Acta Biotheoretica

, Volume 63, Issue 2, pp 113–127 | Cite as

The Effect of Intrinsic and Acquired Resistances on Chemotherapy Effectiveness

Regular Article

Abstract

Although chemotherapy is one of the most common treatments for cancer, it can be only partially successful. Drug resistance is the main cause of the failure of chemotherapy. In this work, we present a mathematical model to study the impact of both intrinsic (preexisting) and acquired (induced by the drugs) resistances on chemotherapy effectiveness. Our simulations show that intrinsic resistance could be as dangerous as acquired resistance. In particular, our simulations suggest that tumors composed by even a small fraction of intrinsically resistant cells may lead to an unsuccessful therapy very quickly. Our results emphasize the importance of monitoring both intrinsic and acquired resistances during treatment in order to succeed and the importance of doing more experimental and genetic research in order to develop a pretreatment clinical test to avoid intrinsic resistance.

Keywords

Mathematical modeling and simulations Chemotherapy  Intrinsic resistance Acquired resistance 

References

  1. Almendro V, Cheng YK, Randles A, Itzkovitz S, Marusyk A, Ametller E, Gonzalez-Farre X, Munoz M, Russnes HG, Helland A, Rye IH, Borresen-Dale AL, Maruyama R, van Oudenaarden A, Dowsett M, Jones RL, Reis-Filho J, Gascon P, Gonen M, Michor F, Polyak K (2014) Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Rep 6:514–527CrossRefGoogle Scholar
  2. Bertuzzi A, d’Onofrio A, Fasano A, Gandolfi A (2003) Regression and regrowth of tumour cords following single-dose anticancer treatment. Bull Math Biol 65:903–931CrossRefGoogle Scholar
  3. Casciari JJ, Sotirchos SV, Sutherland RM (1988) Glucose diffusivity in multicellular tumor spheroids. Cancer Res 48:3905–3909Google Scholar
  4. Chaplain M, Matzavinos A (2006) Mathematical modeling of spatio-temporal phenomena in tumor immunology. In: Friedman A (ed) Tutorials in mathematical biosciences III: cell cycle, proliferation, and cancer. Springer, New York, pp 131–183Google Scholar
  5. Coldman AJ, Goldie JH (1985) Role of mathematical modeling in protocol formulation in cancer chemotherapy. Cancer Treat Rep 69:1041–1048Google Scholar
  6. de Pillis L, Fister KR, Gu W, Collins C, Daub M, Gross D, Moore J, Preskill B (2009) Mathematical model creation for cancer chemo-immunotherapy. Comput Math Methods Med 10:165–184CrossRefGoogle Scholar
  7. DeVita VT (1983) Progress in cancer management. Keynote address. Cancer 51:2401–2409CrossRefGoogle Scholar
  8. DeVita VT, Lawrence TS, Rosenberg SA, DePinho RA, Weinberg RA (2008) DeVita, Hellman, and Rosenberg’s cancer: principles and practice of oncology. Lippincott Williams & Wilkins, Philadelphia, USAGoogle Scholar
  9. d’Onofrio A, Gandolfi A (2010) Resistance to antitumor chemotherapy due to bounded-noise-induced transitions. Phys Rev E 82(061):901Google Scholar
  10. Drasdo D, Höhme S (2005) A single-cell-based model of tumor growth in vitro: monolayers and spheroids. Phys Biol 2:133–147CrossRefGoogle Scholar
  11. Fang J, Sullivan M, McCutchan TF (2004) The effects of glucose concentration on the reciprocal regulation of rRNA promoters in plasmodium falciparum. J Biol Chem 279:720–725CrossRefGoogle Scholar
  12. Foo J, Michor F (2010) Evolution of resistance to anti-cancer therapy during general dosing schedules. J Theor Biol 263:179–188CrossRefGoogle Scholar
  13. Freyer JP, Sutherland RM (1985) A reduction in the in situ rates of oxygen and glucose consumption of cells in EMT6/Ro spheroids during growth. J Cell Physiol 124:516–524CrossRefGoogle Scholar
  14. Frieboes HB, Edgerton ME, Fruehauf JP, Rose FRAJ, Worrall LK, Gatenby RA, Ferrari M, Cristini V (2009) Prediction of drug response in breast cancer using integrative experimental/computational modeling. Cancer Res 69:4484–4492CrossRefGoogle Scholar
  15. Garner AL, Lau YY, Jackson TL, Uhler MD, Jordan DW, Gilgenbach RM (2005) Incorporating spatial dependence into a multicellular tumor spheroid growth model. J Appl Phys 98:1–8CrossRefGoogle Scholar
