Immunohistochemical analysis-based proteomic
subclassification of newly diagnosed glioblastomas
Kazuya Motomura,1 Atsushi Natsume,1,11 Reiko Watanabe,2 Ichiro Ito,2 Yukinari Kato,3 Hiroyuki Momota,1
Ryo Nishikawa,4 Kazuhiko Mishima,4 Yoko Nakasu,5 Tatsuya Abe,6 Hiroki Namba,7 Yoichi Nakazato,8
Hiroshi Tashiro,2 Ichiro Takeuchi,9 Tsutomu Mori10 and Toshihiko Wakabayashi1
1Department of Neurosurgery, Nagoya University School of Medicine, Nagoya; 2Division of Diagnostic Pathology, Shizuoka Cancer Center, Shizuoka;
Molecular Tumor Marker Research Team, Faculty of Medicine, The Oncology Research Center, Advanced Molecular Epidemiology Research Institute,
Yamagata University, Yamagata; 4Department of Neurosurgery, Saitama Medical University International Center, Saitama; 5Department of Neurosurgery,Shizuoka Cancer Center, Shizuoka; 6Department of Neurosurgery, Oita University School of Medicine, Oita; 7Department of Neurosurgery, Hamamatsu
University School of Medicine, Hamamatsu; 8Department of Human Pathology, Gunma University School of Medicine, Gunma; 9Department ofEngineering, Nagoya Institute of Technology, Nagoya; 10Department of Human Lifesciences, Fukushima Medical University School of Nursing, Fukushima,
Japan
3(Received February 16, 2012 ⁄ Revised June 20, 2012 ⁄ Accepted June 21, 2012 ⁄ Accepted manuscript online July 2, 2012)

Recent gene expression and copy number profilings of glioblastoma multiforme (GBM) by The Cancer Genome Atlas (TCGA)
Research Network suggest the existence of distinct subtypes ofthis tumor. However, these approaches might not be easily applicable in routine clinical practice. In the current study, we aimed
to establish a proteomics-based subclassification of GBM by integrating their genomic and epigenomic profiles. We subclassified
79 newly diagnosed GBM based on expression patterns determined by comprehensive immunohistochemical observation in
combination with their DNA copy number and DNA methylation
patterns. The clinical relevance of our classification was independently validated in TCGA datasets. Consensus clustering identified the four distinct GBM subtypes: Oligodendrocyte Precursor(OPC) type, Differentiated Oligodendrocyte (DOC) type, Astrocytic
Mesenchymal (AsMes) type and Mixed type. The OPC type wascharacterized by highly positive scores of Olig2, PDGFRA, p16,
p53 and synaptophysin. In contrast, the AsMes type was strongly
associated with strong expressions of nestin, CD44 and podoplanin, with a high glial fibrillary acidic protein score. The medianoverall survival of OPC-type patients was significantly longer
than that of the AsMes-type patients (19.9 vs 12.8 months). Thisfinding was in agreement with the Oncomine analysis of TCGA
datasets, which revealed that PDGFRA and Olig2 were favorable
prognostic factors and podoplanin and CD44 were associatedwith a poor clinical outcome. This is the first study to establish a
subclassification of GBM on the basis of immunohistochemical
analysis. Our study will shed light on personalized therapies thatmight be feasible in daily neuropathological practice. (Cancer Sci,
doi: 10.1111/j.1349-7006.2012.02377.x, 2012)G

lioblastoma multiforme (GBM) is one of the most common and highly malignant brain tumors in the primarycentral nervous system in adults. GBM was one of the firsttumor types registered in The Cancer Genome Atlas (TCGA),
which is a project that catalogs genomic abnormalities
involved in the development of cancer.(1,2) The techniques currently used in TCGA study for the detection of abnormalities
include gene expression profiling, copy number variation profiling, single-nucleotide polymorphism genotyping, genomewide methylation profiling,(3) microRNA profiling(4) and exon
sequencing. Since the publication of the first TCGA Network
paper,(1) several groups within the TCGA network have presented the results of highly detailed analyses of GBM. Verhaak
et al.(5) recently subclassified GBM into Proneural, Neural,
Classical and Mesenchymal subtypes by integrating multidimensional data on gene expression, somatic mutations anddoi: 10.1111/j.1349-7006.2012.02377.x© 2012 Japanese Cancer Association

