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Direct genome editing of patient-derived xenografts using CRISPR-Cas9 enables rapid in vivo functional genomics

Nature Cancer volume 1, pages 359–369 (2020)Cite this article

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Abstract

Patient-derived xenografts (PDXs) are high-fidelity in vivo tumor models that accurately reflect many key aspects of human cancer. In contrast to cancer cell lines or genetically engineered mouse models, the utility of PDXs has been limited by the inability to perform targeted genome editing of these tumors. To address this limitation, we have developed methods for CRISPR-Cas9 editing of PDXs using a tightly regulated, inducible Cas9 vector that does not require in vitro culture for selection of transduced cells. We demonstrate the utility of this platform in PDXs to analyze genetic dependencies by targeted gene disruption and to analyze mechanisms of acquired drug resistance by site-specific gene editing, using templated homology-directed repair. This flexible system has broad application to other explant models and substantially augments the utility of PDXs as genetically programmable models of human cancer.

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Fig. 1: Development and validation of pSpCTRE, a lentiviral Cas9 vector for dox-inducible genome editing in PDXs.
Fig. 2: Dox induction of EFS promoter activity and CD4T expression above basal levels is a marker for Cas9 expression and genome editing.
Fig. 3: Generation of Cas9-expressing PDXs using pSpCTRE.
Fig. 4: Interrogation of genetic dependencies in SpCTRE PDXs using a competition assay.
Fig. 5: Evaluation of EGFR inhibitor combination therapy in a MET-amplified PDX.
Fig. 6: Introduction of complex drug resistance mutations in SpCTRE PDXs using rAAV.

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Data availability

Unmodified gel images for Figs. 1–3 and 5 are provided as Source Data files (Unmodified_Gels_Fig1 - Unmodified_Gels_Fig4). Numerical source data for Main Figs. 1–6 and Extended Data Figs. 1–4 are provided as Source Data files (SourceData_Fig1 – SourceData_Fig6 and SourceData_ExtendedData_Fig1 – SourceData_ExtendedData_Fig4). All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

The computer code that supports the findings of this study is available from the corresponding author upon request.

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Acknowledgements

We thank the MSKCC Flow Cytometry Core for their technical assistance, members of the Antitumor Assessment Core Facility for assistance with in vivo experiments and all members of the Rudin laboratory for critical comments. We thank the CCIB at Massachusetts General Hospital for the use of the CCIB DNA Core Facility and the Boston Children’s Hospital Viral Core (Core grant 5P30EY012196, NEI). This work was supported by National Institutes of Health U01 CA199215, U24 CA213274, P01 CA129243, P30 CA008748 and R01 CA197936 (C.M.R. and J.T.P.).

Author information

Authors and Affiliations

  1. Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Christopher H. Hulton

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

    Emily A. Costa & Charles M. Rudin

  3. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Nisargbhai S. Shah, Alvaro Quintanal-Villalonga, Elisa de Stanchina & Charles M. Rudin

  4. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Nisargbhai S. Shah, Alvaro Quintanal-Villalonga & Charles M. Rudin

  5. Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Glenn Heller

  6. Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA

    John T. Poirier

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Contributions

C.H.H. performed most of the experiments described here and drafted the manuscript. E.A.C. performed key in vitro assays including editing of GFP in A549GFP cells and of EGFR in PC9 cells. E.dS. directs the xenograft core facility in which the EGFR gene editing in vivo under drug selection was performed. N.S.S. and A.Q.V. provided technical assistance. G.H. provided statistical analysis. C.M.R. and J.T.P. supervised the project and helped draft the manuscript. All authors contributed to editing the manuscript.

Corresponding authors

Correspondence to Charles M. Rudin or John T. Poirier.

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Competing interests

C.M.R. has consulted regarding oncology drug development with AbbVie, Amgen, Ascentage, AstraZeneca, BMS, Celgene, Daiichi Sankyo, Genentech/Roche, Ipsen, Loxo and PharmaMar and is on the scientific advisory boards of Elucida, Bridge and Harpoon. All other authors have no competing interests.

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Extended data

Extended Data Fig. 1 Truncated CD4T is a size-efficient selectable marker for flow cytometry and immunomagnetic selection.

