Effective Reversal of Macrophage Polarization by Inhibitory Combinations Predicted by a Boolean Protein–Protein Interaction Model

SIMPLE SUMMARY: Understanding the effects of the tumor microenvironment is an essential step to advance treatments for cancer, but it is also one that is problematic to reproduce in vitro or study in vivo. When such approaches are difficult to implement, in silico methods are often able to help surmount these barriers. Network models of biological systems focus on the interactions of components (such as proteins or genes) and use available data about the parts of a system to predict emergent qualities. We focused our study on macrophages and how their environment affects their polarization. Thus, we built a Boolean control network model of the early response events going on in macrophages by collating information from a manual search of the literature that interprets the changes in the inner state of the cell. We used this model to simulate combinatorial treatment options that target multiple targets at the same time and have synergistic effects on macrophage polarization. ABSTRACT: Background: The function and polarization of macrophages has a significant impact on the outcome of many diseases. Targeting tumor-associated macrophages (TAMs) is among the greatest challenges to solve because of the low in vitro reproducibility of the heterogeneous tumor microenvironment (TME). To create a more comprehensive model and to understand the inner workings of the macrophage and its dependence on extracellular signals driving polarization, we propose an in silico approach. Methods: A Boolean control network was built based on systematic manual curation of the scientific literature to model the early response events of macrophages by connecting extracellular signals (input) with gene transcription (output). The network consists of 106 nodes, classified as 9 input, 75 inner and 22 output nodes, that are connected by 217 edges. The direction and polarity of edges were manually verified and only included in the model if the literature plainly supported these parameters. Single or combinatory inhibitions were simulated mimicking therapeutic interventions, and output patterns were analyzed to interpret changes in polarization and cell function. Results: We show that inhibiting a single target is inadequate to modify an established polarization, and that in combination therapy, inhibiting numerous targets with individually small effects is frequently required. Our findings show the importance of JAK1, JAK3 and STAT6, and to a lesser extent STK4, Sp1 and Tyk2, in establishing an M1-like pro-inflammatory polarization, and NFAT5 in creating an anti-inflammatory M2-like phenotype. Conclusions: Here, we demonstrate a protein–protein interaction (PPI) network modeling the intracellular signalization driving macrophage polarization, offering the possibility of therapeutic repolarization and demonstrating evidence for multi-target methods.