Literature DB >> 3156189

Macrophage T lymphocyte interactions in the anti-tumor immune response: a mathematical model.

R J De Boer, P Hogeweg, H F Dullens, R A De Weger, W Den Otter.   

Abstract

In this paper we present a model of the macrophage T lymphocyte interactions that generate an anti-tumor immune response. The model specifies i) induction of cytotoxic T lymphocytes, ii) antigen presentation by macrophages, which leads to iii) activation of helper T cells, and iv) production of lymphoid factors, which induce a) cytotoxic macrophages, b) T lymphocyte proliferation, and c) an inflammation reaction. Tumor escape mechanisms (suppression, antigenic heterogeneity) have been deliberately omitted from the model. This research combines hitherto unrelated or even contradictory data within the range of behavior of one model. In the model behavior, helper T cells play a crucial role: Tumors that differ minimally in antigenicity (i.e., helper reactivity) can differ markedly in rejectability. Immunization yields protection against tumor doses that would otherwise be lethal, because it increases the number of helper T cells. The magnitude of the cytotoxic effector cell response depends on the time at which helper T cells become activated: early helper activity steeply increases the magnitude of the immune response. The type of cytotoxic effector cells that eradicates the tumor depends on tumor antigenicity: lowly antigenic tumors are attacked mainly by macrophages, whereas large highly antigenic tumors can be eradicated by cytotoxic T lymphocytes only.

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Year:  1985        PMID: 3156189

Source DB:  PubMed          Journal:  J Immunol        ISSN: 0022-1767            Impact factor:   5.422


  22 in total

1.  T cell state transition produces an emergent change detector.

Authors:  Peter S Kim; Peter P Lee
Journal:  J Theor Biol       Date:  2011-01-27       Impact factor: 2.691

2.  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

Review 3.  A review of quantitative modeling of B cell responses to antigenic challenge.

Authors:  Timothy P Hickling; Xiaoying Chen; Paolo Vicini; Satyaprakash Nayak
Journal:  J Pharmacokinet Pharmacodyn       Date:  2014-10-19       Impact factor: 2.745

4.  Predicting success or failure of immunotherapy for cancer: insights from a clinically applicable mathematical model.

Authors:  Charles F Babbs
Journal:  Am J Cancer Res       Date:  2012-02-15       Impact factor: 6.166

5.  The role of host lymphocytes and host macrophages in antitumor reactions after injection of sensitized lymphocytes and tumor target cells into naive mice.

Authors:  H F Dullens; W Vuist; M Van der Maas; W Den Otter
Journal:  Cancer Immunol Immunother       Date:  1986       Impact factor: 6.968

6.  Chaos synchronization and Nelder-Mead search for parameter estimation in nonlinear pharmacological systems: Estimating tumor antigenicity in a model of immunotherapy.

Authors:  Nikhil Pillai; Morgan Craig; Aristeidis Dokoumetzidis; Sorell L Schwartz; Robert Bies; Immanuel Freedman
Journal:  Prog Biophys Mol Biol       Date:  2018-06-19       Impact factor: 3.667

Review 7.  Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

Authors:  Anna Konstorum; Anthony T Vella; Adam J Adler; Reinhard C Laubenbacher
Journal:  J R Soc Interface       Date:  2017-06       Impact factor: 4.118

Review 8.  A translational bridge to cancer immunotherapy: exploiting costimulation and target antigens for active and passive T cell immunotherapy.

Authors:  Robert H Vonderheide; Carl H June
Journal:  Immunol Res       Date:  2003       Impact factor: 2.829

9.  A tumor-immune interaction model for hepatocellular carcinoma based on measured lymphocyte counts in patients undergoing radiotherapy.

Authors:  Wonmo Sung; Clemens Grassberger; Aimee Louise McNamara; Lucas Basler; Stefanie Ehrbar; Stephanie Tanadini-Lang; Theodore S Hong; Harald Paganetti
Journal:  Radiother Oncol       Date:  2020-07-15       Impact factor: 6.280

Review 10.  Immune surveillance and natural resistance: an evaluation.

Authors:  W Den Otter
Journal:  Cancer Immunol Immunother       Date:  1986       Impact factor: 6.968

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