a negative control, a separate population of 3 million cells was infected with lentivirus containing the empty vector. The day after infection, the medium was replaced with fresh medium containing puromycin, and the cells were cultured for an additional 9 days.

[1254] Ten days after infection, cells infected with the PegRNA sequence and those infected with the empty vector were mixed at a 25:75 ratio. This mixed cell population was divided into untreated and drug-treated conditions. Drugs were used at the following concentrations: afatinib 3 nM and osimertinib 12 nM for K754Q, G930R, and E931K; osimertinib 6 nM, 12 nM, and 25 nM for G796G and C797S. The mixed cell populations were cultured for 10 days (G724S, K754Q, G930R, and E931K) or 6 days (G796G and C797S) before deep sequencing. Each PegRNA-targeted genomic region was amplified using site-specific primers.

[1255] Experimental Example 1.1.17. Isolation and Analysis of Single-Cell-Derived Clones

[1256] MLH1-knockout PC-9 cells expressing PEmax were infected with lentiviruses containing the pegRNA sequence to generate each SNV or empty vector, and selected with puromycin for 10 days. Cells were suspended, passed through a 40 μm cell sieve, and quantified using a hemocytometer. The cell suspensions were diluted to a density of 3 cells / mL, and 100 μL of each cell suspension was transferred to each well of five 96-well plates (0.3 cells / well). After 28 days of clone expansion, a portion of the cells was used for genotyping. Cells were lysed in 100 μL of a buffer containing 50 mM Tris-HCl, 1 mM ethylenediaminetetraacetic acid, 0.05% sodium dodecyl sulfate, and 5% proteinase K (Biofact) at 55°C for 1 hour in a 96-well plate. The target region was amplified through two PCR rounds. In the first round, each reaction mixture contained 5 μL of crude lysate, 20 pmol of site-specific forward and reverse primers (primer pairs 24 / 25 to 40 / 41), 20 μL of 2X Pfu PCR Smart Mix (Solgent), and water in a final volume of 40 μL. The PCR cycling parameters were as follows: an initial 95°C for 2 min, followed by 24 cycles of 95°C for 30 s, 60°C for 30 s, and 72°C for 40 s, and a final extension at 72°C for 5 min. In the second round, 4 μL of the reaction mixture from the first round was used in a 40 μL reaction containing 20 pmol of forward and reverse primers (primer pair 48 / 49). PCR cycling parameters were as follows: 95°C for an initial 2 min, followed by 8 cycles of 95°C for 30 s, 60°C for 30 s, and 72°C for 40 s, and finally an extension at 72°C for 2 min. Amplicons were size-selected by agarose gel electrophoresis and then subjected to deep sequencing.

[1257] Since the PC-9 cells we used had six and two copies of the EGFR and RPL15 genes, respectively, for single-cell-derived clones containing EGFR mutations, we considered that the intended prime editing occurred only in some copies if the number of reads containing SNVs within the clones ranged from 8.3% (= 100 / 6 x 0.5) to 91.7% (100 - 8.3%) for EGFR and from 20% to 80% for RPL15.

[1258] Experimental Example 1.1.18. Cell Proliferation Analysis

[1259] Clonal PC-9 cells containing mutants were seeded in quadruplicate in 96-well plates at a density of 5,000 cells per well. Twenty-four hours after inoculation, the medium was replaced with medium containing afatinib or osimertinib, or with the same concentration of dimethyl sulfoxide (DMSO) for the control cell population. The cells were cultured for an additional 72 hours. MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide) was added to each well and incubated at 37°C for at least 2 hours. The medium was then replaced with DMSO, and the absorbance was measured at OD=570 nm. The raw absorbance values ​​were normalized to those of the DMSO control wells and blank wells, and nonlinear regression was performed to fit the normalized data with a variable slope and IC using the neutralcurve Python library (version 2.0.0). 50 The price has been determined.

[1260] Experimental Example 1.1.19. In vivo xenograft study

[1261] All animal study protocols involving mice were approved by the Institutional Animal Care and Use Committee (IACUC) of Yonsei University Medical Center (Seoul, Korea). Three pegRNAs encoding SNVs with the highest resistance scores (pegRNAs targeting p.C797S, p.S811F, and p.S811Y) were selected. Next, five and 15 pegRNAs encoding SNVs classified as intermediate and sensitive, respectively, were selected. PegRNAs encoding two and three synonymous SNVs classified as intermediate and sensitive, respectively, were also included. The resulting library of 28 pegRNAs was named Syn-vivo-exon20. The 228-bp oligonucleotide for plasmid construction was sequence-synthesized by Twist Bioscience and cloned into plenti-gRNA-puromycin-p2A-luciferase as previously described.

