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A computational model for the cancer field effect

INTRODUCTION: The Cancer Field Effect describes an area of pre-cancerous cells that results from continued exposure to carcinogens. Cells in the cancer field can easily develop into cancer. Removal of the main tumor mass might leave the cancer field behind, increasing risk of recurrence. METHODS: Th...

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Autores principales: Deutscher, Karl, Hillen, Thomas, Newby, Jay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352683/
https://www.ncbi.nlm.nih.gov/pubmed/37469932
http://dx.doi.org/10.3389/frai.2023.1060879
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author Deutscher, Karl
Hillen, Thomas
Newby, Jay
author_facet Deutscher, Karl
Hillen, Thomas
Newby, Jay
author_sort Deutscher, Karl
collection PubMed
description INTRODUCTION: The Cancer Field Effect describes an area of pre-cancerous cells that results from continued exposure to carcinogens. Cells in the cancer field can easily develop into cancer. Removal of the main tumor mass might leave the cancer field behind, increasing risk of recurrence. METHODS: The model we propose for the cancer field effect is a hybrid cellular automaton (CA), which includes a multi-layer perceptron (MLP) to compute the effects of the carcinogens on the gene expression of the genes related to cancer development. We use carcinogen interactions that are typically associated with smoking and alcohol consumption and their effect on cancer fields of the tongue. RESULTS: Using simulations we support the understanding that tobacco smoking is a potent carcinogen, which can be reinforced by alcohol consumption. The effect of alcohol alone is significantly less than the effect of tobacco. We further observe that pairing tumor excision with field removal delays recurrence compared to tumor excision alone. We track cell lineages and find that, in most cases, a polyclonal field develops, where the number of distinct cell lineages decreases over time as some lineages become dominant over others. Finally, we find tumor masses rarely form via monoclonal origin.
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spelling pubmed-103526832023-07-19 A computational model for the cancer field effect Deutscher, Karl Hillen, Thomas Newby, Jay Front Artif Intell Artificial Intelligence INTRODUCTION: The Cancer Field Effect describes an area of pre-cancerous cells that results from continued exposure to carcinogens. Cells in the cancer field can easily develop into cancer. Removal of the main tumor mass might leave the cancer field behind, increasing risk of recurrence. METHODS: The model we propose for the cancer field effect is a hybrid cellular automaton (CA), which includes a multi-layer perceptron (MLP) to compute the effects of the carcinogens on the gene expression of the genes related to cancer development. We use carcinogen interactions that are typically associated with smoking and alcohol consumption and their effect on cancer fields of the tongue. RESULTS: Using simulations we support the understanding that tobacco smoking is a potent carcinogen, which can be reinforced by alcohol consumption. The effect of alcohol alone is significantly less than the effect of tobacco. We further observe that pairing tumor excision with field removal delays recurrence compared to tumor excision alone. We track cell lineages and find that, in most cases, a polyclonal field develops, where the number of distinct cell lineages decreases over time as some lineages become dominant over others. Finally, we find tumor masses rarely form via monoclonal origin. Frontiers Media S.A. 2023-07-04 /pmc/articles/PMC10352683/ /pubmed/37469932 http://dx.doi.org/10.3389/frai.2023.1060879 Text en Copyright © 2023 Deutscher, Hillen and Newby. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Deutscher, Karl
Hillen, Thomas
Newby, Jay
A computational model for the cancer field effect
title A computational model for the cancer field effect
title_full A computational model for the cancer field effect
title_fullStr A computational model for the cancer field effect
title_full_unstemmed A computational model for the cancer field effect
title_short A computational model for the cancer field effect
title_sort computational model for the cancer field effect
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352683/
https://www.ncbi.nlm.nih.gov/pubmed/37469932
http://dx.doi.org/10.3389/frai.2023.1060879
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