A combined computational and experimental approach to successfully predict the behavior of metastatic tumor-associated macrophages in humanbreast cancer

We identified in human breast cancer a scarce population of Tumor-Associated Monocytes (TAMs) endowed with a pro-metastatic activity and associated with reduced distant-metastasis free survival of patients. We developed a novel framework combining computational and experimental methodologies to dampen TAM pro-metastatic activity. Multi-modal experimental data from TAMs exposed in vitro to a series of perturbations were collected to build a Boolean dynamical network of TAMs. This framework successfully identified the biological pathways underlying TAM pro-metastatic activity and predicted potent pharmacological interventions that inhibited the pro-metastatic activity of TAMs isolated from patient tumors. This study showcases the power of integrating computational predictions with experimental validation in identifying new therapeutic avenues that can be extended to other cancer types.