DNA copy number. The main features of the Proneural classare focal amplification of PDGFRA, IDH1 mutation, and TP53
mutation and/or loss of heterozygosity. Moreover, high expression of genes associated with oligodendrocyte development,such as PDGFRA, NKX2-2 and OLIG2, were also associatedwith this subtype. The Neural subtype is characterized by theexpression of neuron markers, such as NEFL, GABRA1, SYT1and SLC12A5. The Classical subtype features high EGFR
expression associated with chromosome 7 amplification andlow expression of p16INK4A and p14ARF, resulting from a
focal 9p21.3 homozygous deletion. Neural stem cell markers,
such as nestin, as well as components of the Notch and Sonic
hedgehog signaling pathways, are highly expressed in the Classical type. The Mesenchymal subtype is characterized by focal
hemizygous deletions at 17q11.2 that contains NF1 and highexpression of YKL-40 (CHI3L1), MET, CD44 and MERTK.
This classification of GBM using gene expression profiles(TCGA) may address the important issue of the inability todefine different patient outcomes on the basis of histopathological features. For ultimately establishing a simple classificationof groups of patients with GBM according to clinicopathological factors, a protein-based immunohistochemical approach,which is routinely used in most neuropathology laboratories,
needs to be applied to avoid more complex molecular biology
techniques.(6)In the present study, we analyzed 79 archival GBM samples
by immunohistochemistry using antibodies against 16 proteins
selected based on Verhaak’s classification for immunohistochemical analysis-based GBM subclassification, including Proneural (Olig2, IDH1-R132H,(7) p53, PDGFRA and PDGFB),Neural (synaptophysin), Classical (p16, EGFR, Hes-1 and nestin) and Mesenchymal types (VEGF, YKL-40, CD44 andpodoplanin [PDPN]), as well as high glial fibrillary acidic protein (GFAP) and Ki-67, and incorporated the results into theexisting genomic and epigenomic data for these samples. We
successfully identified clinically relevant subtypes that partiallyoverlap the Verhaak subgroups.
Materials and Methods
Tumor samples. Samples from 79 consecutive patients with
newly diagnosed GBM from several academic tertiary-careneurosurgical institutions were collected. All the samples werecollected from GBM patients treated with temozolomide

11
To whom correspondence should be addressed.
E-mail: anatsume@med.nagoya-u.ac.jp

Cancer Sci | 2012

(TMZ). Paraffin-embedded surgical samples were collected for
immunohistochemical analysis. All of the specimens had been
fixed in 10% formalin. Three neuropathologists (Y.N., R.W.
and I.I.) independently confirmed the GBM diagnosis according to WHO guidelines.(8,9)
Matched fresh-frozen tissue samples were also obtained.
DNA was prepared as described previously.(10) All the patientsprovided their written informed consent for molecular studiesof their tumor at each participating hospital. The study had theapproval of each of the ethics committees of the Nagoya University Hospital, Shizuoka Cancer Center, Saitama Medical
University Hospital, Oita University Hospital and Hamamatsu
Medical University Hospital (title, “Genetic analysis associatedwith brain tumor”). This study complied with all the provisionsof the Declaration of Helsinki.
Immunohistochemical analysis. Immunohistochemical analysis
was performed as previously described.(11) The antibodies usedin the present study are summarized in Table S1. For each
immunostained slide, the percentage of positively stained GBM
cells on a given slide was evaluated and scored, as shown in
Table S2. This procedure was performed by two pathologists(R.W. and I.I.), and scores were decided through a consensus.
This process was performed twice, and the final scores were
determined at the second round before clustering analysis.
Multiplex ligation-dependent probe amplification. Multiplex
ligation-dependent probe amplification (MLPA) was used for
determining allelic losses and gains of the gene in the tumor
samples. The analysis was performed using the SALSA MLPA
kit P088-B1 and P105-C1 in accordance with the manufacturer’s protocol (MRC Holland, Amsterdam, the Netherlands).(12–15) All the procedures were performed as described
previously.(10)Pyrosequencing. Tumor DNA was modified with bisulfate byusing the EpiTect bisulfite kit (Qiagen, Courtaboeuf Cedex,
France). Pyrosequencing technology was used to determine themethylation status of the CpG island region of MGMT, as
described previously.(10,16,17)
TP53 and IDH1/IDH2 sequencing. Direct sequencing of TP53
exons 5–8, which contain mutation hot spots in gliomas, andIDH1/2 was performed as previously described.(10,18–20) For
IDH sequencing, 129 and 150-bp fragments spanning thesequences encoding the catalytic domains of IDH1 (includingcodon 132) and IDH2 (including codon 172), respectively,
were amplified.
Oncomine data analysis. An independent set of 401 GBM
mRNA expression profiles was analyzed by using the Oncomine Premium Research Edition to assess subtype reproducibility. Details of the standardized normalization techniques andstatistical calculations can be found on the Oncomine website(https://www.oncomine.com).
Statistical analysis. To identify distinct GBM subclasses, weapplied consensus clustering to our immunohistochemical
data.(21) Consensus clustering has been used in many recent
biomedical studies because it can estimate the statistical stability of the identified clusters.(5) Within the consensus clustering,K-means clustering with the Euclidean distance metric wasused as the basic clustering option. For K ranging from 2 to 5,
the K-means clustering was run over 10 000 iterations with a
subsampling ratio of 0.8 for estimating the consensus matrix.
For the purpose of visualization and cluster identification, hierarchical clustering with the Euclidean distance metric and thecomplete linkage option was applied to the estimated consensus matrix. The identified clusters were validated and confirmed using consensus cluster dependence factor plot
analysis(21) and silhouette analysis.(22) To visualize the four
identified clusters, principal component analysis (PCA) wasapplied to the immunohistochemical data and 3-D ellipsoids
representing the covariance structure of each cluster were2

