Transcriptional and Microenvironmental Landscape of Macrophage Transition in Cancer: A Boolean Analysis

Abstract

The balance between pro- and anti-inflammatory immune system responses is crucial to face and counteract complex diseases such as cancer. Macrophages are an essential population that contributes to this balance in collusion with the local tumor microenvironment. Cancer cells evade the attack of macrophages by liberating cytokines and enhancing the transition to the M2 phenotype with pro-tumoral functions. Despite this pernicious effect on immune systems, the M1 phenotype still exists in the environment and can eliminate tumor cells by liberating cytokines that recruit and activate the cytotoxic actions of TH1 effector cells. Here, we used a Boolean modeling approach to understand how the tumor microenvironment shapes macrophage behavior to enhance pro-tumoral functions. Our network reconstruction integrates experimental data and public information that let us study the polarization from monocytes to M1, M2a, M2b, M2c, and M2d subphenotypes. To analyze the dynamics of our model, we modeled macrophage polarization in different conditions and perturbations. Notably, our study identified new hybrid cell populations, undescribed before. Based on the in vivo macrophage behavior, we explained the hybrid macrophages' role in the tumor microenvironment. The in silico model allowed us to postulate transcriptional factors that maintain the balance between macrophages with anti- and pro-tumoral functions. In our pursuit to maintain the balance of macrophage phenotypes to eliminate malignant tumor cells, we emulated a theoretical genetically modified macrophage by modifying the activation of NFκB and a loss of function in HIF1-α and discussed their phenotype implications. Overall, our theoretical approach is as a guide to design new experiments for unraveling the principles of the dual host-protective or -harmful antagonistic roles of transitional macrophages in tumor immunoediting and cancer cell fate decisions.

Keywords: boolean models; cancer immunology; gene regulatory network; macrophage; phenotype; systems immunology.

Figure 1

Gene regulatory network of macrophage polarization in a tumor microenvironment. Green circles represent components of the extracellular space and blue circles represent the components of the internal machinery. Solid green arrows represent activation and dashed red arrows inhibition.

Figure 2

Clusters of the single size attractors obtained in the Boolean simulations. Each circle represents a two-dimensional projection of one attractor, and the color represents a collection of attractors with similar phenotypes between them. The numbers associated with the clusters are explained in Table 1.

Figure 3

Heat Map of the overexpression and knock-out of transcriptional factors. (A) Heat Map of the overexpression of transcriptional factors of macrophage polarization. We maintained the expression of the node as 1, simulated, and reviewed the attractors obtained. All overexpression was compared with the wild-type (original network) with log2 fold change. (B) Heat Map of Knock-outs of transcriptional factors of macrophage polarization. The nodes were permanently fixed with a value of 0; the perturbations were realized one by one until the attractors were reached. We compared the phenotypes of the wild-type with the perturbations by a log-fold change. Green and red regions indicate those attractors whose size of the basin of attraction increased or decreased after the perturbation. In black, we denote those attractors with few effects in the basin attraction size versus WT and after perturbation. Gray areas indicate those attractors that exist in the wild-type but do not remain perturbed.

Figure 4

Heat maps of the microenvironments of macrophage polarization. Microenvironments associated with the six phenotypes evaluated in this work. For these simulations we used the criteria of Table 2. Once the attractors were obtained, we applied a logarithmic transformation on the size of the basin of attraction for each phenotype. Red stands for a low basin of attraction and green for a high basin of attraction. Pro means we modelled the polarization in a microenvironment adjuvant for each macrophage subtype. M0, monocytes.

Figure 5

Cell fate map of the macrophage polarization. Of all the attractors obtained, we changed the node’s value and maintained this perturbation until an attractor was reached. If the attractor’s transition to another phenotype, we represent it with a line and the new phenotype obtained by the perturbation. Plus sign (+) means the node was turned off, and we turn it on, while minus sign (-) means the perturbation was on, and we turned off. (A) Cell fate map of monocyte (M0). (B) Cell fate map of M1 macrophage. (C) Cell fate map of M1M2d macrophage. (D) Cell fate map of M2bM2d macrophage. Colors represent different states of macrophage polarization.

Figure 6

Attractors and cell fate map of our TGEM. (A) Bar plot of the attractors obtained from our theoretical genetically modified macrophage. For this analysis we set the value of NFκB to 1 and HIF1-α to 0, and simulated until we obtained these attractors. (B) Cell fate map of our theoretical genetically modified macrophage. By analyzing the plasticity of phenotypes through single gene perturbation of the genetically modified macrophage, we obtained the rules of genetic perturbation that contribute transition between macrophages phenotypes. Here (-) means the node was turned off and (+) means the node was turned on.

Figure 7

TGEM in a breast cancer microenvironment. (A) We engineered different microenvironments associated with breast cancer and evaluate how our TGEM behaved. (B) Cell fate map of the theoretical genetically modified macrophage in breast cancer microenvironment for the expression of IgG and adenosines. (C) Cell fate map of the theoretical genetically modified macrophage in breast cancer microenvironment for the expression of IL1-β and IL-6. (D) Cell fate map of the theoretical genetically modified macrophage in breast cancer microenvironment for the expression of Hypoxia and glucocorticoids (GCGCR). (E) Cell fate map of the theoretical genetically modified macrophage in breast cancer microenvironment for the expression of IL-10 and TGF-β. This analysis was to evaluate the stability of our pharmaceutical approach in a breast cancer scenario.

Figure 8

Robustness analysis of our transcriptomic regulatory network of macrophage polarization. Sensitivity analysis of each node from our transcriptional regulatory network of macrophage polarization. (A) Derrida curves of our transcriptional regulatory network of macrophage polarization in a tumor microenvironment. (B) Sensitivity analysis of the variables in our transcriptional regulatory network of macrophage polarization in a tumor microenvironment. Golden line is the mean of the value of the sensitivity analysis.