Agent-Based Learning Model for the Obesity Paradox in RCC

Matteo Belenchia; Giacomo Rocchetti; Stefano Maestri; Alessia Cimadamore; Rodolfo Montironi; Matteo Santoni; Emanuela Merelli

doi:10.3389/fbioe.2021.642760

Agent-Based Learning Model for the Obesity Paradox in RCC

Front Bioeng Biotechnol. 2021 Apr 29:9:642760. doi: 10.3389/fbioe.2021.642760. eCollection 2021.

Authors

Matteo Belenchia¹, Giacomo Rocchetti¹, Stefano Maestri^{1

2}, Alessia Cimadamore³, Rodolfo Montironi³, Matteo Santoni⁴, Emanuela Merelli¹

Affiliations

¹ Laboratory of Data Science and Bioshape, School of Science and Technology, University of Camerino, Camerino, Italy.
² Centre de Physique Théorique, Aix-Marseille University, Marseilles, France.
³ Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy.
⁴ Department of Oncology, Macerata Hospital, Macerata, Italy.

Abstract

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.

Keywords: bioagent; cell-cell interaction network; computational biology; dynamical networks; immunotherapy; interaction-as-perception paradigm; multiagent system; renal cell carcinoma.