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Gene expression patterns associated with p53 status in breast cancer.

Troester MA, Herschkowitz JI, Oh DS, He X, Hoadley KA, Barbier CS, Perou CM - BMC Cancer (2006)

Bottom Line: The cell line signatures were compared with p53-mutation associated genes in breast tumors.Further, a common gene expression signature associated with p53 loss across all four cell lines was identified.Our biologically-based refinements excluded genes that were associated with subtype but not downstream of p53 signaling, and identified a signature for p53 loss that is shared across breast cancer subtypes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA. troester@schoolph.umass.edu

ABSTRACT

Background: Breast cancer subtypes identified in genomic studies have different underlying genetic defects. Mutations in the tumor suppressor p53 occur more frequently in estrogen receptor (ER) negative, basal-like and HER2-amplified tumors than in luminal, ER positive tumors. Thus, because p53 mutation status is tightly linked to other characteristics of prognostic importance, it is difficult to identify p53's independent prognostic effects. The relation between p53 status and subtype can be better studied by combining data from primary tumors with data from isogenic cell line pairs (with and without p53 function).

Methods: The p53-dependent gene expression signatures of four cell lines (MCF-7, ZR-75-1, and two immortalized human mammary epithelial cell lines) were identified by comparing p53-RNAi transduced cell lines to their parent cell lines. Cell lines were treated with vehicle only or doxorubicin to identify p53 responses in both non-induced and induced states. The cell line signatures were compared with p53-mutation associated genes in breast tumors.

Results: Each cell line displayed distinct patterns of p53-dependent gene expression, but cell type specific (basal vs. luminal) commonalities were evident. Further, a common gene expression signature associated with p53 loss across all four cell lines was identified. This signature showed overlap with the signature of p53 loss/mutation status in primary breast tumors. Moreover, the common cell-line tumor signature excluded genes that were breast cancer subtype-associated, but not downstream of p53. To validate the biological relevance of the common signature, we demonstrated that this gene set predicted relapse-free, disease-specific, and overall survival in independent test data.

Conclusion: In the presence of breast cancer heterogeneity, experimental and biologically-based methods for assessing gene expression in relation to p53 status provide prognostic and biologically-relevant gene lists. Our biologically-based refinements excluded genes that were associated with subtype but not downstream of p53 signaling, and identified a signature for p53 loss that is shared across breast cancer subtypes.

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Gene expression pattern of tumor samples for 747 genes correlated with p53 status in tumors. The fold change relative to the median expression value across all tumors is shown. Colored bars in A illustrate the location of clusters C and D. The dendrogram in B shows that experiments were divided into two primary branches, one enriched for mutant (red) and one enriched for wildtype (green) tumors. Tumor sample names are red for mutants and green for wildtypes based on sequence analysis. Clusters enriched for proliferation and cell cycle genes (C) and for luminal/estrogen responsive tumor markers (D) are shown.
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Figure 4: Gene expression pattern of tumor samples for 747 genes correlated with p53 status in tumors. The fold change relative to the median expression value across all tumors is shown. Colored bars in A illustrate the location of clusters C and D. The dendrogram in B shows that experiments were divided into two primary branches, one enriched for mutant (red) and one enriched for wildtype (green) tumors. Tumor sample names are red for mutants and green for wildtypes based on sequence analysis. Clusters enriched for proliferation and cell cycle genes (C) and for luminal/estrogen responsive tumor markers (D) are shown.

Mentions: Gene expression data for primary breast tumors with known p53 mutation status is publicly available [2,12]. Using this data, we found that the expression of 747 genes was significantly correlated with p53 status (Figure 4A). The hierarchical cluster of these genes across the primary tumors contained two branches (Figure 4B), one enriched for wild-type tumors (left branch, 45 of 53 wildtype samples) and one enriched for mutant tumors (right branch, 34 of 42 mutant samples). A proliferation cluster/signature was differentially expressed across the two branches of the dendrogram (Figure 4C). This cluster had higher expression in p53 mutants, and included the cell cycle associated genes cyclin A2, CDC28 subunit 1B, CDC2, cyclin-dependent kinase inhibitor 3, polo-like kinase, and topoisomerase IIA. EASE analysis confirmed that genes involved in mitosis and cell cycle progression were significantly over-represented in the set of genes that had higher expression in p53 mutant tumors.


