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Expression of Endoplasmic Reticulum Stress Proteins Is a Candidate Marker of Brain Metastasis in both ErbB-2+ and ErbB-2 Primary Breast Tumors

Open ArchivePublished:June 27, 2011DOI:https://doi.org/10.1016/j.ajpath.2011.04.037
      The increasing incidence of breast cancer brain metastasis in patients with otherwise well-controlled systemic cancer is a key challenge in cancer research. It is necessary to understand the properties of brain-tropic tumor cells to identify patients at risk for brain metastasis. Here we attempt to identify functional phenotypes that might enhance brain metastasis. To obtain an accurate classification of brain metastasis proteins, we mapped organ-specific brain metastasis gene expression signatures onto an experimental protein-protein interaction network based on brain metastatic cells. Thirty-seven proteins were differentially expressed between brain metastases and non-brain metastases. Analysis of metastatic tissues, the use of bioinformatic approaches, and the characterization of protein expression in tumors with or without metastasis identified candidate markers. A multivariate analysis based on stepwise logistic regression revealed GRP94, FN14, and inhibin as the best combination to discriminate between brain and non-brain metastases (ROC AUC = 0.85, 95% CI = 0.73 to 0.96 for the combination of the three proteins). These markers substantially improve the discrimination of brain metastasis compared with ErbB-2 alone (AUC = 0.76, 95% CI = 0.60 to 0.93). Furthermore, GRP94 was a better negative marker (LR = 0.16) than ErbB-2 (LR = 0.42). We conclude that, in breast carcinomas, certain proteins associated with the endoplasmic reticulum stress phenotype are candidate markers of brain metastasis.
      Brain metastases occur in 10% to 15% of breast cancer patients with advanced disease.
      • Weil R.J.
      • Palmieri D.C.
      • Bronder J.L.
      • Stark A.M.
      • Steeg P.S.
      Breast cancer metastasis to the central nervous system.
      • Luck A.A.
      • Evans A.J.
      • Green A.R.
      • Rakha E.A.
      • Paish C.
      • Ellis I.O.
      The influence of basal phenotype on the metastatic pattern of breast cancer.
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      • Ermani M.
      • Brandes A.A.
      The pathogenesis and treatment of brain metastases: a comprehensive review.
      It can be assumed that up to 30% of metastatic breast cancer patients will undergo brain metastasis during the course of their disease.
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      • Elledge R.
      Primary breast cancer phenotypes associated with propensity for central nervous system metastases.
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      • Heinemann V.
      Central nervous system metastases in HER-2-overexpressing metastatic breast cancer: a treatment challenge.
      This rate is increasing, which can be linked to greater survival in patients receiving chemotherapy and to the fact that it is difficult to cross the blood-brain barrier with current systemic treatments.
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      • Moore D.T.
      • Graham M.L.
      Central nervous system metastases in women after multimodality therapy for high risk breast cancer.
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      Risk factors for brain relapse in patients with metastatic breast cancer.
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      • Toms S.A.
      Pathobiology of brain metastases.
      The difficulties in managing brain metastasis therapy result in a median survival of 7 months, with brain metastasis being the cause of death or a major contributing factor in 68% of patients.
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      • Niël C.G.
      • Vecht C.J.
      Therapeutic management of brain metastasis.
      Thus, there is a need for both prevention and improved treatment of brain metastasis.
      • Luck A.A.
      • Evans A.J.
      • Green A.R.
      • Rakha E.A.
      • Paish C.
      • Ellis I.O.
      The influence of basal phenotype on the metastatic pattern of breast cancer.
      • Tosoni A.
      • Ermani M.
      • Brandes A.A.
      The pathogenesis and treatment of brain metastases: a comprehensive review.
      The association of ErbB-2 overexpression with brain metastasis has been attributed to both the inability of a humanized antibody such as trastuzumab to penetrate the blood-brain barrier
      • Palmieri D.
      • Smith Q.R.
      • Lockman P.R.
      • Bronder J.
      • Gril B.
      • Chambers A.F.
      • Weil R.J.
      • Steeg P.S.
      Brain metastases of breast cancer.
      and the longer life span of patients receiving therapy that improves visceral disease control.
      • Bendell J.C.
      • Domchek S.M.
      • Burstein H.J.
      • Harris L.
      • Younger J.
      • Kuter I.
      • Bunnell C.
      • Rue M.
      • Gelman R.
      • Winer E.
      Central nervous system metastases in women who receive trastuzumab-based therapy for metastatic breast carcinoma.
      A longer life can lead to the onset of late tumor spread to the central nervous system. The predilection of ErbB-2+ tumor cells for the central nervous system has also been reported.
      • Palmieri D.
      • Bronder J.L.
      • Herring J.M.
      • Yoneda T.
      • Weil R.J.
      • Stark A.M.
      • Kurek R.
      • Vega-Valle E.
      • Feigenbaum L.
      • Halverson D.
      • Vortmeyer A.O.
      • Steinberg S.M.
      • Aldape K.
      • Steeg P.S.
      Her-2 overexpression increases the metastatic outgrowth of breast cancer cells in the brain.
      Thus, ErbB-2 may affect the development of breast cancer and increase the potential for brain metastasis.
      The development of metastasis in the central nervous system depends on the interaction of tumor cells with host defenses and the brain microenvironment, which, surrounded by the blood-brain barrier and lacking lymphatic drainage, differs from lung, liver, lymph node, or bone microenvironments.
      • Palmieri D.
      • Chambers A.F.
      • Felding-Habermann B.
      • Huang S.
      • Steeg P.S.
      The biology of metastasis to a sanctuary site.
      Moreover, microenvironmental factors at the metastatic foci may affect the response of tumors to chemotherapy and may condition drug resistance.
      • Gu B.
      • España L.
      • Méndez O.
      • Torregrosa A.
      • Sierra A.
      Organ-selective chemoresistance in metastasis from human breast cancer cells: inhibition of apoptosis, genetic variability and microenvironment at the metastatic focus.
      Unraveling the biological pathways that drive brain metastasis promises insight into how to limit or prevent this deadly aspect of cancer progression.
      Our aim was to identify proteins involved in the progression of brain metastasis. Recently, a strategy based on mapping expression profiles with protein interactions has been described.
      • Chuang H.Y.
      • Lee E.
      • Liu Y.T.
      • Lee D.
      • Ideker T.
      Network-based classification of breast cancer metastasis.
      The authors show that it is possible to extract relevant biological information about deregulated functions and the relationship between them, and to identify molecules that could be helpful as metastatic markers or therapeutic targets. We compared data obtained from an experimental protein-protein interaction network (PPIN),
      • Martin B.
      • Aragues R.
      • Sanz R.
      • Oliva B.
      • Boluda S.
      • Martinez A.
      • Sierra A.
      Biological pathways contributing to organ-specific phenotype of brain metastatic cells.
      which identifies biological pathways contributing to the organ-specific phenotype of brain metastatic cells, with gene expression profile data
      • Landemaine T.
      • Jackson A.
      • Bellahcène A.
      • Rucci N.
      • Sin S.
      • Abad B.M.
      • Sierra A.
      • Boudinet A.
      • Guinebretière J.M.
      • Ricevuto E.
      • Noguès C.
      • Briffod M.
      • Bièche I.
      • Cherel P.
      • Garcia T.
      • Castronovo V.
      • Teti A.
      • Lidereau R.
      • Driouch K.
      A six-gene signature predicting breast cancer lung metastasis.
      obtained from published transcriptomic analysis of 23 human breast cancer metastasis samples excised from various anatomical locations, including the brain. To compare the expression and network data sets, we mapped the expression values of each gene onto its corresponding protein in the network and searched for proteins whose activities are highly discriminative of brain metastasis. Protein expression analysis of tissues from metastatic human brain and primary breast tumors provided candidate markers of brain metastasis in both ErbB-2+ and ErbB-2 breast carcinomas.

