TIMP-4 and CD63: New Prognostic Biomarkers in Diffuse Gliomas
Sandrine Rorive 1, Xavier Moles Lopez1,2, Calliope Maris1, Anne-Laure Trepant1 Sébastien Sauvage1, Niloufar Sadeghi3, Isabelle Roland1,
Christine Decaestecker2,4, Isabelle Salmon1
1Department of Pathology, Erasme University Hospital; 2Laboratory of Image Synthesis and Analysis (LISA), Faculty of Applied Sciences; Departments of 3Neuroimaging,
Erasme University Hospital; Université Libre de Bruxelles, Brussels, Belgium.
4 C.D. is a Senior Research Associate with the “Fond National de la Recherche Scientifique”, Brussels, Belgium.
This work has been carried out with the support of grants awarded by the “Fonds Erasme” and the “Fonds Yvonne Boël”, Brussels, Belgium.
TIMP-4 and CD63: new glioma biomarkers
Correspondence to: Sandrine Rorive, MD, PhD student Department of Pathology, Erasme University Hospital 808 route de Lennik- B-1070 Brussels- Belgium
Tel: 322 555 3115 Fax: 322 555 4790 e-mail: email@example.com.
Outcomes for patients with diffuse gliomas are unpredictable. New prognostic markers are needed to identify high-risk patients for whom the standard treatment has low efficacy and who might benefit from more aggressive therapies. Therefore, we aim to investigate the prognostic values of actors recently found to be involved in glioma biology - i.e., TIMP-4, an anti-invasive molecule, and its putative partner, CD63. Tissue microarray and image analysis were first carried out to quantitatively analyse the immunohistochemical expression of these proteins in 471 human gliomas. We next tested for the presence of TIMP-4/CD63 complexes in LN229 astrocytoma cells. Increased CD63 expression was associated with reduced disease- free survival in low-grade diffuse astrocytoma patients (p=0.03) and reduced cancer-specific survival in anaplastic astrocytoma patients (p=0.03). In glioblastoma, high TIMP-4/CD63 co- expression pattern was identified as an independent prognostic factor associated with shorter survival (p=0.006). Finally, in oligoastrocytoma, an astro-like TIMP-4/CD63 co-expression profile was associated with reduced disease-free (p=0.004) and cancer-specific survival (p=0.009) as compared to the oligo-like profile. In conclusion, this work provides the first evidence of a TIMP-4/CD63 interaction in astrocytoma cells, and it highlights the contribution of these markers and their co-expression to the adverse outcomes of patients with diffuse astrocytoma and oligoastrocytoma.
Invasive behaviour is a pathological hallmark of diffuse gliomas that renders complete tumour resection impossible and leads to tumour recurrence and the death of the patient (9). Diffuse gliomas include low-grade diffuse astrocytomas (AST II), anaplastic astrocytomas (ANA), glioblastomas (GBM), oligoastrocytomas (OA) and oligodendrogliomas (OLIGO); the latter two subgroups are subclassified into grades II and III (anaplastic) (18). Among these, GBMs are associated with the worst prognosis, with most patients dying within two years after diagnosis (31). In marked contrast, surgical resection of generally noninfiltrative pilocytic astrocytomas (PILOs) is usually associated with long-term survival or cure (8, 18).
Established prognostic factors such as histopathologic type and grade, age, tumour location, multifocality, and extent of surgical resection are indicators of risk of recurrence after treatment (3). However, the overall outcomes for patients with diffuse gliomas remains unpredictable (18). New prognostic markers are thus needed to identify high-risk patients for whom the standard treatment has poor outcomes and who would thus be suitable for more aggressive therapies. This clinical need motivated us to investigate the prognostic value of proteins involved in the inhibition of the invasion process of gliomas.
