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Programming tumor evolution with selection gene drives to proactively combat drug resistance

Abstract

Most targeted anticancer therapies fail due to drug resistance evolution. Here we show that tumor evolution can be reproducibly redirected to engineer therapeutic opportunity, regardless of the exact ensemble of pre-existing genetic heterogeneity. We develop a selection gene drive system that is stably introduced into cancer cells and is composed of two genes, or switches, that couple an inducible fitness advantage with a shared fitness cost. Using stochastic models of evolutionary dynamics, we identify the design criteria for selection gene drives. We then build prototypes that harness the selective pressure of multiple approved tyrosine kinase inhibitors and employ therapeutic mechanisms as diverse as prodrug catalysis and immune activity induction. We show that selection gene drives can eradicate diverse forms of genetic resistance in vitro. Finally, we demonstrate that model-informed switch engagement effectively targets pre-existing resistance in mouse models of solid tumors. These results establish selection gene drives as a powerful framework for evolution-guided anticancer therapy.

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Fig. 1: Compartmental and agent-based stochastic models of disease evolution establish criteria for gene drive design.
Fig. 2: Modular motifs of genetic switches demonstrate inducible fitness benefits and shared fitness costs.
Fig. 3: Dual-switch selection gene drives demonstrate evolutionary control.
Fig. 4: Selection gene drives are robust to diverse forms of resistance in cis and in trans.
Fig. 5: Diverse molecular designs can achieve evolutionary reprogramming.
Fig. 6: Theoretical models inform optimal treatment regimens in vivo.

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Data availability

Sequencing data associated with this work are publicly available on the NIH NCBI SRA (BioProject PRJNA1081395)77. All other data are available in the main text, in Supplementary Information, or on GitHub in the associated figure directory at https://github.com/pritchardlabatpsu/SelectionGeneDrives74. Source data are provided with this paper.

Code availability

All code associated with this work is publicly available on GitHub (https://github.com/pritchardlabatpsu/SelectionGeneDrives)74.

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Acknowledgements

We acknowledge L. Randolph, V. Rivera, M. Hemann and P. Bruno for their helpful comments on previous versions of the manuscript. We also thank the members of the U01 Synthetic Biology in Cancer consortium for valuable comments during the preparation of the manuscript. We also acknowledge the Huck Flow Cytometry Facility and its members, including D. R. Abrams, M. Koptchak and R. Mani. J.R.P. is supported by U01CA265709, R21EB026617, NSF RECODE CBET 2033673 and NSF Modulus MCB 2141650. This project was supported by Huck Institutes of the Life Sciences at Penn State University through the Huck Innovative and Transformational Seed Grant (HITS). Content is the responsibility of the authors and does not represent the views of the Huck Institutes.

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Authors

Contributions

S.M.L. and J.R.P. conceptualized this work. S.M.L., D.W. and J.R.P. developed the theoretical models. S.M.L., J.A.R., I.S., Z.Y. and H.I. conducted the experiments. S.Y. and M.A. provided support for in vivo studies. S.M.L. wrote the initial draft, and J.R.P. edited the manuscript. J.R.P. acquired the funds to support the project.

Corresponding author

Correspondence to Justin R. Pritchard.

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Competing interests

J.R.P. is a co-founder of Theseus Pharmaceuticals and holds equity in Theseus Pharmaceuticals. J.R.P. consults for and holds equity in MOMA Therapeutics. J.R.P. and S.M.L. are co-founders of Red Ace Bio. J.R.P. and S.M.L. have filed for patent protection of the work described in the manuscript.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Appendices 1 and 2.

Reporting Summary

Supplementary Video 1

Example spatial agent-based model simulation for small kill radius (ρ = 1) and low dispersion (high initial compactness, θ = 1).

Supplementary Video 2

Example spatial agent-based model simulation for small kill radius (ρ = 1) and high dispersion (low initial compactness, θ = 0).

Supplementary Video 3

Example spatial agent-based model simulation for large kill radius (ρ = 5) and low dispersion (high initial compactness, θ = 1).

Supplementary Video 4

Example spatial agent-based model simulation for large kill radius (ρ = 5) and high dispersion (low initial compactness, θ = 0).

Source data

Source Data Fig. 1

Full scan for blot in Fig. 2e (low exposure time).

Source Data Fig. 2

Full scan for blot in Fig. 2e (high exposure time). Membrane slice containing GAPDH control was not included to avoid overexposure.

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Leighow, S.M., Reynolds, J.A., Sokirniy, I. et al. Programming tumor evolution with selection gene drives to proactively combat drug resistance. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02271-7

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