A mathematical model of the interactions between a growing tumor and the immune system is presented. The equations and parameters of the model are based on experimental and clinical results from published studies. The model includes the primary cell populations involved in effector T-cell mediated tumor killing: regulatory T cells, helper T cells, and dendritic cells. A key feature is the inclusion of multiple mechanisms of immunosuppression through the main cytokines and growth factors mediating the interactions between the cell populations. Decreased access of effector cells to the tumor interior with increasing tumor size is accounted for. The model is applied to tumors with different growth rates and antigenicities to gauge the relative importance of various immunosuppressive mechanisms. The most important factors leading to tumor escape are TGF-Β-induced immunosuppression, conversion of helper T cells into regulatory T cells, and the limitation of immune cell access to the full tumor at large tumor sizes. The results suggest that for a given tumor growth rate, there is an optimal antigenicity maximizing the response of the immune system. Further increases in antigenicity result in increased immunosuppression, and therefore a decrease in tumor killing rate. This result may have implications for immunotherapies which modulate the effective antigenicity. Simulation of dendritic cell therapy with the model suggests that for some tumors, there is an optimal dose of transfused dendritic cells. © 2011 Elsevier Ltd.
|Original language||English (US)|
|Number of pages||18|
|Journal||Journal of Theoretical Biology|
|State||Published - Feb 2012|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-C1-013-04
Acknowledgements: This publication is based on the work supported by Award No. KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST), and for based in part on the work supported by the National Science Foundation under grants DMS-0907773 (AG). AG is a Wolfson/Royal Society Merit Award Holder. This publication is based on the work supported by the ARCS Foundation, NSF-VIGRE, and the BIO5 Institute at the University of Arizona (MRT).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.