Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies

Abstract

The tumour microenvironment is the surrounding of a tumour, including blood vessels, fibroblasts, signaling molecules, the extracellular matrix and immune cells, especially neutrophils and monocyte-derived macrophages. In a tumour setting, macrophages encompass a spectrum between a tumour-suppressive (M1) or tumour-promoting (M2) state. The biology of macrophages found in tumours (Tumour Associated Macrophages) remains unclear, but understanding their impact on tumour progression is highly important. In this paper, we perform a comprehensive analysis of a macrophage polarization network, following two lines of enquiry: (i) we reconstruct the macrophage polarization network based on literature, extending it to include important stimuli in a tumour setting, and (ii) we build a dynamical model able to reproduce macrophage polarization in the presence of different stimuli, including the contact with cancer cells. Our simulations recapitulate the documented macrophage phenotypes and their dependencies on specific receptors and transcription factors, while also unravelling the formation of a special type of tumour associated macrophages in an in vitro model of chronic lymphocytic leukaemia. This model constitutes the first step towards elucidating the cross-talk between immune and cancer cells inside tumours, with the ultimate goal of identifying new therapeutic targets that could control the formation of tumour associated macrophages in patients.

Agent-based vs. equation-based multi-scale modeling for macrophage polarization

“…Based on our findings from comparing the two models, we recommend a focus on the main interactions of extracellular mediators and macrophages, where M1/M2 polarization can occur on a continuous spectrum, reflecting the current knowledge and June 17, 2022 20/24 modeling practices of macrophage activation [9,33,34]. Important feedback loops in the pro-and anti-inflammatory phases of the immune response are: the positive feedback loop of M1 activation, upregulation of M2 via M1, and the negative feedback loop in which M2 decreases both M1 and itself.…”

“…Maiti et al modeled the recruitment and activation of STAT3 through binding of STAT3 to the IL-10/IL-10R complex without Jak1 and Tyk2. We also included a multiplier representing inhibition by Suppressors of Cytokine Signaling 1 and 3 (SOCS1 and SOCS3), two IL-10 responsive genes as well as the second term of Eq (33) and Eq (34) which allow for the conservation of STAT3 in the model. SOCS1 inhibits JAK1 function by binding its SH2 domain to JAK1, preventing STAT3 from docking to the IL-10 complex.…”

An Agent-Based Model of Monocyte Differentiation into Tumour-Associated Macrophages in Chronic Lymphocytic Leukemia

“…Finally, we realize the importance of internal regulatory processes that determine the agents’ behaviors and plan to extend this model by combining it with gene regulatory network models of phenotype transitions inside each cell (cancer cells, monocytes and potentially other cells present in the TME of solid tumors in considerable proportions such as lymphocytes or fibroblasts). More specifically, we have been working on a Boolean model of monocyte differentiation into NLCs that we plan to integrate to the presented ABM model using suitable tools in further work (16, 37, 38).…”

“…One of the limitations in the study of TAMs is the difficulty in identifying them in bulk tumor samples, due to their close similarity with other macrophages that are also present in the TME. However, we showed that although NLCs and M2-type macrophages display a similar profile in the CLL microenvironment (15), some distinctions can be highlighted, such as high expression of the RAGE membrane receptor, the HIF1 α and VEGF/EGF transcription factors in NLCs (16). Given the nurturing properties of NLCs in the CLL microenvironment, a high number of NLCs has been reported to lead to disease progression and shorter overall survival (17, 18).…”

Data Integration in Logic-based Models of Biological Mechanisms

“…This step includes data discretization using statistical thresholds to facilitate the comparison with the discrete nature of the logicbased model results. Recent examples include the enrichment of a logical model of macrophage polarisation to describe cancer cell-macrophage interactions and its validation using microarray expression data from in vitro co-culture experiments [18][19]. A similar methodology is employed for the building of a logical model for cancer cell invasion and migration.…”