Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Proteomic identification of the wt-p53-regulated tumor cell secretome

Abstract

Tumor–stroma interactions play a major role in tumor development, maintenance and progression. Yet little is known on how the genetic alterations that underlie cell transformation elicit cell extrinsic changes modulating heterotypic cell interactions. We hypothesized that these events involve a modification in the complement of secreted proteins by the cell, acting as mediators of intercellular communication. To test this hypothesis, we examined the role of wt-p53, a major tumor suppressor, on the tumor microenvironment through its regulation of secreted factors. Using a combination of 2-DE and cICAT proteomic techniques, we found a total of 111 secreted proteins, 39 of which showed enhanced and 21 inhibited secretion in response to wt-p53 expression. The majority of these were not direct targets of p53 transcription factor activity, suggesting a novel role for wt-p53 in the control of intracellular protein trafficking and/or secreted protein stability. Evidence for p53-controlled post-translational modifications on nine secreted proteins was also found. These findings will enhance our understanding of wt-p53 modulated interactions of the tumor with its environment.

Introduction

Traditionally, cancer formation is thought of as a cell autonomous process driven by mutations in genes that increase cell proliferation and survival, where a tumor is primarily composed of transformed cells. Increasing evidence suggests that the tumor microenvironment also contributes to the neoplasm (Hanahan and Weinberg, 2000) and that the tumor–stroma interactions are an active process initiated by transforming events (Bhowmick and Moses, 2005; Taieb et al., 2006). Consequently, we need to understand these tumor–stroma interactions to develop more effective therapies. We hypothesized that loss of tumor-suppressor function during cell transformation may have cell extrinsic effects through the modulation of secreted factors. We focused on p53, as it is frequently mutated in cancer and is a transcription factor that can directly control the synthesis of a large number of proteins (Harris and Levine, 2005).

Tumor-suppressive p53 is best known for its role in maintaining genomic integrity by controlling cell cycle progression and cell survival in response to DNA damage (Steele and Lane, 2005). Nevertheless, some studies have suggested that p53 can influence the tumor microenvironment through suppression of angiogenesis and tumor invasion (Van Meir et al., 1994; Zigrino et al., 2005). These processes might be influenced by p53 through two mechanisms; the induced secretion of inhibitory factors (Van Meir et al., 1994) or the negative regulation of secreted protumorigenic proteins (Chiarugi et al., 1998; Sun et al., 2000). While p53-regulated intracellular proteins are well studied, the extracellular ones have not been systematically analysed. Identification of the p53 controlled secreted proteins will clarify how p53 loss in tumors may lead to the altered regulation and response of the tumor cells to their environment.

To examine the regulation of p53 on the cell's secretome we identified secreted proteins by p53-null tumor cells in the presence or absence of reconstituted wt-p53 expression. This is the first comprehensive study of how p53 plays a role in the process of transformation through its manipulation of the tumor microenvironment. Our studies identified 50 new secreted proteins controlled by p53. These proteins have known roles in cancer-related processes that are dependent on heterotypic cell–cell communication such as immune response, angiogenesis, extracellular matrix (ECM) interaction, and cell survival. Many of these proteins are secreted through receptor-mediated nonclassical secretory pathways. These results are important to advance our understanding on how tumor–stroma interactions contribute to cancer progression.

Results

To identify p53-regulated secreted proteins involved in the cell–cell communication events important for human cell transformation, we selected the LN-Z308 cell line as it was derived from a malignant human glioma that lost both p53 alleles in vivo by well-characterized genetic events suggesting selective pressure for their loss (Albertoni et al., 1998). Reactivating wt-p53 function in these cells would revert or restore the release of p53-regulated secreted proteins and allow their identification in the conditioned media (CM). To this purpose, we used isogenic clones of LN-Z308 with tetracycline-inducible (2024 p53 tet-on) (Albertoni et al., 2002) and repressable (WT11 p53 tet off) (Van Meir et al., 1994) wt-p53 expression (Figure 1). These cells undergo growth arrest but not apoptosis in response to p53 (Van Meir et al., 1995) and show induction of the cell cycle inhibitor p21 upon p53 induction (Figure 1a). Using this system, we generated differential profiles of the cell lines’ secretome with and without wt-p53 expression using two complementary proteomic techniques: two-dimensional gel electrophoresis (2-DE) and cleavable isotope-coded affinity tag (cICAT).

