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Generation of precision preclinical cancer models using regulated in vivo base editing

Nature Biotechnology volume 42, pages 437–447 (2024)Cite this article

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Abstract

Although single-nucleotide variants (SNVs) make up the majority of cancer-associated genetic changes and have been comprehensively catalogued, little is known about their impact on tumor initiation and progression. To enable the functional interrogation of cancer-associated SNVs, we developed a mouse system for temporal and regulatable in vivo base editing. The inducible base editing (iBE) mouse carries a single expression-optimized cytosine base editor transgene under the control of a tetracycline response element and enables robust, doxycycline-dependent expression across a broad range of tissues in vivo. Combined with plasmid-based or synthetic guide RNAs, iBE drives efficient engineering of individual or multiple SNVs in intestinal, lung and pancreatic organoids. Temporal regulation of base editor activity allows controlled sequential genome editing ex vivo and in vivo, and delivery of sgRNAs directly to target tissues facilitates generation of in situ preclinical cancer models.

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Fig. 1: Regulatable BE expression across murine tissues.
Fig. 2: Efficient BE in ex vivo derived iBE organoids.
Fig. 3: iBE enables sequential BE in vitro and in vivo.
Fig. 4: In situ BE with iBE drives liver tumors.
Fig. 5: Efficient engineering of missense mutations in pancreatic tumor models.

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Data availability

All source data (including P values) are available in Supplementary Table 5. Raw FASTQ files have been deposited in the Sequence Read Archive under accession number PRJNA859154. Processed RNA-seq data (transcripts per million values and differentially expressed genes) are available in Supplementary Table 2.

Code availability

Code for analysis and data visualization is available at https://github.com/lukedow/iBE.git.

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Acknowledgements

We thank members of the Dow laboratory for advice and comments on the preparation of the paper. We would like to acknowledge K. Tsanov and J. Leibold for assistance and advice in setting up the pancreas EPO protocol. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health (NIH). This work was supported by a project grant from the NIH (R01CA229773), P01 CA087497 (S.W.L.), an MSKCC Functional Genomics Initiative grant (S.W.L.), an Agilent Technologies Thought Leader Award (S.W.L.) and support from Synthego under a Synthego Innovator Award (L.E.D.). A.K. was supported by an F31 Ruth L. Kirschstein Predoctoral Individual National Research Service Award (F31-CA247351-02). A.V. was supported by a Postdoctoral Fellowship from the Human Frontier Scientific Program (LT0011/2023-L). B.J.D. was supported by an F31 Ruth L. Kirschstein Predoctoral Individual National Research Service Award (F31-CA261061-01). E.E.G. is the Kenneth G. and Elaine A. Langone Fellow of the Damon Runyon Cancer Research Foundation (DRG-2343-18). F.J.S.R. was supported by the MSKCC TROT program (5T32CA160001) and a GMTEC Postdoctoral Researcher Innovation Grant and is a Howard Hughes Medical Institute (HHMI) Hanna Gray Fellow. S.W.L. is an HHMI investigator.

Author information

Author notes

  1. Miguel Foronda

    Present address: Memorial Sloan Kettering Cancer Center, New York, NY, USA

  2. Maria Paz Zafra

    Present address: Biosanitary Research Institute (IBS)–Granada, Granada, Spain

  3. Francisco J. Sánchez Rivera

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

  4. Francisco J. Sánchez Rivera

    Present address: Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

  5. These authors contributed equally: Alyna Katti, Adrián Vega-Pérez.

Authors and Affiliations

  1. Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA

    Alyna Katti, Adrián Vega-Pérez, Miguel Foronda, Jill Zimmerman, Maria Paz Zafra, Elizabeth Granowsky, Sukanya Goswami, Eric E. Gardner, Bianca J. Diaz, Maria Teresa Calvo Fernandez & Lukas E. Dow

  2. Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA

    Alyna Katti, Jill Zimmerman, Bianca J. Diaz & Lukas E. Dow

  3. Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Janelle M. Simon, Alexandra Wuest, Wei Luan, Scott W. Lowe & Francisco J. Sánchez Rivera

  4. Synthego Corporation, Redwood City, CA, USA

    Anastasia P. Kadina, John A. Walker II & Kevin Holden

  5. Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Scott W. Lowe

  6. Department of Medicine, Weill Cornell Medicine, New York, NY, USA

    Lukas E. Dow

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Contributions

A.K. and A.V.P. designed and performed experiments, analyzed data and wrote the paper. M.F., J.Z., M.P.Z., S.G., E.G., J.S., W.L, B.J.D., M.C.F., K.H. and F.J.S.R. performed experiments and/or analyzed data. S.W.L. supervised experimental work. L.E.D. designed and supervised experiments, analyzed data and wrote the paper.

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Correspondence to Lukas E. Dow.

