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. Cellspecific 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.microarray ͉ expression profiles ͉ linear regression ͉ biomarkers ͉ paracine

Tumor–stromal interactions reciprocally modulate gene expression patterns during carcinogenesis and metastasis

“…The collective result of this and other work was effectively summarized by Dawe 12 in a classical study of virally induced salivary gland neoplasia, from which he concluded that the unit responding to the carcinogenic stimulus is an epithelial-mesenchymal complex. Gradually, further evidence has accrued from several laboratories [13][14][15][16][17][18][19] corroborating this concept for both primary and secondary neoplasms.Recent advances in molecular biology and microanalytical techniques have made it possible to investigate the molecular basis of these dynamic, functionally effective cell and tissue interactions. In particular, methods and devices are now available to screen gene expression signatures in the interacting partners and follow this up with accurate measurements of the RNA and protein products.…”

“…The collective result of this and other work was effectively summarized by Dawe 12 in a classical study of virally induced salivary gland neoplasia, from which he concluded that the unit responding to the carcinogenic stimulus is an epithelial-mesenchymal complex. Gradually, further evidence has accrued from several laboratories [13][14][15][16][17][18][19] corroborating this concept for both primary and secondary neoplasms.…”

Breast tumor metastasis: analysis via proteomic profiling

“…Thus, expression differences derived from tissues with categorical labels, such as tumor and nontumor, may primarily reflect varying proportions of the neoplastic and non-neoplastic components. The importance of the cell type contribution has been more fully investigated in gene expression microarray analyses [40,41]. In a study of breast cancer xenograft tissues and lymph node metastases, comparison of the metastasis-associated gene lists produced by oligonucleotide microarrays from whole and microdissected tumors showed remarkable differences, with only 1% of genes common to both techniques [41].…”

From signatures to models: understanding cancer using microarrays

“…We believe that a positive answer lies in the combination of computational and experimental insights. Computational methods should be developed to tackle cell and tissue heterogeneity 56,57 . For example, Stuart et al 56 used histological evaluation of tissue heterogeneity to deconvolve expression profiles and identify cell type-specific expression responses.…”

“…Computational methods should be developed to tackle cell and tissue heterogeneity 56,57 . For example, Stuart et al 56 used histological evaluation of tissue heterogeneity to deconvolve expression profiles and identify cell type-specific expression responses. Experimentally, most cancer genomic studies have focused on tumor samples from the human population and have therefore suffered from inevitable confounding genetic and environmental factors, tissue heterogeneity, lack of time courses for disease progression and unavailability of perturbations instrumental in identifying regulatory events.…”