Figure 1
figure1

Representative 2-DE gels of secreted proteins from glioma cells with inducible wt-p53 expression. (a) Two p53-null clones (WT11 and 2024) with dox-inducible wt-p53 expression were used (See Materials and methods). Western blot shows wt-p53 induction, and downstream activation of the p21 cell cycle inhibitor, 48 h postinduction in serum-free media. (b) Secreted proteins found in the CM from uninduced (left) and wt-p53 induced (right) 2024 cells were analysed by 2-DE analysis using IEF strips pH3-10 NL and 12.5% SDS–Page. Protein spots circled indicate proteins with enhanced secretion (right) or reduced (left) in response to wt-p53. Samples were run in triplicate and location of representative proteins is indicated. Black arrow shows p53-induced post-translational modification of Gal-1. White arrow shows the location of KIAA0828. (c) Enlargement showing acidic shift of Gal 1 in CM from wt p53 expressing cells (arrows).

2-DE of the tumor cell secretome

The secreted proteins were separated by 2-DE analysis using nonlinear pH range of 3–10 and linear range of 4–7 in triplicates to ensure reproducibility (Görg et al., 2004). The proteins were visualized by silver staining and analysed using ImageMaster software. As a further precaution against artefacts, we profiled both 2024 and WT11 clones and only proteins found secreted in both were retained.

The protein spots were next excised from the gel, subjected to in-gel digestion with trypsin, and identified using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF/TOF) MS/MS analysis. We found on average >150 spots on each gel representing 68 individual proteins (Figure 1b; Tables 1, 2 and 3). A semiquantitative analysis of this differential expression was performed by comparing spot intensity and volume using ImageMaster (Figure 2). The levels of 34 proteins in the CM were found to be largely invariable regardless of p53 expression, whereas 32 individual proteins showed differential expression levels in the CM in response to p53 (Tables 1, 2 and 3). Among the differentially expressed proteins, 18 had increased levels and 16 decreased levels in the CM in response to wt-p53 expression in the cells (Figure 3b). The 68 secreted proteins identified in the 2-DE screen belonged to 15 functional categories (Figure 3c; Tables 1 and 2).

Table 1 Secreted proteins with enhanced accumulation in CM upon p53 induction
Table 2 Secreted proteins showing reduced accumulation in the CM upon p53-induction
Table 3 Secreted proteins unchanged by wt-p53 or unclear as found by 2-DE and cICAT analyses
Figure 2
figure2

Semiquantitative analysis of differentially expressed proteins found by 2-DE and identified by MS/MS analysis. 3D representation of differential expression for representative proteins found upregulated (Gal-3, Gal-1 and β-2M), downregulated (SPARC, FGF-4 and TGF-β), and unchanged (TSP-1 and Pre-alb) by 2-DE as analysed by ImageMaster software.

Figure 3
figure3

Comparative analysis of wt-p53-regulated secreted proteins using both proteomic analyses: (a) Ven Diagram showing the total number (111) of secreted proteins found by 2-DE (white), and cICAT (gray). (b) Number of proteins found unchanged (white), up- (dark gray) or downregulated (light gray) by wt-p53 expression using 2-DE and c-ICAT analysis alone and those common to both techniques. (c) Distribution of identified secreted proteins by 2-DE (white) and c-ICAT (gray) analyses according to their general functional categories. Each protein is seen in a single category only even though some might play multiple functions.

Secretome analysis by cICAT

Given that the total number of secreted proteins identified by 2-DE was smaller than we had anticipated, and to avoid potential bias of using a single identification method, we sought a second complementary approach. Recently, internally standardized gel-free quantitative proteomic methods have been developed to alleviate limitations of 2-DE. One of these methods is isotope-coded affinity tag (ICAT) reagent labeling and tandem mass spectrometry (MS/MS) (Gygi et al., 2002). Secreted proteins from 2024 and WT11 cells were very similar in their expression patterns and differed significantly in expression levels for only 10 of the 91 proteins identified by this analysis. Through cICAT alone, we found 34 proteins with increased levels, and 13 with decreased levels by at least 20% while 44 remained unchanged in response to wt-p53 expression (Tables 1, 2 and 3; Figure 3b). These proteins were found differentially expressed in CM (P<0.05) in at least two of the three experiments for both cell lines. The quantification from cICAT was found to be consistent between experiments as seen by small standard deviation values for each tested cell line (Tables 1, 2 and 3). Similar to 2-DE results, the 91 proteins found secreted in the media by cICAT experiments belonged to 15 functional groups (Figure 3c; Tables 1 and 2).

