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

Abstract

BackgroundImmuno-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.MethodsThis 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.ResultsThe 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.ConclusionsThe 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.

Combining microenvironment normalization strategies to improve cancer immunotherapy

“…The proliferation rate of CCs increases with increasing oxygen level, while the proliferation rates of CSCs and ICCs increase under hypoxic conditions (41,74). Other events that determine the population of cancer cells are their killing by effector immune cells and M1-like TAMs (47,75) and the interconversion among CCs, CSCs, and ICSs.…”

“…Systems-level mathematical models for the prediction of anti-CTLA-4 and anti-PD-1/anti-PD-L1 response have been developed previously (43)(44)(45); however, they do not account for spatial variation in the TME. Here, based on our previous study (41) and the work of others (46,47), we developed a continuum mathematical modeling framework to account for interactions among different types of cancer cells, immune cells, tumor blood vessels, oxygen supply, and drug delivery. We build on our previous models (41,48) by incorporating M1-like and M2-like TAMs; CD4 + , CD8 + , and regulatory (Tregs) T cells, and vascular and perivascular cells and their reported involvement in immunotherapy response.…”

Computational modeling of the crosstalk between macrophage polarization and tumor cell plasticity in the tumor microenvironment

“…The tumor microenvironment involves multiple cell types that interact among each other in diverse ways, thus giving rise to a complex adaptive ecological system (49)(50)(51). For such a system, mathematical models can help reveal the mechanisms underlying the emergent behavior and eventually aid in designing effective therapeutic strategies to modulate those aspects of the microenvironment that fuel disease aggressiveness (52)(53)(54)(55)(56)(57)(58).…”

Characterizing chemotherapy-induced neutropenia and monocytopenia through mathematical modelling

“…Conversely, the role of MDMs in the immune response to malignant tumours has been extensively studied (Boemo and Byrne, 2019;Eftimie and Eftimie, 2018;Mahlbacher et al, 2018;Owen et al, 2011). Modelling work has focused on macrophage interactions in hypoxic tumours, with recent work also studying the effects of macrophage polarization in tumour progression (Eftimie and Eftimie, 2018).…”