Based on the molecular profiling of astrocytomas, we previously identified a series of genes involved in astrocytoma invasion (26). Of these, tissue inhibitor of metalloproteinase-4 (TIMP4) was found to be overexpressed in PILOs relative to diffuse astrocytomas of any histological grade (26). TIMPs are natural inhibitors of matrix metalloproteinases (MMPs) and are thought to regulate MMP activity during extracellular matrix (ECM) remodelling (1).
This inhibitory activity of TIMPs might be important in preventing cancer invasion processes and subsequent malignant progression (14). TIMP-4, the most recently identified and least studied member of this family, is widespread in the normal brain (5) and was shown to reduce
the invasive ability of GBM cells in vitro (11). These data suggest that TIMP-4 may be an anti-tumoural actor in glioma. However, this hypothesis has been challenged by some recent findings in other malignancies, such as breast, prostate, pancreatic and cervical cancers, where high TIMP-4 expression has been found (20) to correlate with tumour aggressiveness (2, 17) and to stimulate tumourigenic activity (15). In addition, mounting evidence suggests an authentic signalling capacity for TIMPs distinct from their MMP-inhibitory activity (30). This theory was further supported by recent discoveries of TIMP-binding proteins (16, 24, 28).
Among these, CD63 was shown by yeast two-hybrid screening to interact with TIMP-1 and TIMP-4 (4, 16). While the TIMP-1/CD63 interaction was further confirmed by immunoprecipitation and linked to an increase in cell survival signalling in human breast epithelial cells, TIMP-4/CD63 binding still remains hypothetical. CD63 is a tetraspanin that interacts with many different proteins, including integrins and the Src family tyrosine kinases Lyn and Hck; thus, it regulates intracellular signal transduction pathways involved in cell adhesion, motility and survival (23). To the best of our knowledge, data concerning CD63 expression and its prognostic implications in human gliomas are still missing. This paucity of information on CD63 and its putative link with TIMP-4, whose involvement in glioma aggressiveness remains to be clarified, motivated us to evaluate the prognostic values of these two actors in human gliomas using quantitative immunohistochemistry (IHC) based on image analysis of tissue microarray materials (6). These investigations sought to identify high-risk glioma patients who would be suitable for clinical trials and more aggressive treatments.
MATERIALS AND METHODS
Cell culture and cell block manufacturing
Human LN229 high-grade astrocytoma cells (ATCC number CRL-2611) were maintained as previously described (27). For the preparation of cell blocks, subconfluent LN229 cells were
washed twice with ice-cold PBS, detached with Trypsin/EDTA (Invitrogen, Carlsbad, USA) and centrifuged at 500 g for 5 min at 4°C. Pellets were then submitted to Shandon Cytoblock® reagents according to the manufacturer’s recommendations (Thermo Fischer Scientific Inc., Waltham, USA).
Immunoprecipitation and Immunoblotting
Subconfluent LN229 cells were washed twice with ice-cold PBS and lysed at 4°C for 30 min with 1 ml of lysis buffer 80 mM Tris-HCL, 150 mM NaCl, 1% Brij, 20 mM EDTA, 4 mM Na3VO4, 1 mM PMSF and a protease inhibitor cocktail (Roche Diagnostics, Bale, Switzerland) followed by 10 min of sonification. The lysate was centrifuged at 14,000 g for 10 min at 4°C, and the protein concentration was determined by using the Bio-Rad Dc protein assay (Hercules, CA). Five hundred micrograms of protein was immunoprecipitated overnight at room temperature (RT) with 2 µg of anti-CD63 antibody (mAb, clone MX-49.129.5; Santa Cruz Biotechnology, Santa Cruz, CA, USA), followed by 2 h of incubation with 20 µl of Protein A/G PLUS-Agarose (sc-2003, Santa Cruz Biotechnology, Santa Cruz, CA, USA). The appropriate controls used were water and normal mouse IgG (corresponding to the host species of the primary antibody). The immunoprecipitates were washed three times with ice- cold lysis buffer. The beads were then resuspended in 30 µl of Laemmli sample buffer, boiled for 3 min and centrifuged at 14,000 g for 5 min. The supernatants were fractionated by 12%
SDS PAGE under reducing conditions and transferred to PVDF membrane (Bio-Rad Laboratories Ltd., Hemel Hempstead, United Kingdom). After blocking the nonspecific binding sites, the membrane was incubated overnight at 4°C with a goat polyclonal mAb against human TIMP-4 (C-16; sc-9375, Santa Cruz Biotechnology, Santa Cruz, CA, USA), followed by 1 h of incubation with the corresponding horseradish peroxidase-conjugated secondary antibody (1:10,000; Sigma-Aldrich, St. Louis, MO). Reactive proteins were
detected with the Western LightningTM chemiluminescence reagent (PerkinElmer LAS, Boston, MA). Each condition was run in three independent experiments.