Comparison of 2-DE and cICAT results

Combining both techniques, we were able to identify 111 separate secreted proteins; 68 by 2-DE and 91 by cICAT analysis (Figure 3a). It is noteworthy that 48 of the 91 (50%) secreted proteins identified by cICAT analysis, were identical to the ones identified by 2-DE analysis, showing concordant results between the techniques in identifying the complement of secreted proteins (Tables 1, 2 and 3; Figure 3). Thirty-seven of the 48 (77%) proteins commonly identified by the techniques showed similar responses to p53 activation in the cells. The majority of the remaining 11 (24%) proteins (listed as U in Table 3) were either secreted at a very low level or not differentially expressed to a high degree. When looking at the concordance between p53-regulated proteins, we found 13 proteins upregulated, eight downregulated and 16 unchanged by both 2-DE and cICAT analysis while the remaining 11 (listed as U) were found differentially expressed only by one of the two indicated methods (Figure 3b Tables 1, 2 and 3).

Verification of proteomic results

Some of the p53-regulated secreted proteins found in our analysis had been previously reported and validated, including vascular endothelial growth factor (VEGF), osteopontin (Opn) (Morimoto et al., 2002), secreted protein with acidic and cysteine rich domains (SPARC) and dickkopf (Wang et al., 2000). To confirm our results we picked three proteins whose levels were increased (galectin-1 (Gal-1), galectin-3 (Gal-3) and beta 2 microglobulin (β-2M)) and three decreased (SPARC, fibroblast growth factor-4 (FGF-4) and transforming growth factor beta (TGF-β)) in the CM in response to wt-p53 expression for validation by Western analysis. The levels of Gal-1, Gal-3 and β-2M in the CM were clearly increased by p53 in 2024 cells (Figure 4, compare lanes 2 and 4). In contrast, secreted levels of SPARC, TGF-β and FGF-4 were decreased. The downregulation of SPARC and TGF-β levels in the CM by p53 was particularly strong as it was able to antagonize their increase by doxycycline (dox) as seen in the LNZ308-C16 control cells that lack p53. Thrombospondin-1 (TSP-1) was used as a loading control since its levels are not found to be increased by wt-p53 in our glioma system (Tenan et al., 2000). The data show that our proteomic analysis with 2-DE and cICAT can be used to reliably identify differential expression of secreted proteins in the CM (Tables 1, 2 and 3).

Figure 4
figure4

Verification of selected 2-DE and cICAT results: Western blot analysis on TCA-precipitated serum-free CM collected after 48 h from LNZ308-C16 (control for dox) and 2024 cells with tet-on wt-p53 expression. Differential expression of SPARC, FGF-4, TGF-β, Gal-1, Gal-3, and β-2M in response to wt-p53 expression was examined. TSP1 and Pre-alb were loading controls and remained unchanged.

Investigation of the mechanism underlying p53 control over protein secretion

To examine whether the CM levels of the secreted proteins identified were regulated by p53 at the gene expression level, we examined the differential expression of their mRNAs by microarrays in the 2024 cell line in three independent experiments. None of the mRNAs corresponding to the secreted products found in our analysis appeared to have levels significantly modulated by p53 (Tables 1, 2 and 3; column 5). These findings suggest a role of wt-p53 in the modulation of the extracellular levels of secreted proteins through either enhanced stability or secretion. One way that p53 could potentially affect protein stability and/or secretion is through regulation of post-translational modifications, for example, phosphorylation, glycosylation, acetylation and hydroxylation of proteins, events that may mark certain proteins either for degradation or for localization (Kamemura and Hart, 2003). Preliminary indications of such post-transcriptional modifications were noted for a subset of the identified proteins through 2-DE analysis (Table 4), as seen for example by the horizontal and vertical shifts of Gal-1 protein spots from their original pI and MW positions (Figure 1b, c; black arrows). This suggests a potential novel function of the p53 tumor suppressor, the modulation of post-transcriptional modifications. Alternatively, p53 may also be involved in the regulation of a specific secretory pathway (Yu et al., 2006). Indeed, most proteins whose levels were positively regulated by p53 were found secreted through nonclassical mechanisms including vesicle-mediated pathways like exocytosis, ectocytosis as well as through transporter-mediated pathways. In contrast, most proteins released through classical pathways were downregulated (Tables 1 and 2).

Table 4 Secreted proteins with potential post-translational modifications induced by wt-p53

Discussion

This is the first comprehensive analysis of the tumor cell secretome to identify secreted targets of wt-p53. The term ‘secretome’ refers to proteins released through classical as well as nonclassical secretion pathways (Volmer et al., 2005). In addition, it also includes intracellular proteins and protein fragments that might be released in exosomes as a result of wt-p53 expression. In our analysis, p53 expression led to increased levels of 39 and decreased levels of 21 proteins in the CM of glioma cells (Tables 1, 2 and 3).

