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J Immunother Cancer. 2018 Jan 30;6(1):10. doi: 10.1186/s40425-017-0313-7.

Mathematical modeling of tumor-associated macrophage interactions with the cancer microenvironment.

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Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40208, USA.
Department of Mathematics, University of California, 540H Rowland Hall, Irvine, CA, 92697, USA.
Department of Biomedical Engineering, University of California, Irvine, CA, USA.
Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40208, USA.
James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA.
Department of Pharmacology & Toxicology, University of Louisville, Louisville, KY, USA.



Immuno-oncotherapy has emerged as a promising means to target cancer. In particular, therapeutic manipulation of tumor-associated macrophages holds promise due to their various and sometimes opposing roles in tumor progression. It is established that M1-type macrophages suppress tumor progression while M2-types support it. Recently, Tie2-expressing macrophages (TEM) have been identified as a distinct sub-population influencing tumor angiogenesis and vascular remodeling as well as monocyte differentiation.


This study develops a modeling framework to evaluate macrophage interactions with the tumor microenvironment, enabling assessment of how these interactions may affect tumor progression. M1, M2, and Tie2 expressing variants are integrated into a model of tumor growth representing a metastatic lesion in a highly vascularized organ, such as the liver. Behaviors simulated include M1 release of nitric oxide (NO), M2 release of growth-promoting factors, and TEM facilitation of angiogenesis via Angiopoietin-2 and promotion of monocyte differentiation into M2 via IL-10.


The results show that M2 presence leads to larger tumor growth regardless of TEM effects, implying that immunotherapeutic strategies that lead to TEM ablation may fail to restrain growth when the M2 represents a sizeable population. As TEM pro-tumor effects are less pronounced and on a longer time scale than M1-driven tumor inhibition, a more nuanced approach to influence monocyte differentiation taking into account the tumor state (e.g., under chemotherapy) may be desirable.


The results highlight the dynamic interaction of macrophages within a growing tumor, and, further, establish the initial feasibility of a mathematical framework that could longer term help to optimize cancer immunotherapy.


Cancer immunotherapy; Cancer metastasis; Computational simulation; Mathematical modeling; Tie2 expressing macrophages; Tumor-associated macrophages

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