      Materials and Methods

      Sample Collection

      The Breast Cancer Committee of the Catalan Institute of Oncology and the University Hospital of Bellvitge supplied samples from patients diagnosed between 1988 and 2006. The series of 122 breast cancers included 71 consecutive primary ductal breast carcinomas at initial diagnosis from metastatic patients in treatment at the time of the study, with one or several organs affected (Table 1), and 51 patients with positive lymph nodes at surgery without metastatic progression after a minimum follow-up duration of 5 years. Three patients had brain as the unique metastasis location and 10 patients had dissemination also at bone (n = 7), lung (n = 6), and liver (n = 4). A total of 48 tumors with bone metastasis, 23 with liver metastasis, and 31 with lung metastasis were included.
      Table 1Distribution and Combinations of the Various Metastases from Breast Cancer Tumors Included in the Tissue Array Analysis
      Metastatic involvement of organs
      BrainBoneLiverLungTotal (no.)
      In each organ (no.)
      13482331
      As a unique organ [no. (%)]
      3 (23)11 (23)4 (17)3 (10)21
      Multimetastatic combinations
      ××4
      ××0
      ××1
      ×××2
      ×××1
      ×××0
      ××5
      ××5
      ×××4
      ××2
      ××××2
      Other multimetastatic combinations
      One or more metastases in combination with other organs (lymph nodes, skin, pleura, esophagus, and vagina).
      24
      Total number of patients with metastasis: 71.
      low asterisk One or more metastases in combination with other organs (lymph nodes, skin, pleura, esophagus, and vagina).
      To optimize each immunohistochemical analysis, the corresponding control tissues for the expression of each protein were also used. To validate protein expression, we included in the analysis six brain metastasis samples matched with the corresponding ductal breast carcinoma to validate protein expression. As a validation set, we used a series of 295 breast tumors for which the transcriptomic data were publicly available.
      • van de Vijver M.J.
      • He Y.D.
      • van'T Veer L.J.
      • Dai H.
      • Hart A.A.
      • Voskuil D.W.
      • Schreiber G.J.
      • Peterse J.L.
      • Roberts C.
      • Marton M.J.
      • Parrish M.
      • Atsma D.
      • Witteveen A.
      • Glas A.
      • Delahaye L.
      • van der Velde T.
      • Bartelink H.
      • Rodenhuis S.
      • Rutgers E.T.
      • Friend S.H.
      • Bernards R.
      A gene-expression signature as a predictor of survival in breast cancer.
      • Bos P.D.
      • Zhang X.H.
      • Nadal C.
      • Shu W.
      • Gomis R.R.
      • Nguyen D.X.
      • Minn A.J.
      • van de Vijver M.J.
      • Gerald W.L.
      • Foekens J.A.
      • Massagué J.
      Genes that mediate breast cancer metastasis to the brain.

      Identification of Brain Metastasis Candidate Markers

      The strategy for identifying novel cancer candidates has been described elsewhere.
      • Aragues R.
      • Sander C.
      • Oliva B.
      Predicting cancer involvement of genes from heterogeneous data.
      The general procedure of the study, the steps of the analysis, and the levels of protein expression measured are shown as a flow chart in Figure 1A.
      Figure thumbnail gr1
      Figure 1Identification of candidate genes and pathways. A: Study design flow chart. B: The protein-protein interaction network (PPIN) for interacting proteins identified by mass spectrometry.
      • Martin B.
      • Aragues R.
      • Sanz R.
      • Oliva B.
      • Boluda S.
      • Martinez A.
      • Sierra A.
      Biological pathways contributing to organ-specific phenotype of brain metastatic cells.
      Root proteins are in yellow boxes and linker proteins in blue boxes. C: Specific signature of brain metastasis. Hierarchical clustering of a series of 23 breast cancer metastases using 1193 genes from the MetaBre brain-specific signature.
      • Landemaine T.
      • Jackson A.
      • Bellahcène A.
      • Rucci N.
      • Sin S.
      • Abad B.M.
      • Sierra A.
      • Boudinet A.
      • Guinebretière J.M.
      • Ricevuto E.
      • Noguès C.
      • Briffod M.
      • Bièche I.
      • Cherel P.
      • Garcia T.
      • Castronovo V.
      • Teti A.
      • Lidereau R.
      • Driouch K.
      A six-gene signature predicting breast cancer lung metastasis.

      Experimental Proteomic Analysis and Protein Interaction Network Analysis

      To identify brain metastasis-associated proteins, we used a prior proteomic analysis that compared differential expression of proteins between 435-P and 435-Br1 cells.
      • Martin B.
      • Aragues R.
      • Sanz R.
      • Oliva B.
      • Boluda S.
      • Martinez A.
      • Sierra A.
      Biological pathways contributing to organ-specific phenotype of brain metastatic cells.
      Briefly, the proteins differentially expressed by two-dimensional gel electrophoresis (Amersham Ettan DIGE system; GE Healthcare, Little Chalfont, UK) in 435-Br1 cells were identified by peptide mass fingerprinting spectra recorded by a Voyager STR MALDI-TOF system (Applied Biosystems, Foster City, CA) in positive reflector mode with delayed extraction. The spectra were analyzed using the m/z software package (ProteoMetrics, New York, NY). Proteins were identified against a nonredundant database (NCBInr) using online MASCOT search tool (http://www.matrixscience.com/search_form_select.html).
      The protein network was based on 17 proteins known to be differentially expressed between 435-P breast cancer cells and the brain metastatic variant 435-Br1. We used PIANA
      • Aragues R.
      • Jaeggi D.
      • Oliva B.
      PIANA: protein interactions and network analysis.
      to combine data from DIP 2006.01.16, MIPS 2006.01, HPRD 2005.09.13, BIND 2006.01, and the human interactions from two high-throughput experiments. The final PPIN included 628 proteins from 13 known seeds (interacting proteins) identified by MALDI-TOF (Figure 1B).

      Human Brain Metastasis Transcriptomic Data

      The protein-network approach for identifying markers of brain metastasis was based on results from a previously analyzed microarray hybridization using the GeneChip human genome U133 Plus 2.0 array (Affymetrix, High Wycombe, UK; Santa Clara, CA), which includes more than 47,000 transcripts and variants, according to standard protocols for RNA extraction and probe preparation.
      • Landemaine T.
      • Jackson A.
      • Bellahcène A.
      • Rucci N.
      • Sin S.
      • Abad B.M.
      • Sierra A.
      • Boudinet A.
      • Guinebretière J.M.
      • Ricevuto E.
      • Noguès C.
      • Briffod M.
      • Bièche I.
      • Cherel P.
      • Garcia T.
      • Castronovo V.
      • Teti A.
      • Lidereau R.
      • Driouch K.
      A six-gene signature predicting breast cancer lung metastasis.
      Briefly, to process and normalize Affymetrix chips, robust multichip averaging RMA algorithms were used.
      • Irizarry R.A.
      • Hobbs B.
      • Collin F.
      • Beazer-Barclay Y.D.
      • Antonellis K.J.
      • Scherf U.
      • Speed T.P.
      Exploration, normalization, and summaries of high density oligonucleotide array probe level data.
      All these computations were performed with the Bioconductor package version 2.0.
      • Gentleman R.C.
      • Carey V.J.
      • Bates D.M.
      • Bolstad B.
      • Dettling M.
      • Dudoit S.
      • Ellis B.
      • Gautier L.
      • Ge Y.
      • Gentry J.
      • Hornik K.
      • Hothorn T.
      • Huber W.
      • Iacus S.
      • Irizarry R.
      • Leisch F.
      • Li C.
      • Maechler M.
      • Rossini A.J.
      • Sawitzki G.
      • Smith C.
      • Smyth G.
      • Tierney L.
      • Yang J.Y.
      • Zhang J.
      Bioconductor: open software development for computational biology and bioinformatics.
      Expression profiles were analyzed with BRB Array tools, version 3.3beta3 (Molecular Statistics and Bioinformatics Section, Biometric Research Branch, Division of Cancer Treatment and Diagnosis, NIH-National Cancer Institute, Bethesda, MD).
      The univariate t-test was used to identify genes differentially expressed in four brain metastases and metastases in organs other than the brain (5 lung, 6 liver, 2 skin, and 6 osteolytic bone metastases) (Figure 1C). Differences were considered significant when P < 0.001. This stringent threshold was used to limit the number of false positives. These data sets, under the identification number GSE11078, are freely available from the Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov).

      Identification of Candidate Genes and Pathways

      Gene expression levels obtained from the microarray experiments were mapped onto the network proteins, assuming that a protein might be differentially expressed if the gene encoding for it was found to be differentially expressed at the RNA level. Differential gene expression was found for 556 of the 658 proteins in the initial PPIN.
      To classify proteins by function, we used FatiGO software, an online tool for detecting significant associations between gene ontology terms (GO) and groups of genes.
      • Al-Shahrour F.
      • Minguez P.
      • Tarraga J.
      • Medina I.
      • Alloza E.
      • Montaner D.
      • Dopazo J.
      FatiGO +: a functional profiling tool for genomic data Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.

      TMAs and IHC

      Tissue microarrays (TMAs) were prepared from three representative areas of the tumor that were carefully selected from H&E-stained sections of 122 donor blocks (S.B. and S.H.). Core cylinders, 2 mm in diameter, were removed from each tumor with a skin-biopsy punch and were deposited into recipient paraffin blocks using a specific arraying device (Beecher Instruments, Sun Prairie, WI), as described elsewhere.
      • Fernández P.L.
      • Nayach I.
      • Fernández E.
      • Fresno L.
      • Palacín A.
      • Farré X.
      • Campo E.
      • Cardesa A.
      Tissue macroarrays (“microchops”) for gene expression analysis.
      Sections (3-μm thick) of the resulting microarray block were cut and used for immunohistochemical (IHC) analysis after being transferred to glass slides.
      Experimental conditions, positive control tissues, and the characteristics and source of the antibodies used are listed in Table 2. Staining optimization, evaluation parameters, and analyses were established by two pathologists (P.L.F. and S.B.) who were blinded to the clinical status.
      Table 2Antibodies and Corresponding Conditions for IHC
      AntibodyCloneSupplier
      Suppliers: Abcam, Cambridge, UK; AbD S, AbD Serotec, MorphoSys UK, Oxford, UK; Acris, Acris Antibodies, Herford, Germany; SCB, Santa Cruz Biotechnology, Santa Cruz, CA; Sigma, Sigma-Aldrich, St. Louis, MO.
      ProtocolCellular expressionControl tissue
      GRP 94sc-1794 (C-19)SCB1/2000
      Retrieved in Na-citrate buffer.
      Endoplasmic reticulumBreast carcinoma
      TRAF2SM7106P (clon 33A1293; 205–222 aa)Acris1/100 O/N
      Retrieved in Na-citrate buffer.
      CytoplasmBreast carcinoma
      FN14sc-27143 (C-13)SCB1/3000
      Retrieved in Na-citrate buffer.
      MembraneKidney, heart
      INHAMCA951ST (R1)AbD S1/50
      Retrieved in Na-citrate buffer.
      CytoplasmTestis
      TOP1ab3825 (401–600 aa)Abcam1/100
      Retrieved in Tris/EDTA.
      Nuclei, cytoplasmColorectal tumor
      VAV2sc-20803 (H-200)SCB1/1000
      Retrieved in Na-citrate buffer.
      CytoplasmPancreas
      GFAPZ0334Dako1/8000
      Retrieved in Na-citrate buffer.
      CytoplasmBrain (astrocytes)
      TEM 8ab21270Abcam1/2000
      Retrieved in Na-citrate buffer.
      Cytoplasm, membraneBrain tumor endothelium
      ARFGAPSP1402PAcris1/1000
      Retrieved in Na-citrate buffer.
      CytoplasmTestis
      EIF3s8ab19359 (N-terminal 1–50 aa)Abcam1/1000 O/N
      Retrieved in Na-citrate buffer.
      CytoplasmKidney
      BAT 8G-6919Sigma1/250
      Retrieved in Na-citrate buffer.
      CytoplasmLymph node
      O/N, antibody is incubated overnight.
      low asterisk Suppliers: Abcam, Cambridge, UK; AbD S, AbD Serotec, MorphoSys UK, Oxford, UK; Acris, Acris Antibodies, Herford, Germany; SCB, Santa Cruz Biotechnology, Santa Cruz, CA; Sigma, Sigma-Aldrich, St. Louis, MO.
      Retrieved in Na-citrate buffer.
      Retrieved in Tris/EDTA.
      Antigens were retrieved by heating in a pressure cooker for 7 minutes in the appropriate buffer. Primary antibodies were diluted in Dako real antibody diluent buffer (Dako, Glostrup, Denmark; Carpinteria, CA): Tris buffer, pH 7.2, 15 mmol/L NaN3. LSAB+ system-horseradish peroxidase (Dako) was used, including biotinylated anti-rabbit, anti-mouse, and anti-goat immunoglobulins in PBS; streptavidin conjugated to horseradish peroxidase in PBS; and liquid 3–3′ diaminobenzidine in chromogen solution. A polyclonal antibody anti-ErbB2 (A0485; Dako) was used with an ultraView detection kit in an automatic staining system (Ventana Benchmark XT; Roche, Tucson, AZ).

      Statistical Analysis

      To evaluate the correlation of protein expression with brain metastasis, immunostained samples were graded on a three-category scale (negative, weak positive, and strong positive). The marker was catalogued as overexpressed in strong-positive samples. The association of brain metastasis for each marker was tested using a two-sided Fisher's exact test and summarized by calculating the sensitivity among tumors that developed metastasis, and calculating the specificity among tumors without metastasis, for strong-positive values. Positive and negative likelihood ratios (LR) were also calculated as integrated predictive indexes, as was the area under the ROC curve (AUC). Markers were assessed using a multivariate logistic regression model in a forward stepwise procedure to identify the best combination to discriminate brain metastasis. Because ErbB-2 is a known metastasis risk factor, an analysis including ErbB-2 as the baseline was also performed, as well as a stratified analysis of each candidate marker within ErbB-2+ and ErbB-2 tumors. In all of the analyses, associations were considered significant when P < 0.05. No multiple testing correction was done in this analysis, because the search for the best combination of markers started from a very small set of candidates.

      Results

      Identification of Specific Brain Metastasis Proteins

      We mapped human brain metastasis expression profiles with a PPIN to maximize accuracy in the classification of brain metastasis proteins.
      The signature of brain genes was catalogued as the organ-specific metastasis signature (BOSMS) with a hierarchical clustering that clearly distinguishes among the different metastases.
      • Landemaine T.
      • Jackson A.
      • Bellahcène A.
      • Rucci N.
      • Sin S.
      • Abad B.M.
      • Sierra A.
      • Boudinet A.
      • Guinebretière J.M.
      • Ricevuto E.
      • Noguès C.
      • Briffod M.
      • Bièche I.
      • Cherel P.
      • Garcia T.
      • Castronovo V.
      • Teti A.
      • Lidereau R.
      • Driouch K.
      A six-gene signature predicting breast cancer lung metastasis.
      The BOSMS contained 1193 genes (MetaBre) after the one-versus-all (ONA) class comparisons identified genes differentially expressed in the 4 brain metastases versus the 19 metastases to other organs.
      Integrating genomic and proteomic analyses, we matched the BOSMS with the PPIN,
      • Martin B.
      • Aragues R.
      • Sanz R.
      • Oliva B.
      • Boluda S.
      • Martinez A.
      • Sierra A.
      Biological pathways contributing to organ-specific phenotype of brain metastatic cells.
      and obtained 37 organ-specific proteins (Table 3): seven underexpressed and 30 overexpressed. The FatiGO classifier based on GO terms grouped proteins as follows: 13 nucleic acid metabolism proteins (48%), 10 translation proteins (37%), seven cell death proteins (26%), and six modification and folding proteins (22%), as well as a miscellany of metabolic, transport and signaling proteins, some of them with multiple functions (Figure 2). The cellular components of the analysis were as follows: 74% intracellular organelles, 51% cytoplasm, 22% ribonucleoprotein complex proteins, and 15% proteins intrinsic to membrane.
      Table 3Identities of 37 Brain Metastasis-Specific Proteins Matched in the Proteomic and Transcriptomic Analyses of Human Brain Metastasis
      Gene symbolUniProtKB IDProtein nameFunctionP valueNetwork position (linked to)
      Up-Regulated
      RPL13Q3KQT860S ribosomal protein L13 (breast basic conserved protein 1)Protein biosynthesis0.000840S ribosomal protein s12
      RPS10P4678340S ribosomal protein S10Protein biosynthesis0.0005
      RPL5P4677760S ribosomal protein L5Protein biosynthesis0.0002
      EIF5P55010Eukaryotic translation initiation factor 5Protein biosynthesis0.0007
      EIF3C (prev. EIF3S8)Q99613Eukaryotic translation initiation factor 3, subunit 8Protein biosynthesis0.00002
      EEF1DP29692Eukaryotic translation elongation factor 1-delta, isoform 2Signal transduction0.0006
      EEF1DQ96I38
      Q96I38 is a secondary accession number. The primary (citable) accession number is number is P29692.
      Eukaryotic translation elongation factor 1-delta, isoform 1Signal transduction0.0006
      PARF (syn. C9orf86)Q8IWK1
      Both Q8IWK1 and Q9BU21 link to Q3YEC7 as the main UniProtKB record for the putative GTP-binding protein Parf.
      Putative GTP-binding protein Parf [alt.: C9orf86 protein (fragment)]Signal transduction0.0001
      INHAP05111Inhibin alpha chainSignal transduction<0.000001
      CLN3Q13286Protein CLN3Protein folding0.0008
      FAM3AP98173Protein FAM3A precursor (2–19 protein)No function0.0009
      PARF (syn. C9orf86)Q9BU21
      Both Q8IWK1 and Q9BU21 link to Q3YEC7 as the main UniProtKB record for the putative GTP-binding protein Parf.
      Putative GTP-binding protein Parf (alt.: C9orf86 protein)No function0.0001
      TUBB2AP05218Tubulin beta-2 chainStructural0.0004Root protein
      TBCDQ96E74Tubulin-specific chaperone DStructural0.00005Tubulin beta-2 chain
      MCM4P33991DNA replication licensing factor MCM4DNA binding0.0004
      ARFGAP1Q8N6T3ADP-ribosylation factor GTPase-activating protein 1Transport0.0003
      EHMT2 (syn. BAT8)Q96KQ7Histone-lysine N-methyltransferase EHMT2 (alt.: HLA-B-associated transcript 8)Methylation0.0008
      RNF25Q96BH1Ring finger protein 25Ubiquitinization0.0002
      HMG20BQ9P0W2SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily E member 1-relatedDNA binding0.00001Vimentin
      SIRT6Q8N6T7Sirtuin 6Amino acid metabolism0.000004
      GFAPP14136Glial fibrillary acidic proteinStructural0.0001
      TOP1Q9UJN0
      Q9UJN0 is a secondary accession number. The primary (citable) accession number is number is P11387.
      DNA topoisomerase IDNA binding0.00001
      CRAMP1L (syn. C16orf34, KIAA1426)Q96RY5Protein cramped-like (alt.: uncharacterized protein KIAA1426)DNA binding0.0003Glyoxalase I
      C9orf84Q5VXU9Uncharacterized protein C9orf84No function0.0009
      C16orf34Q9H910Hematological and neurological expressed 1-like proteinNo function0.0003
      MSH6P52701DNA mismatch repair protein MSH6DNA repair0.00002RAD50
      TCERG1O14776Transcription elongation regulator 1DNA binding0.00004HSP 70
      HSP90B1 (prev. TRA1; syn. GRP94)P1462594kDa glucose regulated protein (alt.: GRP94)Protein folding0.0009LINKER (laminin receptor 67 kDa and HSP 27)
      TRAF2Q12933TNF-receptor associated factor 2Signal transduction0.00007PRDX4
      TNFRSF12A (syn. FN14)Q9NP84TNF-receptor superfamily member 12A (alt.: fibroblast growth factor-inducible immediate-early response protein 14; alt.: FN14)Receptor0.0001TRAF2
      Down-Regulated
      RPS12P2539840S ribosomal protein S12Protein Biosynthesis0.0006Root protein
      RPS23P6226640S ribosomal protein S23Protein biosynthesis0.00000140S ribosomal protein s12
      DNM3Q6P2G1Dynamin 3Protein biosynthesis0.0008
      SERPINB9P50453Serpin B9Signal transduction0.0007Tubulin beta-2 chain
      CREB1Q53X93cAMP responsive element binding protein 1, isoform ATranscription0.000005Vimentin
      CREB1P16220cAMP responsive element binding protein 1, isoform BTranscription0.00005
      AOC3Q16853Vascular adhesion protein-1Cell adhesion0.0004Glyoxalase I
      alt., alternative protein name; prev., previous approved gene symbol; syn., gene symbol synonym appearing in the literature.
      low asterisk Q96I38 is a secondary accession number. The primary (citable) accession number is number is P29692.
      Both Q8IWK1 and Q9BU21 link to Q3YEC7 as the main UniProtKB record for the putative GTP-binding protein Parf.
      Q9UJN0 is a secondary accession number. The primary (citable) accession number is number is P11387.
      Figure thumbnail gr2
      Figure 2The PPIN analysis was performed for 556 proteins matched with 1193 differentially expressed brain metastasis genes (transcriptomic comparison of brain metastases versus other metastases), yielding 37 pairs corresponding to 7 underexpressed and 30 overexpressed organ-specific proteins. FatiGO, an online tool for finding significant associations of gene ontology-terms with groups of genes,
      • Al-Shahrour F.
      • Minguez P.
      • Tarraga J.
      • Medina I.
      • Alloza E.
      • Montaner D.
      • Dopazo J.
      FatiGO +: a functional profiling tool for genomic data Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.
      shows the preponderant functions of significant proteins in clusters of coexpression. The classification by function was performed using GO level 6.
      We graphically represented the brain organ-specific metastasis phenotype (Figure 3) in the PPIN-based functional approach from protein interaction databases, providing a novel hypothesis for pathways involved in brain metastasis progression. Indeed, five functions from the PPIN were predominant: i) DNA binding and repair; ii) protein folding and chaperones, which engage one more DNA binding protein (O14776); iii) structural cytoskeleton, which engages four new DNA binding proteins (Q9P0W2, P33991, Q53X93, and Q9UJN0), two new signal transcription factors (P50453 and P16220), one ubiquitinization protein (Q96BH1), one amino acid metabolism protein (Q8N6T7), and one protein involved in methylation (Q96KQ7); iv) protein biosynthesis, which engages four new signal transduction factors (P29692, Q96I38, Q8IWK1, and P05111); and v) vesicle transport, which engages one protein (Q8N6T3).
      Figure thumbnail gr3
      Figure 3Functional classification of the PPIN. Proteins in functional clusters are grouped within a single box containing root and linker proteins. Functions are indicated in black type; the 37 brain metastasis proteins are indicated in white type. Boxes in light gray indicate the previous network
      • Martin B.
      • Aragues R.
      • Sanz R.
      • Oliva B.
      • Boluda S.
      • Martinez A.
      • Sierra A.
      Biological pathways contributing to organ-specific phenotype of brain metastatic cells.
      of brain metastatic cells; boxes in dark gray indicate new functions added from the transcriptomic analysis; boxes shaded from light to dark represent redundant functions identified in proteomics and transcriptomic analysis. Proteins that were validated by IHC are underlined.
      Additional IHC experiments were performed on six matched breast cancer tumor-brain metastasis samples from patients, to corroborate in human brain metastasis the expression of 11 proteins representative of the functions involved. These proteins were chosen on the basis of commercial availability of antibodies (Table 2 and Figure 4). The IHC analysis validated the expression of GRP94, TRAF2, FN14, TOP1, VAV2, GFAP, TEM8, BAT8, and ARFGAP proteins in brain metastasis. In addition, some of these proteins were also expressed in the corresponding primary breast carcinomas, suggesting their functional involvement from the primary tumor to the brain metastasis.
      Figure thumbnail gr4
      Figure 4Validation at the protein expression level (brown) in matched tumor-brain metastasis samples by means of IHC analysis to identify representative functional-type proteins in representative paraffin-embedded tumor-brain metastasis pairs. H&E staining of each tissue is shown as viewed by light microscopy. Original magnification: ×10 (H&E stain); ×20 (all others).
      We searched for references to brain metastasis signatures in published genomic data from experimental and clinical breast cancer and metastasis analysis. From our list of genes, only seven appeared in previous lists of gene expression profiling predicting clinical outcomes of breast cancer
      • van 't Veer L.J.
      • Dai H.
      • van de Vijver M.J.
      • He Y.D.
      • Hart A.A.
      • Mao M.
      • Peterse H.L.
      • van der Kooy K.
      • Marton M.J.
      • Witteveen A.T.
      • Schreiber G.J.
      • Kerkhoven R.M.
      • Roberts C.
      • Linsley P.S.
      • Bernards R.
      • Friend S.H.
      Gene expression profiling predicts clinical outcome of breast cancer.
      • Nevins J.R.
      • Huang E.S.
      • Dressman H.
      • Pittman J.
      • Huang A.T.
      • West M.
      Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction.
      • Wang Y.
      • Klijn J.G.
      • Zhang Y.
      • Sieuwerts A.M.
      • Look M.P.
      • Yang F.
      • Talantov D.
      • Timmermans M.
      • Meijer-van Gelder M.E.
      • Yu J.
      • Jatkoe T.
      • Berns E.M.
      • Atkins D.
      • Foekens J.A.
      Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.
      • Naderi A.
      • Teschendorff A.E.
      • Barbosa-Morais N.L.
      • Pinder S.E.
      • Green A.R.
      • Powe D.G.
      • Robertson J.F.
      • Aparicio S.
      • Ellis I.O.
      • Brenton J.D.
      • Caldas C.
      A gene-expression signature to predict survival in breast cancer across independent data sets.
      • Ramaswamy S.
      • Ross K.N.
      • Lander E.S.
      • Golub T.R.
      A molecular signature of metastasis in primary solid tumors.
      • Nuyten D.S.
      • Kreike B.
      • Hart A.A.
      • Chi J.T.
      • Sneddon J.B.
      • Wessels L.F.
      • Peterse H.J.
      • Bartelink H.
      • Brown P.O.
      • Chang H.Y.
      • van de Vijver M.J.
      Predicting a local recurrence after breast-conserving therapy by gene expression profiling.
      • Feng Y.
      • Sun B.
      • Li X.
      • Zhang L.
      • Niu Y.
      • Xiao C.
      • Ning L.
      • Fang Z.
      • Wang Y.
      • Zhang L.
      • Cheng J.
      • Zhang W.
      • Hao X.
      Differentially expressed genes between primary cancer and paired lymph node metastases predict clinical outcome of node-positive breast cancer patients.
      • Minn A.J.
      • Gupta G.P.
      • Siegel P.M.
      • Bos P.D.
      • Shu W.
      • Giri D.D.
      • Viale A.
      • Olshen A.B.
      • Gerald W.L.
      • Massagué J.
      Genes that mediate breast cancer metastasis to lung.
      • Bos P.M.
      • Boon P.E.
      • van der Voet H.
      • Janer G.
      • Piersma A.H.
      • Brüschweiler B.J.
      • Nielsen E.
      • Slob W.
      A semi-quantitative model for risk appreciation and risk weighing.
      • Klein A.
      • Olendrowitz C.
      • Schmutzler R.
      • Hampl J.
      • Schlag P.M.
      • Maass N.
      • Arnold N.
      • Wessel R.
      • Ramser J.
      • Meindl A.
      • Scherneck S.
      • Seitz S.
      Identification of brain- and bone-specific breast cancer metastasis genes.
      (Table 4): EEF1D, MCM4, RPL5, RPS12, and CLN3
      • van 't Veer L.J.
      • Dai H.
      • van de Vijver M.J.
      • He Y.D.
      • Hart A.A.
      • Mao M.
      • Peterse H.L.
      • van der Kooy K.
      • Marton M.J.
      • Witteveen A.T.
      • Schreiber G.J.
      • Kerkhoven R.M.
      • Roberts C.
      • Linsley P.S.
      • Bernards R.
      • Friend S.H.
      Gene expression profiling predicts clinical outcome of breast cancer.
      and also FAM3A and TBCD.
      • Nevins J.R.
      • Huang E.S.
      • Dressman H.
      • Pittman J.
      • Huang A.T.
      • West M.
      Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction.
      GFAP, encoding a ubiquitous protein in the central nervous system, also appeared in a list of genes differentially expressed between brain and bone breast cancer metastasis.
      • Klein A.
      • Olendrowitz C.
      • Schmutzler R.
      • Hampl J.
      • Schlag P.M.
      • Maass N.
      • Arnold N.
      • Wessel R.
      • Ramser J.
      • Meindl A.
      • Scherneck S.
      • Seitz S.
      Identification of brain- and bone-specific breast cancer metastasis genes.
      Table 4In silico Validation of the Endoplasmic Reticulum Stress Phenotype, Taking Into Account Previous Experimental And Clinical Reports
      ReferenceArray platformDescription of sampleGene signatureMatch to present study
      • van 't Veer L.J.
      • Dai H.
      • van de Vijver M.J.
      • He Y.D.
      • Hart A.A.
      • Mao M.
      • Peterse H.L.
      • van der Kooy K.
      • Marton M.J.
      • Witteveen A.T.
      • Schreiber G.J.
      • Kerkhoven R.M.
      • Roberts C.
      • Linsley P.S.
      • Bernards R.
      • Friend S.H.
      Gene expression profiling predicts clinical outcome of breast cancer.
      Agilent 24479 60-mer oligos97 Samples from LN patients231 Prognosis reporters (risk of distant metastasis)0
      430 Brca1 reporters3 (EEF1D, MCM4, RPL5)
      2460 ER reporters3 (CLN3, MCM4, RPS12)
      • Ramaswamy S.
      • Ross K.N.
      • Lander E.S.
      • Golub T.R.
      A molecular signature of metastasis in primary solid tumors.
      Rosetta inkjet (24479 genes; breast adenocarcinoma) oligonucleotide microarray279 Primary tumors of diverse types (lung, breast, prostate)128 Genes able to distinguish patients with good versus poor prognosis0
      • Nevins J.R.
      • Huang E.S.
      • Dressman H.
      • Pittman J.
      • Huang A.T.
      • West M.
      Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction.
      Multiple gene expression signatures “metagenes”86 LN+ breast cancer patients143 Predictors of lymph node metastasis0
      165 Predictors of breast cancer recurrence2 (FAM3A, TBCD)
      • Wang Y.
      • Klijn J.G.
      • Zhang Y.
      • Sieuwerts A.M.
      • Look M.P.
      • Yang F.
      • Talantov D.
      • Timmermans M.
      • Meijer-van Gelder M.E.
      • Yu J.
      • Jatkoe T.
      • Berns E.M.
      • Atkins D.
      • Foekens J.A.
      Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.
      Affymetrix U133A 25-mer oligosLN and LN+ patients with invasive breast cancer76-Gene signature to distinguish LN primary breast cancer to develop distant metastasis within 5 years0
      • Minn A.J.
      • Gupta G.P.
      • Siegel P.M.
      • Bos P.D.
      • Shu W.
      • Giri D.D.
      • Viale A.
      • Olshen A.B.
      • Gerald W.L.
      • Massagué J.
      Genes that mediate breast cancer metastasis to lung.
      Affymetrix U133A82 Breast cancer patients (primary tumors)95 Genes predictors of lung metastasis0
      • Nuyten D.S.
      • Kreike B.
      • Hart A.A.
      • Chi J.T.
      • Sneddon J.B.
      • Wessels L.F.
      • Peterse H.J.
      • Bartelink H.
      • Brown P.O.
      • Chang H.Y.
      • van de Vijver M.J.
      Predicting a local recurrence after breast-conserving therapy by gene expression profiling.
      Agilent 24479 60-mer oligos161 Patients in stage I and II breast cancer with age <53 years70-Gene predictor of local recurrence0
      • Naderi A.
      • Teschendorff A.E.
      • Barbosa-Morais N.L.
      • Pinder S.E.
      • Green A.R.
      • Powe D.G.
      • Robertson J.F.
      • Aparicio S.
      • Ellis I.O.
      • Brenton J.D.
      • Caldas C.
      A gene-expression signature to predict survival in breast cancer across independent data sets.
      Agilent 22575 60-mer oligos135 Tumor samples (no criteria for selection)70-Gene prognostic signature (risk of metastasis)0
      • Feng Y.
      • Sun B.
      • Li X.
      • Zhang L.
      • Niu Y.
      • Xiao C.
      • Ning L.
      • Fang Z.
      • Wang Y.
      • Zhang L.
      • Cheng J.
      • Zhang W.
      • Hao X.
      Differentially expressed genes between primary cancer and paired lymph node metastases predict clinical outcome of node-positive breast cancer patients.
      Operon 70-mer two-color 21239 probes35 Patients: primary tumor and lymph node metastasis paired samples79 Differentially expressed genes between primary samples and metastasis samples0