The mechanism through which p53 might regulate the secretion of proteins is currently unknown. A number of secreted proteins regulated at the transcriptional level have been reported. However, we did not find any of the secreted proteins found in our analysis to be significantly regulated by p53 at the transcriptional level. Instead, our microarray and Western analyses showed that most p53-regulated secreted proteins were not direct p53 targets and may have accumulated in the CM indirectly through different mechanisms. One possibility is enhanced stability, which could result through multiple means including changes in protein stability and localization or downregulation of proteases like matrix metalloproteinase (MMPs) thus leading to the accumulation of the affected proteins in the media. In fact, MMP-1 and MMP-13 have already been shown to be downregulated by wt-p53 (Sun et al., 2000). Alternatively, p53 could alter the secretion rate of intracellular proteins through either augmented release of specific proteins or through upregulation of a particular secretory pathway, thus leading to enhanced levels of all proteins secreted through that pathway. There is a precedence in the literature for at least one p53-regulated protein (TSAP6) that can facilitate the secretion of another protein (TCTP) through ectocytosis (Amzallag et al., 2004). Recent evidence suggests that p53 may act as a general regulator of this nonclassical secretory pathway (Yu et al., 2006).

Functional implications for tumorigenesis

Wild-type p53 has been shown to inhibit many processes required for tumor growth including migration, angiogenesis, survival and cell proliferation (Fulci and Van Meir, 1999). It has also been implicated in eliciting an immune response against neoplastic cells (Bueter et al., 2006). The results of our screen found wt-p53 regulating the secretion of many proteins that are candidate mediators for the above biological effects.

p53 and metastasis/invasion

Our analysis found several ECM components (growth arrest-specific 6, collagen type XI α-1, proteoglycan PG-M) or proteins involved in adhesion and cell–matrix interactions (galectin-3, lysyl oxidase-like protein 2, Opn, α-catenin and β-5 tubulin) as well as protease inhibitors (TIMP-3 and glioma pathogenesis-related protein) upregulated in the CM from the glioma cells after wt-p53 induction. The induction of these structural and proadhesion proteins would be expected to improve cell–cell and cell–matrix interactions, thus resulting in reduced migratory potential of tumor cells.

In addition to upregulation of antimigratory factors, other proteins directly involved in induction of migration and invasion in multiple tumor types, were found downregulated by wt-p53. These included SPARC, MMP-2, TGF-β, ADAM-10, and Tau (Framson and Sage, 2004; Mazzocca et al., 2005; Stuelten et al., 2005). These findings point to a potential new facet of p53's multimodal function as a tumor-suppressor gene, the downregulation of tumor invasion and metastasis.

p53 and the immune response

In recent years various studies have suggested that wt-p53 could stimulate immune responses against tumor cells. For example, secreted Opn found upregulated in our analysis is one of the key cytokines for type 1 immune responses mediated by macrophages. It has already been reported as a direct target of wt-p53 and has been implicated in suppressing tumor growth in vivo (Morimoto et al., 2002). Our screen identified increased secretion of immune response-related proteins β-2M and macrophage migration and myeloid leukemia inhibitory factors in response to wt-p53. β-2M is a MHC class I molecule and several studies have shown that tumor development might be inhibited by immune responses stimulated by this class of proteins (Bueter et al., 2006). Other immune-related proteins like interleukin-8 (IL-8), attractin, and ANP32A were found downregulated by wt-p53. IL-8 is known to be upregulated in glioma, possibly in response to immune cell infiltration (Desbaillets et al., 1997). Attractin is upregulated in glioma patient cerebrospinal fluid (CSF) and can modulate T-cell motility (Khwaja et al., 2006). These results encourage further research into how p53 may modulate the tumor immune response.

p53 and angiogenesis

Our results show repression of at least five proangiogenic proteins, VEGF, IL-8, TGF-β, PEDF, and CYR61 by wt-p53 in glioma cells. VEGF has been shown to be downregulated by wt-p53 in many systems (Qin et al., 2006) while CYR61 has not been reported as a p53 target before. CYR61 is a secreted ECM-associated signaling molecule that has been shown to promote the adhesion and proliferation of endothelial cells (Babic et al., 1998). CYR61 has been shown to be overexpressed in several cancers including breast and brain tumors, where it promotes angiogenesis and increased tumor growth (Tsai et al., 2000; Xie et al., 2004). Similarly, IL-8 is expressed and secreted at high levels in human gliomas and involved in glial tumor neovascularity and progression (Brat et al., 2005). Overall our findings suggest a model in which p53 loss in tumors activates angiogenesis by an increase in secretion of proangiogenic factors and decrease of inhibitors.

