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Drug dependence in cancer is exploitable by optimally constructed treatment holidays

Nature Ecology & Evolution volume 8, pages 147–162 (2024)Cite this article

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An Author Correction to this article was published on 12 December 2023

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Abstract

Cancers with acquired resistance to targeted therapy can become simultaneously dependent on the presence of the targeted therapy drug for survival, suggesting that intermittent therapy may slow resistance. However, relatively little is known about which tumours are likely to become dependent and how to schedule intermittent therapy optimally. Here we characterized drug dependence across a panel of over 75 MAPK-inhibitor-resistant BRAFV600E mutant melanoma models at the population and single-clone levels. Melanocytic differentiated models exhibited a much greater tendency to give rise to drug-dependent progeny than their dedifferentiated counterparts. Mechanistically, acquired loss of microphthalmia-associated transcription factor in differentiated melanoma models drives ERK–JunB–p21 signalling to enforce drug dependence. We identified the optimal scheduling of ‘drug holidays’ using simple mathematical models that we validated across short and long timescales. Without detailed knowledge of tumour characteristics, we found that a simple adaptive therapy protocol can produce near-optimal outcomes using only measurements of total population size. Finally, a spatial agent-based model showed that optimal schedules derived from exponentially growing cells in culture remain nearly optimal in the context of tumour cell turnover and limited environmental carrying capacity. These findings may guide the implementation of improved evolution-inspired treatment strategies for drug-dependent cancers.

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Fig. 1: Melanocytic melanoma is primed for addiction to MAPK inhibition upon resistance.
Fig. 2: Dedifferentiated melanoma is resistant to ERK-induced cell cycle arrest.
Fig. 3: Adaptive loss of MITF is a driver of drug dependence in MAPKi-resistant melanoma.
Fig. 4: Scheduling drug exposure can optimize growth inhibition in dependent cell lines.
Fig. 5: Optimal scheduling can be approximated with a mixed cell population of unknown growth rates.
Fig. 6: Optimal schedules derived from a well-mixed model approximate those derived from an ABM incorporating cell turnover, spatial competition and carrying capacity.

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Data availability

All data generated or analysed during this study are included in this published article (and its supplementary information files). Source data are provided with this paper.

Code availability

All code used in this study will be available on GitHub.

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Acknowledgements

This study was supported by Duke University School of Medicine start-up funds and the Duke Cancer Institute (K.C.W.), and received grant support from an anonymous donor to the Duke Cancer Institute (K.C.W.), National Institutes of Health awards R01CA207083 and R01CA263593 (K.C.W.), NIH R35GM124875 (K.B.W) and the Duke Medical Scientist Training Program (T32 GM007171 to S.T.K.).

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Author notes

  1. These authors contributed equally: Jeff Maltas, Shane T. Killarney, Katherine R. Singleton.

Authors and Affiliations

  1. Department of Biophysics, University of Michigan, Ann Arbor, MI, USA

    Jeff Maltas & Kevin B. Wood

  2. Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, USA

    Shane T. Killarney, Katherine R. Singleton, Rachel Washart & Kris C. Wood

  3. Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA

    Maximilian A. R. Strobl

  4. Department of Physics, University of Michigan, Ann Arbor, MI, USA

    Kevin B. Wood

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Contributions

K.B.W and K.C.W. conceptualized the project. K.B.W., J.M., S.T.K., K.R.S. and K.C.W. were responsible for the methodology. Mechanistic and validation studies were performed by J.M., S.T.K., K.R.S., R.W. and M.A.R.S. Data were curated by J.M., S.T.K. and K.R.S. J.M., S.T.K. and K.R.S. were responsible for visualization. The original draft was written by K.B.W., J.M., K.R.S. and K.C.W. All authors reviewed and edited the paper. K.B.W. and K.C.W. supervised the project.

Corresponding authors

Correspondence to Kris C. Wood or Kevin B. Wood.

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Competing interests

K.C.W. is a founder, consultant and equity holder at Tavros Therapeutics and Celldom and has performed consulting work for Guidepoint Global, Bantam Pharmaceuticals and Apple Tree Partners. All other authors declare no competing interests.

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Nature Ecology & Evolution thanks Andriy Marusyk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and supplementary discussion on mutation rates.

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Supplementary Data 1

List of melanocytic subtype genes, ssGSEA of melanoma cell lines with melanocytic gene set and top 100 JunB target genes.

Supplementary Data 2

Uncropped and unprocessed western blot images for the supplementary data. We included labels to identify which blot is from which figure.

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Maltas, J., Killarney, S.T., Singleton, K.R. et al. Drug dependence in cancer is exploitable by optimally constructed treatment holidays. Nat Ecol Evol 8, 147–162 (2024). https://doi.org/10.1038/s41559-023-02255-x

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