      • Klein A.
      • Olendrowitz C.
      • Schmutzler R.
      • Hampl J.
      • Schlag P.M.
      • Maass N.
      • Arnold N.
      • Wessel R.
      • Ramser J.
      • Meindl A.
      • Scherneck S.
      • Seitz S.
      Identification of brain- and bone-specific breast cancer metastasis genes.
      Affymetrix U133A8 Bone metastases, 18 brain metastases and 3 primary tumors51 Brain metastasis specific genes (versus bone metastasis)1 (GFAP)
      • Bos P.D.
      • Zhang X.H.
      • Nadal C.
      • Shu W.
      • Gomis R.R.
      • Nguyen D.X.
      • Minn A.J.
      • van de Vijver M.J.
      • Gerald W.L.
      • Foekens J.A.
      • Massagué J.
      Genes that mediate breast cancer metastasis to the brain.
      Affymetrix U133ACN34-BrM2 and MDA231-BrM2 brain metastatic cell lines.17 Genes whose expression was correlated with brain relapse0
      368 Breast cancer primary tumors26 Genes whose expression was increased in brain metastatic cell lines but not in bone or lung metastatic cell lines0
      LN, lymph node.
      These findings indicate that cells metastasizing in brain were enriched in cell structure, chaperones, stress and redox regulation, and intracellular transport proteins. The organ-specific character of this functional signature was also found in the transcriptomic data from breast cancer brain metastasis (Figure 5 and Table 5). From these, the most differentially expressed in brain metastasis, compared with metastases in other organs, were GRP94 (P = 0.002), FN14 (P = 0.002), ARFGAP1 (P = 0.003), TRAF2 (P = 0.003), and PDGFRA (0.002) genes. In contrast, other functions had no relevant expression in brain; for example, amino acid metabolism genes were overexpressed only in liver.
      Figure thumbnail gr5
      Figure 5Differentially expressed genes in brain metastasis (black), compared with metastases in other organs: lung (dark gray), bone (white), and liver (light gray), based on a Mann-Whitney test calculated for each gene using the normalized log intensities (see further in ). *P < 0.05, statistically significant different expression between bracketed organs.
      Table 5Differentially Expressed Genes in Brain Metastasis versus Non-Brain Metastases
      UniProtKB IDProtein nameGene symbolMetastasis
      Based on a Mann-Whitney U-test calculated for each gene using normalized log-intensities.
      P value
      Statistically significant differences are highlighted in bold face type.
      BrainNon-brain
      Chaperones
      P1462594kDa glucose-regulated protein (alt.: GRP94)HSP90B1 (prev. TRA1)9.598.100.002
      P1102178kDa glucose-regulated proteinHSPA511.4410.950.168
      P27824CalnexinCANX6.457.350.035
      P27797CalreticulinCALR11.389.220.006
      P3010158kDa glucose-regulated protein (alt.: p58; ERp57; ERp60)PDIA3 (prev. GRP58)7.824.050.003
      P3864675kDa glucose-regulated proteinHSPA9 (prev. HSPA9B; syn. GRP75)9.469.910.465
      P1080960kDa heat shock protein, mitochondrialHSPD110.479.710.144
      P07900Heat shock protein 90kDa alpha (cytosolic), class A member 1HSP90AA1 (prev. HSPCA)12.0310.520.006
      P08238Heat shock protein 90kDa alpha (cytosolic), class B member 1HSP90AB1 (prev. HSPCB)11.9311.060.168
      Endoplasmic reticulum stress sensors
      P18850Activating transcription factor 6ATF68.018.700.256
      O75460Serine/threonine-protein kinase/endoribonuclease IRE1 (alt.: inositol-requiring protein 1; IRE1a)ERN15.445.270.441
      Q76MJ5Serine/threonine-protein kinase/endoribonuclease IRE2 (alt.: inositol-requiring protein 2; IRE1b)ERN26.395.930.038
      Q9NZJ5Eukaryotic translation initiation factor 2-alpha kinase 3 (alt.: PRKR-like endoplasmic reticulum kinase)EIF2AK3 (syn. PERK)5.024.850.155
      UPR pathways
      P35638DNA damage-inducible transcript 3 proteinDDIT3 (syn. CHOP, GADD153)9.017.690.006
      P18848cAMP-dependent transcription factor ATF-4ATF410.3810.270.626
      P45983Mitogen-activated protein kinase 8 (alt.: c-Jun N-terminal kinase 1)MAPK85.865.380.009
      P45984Mitogen-activated protein kinase 9 (alt.: c-Jun N-terminal kinase 2)MAPK97.966.950.144
      P53779Mitogen-activated protein kinase 10 (alt.: c-Jun N-terminal kinase 3)MAPK106.506.260.441
      P05412Transcription factor AP-1 (alt.: proto-oncogene c-Jun)JUN7.276.080.006
      P17861X-box-binding protein 1XBP110.2710.850.417
      Q14703Membrane-bound transcription factor site-1 protease (alt.: endopeptidase S1P)MBTPS18.107.350.062
      O43462Membrane-bound transcription factor site-2 protease (alt.: endopeptidase S2P)MBTPS25.855.310.155
      Q13217DnaJ homolog subfamily C member 3 (alt.: Protein kinase inhibitor p58)DNAJC34.874.430.006
      O75807Protein phosphatase 1 regulatory subunit 15A (alt.: growth arrest and DNA damage-inducible protein GADD34)PPP1R15A (syn. GADD34)8.086.740.009
      EIF kinases
      Q9BQI3Heme-regulated inhibitorHRI9.989.510.33
      Q9P2K8Eukaryotic translation initiation factor 2-alpha kinase 4 (alt.: GCN2-like protein)EIF2AK46.026.730.052
      P19525Interferon-induced, double-stranded RNA-activated protein kinase (alt.: protein kinase RNA-activated)EIF2A2 (syn. PKR, PRKR)7.847.520.061
      Oxidative stress resistance
      P09601Heme oxygenase 1HMOX18.108.020.516
      Q13501Sequestosome-1SQSTM17.276.150.004
      Q96HE7ERO1-like protein alphaERO1L7.567.190.18
      Proteasome
      Q92611ER degradation-enhancing alpha-mannosidase-like 1EDEM16.535.410.088
      O4324226S proteasome regulatory subunit S3PSMD39.387.450.043
      Q86TM6E3 ubiquitin-protein ligase synoviolinSYVN18.598.770.441
      Q9UBV2Protein sel-1 homolog 1 (alt.: suppressor of lin-12-like protein 1)SEL1L8.369.050.871
      Q96DZ1Endoplasmic reticulum lectin 1 (alt.: XTP3-transactivated gene B protein; XTP-3)ERLEC1 (prev. C2orf30)9.549.930.57
      Glucose transporters
      P11166Solute carrier family 2, facilitated glucose transporter member 1 (alt.: GLUT-1)SLC2A18.967.970.081
      P11168Solute carrier family 2, facilitated glucose transporter member 2 (alt.: GLUT-2) (liver)SLC2A23.434.640.123
      P11169Solute carrier family 2, facilitated glucose transporter member 3 (alt.: GLUT-3) (brain)SLC2A36.606.130.871
      P22732Solute carrier family 2, facilitated glucose transporter member 5 (alt.: GLUT-5)SLC2A55.335.070.074
      Q9UGQ3Solute carrier family 2, facilitated glucose transporter member 6 (alt.: GLUT-6)SLC2A66.035.600.074
      Q9NY64Solute carrier family 2, facilitated glucose transporter member 8 (alt.: GLUT-9)SLC2A87.436.720.035
      Amino acid metabolism
      P48067Sodium- and chloride-dependent glycine transporter 1 (alt.: GlyT-1)SLC6A95.064.780.035
      P08243Asparagine synthetaseASNS7.788.150.49
      P32929Cystathionine gamma-lyaseCTH4.064.750.144
      P11586Methylenetetrahydrofolate dehydrogenaseMTHFD18.078.480.035
      Protein transport
      Q8N6T3ADP-ribosylation factor GTPase-activating protein 1ARFGAP17.256.210.003
      P09496Clathrin light chain ACLTA5.514.800.009
      P09497Clathrin light chain BCLTB4.664.110.015
      Q00610Clathrin heavy chain 1CLTC12.2512.070.685
      P53675Clathrin heavy chain 2CLTCL16.866.720.33
      P61966AP-1 complex subunit sigma-1A (alt.: sigma-adaptin 1A)AP1S1 (prev. CLAPS1)6.025.120.009
      P20340Ras-related protein Rab-6ARAB6A6.666.300.074
      P61019Ras-related protein Rab-2ARAB2A (prev. RAB2)9.678.730.043
      P61106Ras-related protein Rab-14RAB149.658.380.002
      O95197Reticulon-3RTN39.198.730.019
      Receptors and signal transductors
      P04626Receptor tyrosine-protein kinase erbB-2ERBB27.504.170.009
      O95407Tumor necrosis factor receptor superfamily member 6BTNFRSF6B7.376.850.003
      Q9NS68Tumor necrosis factor receptor superfamily member 19TNFRSF195.144.530.006
      Q9NP84Tumor necrosis factor receptor superfamily member 12A (alt.: FN14)TNFRSF12A (syn. FN14)9.077.680.002
      Q9HAV5Ectodysplasin-A2 receptor (alt.: tumor necrosis factor receptor superfamily member 27)EDA2R (syn. TNFRSF27)5.575.410.516
      P16234Alpha-type platelet-derived growth factor receptorPDGFRA5.199.090.002
      P00533Epidermal growth factor receptorEGFR6.926.420.012
      P17948Vascular endothelial growth factor receptor 1FLT17.097.470.088
      Q13077TNF receptor-associated factor 1TRAF15.915.990.655
      Q12933TNF receptor-associated factor 2TRAF26.856.200.003
      Q13114TNF receptor-associated factor 3TRAF36.406.630.417
      Q9BUZ4TNF receptor-associated factor 4TRAF46.605.390.035
      O00463TNF receptor-associated factor 5TRAF56.757.550.330
      alt., alternative name (proteins); prev., previously approved symbol (genes).
      low asterisk Based on a Mann-Whitney U-test calculated for each gene using normalized log-intensities.
      Statistically significant differences are highlighted in bold face type.
      After mapping transcriptomic into proteomic analyses, we concluded that molecules involved in protein folding and chaperones might connect different functions and presumably act by rescuing cells from endoplasmic reticulum stress responses.

      Expression of Endoplasmic Reticulum Stress Phenotype in Breast Cancer Primary Tumors Is Associated with Brain Metastasis Progression

      Because proteins were also expressed in primary tumors, to estimate the probability of specific brain metastasis outcomes we further analyzed the proteins in a series of primary breast carcinomas using TMA technology. We considered a marker to be positive when strong expression was detected, to avoid false positives, and taking into account the known expression in a control tissue (Figure 6).
      Figure thumbnail gr6
      Figure 6ERS response phenotype in breast cancer at first diagnosis. Representative tabulation of protein expression in breast cancer tissues. Tissues are shown as viewed by light microscopy. Low and medium intensities of staining were considered negative for semiquantitative purposes, and only tumors with high intensity staining were considered positive. Insets: Standard positive control tissue sample used in each determination. Original magnification, ×10.
      Statistical analysis of the data showed significant associations between brain metastasis progression and high expression of GRP94 (P < 0.0001), TRAF2 (P < 0.001), and FN14 (P < 0.0001). As expected, ErbB-2 expression was associated with brain metastasis with a high significance (P < 0.0001): 8/13 (62%) breast cancers that progressed to brain metastasis were positive, versus 12% and 5% of breast carcinomas that relapsed in other locations or without metastasis (7/57 and 2/42, respectively). ErbB-2 expression was also associated with the absence of hormone receptors: ER, 55% versus 30% (6/11 and 29/98, respectively, P = 0.016); PR, 73% versus 39% (8/11 versus 37/95, respectively, P = 0.009). A slight association with histological grade (HG) was also observed: HG III 58% versus 47% (7/12 versus 45/96, P = 0.105). In addition, we did not find correlation between lung, bone, or liver metastasis and high expression of these proteins.
      We calculated the positive and negative likelihood ratios (LR) to assess the predictive accuracy of each molecule as a brain metastasis marker, considering the sensitivity and the specificity of each (Table 6). The highest predictive value for the presence of metastasis was ErbB-2 expression (positive LR = 6.7, P < 0.0001), followed by FN14 (positive LR = 3.01, P = 0.001), GRP94 (positive LR = 1.89 P = 0.003), and TRAF2 (positive LR = 1.67, P = 0.055). Furthermore, GRP94 was the best negative predictive marker (negative LR = 0.16), followed by TRAF2 (negative LR = 0.35), FN14 (negative LR = 0.40), and ErbB-2 (negative LR = 0.42). Thus, the absence of the endoplasmic reticulum stress (ERS) response phenotype in tumors predicted the absence of brain metastasis.
      Table 6Risk of Brain Metastasis Associated with Each Marker in Breast Cancer
      Brain metastasis markerUniProtKB IDSensitivity
      Variation in denominators derives from missing values in the IHC (eg, tissue lost, unviable staining, or background).
      Specificity
      Variation in denominators derives from missing values in the IHC (eg, tissue lost, unviable staining, or background).
      LRP value
      Fisher's exact test, 2-sided.
      PosNeg
      ErbB-2P046268/13 (61.5)90/99 (90.9)6.700.42<0.0001
      GRP94P1462512/13 (92.0)55/107 (51.4)1.890.160.003
      FN14Q9NP849/13 (69.2)80/104 (77.0)3.010.400.001
      TRAF2Q129339/11 (81.8)45/88 (51.1)1.670.350.055
      VAV2P527352/13 (15,4)95/107 (88.8)1.380.950.65
      TOP1Q9UJN0
      Secondary accession number. The primary (citable) accession number is number is P11387.
      4/13 (30.8)91/105 (86.6)2.300.800.11
      InhibinP05111)0/13 (0)97/107 (90.7)01.100.60
      LR, likelihood ratio; Neg, negative LR; Pos, positive LR.
      low asterisk Variation in denominators derives from missing values in the IHC (eg, tissue lost, unviable staining, or background).
      Fisher's exact test, 2-sided.
      Secondary accession number. The primary (citable) accession number is number is P11387.
      For a validation set, we used a series of 295 breast tumors for which the transcriptomic data were publicly available.
      • van de Vijver M.J.
      • He Y.D.
      • van'T Veer L.J.
      • Dai H.
      • Hart A.A.
      • Voskuil D.W.
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      • Marton M.J.
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      • Bernards R.
      A gene-expression signature as a predictor of survival in breast cancer.
      • Bos P.D.
      • Zhang X.H.
      • Nadal C.
      • Shu W.
      • Gomis R.R.
      • Nguyen D.X.
      • Minn A.J.
      • van de Vijver M.J.
      • Gerald W.L.
      • Foekens J.A.
      • Massagué J.
      Genes that mediate breast cancer metastasis to the brain.
      As expected, the highest predictive value was ErbB-2 expression (positive LR = 8.27, P < 0.00001). Moreover, TRAF2 (positive LR = 2.36, P = 0.026), GRP94 (positive LR = 1.72 P = 0.028), and FN14 (positive LR = 1.78, P = 0.044) were associated with brain metastasis.
      A multivariate analysis based on stepwise logistic regression retained GRP94, FN14, and inhibin as the best combination to discriminate brain metastasis. The AUC value for this combination was 0.85 (95% CI = 0.73 to 0.96), substantially better than that for ErbB-2, which was the variable more strongly associated with brain metastasis (AUC = 0.76, 95% CI = 0.58 to 0.93). The model that combined ErbB-2, GRP94, FN14, and inhibin expression further increased the discrimination of metastatic disease in brain (AUC = 0.91, 95% CI = 0.77 to 1.00). The ROC curves for the three models are shown in Figure 7.
      Figure thumbnail gr7
      Figure 7The area under the ROC curve (AUC) obtained with the integrated predictive indexes. Markers were assessed in a multivariate logistic regression model using a forward stepwise procedure to identify the best combination to predict brain metastasis. For ErbB-2 alone, AUC = 0.76; for GRP94, FN14, and inhibin in combination, AUC = 0.85; and for ErbB-2, GRP94, FN14, and inhibin in combination, AUC = 0.91.
      We also performed a stratified analysis to check the relationship between ErbB-2 positivity and ERS response phenotype in binary combinations (Table 7). In ErbB-2 tumors, FN14 had a high negative likelihood ratio to predict the absence of brain metastasis progression (LR = 0.26, sensitivity = 0.8, P = 0.015).
      Table 7Risk of Brain Metastasis Associated with Each Marker in Breast Cancer with Regard to ErbB-2 Expression
      Brain metastasis marker
      For novel markers, UniProtKB identifiers are as follows: GRP94, P14625; FN14, Q9NP84; TRAF2, Q12933. For traditional markers: estrogen receptor ER, P03372; progesterone receptor PR, P06401.
      ErbB-2+ErbB-2
      Sensitivity
      Variation in denominators derives from missing values in the IHC (eg, tissue lost, unviable staining, or background).
      Specificity
      Variation in denominators derives from missing values in the IHC (eg, tissue lost, unviable staining, or background).
      P value
      Fisher's test.
      Sensitivity
      Variation in denominators derives from missing values in the IHC (eg, tissue lost, unviable staining, or background).
      Specificity
      Variation in denominators derives from missing values in the IHC (eg, tissue lost, unviable staining, or background).
      P value
      Fisher's test.
      Novel markers
       GRP948/8 (100)2/9 (22.2)0.474/5 (80)48/89 (53.9)0.19
       FN145/8 (62.5)6/9 (66.7)0.354/5 (80)68/88 (77.3)0.015
       TRAF26/7 (85.7)5/9 (55.6)0.153/4 (75)36/71 (50.7)0.62
      Traditional markers
       ER3/7 (42.9)6/8 (75)0.612/4 (50)21/83 (25.3)0.28
       PR2/7 (28.6)6/7 (85.7)1.01/4 (25)29/81 (35.8)0.15
       Histologic grade III6/8 (75.0)3/9 (33.3)1.01/4 (25)42/45 (93.3)0.62
      low asterisk For novel markers, UniProtKB identifiers are as follows: GRP94, P14625; FN14, Q9NP84; TRAF2, Q12933. For traditional markers: estrogen receptor ER, P03372; progesterone receptor PR, P06401.
      Variation in denominators derives from missing values in the IHC (eg, tissue lost, unviable staining, or background).
      Fisher's test.