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Competing interests

L.E.D. is a scientific advisor and holds equity in Mirimus, Inc. L.E.D. has received consulting fees and/or honoraria from Volastra Therapeutics, Revolution Medicines, Repare Therapeutics, Fog Pharma and Frazier Healthcare Partners. S.W.L is an advisor for and has equity in the following biotechnology companies: ORIC Pharmaceuticals, Faeth Therapeutics, Blueprint Medicines, Geras Bio, Mirimus, Inc., PMV Pharmaceuticals and Constellation Pharmaceuticals. S.W.L. acknowledges receiving funding and research support from Agilent Technologies for the purposes of massively parallel oligo synthesis. K.H., A.P.K. and J.A.W. are employees and shareholders of Synthego Corporation.

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Extended data

Extended Data Fig. 1 Regulatable BE expression in vivo.

a. Calculated BE3RA transgene copy number in iBEhem and iBEhom using a Taqman quantiative PCR assay with genomic DNA from H11-LSL-Cas9 mice as a reference. Data are presented as mean values ± s.e.m. (*p<0.05, Student’s t-test). b. Schematic representation of the targeted RMCE site downstream of the Col1a1 locus. Primers flanking the knock-in cassette and a single primer within the targeted transgene can identify wildtype, hemizygous and homozygous animals, as shown in the example genotyping agarose gel c. Mendelian transmission of Col1a1-targeted iBE knock-in (with and without R26-CAGs-rtTA3 allele) and associated p-value (chi-square test) relative to expected Mendelian inheritance. d. Immunofluorescent detection of Cas9 protein in rtTA3 only and iBEhom mice maintained on normal chow (No dox) or doxycycline chow for 14 days (14D dox). (n=3 mice per genotype and condition). e. Immunofluorescent detection of Cas9 protein in iBEhem (top) or iBEhom (bottom) mice maintained on dox chow for 7 days across four tissues. (n=3 mice per genotype and condition).

Extended Data Fig. 2 Expression of BE3RA across different tissues in mice carrying one or two copy of each allele.

Immunofluorescent detection of Cas9 protein in rtTA3+/- iBE+/- (iBEhem), rtTA3+/+ iBE+/-, rtTA3+/- iBE+/+ and rtTA3+/+iBE+/+ (iBEhom) mice maintained on dox chow for 14 days. Cas9 protein(green), DAPI staining for nuclei (blue) across four tissues analyzed. (n=3 mice per genotype and condition).

Extended Data Fig. 3 Dox treatment does not induce abnormalities in iBEhom mice.

a. Hematoxylin and Eosin (H&E) staining in rtTA3+/-iBE-/- and rtTA3+/+iBE+/+ (iBEhom) on normal chow or doxycycline chow for 14 days. (n=3 mice). b. Flow cytometry analysis of spleen and bone marrow cell suspensions of rtTA3+/-iBE-/- and rtTA3+/+iBE+/+ (iBEhom) on normal chow or maintained on doxycycline chow for 14 days (n=3 mice). Data are presented as mean values ± s.d (*p<0.05, Student’s t-test).

Extended Data Fig. 4 iBE induces low off target RNA editing that is reversed by withdrawal of transgene expression.

a. C to U editing in RNA transcripts detected from RNA sequencing data from intestine and liver from rtTAhem and iBEhom on normal chow (-), dox chow (+) for 14 days, or switched from dox chow for 14 days to normal chow for 6 days (SW). Data in the middle and right panels was derived from re-analysis of published datasets, as indicated under each plot. For experiments with multiple comparisons, p-values were calculated by one-way ANOVA, n=3 mice/condition. For individual pairwise comparisons, Student’s t-test was used. b. A to G editing in RNA transcripts detected from RNA sequencing data from intestine and liver from rtTAhem and iBEhom on normal chow (-), dox chow (+) for 14 days, or switched from dox chow for 14 days to normal chow for 6 days (SW). (n=3 mice). c. Transcript abundance (transcripts per million; TPM) in pancreatic organoids, intestine, and liver from rtTAhem and iBEhom on normal chow (-), dox chow (+) for 14 days, or switched from dox chow for 14 days to normal chow for 6 days (SW). All data shown are presented as mean values +/- s.d., n=3 mice/condition.

Extended Data Fig. 5 iBE has low DNA off target activity.

a. Schematic of experimental set up in mouse embryonic stem cells (ESCs). mESCs containing iBE knock in were transduced with LRT2B-gRNA vector and selected for gRNA expression. sgRNA+ cells were plated with and without dox for 6 days after which cells were plated at low density for clonal outgrowth without dox. 3 pools of 10 clones were picked for each dox conditions across to gRNA targeted cell lines (sgRNAs = Apc.Q1405X and Pik3ca.E545K). In total, 12 pools of 10 clones were sequenced at 800-1000-fold coverage across the MSK-IMPACT cancer gene set. b. Pie chart display of frequency of C>T or C>other SNVs found in pooled clones for each condition (on and off dox) for both sgRNAs. c. Sequencing analysis at cancer gene sites in cell conditions (right) described in a. Solid blue boxes represent on-target activity of the sgRNA, dotted orange boxes signify on-target ‘bystander’ editing within the gRNA window. d. Quantification of C>T and C>other SNVs found across both targets. 2-way ANOVA test for multiple comparisons was used to evaluate statistical significance across conditions. Data are presented as mean values ± s.e.m. p-values are displayed.

