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A prime editor mouse to model a broad spectrum of somatic mutations in vivo

Nature Biotechnology (2023)Cite this article

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Abstract

Genetically engineered mouse models only capture a small fraction of the genetic lesions that drive human cancer. Current CRISPR–Cas9 models can expand this fraction but are limited by their reliance on error-prone DNA repair. Here we develop a system for in vivo prime editing by encoding a Cre-inducible prime editor in the mouse germline. This model allows rapid, precise engineering of a wide range of mutations in cell lines and organoids derived from primary tissues, including a clinically relevant Kras mutation associated with drug resistance and Trp53 hotspot mutations commonly observed in pancreatic cancer. With this system, we demonstrate somatic prime editing in vivo using lipid nanoparticles, and we model lung and pancreatic cancer through viral delivery of prime editing guide RNAs or orthotopic transplantation of prime-edited organoids. We believe that this approach will accelerate functional studies of cancer-associated mutations and complex genetic combinations that are challenging to construct with traditional models.

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Fig. 1: Quantification of cancer-associated mutations amenable to modeling by base editing or prime editing.
Fig. 2: Design and functional validation of the Rosa26PE2 prime editor allele.
Fig. 3: Ex vivo prime editing and functional testing of Kras and Trp53 mutations.
Fig. 4: PE GEMMs enable autochthonous and orthotopic modeling of lung and pancreatic cancer.

Data availability

Rosa26PE2 mice on wild-type (JAX stock JR037953) and Trp53flox/flox (JAX stock JR037954; ref. 98) backgrounds are available from the Jackson Laboratory. Plasmids will be made available through Addgene upon publication. Amplicon sequencing data have been deposited in the SRA repository under accession PRJNA951647 (ref. 99). All other materials and data, including Rosa26PE2 cell and organoid lines, are available from the corresponding author upon reasonable request. Source data are provided in this paper.

Code availability

The pipeline, all related scripts, and intermediate data needed to reproduce our results are available at https://github.com/samgould2/prime-vs-base-editing (2022; ref. 100).

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Acknowledgements

We thank the entire Jacks Laboratory with particular thanks to D. Sandel, C. Concepcion, M. Burger, W. Hwang, M. Heileman and A. Jaeger for useful discussions. We also thank K. Yee, J. Teixeira, K. Anderson and M. Magendantz for administrative support. We thank S. Murray, R. Bronson, G. Johnson, J. Kuhn and M. Cornwall-Brady for helpful advice. S.I.G. and F.J.S.R. thank E. Kastenhuber for useful advice on human and mouse ortholog analyses, as well as D. Solit, N. Schultz, M. Berger and B. Gross (MSKCC) for access to MSK-IMPACT data. Z.A.E. acknowledges support from the Ludwig Center at MIT and the National Cancer Institute of the National Institutes of Health under award F31CA268835. S.I.G. was supported by T32GM136540. F.J.S.R. is an HHMI Hanna Gray Fellow and was supported by the V Foundation for Cancer Research (V2022-028), NCI Cancer Center Support Grant P30-CA1405, the Ludwig Center at MIT (2036636), Koch Institute Frontier Awards (2036648 and 2036642) and the MIT Research Support Committee (3189800). G.A.N. was supported by a Helen Hay Whitney Postdoctoral Fellowship and K99 award HL163805. We also thank the Koch Institute Swanson Biotechnology Center for technical support, especially the Histology core facility. This study was also supported by the Howard Hughes Medical Institute, David H. Koch Graduate Fellowship Fund, NCI Cancer Center Support Grant P30-CA1405, NIH Pre-Doctoral Training Grant T32GM007287, NIH grants U01 AI142756, RM1 HG009490 and R35 GM118062 and the Lustgarten Foundation for Pancreatic Cancer Research.

