In situ proteomic analysis of human breast cancer epithelial cells using laser capture microdissection: annotation by protein set enrichment analysis and gene ontology.

Identification of molecular signatures that allow detection of the transition from normal breast epithelial cells to malignant invasive cells is a critical component in the development of diagnostic, therapeutic, and preventative strategies for human breast cancer. Substantial efforts have been devoted to deciphering breast cancer etiology at the genome level, but only a limited number of studies have appeared at the proteome level. In this work, we compared individual in situ proteome profiles of nonpatient matched nine noncancerous, normal breast epithelial (NBE) samples with nine estrogen receptor (ER)-positive (luminal subtype), invasive malignant breast epithelial (MBE) samples by combining laser capture microdissection (LCM) and quantitative shotgun proteomics. A total of 12,970 unique peptides were identified from the 18 samples, and 1623 proteins were selected for quantitative analysis using spectral index (SpI) as a measure of protein abundance. A total of 298 proteins were differentially expressed between NBE and MBE at 95% confidence level, and this differential expression correlated well with immunohistochemistry (IHC) results reported in the Human Protein Atlas (HPA) database. To assess pathway level patterns in the observed expression changes, we developed protein set enrichment analysis (PSEA), a modification of a well-known approach in gene expression analysis, Gene Set Enrichment Analysis (GSEA). Unlike single gene-based functional term enrichment analyses that only examines pathway overrepresentation of proteins above a given significance threshold, PSEA applies a weighted running sum statistic to the entire expression data to discover significantly enriched protein groups. Application of PSEA to the expression data in this study revealed not only well-known ER-dependent and cellular morphology-dependent protein abundance changes, but also significant alterations of downstream targets for multiple transcription factors (TFs), suggesting a role for specific gene regulatory pathways in breast tumorigenesis. A parallel GOMiner analysis revealed both confirmatory and complementary data to PSEA. The combination of the two annotation approaches yielded extensive biological feature mapping for in depth analysis of the quantitative proteomic data.

Persistent parity-induced changes in growth factors, TGF-beta3, and differentiation in the rodent mammary gland.

Epidemiological studies have repeatedly demonstrated that women who undergo an early first full-term pregnancy have a significantly reduced lifetime risk of breast cancer. Similarly, rodents that have previously undergone a full-term pregnancy are highly resistant to carcinogen-induced breast cancer compared with age-matched nulliparous controls. Little progress has been made, however, toward understanding the biological basis of this phenomenon. We have used DNA microarrays to identify a panel of 38 differentially expressed genes that reproducibly distinguishes, in a blinded manner, between the nulliparous and parous states of the mammary gland in multiple strains of mice and rats. We find that parity results in the persistent down-regulation of multiple genes encoding growth factors, such as amphiregulin, pleiotrophin, and IGF-1, as well as the persistent up-regulation of the growth-inhibitory molecule, TGF-beta3, and several of its transcriptional targets. Our studies further indicate that parity results in a persistent increase in the differentiated state of the mammary gland as well as lifelong changes in the hematopoietic cell types resident within the gland. These findings define a developmental state of the mammary gland that is refractory to carcinogenesis and suggest novel hypotheses for the mechanisms by which parity may modulate breast cancer risk.