A contribution to breast cancer cell proteomics: detection of new sequences

Ductal infiltrating carcinoma (DIC) of the breast is the most common and potentially aggressive form of cancer. Knowledge of proteomic profiles, attained both in vivo and in vitro, is fundamental to acquire as much information as possible on the proteins expressed in these pathologic conditions. We used the breast cancer cell line 8701-BC, established from a primary DIC, with the aim of contributing to the databases on mammary cancer cells, which in turn will be very useful for the identification of differentially expressed proteins in normal and neoplastic cells. Within an analysis window comprising about 1750 discernible spots, we have at present catalogued 84 protein spots. The proteins for which an identity was assigned were identified essentially using gel comparison, N-terminal (Nt) microseqencing and immune detection. Among the protein spots Nt-microsequenced, sixteen corresponded to known proteins, four resulted as modified, relative to matching sequences deposited on databases, and seven were unknown. These modified or novel sequences are thus of potential interest to the knowledge of breast cancer proteomics and its applications.     

Membrane protein analysis of human breast cancer cell line MCF-7 by different membrane washing methods

Membrane and membrane-associated proteins are rich in known or potential pharmaceutical drug targets for carcinogenesis. In order to systemically analyze membrane proteins of human breast cancer, we isolated membrane from MCF-7 cells by sequential extraction by washing with three different buffers, namely, phosphate buffer (5 mM, pH 8.0), Tris (40 mM, pH 9.5), and sodium carbonate (100 mM pH 11). The extracted proteins were separated by two-dimensional gel electrophoresis (2-DE) using cup-loading and were then analyzed by peptide mass fingerprinting (PMF). A total of 137 spots from the gels of the three procedures were successfully identified. They corresponded to 79 distinct proteins. Among them, 22 exclusive proteins belonging to each washing procedure were also found, including P-glycoprotein, endoplasmin, Stress-70 protein, ADAM 10, protein disulfide isomerase, and glutamate receptor. These results indicate phosphate buffer to be the most beneficial for enrichment of peripheral membrane proteins, and sodium carbonate is beneficial for the presentation of integral membrane proteins but usually with poor resolution. The reference maps and identified proteins will serve as a basis for the further investigation of breast cancer, especially the proteomic comparison among different cell types of breast cancer, or among the different stages in the drug interfering process of the MCF-7 cell line.     

Quantitative profiling of drug-associated proteomic alterations by combined 2-nitrobenzenesulfenyl chloride (NBS) isotope labeling and 2DE/MS identification

The identification of drug-responsive biomarkers in complex protein mixtures is an important goal of quantitative proteomics. Here, we describe a novel approach for identifying such drug-induced protein alterations, which combines 2-nitrobenzenesulfenyl chloride (NBS) tryptophan labeling with two-dimensional gel electrophoresis (2DE)/mass spectrometry (MS). Lysates from drug-treated and control samples are labeled with light or heavy NBS moiety and separated on a common 2DE gel, and protein alterations are identified by MS through the differential intensity of paired NBS peptide peaks. Using NBS/2DE/MS, we profiled the proteomic alterations induced by tamoxifen (TAM) in the estrogen receptor (ER) positive MCF-7 breast cancer cell line. Of 88 protein spots that significantly changed upon TAM treatment, 44 spots representing 23 distinct protein species were successfully identified with NBS-paired peptides. Of these 23 TAM-altered proteins, 16 (70%) have not been previously associated with TAM or ER activity. We found the NBS labeling procedure to be both technically and biologically reproducible, and the NBS/2DE/MS alterations exhibited good concordance with conventional 2DE differential protein quantitation, with discrepancies largely due to the comigration of distinct proteins in the regular 2DE gels. To validate the NBS/2DE/MS results, we used immunoblotting to confirm GRP78, CK19, and PA2G4 as bona fide TAM-regulated proteins. Furthermore, we demonstrate that PA2G4 expression can serve as a novel prognostic factor for disease-free survival in two independent breast cancer patient cohorts. To our knowledge, this is the first report describing the proteomic changes in breast cancer cells induced by TAM, the most commonly used selective estrogen receptor modulator (SERM). Our results indicate that NBS/2DE/MS may represent a more reliable approach for cellular protein quantitation than conventional 2DE approaches.     

