Advertisement

Competition for Resources and Space Contributes to the Emergence of Drug Resistance in Cancer

  • Peter Rashkov
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 728)

Abstract

Recent experiments reveal targeted therapy of tumours promotes the spread of drug-resistant cancer cells in mixed sensitive-resistant tumours. The hypothesis is that drug-stressed sensitive cells produce diffusible growth factors that stimulate the expansion of drug-resistant cells. A mathematical model employing simple ecological competition and a nonlinear motility law is able to reproduce the magnitude of observed expansion of the resistant populations volume without invoking production of diffusible growth factors. The model shows how the therapy-induced removal of the sensitive population alleviates the competitive pressure on the resistant for resources and space and confirms the in vivo experimental findings, and sheds light onto mechanisms behind the large increase of the drug-resistant cancer cells in the treated tumour.

Notes

Acknowledgements

The author acknowledges funding for his postdoc position at the University of Exeter from AstraZeneca (Cambridge, UK). Thanks to Mark Hewlett and Bogna Pawłowska for proofreading the manuscript.

References

  1. 1.
    Obenauf, A., Zou, Y., Ji, A., Vanharanta, S., Shu, W., Shi, H., Kong, X., Bosenberg, M., Wiesner, T., Rosen, N., Lo, R., Massague, J.: Therapy-induced tumour secretomes promote resistance and tumour progression. Nature 520, 368–372 (2015)Google Scholar
  2. 2.
    Sun, Y., Campisi, J., Higano, C., Beer, T., Porter, P., Coleman, I., True, L., Nelson, P.: Treatment-induced damage to the tumor microenvironment promotes prostate cancer therapy resistance through WNT16B. Nat. Med. 18, 1359–1368 (2012)Google Scholar
  3. 3.
    Wilson, T., Fridlyand, J., Yan, Y., Penuel, E., Burton, L., Chan, E., Peng, J., Lin, E., Wang, Y., Sosman, J., Ribas, A., Li, J., Moffat, J., Sutherlin, D., Koeppen, H., Merchant, M., Neve, R., Settleman, J.: Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors. Nature 487, 505–509 (2012)Google Scholar
  4. 4.
    Korolev, K., Xavier, J., Gore, J.: Turning ecology and evolution against cancer. Nat. Rev. Cancer 14, 371–380 (2014)Google Scholar
  5. 5.
    Aktipis, C., Boddy, A., Gatenby, R., Brown, J., Maley, C.: Life history trade-offs in cancer evolution. Nat. Rev. Cancer 13, 883–892 (2013)Google Scholar
  6. 6.
    Martin, P.: Trade-offs and biological diversity: integrative answers to ecological questions. In: Martin, L., Ghalambor, C., Woods, H. (eds.) Integrative Organismal Biology, pp. 291–308. Wiley (2014)Google Scholar
  7. 7.
    Carré, M., Bondarenko, M., Montero, M-P., Chapuisat, G., Benabdallah, A., Le Grand, M., Braguer, D., André, N., Pasquier, E.: Metronomic scheduling: a promising strategy to manage intratumor heterogeneity and control treatment resistance. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research, April 18–22 2015, Philadelphia, PA (Cancer Res 75(15 Suppl), 2572 (2015))Google Scholar
  8. 8.
    Warburg, O.: On the origin of cancer cells. Science 123, 309–314 (1956)CrossRefGoogle Scholar
  9. 9.
    Kallinowski, F., Vaupel, P., Runkel, S., Berg, G., Fortmeyer, H.P., Baessler, K.H., Wagner, K., Mueller-Klieser, W., Walenta, S.: Glucose uptake, lactate release, ketone body turnover, metabolic micromilieu, and pH distributions in human breast cancer xenografts in nude rats. Cancer Res. 48, 7264–7272 (1988)Google Scholar
  10. 10.
    Peppicelli, S., Bianchini, F., Calorini, L.: Extracellular acidity, a “reappreciated” trait of tumor environment driving malignancy: perspectives in diagnosis and therapy. Cancer Metastasis Rev. 33, 823–832 (2014)Google Scholar
  11. 11.
    Provent, P., Benito, M., Hiba, B., Farion, R., López-Larrubia, P., Ballesteros, P., Rémy, C., Segebarth, C., Cerdán, S., Coles, J., García-Martín, M.: Serial in vivo spectroscopic nuclear magnetic resonance imaging of lactate and extracellular pH in rat gliomas shows redistribution of protons away from sites of glycolysis. Cancer Res. 67, 7638–7645 (2007)CrossRefGoogle Scholar
  12. 12.
    Carmona-Fontaine, C., Bucci, V., Akkari, L., Deforet, M., Joyce, J.A., Xavier, J.B.: Emergence of spatial structure in the tumor microenvironment due to the Warburg effect. Proc. Nat. Acad. Sci. USA 110, 19402–19407 (2013)CrossRefGoogle Scholar
  13. 13.
    Robertson-Tessi, M., Gillies, R.J., Gatenby, R.A., Anderson, A.R.: Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res. 75, 1567–1579 (2015)CrossRefGoogle Scholar
  14. 14.