Patients and Tissue Samples
Ten human glioma tissue microarrays (TMAs) were manufactured using archival formalin- fixed and paraffin-embedded samples from 471 human gliomas collected between 1984 and 2006 in the Department of Pathology of the Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium. At least three tissue cores per tumour targeting the tumour bulk were included in the TMAs. The series includes 354 astrocytomas (42 PILO, 26 AST II, 21 ANA and 265 GBM), 91 oligodendrogliomas (62 OLIGO II and 29 OLIGO III) and 26 oligoastrocytomas (14 OA II and 12 OA III). All of these tumours are from patients who were not previously treated for brain tumours (primary tumours), and their histopathological diagnoses were all reviewed by two pathologists (SR, IS) to ensure consistent tumour grading based on contemporary guidelines from the 2007 revised WHO classification (18). The available clinical data included patients’ ages and genders, their tumour site, multifocality, tumour delineation, the extent of their surgical resection, their post-surgical adjuvant treatment and their follow-ups, as detailed in Table 1 (see Supporting Information Figure S1 for Kaplan-Meier curves illustration). Each tumour was classified as either well- or ill- circumscribed based on the MRI (retrospectively reviewed by a neuroradiologist (NS)) and/or surgical reports. Patient outcomes were characterised in terms of disease-free survival (DFS) and/or cancer-specific survival (CSS). The recurrences were defined as cases presenting MRI evidence of progression that required a second surgery or adjuvant treatments. DFS and CSS were measured from the date of tumour surgery until either the date of recurrence or death (DFS) or the date of death due to tumour progression (CSS). In addition, two normal brain TMAs were manufactured from ten human post mortem brains (patients without
neuropathological symptoms) obtained within 24 hours of death. A minimum of six cores per specific region targeted the cerebral hemisphere (including grey and white matter, centrum semiovale and corpus callosum), the cerebellum, the brainstem and the cervical spinal cord.
As previously described (7, 19), standard IHC was applied to single 5-µm thick sections cut from TMA and the LN229 cell block to display TIMP-4 or CD63 expression using a rabbit polyclonal anti-TIMP-4 (1:100; AB19168; Chemicon Int, Temecula, CA, USA) and a mouse monoclonal anti-CD63 (1:100; sc-5275; Santa Cruz Biotechnology, Santa Cruz, CA, USA) mAb. Each optimal primary antibody concentration was determined by serial dilutions to optimise for the maximal signal without background immunostaining. Negative controls were produced by replacing the primary antibodies with non-immune serum (Dako, Glostrup, Denmark). In addition, technical and fixative controls were prepared on TMA slides using haematoxylin-eosin (H&E) and anti-vimentin staining, respectively. The control slides were checked using Spot Browser® V2e (Alphelys, Plaisir, France) connected to a DXC-390 Sony camera and a motorised stage (Marzhaüser, Wetzlar-Steindorf, Germany) on a BX50 Olympus microscope (Aartselaar, Belgium). A final validation stage was conducted by a pathologist (SR) in order to confirm adequacy of the specific tumour zones targeted and immunostaining compliance. Only the cores satisfying all the control steps were submitted for quantification (6).
