Predicting the therapeutic efficacy of TLR stimulated macrophages for cancer treatment

Abstract Macrophages play a variety of roles in immunity including both pro- and anti-inflammatory functions, which can be controlled by toll-like receptor (TLR) stimulation. Previously, we generated a computational model of interconnected TLR signaling cascades and validated the model using experimental data from the THP-1 monocyte cell line. This project aimed to adapt the computational model to provide insights into the activation of bone marrow-derived macrophages (BMDM) to enable the accurate prediction of BMDM therapeutic efficacy following TLR stimulation. Initially, we quantitated expression of genes encoding TLR signaling proteins in BMDM and THP-1 cells and found distinct differences between the cell types. For instance, IRAK2 was expressed in BMDM at a level over 150 times the expression level in THP-1 cells, while other genes, such as TLR4, were expressed at relatively similar levels. Next, these data were used to adapt the computational model to BMDM. In order to determine whether the model predicted the optimal way to activate the BMDM, the cells were treated with lipopolysaccharide and flagellin and IL-1b production was quantified by ELISA. The data revealed that the model did not predict the optimal way to achieve pro-inflammatory cytokine expression in the BMDM. We are currently altering the model to improve its ability to predict pro-inflammatory cytokine expression following TLR stimulation. In addition, studies are underway exploring the therapeutic efficacy of the TLR-stimulated BMDM using a murine mammary carcinoma model. The ultimate goal of this project is to produce a computational model capable of predicting how BMDM respond to TLR stimulation and to use the model to predict the anti-tumor efficacy of the BMDM.