A proteomic approach to investigate potential biomarkers directed against membrane‐associated breast cancer proteins

Canelle, Ludovic; Bousquet, Jordane; Pionneau, Cédric; Hardouin, Julie; Choquet-Kastylevsky, Geneviève; Joubert‐Caron, Raymonde; Caron, Michel

doi:10.1002/elps.200500712

Cited by 26 publications

(26 citation statements)

References 28 publications

(37 reference statements)

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“…Breast cancer cell lines are commonly used as model systems to investigate the pathways that lead to the development of cancer [37][38][39][40][41][42][43][44][45]. In a previous work, we generated a full proteome profile of the MCF-7 cell line using 2DLC-MS/MS technology [35].…”

Section: Resultsmentioning

confidence: 99%

Differential protein expression analysis using stable isotope labeling and PQD linear ion trap MS technology

Armenta

Hoeschele

Lazar

2009

J. Am. Soc. Mass Spectrom.

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An isotope tags for relative and absolute quantitation (iTRAQ)-based reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS) method was developed for differential protein expression profiling in complex cellular extracts. The estrogen positive MCF-7 cell line, cultured in the presence of 17␤-estradiol (E2) and tamoxifen (Tam), was used as a model system. MS analysis was performed with a linear trap quadrupole (LTQ) instrument operated by using pulsed Q dissociation (PQD) detection. Optimization experiments were conducted to maximize the iTRAQ labeling efficiency and the number of quantified proteins. MS data filtering criteria were chosen to result in a false positive identification rate of Ͻ4%. The reproducibility of protein identifications was ϳ60%-67% between duplicate, and ϳ50% among triplicate LC-MS/MS runs, respectively. The run-to-run reproducibility, in terms of relative standard deviations (RSD) of global mean iTRAQ ratios, was better than 10%. The quantitation accuracy improved with the number of peptides used for protein identification. From a total of 530 identified proteins (P Ͻ 0.001) in the E2/Tam treated MCF-7 cells, a list of 255 proteins (quantified by at least two peptides) was generated for differential expression analysis. A method was developed for the selection, normalization, and statistical evaluation of such datasets. An approximate ϳ2-fold change in protein expression levels was necessary for a protein to be selected as a biomarker candidate. According to this data processing strategy, ϳ16 proteins involved in biological processes such as apoptosis, RNA processing/metabolism, DNA replication/transcription/repair, cell proliferation and metastasis, were found to be up-or down-regulated. R ecently, two-dimensional liquid chromatography (2DLC) with tandem MS detection has emerged as an attractive technology for quantitative proteomic profiling of complex cellular extracts [1][2][3][4]. Stable isotope labeling and label-free quantitation strategies have been explored [4 -13]. Isotope labeling approaches rely on the covalent attachment of stable isotope tags to specific amino acid residues of proteins or peptides during metabolic, enzymatic, or chemical processes. Label-free quantitation methods rely on measuring peak areas, intensities, or spectral counts, and benefit from not having to chemically alter the sample. Throughput, however, is lower, and quantitation errors are higher, as the samples are processed independently. Among chemical labeling techniques, isotope tags for relative and absolute quantitation (iTRAQ) has received much attention [14 -30]. In this approach, peptides are labeled with isobaric tags at the N-terminus and the lysine side chains. MS/MS fragmentation produces signature ions that can be used to obtain quantitative information. Perhaps the most attractive advantage of this approach is that it can be used for the simultaneous quantification (relative or absolute) of up to four/eight different samples. Simplicity, of course, is an added benefit.A...

