A dynamic model for tumour growth and metastasis formation

Area of Science:

• Computational biology
• Cancer research
• Mathematical modeling

Background:

• Tumor growth and metastasis are complex biological processes.
• Understanding the dynamics of tumor progression is crucial for effective cancer treatment.
• Current models may not fully capture the early stages of metastatic dissemination.

Purpose of the Study:

• To develop a simple and fast computational model for tumor growth and metastasis.
• To describe key biological entities such as tumor volume and metastatic colony formation.
• To analyze the chronology of metastatic cascade events.

Main Methods:

• The model calculates successive generations of tumor cells.
• It incorporates two embedded growth models for Gompertzian-like tumor behavior.
• Model training utilized analytical solutions for hepatocellular carcinoma metastasis size distribution.
• Applicability was validated using clinical data from the Munich Cancer Registry.

Main Results:

• The model successfully describes tumor volume, time of first metastatic growth, and number of metastatic colonies.
• Simulations using clinical data from breast cancer cases demonstrate model applicability.
• Calculations indicate that metastasis formation often initiates before primary tumor detection.

Conclusions:

• The developed computational model provides a tool for analyzing tumor growth and metastasis dynamics.
• It offers insights into the early initiation of metastasis relevant to clinical breast cancer research.
• The model supports systematic analyses for improved clinical treatment strategies.