p53 and tumor proliferation and survival

We found several proteins regulating tumor proliferation and survival to be regulated by wt-p53. Brain derived neurotrophic factor (BDNF) exhibited enhanced secretion in response to wt-p53. The secreted form of BDNF mediates apoptosis of cells containing neurotrophin receptors (Lee et al., 2001). Other proteins, including FGF-4, RTVP1, TPM-ALK fusion oncoprotein fragment, TGF-β, PEDF, IGFBPs, and granulin were all found to have inhibited secretion to varying degrees in response to wt-p53. RTVP1, TGF-β (Tsuzuki et al., 1998), and PEDF (Pietras et al., 2002) are previously known targets of wt-p53.

Concluding remarks

In this study we have identified secreted proteins whose extracellular levels are regulated by p53. We found 39 proteins with enhanced and 21 with inhibited levels in response to wt-p53 expression out of a total of 111 proteins identified to be secreted by the cells. None of the tested proteins were found to be transcriptional targets indicating that wt-p53 may have an indirect role in their stability or secretion. These secreted targets will be helpful in better understanding of how wt-p53 may modulate interactions of tumor cells with their environment and establishes p53 loss in tumors as an originator of changes in tumor–stroma interactions. They may also help explain some of the ‘bystander effects’ observed in p53-mediated cancer gene therapy or with radio- and chemotherapies that activate p53.

Materials and methods

Cell lines and culturing conditions

LN-Z308 (p53 null) human glioblastoma cell line (Albertoni et al., 1998), and its isogenic clones LNZ308-C16 (contains a reverse tetracycline transactivator (rtTA)), 2024 (tet-inducible wt-p53) (Albertoni et al., 2002) and WT11 (tet-off for wt-p53) (Van Meir et al., 1994) were grown in Dulbecco's modified Eagle's medium supplemented with 5% tet-tested fetal calf serum. Cells were grown in serum-free media and wt-p53 expression was induced by modulation with 2 μg/ml of dox. CM from the cells was collected after 48 h induction and frozen at −20°C after removal of floating cells and cell debris by centrifugation at 1000 g.

Two-dimensional polyacrylamide gel electrophoresis (2-DE)

Samples were analysed in triplicates using 2-DE as described (Goldman et al., 1980). The first dimension was performed on IPGphor system (Amersham Biosciences, NJ, USA). Isoelectric focusing of 200 μg of trichloroacetic acid (TCA) precipitated protein was performed on 13 cm or 17 cm Immobiline dry strips (IPG strips) using either pH range of 3–10NL or 4–7L (total run=130 000 Vh). Strips were then equilibrated sequentially in equilibration buffer (6 M urea, 2% sodium dodecyl sulfate (SDS), 0.05 M Tris base pH 8.8, 20% glycerol) first containing 10 mg/ml dithiothreitol (DTT) and then 25 mg/ml iodoacetamide followed by separation in the second dimension on 12.5% polyacrylamide gels with 2% SDS using the Protean II xi system (BioRad, CA, USA). Silver Stain Plus kit (BioRad) was used to visualize protein spots and the gels were analysed using Melanie (http://au.expasy.org/melanie/) and the ImageMaster softwares (Amersham Biosciences, NJ, USA).

In-gel digestion of proteins and MALDI-TOF/TOF-MS analysis

Protein spots of interest were excised from the gel and destained using SilverOUT kit (GenoTech, MO, USA). The proteins were digested overnight with 150 ng trypsin (Promega, WI, USA) and the resulting peptides extracted using Montage In-gel peptide extraction kit (Millipore, MA, USA), spotted onto target plates and overlaid with α-cyano-4-hydroxycinnamic acid (Agilent, DE, USA). The plates were analysed using a 4700 Proteomics Analyzer (Applied Biosystems, CA, USA). The combined MS and MS/MS spectra from each spot were processed using GPS Explorer V2.0 (Applied Biosystems, CA, USA) with MASCOT (Matrix Science, MA, USA) as the database search engine. Only proteins that generated multiple peptides with ion scores above 30 were considered positively identified.