drawn in the 3-D plots of the first three principal components.
Most of the statistical analyses (except the 3-D plot, which
was generated by JMP ver.9.0) were performed using R.(23)
We used a Kruskal–Wallis rank test to analyze the differences
between the four GBM subgroups, and the pairwise differencesin the expressions of 16 proteins and genetic/epigenetic alterations between each subgroup and the other three subgroups.
The differences between the GBM subtypes with P < 0.005
were considered to be statistically significant in a more stringent manner, as the four clusters themselves are determined by
the expression of these proteins and genetic/epigenetic alterations. Statistical analysis of survival was performed using thestatistical software SPSS version 17.0 for Windows (SPSS,
Chicago, IL, USA). Survival was estimated using the Kaplan–
Meier method and survival curves were compared using the
log-rank test.
Results
Patient characteristics. The summary of the GBM patient andtreatment characteristics is shown in Table S3. All 79 patients
received surgical treatment followed by standard TMZ-based
chemotherapy and conventional radiation therapy, with daily
concurrent TMZ at 75 mg/m2 throughout the course of theradiation therapy.(24)This study population included 50 male and 29 female
patients aged 13–84 years (median age, 61 years). The medianpreoperative Eastern Cooperative Oncology Group performance status (ECOG PS) score at diagnosis was one (range, 0–4); the preoperative ECOG PS score was <1 in the case of 48
patients (60.8%). All the tumors were located in the supratentorial region: 60 tumors (75.9%) were located in the superficial
area (cortical or subcortical area), and 19 (24.1%) were locatedin deep anatomical structures such as the basal ganglia andcorpus callosum. Surgical gross total resection (GTR) was
achieved in 24 patients (30.4%), and non-GTR was performedin 55 patients (69.6%).
Consensus clustering subclassifies four subtypes. The GBM
subtypes identified by consensus clustering are shown in
Figure 1, with clustering stability increasing from K = 2 toK = 4, but not to K = 5 (Figs 1,2). Furthermore, the identified
clusters were confirmed on the basis of their positive silhouettewidth,(22) indicating higher similarity to their own class than toa member of any other class (Fig. 3).
According to the results, the 79 GBM cases examined werebasically classified into four clusters: clusters I (nine cases), II(17 cases), III (14 cases) and IV (39 cases), depending on the
branch length, which represents the correlation between thescoring data and the similarity in GBM tumor samples(Fig. 4). This analysis identified four discrete groups of sample
sets that differed markedly in GBM protein expression. The 3D ellipsoid of each cluster in PCA in Figure 5 also suggests
the clear separations of each cluster. All the scores for the
immunohistochemical analysis and genetic/epigenetic data lists
for all the analyses are available in Table S4.
These protein groups were named according to the distribution and biological function of the representative protein
expressions of Olig2, IDH1-R132H, PDGFRA, p16, EGFR,
Hes-1, nestin, CD44, PDPN and GFAP; that is, Oligodendrocyte Precursor (OPC) type, Differentiated Oligodendrocyte(DOC) type, Astrocytic Mesenchymal (AsMes) type, andMixed type. Figure 6 shows the immunohistochemical staining
pattern in the 79 GBM cases, aligned according to the four
identified clusters, indicating similarity in immunohistochemical staining patterns within each cluster.
Differentiated Oligodendrocyte type. All the samples clustered in this type showed high positivity for the oligodendroglial marker Olig2 and small round cell morphology (Table 1,

doi: 10.1111/j.1349-7006.2012.02377.x© 2012 Japanese Cancer Association

Four clusters Cj

n = 79j : nj | aveiεcj Si
Cluster 1: 17 | 0.38

Cluster 2: 39 | 0.30

Cluster 3: 14 | 0.26

K=2

K=3

Cluster 4: 9 | 0.71
–0.2

0.2 0.4
0.6
Silhouette width Si
Average silhouette with : 0.36
Fig. 3.