[1262] Transfection was performed at an MOI of 0.3. Twenty-four hours after transfection, the medium was replaced with medium containing puromycin, and the cells were cultured for an additional 9 days. After puromycin removal (day 0), 1X10 7 Cells were injected subcutaneously into NOD / Shi-scid, IL-2Rγ KOJic (NOG) mice along with 50% Matrigel. Mice were divided into three paired groups: each pair consisted of drug-treated and untreated mice. All mice were 6 weeks old at the time of cell transplantation. Ten days after transplantation, tumors were confirmed in all mice by intravital imaging (IVIS). Mice received the following oral drug regimens:

[1263] Untreated mice: Vehicle (5% DMSO, 40% PEG300, 5% TWEEN, 50% distilled water) administered orally (orally) once daily.

[1264] Drug-treated mice: Vehicle + 2.5 mg / kg osimertinib administered orally (orally) once daily.

[1265] The administration schedule was maintained five days a week for two weeks. Mice were observed daily to record general mobility, morbidity, and mortality. Body weight was assessed twice a week. After completion of the treatment regimen, all mice in both experimental groups were sacrificed, and tumors were dissected for genomic DNA preparation and deep sequencing.

[1266] For in vivo tumor growth assays, single-cell-derived clonal PC-9 cells containing mutations were infected with lentivirus generated from plenti-gRNA-puromycin-p2A-luciferase without the pegRNA sequence and expanded for 7 days. Approximately 1 × 10^7 clonal PC-9 cells containing mutations were injected subcutaneously into the right flank of NOG mice. As a control, the same number of non-mutated PC-9 cells were injected into the left flank of NOG mice. All mice were 6 weeks old at the time of cell transplantation. Two weeks after transplantation, tumors were confirmed by IVIS, and mice were randomly assigned to groups receiving vehicle or two doses of treatment (n = 3 per group): afatinib at 5 or 20 mg / kg / day or osimertinib at 1 or 3 mg / kg / day. The dosing schedule was maintained 5 days per week for 12 days, and tumor size was assessed every other day using bilateral caliper measurements. Tumor volume was calculated using the formula: Volume = Length × Width. 2 X 0.591. Tumor growth inhibition was assessed by comparing the mean changes in tumor volume in the left and right flanks.

[1267] Experimental Example 1.1.20. Statistical Analysis

[1268] All statistical tests were performed two-tailed using a Python software package.

[1269] Experimental Example 1.1.21. Data Visualization

[1270] The figures were created in Python. The schematics were generated using BioRender.com. The mutations were mapped to the following crystal structure from the Protein Data Bank using PyMOL (version 2.5.5): 6JXT (EGFR in complex with osimertinib).

[1271] Experimental Example 1.2. Difficulty in Identifying Single Nucleotide Mutations Due to Sequencing Errors

[1272] Previous Saturated Genome Editing (SGE) approaches have been performed using haploid cells or cells partially haploidized in the genetic region of interest to generate and evaluate all possible SNVs in the region of interest. This was due to limited editing efficiency. However, because most cells in our body are at least diploid, evaluating mutations in non-haploid cells can more accurately represent human disease pathophysiology. Furthermore, haploid cells can easily be converted to diploid cells, and the range of available haploid cell types is very limited. Therefore, SGE constructed using haploid cells is considerably difficult to apply generally. The inventors of this specification studied SGE using the non-haploid lung adenocarcinoma cell line PC-9. This non-haploid lung adenocarcinoma cell line harbors an in-frame deletion in EGFR exon 19 (E746-A750 deletion), one of the most common EGFR mutations that confers sensitivity to TKIs in non-small cell lung cancer (NSCLC). In this experimental example, to replicate the conventional SGE approach, we simply introduced single nucleotide mutations using NRCH-PE2max, without introducing the pseudo-single nucleotide mutations described in this specification. Using the Prime Editor system, which includes the NRCH-PE2max prime editor protein, we designed and processed a pegRNA library capable of introducing 558 possible single nucleotide mutations into exon 20 of the EGFR gene. After processing, the cell genome was sequenced to confirm whether the intended single nucleotide mutations were introduced. The experimental results are shown in (Figures 39 to 44). The experimental results show that using the conventional SGE approach, only 34 of the 558 possible single nucleotide mutations could be clearly identified, accounting for only 6% of the total single nucleotide mutations.

