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The Predictive Link between Matrix and Metastasis
Current Opinion in Chemical Engineering
  • Lauren E. Barney, University of Massachusetts Amherst
  • Lauren Jansen, University of Massachusetts Amherst
  • S. R. Polio, University of Massachusetts Amherst
  • Sualyneth Galarza, University of Massachusetts Amherst
  • Maureen E. Lynch, University of Massachusetts Amherst
  • Shelly Peyton, University of Massachusetts Amherst
Publication Date

Cancer spread (metastasis) is responsible for 90% of cancer-related fatalities. Informing patient treatment to prevent metastasis, or kill all cancer cells in a patient's body before it becomes metastatic is extremely powerful. However, aggressive treatment for all non-metastatic patients is detrimental, both for quality of life concerns, and the risk of kidney or liver-related toxicity. Knowing when and where a patient has metastatic risk could revolutionize patient treatment and care. In this review, we attempt to summarize the key work of engineers and quantitative biologists in developing strategies and model systems to predict metastasis, with a particular focus on cell interactions with the extracellular matrix (ECM), as a tool to predict metastatic risk and tropism.

SRP is a Pew Biomedical Scholar supported by the Pew Charitable Trusts. This work was funded by an NIH New Innovator award (1DP2CA186573-01), a grant from the NSF and NCI (DMR-1234852), and start-up funds from the University of Massachusetts Amherst. LEB was partially supported by National Research Service Award T32 GM008515 from the NIH. SRP was supported by a Barry and Afsaneh Siadat faculty development award.
Citation Information
Lauren E. Barney, Lauren Jansen, S. R. Polio, Sualyneth Galarza, et al.. "The Predictive Link between Matrix and Metastasis" Current Opinion in Chemical Engineering Vol. 11 (2016)
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