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.

  • Perspective
  • Published:

OPINION

Cooperation among cancer cells: applying game theory to cancer

Nature Reviews Cancer volume 19, pages 110–117 (2019)Cite this article

Subjects

Abstract

Cell cooperation promotes many of the hallmarks of cancer via the secretion of diffusible factors that can affect cancer cells or stromal cells in the tumour microenvironment. This cooperation cannot be explained simply as the collective action of cells for the benefit of the tumour because non-cooperative subclones can constantly invade and free-ride on the diffusible factors produced by the cooperative cells. A full understanding of cooperation among the cells of a tumour requires methods and concepts from evolutionary game theory, which has been used successfully in other areas of biology to understand similar problems but has been underutilized in cancer research. Game theory can provide insights into the stability of cooperation among cells in a tumour and into the design of potentially evolution-proof therapies that disrupt this cooperation.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Cooperative interactions within the tumour and with the stroma.
Fig. 2: Nonlinear dynamics.

Similar content being viewed by others

References

  1. Jouanneau, J., Moens, G., Bourgeois, Y., Poupon, M. F. & Thiery, J. P. A minority of carcinoma cells producing acidic fibroblast growth factor induces a community effect for tumor progression. Proc. Natl Acad. Sci. USA 91, 286–290 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Axelrod, R., Axelrod, D. & Pienta, K. J. Evolution of cooperation among tumor cells. Proc. Natl Acad. Sci. USA 103, 13474–13479 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 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  PubMed  PubMed Central  Google Scholar 

  4. Cleary, A. S., Leonard, T. L., Gestl, S. A. & Gunther, E. J. Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers. Nature 508, 113–117 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tabassum, D. P. & Polyak, K. Tumorigenesis: it takes a village. Nat. Rev. Cancer 8, 473–483 (2015).

    Article  CAS  Google Scholar 

  6. Joyce, J. A. & Pollard, J. W. Microenvironmental regulation of metastasis. Nat. Rev. Cancer 9, 239–252 (2009).

    Article  CAS  PubMed  Google Scholar 

  7. Quail, D. F. & Joyce, J. A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19, 1423–1437 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hanahan, D. & Coussens, L. M. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21, 309–322 (2012).

    Article  CAS  PubMed  Google Scholar 

  9. MacDougall-Shackleton, S. A. The levels of analysis revisited. Phil. Trans. R. Soc. B 366, 2076–2085 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Mayr, E. Cause and effect in biology. Science 134, 1501–1506 (1961).

    Article  CAS  PubMed  Google Scholar 

  11. Tinbergen, N. On aims and methods in ethology. Z. Tierpsychol. 20, 410–433 (1963).

    Article  Google Scholar 

  12. Maynard Smith, J. Group selection and kin selection. Nature 201, 1145–1147 (1964).

    Article  Google Scholar 

  13. Williams, G. C. Adaptation and Natural Selection: A Critique of Some Current Evolutionary Thought (Princeton Univ. Press, 1972).

  14. Dawkins, R. The Selfish Gene (Oxford University Press, 1976).

  15. Hardin, G. The tragedy of the commons. Science 162, 1243–1248 (1968).

    Article  CAS  PubMed  Google Scholar 

  16. Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).

    Article  CAS  PubMed  Google Scholar 

  17. Cairns, J. Mutation, selection and the natural history of cancer. Nature 255, 197–200 (1975).

    Article  CAS  PubMed  Google Scholar 

  18. Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211, 1390–1396 (1981).

    Article  CAS  PubMed  Google Scholar 

  20. Mesterton-Gibbons, M. & Adams, E. S. The economics of animal cooperation. Science 298, 2146–2147 (2002).

    Article  CAS  PubMed  Google Scholar 

  21. Nowak, M. A. Five rules for the evolution of cooperation. Science 314, 1560–1563 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. West, S. A., Griffin, A. S. & Gardner, A. Evolutionary explanations for cooperation. Curr. Biol. 17, R661–R672 (2007).