  16. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. New Engl J Med 366:883–892CrossRefGoogle Scholar
  17. Goldie JH, Coldman AJ (2009) Drug resistance in cancer: models and mechanisms. Cambridge University Press, Cambridge, UKGoogle Scholar
  18. Jackson TL (2003) Intracellular accumulation and mechanism of action of doxorubicin in a spatio-temporal tumor model. J Theor Biol 220:201–213CrossRefGoogle Scholar
  19. Jiang Y, Pjesivac-Grbovic J, Cantrell C, Freyer JP (2005) A multiscale model for avascular tumor growth. Biophys J 89:3884–3894CrossRefGoogle Scholar
  20. Johnstone RW, Ruefli AA, Lowe SW (2002) Apoptosis: a link between cancer genetics and chemotherapy. Cell 108:153–164CrossRefGoogle Scholar
  21. Kansal AR, Torquato S, Chiocca EA, Deisboeck TS (2000) Emergence of a subpopulation in a computational model of tumor growth. J Teor Biol 207:431–441CrossRefGoogle Scholar
  22. Kole AC, Plaat BEC, Hoekstra HJ, Vaalburg W, Molenaar WM (1999) FDG and L-[1-11C]-tyrosine imaging of soft-tissue tumors before and after therapy. J Nucl Med 40:381–386Google Scholar
  23. Lavi O, Gottesman MM, Levy D (2012) The dynamics of drug resistance: a mathematical perspective. Drug Resist Update 15:90–97CrossRefGoogle Scholar
  24. Lecca P, Morpurgo D (2012) Modelling non-homogeneous stochastic reaction–diffusion systems: the case study of gemcitabine-treated non-small cell lung cancer growth. BMC Bioinform. doi:10.1186/1471-2105-13-S14-S14
  25. Lippert TH, Ruoff HJ, Volm M (2008) Resistance in malignant tumors: Can resistance assays optimize cytostatic chemotherapy? Pharmacology 81:196–203CrossRefGoogle Scholar
  26. Lippert TH, Ruoff H, Volm M (2011) Current status of methods to assess cancer drug resistance. Int J Med Sci 8:245–253CrossRefGoogle Scholar
  27. McKinnell RG, Parchment RE, Perantoni AO, Pierce GB, Damjanov I (2006) The biological basis of cancer. Cambridge University Press, New York, USACrossRefGoogle Scholar
  28. Menchón SA, Condat CA (2008) Cancer growth: predictions of a realistic model. Phys Rev E 78:022901CrossRefGoogle Scholar
  29. Menchón SA, Condat CA (2009) Modeling tumor cell shedding. Eur Biophys J 38:479–485CrossRefGoogle Scholar
  30. Menchón SA, Condat CA (2011) Quiescent cells: a natural way to resist chemotherapy. Phys A 390:3354–3361CrossRefGoogle Scholar
  31. Murray JM, Coldman AJ (2003) The effect of heterogeneity on optimal regimens in cancer chemotherapy. Math Biosci 185:73–87CrossRefGoogle Scholar
  32. Norris ES, King JR, Byrne HM (2006) Modelling the response of spatially structured tumours to chemotherapy: drug kinetics. Math Comput Model 43:820–837CrossRefGoogle Scholar
  33. Panetta JC (1998) A mathematical model of drug resistance: heterogeneous tumors. Math Biosci 147:41–61CrossRefGoogle Scholar
  34. Priestman T (2008) Cancer chemotherapy in clinical practice. Springer-Verlag, LondonGoogle Scholar
  35. Raguz S, Yagüe E (2008) Resistance to chemotherapy: new treatments and novel insights into an old problem. Br J Cancer 99:387–391CrossRefGoogle Scholar
  36. Scalerandi M, Romano A, Pescarmona GP, Delsanto PP, Condat CA (1999) Nutrient competition as a determinant for cancer growth. Phys Rev E 59(2):2206–2217CrossRefGoogle Scholar
  37. Silva AS, Gatenby RA (2010) A theoretical quantitative model for evolution of cancer chemotherapy resistance. Biol Direct 5:25CrossRefGoogle Scholar
  38. Stein WD, Figg WD, Dahut W, Stein AD, Hoshen MB, Price D, Bates SE, Fojo T (2008) Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data. Oncologist 13:1046–1054CrossRefGoogle Scholar
  39. Swan GW (1990) Role of optimal control theory in cancer chemotherapy. Math Biosci 101:237–284CrossRefGoogle Scholar
  40. Swanson KR, Bridge C, Murray JD, Alvord EC (2003) Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. J Neurol Sci 216(1):1–10CrossRefGoogle Scholar
  41. Swierniak A, Kimmel M, Smieja J (2009) Mathematical modeling as a tool for planning anticancer therapy. Eur J Pharmacol 625:108–121CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.IFEG-CONICET and FaMAFUniversidad Nacional de CórdobaCórdobaArgentina

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