[1273] Experimental Example 1.3. Identification of Single Nucleotide Mutations Using the PEER-seq Method

[1274] In this experimental example, we aimed to improve the accuracy of single-nucleotide mutation identification through sequencing by introducing synonymous single-nucleotide mutations. This is schematically illustrated in Figures 1 and 71. As described above, we expected that adding a single synonymous mutation to a single-nucleotide mutation would minimize the activity of the mismatch repair (MMR) system, thereby further increasing prime editing efficiency.

[1275] The cell and prime editing configurations used in this experiment are as follows:

[1276] 1) Prime editor protein: SpCas9 PEmax

[1277] 2) pegRNA library (Syn-exon20):

[1278] A library constructed to introduce all possible quasi-single nucleotide mutations into exon 20 of the EGFR gene, where each pegRNA was designed using DeepPrime-FT.

[1279] 3) SGE library construction cells:

[1280] Cells engineered to express the SpCas9 PEmax protein and have the MLH1 gene knocked out based on the PC-9 cells described in the experimental example above.

[1281] After culturing the cells constituting the above SGE library by treating them with the above pegRNA library, samples of the above library were collected and sequenced to confirm the results.

[1282] The experimental results are shown in (Figs. 4 and 5).

[1283] In both cases where the intended synonymous mutation was introduced and not, the median frequency of the intended single nucleotide mutation was 0.038% (range: 0.0014% to 0.36%). This was 3.4 times higher (= 0.038% / 0.011%) than the result according to the experimental example above. In addition, the median odds ratio increased by 3.8 times (= 4.11 / 1.08). An overall increase in -log10(P-value) was confirmed. When the sequencing results were interpreted based only on single nucleotide mutations, 60% (= 334 / 558) of the 558 possible SNVs were significantly generated (Figures 4 and 5). This is 10 times higher (= 60% / 6%) than the number of single nucleotide mutations that could be identified in the experimental example above, but still falls short of complete SGE. For comparison, the sequencing results were confirmed using the PEER-seq method (a method for introducing and confirming quasi-single nucleotide mutations). The PEER-seq method confirmed that the median frequency of introducing the intended single nucleotide mutation was reduced by 2.5 times to 0.015% (range: 0.00006%–0.29%) (Fig. 4). However, the PEER-seq method increased the odds ratio by 252-fold (= 1,037 / 4.11) and the overall -log10(P-value) compared to confirming the sequencing results based only on single nucleotide mutations. Furthermore, the PEER-seq method was able to generate and identify 99% (555 out of 558) of all possible single nucleotide mutations, achieving nearly perfect SGE.

[1284] The inventors of this specification speculate that the above results may be due to the effective filtering out of false positive reads that were considered to have single nucleotide mutations due to sequencing errors when using the PEER-seq method. In a control experiment in which the exon 20 (186 bp long) region of the EGFR gene was sequenced using untreated, unedited PC-9 cells, it was confirmed that 10% of the reads indicated a single base pair substitution (Fig. 6). Since single nucleotide mutations cannot occur in unedited PC-9 cells, this result is considered to be a false positive result due to sequencing errors. In contrast, when using the PEER-seq method, the frequency of these false positive reads (i.e., reads containing both intended and synonymous substitution errors) in unedited PC-9 cells was only 0.016% of all reads. This value represents a 625-fold (= 10 / 0.016) reduction compared to the results of the method that does not consider additional synonymous substitutions. Furthermore, based on the 10% rate of single point mutations observed in the sequencing of the 186-bp region, the point mutation error rate in PCR and sequencing is calculated to be approximately 0.06% per base pair. This rate is similar to or slightly lower than the generally accepted sequencing error rate of 0.1%. In unedited PC-9 cells, when the analysis was extended to regions other than the above exon 20 region, we found that the false-positive read rate tended to increase as the length of the analyzed region increased (Figures 72-73). Despite this trend, the calculated frequency of single nucleotide mutations per base pair remained consistent across different regions. Specifically, it ranged from 0.038% to 0.095%, with a median of 0.055%.

