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Related Concept Videos

Metastasis02:30

Metastasis

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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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Overview of Cell-Matrix Interactions01:24

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The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...
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The Predictive Link between Matrix and Metastasis.

L E Barney1, L E Jansen1, S R Polio1

  • 1Department of Chemical Engineering, University of Massachusetts, Amherst Amherst, MA 01003.

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|March 5, 2016
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Summary
This summary is machine-generated.

Predicting cancer metastasis, a major cause of death, is crucial for effective patient treatment. Engineers and biologists are developing new models focusing on cell-extracellular matrix interactions to forecast metastatic risk and spread.

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Area of Science:

  • Biomedical Engineering
  • Cancer Biology
  • Quantitative Biology

Background:

  • Metastasis causes 90% of cancer fatalities, necessitating predictive strategies.
  • Overtreatment of non-metastatic cancer poses risks to patient quality of life and organ toxicity.
  • Accurate prediction of metastatic risk and tropism can revolutionize cancer care.

Purpose of the Study:

  • To review engineering and quantitative biology approaches for predicting cancer metastasis.
  • To highlight the role of cell-extracellular matrix (ECM) interactions in metastasis prediction.
  • To summarize model systems developed for assessing metastatic potential.

Main Methods:

  • Review of interdisciplinary research integrating engineering principles and quantitative biology.
  • Focus on experimental and computational models of cancer cell behavior.
  • Analysis of cell interactions with the extracellular matrix (ECM) as a predictive factor.

Main Results:

  • Engineered strategies and model systems show promise in predicting metastatic risk.
  • Understanding cell-ECM interactions is key to forecasting metastasis and tropism.
  • Quantitative approaches offer novel tools for metastasis assessment.

Conclusions:

  • Predictive models for metastasis can guide personalized treatment strategies.
  • Targeting cell-ECM interactions may offer therapeutic avenues.
  • Interdisciplinary collaboration is vital for advancing metastasis research.