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Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies

SIMPLE SUMMARY: The recent success of immunotherapy treatments against cancer relies on helping our own body’s defenses in the fight against tumours, namely reinvigorating the cancer killing action of T cells. Unfortunately, in a large proportion of patients these therapies are ineffective, in part...

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Autores principales: Marku, Malvina, Verstraete, Nina, Raynal, Flavien, Madrid-Mencía, Miguel, Domagala, Marcin, Fournié, Jean-Jacques, Ysebaert, Loïc, Poupot, Mary, Pancaldi, Vera
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762229/
https://www.ncbi.nlm.nih.gov/pubmed/33297362
http://dx.doi.org/10.3390/cancers12123664
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author Marku, Malvina
Verstraete, Nina
Raynal, Flavien
Madrid-Mencía, Miguel
Domagala, Marcin
Fournié, Jean-Jacques
Ysebaert, Loïc
Poupot, Mary
Pancaldi, Vera
author_facet Marku, Malvina
Verstraete, Nina
Raynal, Flavien
Madrid-Mencía, Miguel
Domagala, Marcin
Fournié, Jean-Jacques
Ysebaert, Loïc
Poupot, Mary
Pancaldi, Vera
author_sort Marku, Malvina
collection PubMed
description SIMPLE SUMMARY: The recent success of immunotherapy treatments against cancer relies on helping our own body’s defenses in the fight against tumours, namely reinvigorating the cancer killing action of T cells. Unfortunately, in a large proportion of patients these therapies are ineffective, in part due to the presence of other immune cells, macrophages, which are mis-educated by the cancer cells into promoting tumour growth. Here we start from an existing model of macrophage polarization and extend it to the specific conditions encountered inside a tumour by adding signals, receptors, transcription factors and cytokines that are known to be the key components in establishing the cancer cell-macrophage interaction. Then we use a mathematical Boolean model applied to a gene regulatory network of this biological process to simulate its temporal behaviour and explore scenarios that have not been experimentally tested so far. Additionally, the KO and overexpression simulations successfully reproduce the known experimental results while predicting the potential role of regulators (such as STAT1 and EGF) in preventing the formation of pro-tumoural macrophages, which can be tested experimentally. ABSTRACT: The tumour microenvironment is the surrounding of a tumour, including blood vessels, fibroblasts, signaling molecules, the extracellular matrix and immune cells, especially neutrophils and monocyte-derived macrophages. In a tumour setting, macrophages encompass a spectrum between a tumour-suppressive (M1) or tumour-promoting (M2) state. The biology of macrophages found in tumours (Tumour Associated Macrophages) remains unclear, but understanding their impact on tumour progression is highly important. In this paper, we perform a comprehensive analysis of a macrophage polarization network, following two lines of enquiry: (i) we reconstruct the macrophage polarization network based on literature, extending it to include important stimuli in a tumour setting, and (ii) we build a dynamical model able to reproduce macrophage polarization in the presence of different stimuli, including the contact with cancer cells. Our simulations recapitulate the documented macrophage phenotypes and their dependencies on specific receptors and transcription factors, while also unravelling the formation of a special type of tumour associated macrophages in an in vitro model of chronic lymphocytic leukaemia. This model constitutes the first step towards elucidating the cross-talk between immune and cancer cells inside tumours, with the ultimate goal of identifying new therapeutic targets that could control the formation of tumour associated macrophages in patients.
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spelling pubmed-77622292020-12-26 Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies Marku, Malvina Verstraete, Nina Raynal, Flavien Madrid-Mencía, Miguel Domagala, Marcin Fournié, Jean-Jacques Ysebaert, Loïc Poupot, Mary Pancaldi, Vera Cancers (Basel) Article SIMPLE SUMMARY: The recent success of immunotherapy treatments against cancer relies on helping our own body’s defenses in the fight against tumours, namely reinvigorating the cancer killing action of T cells. Unfortunately, in a large proportion of patients these therapies are ineffective, in part due to the presence of other immune cells, macrophages, which are mis-educated by the cancer cells into promoting tumour growth. Here we start from an existing model of macrophage polarization and extend it to the specific conditions encountered inside a tumour by adding signals, receptors, transcription factors and cytokines that are known to be the key components in establishing the cancer cell-macrophage interaction. Then we use a mathematical Boolean model applied to a gene regulatory network of this biological process to simulate its temporal behaviour and explore scenarios that have not been experimentally tested so far. Additionally, the KO and overexpression simulations successfully reproduce the known experimental results while predicting the potential role of regulators (such as STAT1 and EGF) in preventing the formation of pro-tumoural macrophages, which can be tested experimentally. ABSTRACT: The tumour microenvironment is the surrounding of a tumour, including blood vessels, fibroblasts, signaling molecules, the extracellular matrix and immune cells, especially neutrophils and monocyte-derived macrophages. In a tumour setting, macrophages encompass a spectrum between a tumour-suppressive (M1) or tumour-promoting (M2) state. The biology of macrophages found in tumours (Tumour Associated Macrophages) remains unclear, but understanding their impact on tumour progression is highly important. In this paper, we perform a comprehensive analysis of a macrophage polarization network, following two lines of enquiry: (i) we reconstruct the macrophage polarization network based on literature, extending it to include important stimuli in a tumour setting, and (ii) we build a dynamical model able to reproduce macrophage polarization in the presence of different stimuli, including the contact with cancer cells. Our simulations recapitulate the documented macrophage phenotypes and their dependencies on specific receptors and transcription factors, while also unravelling the formation of a special type of tumour associated macrophages in an in vitro model of chronic lymphocytic leukaemia. This model constitutes the first step towards elucidating the cross-talk between immune and cancer cells inside tumours, with the ultimate goal of identifying new therapeutic targets that could control the formation of tumour associated macrophages in patients. MDPI 2020-12-07 /pmc/articles/PMC7762229/ /pubmed/33297362 http://dx.doi.org/10.3390/cancers12123664 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Marku, Malvina
Verstraete, Nina
Raynal, Flavien
Madrid-Mencía, Miguel
Domagala, Marcin
Fournié, Jean-Jacques
Ysebaert, Loïc
Poupot, Mary
Pancaldi, Vera
Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies
title Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies
title_full Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies
title_fullStr Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies
title_full_unstemmed Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies
title_short Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies
title_sort insights on tam formation from a boolean model of macrophage polarization based on in vitro studies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762229/
https://www.ncbi.nlm.nih.gov/pubmed/33297362
http://dx.doi.org/10.3390/cancers12123664
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