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Metastasis

Metastasis

Metastasis is the spread of cancer cells from the original site to distant locations in the body. Cancer cells can spread via blood vessels (hematogenous) as well as lymph vessels in the body.
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...

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Related Experiment Video

Updated: May 2, 2026

Quantitative Analysis of Cancer Metastasis using an Avian Embryo Model

Quantitative Analysis of Cancer Metastasis using an Avian Embryo Model

Published on: May 30, 2011

Integrating EMT dynamics in model-based metastasis prediction.

Artur Wycislok¹, Malgorzata Kardynska¹, Jaroslaw Smieja¹

¹Silesian University of Technology, Department of Systems Biology and Engineering, Akademicka 16, Gliwice, 44-100, Poland.

Computer Methods and Programs in Biomedicine

|April 30, 2026

Summary

This summary is machine-generated.

Predicting cancer metastasis is vital. Mathematical modeling of transforming growth factor beta (TGF-β) dynamics and epithelial-to-mesenchymal transition (EMT) can improve prognosis accuracy without costly imaging.

Keywords:

EMT Metastasis NSCLC TGF-β

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Last Updated: May 2, 2026

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Area of Science:

Oncology
Mathematical Biology
Computational Pathology

Background:

Metastatic tumors are the leading cause of cancer mortality.
Early detection and treatment of metastases are critical for patient survival.
Current medical imaging for metastasis detection is expensive and resource-intensive.

Purpose of the Study:

To develop a mathematical model for predicting metastasis.
To investigate the role of transforming growth factor beta (TGF-β) dynamics in metastasis.
To create a virtual patient cohort for simulating tumor growth and treatment response.

Main Methods:

Mathematical modeling of TGF-β dynamics and epithelial-to-mesenchymal transition (EMT).
Generation of a virtual patient cohort with patient-specific parameters.
Simulation of tumor growth and response to chemotherapy and radiotherapy.
Comparison of simulation results (metastasis-free and overall survival) with clinical data.

Main Results:

Modeling results show concordance with available clinical data.
The dynamics of TGF-β level changes are more critical than absolute levels for metastasis.
This explains the previously arguable prognostic value of TGF-β.

Conclusions:

TGF-β level dynamics, not just absolute levels, are key in metastasis.
Recommend serial measurements of TGF-β over single measurements for improved prognosis.
The modeling approach enables patient-specific simulations for more accurate population representation.