    Article  CAS  PubMed  Google Scholar 

  23. Gintis, H. & Bowles, S. A Cooperative Species: Human Reciprocity and Its Evolution (Princeton Univ. Press, 2011).

  24. Tomasello, M. Why We Cooperate (MIT Press, 2009).

  25. Axelrod, R. The Evolution of Cooperation (Basic Books, 1984).

  26. Rasmusen, E. Games and Information: An Introduction To Game Theory (Wiley-Blackwell, 2006).

  27. Osborne, M. An Introduction to Game Theory (Oxford Univ. Press, 2003).

  28. Fudenberg, D. & Tirole, J. Game Theory (MIT Press, 1991).

  29. Myerson, R. Game Theory: Analysis of Conflict (Harvard Univ. Press, 1997).

  30. Maynard Smith, J. Evolution and the Theory of Games (Cambridge Univ. Press, 1982).

  31. McElreath, R. & Boyd, R. Mathematical Models of Social Evolution: A Guide For The Perplexed (Univ. of Chicago Press, 2007).

  32. Dugatkin, L. & Reeve, H. Game Theory and Animal Behaviour (Oxford Univ. Press, 1998).

  33. Hofbauer, J. & Sigmund, K. Evolutionary Games and Population Dynamics (Cambridge Univ. Press, 1998).

  34. Tucker, A. in Reading in Games and Information (ed. Rasmusen, E.) 7–8 (Blackwell Publishers, 2001).

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Rapoport, A. & Chammah, A. M. The game of chicken. Am. Behav. Sci. 10, 10–28 (1966).

    Article  Google Scholar 

  38. Sugden, R. The Economics of Rights, Cooperation and Welfare (B. Blackwell, Oxford, 1986).

    Google Scholar 

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

    Article  Google Scholar 

  40. Bach, L. A., Bentzen, S., Alsner, J. & Christiansen, F. B. An evolutionary-game model of tumour cell interactions, possible relevance to gene therapy. Eur. J. Cancer 37, 2116–2120 (2001).

    Article  CAS  PubMed  Google Scholar 

  41. Bach, L. A., Sumpter, D. J. T., Alsner, J. & Loeschke, V. Spatial evolutionary games of interaction among generic cancer cells. J. Theor. Med. 5, 47–58 (2003).

    Article  Google Scholar 

  42. Basanta, D., Hatzikirou, H. & Deutsch, A. Studying the emergence of invasiveness in tumours using game theory. Eur. Phys. J. 63, 393–397 (2008).

    Article  CAS  Google Scholar 

  43. Basanta, D., Simon, M., Hatzikirou, H. & Deutsch, A. Evolutionary game theory elucidates the role of glycolysis in glioma progression and invasion. Cell Prolif. 41, 980–987 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. 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  PubMed  Google Scholar 

  46. Dingli, D., Chalub, F. A., Santos, F. C. & Pacheco, J. Cancer phenotype as the outcome of an evolutionary game between normal and malignant cells. Br. J. Cancer 101, 1130–1136 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Gerstung, M., Nakhoul, H. & Beerenwinkel, N. Evolutionary games with affine fitness functions, applications to cancer. Dyn. Games Appl. 1, 370–385 (2011).

    Article  Google Scholar 

  48. You, L. et al. Spatial versus non-spatial eco-evolutionary dynamics in a tumor growth model. J. Theor. Biol. 435, 78–97 (2017).

    Article  PubMed  Google Scholar 

  49. Zhang, J. S., 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  PubMed  PubMed Central  CAS  Google Scholar 

  50. Archetti, M. Dynamics of growth factor production in monolayers of cancer cells. Evol. Appl. 6, 1146–1159 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Archetti, M. Cooperation among cancer cells as public goods games on Voronoi networks. J. Theor. Biol. 396, 191–203 (2016).

    Article  PubMed  Google Scholar 

  52. Archetti, M. Stable heterogeneity for the production of diffusible factors in cell populations. PLOS ONE 9, e108526 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Yang, J., Zhao, T. J., Yuan, C. Q., Xie, J. H. & Hao, F. F. A nonlinear competitive model of the prostate tumor growth under intermittent androgen suppression. J. Theor. Biol. 404, 66–72 (2016).

    Article  CAS  PubMed  Google Scholar 

  55. Kianercy, A., Veltri, R. & Pienta, K. J. Critical transitions in a game theoretic model of tumour metabolism. Interface Focus 4, 20140014 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  56. 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  PubMed  PubMed Central  Google Scholar 

  57. Archetti, M. Evolutionary dynamics of the Warburg effect, glycolysis as a collective action problem among cancer cells. J. Theor. Biol. 341, 1–8 (2014).

    Article  PubMed  Google Scholar 

  58. Archetti, M. Heterogeneity and proliferation of invasive cancer subclones in game theory models of the Warburg effect. Cell Prolif. 482, 259–269 (2015).

