Agent-Based Learning Model for the Obesity Paradox in RCC - Belenchia, Matteo; Rocchetti, Giacomo; Maestri, Stefano; Cimadamore, Alessia; Montironi, Rodolfo; Santoni, Matteo; Merelli, Emanuela (Frontiers in Bioengineering and Biotechnology, 2021)
A recent study on the immunotherapy treatment of renal cell carcinoma reveals better outcomes in obese patients compared to lean subjects. This enigmatic contradiction has been explained, in the context of the debated obesity paradox, as the effect produced by the cell-cell interaction network on the tumor microenvironment during the immune response. To better understand this hypothesis, we provide a computational framework for the in silico study of the tumor behavior. The starting model of the tumor, based on the cell-cell interaction network, has been described as a multiagent system, whose simulation generates the hypothesized effects on the tumor microenvironment. The medical needs in the immunotherapy design meet the capabilities of a multiagent simulator to reproduce the dynamics of the cell-cell interaction network, meaning a reaction to environmental changes introduced through the experimental data.