Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles.
Front Oncol
; 13: 1116210, 2023.
Article
em En
| MEDLINE
| ID: mdl-37091178
ABSTRACT
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.
Texto completo:
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Front Oncol
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Japão