    Article  CAS  Google Scholar 

  59. Cirri, P. & Chiarugi, P. Cancer associated fibroblasts: the dark side of the coin. Am. J. Cancer Res. 1, 482–497 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Sartakhti, J. S., Manshaei, M. H. & Sadeghi, M. MMP-TIMP interactions in cancer invasion: an evolutionary game-theoretical framework. J. Theor. Biol. 412, 17–26 (2017).

    Article  CAS  Google Scholar 

  61. Sartakhti, J. S., Manshaei, M. H. & Archetti, M. Game theory of tumor–stroma interactions in multiple myeloma: effect of nonlinear benefits. Games 9, 32 (2018).

    Article  Google Scholar 

  62. Sartakhti, J. S., Manshaei, M. H., Bateni, S. & Archetti, M. Evolutionary dynamics of tumor-stroma interactions in multiple myeloma. PLOS ONE 11, e0168856 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Kaiser Wilhelm Institut für Biologie. Über Den Stoffwechsel Der Tumoren: The Metabolism of Tumours (ed. Warburg, O.) (Constable, London, 1930).

    Google Scholar 

  64. Nakajima, E. C. & Van Houten, B. Metabolic symbiosis in cancer: refocusing the Warburg lens. Mol. Carcinog. 52, 329–337 (2013).

    Article  CAS  PubMed  Google Scholar 

  65. Cairns, R. A., Harris, I. S. & Mak, T. W. Regulation of cancer cell metabolism. Nat. Rev. Cancer. 11, 85–95 (2011).

    Article  CAS  PubMed  Google Scholar 

  66. Gatenby, R. & Gillies, R. J. Why do cancers have high aerobic glycolysis? Nat. Rev. Cancer 4, 891–899 (2004).

    Article  CAS  PubMed  Google Scholar 

  67. Pavlides, S. et al. The reverse Warburg effect: aerobic glycolysis in cancer associated fibroblasts and the tumor stroma. Cell Cycle 8, 3984–4001 (2009).

    Article  CAS  PubMed  Google Scholar 

  68. Bonuccelli, G. et al. Ketones and lactate “fuel” tumor growth and metastasis: evidence that epithelial cancer cells use oxidative mitochondrial metabolism. Cell Cycle 9, 3506–3514 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Xing, Y., Zhao, S., Zhou, B. P. & Mi, J. Metabolic reprogramming of the tumour microenvironment. FEBS J. 282, 3892–3898 (2015).

    Article  CAS  PubMed  Google Scholar 

  70. Nieman, K. M. et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat. Med. 17, 1498–1503 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Loo, J. M. et al. Extracellular metabolic energetics can promote cancer progression. Cell 160, 393–406 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Webber, J., Yeung, V. & Clayton, A. Extracellular vesicles as modulators of the cancer microenvironment. Semin. Cell Dev. Biol. 40, 27–34 (2015).

    Article  CAS  PubMed  Google Scholar 

  73. Fong, M. Y. et al. Breast-cancer-secreted miR-122 reprograms glucose metabolism in premetastatic niche to promote metastasis. Nat. Cell Biol. 17, 183–194 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Archetti, M. et al. Economic game theory for mutualism and cooperation. Ecol. Lett. 14, 1300–1312 (2011).

    Article  PubMed  Google Scholar 

  75. Archetti, M. & Scheuring, I. Review: game theory of public goods in one-shot social dilemmas without assortment. J. Theor. Biol. 299, 9–20 (2012).

    Article  PubMed  Google Scholar 

  76. Aktipis, A. Principles of cooperation across systems: from human sharing to multicellularity and cancer. Evol. Appl. 9, 17–36 (2016).

    Article  PubMed  Google Scholar 

  77. Archetti, M. The volunteer’s dilemma and the optimal size of a social group. J. Theor. Biol. 261, 475–480 (2009).

    Article  PubMed  Google Scholar 

  78. Archetti, M. Cooperation as a volunteer’s dilemma and the strategy of conflict in public goods games. J. Evol. Biol. 22, 2192–2200 (2009).

    Article  CAS  PubMed  Google Scholar 

  79. Moreno, E. Is cell competition relevant to cancer? Nat. Rev. Cancer 8, 141–147 (2008).

    Article  CAS  PubMed  Google Scholar 

  80. Merino, M. M., Levayer, R. & Moreno, E. Survival of the fittest: essential roles of cell competition in development, aging, and cancer. Trends Cell Biol. 26, 776–788 (2016).