K=4

K=5

0.0

0.8

1.0

Silhouette plot for identification of core samples.

I

II

III

IV

Fig. 1. Consensus matrix heat maps demonstrating the presence ofseveral clusters within the 79 samples of GBM for K = 2 to K = 5 cluster assignments for each cluster method. The red areas identify thesimilarity between the samples and display samples clustered together
across the bootstrap analysis.

0

I

2

4

Olig2
PDGFRA
p53
IDH1-R132H
PDGFBSynaptophysin
p16
EGFR
Hes-1
Nestin
VEGF
CD44
Podoplanin
GFAP
YKL-40Ki-67

Differentiated oligodendrocyte typeII

Oligodendrocyte precursor type

III

Mixed typeIV

Astrocytic mesenchymal typeFig. 4. Immunohistochemical analysis-based subgroups and comparison with genetic and epigenetic alterations. The heat map anddendrogram show the expression profiles of 16 proteins well characterized in glioblastoma multiforme (GBM) and demonstrate the significant pattern of differential expression among the four subgroups.
The statistical significance of the differential protein expression wasdetermined using one-way ANOVA.

Fig. 2. Consensus clustering cluster dependence factor (CDF) for
K = 2 to K = 5.

Fig. 6). Furthermore, negativity for p53 and p16 was noted.
GFAP was almost always negative in the tumor cell cytoplasm(Fig. 6). Genetically, 1p/19q co-deletion and CDKN2A loss
were more frequently observed in this cluster than in the otherclusters (Fig. 7). The presence of 1p/19q co-deletion wasassessed if the DNA copy numbers at a minimum of threeadjacent loci were less than 0.65 at 1p and 19q.
Oligodendrocyte Precursor type. This cluster was characterized by highly positive scores for PDGFRA, p16, and p53 in
addition to a highly positive score for an oligodendroglial marker, Olig2 (Fig. 6). From the perspective that oligodendrocytes
arise during development from oligodendrocyte precursors,
Motomura et al.

which can be identified by the expression of a number of antigens, including PDGFRA, this subgroup was named the Oligodendrocyte Precursor (OPC) type. On the contrary, few samples
had high scores for nestin, CD44, and PDPN in this group(Table 1; Fig. 6). It is interesting that the genetic alterations
were observed in IDH1 mutations (23.5%) and TP53 mutations(52.9%). These findings were consistent with the results of protein expression. Methylation of the MGMT promoter (41.2%)
was most frequently detected in this cluster (Fig. 7).
Astrocytic Mesenchymal type. This type was generally characterized by: strong membranous and/or stromal positivity for
CD44 and/or PDPN; cytoplasmic positivity for GFAP and/or
nestin in tumor cells; total negativity for p16, except in thecase of four patients; and sparse positivity for p53 (Table 1,
Fig. 6). Morphologically, the tumor cells observed in the
H&E-stained sections showed pleomorphism. In striking contrast to the OPC type, this type was also strongly associatedwith low levels of Olig2, IDH1-R132H, p53, p16 and PDGFRA, and rather strong GFAP expression (Table 1). Thesefindings suggest that this cluster was strongly characterized byastrocytic features.
Genetically, IDH1 and TP53 mutations were rare in thisgroup. Furthermore, methylation of the MGMT promoter(20.5%) was detected at a low frequency (Fig. 7).
Cancer Sci | 2012 | 3© 2012 Japanese Cancer Association

shaped cytoplasmic processes that were haphazardly arranged,
in comparison with the bundle formations in the AsMes type(Fig. 8g,h).
Overview of the immunohistochemical data and genomic/
epigenomic profiles across the four glioblastoma multiforme
subtypes. We sought to select the most significant factors to dis-

DOC type
AsMes type

OPC type

Mixed typeFig. 5. Principal component analysis (PCA) of four glioblastoma multiforme (GBM) subtypes. Ellipsoid bodies represent two SD of the data
distribution for each subgroup.