Author information

Author notes

  1. These authors contributed equally: Zackery A. Ely, Nicolas Mathey-Andrews.

Authors and Affiliations

  1. David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Zackery A. Ely, Nicolas Mathey-Andrews, Santiago Naranjo, Samuel I. Gould, Kim L. Mercer, Christina M. Cabana, William M. Rideout III, Grissel Cervantes Jaramillo, Katie Holland, William A. Freed-Pastor, Zachary Kulstad, Peter M. K. Westcott, Lin Lin, Brendan L. Horton, Nimisha B. Pattada, Sean-Luc Shanahan, Stefani Spranger, Francisco J. Sánchez-Rivera & Tyler Jacks

  2. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Zackery A. Ely, Nicolas Mathey-Andrews, Santiago Naranjo, Samuel I. Gould, Christina M. Cabana, Sean-Luc Shanahan, Stefani Spranger, Francisco J. Sánchez-Rivera & Tyler Jacks

  3. Harvard Medical School, Boston, MA, USA

    Nicolas Mathey-Andrews & William A. Freed-Pastor

  4. Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Gregory A. Newby, Peyton B. Randolph, Jessie R. Davis, Andrew V. Anzalone & David R. Liu

  5. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Gregory A. Newby, Peyton B. Randolph, Jessie R. Davis, Andrew V. Anzalone & David R. Liu

  6. Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA

    Gregory A. Newby, Peyton B. Randolph, Jessie R. Davis, Andrew V. Anzalone & David R. Liu

  7. Harvard-MIT Division of Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Grissel Cervantes Jaramillo

  8. Department of Biomedical Engineering, Tufts University, Medford, MA, USA

    Jennifer M. Khirallah, Zhongfeng Ye & Qiaobing Xu

  9. Department of Biology, Angelo State University, San Angelo, TX, USA

    Katie Holland

  10. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    William A. Freed-Pastor & Zachary Kulstad

  11. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    William A. Freed-Pastor

  12. Cold Spring Harbor Laboratory, Huntington, NY, USA

    Peter M. K. Westcott

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Contributions

Z.A.E. and T.J. conceived of the study. Z.A.E., N.M.A. and T.J. designed the experiments. N.M.A. conducted all autochthonous lung modeling. S.N. and Z.A.E. designed and constructed all DNA vectors described, including the transgene cassette. Z.A.E. designed pegRNAs, with contributions from N.M.A., P.B.R., J.R.D. and K.H.; G.A.N., S.I.G. and F.J.S.R. designed the computational pipeline to analyze patient mutation data; and S.I.G. conducted related bioinformatics analyses. W.A.F.-P., G.C.J. and Z.K. conducted pancreatic orthotopic transplant experiments. L.L. and G.C.J. conducted autochthonous pancreatic modeling. N.M.A., J.M.K., B.L.H. and G.A.N. conducted LNP experiments. C.M.C. and S.N. conducted experiments involving lung organoids. N.M.A., Z.A.E., N.B.P. and W.A.F.-P. conducted in vitro experiments using pancreatic organoids. W.M.R. conducted mESC targeting and chimera generation. K.L.M. provided animal husbandry expertise and conceptual advice. S.N., G.A.N., P.B.R., A.V.A., W.A.F.-P., P.M.K.W., K.L.M., S.L.S., F.J.S.R. and D.R.L. provided conceptual advice. Z.A.E., N.M.A., S.I.G., F.J.S.R. and T.J. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Tyler Jacks.

Ethics declarations

Competing interests

T.J. is a member of the Board of Directors of Amgen and Thermo Fisher Scientific, and a co-founder of Dragonfly Therapeutics and T2 Biosystems. T.J. serves on the Scientific Advisory Board of Dragonfly Therapeutics, SQZ Biotech, and Skyhawk Therapeutics. T.J. is also the President of Break Through Cancer. None of these affiliations represent a conflict of interest with respect to the design or execution of this study or the interpretation of data presented in this manuscript. His laboratory currently receives funding from the Johnson & Johnson Lung Cancer Initiative, but this funding did not support the research described in this manuscript. D.R.L. is a consultant for Prime Medicine, Beam Therapeutics, Pairwise Plants, Chroma Medicine, and Nvelop Therapeutics, companies that use or deliver genome editing or genome engineering agents, and owns equity in these companies. K.H. and A.V.A. are currently employees of Prime Medicine. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Andrea Ventura and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Prime editing has a greater capacity to model recurrent cancer-associated mutations than base editing.

