Fine-tuning anti-tumor immunotherapies via stochastic simulations

BackgroundAnti-tumor therapies aim at reducing to zero the number of tumor cells in a host within their end or, at least, aim at leaving the patient with a sufficiently small number of tumor cells so that the residual tumor can be eradicated by the immune system. Besides severe side-effects, a key problem of such therapies is finding a suitable scheduling of their administration to the patients. In this paper we study the effect of varying therapy-related parameters on the final outcome of the interplay between a tumor and the immune system.ResultsThis work generalizes our previous study on hybrid models of such an interplay where interleukins are modeled as a continuous variable, and the tumor and the immune system as a discrete-state continuous-time stochastic process. The hybrid model we use is obtained by modifying the corresponding deterministic model, originally proposed by Kirschner and Panetta. We consider Adoptive Cellular Immunotherapies and Interleukin-based therapies, as well as their combination. By asymptotic and transitory analyses of the corresponding deterministic model we find conditions guaranteeing tumor eradication, and we tune the parameters of the hybrid model accordingly. We then perform stochastic simulations of the hybrid model under various therapeutic settings: constant, piece-wise constant or impulsive infusion and daily or weekly delivery schedules.ConclusionsResults suggest that, in some cases, the delivery schedule may deeply impact on the therapy-induced tumor eradication time. Indeed, our model suggests that Interleukin-based therapies may not be effective for every patient, and that the piece-wise constant is the most effective delivery to stimulate the immune-response. For Adoptive Cellular Immunotherapies a metronomic delivery seems more effective, as it happens for other anti-angiogenesis therapies and chemotherapies, and the impulsive delivery seems more effective than the piece-wise constant. The expected synergistic effects have been observed when the therapies are combined.

[1]  Alberto d’Onofrio,et al.  Pulse vaccination strategy in the sir epidemic model: Global asymptotic stable eradication in presence of vaccine failures , 2002 .

[2]  Ueli Schibler,et al.  Circadian rhythms: mechanisms and therapeutic implications. , 2007, Annual review of pharmacology and toxicology.

[3]  J. Spratt,et al.  Recurrence of breast cancer. Obesity, tumor size, and axillary lymph node metastases. , 1980, JAMA.

[4]  T. Sawik An exact approach for batch scheduling in flexible flow lines with limited intermediate buffers , 2002 .

[5]  Wilhelm Huisinga,et al.  ADAPTIVE SIMULATION OF HYBRID STOCHASTIC AND DETERMINISTIC MODELS FOR BIOCHEMICAL SYSTEMS , 2005 .

[6]  Ronald F. Boisvert,et al.  NIST Handbook of Mathematical Functions , 2010 .

[7]  D. Kirschner,et al.  A mathematical model of tumor-immune evasion and siRNA treatment , 2003 .

[8]  V. Devita,et al.  Cancer : Principles and Practice of Oncology , 1982 .

[9]  Vipul Periwal,et al.  Numerical Simulation for Biochemical Kinetics , 2006 .

[10]  D. Gillespie Approximate accelerated stochastic simulation of chemically reacting systems , 2001 .

[11]  Roberto Barbuti,et al.  Tumour suppression by immune system through stochastic oscillations. , 2010, Journal of theoretical biology.

[12]  Alberto d’Onofrio,et al.  Tumor evasion from immune control: Strategies of a MISS to become a MASS , 2007 .

[13]  Alberto d'Onofrio,et al.  Bounded-noise-induced transitions in a tumor-immune system interplay. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Vipul Periwal,et al.  System Modeling in Cellular Biology: From Concepts to Nuts and Bolts , 2006 .

[15]  Denise Kirschner,et al.  On the global dynamics of a model for tumor immunotherapy. , 2009, Mathematical biosciences and engineering : MBE.

[16]  Paolo Milazzo,et al.  Delay Stochastic Simulation of Biological Systems: A Purely Delayed Approach , 2011, Trans. Comp. Sys. Biology.

[17]  BMC Bioinformatics , 2005 .

[18]  J. Hale,et al.  Dynamics and Bifurcations , 1991 .

[19]  F. A. Goswitz,et al.  Spontaneous cyclic leukocytosis and thrombocytosis in chronic granulocytic leukemia. , 1972, The New England journal of medicine.

[20]  P Hogeweg,et al.  Macrophage T lymphocyte interactions in the anti-tumor immune response: a mathematical model. , 1985, Journal of immunology.

[21]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[22]  D. Kirschner,et al.  Modeling immunotherapy of the tumor – immune interaction , 1998, Journal of mathematical biology.

[23]  Julien F. Ollivier,et al.  Colored extrinsic fluctuations and stochastic gene expression , 2008, Molecular systems biology.

[24]  Alberto d'Onofrio,et al.  Simple biophysical model of tumor evasion from immune system control. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  A. d’Onofrio A general framework for modeling tumor-immune system competition and immunotherapy: Mathematical analysis and biomedical inferences , 2005, 1309.3337.

[26]  Tianhai Tian,et al.  Oscillatory Regulation of Hes1: Discrete Stochastic Delay Modelling and Simulation , 2006, PLoS Comput. Biol..

[27]  Yiannis Kaznessis,et al.  Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions. , 2005, The Journal of chemical physics.

[28]  Egbert Oosterwijk,et al.  Immunotherapy for renal cell carcinoma. , 2003, European urology.

[29]  Nicola Bellomo,et al.  From the mathematical kinetic, and stochastic game theory to modelling mutations, onset, progression and immune competition of cancer cells ✩ , 2008 .

[30]  G. D. Knott,et al.  Modeling tumor regrowth and immunotherapy , 2001 .

[31]  Filippo Castiglione,et al.  Modeling and simulation of cancer immunoprevention vaccine , 2005, Bioinform..

[32]  C. DeLisi,et al.  Immune surveillance and neoplasia—1 a minimal mathematical model , 1977 .

[33]  Giulio Caravagna,et al.  Formal Modeling and Simulation of Biological Systems with Delays , 2011 .

[34]  A. Gandolfi,et al.  The dynamics of tumour–vasculature interaction suggests low‐dose, time‐dense anti‐angiogenic schedulings , 2009, Cell proliferation.

[35]  P. Holmes,et al.  Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields , 1983, Applied Mathematical Sciences.

[36]  Edmund Taylor Whittaker,et al.  The Hypergeometric Function , 1996 .

[37]  D. Volfson,et al.  Delay-induced stochastic oscillations in gene regulation. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[38]  H. P. de Vladar,et al.  Dynamic response of cancer under the influence of immunological activity and therapy. , 2004, Journal of theoretical biology.

[39]  Christophe Caux,et al.  Tumour escape from immune surveillance through dendritic cell inactivation. , 2002, Seminars in cancer biology.

[40]  Paola Lecca,et al.  A time-dependent extension of gillespie algorithm for biochemical stochastic π-calculus , 2006, SAC.

[41]  Robert N. Hughes,et al.  Cancer: Principles and Practice of Oncology , 2005 .

[42]  Daniel T. Gillespie,et al.  Numerical Simulation for Biochemical Kinetics , 2008 .

[43]  L. D. Pillis,et al.  A Validated Mathematical Model of Cell-Mediated Immune Response to Tumor Growth , 2005 .

[44]  Robert S. Kerbel,et al.  The anti-angiogenic basis of metronomic chemotherapy , 2004, Nature Reviews Cancer.

[45]  S. Agarwala,et al.  New applications of cancer immunotherapy. , 2002, Seminars in oncology.

[46]  Alberto d'Onofrio,et al.  Stability properties of pulse vaccination strategy in SEIR epidemic model. , 2002, Mathematical biosciences.

[47]  A. d’Onofrio TUMOR-IMMUNE SYSTEM INTERACTION: MODELING THE TUMOR-STIMULATED PROLIFERATION OF EFFECTORS AND IMMUNOTHERAPY , 2006 .

[48]  David F Anderson,et al.  A modified next reaction method for simulating chemical systems with time dependent propensities and delays. , 2007, The Journal of chemical physics.

[49]  D. Pardoll,et al.  Does the immune system see tumors as foreign or self? , 2003, Annual review of immunology.

[50]  F. Mandelli,et al.  How long can we give interleukin-2? Clinical and immunological evaluation of AML patients after 10 or more years of IL2 administration , 2002, Leukemia.

[51]  Zvia Agur,et al.  Cancer immunotherapy by interleukin-21: potential treatment strategies evaluated in a mathematical model. , 2006, Cancer research.

[52]  B. Kennedy,et al.  Cyclic leukocyte oscillations in chronic myelogenous leukemia during hydroxyurea therapy. , 1970, Blood.

[53]  R. Schreiber,et al.  The three Es of cancer immunoediting. , 2004, Annual review of immunology.

[54]  A. Goldhirsch,et al.  Prolonged clinical benefit with metronomic chemotherapy in patients with metastatic breast cancer , 2006, Anti-cancer drugs.

[55]  N. Bellomo,et al.  Complex multicellular systems and immune competition: new paradigms looking for a mathematical theory. , 2008, Current topics in developmental biology.

[56]  Yuri Kogan,et al.  Improving alloreactive CTL immunotherapy for malignant gliomas using a simulation model of their interactive dynamics , 2008 .

[57]  D. Gillespie A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions , 1976 .

[58]  T. Whiteside,et al.  Tumor-induced death of immune cells: its mechanisms and consequences. , 2002, Seminars in cancer biology.

[59]  R A Good,et al.  Cyclic leukocytosis in chronic myelogenous leukemia: new perspectives on pathogenesis and therapy. , 1973, Blood.

[60]  P. Holmes,et al.  Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields , 1983, Applied Mathematical Sciences.

[61]  Paolo Milazzo,et al.  On the Interpretation of Delays in Delay Stochastic Simulation of Biological Systems , 2009, COMPMOD.

[62]  Francesco Pappalardo,et al.  Modeling the competition between lung metastases and the immune system using agents , 2010, BMC Bioinformatics.

[63]  Alberto Gandolfi,et al.  Resistance to antitumor chemotherapy due to bounded-noise-induced transitions. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[64]  A. Perelson,et al.  Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis. , 1994, Bulletin of mathematical biology.

[65]  B C Mehta,et al.  Cyclic oscillations in leukocyte count in chronic myeloid leukemia. , 1980, Acta haematologica.

[66]  Alberto d'Onofrio,et al.  Delay-induced oscillatory dynamics of tumour-immune system interaction , 2010, Math. Comput. Model..

[67]  H. I. Freedman,et al.  A mathematical model of cancer treatment by immunotherapy. , 2000, Mathematical biosciences.

[68]  Joseph M Kaminski,et al.  Immunotherapy and prostate cancer. , 2003, Cancer treatment reviews.

[69]  H. Shaw,et al.  Ultra‐late recurrence (15 years or longer) of cutaneous melanoma , 1998, Cancer.