Molecular characterization of human breast tumor vascular cells.

A detailed understanding of the assortment of genes that are expressed in breast tumor vessels is needed to facilitate the development of novel, molecularly targeted anti-angiogenic agents for breast cancer therapies. Rapid immunohistochemistry using factor VIII-related antibodies was performed on sections of frozen human luminal-A breast tumors (n = 5) and normal breast (n = 5), followed by laser capture microdissection of vascular cells. RNA was extracted and amplified, and fluorescently labeled cDNA was synthesized and hybridized to 44,000-element long-oligonucleotide DNA microarrays. Statistical analysis of microarray was used to compare differences in gene expression between tumor and normal vascular cells, and Expression Analysis Systematic Explorer was used to determine enrichment of gene ontology categories. Protein expression of select genes was confirmed using immunohistochemistry. Of the 1176 genes that were differentially expressed between tumor and normal vascular cells, 55 had a greater than fourfold increase in expression level. The extracellular matrix gene ontology category was increased while the ribosome gene ontology category was decreased. Fibroblast activation protein, secreted frizzled-related protein 2, Janus kinase 3, and neutral sphingomyelinase 2 proteins localized to breast tumor endothelium as assessed by immunohistochemistry, showing significantly greater staining compared with normal tissue. These tumor endothelial marker proteins also exhibited increased expression in breast tumor vessels compared with that in normal tissues. Therefore, these genetic markers may serve as potential targets for the development of angiogenesis inhibitors.

The molecular portraits of breast tumors are conserved across microarray platforms.

BACKGROUND: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. RESULTS: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. CONCLUSION: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.