Proteomic signatures for histological types of lung cancer

We performed proteomic studies on lung cancer cells to elucidate the mechanisms that determine histological phenotype. Thirty lung cancer cell lines with three different histological backgrounds (squamous cell carcinoma, small cell lung carcinoma and adenocarcinoma) were subjected to two-dimensional difference gel electrophoresis (2-D DIGE) and grouped by multivariate analyses on the basis of their protein expression profiles. 2-D DIGE achieves more accurate quantification of protein expression by using highly sensitive fluorescence dyes to label the cysteine residues of proteins prior to two-dimensional polyacrylamide gel electrophoresis. We found that hierarchical clustering analysis and principal component analysis divided the cell lines according to their original histology. Spot ranking analysis using a support vector machine algorithm and unsupervised classification methods identified 32 protein spots essential for the classification. The proteins corresponding to the spots were identified by mass spectrometry. Next, lung cancer cells isolated from tumor tissue by laser microdissection were classified on the basis of the expression pattern of these 32 protein spots. Based on the expression profile of the 32 spots, the isolated cancer cells were categorized into three histological groups: the squamous cell carcinoma group, the adenocarcinoma group, and a group of carcinomas with other histological types. In conclusion, our results demonstrate the utility of quantitative proteomic analysis for molecular diagnosis and classification of lung cancer cells.     

In-silico selection of cancer blood plasma proteins by integrating genomic and proteomic databases

Blood protein markers have been studied for the clinical management of cancer. Due to the large number of the proteins existing in blood, it is often necessary to pre-select potential protein markers before experimental studies. However, to date there is a lack of automated method for in-silico selection of cancer blood proteins that integrates the information from both genetic and proteomic studies in a cancer-specific manner. In this work, we synthesized both genomic and proteomic information from several open access databases and established a bioinformatic pipeline for in-silico selection of blood plasma proteins overexpressed in specific type of cancer. We demonstrated the workflow of this pipeline with an example of breast cancer, while the methodology was applicable for other cancer types. With this pipeline we obtained 10 candidate biomarkers for breast cancer. The proposed pipeline provides a useful and convenient tool for in-silico selection of candidate blood protein biomarkers for a variety of cancer research.     

One-dimensional proteomic mapping of human liver cytochromes P450

A method for constructing one-dimensional proteomic maps (1D-PM) based on mass spectrometric identification of proteins from adjacent slices of one-dimensional electrophoregram has been developed. For the proteomic mapping, gel lanes were sectioned into slices less than 0.2 mm thick and each slice was subjected to enzymatic hydrolysis. The resultant mixture of peptide fragments was analyzed by matrix-assisted laser desorption time-of-flight mass spectrometry (MALDI-TOF) and liquid chromatography electrospray ionization tandem mass spectrometry (LC-MS/MS). Proteins were identified by the mass spectra obtained. Data on peptide fragments and corresponding identified proteins were presented as a 1D-PM. Proteomic maps were constructed by assigning individual proteins to gel slices based on number of matching peptides in a corresponding MS-data. On 1D-PM of human liver microsomal fraction, 18 proteins were identified in the region of 40–65 kDa. These included 12 membrane proteins belonging to the superfamily of cytochromes P450. Pooling of mass spectrometric data, obtained from several adjacent gel slices (molecular zooming) increased sequence coverage of CYP2A (cytochrome P450 family 2A). The maximal coverage of 66% significantly exceeded the level of 48% that could be obtained using one (even the most informative) slice. This method can be applied to the proteomic profiling of membrane-bound proteins.     

