A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-04

AUTHORS

Elvin Wagenblast, Mar Soto, Sara Gutiérrez-Ángel, Christina A. Hartl, Annika L. Gable, Ashley R. Maceli, Nicolas Erard, Alissa M. Williams, Sun Y. Kim, Steffen Dickopf, J. Chuck Harrell, Andrew D. Smith, Charles M. Perou, John E. Wilkinson, Gregory J. Hannon, Simon R. V. Knott

ABSTRACT

Cancer metastasis requires that primary tumour cells evolve the capacity to intravasate into the lymphatic system or vasculature, and extravasate into and colonize secondary sites. Others have demonstrated that individual cells within complex populations show heterogeneity in their capacity to form secondary lesions. Here we develop a polyclonal mouse model of breast tumour heterogeneity, and show that distinct clones within a mixed population display specialization, for example, dominating the primary tumour, contributing to metastatic populations, or showing tropism for entering the lymphatic or vasculature systems. We correlate these stable properties to distinct gene expression profiles. Those clones that efficiently enter the vasculature express two secreted proteins, Serpine2 and Slpi, which were necessary and sufficient to program these cells for vascular mimicry. Our data indicate that these proteins not only drive the formation of extravascular networks but also ensure their perfusion by acting as anticoagulants. We propose that vascular mimicry drives the ability of some breast tumour cells to contribute to distant metastases while simultaneously satisfying a critical need of the primary tumour to be fed by the vasculature. Enforced expression of SERPINE2 and SLPI in human breast cancer cell lines also programmed them for vascular mimicry, and SERPINE2 and SLPI were overexpressed preferentially in human patients that had lung-metastatic relapse. Thus, these two secreted proteins, and the phenotype they promote, may be broadly relevant as drivers of metastatic progression in human cancer. More... »

PAGES

358

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nature14403

DOI

http://dx.doi.org/10.1038/nature14403

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043892984

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25855289


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/nature14403'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/nature14403'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/nature14403'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/nature14403'


 

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