Comprehensive resistance profiling of chronic myeloid leukaemia associated ABL1 variants against five tyrosine kinase inhibitors using prime editing

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comprehensive-resistance-profiling-of-chronic-myeloid-leukaemia-associated-abl1-variants-against-five-tyrosine-kinase-inhibitors-using-prime-editing
Comprehensive resistance profiling of chronic myeloid leukaemia associated ABL1 variants against five tyrosine kinase inhibitors using prime editing

Data availability

We have submitted the deep sequencing data from this study to the National Center of Biotechnology Information’s Sequence Read Archive under accession number PRJNA1048659 (ref. 78). We have provided the datasets used in this study as Supplementary Tables 2, 3, 5–8, 10–13 and 15–17. The 3D structure of the ABL1 protein was obtained from the Protein Data Bank (PDB 5MO4).

Code availability

High-throughput evaluation data were analysed with in-house custom Python scripts (version >3.7) and MAGeCK (version 0.5.9.3). They are available via GitHub at https://github.com/Goosang-Yu/CML_VUS.

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Acknowledgements

We are very grateful to Younggwang Kim and S. Lee for helpful discussions and technical advice. We also thank Younghye Kim, S. Park and G. Baek for assisting with the experiments. We thank Medical Illustration & Design, as a member of the Medical Research Support Services of Yonsei University College of Medicine, for providing excellent support with medical illustration. We also thank B. G. Kim and J. H. Ji for their support with in vivo experiments. This work was supported, in part, by a National Research Foundation (NRF) of Korea grant funded by the Korean government (Ministry of Science and ICT, MSIT) (RS-2022-NR070713 (H.H.K.) and RS-2025-02214844 (H.H.K.)); the Bio and Medical Technology Development Program of the NRF funded by the Korean government (MSIT) (RS-2022-NR067326 (H.H.K.), RS-2022-NR067345 (H.H.K.), RS-2023-00260968 (H.H.K.) and NRF-2021R1A2C3011992 (T.S.)); the Korea Health Technology R&D Project through the Korea Health Industry Development Institute funded by the Ministry of Health and Welfare, Republic of Korea (RS-2024-00404555 (T.S.)); the Yonsei Signature Research Cluster Program of 2025-22-0015 (H.H.K.); the Brain Korea 21 FOUR Project for Medical Science (Yonsei University College of Medicine); the SNUH Kun-hee Lee Child Cancer and Rare Disease Project, Republic of Korea (22B-000-0101 (H.H.K.)); the Yonsei Fellow Program, funded by Lee Youn Jae; the Korea–US Collaborative Research Fund, funded by the Ministry of Science and ICT and Ministry of Health and Welfare, Republic of Korea (grant number RS-2024-00467177 (H.H.K.)); the ‘Regional Innovation System & Education (RISE)’ through the Seoul RISE Center, funded by the Ministry of Education (MOE) and the Seoul Metropolitan Government (2025-RISE-01-022-05 (H.H.K.)); and a grant of the MD–PhD/Medical Scientist Training Program (Y.J.) through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea.

Author information

Author notes

  1. These authors contributed equally: Yusang Jung, Goosang Yu.

Authors and Affiliations

  1. Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea

    Yusang Jung, Goosang Yu, Hyeong-Cheol Oh, Jueng-Hu Lee & Hyongbum Henry Kim

  2. Graduate School of Medical Science, Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea

    Goosang Yu, Eunhye Jeon, Juhyeon Bae, Taebo Sim & Hyongbum Henry Kim

  3. Department of Biomedical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea

    Eunhye Jeon, Juhyeon Bae & Taebo Sim

  4. Clinical Candidate Discovery & Development Institute, Yonsei University College of Medicine, Seoul, Republic of Korea

    Taebo Sim

  5. Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  6. Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea

    Hyongbum Henry Kim

  7. Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea

    Hyongbum Henry Kim

  8. Woo Choo Lee Institute for Precision Drug Development, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  9. Institute for Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  10. Won-Sang Lee Institute for Hearing Loss, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

Authors

  1. Yusang Jung
  2. Goosang Yu
  3. Hyeong-Cheol Oh
  4. Jueng-Hu Lee
  5. Eunhye Jeon
  6. Juhyeon Bae
  7. Taebo Sim
  8. Hyongbum Henry Kim

Contributions

Y.J., G.Y. and H.H.K. conceived and designed the research. Y.J., G.Y., H.-C.O. and J.-H.L. performed the experiments. E.J., J.B. and T.S. performed the experiments using Ba/F3 cells and single-cell-derived clones (K562-PE4K cells). Y.J. and G.Y. performed the data analysis. G.Y. developed an algorithm to design the SGE library and built the web tool. Y.J., G.Y. and H.H.K. wrote the manuscript.