cICAT analysis

cICAT technology uses stable isotope tags in combination with two-dimensional (2D) chromatography of complex peptide mixtures (Applied Biosystems) (Gygi et al., 2002). Hundred micrograms each of precipitated secreted protein from the CM were treated with denaturing (50 mM Tris; 0.1% SDS) and reducing (50 mM TCEP (Tris(2-carboxyethyl)phosphine hydrochloride)) reagents. Next, the control and wt-p53 induced samples were respectively labeled with light (9 12C atoms) and heavy (9 13C atoms) reagents for 2 h at 37°C. After trypsin digestion and purification, the peptides were analysed using an Ultimate nanoHPLC LC-MS/MS (Dionex/LC Packings, CA, USA) interfaced to a QSTAR XL mass spectrometer (Applied Biosystems). The MS/MS data were processed using ProICAT software for protein identification and quantification. Only proteins with ProtScore >1.0 (>85% confidence) were considered. Also, the heavy to light ratios were tested for significance using Student t-test and P<0.05 was considered significant.

Western blot analysis

Immunoblots were performed on cell lysates (lysed in 8 M urea, 4% SDS, in 10 mM Tris, pH 7.4). The CM was precipitated by 15% TCA for 2 h at 4°C, washed twice with ice-cold acetone, and then resuspended in lysis buffer (8 M urea, 4% SDS, 100 mM protease inhibitor cocktail (Roche, Germany), in 10 mM Tris, pH 7.4). Antibodies used were: α-TSP1 (Ab-4 NeoMarkers, Freemont, CA, USA; 1:1000), α-FGF-4 (sc-16812, Santa Cruz, CA, USA; 1:500), α-SPARC (sc-13324, Santa Cruz; 1:500), α-VEGF (Santa Cruz; 1:500), α-β-2M (Clone B2M-01; Abcam, MA, USA; 1:250), α-TGFβ (AE1109.1, Immunodiagnostik, Germany; 1:100), α-galectin-3 (Santa Cruz, CA, USA; sc-14364; 1:500), α-galectin-1 (Santa Cruz, CA, USA; 1: 500). Pre-albumin (Pre-alb) (sc-13098; Santa Cruz; 1:1000) and actin (sc-1615; Santa Cruz, CA, USA; 1:1000) were used as a loading controls.

Accession codes

Accessions

GenBank/EMBL/DDBJ

Abbreviations

β-2M:

beta-2-microglobulin

2-DE:

two-dimensional gel electrophoresis

cICAT, cleavable isotope-coded affinity tag technology; CM:

conditioned media

ECM:

extracellular matrix

FGF-4:

fibroblast growth factor-4

Gal-1:

Galectin-1

Gal-3:

galectin-3

Pre-alb:

pre-albumin

SPARC:

secreted protein with acidic and cysteine-rich domains

TGF-β:

transforming growth factor beta

TSP1:

thrombospondin-1

References

  1. Albertoni MD, Daub M, Arden KC, Viars CS, Powell C, Van Meir EG . (1998). Genetic instability leads to loss of both p53 alleles in a human glioblastoma. Oncogene 16: 321–326.

    CAS  Article  Google Scholar 

  2. Albertoni M, Shaw PH, Nozaki M, Godard S, Tenan M, Hamou M-F et al. (2002). Anoxia induces macrophage inhibitory cytokine-1 (MIC-1) in glioblastoma cells independently of p53 and HIF-1. Oncogene 21: 4212–4219.

    CAS  Article  Google Scholar 

  3. Amzallag N, Passer BJ, Allanic D, Segura E, Thery C, Goud B et al. (2004). TSAP6 facilitates the secretion of translationally controlled tumor protein/histamine-releasing factor via a nonclassical pathway. J Biol Chem 279: 46104–46112.

    CAS  Article  Google Scholar 

  4. Babic AM, Kireeva ML, Kolesnikova TV, Lau LF . (1998). CYR61, a product of a growth factor-inducible immediate early gene, promotes angiogenesis and tumor growth. Proc Natl Acad Sci USA 95: 6355–6360.

    CAS  Article  Google Scholar 

  5. Bhowmick NA, Moses HL . (2005). Tumor–stroma interactions. Curr Opin Genet Dev 15: 97–101.

    CAS  Article  Google Scholar 

  6. Brat DJ, Bellail AC, Van Meir EG . (2005). The role of interleukin-8 and its receptors in gliomagenesis and tumoral angiogenesis. Neuro-oncol 7: 122–133.

    CAS  Article  Google Scholar 

  7. Bueter M, Gasser M, Lebedeva T, Benichou G, Waaga-Gasser AM . (2006). Influence of p53 on anti-tumor immunity (review). Int J Oncol 28: 519–525.