a Quantification of mutations that are detected in multiple patients in cancer-associated genes, depicted for each mutant variant type (Single nucleotide variants = SNV, deletions = DEL, insertions = INS, di-nucleotide variants = DNV, oligo-nucleotide variants = ONV). The y-axis in all plots indicates the total number of unique mutations per variant type. b Quantification of recurrent mutations potentially amenable to modeling by a base editor with an NGG PAM (top) or a prime editor with an NGG PAM and a 30 base pair RT template (bottom). The columns show results considering mutations that occur in ≥5 patients (left) or ≥10 patients (right). ‘CBE/ABE high’ indicates that the SNV falls in the high efficiency editing window (position +4 to +8 in the protospacer), while ‘CBE/ABE low’ indicates the SNV falls within the protospacer but outside the high efficiency window. The data include SNVs (blue outer circle; 78.8% of mutations) and other mutation types (gray outer circle; 20.2% of mutations). All calculations assume a base or prime editor that recognizes only NGG PAMs. c Total percentage of recurrent mutations amenable to modeling by a base editor with an NGG PAM, quantified at multiple thresholds of mutation frequency, from mutations that occur ≥1 patient to those that occur ≥10 patients. d Total percentage of recurrent mutations amenable to modeling by a prime editor with an NGG PAM and a 30 base pair RT template at multiple thresholds of mutation frequency, from mutations that occur ≥1 patient to those that occur ≥10 patients. e Capabilities of prime versus base editing to model orthologous mutations in mice at multiple thresholds of mutation frequency, from ≥1 to ≥10 patients with each mutation. Prime editing can model approximately double the number of orthologous mutations in mice at all thresholds of mutation frequency. Base editing (BE) shown in red. Prime editing (PE) shown in blue. f Quantification of the ability of base editing (left) and prime editing (right) to model orthologous mutations in mice for mutations that occur in ≥5 or ≥ 10 patients.

Extended Data Fig. 2 Prime editing enables modeling a broader scope of cancer-associated mutations from residues conserved in mice at various homology stringencies.

a The percentage of mutations, categorized by variant type, that fall in a region of homology as a function of flank size. Flank size is defined as the number of amino acids on either side of the mutant codon that must match between the human and mouse orthologs for the mutated codon to be considered orthologous (for example, a flank size of two would mean that a total of five amino acids–two upstream and two downstream of the mutant codon–would need to match in the human and mouse protein to be considered orthologous.). b Capabilities of prime editing (blue) and base editing (red) to model mutations that derive from a wild-type amino acid residue conserved in mice as a function of conserved flank size (that is, the stringency of homology). All calculations assume a base or prime editor that recognizes only NGG PAMs. c Quantification of the ability of base editing (top) and prime editing (bottom) to model orthologous mutations in mice at different flank size values (2, 3, 5, and 10 from left to right). These plots correspond with the data points in panel (b).

Extended Data Fig. 3 Prime editing in Trp53-proficient cells and off-target activity at computationally predicted loci.

a Prime editing in Trp53flox/flox;Rosa26PE2/+ versus Trp53+/+;Rosa26PE2/+ TTFs across several indicated loci. Data and error bars indicate the mean and standard deviation of three independent transductions. b Prime editing in Trp53flox/flox;Rosa26PE2/+ versus Trp53+/+;Rosa26PE2/+ pancreatic across several indicated loci. Note that the editing data for Dnmt1+GGG in Trp53flox/flox organoids are the same depicted in Fig. 2f. Data and error bars indicate the mean and standard deviation of three independent transductions. c Quantification of off-target prime editing for a pegRNA targeted to Dnmt1. Off-targets represent three computationally predicted loci for tail-tip fibroblasts cultured for more than one month in the presence of the Dnmt1+GGG pegRNA or a control pegRNA. Data and error bars indicate the mean and standard deviation of three independent transductions. Off-target locus details are described in Supplementary Table 4. d Quantification of off-target prime editing at four computationally predicted loci for tail-tip fibroblasts cultured with a KrasG12A epegRNA or a control pegRNA. Data and error bars indicate the mean and standard deviation of three independent transductions.

Extended Data Fig. 4 Mismatch repair-evasive edits amplify prime editing efficiency across multiple cancer-associated loci.

a Design of epegRNAs encoding EgfrL860R (right) or KrasG12C (left) with (bottom) or without (top) silent mutations intended to promote MMR evasion. The black line on the sequence schematics indicates the Cas9 nicking site. b Editing efficiency and indel byproduct frequency of epegRNAs encoding KrasG12C with or without silent mutations in tail tip-derived fibroblasts. Note that the data for KrasG12C without silent edits is also featured in Fig. 3. Data and error bars indicate the mean and standard deviation of three independent transductions. c Editing efficiency and indel byproduct frequency of epegRNAs encoding EgfrL860R with or without silent mutations. Data and error bars indicate the mean and standard deviation of three independent transductions. d Editing efficiency and indel byproduct frequency of epegRNAs encoding KrasG12C with or without silent mutations in pancreatic organoids. Note that the data for KrasG12C without silent edits is also featured in Fig. 3. Data and error bars indicate the mean and standard deviation of three independent transductions.

