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Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse
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 tha...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3303043/ https://www.ncbi.nlm.nih.gov/pubmed/21671017 http://dx.doi.org/10.1007/s10549-011-1619-7 |
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author | Harrell, J. Chuck Prat, Aleix Parker, Joel S. Fan, Cheng He, Xiaping Carey, Lisa Anders, Carey Ewend, Matthew Perou, Charles M. |
author_facet | Harrell, J. Chuck Prat, Aleix Parker, Joel S. Fan, Cheng He, Xiaping Carey, Lisa Anders, Carey Ewend, Matthew Perou, Charles M. |
author_sort | Harrell, J. Chuck |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-011-1619-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-3303043 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-33030432012-03-22 Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse Harrell, J. Chuck Prat, Aleix Parker, Joel S. Fan, Cheng He, Xiaping Carey, Lisa Anders, Carey Ewend, Matthew Perou, Charles M. Breast Cancer Res Treat Preclinical Study 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-011-1619-7) contains supplementary material, which is available to authorized users. Springer US 2011-06-14 2012-04 /pmc/articles/PMC3303043/ /pubmed/21671017 http://dx.doi.org/10.1007/s10549-011-1619-7 Text en © Springer Science+Business Media, LLC. 2011 |
spellingShingle | Preclinical Study Harrell, J. Chuck Prat, Aleix Parker, Joel S. Fan, Cheng He, Xiaping Carey, Lisa Anders, Carey Ewend, Matthew Perou, Charles M. Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse |
title | Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse |
title_full | Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse |
title_fullStr | Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse |
title_full_unstemmed | Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse |
title_short | Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse |
title_sort | genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse |
topic | Preclinical Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3303043/ https://www.ncbi.nlm.nih.gov/pubmed/21671017 http://dx.doi.org/10.1007/s10549-011-1619-7 |
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