Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Fibroblasts and alectinib switch the evolutionary games played by non-small cell lung cancer

Nature Ecology & Evolution volume 3, pages 450–456 (2019)Cite this article

Subjects

Abstract

Heterogeneity in strategies for survival and proliferation among the cells that constitute a tumour is a driving force behind the evolution of resistance to cancer therapy. The rules mapping the tumour’s strategy distribution to the fitness of individual strategies can be represented as an evolutionary game. We develop a game assay to measure effective evolutionary games in co-cultures of non-small cell lung cancer cells that are sensitive and resistant to the anaplastic lymphoma kinase inhibitor alectinib. The games are not only quantitatively different between different environments, but targeted therapy and cancer-associated fibroblasts qualitatively switch the type of game being played by the in vitro population from Leader to Deadlock. This observation provides empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game. Although we concentrate on measuring games played by cancer cells, the measurement methodology we develop can be used to advance the study of games in other microscopic systems by providing a quantitative description of non-cell-autonomous effects.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Code availability

Image analysis code is available on GitHub at https://github.com/kaznatcheev/CV4Microscopy. The game assay analysis code is available on GitHub at https://github.com/kaznatcheev/GameAssay.

Data availability

Due to size constraints, raw image data from experiments are available upon request. Post-image processing data (that is, population size time-series for each experimental replicate) are available on GitHub at https://github.com/kaznatcheev/GameAssay.

References

  1. Merlo, L. M. F., Pepper, J. W., Reid, B. J. & Maley, C. C. Cancer as an evolutionary and ecological process. Nat. Rev. Cancer 6, 924–935 (2006).

    Article  CAS  Google Scholar 

  2. Heppner, G. H. Tumor heterogeneity. Cancer Res. 44, 2259–2265 (1984).

    CAS  PubMed  Google Scholar 

  3. Ibrahim-Hashim, A. et al. Defining cancer subpopulations by adaptive strategies rather than molecular properties provides novel insights into intratumoral evolution. Cancer Res. 77, 2242–2254 (2017).

    Article  CAS  Google Scholar 

  4. Scott, J. & Marusyk, A. Somatic clonal evolution: a selection-centric perspective. Biochim. Biophys. Acta 1867, 139–150 (2017).

    CAS  Google Scholar 

  5. Shaw, A. T. et al. Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N. Engl. J. Med. 368, 2385–2394 (2013).

    Article  CAS  Google Scholar 

  6. Peters, S. et al. Alectinib versus crizotinib in untreated ALK-positive non-small-cell lung cancer. N. Engl. J. Med. 377, 829–838 (2017).

    Article  CAS  Google Scholar 

  7. Shaw, A. T. & Engelman, J. A. ALK in lung cancer: past, present, and future. J. Clin. Oncol. 31, 1105–1111 (2013).

    Article  CAS  Google Scholar 

  8. Gillies, R. J., Verduzco, D. & Gatenby, R. A. Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nat. Rev. Cancer 12, 487–493 (2012).

    Article  CAS  Google Scholar 

  9. Katayama, R., Lovly, C. M. & Shaw, A. T. Therapeutic targeting of anaplastic lymphoma kinase in lung cancer: a paradigm for precision cancer medicine. Clin. Cancer Res. 21, 2227–2235 (2015).

    Article  CAS  Google Scholar 

  10. Marusyk, A. et al. Spatial proximity to fibroblasts impacts molecular features and therapeutic sensitivity of breast cancer cells influencing clinical outcomes. Cancer Res. 76, 6495–6506 (2016).

    Article  CAS  Google Scholar 

  11. Yamada, T. et al. Paracrine receptor activation by microenvironment triggers bypass survival signals and ALK inhibitor resistance in EML4–ALK lung cancer cells. Clin. Cancer Res. 18, 3592–3602 (2012).

    Article  CAS  Google Scholar 

  12. Ou, S.-H. I. et al. Alectinib in crizotinib-refractory ALK-rearranged non-small-cell lung cancer: a phase II global study. J. Clin. Oncol. 34, 661–668 (2015).

    Article  Google Scholar 

  13. Dhawan, A. et al. Collateral sensitivity networks reveal evolutionary instability and novel treatment strategies in ALK mutated non-small cell lung cancer. Sci. Rep. 7, 1232 (2017).