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Section: Resultsmentioning

confidence: 99%

Differential protein expression analysis using stable isotope labeling and PQD linear ion trap MS technology

Armenta

Hoeschele

Lazar

2009

J. Am. Soc. Mass Spectrom.

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“…Sera from cancer patients or healthy subjects are screened individually allowing immunodetection of relevant antigens among the several thousand individual proteins separated using 2-DE. Comparative probing of blots with sera from patients and healthy subjects may allow the characterization of proteins that react specifically with sera from cancer patients (17,22,23,31,32).…”

Section: Serological Proteome Analysismentioning

confidence: 99%

“…Our laboratory has embarked on a long term immunomics study of breast cancer (18,32,33) as this disease is the most common malignancy among women (34). During this study, we applied the SERPA approach to the identification of breast cancer proteins that elicit a humoral response.…”

Section: Serological Proteome Analysismentioning

confidence: 99%

Cancer Immunomics Using Autoantibody Signatures for Biomarker Discovery

Caron

Choquet-Kastylevsky

Joubert‐Caron

2007

Molecular & Cellular Proteomics

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140

127

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The increased incidence of autoantibodies in malignancies has been described since the 1970s. Thus the ability to determine molecular fingerprinting of autoantibodies (antibody signatures) may provide useful clinical diagnostic and prognostic information. This review describes the use of several proteomics approaches for the identification of antigens recognized by these autoantibodies. Serological proteome analysis combines separation of tumor cell proteins on two-dimensional gel electrophoresis gels, Western blotting with sera of patients and healthy subjects, and identification of the detected antigens by MS. Alternatively multiple affinity protein profiling combines isolation of the antigens recognized by patient antibodies by two-dimensional immunoaffinity chromatography and identification by MS/MS. The use and limitations of reverse phase protein microarrays for testing patient serum containing autoantibodies are also considered. Lastly

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“…A number of studies have used a cell culture model system in which the cells were grown in serum-free media to perform proteomics analysis (23)(24)(25)(26)(27)(28). The clinical relevance of using a cell culture model to understand biological processes and functions has been examined.…”

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confidence: 99%

Proteomics Analysis of Conditioned Media from Three Breast Cancer Cell Lines

Kulasingam

Diamandis

2007

Molecular & Cellular Proteomics

191

209

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A "bottom-up" proteomics approach and a two-dimensional (strong cation exchange followed by reversedphase) LC-MS/MS strategy on a linear ion trap (LTQ) were utilized to identify and compare expressions of extracellular and membrane-bound proteins in the conditioned media of three breast cell lines (MCF-10A, BT474, and MDA-MB-468). Proteomics analysis of the media identified in excess of 600, 500, and 700 proteins in MCF-10A, BT474, and MDA-MB-468, respectively. We successfully identified the internal control proteins, kallikreins 5, 6, and 10 (ranging in concentration from 2 to 50 g/liter) in MDA-MB-468 conditioned medium as validated by ELISA and confidently identified Her-2/neu in BT474 cells. Subcellular localization was determined based on Genome Ontology terms for all the 1,139 proteins of which 34% were classified as extracellular and membrane-bound. Proteomics analysis of MDA-MB-468 cell lysate demonstrated that only 5% of all identified proteins were extracellular. This confirmed our hypothesis that examining the CM of cell lines, as opposed to the cell lysates, leads to a significant enrichment in secreted proteins. Tissue specificity, functional classifications, and spectral counting were performed. Elafin, a protease inhibitor, identified in the conditioned media of BT474 and MDA-MB-468 and the three kallikreins (KLK5, KLK6, and KLK10) were validated using an immunoassay on various serum and biological samples. Some of the secreted proteins identified have established roles in breast cancer development (cell growth, differentiation, and metastasis) and/or are linked to early onset breast cancer. Our approach to mining for low abundance molecules could identify proteins in various stages of breast cancer development. Many of the identified proteins are potentially useful to investigate as circulating serum breast cancer biomarkers. Molecular

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A proteomic approach to investigate potential biomarkers directed against membrane‐associated breast cancer proteins

Cited by 26 publications

References 28 publications

Differential protein expression analysis using stable isotope labeling and PQD linear ion trap MS technology

Differential protein expression analysis using stable isotope labeling and PQD linear ion trap MS technology

Cancer Immunomics Using Autoantibody Signatures for Biomarker Discovery

Proteomics Analysis of Conditioned Media from Three Breast Cancer Cell Lines

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