Abstract
We present a mathematical model of dynamic changes in clinical parameters following drug therapy for chronic myeloid leukemia (CML) using a system of ordinary differential equations (ODE), describing the interactions between effector T cells and leukemic cancer cells. The model successfully predicts clinical response to two separate drug therapies: targeted therapy with the tyrosine kinase inhibitor imatinib and immunotherapy with interferon alfa-2. Development of this model enables the identification of the treatment regimen for a determined time period, in order to reach an admissible concentration of cancer cells. To mathematically model the dynamics of CML progression, both without and with treatment, we have obtained the local and global stability and the local relative controllability conditions for this ODE system.
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World Cancer Research Fund International (WCRF International) statistics (2008). http://www.wcrf.org/cancer_statistics/world_cancer_statistics.php
Jemal, A., Bray, F., Center, M.M., Ferlay, J., Ward, E., Forman, D.: Statistics. CA Cancer J. Clin. 61, 69–90 (2011)
Faderl, S., Talpaz, M., Estrov, Z., O’Brien, S., Kurzrock, R., Kantarjian, H.: The biology of chronic myeloid leukemia. N. Engl. J. Med. 341(3), 164–172 (1999)
Druker, B.J.: Imatinib as a paradigm of targeted therapies. Adv. Cancer Res. 91, 1–30 (2004)
Druker, B.J., Lydon, N.B.: Lessons learned from the development of an ABL tyrosine kinase inhibitor for chronic myelogenous leukemia. J. Clin. Investig. 105(1), 3–7 (2000)
Druker, B.J.: Overcoming resistance to imatinib by combining targeted agents. Mol. Cancer Ther. 2, 225–226 (2003)
Nardi, V., Azam, M., Daley, G.Q.: Mechanisms and implications of imatinib resistance mutations in BCR-ABL. Curr. Opin. Hematol. 11, 35–43 (2004)
Goldman, J.M.: Treatment strategies for CML. Best Pract. Res. Clin. Haematol. 22(3), 303–313 (2009)
Campbell, J.D., Cook, G., Holyoake, T.L.: Evolution of bone marrow transplantation the original immunotherapy. Trends Immunol. 22(2), 88–92 (2001)
Vonka, V.: Immunotherapy of chronic myeloid leukemia: present state and future prospects. Immunotherapy 2(2), 227–241 (2010)
Burchert, A., Neubauer, A.: Interferon alpha and T-cell responses in chronic myeloid leukemia. Leuk. Lymphoma 46(2), 167–175 (2005)
Burchert, A., Muller, M.C., Kostrewa, P., Erben, P., Bostel, T., Liebler, S., Hehlmann, R., Neubauer, A., Hochhaus, A.: Sustained molecular response with interferon alfa maintenance after induction therapy with imatinib plus interferon alfa in patients with chronic myeloid leukemia. J. Clin. Oncol. 28, 1429–1435 (2010)
Bellomo, N., Preziosi, L.: Modelling and mathematical problems related to tumour evolution and its interaction with the immune system. Math. Comput. Model. 32, 413–452 (2000)
Wodarz, D., Jansen, V.A.A.: A dynamical perspective of CTL cross-priming and regulation: implications for cancer immunology. Immunol. Lett. 86, 213–227 (2003)
Bellomo, N., Bellouquid, A., Delitala, M.: Mathematical topics on the modelling complex multicellular systems and tumour immune cells competition. Math. Models Methods Appl. Sci. 14, 1683–1733 (2004)
d’Onofrio, A.: A general framework for modeling tumor–immune system competition and immunotherapy: mathematical analysis and biomedical inferences. Phys D. 208, 220–235 (2005)
d’Onofrio, A.: Tumor–immune system interaction: modeling the tumor-stimulated proliferation of effectors and immunotherapy. Math. Models Methods Appl. Sci. 16, 1375–1401 (2006)
Kronik, N., Kogan, Y., Vainstein, V., Agur, Z.: Improving alloreactive CTL immunotherapy for malignant gliomas using a simulation model of their interactive dynamics. Cancer Immunol. Immunother. 57, 425–439 (2008)