    Carmona-Fontaine, C., Deforet, M., Akkari, L., Thompson, C.B., Joyce, J.A., Xavier, J.B.: Metabolic origins of spatial organization in the tumor microenvironment. Proc. Nat. Acad. Sci. USA 114, 2934–2939 (2017)CrossRefGoogle Scholar
  15. 15.
    Hanahan, D., Weinberg, R.: Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011)CrossRefGoogle Scholar
  16. 16.
    Gatenby, R., Silva, A., Gillies, R., Frieden, B.: Adaptive therapy. Cancer Res. 69, 4894–4903 (2009)CrossRefGoogle Scholar
  17. 17.
    Broxterman, H., Pinedo, H., Kuiper, C., Kaptein, L., Schuurhuis, G., Lankelma, J.: Induction by verapamil of a rapid increase in ATP consumption in multidrug-resistant tumor cells. FASEB J. 2, 2278–2282 (1988)Google Scholar
  18. 18.
    Harada, D., Takigawa, N., Ochi, N., Ninomiya, T., Yasugi, M., Kubo, T., Takeda, H., Ichihara, E., Ohashi, K., Takata, S., Tanimoto, M., Kiura, K.: JAK2-related pathway induces acquired erlotinib resistance in lung cancer cells harboring an epidermal growth factor receptor-activating mutation. Cancer Sci. 103, 1795–1802 (2012)CrossRefGoogle Scholar
  19. 19.
    Wang, Q., Cui, K., Espin-Garcia, O., Cheng, D., Qiu, X., Chen, Z., Moore, M., Bristow, R., Xu, W., Der, S., Liu, G.: Resistance to bleomycin in cancer cell lines is characterized by prolonged doubling time, reduced DNA damage and evasion of G2/M arrest and apoptosis. PLoS ONE 8, e82363 (2013)CrossRefGoogle Scholar
  20. 20.
    Abercrombie, M.: Contact inhibition and malignancy. Nature 281, 259–262 (1979)CrossRefGoogle Scholar
  21. 21.
    Sherratt, J.: Wavefront propagation in a competition equation with a new motility term modelling contact inhibition between cell populations. Proc. R. Soc. A. 456, 2365–2386 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  22. 22.
    Sherratt, J., Chaplain, M.: A new mathematical model for avascular tumour growth. J. Math. Biol. 43, 291–312 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Bertsch, M., Hilhorst, D., Izuhara, H., Mimura, M.: A nonlinear parabolic-hyperbolic system for contact inhibition of cell-growth. Diff. Equ. Appl. 4, 137–157 (2012)Google Scholar
  24. 24.
    Bertsch, M., Hilhorst, D., Izuhara, H., Mimura, M., Wakasa, T.: Travelling wave solutions of a parabolic-hyperbolic system for contact inhibition of cell-growth. Eur. J. Appl. Math. 26, 297–323 (2015)Google Scholar
  25. 25.
    Bertsch, M., Passo, R.D., Mimura, M.: A free boundary problem arising in a simplified tumour growth model of contact inhibition. Interfaces Free Bound. 12, 235–250 (2010)Google Scholar
  26. 26.
    Murray, J.D.: Mathematical Biology. Springer, New York (1993)CrossRefzbMATHGoogle Scholar
  27. 27.
    Maezawa, H., Wong, K., Urano, M.: Radiosensitivity of mouse skin epithelial cell line established in serum-free culture: an alternative to animal use. Int. J. Radiat. Oncol. Biol. Phys. 24, 533–541 (1992)Google Scholar
  28. 28.
    Gatenby, R., Gawlinski, E.: A reaction-diffusion model of cancer invasion. Cancer Res. 56, 5745–5733 (1996)Google Scholar
  29. 29.
    Kolmogorov, A., Petrovskii, I., Piscounov, N.: A study of the diffusion equation with increase in the amount of substance, and its application to a biological problem. Bull. Moscow Univ. Math. Mech. 1, 1–25 (1937)Google Scholar
  30. 30.
    Benga, G.: Basic studies on gene therapy of human malignant melanoma by use of the human interferon \(\beta \)-gene entrapped in cationic multilamellar liposomes.: 1. morphology and growth rate of six melanoma cell lines used in transfection experiments with the human interferon \(\beta \)-gene. J. Cell. Mol. Med. 5, 402–408 (2001)Google Scholar
  31. 31.
    Walenta, S., Wetterling, M., Lehrke, M., Schwickert, G., Sundfør, K., Rofstad, E., Mueller-Klieser, W.: High lactate levels predict likelihood of metastases, tumor recurrence, and restricted patient survival in human cervical cancers. Cancer Res. 60, 916–921 (2000)Google Scholar
  32. 32.
    Foo, J., Michor, F.: Evolution of acquired resistance to anti-cancer therapy. J. Theor. Biol. 355, 10–20 (2014)CrossRefGoogle Scholar
  33. 33.
    Pisco, A.O., Huang, S.: Non-genetic cancer cell plasticity and therapy-induced stemness in tumour relapse: what does not kill me strengthens me. Br. J. Cancer 112, 1725–1732 (2015)Google Scholar
  34. 34.
    Weiner, R., Schmitt, B., Podhaisky, H.: ROWMAP—a ROW-code with Krylov techniques for large stiff ODEs. Appl. Numer. Math. 25, 303–319 (1997)Google Scholar
  35. 35.
    Hecht, F.: New development in FreeFem++. J. Numer. Math. 20, 251–265 (2012)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institute of Mathematics and InformaticsBulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations