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• W2133315981 abstract "Molecular subtypes of breast cancer with relevant biological and clinical features have been defined recently, notably ERBB2-overexpressing, basal-like, and luminal-like subtypes. To investigate the ability of mass spectrometry-based proteomics technologies to analyze the molecular complexity of human breast cancer, we performed a SELDI-TOF MS-based protein profiling of human breast cell lines (BCLs). Triton-soluble proteins from 27 BCLs were incubated with ProteinChip arrays and subjected to SELDI analysis. Unsupervised global hierarchical clustering spontaneously discriminated two groups of BCLs corresponding to “luminal-like” cell lines and to “basal-like” cell lines, respectively. These groups of BCLs were also different in terms of estrogen receptor status as well as expression of epidermal growth factor receptor and other basal markers. Supervised analysis revealed various protein biomarkers with differential expression in basal-like versus luminal-like cell lines. We identified two of them as a carboxyl terminus-truncated form of ubiquitin and S100A9. In a small series of frozen human breast tumors, we confirmed that carboxyl terminus-truncated ubiquitin is observed in primary breast samples, and our results suggest its higher expression in luminal-like tumors. S100A9 up-regulation was found as part of the transcriptionally defined basal-like cluster in DNA microarrays analysis of human tumors. S100A9 association with basal subtypes as well as its poor prognosis value was demonstrated on a series of 547 tumor samples from early breast cancer deposited in a tissue microarray. Our study shows the potential of integrated genomics and proteomics profiling to improve molecular knowledge of complex tumor phenotypes and identify biomarkers with valuable diagnostic or prognostic values. Molecular subtypes of breast cancer with relevant biological and clinical features have been defined recently, notably ERBB2-overexpressing, basal-like, and luminal-like subtypes. To investigate the ability of mass spectrometry-based proteomics technologies to analyze the molecular complexity of human breast cancer, we performed a SELDI-TOF MS-based protein profiling of human breast cell lines (BCLs). Triton-soluble proteins from 27 BCLs were incubated with ProteinChip arrays and subjected to SELDI analysis. Unsupervised global hierarchical clustering spontaneously discriminated two groups of BCLs corresponding to “luminal-like” cell lines and to “basal-like” cell lines, respectively. These groups of BCLs were also different in terms of estrogen receptor status as well as expression of epidermal growth factor receptor and other basal markers. Supervised analysis revealed various protein biomarkers with differential expression in basal-like versus luminal-like cell lines. We identified two of them as a carboxyl terminus-truncated form of ubiquitin and S100A9. In a small series of frozen human breast tumors, we confirmed that carboxyl terminus-truncated ubiquitin is observed in primary breast samples, and our results suggest its higher expression in luminal-like tumors. S100A9 up-regulation was found as part of the transcriptionally defined basal-like cluster in DNA microarrays analysis of human tumors. S100A9 association with basal subtypes as well as its poor prognosis value was demonstrated on a series of 547 tumor samples from early breast cancer deposited in a tissue microarray. Our study shows the potential of integrated genomics and proteomics profiling to improve molecular knowledge of complex tumor phenotypes and identify biomarkers with valuable diagnostic or prognostic values. Breast cancer (BC) 1The abbreviations used are: BC, breast cancer; BCL, breast cell lines; ER, estrogen receptor; PR, progesterone receptor; IHC, immunohistochemistry; PTM, post-translational modification; TMA, tissue microarray; EGFR, epidermal growth factor receptor; bis-tris, 2-[bis(2-hydroxyethyl)amino]-2-(hydroxymethyl)propane-1,3-diol; IPI, International Protein Index; CMA, cell microarray; CI 95%, 95% confidence interval. is a complex and heterogeneous disease resulting from accumulation of genetic alterations. This molecular heterogeneity explains in part the extensive diversity of clinical outcome and needs to be better delineated to improve therapeutic management and to identify relevant targets for novel treatments. A molecular taxonomy of BCs has been defined based on DNA microarray data (1Perou C.M. Sorlie T. Eisen M.B. van de Rijn M. Jeffrey S.S. Rees C.A. Pollack J.R. Ross D.T. Johnsen H. Akslen L.A. Fluge O. Pergamenschikov A. Williams C. Zhu S.X. Lonning P.E. Borresen-Dale A.L. Brown P.O. Botstein D. Molecular portraits of human breast tumours.Nature. 