Gene expression patterns associated with p53 status in breast cancer.

Troester MA, Herschkowitz JI, Oh DS, He X, Hoadley KA, Barbier CS, Perou CM - BMC Cancer (2006)

Gene expression pattern of tumor samples for 747 genes correlated with p53 status in tumors. The fold change relative to the median expression value across all tumors is shown. Colored bars in A illustrate the location of clusters C and D. The dendrogram in B shows that experiments were divided into two primary branches, one enriched for mutant (red) and one enriched for wildtype (green) tumors. Tumor sample names are red for mutants and green for wildtypes based on sequence analysis. Clusters enriched for proliferation and cell cycle genes (C) and for luminal/estrogen responsive tumor markers (D) are shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1764759&req=5

Figure 4: Gene expression pattern of tumor samples for 747 genes correlated with p53 status in tumors. The fold change relative to the median expression value across all tumors is shown. Colored bars in A illustrate the location of clusters C and D. The dendrogram in B shows that experiments were divided into two primary branches, one enriched for mutant (red) and one enriched for wildtype (green) tumors. Tumor sample names are red for mutants and green for wildtypes based on sequence analysis. Clusters enriched for proliferation and cell cycle genes (C) and for luminal/estrogen responsive tumor markers (D) are shown.
Mentions: Gene expression data for primary breast tumors with known p53 mutation status is publicly available [2,12]. Using this data, we found that the expression of 747 genes was significantly correlated with p53 status (Figure 4A). The hierarchical cluster of these genes across the primary tumors contained two branches (Figure 4B), one enriched for wild-type tumors (left branch, 45 of 53 wildtype samples) and one enriched for mutant tumors (right branch, 34 of 42 mutant samples). A proliferation cluster/signature was differentially expressed across the two branches of the dendrogram (Figure 4C). This cluster had higher expression in p53 mutants, and included the cell cycle associated genes cyclin A2, CDC28 subunit 1B, CDC2, cyclin-dependent kinase inhibitor 3, polo-like kinase, and topoisomerase IIA. EASE analysis confirmed that genes involved in mitosis and cell cycle progression were significantly over-represented in the set of genes that had higher expression in p53 mutant tumors.

Bottom Line: The cell line signatures were compared with p53-mutation associated genes in breast tumors.Further, a common gene expression signature associated with p53 loss across all four cell lines was identified.Our biologically-based refinements excluded genes that were associated with subtype but not downstream of p53 signaling, and identified a signature for p53 loss that is shared across breast cancer subtypes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA. troester@schoolph.umass.edu

ABSTRACT

Background: Breast cancer subtypes identified in genomic studies have different underlying genetic defects. Mutations in the tumor suppressor p53 occur more frequently in estrogen receptor (ER) negative, basal-like and HER2-amplified tumors than in luminal, ER positive tumors. Thus, because p53 mutation status is tightly linked to other characteristics of prognostic importance, it is difficult to identify p53's independent prognostic effects. The relation between p53 status and subtype can be better studied by combining data from primary tumors with data from isogenic cell line pairs (with and without p53 function).

Methods: The p53-dependent gene expression signatures of four cell lines (MCF-7, ZR-75-1, and two immortalized human mammary epithelial cell lines) were identified by comparing p53-RNAi transduced cell lines to their parent cell lines. Cell lines were treated with vehicle only or doxorubicin to identify p53 responses in both non-induced and induced states. The cell line signatures were compared with p53-mutation associated genes in breast tumors.

Results: Each cell line displayed distinct patterns of p53-dependent gene expression, but cell type specific (basal vs. luminal) commonalities were evident. Further, a common gene expression signature associated with p53 loss across all four cell lines was identified. This signature showed overlap with the signature of p53 loss/mutation status in primary breast tumors. Moreover, the common cell-line tumor signature excluded genes that were breast cancer subtype-associated, but not downstream of p53. To validate the biological relevance of the common signature, we demonstrated that this gene set predicted relapse-free, disease-specific, and overall survival in independent test data.

Conclusion: In the presence of breast cancer heterogeneity, experimental and biologically-based methods for assessing gene expression in relation to p53 status provide prognostic and biologically-relevant gene lists. Our biologically-based refinements excluded genes that were associated with subtype but not downstream of p53 signaling, and identified a signature for p53 loss that is shared across breast cancer subtypes.

Show MeSH
Related in: MedlinePlus