Mixed type. Compared with TCGA’s Classical-type markers,
frequent expressions of p16 (79%), EGFR (36%) and Hes-1(64%), as well as a Proneural-type marker, p53 (64%), werepredominant in this class (Table 1, Fig. 6).
Strong expression of the downstream Notch transcriptionaltarget Hes-1 suggested that the prominent Notch-Hes-1 pathway was activated in this class. Furthermore, this cluster wascharacterized by positivity for CD44 and GFAP, and morphologically, by tumor cells with eosinophilic cytoplasm. Thismorphological characteristic was compatible with limited or
scant positivity for Olig2. CD44 and PDPN were detected inmany tumor cells.
In addition, this cluster had a genetically high frequency of
EGFR amplification (57.1%) and low frequency of CDKN2Aloss, and these findings are consistent with the protein expression data (Fig. 7). Thus, we named this cluster Mixed typebecause it shares characteristics of the OPC type and AsMes
type or those of TCGA’s Proneural and Classical types.
Morphological characteristics of the four types. On the H&
E-stained sections, the morphological findings of the four types
were fairly characteristic, although not specific (Fig. 8). In the
DOC type, the tumor cells had small round or oval nuclei,
scant cytoplasm and few cytoplasmic processes. The tumor
cell nuclei showed a fine and diffuse chromatin (Fig. 8a,b). In
some cases in which the tumor cells had faint processes, thecells tended to gather around vessels.
In the OPC type, in addition to small round or oval nucleatedcells similar to those of the DOC type, there were scattered intermediate to large pleomorphic and/or multinucleated neoplastic
cells, cells with vesicular chromatin, and/or cells with short spindle-shaped or irregularly-shaped nuclei that were slightly larger
than the small round or oval cells (Fig. 8c,d).
In the AsMes type, many neoplastic cells had spindle-shaped
nuclei and almost bipolar, distinct cytoplasmic processes. They
were generally arranged in bundle-like and interlacing patterns.
Some neoplastic cells had nuclei with vesicular open chromatin. The boundaries of the cytoplasmic processes were generally well defined (Fig. 8e,f).
In the Mixed type, there were scattered large pleomorphic
cells on a background of intermediate or small cells. The latterbackground cells had irregularly-shaped nuclei and spindle-

4

tinguish the four GBM subgroups by a Kruskal–Wallis rank test.
As indicated in Tables S5 and S6, the differences between the
GBM subtypes showing P < 0.005 were considered to be statistically significant in a more stringent manner, as the four clusters
themselves are determined by the expression of these proteinsand genetic/epigenetic alterations. Of these, Olig2, p53, PDGFRA, synaptophysin, p16 and the IDH1 mutation were positively
correlated with the OPC type, whereas positive correlations withnestin, PDPN, CD44 and GFAP were predominant in the AsMes
type. The DOC type showed a significant positive correlation
with Olig2, and there was a significant positive correlation withthe p16 expression in the Mixed type.
Proteomic clusters correlate with survival. The Kaplan–Meiersurvival analysis revealed that the four proteomic clusters differed significantly in their correlation with survival (Fig. 9 andTable 2). There were no significant differences in any of theclinical parameters (i.e. age, sex, preoperative ECOG PS,
tumor location and extent of resection; Table S3) between thefour cluster groups, as determined using the Fisher exact test.
It is interesting that the median overall survival (OS) associated with the OPC type was significantly longer (19.9 months[95% CI, 8.3–31.4]) than that of the patients with the AsMes
type (12.8 months [95% CI, 10.0–15.7; P = 0.041]; Fig. 9).
The difference was statistically significant, as determined by
the log-rank test and univariate analysis. These findings wereconsistent with the OPC type being characterized by higher
positive scores for IDH1-R132H (29%) in the immunohistochemical analysis and a high frequency of IDH1 mutation(23.5%) in the genetic analysis, which are known to predict
long-term survival.(25) Although the survival period of thepatients in the Mixed type appeared to be the longest (median
OS: 21.3 months [95% CI, 7.9–34.8]) among those of theother subgroups, the difference between these three subgroups
was not statistically significant, presumably owing to the limited sample size in this study; the Kaplan–Meier curve of the
DOC type (median OS: 14.8 months [95% CI, 2.6–27.0]) was
similar to those of the OPC and Mixed types (Fig. 9).
Subgroup-specific outcome based on mRNA expression in The
Cancer Genome Atlas datasets. An independent set of 401

GBM mRNA expression profiles was compiled from theOncomine Premium Research Edition to assess subtype reproducibility. Among our selected 16 protein markers, information
about the mRNA expressions of 12 markers and clinical outcomes could be obtained from TCGA brain dataset. IDH1R132H, p53, p16 and Ki-67 were not available in the 401
GBM mRNA expression profiles of TCGA dataset because
IDH1-R132H and p53 antibodies were used to detect mutation
status, and this did not correlate with the mRNA expression ofeach gene. Moreover, because p16 protein expression correlates with the homozygous deletion of CDKN2A, we also
excluded this protein from the analysis. In this analysis, theOlig2, PDGFB and PDGFRA mRNA expression levels weresignificantly low in the tumors of patients who died at 1 year
compared with those who survived for 1 year after the treatment. Notably, PDGFRA was the most favorable prognostic factor among these factors (P = 0.002; Fig. 10 and
Tables 3 and 4).
Furthermore, PDPN, CD44, YKL-40 and EGFR mRNA
were significantly overexpressed in the tumors of the patients
who died 1 year after the treatment. These results indicate that
PDPN is significantly associated with a poor clinical outcome(P = 0.0003; Fig. 10).
doi: 10.1111/j.1349-7006.2012.02377.x© 2012 Japanese Cancer Association