    Article  PubMed  Google Scholar 

  81. Peter, M. E. et al. The role of CD95 and CD95 ligand in cancer. Cell Death Differ. 22, 549–559 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Shubik, M. Readings in Game Theory and Political Behavior 43–46 (Doubleday, 1954).

  83. Archetti, M. Survival of the weakest in N-person duels and the maintenance of variation under constant selection. Evolution 66, 637–650 (2012).

    Article  PubMed  Google Scholar 

  84. Fox, J. & Guyer, M. Public choice and cooperation in N-person prisoner’s dilemma. J. Conflict Resolut. 22, 469–481 (1978).

    Article  Google Scholar 

  85. Hamburger, H. N-person prisoner’s dilemma. J. Math. Sociol. 3, 27–48 (1973).

    Article  Google Scholar 

  86. Grafen, A. & Archetti, M. Natural selection of altruism in inelastic homogeneous viscous populations. J. Theor. Biol. 252, 694–710 (2008).

    Article  PubMed  Google Scholar 

  87. Cornish-Bowden, A. Fundamentals of Enzyme Kinetics (Wiley, 2012).

  88. Archetti, M. & Scheuring, I. Coexistence of cooperation and defection in public goods games. Evolution 65, 1140–1148 (2011).

    Article  PubMed  Google Scholar 

  89. Archetti, M. How to analyze models of nonlinear public goods. Games 9, 17 (2018).

    Article  Google Scholar 

  90. de Groot, A. E., Roy, S., Brown, J. S., Pienta, K. J. & Amend, S. R. Revisiting seed and soil: examining the primary tumor and cancer cell foraging in metastasis. Mol. Cancer Res. 15, 361–370 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Nagy, J. D. Competition and natural selection in a mathematical model of cancer. Bull. Math. Biol. 66, 663–687 (2004).

    Article  PubMed  Google Scholar 

  92. Archetti, M. & Scheuring, I. Trading public goods stabilizes interspecific mutualism. J. Theor. Biol. 318, 58–67 (2013).

    Article  PubMed  Google Scholar 

  93. Valkenburg, K. C., de Groot, A. E. & Pienta, K. J. Targeting the tumour stroma to improve cancer therapy. Nat. Rev. Clin. Oncol. 15, 366–381 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Aktipis, C. A., Kwan, V. S. Y., Johnson, K. A., Neuberg, S. L. & Maley, C. C. Overlooking evolution: a systematic analysis of cancer relapse and therapeutic resistance research. PLOS ONE 6, e26100 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  95. Myerson, R. B. in The New Palgrave Dictionary of Economics 2nd edn (eds Durlauf, S. N. & Blume, L. E.) 533–542 (Palgrave Macmillan UK, 2008).

    Google Scholar 

  96. 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  PubMed  PubMed Central  Google Scholar 

  97. Gatenby, R. A. A change of strategy in the war on cancer. Nature 459, 508–509 (2009).

    Article  CAS  PubMed  Google Scholar 

  98. Gatenby, R. A., Silva, A. S., Gillies, R. J. & Frieden, B. R. Adaptive therapy. Cancer Res. 69, 4894–4903 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Enriquez-Navas, P. M. et al. Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer. Sci. Transl Med. 8, 327ra24 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Read, A. F., Day, T. & Huijben, S. The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy. Proc. Natl Acad. Sci. USA 108, 10871–10877 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Day, T., Huijben, V. & Read, A. F. Is selection relevant in the evolutionary emergence of drug resistance? Trends Microbiol. 23, 126–133 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Day, T. & Read, A. F. Does high-dose antimicrobial chemotherapy prevent the evolution of resistance? PLOS Comput. Biol. 12, e1004689 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Martin, R., Fisher, M., Minchin, R. & Teo, K. Low-intensity combination chemotherapy maximizes host survival time for tumors containing drug-resistant cells. Math. Biosci. 110, 221–152 (1992).