Image acquisition and staining quantification
The TMA core image acquisition (field magnification x10) and staining quantification was performed using Spot Browser® V2e as recently described (6). Briefly, the first stage consists of standardising and optimising the acquisition chain integrated into the imaging system. This
is done to control the sources of variation due to illumination and camera response. The second stage focuses on image segmentation and is required to identify positive (i.e., immunoreactive) vs. negative (i.e., counterstained) tissue areas. The third stage aims to provide a valid quantitative characterisation of antigen expression by combining the multiple measurements carried out on the set of tissue cores sampled from the same case or the same specific region (in the case of normal brain samples). For each valid core, we measured the analysed (i.e., positive and negative) tissue area and the positive (i.e., stained) area. To characterise each glioma, or each specific brain region under study, we pooled all the concerned cores and computed the labelling index (LI), which is the percentage of the immunostained tissue area.
The comparison of proportions was carried out using the exact Fisher test (2 x 2 cases). The Kruskal-Wallis test was used to compare independent groups of numerical data. When this multi-group test was significant, post-hoc tests (the Dunn procedure) were used to compare the group pairs of interest, thus avoiding multiple comparison effects. Survival data were analysed using the standard Kaplan-Meier analysis and the multivariate Cox regression.
Survival curves were compared using the log-rank test (L-R) and the Gehan-Wilcoxon (G-W) test. While the L-R method gives equal weight to all time points, the G-W method gives more weight to deaths at early time points. The latter is thus more sensitive for detecting early differences in survival. Detailed results are provided only if the two tests were discordant (indicating a specific difference in terms of either early or late events); if this was not the case, only the L-R p-values are mentioned. All the statistical analyses were carried out using Statistica (Statsoft, Tulsa, OK, USA).
TIMP-4 and CD63 are differentially expressed in different glioma types.
The baseline characteristics of the 471 patients with newly diagnosed gliomas are described in Table 1. The series was comprised of 354 astrocytomas, including 265 (75%) diagnosed as GBM. To complete the follow-up data given in Table 1, Kaplan-Meier curves of disease-free and cancer-specific survival according to glioma type and grade are given in the Supporting Information, Figure S1.
The immunohistochemical staining of TIMP-4 and CD63 in normal brain and in gliomas is shown in Figure 1, and quantitative staining evaluations are shown in Figure 2. Increased TIMP-4 LI was detected in normal brain (Fig. 2A), with a weak predilection for cerebellar samples as compared to cerebral ones (p<0.05; data not shown). Excepting oligodendrocytes, all the cells (i.e., neurons, astrocytes, endothelial cells and microglia) expressed TIMP-4 (data not shown). Compared to TIMP-4 expression in normal brain tissue, its expression was lower in all glioma types (p<0.01; Fig. 2A), particularly in OLIGO and OA (p<0.001, Fig. 2A). In marked contrast, CD63 was weakly expressed in normal brain (Fig. 2B), but its expression was significantly greater in ASTRO compared to normal brain (p<0.01; Fig. 2B) or to both OLIGO and OA (p<0.001; Fig. 2B). When focussing on glioma type, we noted similarities in the distribution of TIMP-4 and CD63 expression (Fig. 2C-D). In ASTRO, both markers were dramatically overexpressed in PILO as compared to AST II (p<0.001), and both were elevated in high-grade diffuse astrocytomas (i.e., ANA and/or GBM) as compared to AST II (Fig. 2C-D). In addition, TIMP-4 and CD63 were both weakly expressed in OLIGO II and III (Fig. 2C-D). Finally, CD63 expression significantly increased with tumour grade in OA (p<0.05; Fig. 2D), whereas the slight increase in TIMP-4 expression observed in OA III was not significant (Fig. 2C).
TIMP-4/CD63 co-expression patterns enable diagnosis of oligodendrogliomas.
In view of the expression data described above, we evaluated whether the co-expression of both markers separates tumour types. To do this, the value distributions of the TIMP-4 and CD63 LI were analysed and threshold values were chosen in order to separate ASTROs and OLIGOs (data not shown). Threshold values of 2% and 10% were identified for the CD63 LI, and 10% and 25% were identified for the TIMP-4 LI. The resulting categorisation into nine classes of co-expression enabled us to discriminate between gliomas with oligo-profiles (grey cells in Table 2; i.e., CD63 LI 2% associated with TIMP-4 LI 25% or CD63 LI 10%
associated with TIMP-4 LI 10%) and those with astro-profiles (white cells in Table 2; i.e., the six remaining categories). The oligo-profile covered 84 of the 91 OLIGOs (92%) and includes gliomas with the lowest levels of TIMP-4 and CD63 co-expression. This profiling of the 471 gliomas separated ASTROs from OLIGOs with a specificity of 87% and a sensitivity of 92% (Fisher test: p<10-6). In contrast, OA showed a more heterogeneous distribution in both the oligo- and astro-profiles (Table 2).
TIMP-4 and CD63 are prognostic markers in diffuse astrocytomas
To evaluate the prognostic contributions of TIMP-4 and CD63 in patients with astrocytomas, we first analysed the impact of the clinical factors (listed in Table 1) on disease-free and cancer-specific survival. Considering the strong impact of histological grade on ASTRO patient survival (Figure S1A-B, see supporting information), the analyses were carried out per grade. Univariate survival analyses revealed that none of the clinical factors influenced patient outcomes with either AST II or ANA in our series (data not shown). In contrast, partial surgery was associated with a higher rate of tumour recurrence for patients with PILO (p=0.02; data not shown). Significant effects observed in the GBM patients are detailed in Table 3. Older age was significantly associated with reduced median survival for patients with
GBM (p=0.0003; Table 3). As expected, a macroscopically complete surgery significantly improved the median survival of these patients (from 8.2 to 12.7 months) as did the addition of temozolomide to radiotherapy (from 10.2 to 14.2 months) (p=0.0003 and p=0.008; Table 3).
Concerning TIMP-4 and CD63 expression patterns, we did not find any impact on the survival of patients with PILO (data not shown). In contrast, a CD63 LI larger than 2% was associated with bad prognoses, in terms of early recurrence for AST II patients (G-W p=0.03, see Fig. 3A; L-R p>0.05) and in terms of late cancer-specific survival for ANA patients (L-R p=0.03, see Fig. 3B; G-W p>0.05), whereas TIMP-4 LI had no prognostic value for these two patient groups.
Regarding the GBM patients, high co-expression of TIMP-4 and CD63 (i.e., TIMP-4 LI>25%
associated with CD63 LI>10%) was associated with reduced survival relative to patients with a GBM characterised by any other expression pattern (p=0.007; data not shown). As shown in Figure 3C, the impact of this co-expression was particularly relevant for the group of GBM patients who had macroscopically complete resections. In this group, the median survival dropped to 9.8 months for GBMs exhibiting a high TIMP-4/CD63 co-expression level, as compared to 16.2 months for GBMs with another expression pattern (p=0.002). This latter result suggests that high TIMP-4/CD63 co-expression could be an independent prognostic factor regarding the extent of surgery for patients with GBM. To confirm this property, we performed a multivariate survival analysis by combining TIMP-4/CD63 co-expression with the clinical factors for which the univariate results in Table 3 indicated a p-value smaller than 0.10 (i.e., all except “corticosteroids”). Table 4 shows the final model from which
“multifocality” was excluded (p>0.1 in the complete multivariate model). The final model highlighted a high TIMP-4/CD63 co-expression pattern as being an independent prognostic factor associated with worse survival for GBM patients (p=0.006).
TIMP-4, CD63 and the TIMP4/CD63 co-expression do not influence outcome in oligodendrogliomas.