(a)

(b)

DOC type

H&E

GFAP

NestinOlig2

PDGFRA

IDH1-R132H

Ki67

p16

p53

CD44

PDPN

EGFR

Hes-1

H&E

GFAP

NestinOlig2

PDGFRA

IDH1-R132H

Ki67

p16

p53

CD44

PDPN

EGFRHes-1

H&E

GFAP

NestinOlig2

PDGFRA

IDH1-R132H

Ki67

p16

p53

CD44

PDPN

EGFR

Hes-1

H&E

GFAP

NestinOlig2

PDGFRA

IDH1-R132H

p16

p53

CD44

PDPN

EGFR

Hes-1

OPC type(c) AsMes type(d) Mixed type

Ki67

Fig. 6. Representative immunohistochemical images used in this study. (a) Differentiated Oligodendrocyte type (DOC type), glioblastoma multiforme (GBM) case.44. (b) Oligodendrocyte Precursor type (OPC type), GBM case.01. (c) Astorocytic Mesenchymal type (AsMes type), GBM case 03.
(d) Mixed type, GBM case 04.

Discussion

A large number of studies have shown that GBM can be classified by gene and protein expression profiling.(5,26–28) The TCGA
Research Network classifies GBM according to gene expression
profiles into Proneural, Neural, Classical and Mesenchymal subtypes.(5) However, these transcriptomic approaches might not beeasily applied in routine clinical practice because complicatedtechniques are necessary to perform several of the experiments.
Compared with these approaches, an immunohistochemistrybased approach could have widespread utility in the clinical setMotomura et al.

ting and lead to significantly improved patient stratification. The
goal of the current study was to subclassify GBM using an
immunohistochemical approach that is feasible in daily neuropathological practice, using a dataset from the TCGA Research
Network as a reference. Our classification based on immunohistochemical analyses may enable the prediction of clinical chemosensitivity and survival in TMZ-treated patients with GBM.
Identification of four novel clusters by immunohistochemical
analysis. We identified four novel clusters (OPC type, DOC

type, AsMes type and Mixed type) with a considerably different
expression profile of GBM tumors; to our knowledge, such an
Cancer Sci | 2012 | 5© 2012 Japanese Cancer Association

Table 1. Frequency of positive score >3

Proteins

Proneural
Olig2
IDH1-R132H
p53
PDGFRAPDGFBNeuralSynaptophysin
Classical
p16
EGFR
Hes-1
NestinMesenchymal
VEGF
YKL-40Podoplanin
CD44
GFAP
Ki-67

DOC typeDOC
type(n = 9)
(%)

OPC
type
(n = 17)
(%)

Mixed
type
(n = 14)
(%)

AsMes
type
(n = 39)
(%)

Total

9 (100)0 (0)1 (11)
3 (33)
2 (22)

14 (82)5 (29)
9 (53)
10 (59)1 (6)4 (29)0 (0)9 (64)
5 (36)
5 (35)

16 (41)1 (3)5 (13)3 (8)
9 (23)

43
6
24
21
17

0 (0)

3 (18)

3 (21)

0 (0)

6

0 (0)
0 (0)
0 (0)
2 (22)

9 (53)0 (0)3 (17
2 (12)

11 (79)5 (36)
9 (64)
4 (29)2 (5)
5 (13)
11 (28)24 (62)

22
10
23
32

1 (11)0 (0)0 (0)5 (56)

6 (35)
1 (6)
0 (0)
1 (6)

4 (29)0 (0)4 (29)
11 (79)11 (28)2 (5)
18 (4637 (95)

22
3
22
54

0 (0
5 (56)

3 (18)
11 (65)

10 (71)5 (29)

32 (82)18 (46)

45
39

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

OPC type

AsMes type

Mixed type

AsMes, astrocytic mesenchymal; DOC, differentiated oligodendrocyte;
OPC, oligodendrocyte precursor.

Differentiatedoligodendrocyte type

Mixed type

Oligodendrocyte
precursor type

Astrocytic
Mesenchymal type

60
50
40

Fig. 8. Morphological findings of four types on the H&E sections. In
the Differentiated Oligodendrocyte (DOC) type (original magnification: (a) 920; (b) 940), the tumor cells have small round/oval nuclei
and indistinct processes. In the Oligodendrocyte Precursor (OPC) type(original magnification: (c) 920; (d) 940), there are scattered intermediate to large pleomorphic and/or multinucleated cells. In the Astrocytic Mesenchymal (AsMes) type (original magnification: (e) 920; (f)940), spindle-shaped cytoplasmic processes are distinct and form bundles in an interlacing fashion. In the Mixed type (original magnification: (g) 920; (h) 940), in the background of small-sized tointermediate-sized spindle-shaped cells arranged in a haphazard fashion, there are several pleomorphic large cells.

