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Multifaceted Kinetics of Immuno-Evasion from Tumor Dormancy

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Systems Biology of Tumor Dormancy

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 734))

Abstract

Tumor progression is subject to modulation by the immune system. The immune system can eliminate tumors or keep them at a dormant equilibrium size, while some tumors escape immunomodulation and advance to malignancy. Herein, we discuss some aspects of immune evasion of dormant tumors from a theoretical biophysics point of view that can be modeled mathematically. We go on to analyze the mathematical system on multiple timescales. First, we consider a long timescale where tumor evasion is likely due to adaptive (and somewhat deterministic) immuno-editing. Then, we consider the temporal mesoscale and hypothesize that extrinsic noise could be a major factor in induction of immuno-evasion. Implications of immuno-evasive mechanisms for the outcome of immunotherapies are also discussed. In addition, we discuss the ideas that population level tumor dormancy may not be a quiescence phenomenon and that dormant tumors can, at least if modulated by the immune system, live a very active and noisy life!

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References

  1. Agarwal SA (Guest Editor) (2003) Seminars in oncology. I madew reference to a special issue of a journal. 29-3 (Suppl 7)

    Google Scholar 

  2. Ai BQ, Wang XJ, Liu GT, Liu LG (2003) Correlated noise in a logistic growth model. Phys Rev E 67:022903

    Article  Google Scholar 

  3. Al Taamemi M, Chaplain M, d’Onofrio A (2011) Evasion of tumours from the control of the immune system: consequences of brief encounters. Biology Direct (in press)

    Google Scholar 

  4. Bazzani A, Freguglia P (2003) Evolution: Geometrical and dynamical aspects. Biol Forum 96:123–136

    Google Scholar 

  5. Bazzani A, Freguglia P (2004) An evolution model of phenotipic characters. WSEAS Trans Biol Biomed 4:369–373

    Google Scholar 

  6. Behera A, O’Rourke S (2008) Comment on “correlated noise in a logistic growth model”. Phys Rev E 77:013901

    Article  Google Scholar 

  7. Bellomo N (2010) Modeling the hiding-learning dynamics in large living systems. Appl Math Lett 23:907–911

    Article  Google Scholar 

  8. Bellomo N, Delitala M (2008) From the mathematical kinetic stochastic game theory to modelling mutations, onset, progression and immune competition of cancer cells. Phys Life Rev 5:183–206

    Article  Google Scholar 

  9. Bellomo N, Forni G (2008) Complex multicellular systems and immune competition: New paradigms looking for a mathematical theory. Curr Topics Dev Biol 81:485–502

    Article  Google Scholar 

  10. Bleumer I, Oosterwijk E, de Mulder P, Mulders PF (2003) Immunotherapy for renal cell carcinoma. Eur Urol 44:65–75

    Article  PubMed  CAS  Google Scholar 

  11. Bobryk RB, Chrzeszczyk A (2005) Transitions induced by bounded noise. Physica A 358:263–272

    Article  Google Scholar 

  12. Bobryk RB, Chrzeszczyk A (2009) Stability regions for Mathieu equation with imperfect periodicity. Phys Lett A 373:3532–3535

    Article  CAS  Google Scholar 

  13. Borland L (1998) Ito-Langevin equations within generalized thermostatistics. Phys Lett A 245:67–72

    Article  CAS  Google Scholar 

  14. Bose T, Trimper S (2009) Stochastic model for tumor growth with immunization. Phys Rev E 79:051903

    Article  Google Scholar 

  15. Burnet FM (1957) Cancer – a biological approach. Br Med J 1:841–847

    Article  PubMed  CAS  Google Scholar 

  16. Burnet FM (1964) Immunological factors in the process of carcinogenesis. Br Med Bull 20:154–158

    PubMed  CAS  Google Scholar 

  17. Burnet FM (1971) Immunological surveillance in neoplasia. Transplant Rev 7:3–25

    PubMed  CAS  Google Scholar 

  18. Cai GQ, Lin YK (1996) Generation of non-Gaussian stationary stochastic process. Phys Rev E 54: 299–303

    Article  CAS  Google Scholar 

  19. Cai GQ, Suzuki Y (2005) Response of systems under non-gaussian random excitation. Nonl Dyn 45:95–108

    Article  Google Scholar 

  20. Cappuccio A, Elishmereni M, Agur Z (2006) Cancer immunotherapy by interleukin-21 treatment strategies evaluated in a mathematical model. Canc Res 66:7293–7300