[1285] Experimental Example 1.4. Verifying the Accuracy of the PEER-seq Method by Generating Saturated Single-Nucleotide Mutations in the RPL15 Gene

[1286] To determine whether the PEER-seq method could be used to functionally assess single nucleotide mutations in essential genes, a saturated single nucleotide mutation library was constructed for the RPL15 gene. Specifically, referring to Experimental Example 1, a pegRNA library capable of introducing all possible single nucleotide mutations (a total of 516) into exon 2 (172 bp) of the RPL15 gene was designed. This pegRNA library, designated Syn-RPL15, contained a total of 1,555 pegRNAs. Meanwhile, PC-9 cells expressing the SpCas9 PEmax protein and lacking the MLH1 gene were treated with the Syn-RPL15 library and selected with puromycin (Fig. 7).

[1287] First, cell samples were harvested on the fifth day after library delivery and deep-sequenced. The sequencing results showed that the median number of cells introducing quasi-single nucleotide mutations (including the intended single nucleotide mutation and synonymous mutations) was 0.19%, with a range of 7.0x10^-5% to 0.29%. In the negative control cells (hereinafter referred to as the control) not treated with the prime editing system, the median detection rate of quasi-single nucleotide mutations was 0% (meaning they were not detected by deep sequencing), with a range of 0% to 0.047%. This suggests that false-positive results are rarely detected in the case of quasi-single nucleotide mutations. Meanwhile, the PEER-seq method significantly identified 511 single nucleotide mutations, which correspond to almost all single nucleotide mutations (511 / 516 = 99%).

[1288] Afterwards, the above cells were cultured for an additional 5 days (10 days in total), and samples were collected and sequenced once more. The Log2-Fold Changes (LFC) for each single nucleotide mutation were calculated using the sequencing results on day 5 and day 10. Based on the LFC, the degree of functional loss of the RPL15 gene associated with the single nucleotide mutation was assessed (Fig. 7). The positional bias of the mutation corresponding to the silent mutation among the single nucleotide mutations was modeled using Locally Weighted Scatterplot Smoothing (LOWESS) regression, and the LFC was corrected using this method (Fig. 45, Experimental Example 1.1). As a result of the correction, the area under the ROC curve (AUROC) slightly increased from 0.99 to 1.0. These adjusted LFC values ​​were well correlated between biological replicates (Fig. 47).

[1289] The experimental results are shown in Figs. 48 and 49.

[1290] As expected, the number of single nucleotide variants (nonsense SNVs) causing nonsense mutations was significantly reduced compared to single nucleotide variants (synonymous SNVs) causing synonymous mutations (P = 2.3 X10^-14, Student's t-test, Fig. 13). The inventors of this specification hypothesized that the above nonsense SNVs eliminate RPL15 function, resulting in a significant reduction in the number of cells containing nonsense SNVs. In contrast, the synonymous SNVs assumed that RPL15 function was intact. Under the above assumption, Receiver-Operator Characteristics (ROC) and Precision-Recall (PRC) analyses were performed (Figs. 14 and 15).

[1291] Evaluation of the library cells generated using the PEER-seq method demonstrated that it could effectively distinguish between nonsense and synonymous SNVs. This is evident from the calculated area under the PRC curve (AUROC) and area under the PRC curve (AUPRC) values ​​of 1.0 (Figures 14 and 15). This suggests that the PEER-seq method is highly accurate.

[1292] A total of 86 single nucleotide variants (17% of 511) were classified as loss-of-function mutations in RPL15. These single nucleotide variants included 70 single amino acid variants (SAAVs) (20% of 356). These variants had LFC values ​​below the 5th percentile of those of synonymous mutations.

[1293] Meanwhile, a total of 340 (66%) single-nucleotide mutations were classified as mutations that do not significantly affect the function of RPL15. These single-nucleotide mutations include a total of 229 (64%) single-amino acid mutations. These mutations have LFC values ​​exceeding the 20th percentile of the LFC values ​​of synonymous mutations.

[1294] The remaining 85 (17%) single-nucleotide mutations were classified as intermediate. These single-nucleotide mutations comprised a total of 57 (16%) single-amino acid mutations. These mutations had LFC values ​​between the 5th and 20th percentiles of the LFC values ​​of synonymous mutations.

[1295] In summary, using the PEER-seq approach, we functionally classified 511 SNVs (99% of all possible SNVs), which account for 99% of the total possible protein variants encoded in RPL15 exon 2.