Extended Data Fig. 5 Treatment of prime edited KrasG12D and KrasG12C pancreatic organoids with EGFR and KRAS inhibitors in the presence or absence of a secondary resistance mutation.

a Bright-field images of unedited Rosa26PE2/+ pancreatic organoids treated with gefitinib after infection with the template UPEC vector (that is, lacking a pegRNA). Scale bars: 100 µm. b Additional bright-field images supplementing Fig. 3d and depicting prime edited KrasG12D and KrasG12C organoids treated either with DMSO, gefitinib, or sotorasib. Scale bars: 100 µm. c Allele frequencies of KrasG12R, KrasG12A, or KrasG12C+silent edits mutations in pancreatic organoids before and after two passages of treatment with gefitinib (1 µM). Data and error bars indicate the mean and standard deviation of three independent transductions pre- and post-treatment. Note that pre-treatment replicate data is also represented in Extended Data Fig. 4 (KrasG12C + silent edits) and Fig. 3 (KrasG12R and KrasG12A). d Editing efficiency and indel frequencies of epegRNAs templating the KrasY96C transversion in Rosa26PE2/+ TTFs. Data and error bars indicate the mean and standard deviation of three independent transductions. e Nucleotide percentage quilts and allele frequency tables output by CRISPResso2 depicting sequencing reads derived from prime edited KrasG12C and KrasG12D pancreatic organoids. Prime edited alleles were previously selected with gefitinib, and these sequencing data derive from organoids after treatment with gefitinib in combination with mutant KRAS inhibitors.

Extended Data Fig. 6 On- and off-target prime editing at Trp53 in pancreatic organoids.

a Editing efficiency and indel byproduct frequency of epegRNAs encoding Trp53M240FS-14nt (n = 4 independent transductions) or Trp53M240S* (n = 4 independent transductions) in Trp53flox/+ pancreatic organoids. Data and error bars indicate the mean and standard deviation across independent transductions. b Design of the epegRNAs for Trp53M240FS-14nt or Trp53M240S*. The black line below the sequence schematics indicates the Cas9 nicking site. These epegRNAs were designed to both evade MMR and modify the seed and PAM sequences. c CRISPResso allele frequency plots of prime edited reads for Trp53M240FS-14nt before and after nutlin selection in pancreatic organoids. d Quantification of off-target prime editing for an epegRNA encoding Trp53M240FS-14nt. Off-targets represent four computationally predicted loci for pancreatic organoids cultured for more than one month in the presence of the Trp53M240FS-14nt epegRNA and nutlin-3a or a control pegRNA. Data and error bars indicate the mean and standard deviation of three independent transductions. e Quantification of off-target prime editing for an epegRNA encoding Trp53R245Q. Off-targets represent four computationally predicted loci for pancreatic organoids cultured for more than one month in the presence of the Trp53R245Q epegRNA and nutlin-3a or a control pegRNA. Data and error bars indicate the mean and standard deviation of three independent transductions. f Quantification of off-target prime editing for an epegRNA encoding Trp53R245W. Off-targets represent four computationally predicted loci for pancreatic organoids cultured for more than one month in the presence of the Trp53R245W epegRNA and nutlin-3a or a control pegRNA. Data and error bars indicate the mean and standard deviation of three independent transductions. Statistical significance was calculated using an unpaired, one-tailed Welch’s t-test (P = 0.0045 for the leftmost off-target site).