      Discussion

      Fewer than 10% of breast cancer patients have detectable distant metastasis at diagnosis.
      • Steeg P.S.
      Tumor metastasis: mechanistic insights and clinical challenges.
      Thus, it is necessary to understand the properties of brain-tropic tumor cells to identify patients with risk of brain metastasis and to effectively prevent it. Because we assume that metastasis colonization could already be underway at the time of diagnosis, the ERS response phenotype might be a predictive tool to help decide on treatment under the risk of brain metastasis progression.
      The phenotype includes the overexpression of GRP94, FN14, and TRAF2, which are well correlated with brain metastasis in breast cancer patients. Our search for a multivariate panel of markers to predict brain metastasis revealed that the combination of GRP94, FN14, and inhibin together has a better discriminate accuracy than ErbB-2 alone, and that the best accuracy is obtained combining all four markers. Although all variables in these models were significantly associated with brain metastasis in the multivariate models, the increase in predictive accuracy measured by the difference in AUC was not (because of the small sample size in the present study). Indeed, TRAF2, which was associated with brain metastasis, had many missing values and could not be included in the multivariate analysis. The ERS response phenotype is indicative of a new tool to discriminate the risk of brain metastasis in both ErbB-2+ and ErbB-2 breast cancers. Moreover, the absence of the ERS response phenotype in tumors might predict the absence of metastasis.
      These biomarkers can help in selection of treatment strategies, furthering the current ambitious aim of identifying treatment strategies that will cure patients with ErbB-2+ disease while ensuring minimal toxicity for each individual patient.
      • Ignatiadis M.
      • Desmedt C.
      • Sotiriou C.
      • de Azambuja E.
      • Piccart M.
      HER-2 as a target for breast cancer therapy.
      Hicks et al
      • Hicks D.G.
      • Short S.M.
      • Prescott N.L.
      • Tarr S.M.
      • Coleman K.A.
      • Yoder B.J.
      • Crowe J.P.
      • Choueiri T.K.
      • Dawson A.E.
      • Budd G.T.
      • Tubbs R.R.
      • Casey G.
      • Weil R.J.
      Breast cancers with brain metastases are more likely to be estrogen receptor negative, express the basal cytokeratin CK5/6, and overexpress HER2 or EGFR.
      reported that EGFR expression, like ErbB-2, predicted the development of brain metastasis. The incidence of brain metastasis in patients with breast cancer overexpressing ErbB-2 who are being treated with trastuzumab is double that in other breast cancer patients; one-third will develop central nervous system metastasis, and this often occurs when they are responding to therapy at other sites or have a stable disease.
      • Lin N.U.
      • Winer E.P.
      Brain metastases: the HER2 paradigm.
      One of the clinical questions is whether this receptor remains a viable target after disease progression,
      • Piccart M.
      Circumventing de novo and acquired resistance to trastuzumab: new hope for the care of ErbB2-positive breast cancer.
      and whether trastuzumab treatment can prevent brain metastasis or whether it encourages the development of metastatic cells that have crossed the blood-brain barrier.
      GRP94 overexpression in brain metastasis might modulate ERS responses through activation of PERK, ATF6, and IRE1.
      • Dollins D.E.
      • Warren J.J.
      • Immormino R.M.
      • Gewirth D.T.
      Structures of GRP94-nucleotide complexes reveal mechanistic differences between the hsp90 chaperones.
      Downstream from GRP94 activation, transcription of chaperones and protein degradation might increase in brain metastatic cells. (We are currently exploring these pathways in our laboratory.) Because therapy decisions should depend on the tumor phenotype, the known close correlation between brain metastasis potential and ERS response phenotype in primary tumors suggests that HSP90 and proteasome inhibitors might be alternatives for treatment of breast cancer patients with a high risk of brain metastasis.
      • Orlowski R.Z.
      • Kuhn D.J.
      Proteasome inhibitors in cancer therapy: lessons from the first decade.
      It has been reported that inhibition of HSP90, which helps expression and folding of its client proteins, such as ErbB-2, could simultaneously inhibit the expression of viable receptors.
      • Piccart M.
      Circumventing de novo and acquired resistance to trastuzumab: new hope for the care of ErbB2-positive breast cancer.
      Furthermore, the expression of GRP94 has been associated with poor prognosis in gastric carcinomas,
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      • Hara T.
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      • Guan Y.F.
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      Overexpression of GRP78 and GRP94 are markers for aggressive behavior and poor prognosis in gastric carcinomas.
      and with chemotherapy resistance of lung cancer cells and ovarian carcinoma cells.
      • Zhang L.
      • Wang S.
      • Wangtao
      • Wang Y.
      • Wang J.
      • Jiang L.
      • Li S.
      • Hu X.
      • Wang Q.
      Upregulation of GRP78 and GRP94 and its function in chemotherapy resistance to VP-16 in human lung cancer cell line SK-MES-1.
      The switch from dormancy to growth of cancer cells in the brain may be dependent on stress response mechanisms, subsequent coordination of detoxification and redox pathways,
      • Martin B.
      • Aragues R.
      • Sanz R.
      • Oliva B.
      • Boluda S.
      • Martinez A.
      • Sierra A.
      Biological pathways contributing to organ-specific phenotype of brain metastatic cells.
      and cytokines produced by glial cells, which may contribute, in a paracrine manner, to the final development of brain metastasis. We identified overexpression of the FN14 gene, a member of the tumor necrosis factor (TNF) superfamily of receptors.
      • Wiley S.R.
      • Winkles J.A.
      TWEAK, a member of the TNF superfamily, is a multifunctional cytokine that binds the TweakR/Fn14 receptor.
      FN14 is an immediate early response gene whose expression is directly activated after exposure to growth factors in fibroblasts; it is up-regulated in migration-stimulated glioma cells in vitro, and it has been associated with high-grade tumors.
      • Tran N.L.
      • McDonough W.S.
      • Savitch B.A.
      • Sawyer T.F.
      • Winkles J.A.
      • Berens M.E.
      The tumor necrosis factor-like weak inducer of apoptosis (TWEAK)-fibroblast growth factor-inducible 14 (Fn14) signaling system regulates glioma cell survival via NFkappaB pathway activation and BCL-XL/BCL-W expression.
      Through activation of MAPK8/JNK and NF-κB, the TRAF proteins mediate signal transduction of the TNF receptor superfamily members
      • Hu P.
      • Han Z.
      • Couvillon A.D.
      • Kaufman R.J.
      • Exton J.H.
      Autocrine tumor necrosis factor alpha links endoplasmic reticulum stress to the membrane death receptor pathway through IRE1alpha-mediated NF-kappaB activation and down-regulation of TRAF2 expression.
      ; they could connect ERS responses and FN14 signaling pathway activation. Because FN14 and TRAF2 are overexpressed in breast cancer tumors that develop brain metastasis, and in brain metastatic cells, the disruption of the TWEAK/FN14 feedback loop also emerges as a molecular targeting strategy.
      FN14 overexpression can stimulate survival through interaction with the inhibitor-of-apoptosis proteins (IAPs).
      • Vince J.E.
      • Chau D.
      • Callus B.
      • Wong W.W.
      • Hawkins C.J.
      • Schneider P.
      • McKinlay M.
      • Benetatos C.A.
      • Condon S.M.
      • Chunduru S.K.
      • Yeoh G.
      • Brink R.
      • Vaux D.L.
      • Silke J.
      TWEAK-FN14 signaling induces lysosomal degradation of a cIAP1-TRAF2 complex to sensitize tumor cells to TNFalpha.
      TNF-like weak inducer of apoptosis (TWEAK)/FN14 signaling recruits cIAP1-TRAF2 complex, which is then targeted for lysosomal degradation. Cell sensitivity to TWEAK correlates with sensitivity to synthetic IAP antagonist. Studies with FN14-overexpressing tumor cells that could be selectively destroyed using a TWEAK-cytotoxin protein fusion suggest that FN14 could be a new molecular target for treating metastasis.
      • Winkles J.A.
      • Tran N.L.
      • Berens M.E.
      TWEAK and Fn14: new molecular targets for cancer therapy?.
      Indeed, the ERS response phenotype might indicate new opportunities in anticancer strategies for sanctuary sites and micrometastatic disease. Evidence of such phenotypes can be used to develop more specifically addressed therapies.
      Microarray-based gene studies are difficult to interpret, because of the huge amount of data involved, and it is therefore a challenge to derive biological insights. We applied a PPIN-based approach that identifies markers not as individual genes but as subnetworks extracted from PPINs, thus providing a systemic view of the interactome. This method serves to filter information by picking out key protein functions as metastasis markers. Indeed, we have delineated a pathogenic mechanism of metastasis to the brain based on the information from a proteomic study of brain metastasis cellular proteins. Further work is needed to confirm the prognostic value of the ERS response phenotype in a second validation step that includes a large independent group to increase the statistic power of the study and to assess the usefulness of the ERS response phenotype as a predictive tool at first diagnosis.
      To validate these markers, the main objective is to obtain a large collection of retrospective samples, far in excess of the typical numbers required to obtain statistical significance in the data. This could also lead to preventive therapy for brain metastases at initial diagnosis, not only in breast cancer patients, but also for other carcinomas with brain tropism.

      Acknowledgments

      We thank Dr. Marta Brell from the Neurosurgery Service and Dr. Sergio Herrero from the Pathology Service, both at the Bellvitge Hospital (C.S.U.B.), for their expert assistance and providing human metastasis samples. We also thank Mr. R. Rycroft for expert language advice. We acknowledge all of the partners of the MetaBre consortium for their collaboration and stimulating criticism: Marc Bracke (Ghent University Hospital, Belgium), Roberto Buccione (Consorzio Mario Negri Sud, Italy), Vincent Castronovo (University of Liège, Belgium), Philippe Clément-Lacroix (Prostrakan, France), Philippe Clézardin (INSERM, France), Suzanne Eccles (Institute of Cancer Research, UK), Anna Teti (University of l'Aquila, Italy), Maciej Ugorski (Wroclaw Agriculture University, Poland), and Gabri van der Pluijm (Leiden University Medical Center, The Netherlands).

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