[1296] Experimental Example 1.5. Verifying the Accuracy of the PEER-seq Method by Generating Saturated Single-Nucleotide Mutations in the BRCA1 Gene

[1297] In this experimental example, a library prepared using the PEER-seq method was compared with a library prepared using the HDR method disclosed in a prior literature (Findlay et al., 2018, Accurate classification of BRCA1 variants with saturation genome editing.). Specifically, referring to the method disclosed in a prior literature (Findlay et al., 2018, Accurate classification of BRCA1 variants with saturation genome editing.), a pegRNA library introducing all possible quasi-single nucleotide mutations into exon 19 of the BRCA1 gene of HAP1 cells was prepared and processed to prepare a saturated single nucleotide mutation library for BRCA1 exon 19. The results comparing this with the library prepared using HDR are shown in (see Figs. 53 to 55, Experimental Example 1.1)).

[1298] Experimental results showed that the correlation of adjusted Log Fold Change (LFC) between replicate experiments for the biological replicate set was very high at r = 0.94 (Fig. 54). In addition, the correlation between PEER-seq scores (adjusted LFC) and HDR functional scores was also very high at r = 0.75 (Fig. 55). As expected, (pseudo) single nucleotide mutations causing nonsense mutations and canonical splicing mutations were significantly reduced compared to single nucleotide mutations causing synonymous mutations (P = 1.9 X 10^-16 and 1.9 X 10^-9, respectively, Student's t-test, Fig. 56). ROC analysis results showed that the AUROC of the PEER-seq method based on ClinVar annotations and the AUROC based on variant categories (i.e., the assumption that nonsense and canonical splicing variants would be reduced compared to synonymous mutations) were 1 and 0.995, respectively, which were comparable to the AUROC of the HDR-based approach (1 and 1, respectively) (Figures 57 and 58). This suggests that both approaches are highly accurate. Furthermore, it can be confirmed that the above PEER-seq method can be applied well to haploid cells.

[1299] Experimental Example 1.6. Comparison of the Accuracy of PEER-seq and pegRNA Abundance-Based Analysis

[1300] In this experimental example, we compared the accuracy of 1) a method for identifying quasi-single nucleotide mutations by directly sequencing endogenous genes of cells (PEER-seq method) and 2) a method for identifying single nucleotide mutations by sequencing pegRNA integrated into cells via lentivirus (hereinafter, pegRNA abundance-based analysis). Specifically, cells with saturated single nucleotide mutations for the RPL15 gene in the experimental example above were sequenced to confirm whether each pegRNA was integrated, and based on this, LFC was calculated using the MAGeCK algorithm.

[1301] The experimental results are shown in Fig. 50.

[1302] First, in repeated experiments with two sets of biological replicates, the correlation between the frequency of single nucleotide mutations (LFC) calculated from pegRNA abundance data was not very high (Pearson r = 0.27, Figure 50)....

Claims

1. An engineered cell population for studying single nucleotide mutations in a genetic region of interest. Hereinafter, the terms used in this claim are defined as follows: GOI is a Genetic Region of Interest containing a coding sequence; CDS is a coding sequence included in the above GOI; l is the total number of base pairs contained in the CDS; x is an integer greater than or equal to 1 and less than or equal to l; The xth position of the CDS refers to the xth nucleotide from the 5' end of the CDS, based on a particular strand; Nx refers to the type of nucleotide at the x-th position of the CDS, for example, A, T, C, or G; f(.) is a function that maps one nucleotide type to another, defined as: f(A) = T; f(T) = A; f(C) = G; and f(G) = C; g(.) is a function that maps one nucleotide type to another, defined as: g(A) = C; g(T) = G; g(C) = A; and g(G) = T; h(.) is a function that maps one nucleotide type to another, defined as: h(A) = G; h(T) = C; h(C) = T; and h(G) = A; wherein the engineered cell population comprises at least (2.4 Х l) different types of cells, All cells in the engineered cell population above belong to one species selected from: (i) as a reference type, containing the wild type GOI; (ii) a mutated GOI comprising a first substitution and a synonymous substitution, of type (3x-2), The above first substitution means that the x-th position of CDS is substituted from Nx to f(Nx); (iii) a mutated GOI comprising a second substitution and a synonymous substitution, of type (3x-1), The above second substitution means that the x-th position of CDS is substituted from Nx to g(Nx); (vi) (3x) type, including a mutated GOI including a third substitution and a synonymous substitution, The above third substitution means that the x-th position of CDS is substituted from Nx to h(Nx); (v) Other types other than (i), (ii), (iii), and (iv).

