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Why Is Evolution Important in Cancer and What Mathematics Should Be Used to Treat Cancer? Focus on Drug Resistance

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Trends in Biomathematics: Modeling, Optimization and Computational Problems

Abstract

The clinical question of drug resistance in cancer, our initial motivation to study continuous models of adaptive cell population dynamics, leads naturally and more generally to consider the cancer disease itself from an evolutionary biology viewpoint, a consideration without which even the best targeted therapies will likely most often eventually fail. Among the challenging questions to mathematicians who tackle the task of understanding this disease and optimising its treatment are the representation of phenotypic heterogeneity of cancer cell populations and of their plasticity in response to anticancer drug insults. Such representation can be obtained using phenotype-structured models of healthy and cancer cell populations, and optimal control methods to optimise drug effects, with the perspective to implement them in the therapeutics of cancer, aiming at both avoiding the emergence of drug resistance in tumours and taking into account a constraint of limiting unwanted adverse effects to healthy tissues.

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References

  1. S. Benzekry, Ph. Hahnfeldt, J. Theor. Biol. 335, 235 (2013)

    Article  Google Scholar 

  2. F. Billy, J. Clairambault, Discr. Cont. Dyn. Syst. Ser. B 18, 865 (2013)

    Article  Google Scholar 

  3. H.M. Byrne, D. Drasdo, J. Math. Biol. 58, 657 (2009)

    Article  MathSciNet  Google Scholar 

  4. C. Carrère, J. Theor. Biol. 413, 24 (2017)

    Article  MathSciNet  Google Scholar 

  5. R.H. Chisholm, T. Lorenzi, A. Lorz, A.K. Larsen, L. Almeida, A. Escargueil, J. Clairambault, Cancer Res. 75, 930 (2015)

    Article  Google Scholar 

  6. R.H. Chisholm, T. Lorenzi, J. Clairambault, Biochem. Biophys. Acta Gen. Subj. 1860, 2627 (2016)

    Article  Google Scholar 

  7. R.H. Chisholm, T. Lorenzi, A. Lorz, Commun. Math. Sci. 14, 1181 (2016)

    Article  MathSciNet  Google Scholar 

  8. R.H. Chisholm, T. Lorenzi, L. Desvillettes, B.D. Hughes, Z. Angew. Math. Phys. 67:100, 1 (2016)

    Google Scholar 

  9. P.C.W. Davies, C.H. Lineweaver, Phys. Biol. 7, 1 (2011)

    Google Scholar 

  10. M. Gerlinger et al., N. Engl. J. Med. 336, 883 (2012)

    Article  Google Scholar 

  11. R.J. Gillies, D. Verduzco, R.A. Gatenby, Nat. Rev. Cancer 12, 487 (2012)

    Article  Google Scholar 

  12. A. Goldman, M. Kohandel, J. Clairambault, Curr. Stem Cell Rep. 3, 253 (2017)

    Article  Google Scholar 

  13. A. Goldman, M. Kohandel, J. Clairambault, Curr. Stem Cell Rep. 3, 260 (2017)

    Article  Google Scholar 

  14. M.M. Gottesman, T. Fojo, S.E. Bates. Nat. Rev. Cancer 2, 48 (2002)

    Article  Google Scholar 

  15. S. Huang, Semin. Cancer Biol. 21, 183 (2011)

    Article  Google Scholar 

  16. S. Huang, Cancer Metastasis Rev. 32, 423 (2013)

    Article  Google Scholar 

  17. S. Huang, Y.P. Guo, G. May, T. Enver, Dev. Biol. 305, 695 (2007)

    Article  Google Scholar 

  18. F. Jacob, Science 196, 1161 (1977)

    Article  Google Scholar 

  19. U. Łedżewicz, H. Schättler, Discr. Cont. Dyn. Syst. Ser. B 6, 129 (2006)

    Google Scholar 

  20. U. Łedżewicz, H. Schättler, Mathematical Models of Tumor-Immune Dynamics (Springer, New York, 2013), p. 157

    Google Scholar 

  21. Y. Li, J. Laterra, Cancer Res. 72, 576 (2012)

    Article  Google Scholar 

  22. A. Lorz, T. Lorenzi, J. Clairambault, A. Escargueil, B. Perthame, Bull. Math. Biol. 77, 1 (2015)

    Article  MathSciNet  Google Scholar 

  23. S.E. Luria, M. Delbrück, Genetics 28, 491 (1943)

    Google Scholar 

  24. E. Pasquier, M. Kavallaris, N. André, Nat. Rev. Clin. Oncol. 7, 455 (2010)

    Article  Google Scholar 

  25. B. Perthame, Transport Equations in Biology (Birkhäuser, Basel, 2007)

    MATH  Google Scholar 

  26. T. Philippi, J. Seger, Tends Ecol. Evol. 4, 41 (1989)

    Article  Google Scholar 

  27. C. Pouchol, Modelling interactions between tumour cells and supporting adipocytes in breast cancer. https://hal.inria.fr/hal-01252122 (2015)

  28. C. Pouchol, E. Trélat, arXiv 1702.06187. https://hal.inria.fr/hal-01618357 (2017)

  29. C. Pouchol, J. Clairambault, A. Lorz, E. Trélat, arXiv 1612.04698 (2016); J. Maths Pures Appl. (2017, to appear)

    Google Scholar 

  30. S.M. Shaffer et al., Nature 546, 431 (2017)

    Article  Google Scholar 

  31. S.V. Sharma et al., Cell 141, 69 (2010)

    Article  Google Scholar 

  32. E. Trélat, Contrôle Optimal (Vuibert, Paris, 2005), 246 pp. Reviewed in Mathscinet MR2224013, 2007f:49001

    Google Scholar 

  33. B. Ujvari, B. Roche, F. Thomas (eds.), Ecology and Evolution of Cancer (Academic, London, 2017)

    Google Scholar 

  34. C.H. Waddington, The Strategy of Genes (George Allen & Unwin, London, 1957)

    Google Scholar 

  35. A. Wu et al., Proc. Natl. Acad. Sci. USA 112, 10467 (2015)

    Article  Google Scholar 

  36. L. Zitvogel, L. Apetoh, F. Ghiringhelli, G. Kroemer, Nat. Rev. Immunol. 8, 59 (2008)

    Article  Google Scholar 

  37. L. Zitvogel, O. Kepp, G. Kroemer, Nat. Rev. Clin. Oncol. 8, 151 (2011)

    Article  Google Scholar 

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Correspondence to Jean Clairambault .

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Almeida, L. et al. (2018). Why Is Evolution Important in Cancer and What Mathematics Should Be Used to Treat Cancer? Focus on Drug Resistance. In: Mondaini, R. (eds) Trends in Biomathematics: Modeling, Optimization and Computational Problems. Springer, Cham. https://doi.org/10.1007/978-3-319-91092-5_8

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