    Article  CAS  Google Scholar 

  21. Caravagna G, d’Onofrio A, Milazzo P, Barbuti R (2010) Antitumor immune surveillance through stochastic oscillations. J Th Bio 265:336–345

    Article  CAS  Google Scholar 

  22. Cattani C, Ciancio A, d’Onofrio A (2010) Metamodeling of the learning-hiding competition between tumors and the immune system: A kinematic approach. Math Comp Model 52:62–69

    Article  Google Scholar 

  23. Chakraborty AK, Kosmrlj A (2010) Statistical mechanical concepts in immunology. Annu Rev Phys Chem 61:283–303

    Article  PubMed  CAS  Google Scholar 

  24. Chaplain M, Matzavinos A (2006) Mathematical modelling of spatio-temporal phenomena in tumor immunology. Lect Notes Math Sci 1872:131–183

    Article  Google Scholar 

  25. Chaplain M, Kuznetsov VA, James ZH, Stepanova LA (1998) Spatiotemporal dynamics of the immune system response to cancer. In: Horn MA, Simonett G, Webb G (eds) Mathematical models in medical and health sciences. Vanderbilt University Press, Nashville, pp 1–20

    Google Scholar 

  26. Cheon T (2003) Evolutionary stability of ecological hierarchy. Phys Rev Lett 90:258105

    Article  PubMed  Google Scholar 

  27. O’Connell J, Bennett MW, O’Sullivan GC, Collins JK, Shanahan F (1999) The Fas counterattack: Cancer as a site of immune privilege. Immunol Today 20:46–50

    Article  PubMed  Google Scholar 

  28. DeBoer RJ, Hogeweg P, Hub F, Dullens J, DeWeger R, DenOtter W (1985) Macrophage T lymphocyte interactions in the anti-tumor immune response: A mathematical model. J Immunol 134:2748–2758

    CAS  Google Scholar 

  29. Delves P, Martin S, Burton D, Roitt I (2006) Essential immunology. Wiley-Blackwell, New York

    Google Scholar 

  30. De Pillis LG, Radunskaya AE, Wiseman CL (2005) A validated mathematical model of cell-mediated immune response to tumor growth. Canc Res 65:7950–7958

    Google Scholar 

  31. De Vito VT Jr, Hellman J, Rosenberg SA (eds) (2005) Cancer: Principles and practice of oncology. J P Lippincott, Philadelphia

    Google Scholar 

  32. De Vladar HP, Gonzalez JA (2004) Dynamic response of cancer under the influence of immunological activity and therapy. J Theor Biol 227:335–348

    Article  PubMed  Google Scholar 

  33. Deza R, Wio HS, Fuentes MA (2007) Noise-induced phase transitions: Effects of the noises, statistics and spectrum. AIP Conf. Proc. 913:62–67

    Article  Google Scholar 

  34. Dimentberg MF (1988) Statistical dynamics of nonlinear and time-varying systems. Wiley, New York

    Google Scholar 

  35. d’Onofrio A (2005) A general framework for modeling tumor-immune system competition and immunotherapy: Analysis and medical inferences. Phys D 208:220–235

    Article  Google Scholar 

  36. d’Onofrio A (2006) Tumor-immune system interaction: Modeling the tumor-stimulated proliferation of effectors and immunotherapy. Math Models Meth Appl Sci 16:1375–1401

    Article  Google Scholar 

  37. d’Onofrio A (2007) Noisy oncology. In: Venturino E, Hoskins RH (eds) Aspects of mathematical modeling. Birkhauser, Basel, pp 229–234

    Google Scholar 

  38. d’Onofrio A (2007) Tumor evasion from immune system control: Strategies of a MISS to become a MASS. Chaos, Solitons and Fractals 31:261–268

    Article  Google Scholar 

  39. d’Onofrio A (2008) Fuzzy oncology. Appl Math Lett 21:662–668

    Article  Google Scholar 

  40. d’Onofrio A (2009) Fractal growth of tumors and other cellular populations: Linking the mechanistic to the phenomenological modeling and vice versa. Chaos, Solitons and Fractals 41:875–880

