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2015, 12(5): 1037-1053. doi: 10.3934/mbe.2015.12.1037

Analysis of a cancer dormancy model and control of immuno-therapy

Ben Sheller 1, and  Domenico D'Alessandro 1,

1. 

Department of Mathematics, Iowa State University, Ames, IA 50011, United States, United States

Received  July 2014 Revised  February 2015 Published  June 2015

The goal of this paper is to analyze a model of cancer-immune system interactions from [16], and to show how the introduction of control in this model can dramatically improve the hypothetical patient response and in effect prevent the cancer from growing. We examine all the equilibrium points of the model and classify them according to their stability properties. We identify an equilibrium point corresponding to a survivable amount of cancer cells which are exactly balanced by the immune response. This situation corresponds to cancer dormancy. By using Lyapunov stability theory we estimate the region of attraction of this equilibrium and propose two control laws which are able to stabilize the system effectively, improving the results of [16]. Ultimately, the analysis presented in this paper reveals that a slower, continuous introduction of antibodies over a short time scale, as opposed to mere inoculation, may lead to more efficient and safer treatments.
Keywords: Lyapunov stability., Modeling of immuno-therapy, application of control theory to cancer therapy.
Mathematics Subject Classification: Primary: 92C50, 93C10; Secondary: 93D0.
Citation: Ben Sheller, Domenico D'Alessandro. Analysis of a cancer dormancy model and control of immuno-therapy. Mathematical Biosciences & Engineering, 2015, 12 (5) : 1037-1053. doi: 10.3934/mbe.2015.12.1037
References:
[1]

A. M. Baker, et. al., Lysyl Oxidase Plays a Critical Role in Endothelial Cell Stimulation to Drive Tumor Angiogenesis, Cancer Research, 73 (2013), 583-594. doi: 10.1158/0008-5472.CAN-12-2447.  Google Scholar

[2]

A. D'Onofrio, A general framework for modeling tumor-immune system competition and immuno-therapy, mathematical analysis and biomedical inferences, Physica D, 208 (2005), 220-235. doi: 10.1016/j.physd.2005.06.032.  Google Scholar

[3]

G. P. Dunn, L. J. Old and R. D. Schreiber, The three E's of cancer immuno-editing, Annu. Rev. Immunol., 22 (2004), 329-360. Google Scholar

[4]

R. Eftimie, J. L. Bramson and D. J. Earn, Interactions between the immune systems and cancer: A brief review of non-spatial mathematical models, Bull. Math. Biol., 73 (2011), 2-32. doi: 10.1007/s11538-010-9526-3.  Google Scholar

[5]

J. Erler, et. al., Lysyl oxidase is essential for hypoxia-induced metastasis, Nature, 440 (2006), 1222-1226. doi: 10.1038/nature04695.  Google Scholar

[6]

R. Genesio, M. Tartaglia and A. Vicino, On the estimation of asymptotic stability regions: State of the art and new proposals, IEEE Transactions on Automatic Control, 30 (1985), 747-755. doi: 10.1109/TAC.1985.1104057.  Google Scholar

[7]

W. Hahn, Stability of Motion, Springer Verlag, Heidelberg-Berlin, 1967.  Google Scholar

[8]

T. Kailath, Linear Systems, Prentice-Hall Information and System Sciences Series. Prentice-Hall, Inc., Englewood Cliffs, N.J., 1980.  Google Scholar

[9]

M. Krstić, I. Kanellakopoulos and P. V. Kokotović, Nonlinear and Adaptive Control Design, John Wiley and Sons, 1995. Google Scholar

[10]

V. A. Kuznetsov, Mathematical modeling of the development of dormant tumors and immune stimulation of their growth, Cybern. syst. Anal, 23 (1988), 556-564. Google Scholar

[11]

V. A. Kuznetsov, I. A. Makalkin, M. A. Taylor and A. S. Perelson, Nonlinear dynamics of immunogenic tumors: Parameter estimation and global bifurcation analysis, Bull. Math. Biol, 56 (1994), 295-321. Google Scholar

[12]

U. Ledzewicz, M. Faraji and H. Schaettler, Mathematical model of tumor-immune interactions under chemotherapy with immune boost, Discrete and Continuous Dynamical Systems, Series B, 18 (2013), 1031-1051. doi: 10.3934/dcdsb.2013.18.1031.  Google Scholar

[13]