Corresponding author

Correspondence to Hyongbum Henry Kim.

Ethics declarations

Competing interests

Yonsei University has filed a patent application based on this work, in which Y.J., G.Y., H.-C.O. and H.H.K. are listed as inventors. H.H.K. is the founder of cisionMed. T.S. is a stockholder of MagicBulletTherapeutics Inc. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Francisco Sánchez-Rivera and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Generation and characterization of the K562-PE4K cell line.

(a) The introduction of a premature stop codon into MSH6 in K562-PE4K cell using cytosine base editing. (b) Gene copy numbers for chromosome 9 in K562-PE4K cells determined using exome sequencing. Locations of probes corresponding to exons 4 to 9 of ABL1 are marked with yellow vertical lines.

Extended Data Fig. 2 Evaluation of ABL1 kinase variants using epegRNA abundance-based analysis.

(a) Distribution of DeepPrime-FT scores for the epegRNAs designed to generate SNVs in exons encoding the ABL1 kinase. In the boxes, the top, middle, and bottom lines represent the 25th, 50th, and 75th percentiles, respectively, with whiskers indicating the 5th and 95th percentiles. N = 2,456, 765, 1,602, 1,664, 1,377, and 809 for exon 4–9, respectively. (b) Fluorescence microscopy of K562-PE4K cells to visualize green fluorescent protein (GFP) and red fluorescent protein (RFP), markers of hMLH1dn and epegRNA expression, respectively. Representative images from two independent experiments with similar results are shown. Scale bar, 1 mm. (c, d) Volcano plots showing log2-fold changes and adjusted P values of epegRNAs designed to generate SNVs in ABL1 after treatment with bosutinib (c) and ponatinib (d). The P values and LFCs were generated by the MAGeCK-test module using a modified robust ranking aggregation (α-RRA) analysis. Each dot represents the results from two to three epegRNAs designed to induce a single SNV (previously reported SNVs and VUSs conferring resistance), a negative control epegRNA (or sgRNA), or ten epegRNAs targeting an essential gene. KO, knockout. (e) The number of resistant SAAVs identified by the epegRNA abundance-based analyses.

Extended Data Fig. 3 Individual evaluation of prime edited cells and clones.

(a) Prime editing efficiencies determined by deep sequencing in K562-PE4K cells after the transduction of individual epegRNAs intended to generate the indicated SAAVs. (b) Median relative numbers of live K562-PE4K cells expressing ABL1WT (wild-type), ABL1Y253H, ABL1E255V, and ABL1T315I SAAVs after treatment with imatinib (left), nilotinib (middle), or bosutinib (right). The relative numbers of live cells were measured with a CCK-8 assay and normalized to the numbers of cells containing ABL1WT treated with a solvent control (DMSO) (the number of independent transductions n = 3). (c) Frequency of reads in each single cell-derived clone containing the Y253H mutation in ABL1. “Intended edit’ (shown in sky blue) indicates reads that contain the indicated SNVs without any other mutations. “Wild type’ (dark blue) indicates reads without the intended SNV or any other mutations. “Others’ (gray) indicates reads that fall into neither of these categories. Considering that the K562-PE4K cells contain 13 copies of ABL1, the calculated prime-edited ABL1 gene copy number is indicated on the y-axis. (d) The relative viability of K562-PE4K clones containing the Y253H mutation with different edited gene copy numbers was assessed using the CellTiterGlo assay after three days of treatment with various concentrations of the five TKIs. Data are presented as the mean, with error bars representing the standard deviation. EC, edited gene copy number.

Extended Data Fig. 4 Evaluation of off-target effects.

Prime editing efficiency at on- and off-target sites in K562-PE4K cells transduced with lentiviral vectors expressing pegRNAs. Prime editing efficiencies were evaluated at 13 potential off-target sites (Off) and corresponding on-target sites (On). The numbers at the top represent positions in the protospacer (purple), protospacer adjacent motif (PAM, yellow), and reverse transcription template (white). Intended edit sites (blue), synonymous edit sites (green), and base pair mismatches at on- and off-target sites (red) are highlighted. The red vertical line indicates the site that is nicked by the prime editor nickase domain. For each potential off-target site, cells edited with pegRNAs (Edit) and un-edited negative control cells (UE) were evaluated. Deep sequencing analyses of target DNA sequences that correspond to RTTs (13 bp for On1 and Off1, 9 bp for the remaining sites) revealed the frequencies of wild-type sequences (WT), sequences containing both intended and synonymous edits (Double edit, Dbl. edit), sequences containing only intended edits (Intended edit only, Inten. Only), sequences containing only synonymous edits (Syn. Only), and other sequences (Other). Inten., intended editing.

Extended Data Fig. 5 Resistance scores of 1,954 SAAVs against each of the five TKIs.