    Article  Google Scholar 

  41. d’Onofrio A (2010) Bounded-noise-induced transitions in a tumor-immune system interplay. Phys Rev E 81:021923

    Google Scholar 

  42. d’Onofrio A (2011) Spatiotemporal effects of a possible chemorepulsion of tumor cells by immune system effectors. J Theor Biol 296:41–48

    Article  PubMed  Google Scholar 

  43. d’Onofrio A, Ciancio A (2011) Simple biophysical model of tumor evasion from immune system control. Phys Rev E 84:031910

    Google Scholar 

  44. d’Onofrio A, Tomlinson IPM (2007) A nonlinear mathematical model of cell turnover, differentiation and tumorigenesis in the intestinal crypt. J Theor Biol 224:367–374

    Article  Google Scholar 

  45. d’Onofrio A, Gatti F, Cerrai P, Freschi L (2010) Delay-induced oscillatory dynamics of tumorimmune system interaction. Math Comp Mod 51:572–591

    Article  Google Scholar 

  46. Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD (2002) Cancer immunoediting: From immunosurveillance to tumor escape. Nat Immunol 3:991–998

    Article  PubMed  CAS  Google Scholar 

  47. Dunn GP, Old LJ, Schreiber RD (2004) The three Es of cancer immunoediting. Annu Rev Immunol 22:322–360

    Article  Google Scholar 

  48. Ehrlich P (1909) Ueber den jetzigen Stand der Karzinomforschung. Ned Tijdschr Geneeskd 5:273–290

    Google Scholar 

  49. Fishelson Z, Berke G (1981) Tumor cell destruction by cytotoxic T lymphocytes: The basis of reduced antitumor cell activity in syngeneic hosts. J Immunol 25:2048–2052

    Google Scholar 

  50. Frey E (2010) Game theory: Theoretical concepts and applications to microbial communities. Physica A 389:3265–3298

    Article  Google Scholar 

  51. Fuentes MA, Toral R, Wio HS (2001) Enhancement of stochastic resonance: The role of non Gaussian noises. Physica A 295:114–122

    Article  Google Scholar 

  52. Gabrilovich DI, Hurwitz AA (eds) (2008) Tumor-induced immune suppression. Springer, Heidelberg

    Google Scholar 

  53. Gatti R, Robinson WA, Deinard AS, Nesbit M, McCullough JJ, Ballow M, Good RA (1973) Cyclic leukocytosis in chronic myelogenous leukemia: New perspectives on pathogenesis and therapy. Blood 41:771–783

    PubMed  CAS  Google Scholar 

  54. Hedrich H (ed) The laboratory mouse. Elsevier, Amsterdam

    Google Scholar 

  55. Horsthemke W, Lefever R (1977) Phase transition induced by external noise. Phys Lett A 64:19–21

    Article  Google Scholar 

  56. Horsthemke W, Lefever R (2007) Noise-induced transitions in physics, chemistry and biology. Springer, Heidelberg

    Google Scholar 

  57. Janeway CA Jr, Travers P, Walport M, Shlomchik MA (2001) Immunobiology, 5th edn. Garland, New York

    Google Scholar 

  58. Kaminski JM, Summers JB, Ward MB, Huber MR, Minev B (2004) Immunotherapy and prostate cancer. Canc Treat Rev 29:199–209

    Article  Google Scholar 

  59. Kennedy BJ (1970) Cyclic leukocyte oscillations in chronic myelogenous leukemia during hydroxyurea therapy. Blood 35:751–760

    PubMed  CAS  Google Scholar 

  60. Kim R, Emi M, Tanabe K (2007) Cancer immunoediting from immune surveillance to immune escape. Immunology 121:1–14

    Article  PubMed  CAS  Google Scholar 

  61. Kindt TJ, Osborne BA, Goldsby RA (2006) Kuby immunology. W H Freeman, New York

    Google Scholar 

  62. Kirschner D, Panetta JC (1998) Modeling immunotherapy of the tumor - immune interaction. J Math Biol 37:235–252

    Article  PubMed  CAS  Google Scholar 

  63. Kirschner D, Tsygvintsev A (2009) On the global dynamics of a model for tumor immunotherapy. Math Biosci Eng 6:573–583