    Article  Google Scholar 

  14. Seto, T. et al. CH5424802 (RO5424802) for patients with ALK-rearranged advanced non-small-cell lung cancer (AF-001JP study): a single-arm, open-label, phase 1–2 study. Lancet Oncol. 14, 590–598 (2013).

    Article  CAS  Google Scholar 

  15. Kaznatcheev, A. Two conceptions of evolutionary games: reductive vs effective. Preprint at https://www.biorxiv.org/content/early/2017/12/11/231993 (2017).

  16. Kaznatcheev, A. Effective games and the confusion over spatial structure. Proc. Natl Acad. Sci. USA 115, E1709 (2018).

    Article  CAS  Google Scholar 

  17. Marusyk, A. et al. Non-cell autonomous tumor-growth driving supports sub-clonal heterogeneity. Nature 514, 54–58 (2014).

    Article  CAS  Google Scholar 

  18. Basanta, D. et al. Investigating prostate cancer tumour–stroma interactions: clinical and biological insights from an evolutionary game. Br. J. Cancer 106, 174–181 (2012).

    Article  CAS  Google Scholar 

  19. Kaznatcheev, A., Scott, J. G. & Basanta, D. Edge effects in game-theoretic dynamics of spatially structured tumours. J. R. Soc. Interface 12, 20150154 (2015).

    Article  Google Scholar 

  20. Kaznatcheev, A., Vander Velde, R., Scott, J. G. & Basanta, D. Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature. Br. J. Cancer 116, 785–792 (2017).

    Article  Google Scholar 

  21. Kerr, B., Riley, M. A., Feldman, M. W. & Bohannan, B. J. M. Local dispersal promotes biodiversity in a real-life game of rock–paper–scissors. Nature 418, 171–174 (2002).

    Article  CAS  Google Scholar 

  22. Maddamsetti, R., Lenski, R. E. & Barrick, J. E. Adaptation, clonal interference, and frequency-dependent interactions in a long-term evolution experiment with Escherichia coli. Genetics 200, 619–631 (2015).

    Article  CAS  Google Scholar 

  23. MacLean, R. C. & Gudelj, I. Resource competition and social conflict in experimental populations of yeast. Nature 441, 498–501 (2006).

    Article  CAS  Google Scholar 

  24. Gore, J., Youk, H. & Van Oudenaarden, A. Snowdrift game dynamics and facultative cheating in yeast. Nature 459, 253–256 (2009).

    Article  CAS  Google Scholar 

  25. Li, X.-Y. et al. Which games are growing bacterial populations playing? J. R. Soc. Interface 12, 20150121 (2015).

    Article  Google Scholar 

  26. Archetti, M., Ferraro, D. A. & Christofori, G. Heterogeneity for IGF-II production maintained by public goods dynamics in neuroendocrine pancreatic cancer. Proc. Natl Acad. Sci. USA 112, 1833–1838 (2015).

    Article  CAS  Google Scholar 

  27. Maynard Smith, J. & Price, G. R. The logic of animal conflict. Nature 246, 15–18 (1973).

    Article  Google Scholar 

  28. Tomlinson, I. P. & Bodmer, W. F. Modelling the consequences of interactions between tumour cells. Br. J. Cancer 75, 157–160 (1997).

    Article  CAS  Google Scholar 

  29. Tomlinson, I. P. Game-theory models of interactions between tumour cells. Eur. J. Cancer 33, 1495–1500 (1997).

    Article  CAS  Google Scholar 

  30. Archetti, M. Evolutionary game theory of growth factor production: implications for tumour heterogeneity and resistance to therapies. Br. J. Cancer 109, 1056–1062 (2013).

    Article  CAS  Google Scholar 

  31. Peña, J., Lehmann, L. & Nöldeke, G. Gains from switching and evolutionary stability in multi-player matrix games. J. Theor. Biol. 346, 23–33 (2014).

    Article  Google Scholar 

  32. Robinson, D. & Goforth, D. The Topology of the 2 × 2 Games: a New Periodic Table Vol. 3 (Psychology Press, New York, 2005).

