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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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Advanced Animal Model of Colorectal Metastasis in Liver: Imaging Techniques and Properties of Metastatic Clones

Advanced Animal Model of Colorectal Metastasis in Liver: Imaging Techniques and Properties of Metastatic Clones

Published on: November 30, 2016

How Subclonal Modeling Is Changing the Metastatic Paradigm.

Geoff Macintyre¹, Peter Van Loo^2,3, Niall M Corcoran^4,5

¹Cancer Research UK Cambridge Institute, University of Cambridge, United Kingdom.

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research

|November 20, 2016

Summary

Understanding human metastasis is improving through sequencing primary and metastatic tumors. New algorithms enable high-resolution subclonal modeling, revealing diverse metastatic patterns and influencing cancer research.

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Models of Bone Metastasis

Models of Bone Metastasis

Published on: September 4, 2012

Area of Science:

Oncology
Genomics
Computational Biology

Background:

Sequencing matched primary and metastatic tumors is advancing the understanding of human metastasis.
Emerging evidence highlights diverse and complex metastatic patterns.
Subclonal modeling offers a high-resolution approach to study metastatic spread.

Purpose of the Study:

To summarize how subclonal models of metastasis are influencing the current understanding of the metastatic paradigm.
To highlight the impact of advanced algorithms in analyzing metastatic patterns.

Main Methods:

Utilizing data from sequenced matched primary and metastatic tumors.
Applying a new class of algorithms for high-resolution subclonal modeling.
Analyzing patterns of metastatic spread.

Main Results:

Subclonal modeling provides detailed insights into the evolutionary dynamics of metastasis.
Diverse and previously unrecognized metastatic patterns have been identified.
These models are refining the understanding of how cancer spreads.

Conclusions:

Subclonal modeling is a powerful tool for dissecting the complexities of human metastasis.
Continued integration of sequencing and computational approaches will further elucidate metastatic processes.
This research paradigm shift impacts cancer progression and treatment strategies.