2000; 406: 747-752Crossref PubMed Scopus (11740) Google Scholar, 2Sorlie T. Perou C.M. Tibshirani R. Aas T. Geisler S. Johnsen H. Hastie T. Eisen M.B. van de Rijn M. Jeffrey S.S. Thorsen T. Quist H. Matese J.C. Brown P.O. Botstein D. Eystein Lonning P. Borresen-Dale A.L. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 10869-10874Crossref PubMed Scopus (8560) Google Scholar, 3Sorlie T. Tibshirani R. Parker J. Hastie T. Marron J.S. Nobel A. Deng S. Johnsen H. Pesich R. Geisler S. Demeter J. Perou C.M. Lonning P.E. Brown P.O. Borresen-Dale A.L. Botstein D. Repeated observation of breast tumor subtypes in independent gene expression data sets.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 8418-8423Crossref PubMed Scopus (4182) Google Scholar). Five major molecular subtypes have been identified: luminal A and B, ERBB2-overexpressing, basal-like, and normal-like. These different BCs have a distinct clinical course and response to therapeutic agents (4Troester M.A. Hoadley K.A. Sorlie T. Herbert B.S. Borresen-Dale A.L. Lonning P.E. Shay J.W. Kaufmann W.K. Perou C.M. Cell-type-specific responses to chemotherapeutics in breast cancer.Cancer Res. 2004; 64: 4218-4226Crossref PubMed Scopus (154) Google Scholar, 5Bertucci F. Finetti P. Rougemont J. Charafe-Jauffret E. Cervera N. Tarpin C. Nguyen C. Xerri L. Houlgatte R. Jacquemier J. Viens P. Birnbaum D. Gene expression profiling identifies molecular subtypes of inflammatory breast cancer.Cancer Res. 2005; 65: 2170-2178Crossref PubMed Scopus (204) Google Scholar, 6Rouzier R. Perou C.M. Symmans W.F. Ibrahim N. Cristofanilli M. Anderson K. Hess K.R. Stec J. Ayers M. Wagner P. Morandi P. Fan C. Rabiul I. Ross J.S. Hortobagyi G.N. Pusztai L. Breast cancer molecular subtypes respond differently to preoperative chemotherapy.Clin. Cancer Res. 2005; 11: 5678-5685Crossref PubMed Scopus (1525) Google Scholar). Overall luminal cancers (estrogen receptor (ER)-positive, 60% of BCs) have a good prognosis (although subtype B, which has a lower ER and higher proliferative profile, has a poor prognosis in comparison with subtype A). ERBB2-overexpressing (ER-negative and overexpressing ERBB2, 20–30% of BCs) and basal-like BCs (ER-negative and HER-2-negative, 10–20% of BCs) are unanimously considered as poor prognosis subtypes (1Perou C.M. Sorlie T. Eisen M.B. van de Rijn M. Jeffrey S.S. Rees C.A. Pollack J.R. Ross D.T. Johnsen H. Akslen L.A. Fluge O. Pergamenschikov A. Williams C. Zhu S.X. Lonning P.E. Borresen-Dale A.L. Brown P.O. Botstein D. Molecular portraits of human breast tumours.Nature. 2000; 406: 747-752Crossref PubMed Scopus (11740) Google Scholar, 2Sorlie T. Perou C.M. Tibshirani R. Aas T. Geisler S. Johnsen H. Hastie T. Eisen M.B. van de Rijn M. Jeffrey S.S. Thorsen T. Quist H. Matese J.C. Brown P.O. Botstein D. Eystein Lonning P. Borresen-Dale A.L. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 10869-10874Crossref PubMed Scopus (8560) Google Scholar, 3Sorlie T. Tibshirani R. Parker J. Hastie T. Marron J.S. Nobel A. Deng S. Johnsen H. Pesich R. Geisler S. Demeter J. Perou C.M. Lonning P.E. Brown P.O. Borresen-Dale A.L. Botstein D. Repeated observation of breast tumor subtypes in independent gene expression data sets.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 8418-8423Crossref PubMed Scopus (4182) Google Scholar, 7Sotiriou C. Neo S.Y. McShane L.M. Korn E.L. Long P.M. Jazaeri A. Martiat P. Fox S.B. Harris A.L. Liu E.T. Breast cancer classification and prognosis based on gene expression profiles from a population-based study.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 10393-10398Crossref PubMed Scopus (1659) Google Scholar). Importantly if molecularly targeted approaches are available for luminal (hormonal therapy) and ERBB2 BCs (trastuzumab), no similar treatment exists for basal-like BCs, justifying the need for a better molecular definition of this subtype. This definition may allow specific management and help identify novel molecular targets for innovative treatments. Using whole-genome DNA microarrays and immunohistochemistry (IHC), we have recently established gene and protein expression profiles of 31 breast cell lines (BCLs) and generated a 10-protein signature (CAV1,CD44, EGFR, MET, ETS1, GATA3, luminal cytokeratin CK19, basal cytokeratin CK5/6, CD10, and Moesin) allowing accurate distinction between luminal and basal BCLs (8Charafe-Jauffret E. Ginestier C. Monville F. Finetti P. Adelaide J. Cervera N. Fekairi S. Xerri L. Jacquemier J. Birnbaum D. Bertucci F. Gene expression profiling of breast cell lines identifies potential new basal markers.Oncogene. 2006; 25: 2273-2284Crossref PubMed Scopus (437) Google Scholar). A promising way to complement molecular typing of tumors is to perform protein expression profiling using MS-based approaches (9Bertucci F. Birnbaum D. Goncalves A. Proteomics of breast cancer: principles and potential clinical applications.Mol. Cell. Proteomics. 2006; 5: 1772-1786Abstract Full Text Full Text PDF PubMed Scopus (73) Google Scholar). These approaches take advantage of the ability of mass spectrometers to separate peptides or proteins according to their m/z. They may identify peptides after enzymatic digestion of proteins separated from complex mixtures or may be applied directly to biological samples to generate a protein signature that correlates with a given phenotype. Theoretical advantages of this technology include the lack of requirement for an “a priori” hypothesis based on previous biological foreknowledge, allowing examination and quantification of a large number of initially unknown protein parameters as well as the potential to capture post-translational modifications (PTMs). PTMs are not detectable at the mRNA level but often play significant roles in protein functions. Among MS-based approaches, SELDI-TOF technology, which couples protein separation using chromatographic surfaces (ProteinChip arrays) and direct presentation to spectrometers, was made popular as a promising way to profile complex biological samples, notably biological fluids such as serum, plasma, or urine, to identify diagnostic or prognostic biomarkers (10Petricoin E.F. Liotta L.A. SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer.Curr. Opin. Biotechnol. 2004; 15: 24-30Crossref PubMed Scopus (296) Google Scholar, 11Kozak K.R. Amneus M.W. Pusey S.M. Su F. Luong M.N. Luong S.A. Reddy S.T. Farias-Eisner R. Identification of biomarkers for ovarian cancer using strong anion-exchange ProteinChips: potential use in diagnosis and prognosis.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 12343-12348Crossref PubMed Scopus (240) Google Scholar, 12Petricoin E.F. Ardekani A.M. Hitt B.A. Levine P.J. Fusaro V.A. Steinberg S.M. Mills G.B. Simone C. Fishman D.A. Kohn E.C. Liotta L.A. Use of proteomic patterns in serum to identify ovarian cancer.Lancet. 2002; 359: 572-577Abstract Full Text Full Text PDF PubMed Scopus (2857) Google Scholar, 13Zhang Z. Bast Jr., R.C. Yu Y. Li J. Sokoll L.J. Rai A.J. Rosenzweig J.M. Cameron B. Wang Y.Y. Meng X.-Y. Berchuck A. van Haaften-Day C. Hacker N.F. de Bruijn H.W.A. van der Zee A.G.J. Jacobs I.J. Fung E.T. Chan D.W. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer.Cancer Res. 2004; 64: 5882-5890Crossref PubMed Scopus (828) Google Scholar, 14Goncalves A. Esterni B. Bertucci F. Sauvan R. Chabannon C. Cubizolles M. Bardou V.J. Houvenaegel G. Jacquemier J. Granjeaud S. Meng X.Y. Fung E.T. Birnbaum D. Maraninchi D. Viens P. Borg J.P. Postoperative serum proteomic profiles may predict metastatic relapse in high-risk primary breast cancer patients receiving adjuvant chemotherapy.Oncogene. 2006; 25: 981-989Crossref PubMed Scopus (114) Google Scholar). Here we performed SELDI-TOF MS profiling of Triton-based protein lysates from 27 BCLs characterized previously at the transcriptional and IHC protein level. Our objectives were to explore how SELDI protein profiles may correlate with previously reported molecular subtypes identified in BCs and to identify specific biomarkers associated with these subtypes by taking advantage of specific MS features. 27 of the 31 breast cell lines referenced in Charafe-Jauffret et al. (8Charafe-Jauffret E. Ginestier C. Monville F. Finetti P. Adelaide J. Cervera N. Fekairi S. Xerri L. Jacquemier J. Birnbaum D. Bertucci F. Gene expression profiling of breast cell lines identifies potential new basal markers.Oncogene. 2006; 25: 2273-2284Crossref PubMed Scopus (437) Google Scholar) were analyzed: BT-20, BT-474, BT-483, CAMA-1, HCC1937, HCC38, HCC1954, HME-1, MCF-7, MCF-10A, MDA-MB-134, MDA-MB-157, MDA-MB-175, MDA-MB-231, MDA-MB-453, SK-BR-3, SK-BR-7, T47D, UACC-812, ZR-75-1, ZR-75-30, 184B5, BrCa-MZ-01, SUM-185, SUM-190, SUM-225, and S68. All cell lines are derived from human carcinomas except MCF-10A, which is derived from a fibrocystic disease, and HME-1 and 184B5, which represent immortalized normal mammary tissue. The cell lines were grown using the recommended culture conditions. Cells were rinsed twice in cold PBS and lysed in buffer containing 50 mm HEPES, pH 7.5, 1 mm EGTA, 150 mm NaCl, 1.5 mm MgCl2, 10% glycerol, 1% Triton X-100 supplemented with 1 mm PMSF, 1 mm orthovanadate, 10 nm aprotinin, and 1 μm leupeptin as antiprotease mixture. Triton-soluble proteins were recovered in the supernatant of a 20-min centrifugation at 13,000 × g and 4 °C. For tumors, frozen tissues were first cryoground and then subjected to the same lysis method. Protein concentrations were assessed using the Bradford assay, and an equal amount of total protein (20 μg) was investigated for each cell line. Samples were subjected to SELDI-TOF MS profiling using the ProteinChip Biomarker System as recommended by Ciphergen Biosystems (Fremont, CA). Briefly Triton-based cell lysates were bound in triplicate with a randomized chip/spot allocation scheme to IMAC-Cu and CM10 ProteinChip arrays. The energy absorbing molecule (crystallization matrix), 50% saturated sinapinic acid dissolved in 50% acetonitrile, 0.5% trifluoroacetic acid, was promptly applied. These steps were automated using a customized Tecan Evo Platform. All samples to be compared in a given experimental condition were processed in a one-step procedure. Spotted arrays were then read using a PBS IIC ProteinChip reader. For each experimental condition, readings were optimized for low molecular weight (2,000–30,000 Da). A pool of randomly spotted human serum specimens was used for monitoring the intra-assay reproducibility. External mass calibration was performed daily. Spectra were externally calibrated, base line-subtracted, and normalized to total ion current. Qualified mass peaks (signal/noise >5; cluster mass window at 0.3%) within the m/z range of 2–30 kDa were selected automatically using integrated Biomarker Wizard software. Resulting Excel files containing absolute intensity and m/z of protein peaks resolved were obtained and subjected to biostatistic processing. All data were log-transformed and analyzed by a combination of unsupervised and supervised methods. Unsupervised hierarchical clustering of expression data was done with the Cluster program (15Eisen M.B. Spellman P.T. Brown P.O. Botstein D. Cluster analysis and display of genome-wide expression patterns.Proc. Natl. Acad. Sci. U. S. A. 1998; 95: 14863-14868Crossref PubMed Scopus (13235) Google Scholar) using Pearson correlation as the similarity metric and centroid linkage clustering. Results were displayed using the TreeView program (15Eisen M.B. Spellman P.T. Brown P.O. Botstein D. Cluster analysis and display of genome-wide expression patterns.Proc. Natl. Acad. Sci. U. S. A. 1998; 95: 14863-14868Crossref PubMed Scopus (13235) Google Scholar). Supervised analysis was applied to the 327 peaks resolved and 27 cell lines to identify and rank proteins that discriminate between distinct relevant subgroups of cell lines using the nonparametric Wilcoxon Mann-Whitney test. Differentially expressed proteins were selected at an unadjusted p value of <0.05. Candidate biomarkers identified were then purified using IMAC-Cu-based chromatographic minicolumns (Hypercel, Ciphergen Biosystems, Fremont, CA) according to the manufacturer’s instructions. These minicolumns allow recapitulating protein capture on IMAC ProteinChips as performed during the profiling phase. Briefly IMAC Hypercel columns were loaded with Cooper buffer and incubated with 300 μg of selected cell lysate samples in optimized binding buffer. After washing, proteins were eluted using 10 mm imidazole-containing buffer and concentrated to a final volume of 15 μl using a SpeedVac concentrator system. The purification process was monitored at all steps using NP20 ProteinChips. The purified biomarker was separated in an Xcell sure lock electrophoresis unit with 4–12% bis-tris gradient precast NuPAGE gels in MES running buffer according to the manufacturer’s instruction (Invitrogen). Coomassie Blue-stained samples were washed, reduced, alkylated with 55 mm iodoacetamide, and digested at 37 °C for 16 h using 12.5 ng/μl specific enzymes (trypsin (Promega, Madison, WI) or endolysin (Sigma-Aldrich)) according to Shevchenko et al. (16Shevchenko A. Wilm M. Vorm O. Jensen O.N. Podtelejnikov A.V. Neubauer G. Mortensen P. Mann M. A strategy for identifying gel-separated proteins in sequence databases by MS alone.Biochem. Soc. Trans. 1996; 24: 893-896Crossref PubMed Scopus (196) Google Scholar). Peptides were extracted from the acrylamide gel by adding 75 μl of a 5% formic acid solution for 10 min and then 75 μl of the mixture acetonitrile/water/formic acid (60:35:5). Peptide extraction was increased using bath sonication. Extracted peptides were dried in a SpeedVac concentrator system and mixed with 4 μl of HCCA matrix solution (α-cyano-4-hydroxycinnamic acid in acetonitrile/water/trifluoroacetic acid (50:49.7:0.3)). 1 μl of the mixture was loaded on a standard Bruker 384 MALDI target plate. Mass spectrometry analyses were done with a MALDI-TOF instrument (Ultraflex, Bruker Daltonics, Billerica, MA) using reflectron and positive modes with an ion acceleration of 25 keV. 600 laser shots were accumulated for each spectrum. Mass spectra were processed with FlexAnalysis 2.0 software (Bruker Daltonics). Only peaks with a signal/noise higher than 5 were retained. Internal calibration with peptides 842.509, 1045.564, 2211.104, and 2283.180 corresponding to trypsin autolysis was used. A control spectrum corresponding to background peak (control piece of gel treated and digested the same as gel containing protein) was used to manually remove background peaks. Protein identification was carried out by peptide mass fingerprint using an in-house Mascot server (version 2.2.0), Matrix Science Inc., London, UK. The MS spectra were searched against the International Protein Index (IPI) human database (version 3.26) from the European Bioinformatics Institute for peptide mass fingerprint identification. Criteria for searches were as follows: fixed carbamidomethylcysteine, optional methionine oxidation, no missing cleavage allowed, and a peptide search tolerance of 50 and 75 ppm for the trypsin and endolysin digest, respectively. Identification results were based on both the Mascot probability-based Mowse scores and the manual validation of mass assignments. For Western blot analysis of full-length ubiquitin, cytosolic lysates from CAMA-1 and SUM-225 cells were separated by SDS-PAGE, transferred, and immunoblotted onto nitrocellulose as described previously (17Jaulin-Bastard F. Saito H. Le Bivic A. Ollendorff V. Marchetto S. Birnbaum D. Borg J.-P. The ERBB2/HER2 receptor differentially interacts with ERBIN and PICK1 PSD-95/DLG/ZO-1 domain proteins.J. Biol. Chem. 2001; 276: 15256-15263Abstract Full Text Full Text PDF PubMed Scopus (81) Google Scholar) using anti-ubiquitin monoclonal antibody P4D1 (Cell Signaling Technology, Danvers, MA). RNA expression was profiled with Affymetrix U133 Plus 2.0 human oligonucleotide, representing over 47,000 transcripts and variants from human genes as described elsewhere (8Charafe-Jauffret E. Ginestier C. Monville F. Finetti P. Adelaide J. Cervera N. Fekairi S. Xerri L. Jacquemier J. Birnbaum D. Bertucci F. Gene expression profiling of breast cell lines identifies potential new basal markers.Oncogene. 2006; 25: 2273-2284Crossref PubMed Scopus (437) Google Scholar). The TMA from 547 patients with early breast cancer has been described previously (18Jacquemier J. Ginestier C. Rougemont J. Bardou V.J. Charafe-Jauffret E. Geneix J. Adelaide J. Koki A. Houvenaeghel G. Hassoun J. Maraninchi D. Viens P. Birnbaum D. Bertucci F. Protein expression profiling identifies subclasses of breast cancer and predicts prognosis.Cancer Res. 2005; 65: 767-779Crossref PubMed Google Scholar). The CMA was constructed as described previously to circumvent the scattering of cells in paraffin-embedded cell lines (8Charafe-Jauffret E. Ginestier C. Monville F. Finetti P. Adelaide J. Cervera N. Fekairi S. Xerri L. Jacquemier J. Birnbaum D. Bertucci F. Gene expression profiling of breast cell lines identifies potential new basal markers.Oncogene. 2006; 25: 2273-2284Crossref PubMed Scopus (437) Google Scholar). Briefly formaldehyde-fixed cell line pellets were resuspended at 37 °C in 1% low melting point agarose in 2-ml syringes and placed on ice, and the agarose cylinders obtained after cutting the terminal end of the syringe were fixed in ice cooled formalin-alcohol fixative. Cylinders were then processed in an automated tissue processor (ASP300, Leica) for an overnight run. The processed cylinders were then paraffin-embedded. CMA was prepared as for tissue microarrays with some modifications, mainly using a core cylinder with a diameter of 2 mm. 5-μm sections of the resulting blocks were made and used for IHC analysis after transfer onto glass slides as described previously (18Jacquemier J. Ginestier C. Rougemont J. Bardou V.J. Charafe-Jauffret E. Geneix J. Adelaide J. Koki A. Houvenaeghel G. Hassoun J. Maraninchi D. Viens P. Birnbaum D. Bertucci F. Protein expression profiling identifies subclasses of breast cancer and predicts prognosis.Cancer Res. 2005; 65: 767-779Crossref PubMed Google Scholar) using a Dako LSABR2 Kit in the autoimmunostainer (Dako Autostainer, Glostrup, Denmark). Sections were deparaffinized in Histolemon (Carlo Erba Reagenti, Rodano, Italy) and rehydrated in a graded ethanol solution. Goat polyclonal anti-calgranulin B (C-19) antibody (Santa Cruz Biotechnology, Inc., Santa Cruz, CA) was applied at a dilution of 1:100. After staining, slides were evaluated by two pathologists (E. C. J. and J. J.). Results were scored by estimating the percentage (P) of tumor cells showing characteristic staining (from undetectable level or 0% to homogeneous staining or 100%) and by estimating the intensity (I) of staining (1, weak staining; 2, moderate staining; or 3, strong staining). Results were scored by multiplying the percentage of positive cells by the intensity, i.e. by the so-called quick score (Q) (Q = P × I; maximum = 300). For each cell line and core biopsies, the mean of the score of a minimum of two core biopsies on two different slides was calculated. Discrepancies were resolved under the multiheaded microscope. For CMA, comparison between SELDI and IHC data were expressed as continuous values. For TMA, S100A9 staining had to be compared with other clinical and pathological data, and the cutoff value selected for S100A9 expression was >30 (median Q value of stained samples). Distributions of molecular markers and other categorical variables were compared using either the χ2 or Fisher’s exact tests. For continuous variables, Wilcoxon test was used. Metastasis-free survival was calculated from the date of diagnosis, the first distant metastasis being scored as an event. All other patients were censored at the time of the last follow-up, death, recurrence of local or regional disease, or development of a second primary cancer. Overall survival was calculated from the date of the diagnosis to the date of death or date of the last news. Survival curves were derived from Kaplan-Meier estimates (19Kaplan E.L. Meier P. Nonparametric estimation from incomplete observations.J. Am. Stat. Assoc. 1958; 53: 457-481Crossref Scopus (48352) Google Scholar) and compared by log-rank test. Survival rates are presented with their 95% confidence intervals (CI 95%). For multivariate analysis, Cox proportional hazards model regression was performed using a backward stepwise procedure based on Akaike information criterion. Statistical tests were two-sided at the 5% level of significance. All statistical tests were done using SAS version 8.02. Protein lysates from a total of 27 BCLs, previously characterized by IHC and DNA microarrays profiling (8Charafe-Jauffret E. Ginestier C. Monville F. Finetti P. Adelaide J. Cervera N. Fekairi S. Xerri L. Jacquemier J. Birnbaum D. Bertucci F. Gene expression profiling of breast cell lines identifies potential new basal markers.Oncogene. 