We then carried out a similar analysis for patients with OLIGOs. We again proceeded per histological grade because of its impact on OLIGO patient survival (see Figure S1-C in the Supporting Information). We detected no effect of either TIMP-4 or CD63 expression pattern on the survival of patients with OLIGO II or OLIGO III.
TIMP-4/CD63 co-expression is a prognostic marker in oligoastrocytomas
Finally, we concluded our survival analyses by considering all the patients with OA (because their outcomes were not significantly influenced by tumour grade; see Figure S1-D in supporting information). Univariate analyses of clinical factors revealed that “extent of surgery” had a significant impact (p=0.008) and “age” had a slight influence (p=0.05) on this group of patients, as detailed in Table 3. In addition, our “astro/oligo tumour profiling”
(defined in Table 2) identified significantly different outcomes, as illustrated in Figure 4.
Compared to the oligo-profile, the astro-profile was associated with significantly reduced disease-free (p=0.004) and cancer-specific survival (p=0.009) periods, independent of grade (see the arrows in Figure 4). As detailed in Table 4, a multivariate Cox analysis identified this tumour profile as a significant determinant of reduced disease-free survival that is independent of age and extent of surgery (p=0.0006). The reduced number of deaths among the OA patients, who are all associated with the astro-profile (see Fig. 4B), prevented us from carrying out a multivariate analysis for cancer-specific survival.
TIMP-4 interacts with CD63 in LN229 astrocytoma cells.
To confirm that TIMP-4 is able to interact with CD63 in gliomas, we carried out in vitro immunoprecipitation experiments using the human LN229 astrocytoma cell line. As illustrated in Figure 5A-B, LN229 cells express both CD63 and TIMP-4 proteins. We showed that the anti-CD63 antibody co-immunoprecipitated endogenous CD63 with endogenous TIMP-4 (Figure 5, Lane 3), whereas the specific mouse isotype had no effect (Figure 5, Lane 2).
Molecular characterisation and research efforts in cancer therapy have enabled improvements in the outcomes of patients with diffuse gliomas. Concurrent deletion of chromosomal arms 1p and 19q constitutes the hallmark molecular alteration in oligodendroglial tumours, as they are associated with favourable outcomes and better responses to adjuvant treatment (18). The recent addition of temozolomide to radiotherapy in the management of GBMs has provided a statistically significant survival benefit (31). Nevertheless, tumour recurrence and progression is common for gliomas, thus continued research efforts are needed to improve clinical treatments and outcomes. While previous studies identified several clinical and molecular factors that help to explain the variability in the outcomes of patients with gliomas (3), new markers are needed to more accurately recognise the high-risk patients in the different glioma subgroups. The present work identifies, for the first time, the contribution of CD63 and TIMP-4 and their co-expression in this particular field of study.
To our knowledge, this study is based on the largest glioma series analysed for TIMP-4 expression and is also the first that characterised CD63 expression in normal brain and in gliomas. Data regarding CD63 in gliomas was only provided in the work of Shirahata et al., in which CD63 appeared in a list of genes highly overexpressed in GBM as compared to OLIGO
III (29). Our study extended this result by detecting low CD63 expression levels in OLIGOs of all grades compared to ASTROs. CD63 was first discovered as an abundantly expressed surface antigen in early stage melanoma cells (12). A negative relationship between CD63 expression and increased malignancy or invasiveness has been reported in many tumours, including ovarian, lung, breast, and colon cancers as well as in melanoma (23). While these reports contradict our results on diffuse gliomas, they correlate with the high CD63 expression level noted in PILO, an indolent, noninvasive tumour. PILOs strongly express both CD63 and TIMP-4, but none of these markers influence patient outcomes in this specific tumour subgroup. In contrast, TIMP-4/CD63 co-expression negatively impacts patients’
survival in other glioma groups, in particular in GBM and OA. This result agrees with previous studies suggesting that TIMP-4 has a signalling capacity distinct from its MMP- inhibitory activity that, like that of TIMP-1 (16), can be initiated by binding to CD63 (30).