ER/PR阳性和阴性乳腺癌的定量蛋白质组学和生物信息学比较研究

目的:建立雌/孕激素受体(ER/PR)阴性和阳性乳腺癌的蛋白质表达谱,寻找ER/PR阴性和阳性乳腺癌中差异表达蛋白,为乳腺癌患者提供新的预后预测指标和治疗新靶点。方法:应用蛋白质组学i TRAQ技术建立ER/PR阳性和阴性乳腺癌的蛋白质差异表达谱,鉴定两组乳腺癌的差异表达蛋白,对部分差异表达蛋白进行生物信息学分析,包括蛋白功能注释和分类GO分析和KEGG通路分析。结果:应用i TRAQ蛋白质组学技术对乳腺癌组织进行了蛋白组学分析,鉴定出ER/PR阳性和阴性组间有差异表达的蛋白4999种,以ER/PR阳性:ER/PR阴性≥3为上调标准,确定ER/PR阳性组上调蛋白101种。以ER/PR阳性:ER/PR阴性≤0.5为下调标准,ER/PR阳性组下调蛋白122种。GO分析结果显示ER/PR受体阴性和阳性乳腺癌的差异表达蛋白的分子功能、生物过程、细胞定位较为复杂,并且在上调蛋白和下调蛋白上存在分布差异。KEGG通路分析发现部分差异表达蛋白涉及201条信号通路。结论:ER/PR阳性和阴性乳腺癌间存在差异表达蛋白,这些蛋白涉及复杂的分子功能、生物过程和信号通路。     

The proteomic reactor: a microfluidic device for processing minute amounts of protein prior to mass spectrometry analysis

Gel-free proteomics has emerged as a complement to conventional gel-based proteomics. Gel-free approaches focus on peptide or protein fractionation, but they do not address the efficiency of protein processing. We report the development of a microfluidic proteomic reactor that greatly simplifies the processing of complex proteomic samples by combining multiple proteomic steps. Rapid extraction and enrichment of proteins from complex proteomic samples or directly from cells are readily performed on the reactor. Furthermore, chemical and enzymatic treatments of proteins are performed in 50 nL effective volume, which results in an increased number of generated peptides. The products are compatible with mass spectrometry. We demonstrated that the proteomic reactor is at least 10 times more sensitive than current gel-free methodologies with one protein identified per 440 pg of protein lysate injected on the reactor. Furthermore, as little as 300 cells can be directly introduced on the proteomic reactor and analyzed by mass spectrometry.     

Urinary proteome alterations in HER2 enriched breast cancer revealed by multipronged quantitative proteomics

Globally, breast cancer is the second most common cancer among women. Although biomarker discoveries through various proteomic approaches of tissue and serum samples have been studied in breast cancer, urinary proteome alterations in breast cancer are least studied. Urine being a noninvasive biofluid and a significant source of proteins, it has the potential in early diagnosis of breast cancer. This study used complementary quantitative gel‐based and gel‐free proteomic approaches to find a panel of urinary protein markers that could discriminate HER2 enriched (HE) subtype breast cancer from the healthy controls. A total of 183 differentially expressed proteins were identified using three complementary approaches, namely 2D‐DIGE, iTRAQ, and sequential window acquisition of all theoretical mass spectra. The differentially expressed proteins were subjected to various bioinformatics analyses for deciphering the biological context of these proteins using protein analysis through evolutionary relationships, database for annotation, visualization and integrated discovery, and STRING. Multivariate statistical analysis was undertaken to identify the set of most significant proteins, which could discriminate HE breast cancer from healthy controls. Immunoblotting and MRM‐based validation in a separate cohort testified a panel of 21 proteins such as zinc‐alpha2‐glycoprotein, A2GL, retinol‐binding protein 4, annexin A1, SAP3, SRC8, gelsolin, kininogen 1, CO9, clusterin, ceruloplasmin, and α1‐antitrypsin could be a panel of candidate markers that could discriminate HE breast cancer from healthy controls.     

Plasma proteome profiling of a mouse model of breast cancer identifies a set of up-regulated proteins in common with human breast cancer cells

We have applied an in-depth quantitative proteomic approach, combining isotopic labeling extensive intact protein separation and mass spectrometry, for high confidence identification of protein changes in plasmas from a mouse model of breast cancer. We hypothesized that a wide spectrum of proteins may be up-regulated in plasma with tumor development and that comparisons with proteins expressed in human breast cancer cell lines may identify a subset of up-regulated proteins in common with proteins expressed in breast cancer cell lines that may represent candidate biomarkers for breast cancer. Plasma from PyMT transgenic tumor-bearing mice and matched controls were obtained at two time points during tumor growth. A total of 133 proteins were found to be increased by 1.5-fold or greater at one or both time points. A comparison of this set of proteins with published findings from proteomic analysis of human breast cancer cell lines yielded 49 proteins with increased levels in mouse plasma that were identified in breast cancer cell lines. Pathway analysis comparing the subset of up-regulated proteins known to be expressed in breast cancer cell lines with other up-regulated proteins indicated a cancer related function for the former and a host-response function for the latter. We conclude that integration of proteomic findings from mouse models of breast cancer and from human breast cancer cell lines may help identify a subset of proteins released by breast cancer cells into the circulation and that occur at increased levels in breast cancer.     