2. In the engineered cell population of paragraph 1, Here, there is at least one integer y greater than or equal to 1 and less than or equal to l, in which at least one mutated GOI of the (3y-2) type, the (3y-1) type, or the (3y) type is mutated such that the 5'-NGG-3' of the wild-type GOI sequence is not changed.

3. In the engineered cell population of paragraph 1, Each engineered cell contains nucleic acid encoding a prime editor protein, The above prime editor protein recognizes PAM, At least one mutated GOI of the (3y-2) type, the (3y-1) type, or the (3y) type is mutated so as not to change the PAM contained in the wild-type GOI sequence, and there is at least one integer y greater than or equal to 1 and less than or equal to l.

4. A method for producing a saturated single nucleotide mutation library for a genetic region of interest, The types of cells included in the above library cover more than 80% of all possible single nucleotide mutation cell types, The above method includes: (a) the process of preparing a cell population, Here, each cell contains a nucleic acid encoding a prime editor protein; (b) a process of treating the pegRNA library to the cell population; Here, the pegRNA library comprises pegRNAs targeting the coding sequence of the genetic region of interest, or comprises nucleic acids encoding the pegRNAs, Each pegRNA contained in the above pegRNA can work with the prime editor protein to: (i) changing one nucleotide in the coding sequence of the genetic region of interest to another nucleotide; and (ii) introducing a single synonymous mutation into the coding sequence of the genetic region of interest; Here, the pegRNA library comprises pegRNAs capable of inducing more than 90% of all single nucleotide mutations that can occur in the genetic region of interest; (c) (b) After that, the process of collecting the first sample; (d) a process of obtaining sequence information of a genetic region of interest from the first sample; (e) A process of verifying whether the cell population that performed process (b) is a saturated single nucleotide mutation library using the sequence information obtained in (d); Here, among the above sequence information, only the nucleic acid sequences that satisfy the following criteria are treated as nucleic acid sequences in which a single nucleotide mutation has occurred in the genetic region of interest: (i) one nucleotide in the genetic region of interest of the wild type is changed to a different nucleotide; and (ii) One synonymous mutation was introduced into the genetic region of interest of the wild type, which is distinct from the mutation in (i).

5. A method for producing a saturated single nucleotide mutation library for a genetic region of interest, The types of cells included in the above library cover more than 80% of all possible single nucleotide mutation cell types, The above method includes: (a) the process of preparing a cell population, (b) a process of treating the cell population with a prime editor protein or a nucleic acid encoding the prime editor protein; and a pegRNA library; Here, the pegRNA library comprises pegRNAs targeting the coding sequence of the genetic region of interest, or comprises nucleic acids encoding the pegRNAs, Each pegRNA contained in the above pegRNA can work with the prime editor protein to: (i) changing one nucleotide in the coding sequence of the genetic region of interest to another nucleotide; and (ii) introducing a single synonymous mutation into the coding sequence of the genetic region of interest; Here, the pegRNA library comprises pegRNAs capable of inducing more than 90% of all single nucleotide mutations that can occur in the genetic region of interest; (c) (b) After that, the process of collecting the first sample; (d) a process of obtaining sequence information of a genetic region of interest from the first sample; (e) A process of verifying whether the cell population that performed process (b) is a saturated single nucleotide mutation library using the sequence information obtained in (d); Here, among the above sequence information, only the nucleic acid sequences that satisfy the following criteria are treated as nucleic acid sequences in which a single nucleotide mutation has occurred in the genetic region of interest: (i) one nucleotide in the genetic region of interest of the wild type is changed to a different nucleotide; and (ii) One synonymous mutation was introduced into the genetic region of interest of the wild type, which is distinct from the mutation in (i).

6. A method for producing a saturated single nucleotide mutation library for the genetic region of interest of Article 5, The above (b) process includes one of the following processes: (i) treating the prime editor protein or a nucleic acid encoding the prime editor protein prior to the pegRNA library; (ii) treating said pegRNA library before said prime editor protein or a nucleic acid encoding said prime editor protein; or (iii) Simultaneously processing the above prime editor protein or the nucleic acid encoding the above prime editor protein and the above pegRNA library.