This concept is reinforced in the present study by novel evidence for an interaction between TIMP-4 and CD63 in a human high-grade astrocytoma cell line. Of the signalling roles involved in tumour aggressiveness, anti-apoptotic functions are reported for both TIMP-1 and TIMP-4, and they might share the same receptor (15, 16, 30). Similarly, a decrease in TIMP4 expression was recently reported to be associated with a decrease of aggressiveness in a GBM-transfected cell line in vitro (32). In addition, the negative influence of CD63 alone observed for both AST II and ANA patients suggests that CD63 may also act with partners other than TIMP-4 to promote diffuse astrocytoma malignancy and progression. CD63 was observed to interact with many different molecules, including TIMP-1, integrins and some members of the Src family tyrosine kinases (13, 16). Recently, Milano et al. showed that a novel inhibitor of Src kinases improved the therapeutic efficacy of temozolomide, resulting in a significant increase in glioma cell cycle disruption and autophagic cell death (21).
Interestingly, the growth-inhibitory effects of the same inhibitor in HMC-1 mast cells were
found to be associated with a decrease in CD63 expression in tumour cells (10). To the best of our knowledge, this relationship has never been investigated in glioma cells.
In our series, no OA associated with an astro-like TIMP-4/CD63 co-expression profile (i.e., medium to high co-expression) was found to be 1p and/or 19q deleted, in contrast to some of those with an oligo-like profile (data not shown). In this regard, TIMP-4 and CD63 expression (whose genes are located on 3p and 12q, respectively) may provide additional prognostic information in the setting of OA patients.
In conclusion, this work identified CD63 expression and TIMP-4/CD63 co-expression as new independent prognostic markers associated with adverse outcome for diffuse ASTRO and OA patients. The use of these markers in clinical practise should help to identify high-risk patients who would be suitable for more aggressive therapies or new clinical trials. In addition, these new findings regarding TIMP-4 and CD63 involvement in human glioma aggressiveness may open new avenues in the search for novel targets in the treatment of these tumours.
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LEGENDS TO FIGURES
Figure 1. Morphological illustration of the immunohistochemical expression of TIMP-4 and CD63 in normal brain (A-B: x400; A=cerebellum; B=cerebral hemisphere), grade I pilocytic astrocytoma (C-D: x400), grade II diffuse astrocytoma (E-F: x400), glioblastoma (G-H: x400) and anaplastic oligodendroglioma (I-J: x400). The arrows illustrate TIMP-4 and CD63 staining in microgemistocytes and reactive astrocytes.
Figure 2. Quantitative evaluation (by computer-assisted microscopy) of the percentage of tissue area exhibiting TIMP-4 or CD63 immunopositivity (LI = labelling index) in ten normal brains (NORMAL) and 471 gliomas with respect to type (A-B) and grade (C-D). ASTRO = astrocytoma (n=354); OLIGO = oligodendroglioma (n=91); OA = oligoastrocytoma (n=26).
The data are expressed in terms of means SE. Only the significant differences (post hoc multicomparison tests) are indicated, as *p<0.05, **p<0.01, and ***p<0.001.
Figure 3. Kaplan-Meier curves characterising (A) disease-free and (B) cancer-specific survival for AST II and ANA patients, respectively, according to the CD63 labelling index (i.e., CD63 LI 2% vs. CD63 LI>2%). (C) Kaplan-Meier curves characterising cancer- specific survival in the case of GBM patients (post-macroscopically complete surgery), according to TIMP-4 and CD63 co-expression (i.e., TIMP-4 LI>25% associated with CD63 LI>10% vs. all the other cases). The significant differences are indicated in terms of the Gehan/Wilcoxon (A) or the log-rank test (B-C) p-values (see text for more details).