    Article  PubMed  Google Scholar 

  64. Koebel CM, Vermi W, Swann JB, Zerafa N, Rodrig SJ, Old LJ, Smyth MJ, Schreiber RD (2007) Adaptive immunity maintains occult cancer in an equilibrium state. Nature 450:903–907

    Article  PubMed  CAS  Google Scholar 

  65. Kogan Y, Forys U, Shukron O, Kronik N, Agur Z (2010) Cellular immunotherapy for high grade gliomas: Mathematical analysis deriving efficacious infusion rates based on patient requirements. SIAM J Appl Math 70:1953–1976

    Article  CAS  Google Scholar 

  66. Kondoh CM (2003) Foraging adaptation and the relationship between food-web complexity and stability. Science 299:5611–5613

    Article  Google Scholar 

  67. Kronik N, Kogan Y, Vainstein V, Agur Z (2008) Improving alloreactive CTL immunotherapy for malignant gliomas using a simulation model of their interactive dynamics. Canc Immunol Immunother 57:425–439

    Article  Google Scholar 

  68. Kurnick JT, Ramirez-Montagut T, Boyle LA, Andrews DM, Pandolfi F, Durda PJ, Butera D, Dunn IS, Benson EM, Gobin SJ, van den Elsen PJ (2011) A novel autocrine pathway of tumor escape from immune recognition: Melanoma cell lines produce a soluble protein that diminishes expression of the gene encoding the melanocyte lineage melan-A/MART-1 antigen through down-modulation of its promoter. J Immunol 167:1204–1211

    Google Scholar 

  69. Kuznetsov VA (1979) Dynamics of cellular immune anti-tumor reactions I. Synthesis of a multi-level model mathematical methods. In: Fedorov (ed) The theory of systems. Kyrghyz State University Press, Frunze, pp 57–71 (in Russian)

    Google Scholar 

  70. Kuznetsov VA, Knott GD (2001) Modeling tumor regrowth and immunotherapy. Math Comp Mod 33:1275–1287

    Article  Google Scholar 

  71. Kuznetsov VA, Makalkin IA, Taylor MA, Perelson AS (1994) Nonlinear dynamics of immunogenic tumors: Parameter estimation and global bifurcations analysis. Bull Math Biol 56:295–321

    PubMed  CAS  Google Scholar 

  72. Lefever R, Horsthemke W (1979) Bistability in fluctuating environments Implications in tumor immunology. Bull Math Biol 41:469–490

    PubMed  CAS  Google Scholar 

  73. De Lisi C, Rescigno A (1977) Immune surveillance and neoplasia: A minimal mathematical model. Bull Math Biol 39:201–221

    Article  Google Scholar 

  74. Matzavinos A, Chaplain M, Kuznetsov VA (2004) Mathematical modelling of the spatio-temporal response of cytotoxic T-lymphocytes to a solid tumor. Math Med Biol 21:1–34

    Article  PubMed  Google Scholar 

  75. Mehta BC, Agarwal MB (1980) Cyclic oscillations in leukocyte count in chronic myeloid leukemia. Acta Hematol 63:68–70

    Article  CAS  Google Scholar 

  76. Meloni G, Trisolini SM et al (2002) How long can we give interleukin-2? Clinical and immunological evaluation of AML patients after 10 or more years of IL2 administration. Leukemia 16:2016–2018

    Article  PubMed  CAS  Google Scholar 

  77. Owen M, Sherratt J (1997) Pattern formation and spatiotemporal irregularity in a model for macrophage-tumor interaction. J Theor Biol 189:63–80

    Article  PubMed  CAS  Google Scholar 

  78. Owen M, Sherratt J (1999) Mathematical modelling of macrophage dynamics in tumors. Math Meth Mod Appl Sci 9:513–539

    Article  Google Scholar 

  79. Pappalardo F, Lollini PL, Castiglione F, Motta S (2005) Modeling and simulation of cancer immunoprevention vaccine. Bioinformatics 21:2891–2897