7. A method for evaluating the impact of single nucleotide mutations occurring in a genetic region of interest on cell function. The above method includes: (a) the process of preparing a cell population, Here, each cell contains a nucleic acid encoding a prime editor protein; (b) a process of treating the pegRNA library to the cell population; Here, the pegRNA library comprises pegRNAs targeting the coding sequence of the genetic region of interest, or comprises nucleic acids encoding the pegRNAs, Each pegRNA contained in the above pegRNA can work with the prime editor protein to: (i) changing one nucleotide in the coding sequence of the genetic region of interest to another nucleotide; and (ii) introducing a single synonymous mutation into the coding sequence of the genetic region of interest; Here, the pegRNA library comprises pegRNAs capable of inducing more than 90% of all single nucleotide mutations that can occur in the genetic region of interest; (c) at a first time point, a process of obtaining first sequence information from the cell population; (d) at a second time point, a process of obtaining second sequence information from the cell population; and (e) a process of evaluating the impact of a single nucleotide mutation occurring in a genetic region of interest on cell function using the first sequence information and the second sequence information; Here, when utilizing the first sequence information and the second sequence information, only nucleic acid sequences satisfying the following criteria are treated as nucleic acid sequences in which a single nucleotide mutation has occurred in the genetic region of interest: (i) one nucleotide in the genetic region of interest of the wild type is changed to a different nucleotide; and (ii) One synonymous mutation was introduced into the genetic region of interest of the wild type, which is distinct from the mutation in (i).

8. A method for evaluating the impact of single nucleotide mutations occurring in a genetic region of interest on drug sensitivity. The above method includes: (a) the process of preparing a cell population, Here, each cell contains a nucleic acid encoding a prime editor protein; (b) a process of treating the pegRNA library to the cell population; Here, the pegRNA library comprises pegRNAs targeting the coding sequence of the genetic region of interest, or comprises nucleic acids encoding the pegRNAs, Each pegRNA contained in the above pegRNA can work with the prime editor protein to: (i) changing one nucleotide in the coding sequence of the genetic region of interest to another nucleotide; and (ii) introducing a single synonymous mutation into the coding sequence of the genetic region of interest; Here, the pegRNA library comprises pegRNAs capable of inducing more than 90% of all single nucleotide mutations that can occur in the genetic region of interest; (c) a process of dividing the cell population into at least two groups including group 1 and group 2; (d) a process of obtaining first sequence information from the first group; (e) the process of administering the above drug to the second group; (f) a process of obtaining second sequence information from the second group after the above process (e); and (g) a process of evaluating the effect of a single nucleotide mutation occurring in a genetic region of interest on the sensitivity of the drug using the first sequence information and the second sequence information; Here, when utilizing the first sequence information and the second sequence information, only nucleic acid sequences satisfying the following criteria are treated as nucleic acid sequences in which a single nucleotide mutation has occurred in the genetic region of interest: (i) one nucleotide in the genetic region of interest of the wild type is changed to a different nucleotide; and (ii) One synonymous mutation was introduced into the genetic region of interest of the wild type, which is distinct from the mutation in (i).

9. In any one of the methods of paragraphs 4 to 8, The pegRNA library comprises a first pegRNA or a DNA encoding the first pegRNA, The synonymous mutations introduced by the first pegRNA and the prime editor protein do not destroy the PAM of the prime editor protein.

10. A method for providing information on a tyrosine kinase inhibitor (TKI) suitable for treatment using the Epidermal Growth Factor Receptor (EGFR) gene sequence information of a cancer patient's tumor, comprising: (a) A process for obtaining EGFR gene sequence information of a patient's tumor cells; (b) a process for obtaining sensitivity to one or more tyrosine kinase inhibitors corresponding to the EGFR gene sequence obtained in (a), with reference to [Table 1] of this specification; and (c) A process of comparing the drug sensitivity information obtained in (b) with a predetermined standard to provide information on a tyrosine kinase inhibitor suitable for treating the above patient.

11. A method for providing information on a tyrosine kinase inhibitor (TKI) suitable for treatment using ABL1 gene sequence information of a cancer patient's tumor, comprising: (a) A process of obtaining ABL1 gene sequence information of a patient's tumor cells; (b) a process for obtaining sensitivity to one or more tyrosine kinase inhibitors corresponding to the EGFR gene sequence obtained in (a), with reference to [Table 2] and [Table 3] of this specification; and (c) A process of comparing the drug sensitivity information obtained in (b) with a predetermined standard to provide information on a tyrosine kinase inhibitor suitable for treating the above patient.

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