Heatmaps show resistance scores of SAAVs in the ABL1 kinase (encoded by exons 4 – 9) against imatinib (a), nilotinib (b), bosutinib (c), ponatinib (d), and asciminib (e) determined using the endogenous region sequencing approach in K562-PE4K cells. Boxes outlined in black and gray indicate SAAVs causing resistant and intermediate phenotypes, respectively. The numbers at the top of each heatmap represent the locations within the ABL1 amino acid sequence, with the reference amino acid sequence displayed at the top. Forty-four protein variants with adjusted P values exceeding 0.05, odds ratios less than 2, or reads per millions less than 10 were excluded from the analysis and are presented with a gray background.

Extended Data Fig. 6 Evaluation of TKI resistance using K562-PEmax cells.

(a) Frequencies of sequencing reads with both intended and synonymous substitutions in both unedited and prime-edited K562-PE4K and K562-PEmax cells. Prime editing was induced by transducing library E5. Boxes represent the 25th, 50th, and 75th percentiles, and whiskers show the 10th and 90th percentiles. The number of SNVs n = 496. P values were calculated by one-way ANOVA followed by two-sided Tukey’s post hoc test. (b) Proportion of sequencing reads in each of the indicated categories in K562-PE4K (left) and in K562-PEmax (right) cells 30 days after transduction with library E5. (c) Correlations between resistance scores determined in cultured K562-PE4K cells (the y-axis) and K562-PEmax cells (the x-axis). The Pearson correlation coefficients (r) are shown. The functional classification of each SAAV based on the results using cultured K562-PE4K cells is indicated by the dot color (gray: sensitive, blue: intermediate, red: resistant). The number of SAAVs n = 175.

Extended Data Fig. 7 Viability of K562-PE4K cells in the presence of various concentrations of TKIs.

(a) The viability of K562-PE4K cells expressing wild-type ABL1 treated with various concentrations of TKIs for two days. The viability of cells was assessed using the CCK-8 assay and normalized to that of cells treated with a solvent control (DMSO). Vertical yellow lines represent the concentrations used for the high-throughput evaluations. Data are presented as mean values +/- standard deviation of three replicates. (b, c) Relative cell counts after treatment with the specified doses of imatinib (b) or asciminib (c). The relative cell counts were normalized to counts of cells treated with a solvent control (DMSO). The number of replicates n = 2.

Extended Data Fig. 8 Evaluation of TKI resistance using diverse concentrations of TKIs.

(a, b) Correlations between resistance scores of SAAVs in exon 5 of ABL1 following treatment with diverse concentrations of imatinib (a) and asciminib (b) (the y-axis) versus 100 nM imatinib (a) and 5 nM asciminib (b) (the x-axis). The classification of each SAAV is indicated by the dot color (gray: sensitive, blue: intermediate, red: resistant). The Pearson correlation coefficients (r) are shown. The number of SAAVs n = 205.

Extended Data Fig. 9 Correlations between replicates or SNVs encoding the same SAAVs in the epegRNA abundance-based (a, c) and endogenous region sequencing (b, d) analyses.

(a, b) Correlations between normalized LFCs of SNVs from two replicates independently treated with the indicated TKI in the epegRNA abundance-based (a) and endogenous region sequencing (b) analyses. The Pearson’s correlation coefficient (r) is shown. The classification of each SNV is indicated by the dot color (gray: sensitive, blue: intermediate, red: resistant). The number of SNVs n = 2,892 (a), 2,802 (b). (c, d) Correlations between adjusted LFCs following treatment with the indicated TKI in pairs of SNVs encoding the same SAAVs in the epegRNA abundance-based (c) and endogenous region sequencing (d) analyses. The colors of the dots represent their SNV categories (gray: missense, red: nonsense). The Pearson’s correlation coefficient (r) is shown. The number of SNV pairs n = 289 (c), 278 (d).

Extended Data Fig. 10 Comparison of classification results determined by the epegRNA abundance-based and endogenous region sequencing analyses.

(a) A heatmap that compares the functional classification results of 1,954 SAAVs with regards to their effect on resistance against the indicated TKIs (imatinib, nilotinib, bosutinib, and ponatinib) determined using epegRNA abundance- and endogenous region sequencing-based analyses. (b) A heatmap that compares the functional classification results for 9,770 (=1,885 ×5) pairs of SAAVs and TKIs determined by the two methods. This heatmap is the summary of results shown in (a).

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Jung, Y., Yu, G., Oh, HC. et al. Comprehensive resistance profiling of chronic myeloid leukaemia associated ABL1 variants against five tyrosine kinase inhibitors using prime editing. Nat. Biomed. Eng (2025). https://doi.org/10.1038/s41551-025-01531-4

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