AUTHOR=Corral-Jara Karla F. , Rosas da Silva Gonçalo , Fierro Nora A. , Soumelis Vassili 

TITLE=Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.675099

DOI=10.3389/fcell.2021.675099

ISSN=2296-634X

ABSTRACT=CD4+ T cell differentiation is governed by gene regulatory and metabolic networks. Both networks are highly interconnected and can adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Treg cells are involved in tumor development and progression by inhibiting antitumor immunity. TME factors mediate the recruitment and expansion of Th17 cells, but these cells can progressively transdifferentiate into IL17A+FOXP3+ during tumor development. Due to the complexity of the underlying molecular pathways, modelling biological systems has emerged as a promising solution for better understanding both CD4+ T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4+T cell transcriptomic and metabolic network adaptability. We then discuss CD4+ T cell knowledge-based models to extract the regulatory elements of Th17 and Treg differentiation in multiple CD4+ T cell levels. We highlight the importance of complementing these models with data from «omics» technologies such as transcriptomics and metabolomics in order to better delineate the existing Th17 and Treg bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Treg differentiation in a normal cell, which we then connected with biological evidence in the TME context to better understand CD4+T cell behavior in cancer.  From the integration of mechanistic models with «omics» data, the reprogramming of Th17 and Treg cell transcriptomes and metabolomes can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.