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In silico dissection of cell-type-associated patterns of gene expression in prostate cancer.

ABSTRACT: Prostate tumors are complex entities composed of malignant cells mixed and interacting with nonmalignant cells. However, molecular analyses by standard gene expression profiling are limited because spatial information and nontumor cell types are lost in sample preparation. We scored 88 prostate specimens for relative content of tumor, benign hyperplastic epithelium, stroma, and dilated cystic glands. The proportions of these cell types were then linked in silico to gene expression levels determined by microarray analysis, revealing unique cell-specific profiles. Gene expression differences for malignant and nonmalignant epithelial cells (tumor versus benign hyperplastic epithelium) could be identified without being confounded by contributions from stroma that dominate many samples or sacrificing possible paracrine influences. Cell-specific expression of selected genes was validated by immunohistochemistry and quantitative PCR. The results provide patterns of gene expression for these three lineages with relevance to pathogenetic, diagnostic, and therapeutic considerations.

SUBMITTER: Stuart RO

PROVIDER: S-EPMC327196 | biostudies-literature | 2004 Jan

REPOSITORIES: biostudies-literature

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In silico dissection of cell-type-associated patterns of gene expression in prostate cancer.

Stuart Robert O RO Wachsman William W Berry Charles C CC Wang-Rodriguez Jessica J Wasserman Linda L Klacansky Igor I Masys Dan D Arden Karen K Goodison Steven S McClelland Michael M Wang Yipeng Y Sawyers Anne A Kalcheva Iveta I Tarin David D Mercola Dan D

Proceedings of the National Academy of Sciences of the United States of America 20040101 2

Prostate tumors are complex entities composed of malignant cells mixed and interacting with nonmalignant cells. However, molecular analyses by standard gene expression profiling are limited because spatial information and nontumor cell types are lost in sample preparation. We scored 88 prostate specimens for relative content of tumor, benign hyperplastic epithelium, stroma, and dilated cystic glands. The proportions of these cell types were then linked in silico to gene expression levels determi ...[more]

PMID: 14722351

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Project description: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).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.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.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.

Dataset Information

In silico dissection of cell-type-associated patterns of gene expression in prostate cancer.

Publications

In silico dissection of cell-type-associated patterns of gene expression in prostate cancer.

Similar Datasets

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