U. Ledzewicz, M. Naghneian and H. Schaettler, Optimal response to chemotherapy for a mathematical model of tumor-immune dynamics, Journal of Mathematical Biology, 64 (2012), 557-577. doi: 10.1007/s00285-011-0424-6.  Google Scholar

[14]

L. Norton and R. Simon, Growth curve of an experimental solid tumor following radiotherapy, J. of the National Cancer Institute, 58 (1977), 1735-1741. Google Scholar

[15]

L. Norton, A Goempertzian model of human breast cancer growth, Cancer Research, 48 (1988), 7067-7071. Google Scholar

[16]

K. Page and J. Uhr, Mathematical models of cancer dormancy, Leukemia and Lymphoma, 46 (2005), 313-327. doi: 10.1080/10428190400011625.  Google Scholar

[17]

G. Phan et. al., Cancer regression and autoimmunity induced by cytotoxic T lymphocyte-associated antigen 4 blockade in patients with metastatic melanoma, PNAS, 100 (2003), 8372-8377. Google Scholar

[18]

S. Ratschan and Z. She, Providing a basin of attraction to a target region of polynomial systems by computation of Lyapunov-like functions, SIAM J. Control Optim., 48 (2010), 4377-4394. doi: 10.1137/090749955.  Google Scholar

[19]

S. Sastry and M. Bodson, Adaptive Control: Stability, Convergence and Robustness, Prentice-Hall, Advanced Reference Series (Engineering), 1989. Google Scholar

[20]

A. Scott, J. Wolchock and L. Old, Antibody therapy of cancer, Nature Reviews Cancer, 12 (2012), 278-287. doi: 10.1038/nrc3236.  Google Scholar

[21]

N. V. Stepanova, Course of the immune reaction during the development of a malignant tumor, Biophysics, 24 (1980), 917-923. Google Scholar

[22]

T. Takayanagi, H. Kawamura and A. Ohuchi, Cellular automaton model of a tumor tissue consisting of tumor cells, cytoxic T lymphocytes (CTLs), and cytokine produced by CTLs, IPSJ Trans Math Model Appl., 47 (2006), 61-67. Google Scholar

[23]

A. Vannelli and M. Vidyasagar, Maximal Lyapunov functions and domains of attraction for autonomous nonlinear systems, Automatica, 21 (1985), 69-80. doi: 10.1016/0005-1098(85)90099-8.  Google Scholar

[24]

K. P. Wilkie, A Review of Mathematical Models of Cancer-Immune Interactions in the Context of Tumor Dormancy, Systems Biology of Tumor Dormancy, Springer, New York, 2013. Google Scholar

[25]

V. I. Zubov, Mathematical Methods for the Study of Automatic Control Systems, Israel Jerusalem Academic Press, 1962.  Google Scholar

[26]

V. I. Zubov, Methods of A.M. Lyapunov and Their Application, the Netherlands, Noordhoff, 1964.  Google Scholar

[27]

, Sydney International Workshop on Math Models of Tumor-Immune System Dynamics,, January 7-10, (2013), 7.   Google Scholar

show all references

References:
[1]

A. M. Baker, et. al., Lysyl Oxidase Plays a Critical Role in Endothelial Cell Stimulation to Drive Tumor Angiogenesis, Cancer Research, 73 (2013), 583-594. doi: 10.1158/0008-5472.CAN-12-2447.  Google Scholar

[2]

A. D'Onofrio, A general framework for modeling tumor-immune system competition and immuno-therapy, mathematical analysis and biomedical inferences, Physica D, 208 (2005), 220-235. doi: 10.1016/j.physd.2005.06.032.  Google Scholar

[3]

G. P. Dunn, L. J. Old and R. D. Schreiber, The three E's of cancer immuno-editing, Annu. Rev. Immunol., 22 (2004), 329-360. Google Scholar

[4]

R. Eftimie, J. L. Bramson and D. J. Earn, Interactions between the immune systems and cancer: A brief review of non-spatial mathematical models, Bull. Math. Biol., 73 (2011), 2-32. doi: 10.1007/s11538-010-9526-3.  Google Scholar

[5]

J. Erler, et. al., Lysyl oxidase is essential for hypoxia-induced metastasis, Nature, 440 (2006), 1222-1226. doi: 10.1038/nature04695.  Google Scholar

[6]

R. Genesio, M. Tartaglia and A. Vicino, On the estimation of asymptotic stability regions: State of the art and new proposals, IEEE Transactions on Automatic Control, 30 (1985), 747-755. doi: 10.1109/TAC.1985.1104057.  Google Scholar