    Article  CAS  PubMed  Google Scholar 

  104. Hansen, E., Woods, R. J. & Read, A. F. How to use a chemotherapeutic agent when resistance to it threatens the patient. PLOS Biol. 15, e2001110 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. 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  PubMed  Google Scholar 

  106. Silva, A. S. & Gatenby, R. A. A theoretical quantitative model for evolution of cancer chemotherapy resistance. Biol. Direct. 5, 25 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Gatenby, R. A., Gillies, R. J. & Brown, J. S. The evolutionary dynamics of cancer prevention. Nat. Rev. Cancer 10, 526–527 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Basanta, D., Gatenby, R. A. & Anderson, A. R. An exploiting evolution to treat drug resistance: combination therapy and the double bind. Mol. Pharm. 9, 914–921 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Aktipis, C. A. & Nesse, R. M. Evolutionary foundations for cancer biology. Evol. Appl. 6, 144–159 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  110. André, J. B. & Godelle, B. Multicellular organization in bacteria as a target for drug therapy. Ecol. Lett. 8, 800–810 (2005).

    Article  Google Scholar 

  111. Pepper, J. W. Drugs that target pathogen public goods are robust against evolved drug resistance. Evol. Appl. 5, 757–761 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Jansen, G., Gatenby, R. & Aktipis, C. A. Opinion: control versus eradication: applying infectious disease treatment strategies to cancer. Proc. Natl Acad. Sci. USA 112, 937–938 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Archetti, M. Evolutionarily stable anti-cancer therapies by autologous cell defection. Evol. Med. Public Health 1, 161–172 (2013).

    Article  Google Scholar 

  114. Loberg, R. D., Bradley, D. A., Tomlins, S. A., Chinnaiyan, A. M. & Pienta, K. J. The lethal phenotype of cancer: the molecular basis of death due to malignancy. CA Cancer J. Clin. 57, 225–241 (2007).

    Article  PubMed  Google Scholar 

  115. Maynard Smith, J. & Szathmáry, E. The Major Transitions in Evolution (Oxford Univ. Press, 1995).

Download references

Acknowledgements

This work was supported by P01-CA093900, U01-CA196390 and U54-CA210173 and the Prostate Cancer Foundation to K.J.P. and the 7th European Community Framework Program grant agreement No 627816 to M.A.

Reviewer information

Nature Reviews Cancer thanks A. Aktipis, D. Basanta and F. Fu for their contribution to the peer review of this work.

Author information

Authors and Affiliations

  1. Department of Biology, Pennsylvania State University, State College, PA, USA

    Marco Archetti

  2. School of Biological Sciences, University of East Anglia, Norwich, UK

    Marco Archetti

  3. Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Kenneth J. Pienta

Authors
  1. Marco Archetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kenneth J. Pienta
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Both authors researched data for the article, substantially contributed to the discussion of content, and wrote, reviewed and edited the manuscript.

Corresponding author

Correspondence to Marco Archetti.

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.

Glossary

Clonal selection

Natural selection (the preferential survival of the fitter phenotypes) within populations of cells.

Cooperators

Players that pay a cost to produce a benefit for their opponents or contribute to a public good (for example, a growth factor producer).

Defectors

Players that do not produce a benefit for their opponents or do not contribute to a public good (for example, a growth factor non-producer).

Equilibrium

An evolutionarily stable state to which a population converges over time.

Frequency-dependent

A type of natural (clonal) selection in which fitness depends on the frequency of other phenotypes in the population.

Games

The formal description of strategic interactions; they include the definitions of the players, strategies and payoffs.

Linear effects

The effects of cooperation on fitness when the sum of the contributions is additive (each contribution produces the same increment in benefit).

Multiplayer games

Games with multiple players (which can be made of multiple pairwise interactions or a single public goods game).

Nonlinear effects

The effects of cooperation on fitness when the sum of the contributions is not additive but has increasing and/or diminishing returns.

Optimization

The choice of the best set of actions to maximize a payoff function.

Pairwise game

A game with only two players.

Payoff

The reward from the outcome of the interaction (in biology, this is evolutionary fitness).

Players

The individuals (or cells or other entities) that adopt strategies and obtain payoffs.

Public goods games

Multiplayer games in which the payoff depends on the collective decision of multiple players rather than their pairwise interactions.

Strategy

The decision or type adopted by a player (in biology, this is phenotype).

Warburg effect

The switch from aerobic energy production through oxidative phosphorylation to anaerobic energy production through glycolysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Archetti, M., Pienta, K.J. Cooperation among cancer cells: applying game theory to cancer. Nat Rev Cancer 19, 110–117 (2019). https://doi.org/10.1038/s41568-018-0083-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41568-018-0083-7

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