Over-Expression of RNA Processing, Heat Shock, and DNA Repair Proteins in Breast Tumor Compared to Normal Tissue.

This study identifies the main changes in protein expression in human breast tumors compared to normal breast tissue. Malignant tumors (32) and normal breast tissue samples (23), from formaldehyde-fixed, paraffin-embedded specimens are subjected to discovery proteomics using liquid chromatography/tandem mass spectrometry, with spectral counts for quantitation. The dataset contains 1406 proteins. Differential expression is measured using a method that takes advantage of estimates of the percentage of tumor on a slide. This analysis shows that the major classes of proteins over-expressed by tumors are RNA-binding, heat shock and DNA repair proteins. RNA-binding proteins, including heterogeneous nuclear ribonucleoproteins (HNRNPs), SR splice factors (SRSF) and elongation factors form the largest group. Comparison with results from another study demonstrates that the RNA-binding proteins are associated specifically with malignant transformation, rather than with cell proliferation. HNRNP and SRSF proteins help define splice sites in normal cells. Their over-expression may dysregulate splicing, which in turn has the potential to promote malignant transformation.

Glycoproteins in Claudin-Low Breast Cancer Cell Lines Have a Unique Expression Profile.

Claudin proteins are components of epithelial tight junctions; a subtype of breast cancer has been defined by the reduced expression of mRNA for claudins and other genes. Here, we characterize the expression of glycoproteins in breast cell lines for the claudin-low subtype using liquid chromatography/tandem mass spectrometry. Unsupervised clustering techniques reveal a group of claudin-low cell lines that is distinct from nonmalignant, basal, and luminal lines. The claudin-low cell lines express F11R, EPCAM, and other proteins at very low levels, whereas CD44 is expressed at a high level. Comparison of mRNA expression to glycoprotein expression shows modest correlation; the best agreement occurs when the mRNA expression level is lowest and little or no protein is detected. These findings from cell lines are compared to those for tumor samples by the Clinical Proteomic Tumor Analysis Consortium (CPTAC). The CPTAC samples contain a group low in CLDN3. The samples low in CLDN3 proteins share many differentially expressed glycoproteins with the claudin-low cell lines. In contrast to the situation for cell lines or patient samples classified as claudin-low by RNA expression, however, most of the tumor samples low in CLDN3 protein express the estrogen receptor or HER2. These tumor samples express CD44 protein at low rather than high levels. There is no correlation between CLDN3 gene expression and protein expression in these CPTAC samples; hence, the claudin-low subtype defined by gene expression is not the same group of tumors as that defined by low expression of CLDN3 protein.

Mining the Breast Cancer Proteome for Predictors of Drug Sensitivity.

Approximately 20 drugs have been approved by the FDA for breast cancer treatment, yet predictive biomarkers are known for only a few of these. The identification of additional biomarkers would be useful both for drugs currently approved for breast cancer treatment and for new drug development. Using glycoprotein expression data collected via mass spectrometry, in conjunction with statistical models constructed by elastic net or lasso regression, we modeled quantitatively the responses of breast cancer cell lines to ~90 drugs. Lasso and elastic net regression identified HER2 as a predictor protein for lapatinib, afatinib, gefitinib and erlotinib, which target HER2 or the EGF receptor, thus providing an internal control for the approach. Two additional protein datasets and two RNA datasets were also tested as sources of predictor proteins for modeling drug sensitivity. Protein expression measured by mass spectrometry gave models with higher coefficients of determination than did reverse phase protein array (RPPA) predictor data. Further, cross validation of the elastic net models shows that, for many drugs, the prediction error is lower when the predictor data is from proteins, rather than mRNA expression measured on microarrays. Drugs that could be modeled effectively include PI3K inhibitors, Akt inhibitors, paclitaxel and docetaxel, rapamycin, everolimus and temsirolimus, gemcitabine and vinorelbine. Strikingly, this modeling approach with protein predictors often succeeds for drugs that are targeted agents, even when the nominal target is not in the dataset.

Using a cell line breast cancer progression system to identify biomarker candidates.