[7]

W. Hahn, Stability of Motion, Springer Verlag, Heidelberg-Berlin, 1967.  Google Scholar

[8]

T. Kailath, Linear Systems, Prentice-Hall Information and System Sciences Series. Prentice-Hall, Inc., Englewood Cliffs, N.J., 1980.  Google Scholar

[9]

M. Krstić, I. Kanellakopoulos and P. V. Kokotović, Nonlinear and Adaptive Control Design, John Wiley and Sons, 1995. Google Scholar

[10]

V. A. Kuznetsov, Mathematical modeling of the development of dormant tumors and immune stimulation of their growth, Cybern. syst. Anal, 23 (1988), 556-564. Google Scholar

[11]

V. A. Kuznetsov, I. A. Makalkin, M. A. Taylor and A. S. Perelson, Nonlinear dynamics of immunogenic tumors: Parameter estimation and global bifurcation analysis, Bull. Math. Biol, 56 (1994), 295-321. Google Scholar

[12]

U. Ledzewicz, M. Faraji and H. Schaettler, Mathematical model of tumor-immune interactions under chemotherapy with immune boost, Discrete and Continuous Dynamical Systems, Series B, 18 (2013), 1031-1051. doi: 10.3934/dcdsb.2013.18.1031.  Google Scholar

[13]

U. Ledzewicz, M. Naghneian and H. Schaettler, Optimal response to chemotherapy for a mathematical model of tumor-immune dynamics, Journal of Mathematical Biology, 64 (2012), 557-577. doi: 10.1007/s00285-011-0424-6.  Google Scholar

[14]

L. Norton and R. Simon, Growth curve of an experimental solid tumor following radiotherapy, J. of the National Cancer Institute, 58 (1977), 1735-1741. Google Scholar

[15]

L. Norton, A Goempertzian model of human breast cancer growth, Cancer Research, 48 (1988), 7067-7071. Google Scholar

[16]

K. Page and J. Uhr, Mathematical models of cancer dormancy, Leukemia and Lymphoma, 46 (2005), 313-327. doi: 10.1080/10428190400011625.  Google Scholar

[17]

G. Phan et. al., Cancer regression and autoimmunity induced by cytotoxic T lymphocyte-associated antigen 4 blockade in patients with metastatic melanoma, PNAS, 100 (2003), 8372-8377. Google Scholar

[18]

S. Ratschan and Z. She, Providing a basin of attraction to a target region of polynomial systems by computation of Lyapunov-like functions, SIAM J. Control Optim., 48 (2010), 4377-4394. doi: 10.1137/090749955.  Google Scholar

[19]

S. Sastry and M. Bodson, Adaptive Control: Stability, Convergence and Robustness, Prentice-Hall, Advanced Reference Series (Engineering), 1989. Google Scholar

[20]

A. Scott, J. Wolchock and L. Old, Antibody therapy of cancer, Nature Reviews Cancer, 12 (2012), 278-287. doi: 10.1038/nrc3236.  Google Scholar

[21]

N. V. Stepanova, Course of the immune reaction during the development of a malignant tumor, Biophysics, 24 (1980), 917-923. Google Scholar

[22]

T. Takayanagi, H. Kawamura and A. Ohuchi, Cellular automaton model of a tumor tissue consisting of tumor cells, cytoxic T lymphocytes (CTLs), and cytokine produced by CTLs, IPSJ Trans Math Model Appl., 47 (2006), 61-67. Google Scholar

[23]

A. Vannelli and M. Vidyasagar, Maximal Lyapunov functions and domains of attraction for autonomous nonlinear systems, Automatica, 21 (1985), 69-80. doi: 10.1016/0005-1098(85)90099-8.  Google Scholar

[24]

K. P. Wilkie, A Review of Mathematical Models of Cancer-Immune Interactions in the Context of Tumor Dormancy, Systems Biology of Tumor Dormancy, Springer, New York, 2013. Google Scholar

[25]

V. I. Zubov, Mathematical Methods for the Study of Automatic Control Systems, Israel Jerusalem Academic Press, 1962.  Google Scholar

[26]

V. I. Zubov, Methods of A.M. Lyapunov and Their Application, the Netherlands, Noordhoff, 1964.  Google Scholar

[27]

, Sydney International Workshop on Math Models of Tumor-Immune System Dynamics,, January 7-10, (2013), 7.   Google Scholar

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