  33. Rapoport, A. Exploiter, leader, hero, and martyr: the four archetypes of the 2 × 2 game. Syst. Res. Behav. Sci. 12, 81–84 (1967).

    Article  CAS  Google Scholar 

  34. Anderson, A. R. A., Weaver, A. M., Cummings, P. T. & Quaranta, V. Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 127, 905–915 (2006).

    Article  CAS  Google Scholar 

  35. Nichol, D. et al. Steering evolution with sequential therapy to prevent the emergence of bacterial antibiotic resistance. PLoS Comput. Biol. 11, e1004493 (2015).

    Article  Google Scholar 

  36. Basanta, D., Scott, J. G., Rockne, R., Swanson, K. R. & Anderson, A. R. A. The role of IDH1 mutated tumour cells in secondary glioblastomas: an evolutionary game theoretical view. Phys. Biol. 8, 015016 (2011).

    Article  Google Scholar 

  37. Gatenby, R. A., Gawlinski, E. T., Gmitro, A. F., Kaylor, B. & Gillies, R. J. Acid-mediated tumor invasion: a multidisciplinary study. Cancer Res. 66, 5216–5223 (2006).

    Article  CAS  Google Scholar 

  38. Jain, R. K. Normalizing tumor microenvironment to treat cancer: bench to bedside to biomarkers. J. Clin. Oncol. 31, 2205–2218 (2013).

    Article  CAS  Google Scholar 

  39. Zhang, J., Cunningham, J. J., Brown, J. S. & Gatenby, R. A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat. Commun. 8, 1816 (2017).

    Article  Google Scholar 

  40. Gerlee, P. & Altrock, P. M. Extinction rates in tumour public goods games. J. R. Soc. Interface 14, 20170342 (2017).

    Article  Google Scholar 

  41. Conitzer, V. The exact computational complexity of evolutionarily stable strategies. In International Conference on Web and Internet Economics 96–108 (Springer, 2013).

  42. Kaznatcheev, A. Computational complexity as an ultimate constraint on evolution. Preprint at https://www.biorxiv.org/content/early/2018/06/18/187682 (2018).

  43. Mediavilla-Varela, M., Boateng, K., Noyes, D. & Antonia, S. J. The anti-fibrotic agent pirfenidone synergizes with cisplatin in killing tumor cells and cancer-associated fibroblasts. BMC Cancer 16, 176 (2016).

    Article  Google Scholar 

Download references

Acknowledgements

J.G.S. acknowledges the NIH Loan Repayment Programs for generous support of his research in general, as well as Miles for Moffitt and the NIH Case Comprehensive Cancer Center (support grant P30CA043703), and the Calabresi Clinical Oncology Research Program, National Cancer Institute (award number K12CA076917). We also thank M. Abazeed, P. Jeavons and K. Kaznatcheev for helpful feedback and discussions.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Oxford, Oxford, UK

    Artem Kaznatcheev

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

    Artem Kaznatcheev & Jacob G. Scott

  3. Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA

    Jeffrey Peacock

  4. Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA

    David Basanta

  5. Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL, USA

    Andriy Marusyk

  6. Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA

    Jacob G. Scott

Authors
  1. Artem Kaznatcheev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeffrey Peacock
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Basanta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andriy Marusyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jacob G. Scott
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.K., J.P., A.M. and J.G.S. conceived and designed the study. J.P. and A.M. performed the experiments. A.K. designed the mathematical model, wrote the image analysis and game assay code, and analysed the data. A.K., A.M. and J.G.S. wrote the main text. A.K. and J.P. wrote the Supplementary Information. D.B., A.M. and J.G.S. supervised the project. All authors discussed the results and implications, commented on the work at all stages and approved the final submission.

Corresponding authors

Correspondence to Artem Kaznatcheev, Andriy Marusyk or Jacob G. Scott.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Sections A–F, including Supplementary Figures 1–8 and Supplementary References

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaznatcheev, A., Peacock, J., Basanta, D. et al. Fibroblasts and alectinib switch the evolutionary games played by non-small cell lung cancer. Nat Ecol Evol 3, 450–456 (2019). https://doi.org/10.1038/s41559-018-0768-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-018-0768-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer