Literature DB >> 15019501

Dynamic response of cancer under the influence of immunological activity and therapy.

Harold P de Vladar1, Jorge A González.   

Abstract

The dynamical basis of tumoral growth has been controversial. Many models have been proposed to explain cancer development. The descriptions employ exponential, potential, logistic or Gompertzian growth laws. Some of these models are concerned with the interaction between cancer and the immunological system. Among other properties, these models are concerned with the microscopic behavior of tumors and the emergence of cancer. We propose a modification of a previous model by Stepanova, which describes the specific immunological response against cancer. The modification consists of the substitution of a Gompertian law for the exponential rate used for tumoral growth. This modification is motivated by the numerous works confirming that Gompertz's equation correctly describes solid tumor growth. The modified model predicts that near zero, tumors always tend to grow. Immunological contraposition never suffices to induce a complete regression of the tumor. Instead, a stable microscopic equilibrium between cancer and immunological activity can be attained. In other words, our model predicts that the theory of immune surveillance is plausible. A macroscopic equilibrium in which the system develops cancer is also possible. In this case, immunological activity is depleted. This is consistent with the phenomena of cancer tolerance. Both equilibrium points can coexist or can exist without the other. In all cases the fixed point at zero tumor size is unstable. Since immunity cannot induce a complete tumor regression, a therapy is required. We include constant-dose therapies and show that they are insufficient. Final levels of immunocompetent cells and tumoral cells are finite, thus post-treatment regrowth of the tumor is certain. We also evaluate late-intensification therapies which are successful. They induce an asymptotic regression to zero tumor size. Immune response is also suppressed by the therapy, and thus plays a negligible role in the remission. We conclude that treatment evaluation should be successful without taking into account immunological effects.

Entities:  

Mesh:

Substances:

Year:  2004        PMID: 15019501     DOI: 10.1016/j.jtbi.2003.11.012

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  11 in total

1.  Fine-tuning anti-tumor immunotherapies via stochastic simulations.

Authors:  Giulio Caravagna; Roberto Barbuti; Alberto d'Onofrio
Journal:  BMC Bioinformatics       Date:  2012-03-28       Impact factor: 3.169

2.  Dynamical properties of a minimally parameterized mathematical model for metronomic chemotherapy.

Authors:  Heinz Schättler; Urszula Ledzewicz; Behrooz Amini
Journal:  J Math Biol       Date:  2015-06-19       Impact factor: 2.259

3.  Modelling lymphoma therapy and outcome.

Authors:  Katja Roesch; Dirk Hasenclever; Markus Scholz
Journal:  Bull Math Biol       Date:  2013-12-14       Impact factor: 1.758

4.  T model of growth and its application in systems of tumor-immune dynamics.

Authors:  Mohammad A Tabatabai; Wayne M Eby; Karan P Singh; Sejong Bae
Journal:  Math Biosci Eng       Date:  2013-06       Impact factor: 2.080

5.  Optimal response to chemotherapy for a mathematical model of tumor-immune dynamics.

Authors:  Urszula Ledzewicz; Mohammad Naghnaeian; Heinz Schättler
Journal:  J Math Biol       Date:  2011-05-08       Impact factor: 2.259

6.  Modeling cancer-immune responses to therapy.

Authors:  L G dePillis; A Eladdadi; A E Radunskaya
Journal:  J Pharmacokinet Pharmacodyn       Date:  2014-10-04       Impact factor: 2.745

7.  A race between tumor immunoescape and genome maintenance selects for optimum levels of (epi)genetic instability.

Authors:  Shingo Iwami; Hiroshi Haeno; Franziska Michor
Journal:  PLoS Comput Biol       Date:  2012-02-16       Impact factor: 4.475

8.  Periodically Pulsed Immunotherapy in a Mathematical Model of Tumor, CD4+ T Cells, and Antitumor Cytokine Interactions.

Authors:  Hsiu-Chuan Wei; Jui-Ling Yu; Chia-Yu Hsu
Journal:  Comput Math Methods Med       Date:  2017-11-09       Impact factor: 2.238

9.  Cancer-induced immunosuppression can enable effectiveness of immunotherapy through bistability generation: A mathematical and computational examination.

Authors:  Victor Garcia; Sebastian Bonhoeffer; Feng Fu
Journal:  J Theor Biol       Date:  2020-02-06       Impact factor: 2.691

10.  Optimal minimum variance-entropy control of tumour growth processes based on the Fokker-Planck equation.

Authors:  Maliheh Sargolzaei; Gholamreza Latif-Shabgahi; Mahdi Afshar
Journal:  IET Syst Biol       Date:  2020-12       Impact factor: 1.615

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.