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Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles

INTRODUCTION: Local and regional recurrence after surgical intervention is a significant problem in cancer management. The multistage theory of carcinogenesis precisely places the presence of histologically normal but mutated premalignant lesions surrounding the tumor - field cancerization, as a sig...

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Autores principales: Abubakar, Sharafudeen Dahiru, Takaki, Mitsuaki, Haeno, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117647/
https://www.ncbi.nlm.nih.gov/pubmed/37091178
http://dx.doi.org/10.3389/fonc.2023.1116210
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author Abubakar, Sharafudeen Dahiru
Takaki, Mitsuaki
Haeno, Hiroshi
author_facet Abubakar, Sharafudeen Dahiru
Takaki, Mitsuaki
Haeno, Hiroshi
author_sort Abubakar, Sharafudeen Dahiru
collection PubMed
description INTRODUCTION: Local and regional recurrence after surgical intervention is a significant problem in cancer management. The multistage theory of carcinogenesis precisely places the presence of histologically normal but mutated premalignant lesions surrounding the tumor - field cancerization, as a significant cause of cancer recurrence. The relationship between tissue dynamics, cancer initiation and cancer recurrence in multistage carcinogenesis is not well known. METHODS: This study constructs a computational model for cancer initiation and recurrence by combining the Moran and branching processes in which cells requires 3 or more mutations to become malignant. In addition, a spatial structure-setting is included in the model to account for positional relativity in cell turnover towards malignant transformation. The model consists of a population of normal cells with no mutation; several populations of premalignant cells with varying number of mutations and a population of malignant cells. The model computes a stage of cancer detection and surgery to eliminate malignant cells but spares premalignant cells and then estimates the time for malignant cells to re-emerge. RESULTS: We report the cellular conditions that give rise to different patterns of cancer initiation and the conditions favoring a shorter cancer recurrence by analyzing premalignant cell types at the time of surgery. In addition, the model is fitted to disease-free clinical data of 8,957 patients in 27 different cancer types; From this fitting, we estimate the turnover rate per month, relative fitness of premalignant cells, growth rate and death rate of cancer cells in each cancer type. DISCUSSION: Our study provides insights into how to identify patients who are likely to have a shorter recurrence and where to target the therapeutic intervention.
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spelling pubmed-101176472023-04-21 Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles Abubakar, Sharafudeen Dahiru Takaki, Mitsuaki Haeno, Hiroshi Front Oncol Oncology INTRODUCTION: Local and regional recurrence after surgical intervention is a significant problem in cancer management. The multistage theory of carcinogenesis precisely places the presence of histologically normal but mutated premalignant lesions surrounding the tumor - field cancerization, as a significant cause of cancer recurrence. The relationship between tissue dynamics, cancer initiation and cancer recurrence in multistage carcinogenesis is not well known. METHODS: This study constructs a computational model for cancer initiation and recurrence by combining the Moran and branching processes in which cells requires 3 or more mutations to become malignant. In addition, a spatial structure-setting is included in the model to account for positional relativity in cell turnover towards malignant transformation. The model consists of a population of normal cells with no mutation; several populations of premalignant cells with varying number of mutations and a population of malignant cells. The model computes a stage of cancer detection and surgery to eliminate malignant cells but spares premalignant cells and then estimates the time for malignant cells to re-emerge. RESULTS: We report the cellular conditions that give rise to different patterns of cancer initiation and the conditions favoring a shorter cancer recurrence by analyzing premalignant cell types at the time of surgery. In addition, the model is fitted to disease-free clinical data of 8,957 patients in 27 different cancer types; From this fitting, we estimate the turnover rate per month, relative fitness of premalignant cells, growth rate and death rate of cancer cells in each cancer type. DISCUSSION: Our study provides insights into how to identify patients who are likely to have a shorter recurrence and where to target the therapeutic intervention. Frontiers Media S.A. 2023-04-06 /pmc/articles/PMC10117647/ /pubmed/37091178 http://dx.doi.org/10.3389/fonc.2023.1116210 Text en Copyright © 2023 Abubakar, Takaki and Haeno 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 Oncology
Abubakar, Sharafudeen Dahiru
Takaki, Mitsuaki
Haeno, Hiroshi
Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles
title Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles
title_full Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles
title_fullStr Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles
title_full_unstemmed Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles
title_short Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles
title_sort computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117647/
https://www.ncbi.nlm.nih.gov/pubmed/37091178
http://dx.doi.org/10.3389/fonc.2023.1116210
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