d’Onofrio, A., Gatti, F., Cerrai, P., Freschi, L.: Delay-induced oscillatory dynamics of tumour–immune system interaction. Math. Comput. Model. 51, 572–591 (2010)
Caravagna, G., Barbuti, R., d’Onofrio, A.: Fine-tuning anti-tumor immunotherapies via stochastic simulations. BMC Bioinform. 13(Suppl 4), S8 (2012)
Vincent, P.C., Cronkite, E.P., Greenberg, M.L., Kirsten, C., Schiffer, L.M., Stryckmans, P.A.: Leukocyte kinetics in chronic myeloid leukemia. I. DNA synthesis time in blood and marrow myelocytes. Blood 33(6), 843–850 (1969)
Fokas, A.S., Keller, J.B., Clarkson, B.D.: A mathematical model of granulocytopoiesis and chronic myelogenous leukemia. Cancer 51, 2084–2091 (1991)
Moore, H., Li, N.K.: A mathematical model of chronic myelogenous leukemia (CML) and T cell interaction. J. Theor. Biol. 227, 513 (2004)
Komarova, N., Wodarz, D.: Drug resistance in cancer: principles of emergence and prevention. Proc. Natl. Acad. Sci. USA 102, 9714–9719 (2005)
Michor, F., Hughes, T., Iwasa, Y., Branford, S., Shah, N., Sawyers, C., Nowak, M.: Dynamics of chronic myeloid leukemia. Nature 435, 1267–1270 (2005)
Kim, P., Lee, P., Levy, D.: Dynamics and potential impact of the immune response to chronic myelogenous leukemia. PLoS Comput. Biol. 4, e1000095 (2008)
Paquin, D., Kim, P.S., Lee, P.P., Levy, D.: Strategic treatment interruptions during imatinib treatment of chronic myelogenous leukemia. Bull. Math. Biol. 73, 1082–1100 (2011)
Nanda, S., Moore, H., Lenhart, S.: Optimal control of treatment in a mathematical model of chronic myelogenous leukemia. Math. Biosci. 210, 143 (2007)
Ainseba, B., Benosman, C.: Optimal control for resistance and suboptimal response in CML. Math. Biosci. 227, 81–93 (2010)
Berezansky, L., Bunimovich-Mendrazitsky, S., Domoshnitsky, A.: A mathematical model with time-varying delays in the combined treatment of chronic myeloid leukemia. Adv. Differ. Equ. 217, 257–266 (2012)
Norton, L.: A Gompertzian model of human breast cancer growth. Cancer Res. 48, 7067–7071 (1988)
Laird, A.K.: Dynamics of tumor growth. Br. J. Cancer. 18, 490–502 (1964)
d’Onofrio, A., Gandolfi, A.: Tumor eradication by antiangiogenic therapy: analysis and extensions of the model by Hahnfeldt et al. Math. Biosci. 191, 159–184 (2004)
d’Onofrio, A., Gandolfi, A., Rocca, A.: The dynamics of tumour–vasculature interaction suggests low dose, time-dense antiangiogenic scheduling. Cell Prolif. 42, 317–329 (2009)
d’Onofrio, A.: Metamodeling tumour–immune system interaction, tumour evasion and immunotherapy. Math. Comput. Model. 47, 614–637 (2008)
Gabasov, R., Kirillova, F.: Qualitative Theory of Optimal Processes. M. Nauka, Moscow (1972)
Breccia, M., Alimena, G.: Discontinuation of tyrosine kinase inhibitors and new approaches to target leukemic stem cells: treatment-free remission as a new goal in chronic myeloid leukemia. Cancer Lett. 347(1), 22–28 (2014)
Jabbour, E., Fava, C., Kantarjian, H.: Advances in the biology and therapy of patients with chronic myeloid leukaemia. Best. Pract. Res. Clin. Haematol. 22, 395–407 (2009)
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The authors would like to thank the editors and the referees for their valuable comments and suggestions which improved the original submission of this paper.
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Berezansky, L., Bunimovich-Mendrazitsky, S. & Shklyar, B. Stability and Controllability Issues in Mathematical Modeling of the Intensive Treatment of Leukemia. J Optim Theory Appl 167, 326–341 (2015). https://doi.org/10.1007/s10957-015-0717-9
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DOI: https://doi.org/10.1007/s10957-015-0717-9