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Genetic background may contribute to PAM50 gene expression breast cancer subtype assignments.

Hu Y, Bai L, Geiger T, Goldberger N, Walker RC, Green JE, Wakefield LM, Hunter KW - PLoS ONE (2013)

Bottom Line: Recent advances in genome wide transcriptional analysis have provided greater insights into the etiology and heterogeneity of breast cancer.It is thought that the expression patterns of the molecular subtypes primarily reflect cell-of-origin or tumor driver mutations.These results have important implications for interpretation of "snapshot" expression profiles, as well as suggesting that incorporation of genetic background effects may allow investigation into phenotypes not initially anticipated in individual mouse models of cancer.

View Article: PubMed Central - PubMed

Affiliation: Center for Biomedical Informatics and Information Technology, Bethesda, Maryland, United States of America.

ABSTRACT
Recent advances in genome wide transcriptional analysis have provided greater insights into the etiology and heterogeneity of breast cancer. Molecular signatures have been developed that stratify the conventional estrogen receptor positive or negative categories into subtypes that are associated with differing clinical outcomes. It is thought that the expression patterns of the molecular subtypes primarily reflect cell-of-origin or tumor driver mutations. In this study however, using a genetically engineered mouse mammary tumor model we demonstrate that the PAM50 subtype signature of tumors driven by a common oncogenic event can be significantly influenced by the genetic background on which the tumor arises. These results have important implications for interpretation of "snapshot" expression profiles, as well as suggesting that incorporation of genetic background effects may allow investigation into phenotypes not initially anticipated in individual mouse models of cancer.

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Related in: MedlinePlus

PAM50 clustering.A) NZB cross samples after unsupervised hierarchical clustered using the mouse orthologs of the PAM50 signature. B) The human breast cancer U133A gene expression data set after PAM50 subtype clustering using the Genefu R subtyping algorithm. Subtype classifications are indicated along the top of the heatmap.
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pone-0072287-g001: PAM50 clustering.A) NZB cross samples after unsupervised hierarchical clustered using the mouse orthologs of the PAM50 signature. B) The human breast cancer U133A gene expression data set after PAM50 subtype clustering using the Genefu R subtyping algorithm. Subtype classifications are indicated along the top of the heatmap.

Mentions: To address this hypothesis, unsupervised hierarchical clustering of the gene expression data from an NZB backcross was performed. This backcross expression data set consists of 68 mammary tumors from a genetic mapping backcross performed between NZB/B1NJ and MMTV-PyMT mice [16], arrayed on the MOE430 v2 chip [21]. The data was filtered for the 42 genes comprising the PAM50 intrinsic subtype classifier and clustering performed. As can been observed in figure 1a, distinct subgroups within the mouse samples were observed, reminiscent of the subtypes observed by PAM50 subtyping of the GSE2034 human breast cancer data (figure 1b) [22] set. These results were therefore consistent with the possibility that genetic background significantly contributes to gene expression-based breast cancer subtype assignments.


Genetic background may contribute to PAM50 gene expression breast cancer subtype assignments.

Hu Y, Bai L, Geiger T, Goldberger N, Walker RC, Green JE, Wakefield LM, Hunter KW - PLoS ONE (2013)

PAM50 clustering.A) NZB cross samples after unsupervised hierarchical clustered using the mouse orthologs of the PAM50 signature. B) The human breast cancer U133A gene expression data set after PAM50 subtype clustering using the Genefu R subtyping algorithm. Subtype classifications are indicated along the top of the heatmap.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756056&req=5

pone-0072287-g001: PAM50 clustering.A) NZB cross samples after unsupervised hierarchical clustered using the mouse orthologs of the PAM50 signature. B) The human breast cancer U133A gene expression data set after PAM50 subtype clustering using the Genefu R subtyping algorithm. Subtype classifications are indicated along the top of the heatmap.
Mentions: To address this hypothesis, unsupervised hierarchical clustering of the gene expression data from an NZB backcross was performed. This backcross expression data set consists of 68 mammary tumors from a genetic mapping backcross performed between NZB/B1NJ and MMTV-PyMT mice [16], arrayed on the MOE430 v2 chip [21]. The data was filtered for the 42 genes comprising the PAM50 intrinsic subtype classifier and clustering performed. As can been observed in figure 1a, distinct subgroups within the mouse samples were observed, reminiscent of the subtypes observed by PAM50 subtyping of the GSE2034 human breast cancer data (figure 1b) [22] set. These results were therefore consistent with the possibility that genetic background significantly contributes to gene expression-based breast cancer subtype assignments.

Bottom Line: Recent advances in genome wide transcriptional analysis have provided greater insights into the etiology and heterogeneity of breast cancer.It is thought that the expression patterns of the molecular subtypes primarily reflect cell-of-origin or tumor driver mutations.These results have important implications for interpretation of "snapshot" expression profiles, as well as suggesting that incorporation of genetic background effects may allow investigation into phenotypes not initially anticipated in individual mouse models of cancer.

View Article: PubMed Central - PubMed

Affiliation: Center for Biomedical Informatics and Information Technology, Bethesda, Maryland, United States of America.

ABSTRACT
Recent advances in genome wide transcriptional analysis have provided greater insights into the etiology and heterogeneity of breast cancer. Molecular signatures have been developed that stratify the conventional estrogen receptor positive or negative categories into subtypes that are associated with differing clinical outcomes. It is thought that the expression patterns of the molecular subtypes primarily reflect cell-of-origin or tumor driver mutations. In this study however, using a genetically engineered mouse mammary tumor model we demonstrate that the PAM50 subtype signature of tumors driven by a common oncogenic event can be significantly influenced by the genetic background on which the tumor arises. These results have important implications for interpretation of "snapshot" expression profiles, as well as suggesting that incorporation of genetic background effects may allow investigation into phenotypes not initially anticipated in individual mouse models of cancer.

Show MeSH
Related in: MedlinePlus