Dynamics of metastasis suppressor gene inactivation
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Published on: March 8, 2022
1Harvard Society of Fellows, Harvard Program for Evolutionary Dynamics, Cambridge, MA 02138, USA. michor@fas.harvard.edu
Abstract:
For most cancer cell types, the acquisition of metastatic ability leads to clinically incurable disease. Twelve metastasis suppressor genes (MSGs) have been identified that reduce the metastatic propensity of cancer cells. If these genes are inactivated in both alleles, metastatic ability is promoted. Here, we develop a mathematical model of the dynamics of MSG inactivation and calculate the expected number of metastases formed by a tumor. We analyse the effects of increased mutation rates and different fitness values of cells with one or two inactivated alleles on the ability of a tumor to form metastases. We find that mutations that are negatively selected in the main tumor are unlikely to be responsible for the majority of metastases produced by a tumor. Most metastases-causing mutations will be present in all (or most) cells in the main tumor.
Insights
Metastasis suppressor genes (MSGs) control cancer spread. Mathematical modeling reveals that most metastasis-causing mutations originate from widespread genetic changes within the primary tumor, not rare events.
Area of Science:
- Oncology
- Cancer Metastasis Research
- Mathematical Biology
Background:
- Metastasis acquisition renders most cancers clinically incurable.
- Twelve metastasis suppressor genes (MSGs) are known to reduce cancer cell metastatic potential.
- Inactivation of both alleles of MSGs promotes metastatic ability.
Purpose of the Study:
- To develop a mathematical model for MSG inactivation dynamics.
- To calculate the expected number of metastases formed by a tumor.
- To analyze the impact of mutation rates and cell fitness on metastasis formation.
Main Methods:
- Development of a mathematical model simulating MSG inactivation.
- Calculation of expected metastasis numbers based on model dynamics.
- Analysis of mutation rates and fitness effects on metastatic potential.
Main Results:
- Mutations negatively selected in the primary tumor are unlikely to drive widespread metastasis.
- The majority of metastasis-causing mutations are likely present in most or all cells of the primary tumor.
- Model predicts the number of metastases based on MSG inactivation dynamics.
Conclusions:
- Understanding MSG inactivation dynamics is crucial for predicting cancer metastasis.
- Widespread genetic alterations in the primary tumor are key drivers of metastasis.
- This model provides insights into the genetic basis of metastatic disease.
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