Secreted and plasma membrane glycoproteins are considered excellent candidates for disease biomarkers. Herein we describe the identification of secreted and plasma membrane glycoproteins that are differentially expressed among a family of three breast cancer cell lines that models the progression of breast cancer. Using two-dimensional liquid chromatography-tandem mass spectrometry we identified more than 40 glycoproteins that were differentially expressed in either the premalignant (MCF10AT) or the fully malignant (MCF10CA1a) cell lines of this model system. Comparative analysis revealed that the differentially expressed breast cancer progression-associated glycoproteins were among the most highly expressed in the malignant (MCF10CA1a) breast cancer cell line; a subset of these was detected only in the malignant line; and others were detected in the malignant line at levels 25 to 50 times greater than in the benign (MCF10A) line. Using the results from this model cell system as a guide, we then carried out glycoproteomic analyses of normal and cancerous breast tissue lysates. Eleven of the glycoproteins differentially expressed in the breast cell lines were identified in the tissue lysates. Among these glycoproteins, collagen alpha-1 (XII) chain was expressed at dramatically higher (~10-fold) levels in breast cancer than in normal tissue. BIOLOGICAL SIGNIFICANCE: Identifying glycoproteins differentially expressed during cancer progression results in information on the biological processes and key pathways associated with cancer. In addition, new hypotheses and potential biomarkers result from these glycoproteomic studies. Our glycoproteomic analysis of this model of breast cancer provides a roadmap for future experimental interventions to further tease apart critical components of tumor progression.

Systemic alteration of cell-surface and secreted glycoprotein expression in malignant breast cancer cell lines.

Breast cancer cell lines express fewer transmembrane and secreted glycoproteins than nonmalignant ones. The objective of these experiments was to characterize the changes in the expression of several hundred glycoproteins quantitatively. Secreted and cell-surface glycoproteins were isolated using a glycoprotein capture protocol and then identified by tandem mass spectrometry. Glycoproteins expressed by a group of cell lines originating from malignant tumors of the breast were compared with those expressed by a nonmalignant set. The average number of spectral counts (proportional to relative protein abundance) and the total number of glycopeptides in the malignant samples were reduced to about two-thirds of the level in the nonmalignant samples. Most glycoproteins were expressed at a different level in the malignant samples, with nearly as many increasing as decreasing. The glycoproteins with reduced expression accounted for a larger change in spectral counts, and hence for the net loss of spectral counts in the malignant lines. Similar results were found when the glycoproteins were studied via identified glycosylation sites only, or through identified sites together with non-glycopeptides. The overall reduction is largely due to the loss of integrins, laminins and other proteins that form or interact with the basement membrane.

Overcoming challenges and opening new opportunities in glycoproteomics.

Glycoproteomics has emerged as a prime area of interest within the field of proteomics because glycoproteins have been shown to function as biomarkers for disease and as promising therapeutic targets. A significant challenge in the study of glycoproteins is the fact that they are expressed in relatively low abundance in cells. In response, various enrichment methods have been developed to improve the detection of glycoproteins. One such method involves their capture via oxidation of their glycan chains and covalent attachment with hydrazide resins which, when catalyzed by PNGase F, release N-linked glycans and convert the glycosite Asn to Asp; this conversion is identifiable with LC/ESI-MS/MS as a corresponding increase of 0.984 Da in molecular weight. The present study builds on this body of work, providing evidence of three additional strategies that improve glycoprotein identification: (1) use of a high resolution mass spectrometer-the Q Exactive MS-which delivers 2-3 times more glycoprotein identifications than a low resolution MS; (2) optimization of instrument settings and database search parameters to reduce misidentification of N-linked glycopeptides to ~1 percent; and (3) labeling glycopeptides with (18)O during PNGase F treatment to locate N-linked glycosites within peptides containing multiple N-linked sequons.

Glycoprotein profiles of human breast cells demonstrate a clear clustering of normal/benign versus malignant cell lines and basal versus luminal cell lines.

Gene expression profiling has defined molecular subtypes of breast cancer including those identified as luminal and basal. To determine if glycoproteins distinguish various subtypes of breast cancer, we obtained glycoprotein profiles from 14 breast cell lines. Unsupervised hierarchical cluster analysis demonstrated that the glycoprotein profiles obtained can serve as molecular signatures to classify subtypes of breast cancer, as well as to distinguish normal and benign breast cells from breast cancer cells. Statistical analyses were used to identify glycoproteins that are overexpressed in normal versus cancer breast cells, and those that are overexpressed in luminal versus basal breast cancer. Among the glycoproteins distinguishing normal breast cells from cancer cells are several proteins known to be involved in cell adhesion, including proteins previously identified as being altered in breast cancer. Basal breast cancer cell lines overexpressed a number of CD antigens, including several integrin subunits, relative to luminal breast cancer cell lines, whereas luminal breast cancer cells overexpressed carbonic anhydrase 12, clusterin, and cell adhesion molecule 1. The differential expression of glycoproteins in these breast cancer cell lines readily allows the classification of the lines into normal, benign, malignant, basal, and luminal groups.