Extended Data Fig. 7 Prime edited tumors recapitulate histological features of gold standard GEMMs of lung adenocarcinoma.

a Aiforia grade analysis of 16-week tumor-bearing lungs initiated by in vivo prime editing (red = Grade 1, green = Grade 2, blue = Grade 3, orange = Grade 4). b H&E and immunohistochemical (IHC) staining of a representative Grade 2 tumor 16 weeks post-initiation. IHC staining was performed with antibodies specific to Nkx2.1, a lung lineage transcription factor, or Surfactant Protein C (Sftpc), a marker of AT2 cells. Scale bars: 200 µm. c H&E and IHC staining of a Grade 3/4 tumor 16 weeks post-initiation. IHC staining was performed with antibodies specific to NKX2-1 or HMGA2, a lung embryonic chromatin regulator. Scale bars: 200 µm.

Extended Data Fig. 8 Autochthonous modeling of diverse Kras variants in the lung.

a Tumor burden quantification using Aiforia grading algorithm for KrasG12D (n = 5 mice), KrasG12A (n = 9 mice), and KrasG12R (n = 9 mice), tumors at 16 weeks. Tumor burden is defined as the ratio of tumor area (Grades 1–4) over total lung area. Data and error bars indicate the mean and standard deviation of five to nine biological replicates. b Representative fluorescence imaging of lungs from Trp53flox/flox;Rosa26PE2/+ and Trp53flox/flox;Rosa26PE2/PE2 mice infected with UPEC-KrasG12A (n = 10), UPEC-KrasG12R (n = 9), UPEC-KrasG12D (n = 20), all harvested at 16 weeks, as well as mice infected with UPEC-KrasG12C+silent edits harvested at 12 weeks. c H&E staining of representative tissue from a mouse infected with UPEC-KrasG12C+silent edits. Tissue was collected at 12 weeks. Scale bars indicate 2 mm, 100 µm, and 20 µm respectively. Grading analysis indicates proportion of Grade 1, 2, and 3 lesions from four representative animals. d CRISPResso allele frequency plots depicting prime edited reads derived from lung tumors from a Trp53flox/flox;Rosa26PE2/PE2 mouse infected with UPEC-KrasG12C (top plot) and three different Trp53flox/flox;Rosa26PE2/PE2 mice infected with UPEC-KrasG12C+silent edits (bottom three plots). The black line on the sequence schematics indicates the Cas9 nicking site. The intended mutations are indicated by black boxes. The red box in the top plot indicates an unintended, silent KrasA11A substitution. The percentages indicated represent the percentage of all analyzed reads (edited + unedited reads) that bear this sequence. e H&E staining of representative tissue from a Trp53+/+;Rosa26PE2/+ mouse infected with UPEC-KrasG12D. H&E indicates histopathology consistent with a low grade adenoma. Scale bars indicate 2 mm (left) and 100 µm (right). f CRISPResso allele frequency plot depicting prime edited reads derived from an adenoma from a Trp53+/+;Rosa26PE2/+ mouse infected with UPEC-KrasG12D. The intended mutation is indicated by a black box. Note that the percentage indicated represents the percentage of all analyzed reads (including unedited reads) that bear this sequence. g Representative bright-field and fluorescence imaging of adenomas from two Trp53+/+;Rosa26PE2/+ mice infected with UPEC-KrasG12D (harvested at 10 weeks). White arrows indicate smaller fluorescent adenomas.

Extended Data Fig. 9 Modeling of pancreatic cancer through organoid transplantation and lentiviral initiation.

a Stacked bar plots indicating incidence of tumors and metastases in animals orthotopically transplanted with UPEC-KrasG12D (n = 9) and UPEC-KrasG12C (n = 9) prime-edited pancreatic organoids. b Bright-field and fluorescent imaging of an omental metastasis and surrounding tissue in an animal transplanted with UPEC-KrasG12D prime-edited pancreatic organoids. c H&E staining of a representative omental metastasis. Scale bar indicates 100 µm. d Experimental workflow for autochthonous pancreatic tumor modeling through retrograde pancreatic ductal infection of lentivirus templating KrasG12D (n = 4) or KrasY96C (n = 3) pegRNAs. e Representative H&E staining of autochthonous pancreatic adenocarcinoma initiated through pancreatic ductal infection of UPEC-KrasG12D. Scale bars indicate 1 mm and 50 µm, respectively.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Supplementary Data (Flow Cytometry Gating Strategy) and Supplementary Note.

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Supplementary Tables

Supplementary Tables 1–5.

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Unprocessed western blots.

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Ely, Z.A., Mathey-Andrews, N., Naranjo, S. et al. A prime editor mouse to model a broad spectrum of somatic mutations in vivo. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01783-y

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