    Article  PubMed  CAS  Google Scholar 

  80. Pardoll D (2003) Does the immune system see tumors as foreign or self? Annu Rev Immunol 21:807–839

    Article  PubMed  CAS  Google Scholar 

  81. Parisi G (1990) A simple model for the immune network. Proc Nat Acad Sci USA 87:429–433

    Article  PubMed  CAS  Google Scholar 

  82. Pennisi M, Pappalardo F, Palladini A, Nicoletti G, Nanni P, Lollini PL, Motta S (2010) Modeling the competition between lung metastases and the immune system using agents. BMC Bioinformatics 11(Suppl 7):S13

    Article  PubMed  Google Scholar 

  83. Perelson AS, Weisbuch G (1997) Immunology for physicists. Rev Mod Phys 69:1219–1268

    Article  CAS  Google Scholar 

  84. Reiche EM, Nunes SO, Morimoto HK (2004) Stress, depression, the immune system, and cancer. Lancet Oncol 5:617–625

    Article  PubMed  CAS  Google Scholar 

  85. Revelli JA, Sanchez AD, Wio HS (2002) Effect of non-Gaussian noises on the stochastic resonance-like phenomenon in gated traps. Physica D 168–169:165–170

    Article  Google Scholar 

  86. Schmielau J, Finn OJ (2001) Activated granulocytes and granulocyte-derived hydrogen peroxide are the underlying mechanism of suppression of t-cell function in advanced cancer patients. Canc Res 61:4756–4760

    CAS  Google Scholar 

  87. Stariolo DA (1994) The Langevin and Fokker-Planck equations in the framework of a generalized statistical mechanics. Phys Lett A 185:262–264

    Article  Google Scholar 

  88. Stepanova NV (1980) Course of the immune reaction during the development of a malignant tumor. Biophizika 24:917–923

    Google Scholar 

  89. Stewart TJ, Abrams SI (2008) How tumors escape mass destruction. Oncogene 27:5894–5903

    Article  PubMed  CAS  Google Scholar 

  90. Thomas L (1959) Discussion. In: Lawrence HS (ed) Cellular and humoral aspects of the hypersensitive states. Hoeber-Harper, New York, pp 529–532

    Google Scholar 

  91. Tomlinson IPM, Bodmer WF (1997) Modelling the consequences of interactions between tumor cells. Br J Canc 75:157–160

    Article  CAS  Google Scholar 

  92. Vianello F, Papeta N, Chen T, Kraft P, White N, Hart WK, Kircher MF, Swart E, Rhee S, Palù G, Irimia D, Toner M, Weissleder R, Poznansky MC (2006) Murine B16 melanomas expressing high levels of the chemokine stromal-derived factor-1/CXCL12 induce tumor-specific T cell chemorepulsion and escape from immune control. J Immunol 176(5):2902–2914

    PubMed  CAS  Google Scholar 

  93. Vodopick H, Rupp EM, Edwards CL, Goswitz FA, Beauchamp JJ (1972) Spontaneous cyclic leukocytosis and thrombocytosis in chronic granulocytic leukemia. New Engl J Med 286:284–290

    Article  PubMed  CAS  Google Scholar 

  94. Wie RZ, Shao YZ, He YZ (2006) Pure multiplicative stochastic resonance of a theoretical anti-tumor model with seasonal modulability. Phys Rev E 73:060902

    Article  Google Scholar 

  95. Wio HS, Toral R (2004) Effect of non-Gaussian noise sources in a noise-induced transition. Physica D 193:161–168

    Article  Google Scholar 

  96. Zitvogel L, Tesniere A, Kroemer G (2006) Cancer despite immunosurveillance: Immunoselection and immunosubversion. Nat Rev Imm 6:715–727

    Article  CAS  Google Scholar 

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Acknowledgments

I very much thank Heiko Enderling, Nava Almog, and Lynn Hlatky for inviting me to contribute to this book! I also extend my thanks to the anonymous referees for their very useful suggestions.

This work was performed in the framework of the Integrated Project “P-medicine—from data sharing and integration via VPH models to personalized medicine” (project ID: 270089), which is partially funded by the European Commission under the 7th framework program.

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d’Onofrio, A. (2013). Multifaceted Kinetics of Immuno-Evasion from Tumor Dormancy. In: Enderling, H., Almog, N., Hlatky, L. (eds) Systems Biology of Tumor Dormancy. Advances in Experimental Medicine and Biology, vol 734. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1445-2_7

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