    PubMed  Google Scholar 

  8. Chiarugi V, Magnelli L, Gallo O . (1998). Cox-2, iNOS and p53 as play-makers of tumor angiogenesis (review). Int J Mol Med 2: 715–719.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Desbaillets I, Diserens AC, Tribolet N, Hamou MF, Van Meir EG . (1997). Upregulation of interleukin 8 by oxygen-deprived cells in glioblastoma suggests a role in leukocyte activation, chemotaxis, and angiogenesis. J Exp Med 186: 1201–1212.

    CAS  Article  Google Scholar 

  10. Framson PE, Sage EH . (2004). SPARC and tumor growth: where the seed meets the soil? J Cell Biochem 92: 679–690.

    CAS  Article  Google Scholar 

  11. Fulci G, Van Meir EG . (1999). p53 and the CNS: tumors and developmental abnormalities. Mol Neurobiol 19: 61–77.

    CAS  Article  Google Scholar 

  12. Goldman D, Merril CR, Ebert MH . (1980). Two-dimensional gel electrophoresis of cerebrospinal fluid proteins. Clin Chem 26: 1317–1322.

    CAS  PubMed  Google Scholar 

  13. Görg A, Weiss W, Dunn MJ . (2004). Current two-dimensional electrophoresis technology for proteomics. Proteomics 4: 3665–3685.

    Article  Google Scholar 

  14. Gygi SP, Rist B, Griffin TJ, Eng J, Aebersold R . (2002). Proteome analysis of low-abundance proteins using multidimensional chromatography and isotope-coded affinity tags. J Proteome Res 1: 47–54.

    CAS  Article  Google Scholar 

  15. Hanahan D, Weinberg RA . (2000). The hallmarks of cancer. Cell 100: 57–70.

    CAS  Article  Google Scholar 

  16. Harris SL, Levine AJ . (2005). The p53 pathway: positive and negative feedback loops. Oncogene 24: 2899–2908.

    CAS  Article  Google Scholar 

  17. Kamemura K, Hart GW . (2003). Dynamic interplay between O-glycosylation and O-phosphorylation of nucleocytoplasmic proteins: a new paradigm for metabolic control of signal transduction and transcription. Prog Nucleic Acid Res Mol Biol 73: 107–136.

    CAS  Article  Google Scholar 

  18. Khwaja FW, Duke-Cohan JS, Brat DJ, Van Meir EG . (2006). Attractin is elevated in the cerebrospinal fluid (CSF) of patients with malignant astrocytoma and mediates glioma cell migration. Clinical Cancer Research, in press.

  19. Lee R, Kermani P, Teng KK, Hempstead BL . (2001). Regulation of cell survival by secreted proneurotrophins. Science 294: 1945–1948.

    CAS  Article  Google Scholar 

  20. Mazzocca A, Coppari R, De Franco R, Cho JY, Libermann TA, Pinzani M et al. (2005). A secreted form of ADAM9 promotes carcinoma invasion through tumor–stromal interactions. Cancer Res 65: 4728–4738.

    CAS  Article  Google Scholar 

  21. Morimoto I, Sasaki Y, Ishida S, Imai K, Tokino T . (2002). Identification of the osteopontin gene as a direct target of TP53. Genes Chromosomes Cancer 33: 270–278.

    CAS  Article  Google Scholar 

  22. Pietras K, Rubin K, Sjoblom T, Buchdunger E, Sjoquist M, Heldin CH et al. (2002). Inhibition of PDGF receptor signaling in tumor stroma enhances antitumor effect of chemotherapy. Cancer Res 62: 5476–5484.

    CAS  PubMed  Google Scholar 

  23. Qin G, Kishore R, Dolan CM, Silver M, Wecker A, Luedemann CN et al. (2006). Cell cycle regulator E2F1 modulates angiogenesis via p53-dependent transcriptional control of VEGF. Proc Natl Acad Sci USA 103: 11015–11020.

    CAS  Article  Google Scholar 

  24. Steele RJ, Lane DP . (2005). P53 in cancer: a paradigm for modern management of cancer. Surgeon 3: 197–205.

    CAS  Article  Google Scholar 

  25. Stuelten CH, DaCosta Byfield S, Arany PR, Karpova TS, Stetler-Stevenson WG, Roberts AB . (2005). Breast cancer cells induce stromal fibroblasts to express MMP-9 via secretion of TNF-alpha and TGF-beta. J Cell Sci 118 (Part 10): 2143–2153.