Figure 4. Kaplan-Meier curves characterising (A) disease-free and (B) cancer-specific survival for OA patients according to the TIMP-4/CD63 co-expression patterns identifying astro- and oligo-profiles (see Table 2 and text for more details). The arrows indicate the
occurrences of OA III patients in the two profiles. The significant differences are indicated in terms of the log-rank test p-value.
Figure 5. (A-B) Illustration of the immunohistochemical staining of (A) CD63 and (B) TIMP-4 in LN229 astrocytoma cells. (C) Western blot analysis showing binding between TIMP-4 and CD63 in LN229 astrocytoma cells. LN229 cell lysates were immunoprecipitated (IP) with anti-CD63 mAb (lane 3), the corresponding IgG isotype (negative control, lane 2) or water (negative control, lane 1), respectively, and separated by SDS-PAGE. The blotted proteins were detected with an anti-TIMP-4 mAb. Each condition was run in three independent experiments.
Figure S1. Kaplan-Meier curves characterising (A) disease-free and (B) cancer-specific survival for all ASTRO patients with respect to grade. Kaplan-Meier curves characterising disease-free survival for all OLIGO (C) and OA (D) patients with respect to grade. Complete and censured data are shown by dots and crosses, respectively. Intergroup comparisons showed significant differences in terms of disease-free and cancer-specific survival for patients with ASTRO (p<10-6; Fig A and B). OLIGO III were associated with worse disease- free survival as compared to OLIGO II (p=0.0006; Fig C), whereas OA II and III did not differ (p>0.1; Fig D).
LEGENDS TO TABLES
Table 1. Clinicopathological data characterising the series of 471 patients with gliomas. The table displays the numbers (or percentages) of cases in the different glioma subtypes, except where other features are indicated (such as range and median). * Including non-standard therapy for GBM patients, chemotherapy alone or combined with radiotherapy or palliative management.
Table 2. Distribution of 471 gliomas (including 91 OLIGOs, 26 OAs and 354 ASTROs) categorised according to TIMP-4 and CD63 labelling indices. The threshold values were chosen in order to separate ASTROs and OLIGOs. The grey cells identify the oligo-profile and the white ones the astro-profile. The oligo-profile covers 84 of the 91 OLIGOs (92%) and the astro-profile covers 308 of the 354 ASTROs (87%).
Table 3. Univariate survival analyses of clinical prognostic factors in patients with GBM and OA. Age was considered as either a continuous variable in a univariate Cox regression (see *) or a 2-class factor analysed by the log-rank test (similar to all the other factors). Each category is characterised by the median survival or the median recurrence time (in months) and their standard error (SE). Missing values (-) are due to insufficient uncensored cases in the particular patient group, which prevented complete data accumulation and evaluation.
Table 4. Cox proportional hazard analyses of survival or disease-free prognostic factors in patients with GBM and OA. The Model/p-value indicates the overall level of significance of the model. Aside from age, which is treated as a continuous variable, all of the other variables are binary. These variables distinguish between complete and partial surgery, radiotherapy + temozolomide compared to radiotherapy alone, high CD63 and TIMP-4 co-expression (i.e.,
TIMP-4 LI>25% associated with CD63 LI>10%) compared to all the other expression patterns, and, in the OA cases, TIMP-4/CD63 astro-profile compared to the oligo-profile. The equation at the basis of the Cox Regression model is an exponential function of a linear combination of the considered variables, where b indicates the coefficient of each variable in the linear combination. The eb value indicates that the risk of death/recurrence is increased by eb percentage for patients belonging to the indicated category (in the case of binary variables) or per year of patients’ ages. A negative value thus means a positive impact on survival. The individual p-values are the levels of significance of the independent contributions of each variable to the model. If p<0.05, the variable is associated with a significant prognostic value independent of all of the other parameters included in the model.
We thank Miss Andra Negulescu, Dina Milowic, Françoise Hulot and Delphyn Hastir for their help in patients’ data collecting. We thank Miss Sarah Ballatori for her redacting assistance.