a, Domain structure of wildtype human CD4 and truncated constructs CD4ΔD2-4;ΔIC and CD4ΔD3-4;ΔIC (hereafter CD4T), where the indicated deletions are replaced by flexible linkers. Heatmap depicts flow cytometry staining intensity of commercially available α-CD4 antibodies, which target the indicated extracellular domains of CD4, to the indicated CD4 constructs. S, signal peptide; TM, transmembrane domain; IC, intracellular domain; MFI, mean fluorescence intensity b, Flow cytometry analysis of A549 cells expressing CD4T and enriched by fluorescence activated cell sorting (FACS). Data is representative of three independent experiments with similar results. c, Immunomagnetic enrichment of A549 cells expressing CD4T, with the indicated enrichment factors (above). Mean percent CD4 positive is displayed for n = 3 independent cell culture replicates. Data is representative of two independent experiments with similar results. Error bars are SD. d, α-CD4 staining strategy with domain D1 and D3 targeting antibodies to differentiate CD4T and full-length CD4 using flow cytometry. A mixture of GFP-negative cells expressing full length CD4 and GFP-positive cells expressing CD4T were stained with the indicated α-CD4 antibody clones. Double positive cells were exclusively GFP-negative (expressing full length CD4) and cells single positive for the domain D1 targeting α-CD4 antibody were exclusively GFP-positive (expressing CD4T). Data is representative of n = 3 independent cell culture replicates from a single experiment. Data for experiments in panels b-d are available as source data (SourceData_ExtendedData_Fig1).

Source data

Extended Data Fig. 2 Competition assay utilizing sgTrack vectors effectively determines fitness effects of gene disruption.

a, Competition assay of sgRPA1-2 in A549 with no Cas9, constitutive Cas9 expression from lentiCas9-Blast, or dox-inducible Cas9 expression from pSpCTRE (left, middle and right panels, respectively). Competition assays were performed with (solid) or without (dashed) dox. Mean fluorescence expression is displayed for n = 3 independent cell culture replicates from a single experiment. Error bars are SD, with SD < plotting character not drawn. b, Formulas for fitness score and log ratio (left). Comparison of fitness score and log ratio analysis methods (right). Mean fitness score or mean log ratio are displayed for n = 3 independent cell culture replicates from a single experiment. Error bars are SD, with SD < plotting character not drawn. c, In vitro competition assays in A549 with sgRPA1-1, sgKRAS-1, and sgKRAS-2 (left, middle and right panels, respectively). Log ratio calculations and line assignments are as described in panel b. Mean log ratio is displayed for n = 3 independent cell culture replicates from a single experiment. Error bars are SD, with SD < plotting character not drawn. Data for experiments in panels a-c are available as source data (SourceData_ExtendedData_Fig2).

Source data

Extended Data Fig. 3 CD4T induction in SpCTRE PDXs and gating strategy for in vivo competition assay analysis.

a, Representative flow cytometry analysis of CD4T induction for the competition assay with sgRPA1-1 in MSK-LX369. Data is representative of n = 5 mice. b, Flow cytometry analysis of sgRPA1-1 (mCherry) and sgNTC (GFP) in tumors from panel a. Cells are first gated on CD4T positive or CD4T induced populations as shown in panel a. Data is representative of n = 5 mice. c, Log ratio of MSK-LX369 competition assays with the indicated sgRNAs for control or dox-treated mice gated on either CD4T positive or CD4T induced populations. Mean log ratio is displayed for n = 5 mice. Error bars are SD, with SD < plotting character not drawn. A two-sided Wilcoxon rank sum test was used to determine statistical significance. d, Percent of CD4T induced cells from dox-treated mice for competition assays with the indicated sgRNAs in the SpCTRE PDXs MSK-LX369, JHU-LX55a, and MSK-LX29 (left, middle, and right panels, respectively). Mean percent CD4 positive cells is displayed for n = 4 or n = 5 mice as indicated. Error bars are SD. e, Mean time to sacrifice in days for parental and SpCTRE PDXs for n = 2 to n = 46 mice. Error bars are SD, with SD < plotting character not drawn. Data for experiments in panels a-e are available as source data (SourceData_ExtendedData_Fig3).

Source data

Extended Data Fig. 4 Evaluation of acquired osimertinib resistance driven by rAAV-mediated homology-directed repair in PC9 cells in vitro.

a, Number of PC9 cells under the indicated conditions for n = 2 independent cell culture replicates from a single experiment. Error bars are SD, with SD < plotting character not drawn. b, Osimertinib dose response curves of PC9 cells from the rAAV, osimertinib or control, DMSO treatment arms from panel a. Mean relative luminescence is displayed for n = 3 independent cell culture replicates from a single experiment. Error bars are SD, with SD < plotting character not drawn. c, d, Sequencing analysis of PC9 cells from the rAAV, osimertinib group at (c) t = 7 and (d) t = 35 days for n = 2 biologically independent replicates from panel a. Data for experiments in panels a-d are available as source data (SourceData_ExtendedData_Fig4).

Source data

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Hulton, C.H., Costa, E.A., Shah, N.S. et al. Direct genome editing of patient-derived xenografts using CRISPR-Cas9 enables rapid in vivo functional genomics. Nat Cancer 1, 359–369 (2020). https://doi.org/10.1038/s43018-020-0040-8

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