2006; 25: 2273-2284Crossref PubMed Scopus (437) Google Scholar), were profiled by SELDI-based mass spectrometry using CM10 and IMAC-Cu ProteinChip arrays. These two conditions generated a total of 326 protein peaks. Cell lines clustered according to the similarities of their SELDI-generated protein expression profiles, whereas proteins clustered according to their expression similarity across the sample population. Results of hierarchical clustering are shown in Fig. 1. BCLs displayed heterogeneous protein expression profiles as reflected by the dendrogram branch length. Overall they fell in two groups. Group I (n = 12) included only carcinoma cell lines with the majority (nine of 12) derived from ductal carcinoma (BT-483, UACC-812, T47D, ZR-75-1, MDA-MB-134, MCF-7, ZR-75-30, MDA-MB-175, and BT-474); the histological type of the other cell lines of this group (SUM-185, SUM-225, and S68) were not available. Group II (n = 15) comprised six ductal carcinoma cell lines (BT-20, HCC38, BRCA1-mutated HCC1937, HCC1954, SK-BR-7, and SK-BR-3), mesenchymal-like MDA-MB-231, two medullary BCLs (MDA-MB-157 and BrCa-MZ-01), three non-cancerous BCLs (184B5, MCF-10A, and HME-1), and three BCLs with unknown histological type (CAMA-1, MDA-MB-453, and SUM-190). A strong correlation existed between the two groups and the ER status of cell lines as determined in our previous study (8Charafe-Jauffret E. Ginestier C. Monville F. Finetti P. Adelaide J. Cervera N. Fekairi S. Xerri L. Jacquemier J. Birnbaum D. Bertucci F. Gene expression profiling of breast cell lines identifies potential new basal markers.Oncogene. 2006; 25: 2273-2284Crossref PubMed Scopus (437) Google Scholar). This was true when ER status was evaluated either qualitatively by IHC (p = 0.008473, Fisher’s exact test) with more ER-positive cell lines in group I (seven of 12) as compared with group II (one of 15) or quantitatively by DNA microarray-based measurement of ESR1 gene expression (p = 4.28·10−5, Wilcoxon test). We previously defined the same BCLs as “luminal-like” (n = 13) or “basal-like” (n = 10) according to breast cancer molecular subtyping generated from DNA microarray studies (2Sorlie T. Perou C.M. Tibshirani R. Aas T. Geisler S. Johnsen H. Hastie T. Eisen M.B. van de Rijn M. Jeffrey S.S. Thorsen T. Quist H. Matese J.C. Brown P.O. Botstein D. Eystein Lonning P. Borresen-Dale A.L. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 10869-10874Crossref PubMed Scopus (8560) Google Scholar, 8Charafe-Jauffret E. Ginestier C. Monville F. Finetti P. Adelaide J. Cervera N. Fekairi S. Xerri L. Jacquemier J. Birnbaum D. Bertucci F. Gene expression profiling of breast cell lines identifies potential new basal markers.Oncogene. 2006; 25: 2273-2284Crossref PubMed Scopus (437) Google Scholar, 20Ross D.T. Perou C.M. A comparison of gene expression signatures from breast tumors and breast tissue derived cell lines.Dis. Markers. 2001; 17: 99-109Crossref PubMed Scopus (118) Google Scholar). As shown in Fig. 1, the major subgrouping of BCLs based on global clustering was in agreement with the subtype to which they were allocated: group I included 10 luminal-like cell lines of 13, and group II included nine basal-like cell lines of 10 (p = 0.00275, Fisher’s exact test). This represents an 82% rate of concordance. Interestingly this SELDI-based subgrouping strongly correlated with differential expression of a molecular signature involving 10 potential basal markers (GATA3, CK19, EGFR, CD10, MET, CK5/6, CAV1, Moesin, CD44, and ETS1), which we generated from a DNA microarray study and validated by cell microarrays (supplemental Table 1). Thus, mass spectrometry-based profiling was able to capture protein expression information allowing the separation of BCLs according to their major pathological and molecular features, including ER status and “luminal/basal” molecular subtyping. We then applied a supervised analysis bas" @default.
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• W2133315981 title "Protein Profiling of Human Breast Tumor Cells Identifies Novel Biomarkers Associated with Molecular Subtypes" @default.
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• W2133315981 doi "https://doi.org/10.1074/mcp.m700487-mcp200" @default.
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