Extended Data Fig. 6 iBE does not induce off target RNA editing in organoids.

a. Schematic of experimental set up in iBE derived pancreatic organoids. Organoids were transduced and selected with GFPGO reporter (mScarlet+). Organoids maintained off dox were then split into dox conditions to induce BE expression for 4 days and then split again into + and – dox conditions for an additional day. b. Editing of organoids in each condition (OFF, D4, D8, and D4 sw) was quantified by flow cytometry, calculating the percentage of GFP+ cells within the mScarlet+ population. Data are presented as mean values ± s.e.m. One-way ANOVA with Tukey’s correction c. PCA analysis of RNA sequencing data from OFF, D8, and D4 SW organoids. Colors correspond to dox condition and shape delineates organoid replicate/mouse origin (n=3). d. Volcano plots from RNA-seq data comparing iBE pancreatic organoids culture on dox-containing media vs regular media. e. Off-target RNA editing analysis, processed as described for Supplementary Fig. 4. No significant differences in RNA variants were observed, n=3, one-way ANOVA with Tukey’s correction. Data are presented as mean values ± s.e.m. For all data shown, n=3 independent organoid lines/condition.

Extended Data Fig. 7 Editing dynamics of iBE organoids.

a. Flow cytometry analysis of three independent pancreatic KP mutant organoid lines integrated with GFPGO reporter following dox treatment for 0-8 days (black), transient exposure for 2h or 12h (grey), or transient exposure then re-treatment at 4 days (green). b. Targeted deep sequencing quantification of target C:G to T:A conversion at the ApcQ1405X locus in 2D small intestinal derived iBE cell line following dox addition for 21 days (dark blue), or transient dox treatment for 3 days and withdrawn for 18 days (light blue). c. Targeted deep sequencing quantification of indel conversion of b. Data are presented as mean values ± s.e.m. (*p<0.05, Student’s t-test) (n=3 independently derived line).

Extended Data Fig. 8 Efficient BE in iBE organoids with low collateral editing.

a. Targeted deep sequencing quantification of corresponding target C>T/A/G and indel conversion in small intestinal iBE organoids nucleofected with plasmid (light blue) or synthetic (indigo) gRNAs (ApcQ1405, Trp53Q97, CR8.OS2) as indicated, with and without dox treatment. b. Targeted deep sequencing quantification of target C>T/A/G and indel conversion in small intestinal iBE organoids nucleofected with synthetic gRNAs targeting cancer associated SNVs from Fig. 2f. c-j. Quantification of collateral editing of adjacent cytosines for samples shown in Fig. 2f. Predicted translation of each quantified read is shown below with targeted amino acid substitution (dark grey) and additional amino acid substitution (pink). All data are presented as mean values ± s.e.m.

Extended Data Fig. 9 Analysis of collateral editing before and after functional selection.

a-h. Quantification of collateral editing of adjacent cytosines for data shown in Fig. 2f, unselected (white) and selected (color) in small intestinal iBE organoids nucleofected with various synthetic gRNAs targeting cancer associated SNVs.

Extended Data Fig. 10 In situ base editing with iBE by synthetic gRNA delivery drives liver tumors.

a. HTVI delivery of synthetic gRNAs with SB-Myc as in Fig. 4. BF, H&E images, and IF staining for ß-catenin (green) and glutamine synthetase (GS, red) in livers with tumors. Number of transfected mice with palpable tumors is shown below each column. b. Quantification of target C:G to T:A conversion from tumors described in a). Each point corresponds to an isolated bulk tumor. (n=2-7 mice for a given gRNA target). Individual editing data color-coded by animal in Supplementary Fig. 3. All data are presented as mean values ± s.e.m.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8.

Reporting Summary

Supplementary Table 1

Mendelian transmission of Cola1-targeted iBE knockin.

Supplementary Table 2

RNA-seq results.

Supplementary Table 3

DNA off-target effects using iBE-targeted ESCs expressing ApcQ1405 and Pik3caE545K sgRNAs.

Supplementary Table 4

sgRNAs and primers used in the study.

Supplementary Table 5

Source data P values

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Katti, A., Vega-Pérez, A., Foronda, M. et al. Generation of precision preclinical cancer models using regulated in vivo base editing. Nat Biotechnol 42, 437–447 (2024). https://doi.org/10.1038/s41587-023-01900-x

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