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Published in: Breast Cancer Research and Treatment 2/2012

Open Access 01-04-2012 | Preclinical Study

Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse

Authors: J. Chuck Harrell, Aleix Prat, Joel S. Parker, Cheng Fan, Xiaping He, Lisa Carey, Carey Anders, Matthew Ewend, Charles M. Perou

Published in: Breast Cancer Research and Treatment | Issue 2/2012

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Abstract

The ability to predict metastatic potential could be of great clinical importance, however, it is uncertain if predicting metastasis to specific vital organs is feasible. As a first step in evaluating metastatic predictions, we analyzed multiple primary tumors and metastasis pairs and determined that >90% of 298 gene expression signatures were found to be similarly expressed between matched pairs of tumors and metastases; therefore, primary tumors may be a good predictor of metastatic propensity. Next, using a dataset of >1,000 human breast tumor gene expression microarrays we determined that HER2-enriched subtype tumors aggressively spread to the liver, while basal-like and claudin-low subtypes colonize the brain and lung. Correspondingly, brain and lung metastasis signatures, along with embryonic stem cell, tumor initiating cell, and hypoxia signatures, were also strongly expressed in the basal-like and claudin-low tumors. Interestingly, low “Differentiation Scores,” or high expression of the aforementioned signatures, further predicted for brain and lung metastases. In total, these data identify that depending upon the organ of relapse, a combination of gene expression signatures most accurately predicts metastatic behavior.
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Metadata
Title
Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse
Authors
J. Chuck Harrell
Aleix Prat
Joel S. Parker
Cheng Fan
Xiaping He
Lisa Carey
Carey Anders
Matthew Ewend
Charles M. Perou
Publication date
01-04-2012
Publisher
Springer US
Published in
Breast Cancer Research and Treatment / Issue 2/2012
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-011-1619-7

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