NanoLC-FT-ICR MS improves proteome coverage attainable for approximately 3000 laser-microdissected breast carcinoma cells

Proteomics assays hold great promise for unraveling molecular events that underlie human diseases. Effective analysis of clinical samples is essential, but this task is considerably complicated by tissue heterogeneity. Laser capture microdissection (LCM) can be used to selectively isolate target cells from their native tissue environment. However, the small number of cells that is typically procured by LCM severely limits proteome coverage and biomarker discovery potential achievable by conventional proteomics platforms. Herein, we describe the use of nanoLC-FT-ICR MS for analyzing protein digests of 3000 LCM-derived tumor cells from breast carcinoma tissue, corresponding to 300 ng of total protein. A total of 2282 peptides were identified by matching LC-MS data to accurate mass and time (AMT) tag databases that were previously established for human breast (cancer) cell lines. One thousand and three unique proteins were confidently identified with two or more peptides. Based on gene ontology categorization, identified proteins appear to cover a wide variety of biological functions and cellular compartments. This work demonstrates that a substantial number of proteins can be detected and identified from limited number of cells using the AMT tag approach, and opens doors for high-throughput in-depth proteomics analysis of clinical samples.     

Proteomic characterization of Her2/neu-overexpressing breast cancer cells

The receptor tyrosine kinase HER2 is an oncogene amplified in invasive breast cancer and its overexpression in mammary epithelial cell lines is a strong determinant of a tumorigenic phenotype. Accordingly, HER2-overexpressing mammary tumors are commonly indicative of a poor prognosis in patients. Several quantitative proteomic studies have employed two-dimensional gel electrophoresis in combination with MS/MS, which provides only limited information about the molecular mechanisms underlying HER2/neu signaling. In the present study, we used a SILAC-based approach to compare the proteomic profile of normal breast epithelial cells with that of Her2/neu-overexpressing mammary epithelial cells, isolated from primary mammary tumors arising in mouse mammary tumor virus-Her2/neu transgenic mice. We identified 23 proteins with relevant annotated functions in breast cancer, showing a substantial differential expression. This included overexpression of creatine kinase, retinol-binding protein 1, thymosin 4 and tumor protein D52, which correlated with the tumorigenic phenotype of Her2-overexpressing cells. The differential expression pattern of two genes, gelsolin and retinol binding protein 1, was further validated in normal and tumor tissues. Finally, an in silico analysis of published cancer microarray data sets revealed a 23-gene signature, which can be used to predict the probability of metastasis-free survival in breast cancer patients.     

Proteome profiling of human epithelial ovarian cancer cell line TOV-112D

A proteome profiling of the epithelial ovarian cancer cell line TOV-112D was initiated as a protein expression reference in the study of ovarian cancer. Two complementary proteomic approaches were used in order to maximise protein identification: two-dimensional gel electrophoresis (2DE) protein separation coupled to matrix assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and one-dimensional gel electrophoresis (1DE) coupled to liquid-chromatography tandem mass spectrometry (LC MS/MS). One hundred and seventy-two proteins have been identified among 288 spots selected on two-dimensional gels and a total of 579 proteins were identified with the 1DE LC MS/MS approach. This proteome profiling covers a wide range of protein expression and identifies several proteins known for their oncogenic properties. Bioinformatics tools were used to mine databases in order to determine whether the identified proteins have previously been implicated in pathways associated with carcinogenesis or cell proliferation. Indeed, several of the proteins have been reported to be specific ovarian cancer markers while others are common to many tumorigenic tissues or proliferating cells. The diversity of proteins found and their association with known oncogenic pathways validate this proteomic approach. The proteome 2D map of the TOV-112D cell line will provide a valuable resource in studies on differential protein expression of human ovarian carcinomas while the 1DE LC MS/MS approach gives a picture of the actual protein profile of the TOV-112D cell line. This work represents one of the most complete ovarian protein expression analysis reports to date and the first comparative study of gene expression profiling and proteomic patterns in ovarian cancer.     