    CAS  Article  Google Scholar 

  26. Sun Y, Cheung JM, Martel-Pelletier J, Pelletier JP, Wenger L, Altman RD et al. (2000). Wild type and mutant p53 differentially regulate the gene expression of human collagenase-3 (hMMP-13). J Biol Chem 275: 11327–11332.

    CAS  Article  Google Scholar 

  27. Taieb J, Chaput N, Menard C, Apetoh L, Ullrich E, Bonmort M et al. (2006). A novel dendritic cell subset involved in tumor immunosurveillance. Nat Med 12: 214–219.

    CAS  Article  Google Scholar 

  28. Tenan M, Fulci G, Albertoni M, Diserens AC, Hamou MF, El Atifi-Borel M et al. (2000). Thrombospondin-1 is downregulated by anoxia and suppresses tumorigenicity of human glioblastoma cells. J Exp Med 191: 1789–1798.

    CAS  Article  Google Scholar 

  29. Tsai MS, Hornby AE, Lakins J, Lupu R . (2000). Expression and function of CYR61, an angiogenic factor, in breast cancer cell lines and tumor biopsies. Cancer Res 60: 5603–5607.

    CAS  PubMed  Google Scholar 

  30. Tsuzuki T, Izumoto S, Ohnishi T, Hiraga S, Arita N, Hayakawa T . (1998). Neural cell adhesion molecule L1 in gliomas: correlation with TGF-beta and p53. J Clin Pathol 51: 13–17.

    CAS  Article  Google Scholar 

  31. Van Meir EG, Polverini PJ, Chazin VR, Su Huang HJ, de Tribolet N, Cavenee WK . (1994). Release of an inhibitor of angiogenesis upon induction of wild type p53 expression in glioblastoma cells. Nat Genet 8: 171–176.

    CAS  Article  Google Scholar 

  32. Van Meir EG, Roemer K, Diserens A-C, Kikuchi T, Rempel SA, Haas M et al. (1995). Single-cell monitoring of growth arrest and morphological changes induced by transfer of wild type p53 alleles to glioblastoma cells. Proc Natl Acad Sci USA 92: 1008–1012.

    CAS  Article  Google Scholar 

  33. Volmer MW, Stuhler K, Zapatka M, Schoneck A, Klein-Scory S, Schmiegel W et al. (2005). Differential proteome analysis of conditioned media to detect Smad4 regulated secreted biomarkers in colon cancer. Proteomics 5: 2587–2601.

    CAS  Article  Google Scholar 

  34. Wang J, Shou J, Chen X . (2000). Dickkopf-1, an inhibitor of the Wnt signaling pathway, is induced by p53. Oncogene 19: 1843–1848.

    CAS  Article  Google Scholar 

  35. Xie D, Yin D, Wang HJ, Liu GT, Elashoff R, Black K et al. (2004). Levels of expression of CYR61 and CTGF are prognostic for tumor progression and survival of individuals with gliomas. Clin Cancer Res 10: 2072–2081.

    CAS  Article  Google Scholar 

  36. Yu X, Harris SL, Levine AJ . (2006). The regulation of exosome secretion: a novel function of the p53 protein. Cancer Res 66: 4795–4801.

    CAS  Article  Google Scholar 

  37. Zigrino P, Loffek S, Mauch C . (2005). Tumor-stroma interactions: their role in the control of tumor cell invasion. Biochimie 87: 321–328.

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank Drs JC Lucchesi, D Pallas, I Matsumura and P Vertino for their support. This work was supported by National Institutes of Health (NIH) grants CA 86335 (to EGVM); NCRR 02878, 12878, 13948 (to Microchemical and Proteomics Facility), the Pediatric Brain Tumor Foundation of the US (to EGVM) and the American Brain Tumor Association (to BP), the Genetics and Molecular Biology (GMB) program of the Graduate Division of Biological and Biomedical Sciences (GDBBS) of Emory University, and the National Science Foundation (NSF) (PRISM; DGE0231900).

Author information

Affiliations

Authors

Corresponding author

Correspondence to E G Van Meir.

Additional information

FWK and EGVM designed and interpreted experiments and wrote the manuscript. FWK performed experiments with the help of PS, MR and JP for the MS analyses. BP performed the microarrays and Northern blot. All authors read the manuscript.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Khwaja, F., Svoboda, P., Reed, M. et al. Proteomic identification of the wt-p53-regulated tumor cell secretome. Oncogene 25, 7650–7661 (2006). https://doi.org/10.1038/sj.onc.1209969

Download citation

Keywords

  • p53
  • proteomics
  • secretion
  • glioma
  • brain cancer
  • two-dimensional electrophoresis

Further reading

Search

Quick links