Identification of glycosylphosphatidylinositol-anchored proteins in Arabidopsis. A proteomic and genomic analysis

In a recent bioinformatic analysis, we predicted the presence of multiple families of cell surface glycosylphosphatidylinositol (GPI)-anchored proteins (GAPs) in Arabidopsis (G.H.H. Borner, D.J. Sherrier, T.J. Stevens, I.T. Arkin, P. Dupree [2002] Plant Physiol 129: 486-499). A number of publications have since demonstrated the importance of predicted GAPs in diverse physiological processes including root development, cell wall integrity, and adhesion. However, direct experimental evidence for their GPI anchoring is mostly lacking. Here, we present the first, to our knowledge, large-scale proteomic identification of plant GAPs. Triton X-114 phase partitioning and sensitivity to phosphatidylinositol-specific phospholipase C were used to prepare GAP-rich fractions from Arabidopsis callus cells. Two-dimensional fluorescence difference gel electrophoresis and one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis demonstrated the existence of a large number of phospholipase C-sensitive Arabidopsis proteins. Using liquid chromatography-tandem mass spectrometry, 30 GAPs were identified, including six beta-1,3 glucanases, five phytocyanins, four fasciclin-like arabinogalactan proteins, four receptor-like proteins, two Hedgehog-interacting-like proteins, two putative glycerophosphodiesterases, a lipid transfer-like protein, a COBRA-like protein, SKU5, and SKS1. These results validate our previous bioinformatic analysis of the Arabidopsis protein database. Using the confirmed GAPs from the proteomic analysis to train the search algorithm, as well as improved genomic annotation, an updated in silico screen yielded 64 new candidates, raising the total to 248 predicted GAPs in Arabidopsis.     

Searchable high-resolution 2D gel proteome of the human colon crypt

We seek alterations in protein patterns at the earliest possible step on the path to cancer, namely, in cells of the target tissue from normal persons versus the corresponding normally appearing cells from persons who are heterozygous for mutation in a tumor suppressor gene that predisposes strongly to carcinoma in that tissue. To begin a systematic comparison of the proteomes of cells from normal and from neoplastic colons, we have undertaken the isolation of human colon crypts that are derived from the normal-appearing mucosa of left (descending) colon of patients with sporadic colorectal cancer. Two-dimensional (2D) gel electrophoresis is a proteomic approach that excels in the resolution of protein isoforms. Here, we document the practicality of this approach with human samples using gels of three overlapping pH ranges. For the first time, about 800 nonredundant proteins and 900 isoforms from purified human colonic crypts were identified, permitting an assessment of the contributions of protein isoforms. These interactive, searchable, hyperlink-enabled proteome maps and gene ontology analyses will facilitate future studies to discover the earliest markers and intervention targets during progression to colon cancer.     

Arabidopsis cell wall proteome defined using multidimensional protein identification technology

With the completion of the sequencing of the Arabidopsis genome and the recent advances in proteomic technology, the identification of proteins from highly complex mixtures is now possible. Rather than using gel electrophoresis and peptide mass fingerprinting, we have used multidimensional protein identification technology (MudPIT) to analyse the "tightly-bound" proteome for purified cell walls from Arabidopsis cell suspension cultures. Using bioinformatics for the prediction of signal peptides for targeting to the secretory pathway and for the absence of ER retention signal, 89 proteins were selected as potential extracellular proteins. Only 33% of these were identified in previous proteomic analyses of Arabidopsis cell walls. A functional classification revealed that a large proportion of the proteins were enzymes, notably carbohydrate active enzymes, peroxidases and proteases. Comparison of all the published proteomic analyses for the Arabidopsis cell wall identified 268 non-redundant genes encoding wall proteins. Sixty of these (22%) were derived from our analysis of tightly-bound wall proteins.