30

1.0

DOC type
OPC type
Mixed type
AsMes type

100Fig. 7. Frequency and pattern of genetic and epigenetic alterations
in four glioblastoma multiforme (GBM) subtypes.

expression profile has not been described elsewhere. However,the limitation of unsupervised clustering, which does not guarantee a clinically relevant classification, must be considered.
Among the four clusters, the OPC and AsMes types in particularshowed unique immunohistochemical patterns.
In addition, the survival patterns of the patients with GBM
tumors classified into these types were significantly different.
The OPC type was characterized by a favorable outcome andhigh positivity for Olig2, PDGFRA and IDH1-R132H on the
immunohistochemical staining. The Kaplan–Meier log-rank
test revealed that the OPC type was associated with a median6

Probability of survival

20

0.5

0

12

24

36

48

Months after diagnosis (months)
Fig. 9. Kaplan–Meier estimates of overall survival (OS) for all the
glioblastoma multiforme (GBM) patients (n = 79) separated into four
subgroups.

OS of 19.9 months (Fig. 9). This is possibly a result of thehigh positive score for IDH1-R132H, which is well recognized
as a predictive biomarker and may influence this favorable surdoi: 10.1111/j.1349-7006.2012.02377.x© 2012 Japanese Cancer Association

Table 2. Median overall survival of each of the four glioblastoma
multiforme subtypes
Median overall
survival (months)

95% CI

19.9†
14.8
21.3
12.8

8.3–31.4
2.6–27.0
7.9–34.8
10.0–15.7

Proteomic clusters
OPC type
DOC type
Mixed type
AsMes type

OLIG2

PDGFB

Log2 medi an-centered intensity

Log2 medi an-centered intensity†The median overall survival of the OPC type was significantly longer(19.9 months [95% CI, 8.3–31.4]) than that of the AsMes type(12.8 months [95% CI, 10.0–15.7]) (P = 0.041). AsMes, astrocytic
mesenchymal; DOC, differentiated oligodendrocyte. OPC,
oligodendrocyte precursor.

PDPN

CD44

PDGFRAA

B

YKL-40C

A. No value (45)
B. Alive at 1 year (162)
C. Dead at 1 year (194)A

vival in GBM. Oligodendrocyte precursor cells are thought todevelop from neural stem cells through multiple genetic andmorphological changes.(29) Oligodendrocyte precursor cells
express Olig1, Olig2, Sox10, Nkx2.2(30) and PDGFRA.(31,32) In
particular, PDGFRA signaling is known to regulate the proliferation of oligodendrocyte precursor cells during neonatal
development and regeneration in adulthood.(33)
In contrast, the characteristics of the AsMes type were highpositivity for PDPN and CD44 in the stroma and GFAP in tumor
cells. PDPN is a mucin-like transmembrane sialoglycoprotein
putatively involved in migration, invasion, metastasis and malignant progression of several tumors, such as squamous cell carcinomas, mesothelioma and testicular tumors.(34–36) Furthermore,
PDPN expression is thought to be associated with malignant progression of astrocytomas.(37) A study showed the presence of
putative binding sites for NF1 within the basic transcription factor of the PDPN promoter lesion,(38) suggesting that NF1 negatively downregulates the expression of PDPN. CD44 is a major
cell surface hyaluronan receptor and cancer stem cell marker
that has been implicated in the progression of various cancer
types.(39) Recently, CD44 was found to be upregulated in a
broad range of GBM, and its elevated expression was correlatedwith poor prognosis.(40) An interesting finding is that CD44 and
PDPN colocalize on cell surface protrusions in carcinoma cells,
and the PDPN-CD44 interaction is important for driving directional cell migration in malignant tumors.(41)
The DOC type was clustered adjacent to the OPC type, suggesting that these clusters are closer to each other than to theother two clusters. A unique characteristic of the DOC type
was that high positivity for Olig2 was observed in all thetumor sections in this cluster, whereas the positivity for theother markers was unremarkable. Genetically, the highest frequency of 1p/19q co-deletion, which refers to the combinationof both 1p and 19q partial loss, and both hemizygous andhomozygous deletions of the CDKN2A gene were observed inthis class. This type is a heterogeneous group consisting ofeither 1p/19q co-deletion or CDKN2A-loss tumors. Taken
together, this class may be differentiated into an oligodendroglioma-like lineage from oligodendrocyte precursor cells. The
Mixed type was indeed mixed between the OPC and AsMes
types. Moreover, PCA revealed that this type was a combination of the OPC and AsMes types.

EGFR

Comparison with The Cancer Genome Atlas subclassificationand validation of The Cancer Genome Atlas brain dataset. Muta-

C

tions of the IDH1 and TP53 genes and high expression levels
and high copy numbers of PDFGRA were frequently observedin the Proneural subtype advocated by Phillips andVerhaak,(5,26) suggesting that the OPC type is similar to theProneural type. Striking characteristics common to the OPC
type and the DOC type were high Olig2 expression and low

BFig. 10. Seven factors among our 16 markers that are correlated significantly with clinical outcomes from TCGA datasets. B and C show
the log2 median-centered intensity of tumors of patients who werealive or dead at 1 year, respectively.

Table 3. Underexpression: dead at 1 year
Gene symbol

Reporter ID

t-testP-valueQ-value

Fold change

OLIG2
PDGFB
PDGFRA

213824_at
217112_at
211533_at

2.105792
2.3643584
2.8829412

0.018113879
0.009313148
0.002104464

0.220443238
0.188830637
0.141673037

1.0732534
1.0354494
1.0440509

Table 4. Overexpression: dead at 1 year
Gene symbol

Reporter ID

t-testP-valueQ-value

Fold change

PDPN
CD44
YKL-40
EGFR

204879_at
204490_s_at
209395_at
211551_at

3.4651806
2.6668687
2.2250252
1.8736842

3.04E-04
0.004021729
0.013421925
0.030904011

0.322990973
0.527154075
0.672091602
0.840823061

1.4722846
1.296043
1.3869164
1.0449007

Motomura et al.

Cancer Sci | 2012 | 7© 2012 Japanese Cancer Association

expression levels of GFAP. However, the OPC type was characterized by higher positive scores for PDGFRA, p16, p53 andsynaptophysin. The OPC type may be a mixture of Proneuraland Neural subtypes.
In the Classical subtype, EGFR amplification and
CDKN2A homozygous deletions were the frequent genetic
alterations observed. Moreover, components of the nestin
and Notch signaling pathways were highly expressed.
According to our classification, overexpression of EGFR andthe downstream effector of Notch signaling Hes-1 were mostfrequently observed in the Mixed type. Deletion of CDKN2A
and downregulation of p16 were characteristic of the DOC
type, and strong nestin expression was most frequently
observed in the AsMes type. The Mesenchymal subtype wascharacterized by high expression levels of YKL-40 and
MET and high frequency of NF1 mutation/deletion. In ourdataset, the mesenchymal markers PDPN and CD44 werehighly expressed in the AsMes type, which is also characterized by strong GFAP expression. The combination of higher
activity of mesenchymal and astrocytic markers is suggestiveof an epithelial-to-mesenchymal transition.
In the present study, the outcomes of the patients with the
OPC and AsMes types were significantly different. Contrary to
Verhaak’s classification, the outcomes of the patients with the
OPC and AsMes types, on the basis of our classification, weresignificantly different.
Furthermore, we could easily distinguish these two subtypes
using PDGFRA, p16, p53, PDPN and CD44, as well as theroutinely used proteins, GFAP, Olig2, synaptophysin and nestin. The ability to discriminate these two subtypes will contribute to the development of different therapeutic approaches foreach GBM subtype.
Although there were no independent validation samples ofparaffin-embedded sections of GBM, determining the associa-

tion between the expressions of these markers and the outcomes in 406 TCGA brain datasets would be of interest. Of
note, Oncomine suggested that the overexpressions of PDPN
and CD44 were associated with poor prognosis, and highexpression levels of Olig2 and PDGFRA in tumor cells significantly improved the patient outcome. We are currently conducting a prospective randomized clinical trial, the Japan
Clinical Oncology Group Study 0911, to validate the additive
efficacy of interferon-b in 120 patients with newly diagnosed
GBM.(42,43) It is anticipated that his clinical trial will validate
our immunohistochemical approach.
In conclusion, the data obtained by expression profiling of
79 GBM tumors based on immunohistochemical studies
suggest the existence of four proteomic subgroups of GBM
tumors. To the best of our knowledge, this is the first study toestablish the subclassification of GBM on the basis of
immunohistochemical analysis. Among the four subtypes, thepatients with the OPC type showed favorable outcomes. To
develop more effective and less toxic GBM treatment
regimens, it is necessary to identify and correctly classify theproteomic subtypes, as well as understand the underlying
oncogenic driving pathways for each type.

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8

Acknowledgments
The authors would like to thank Mr Akiyoshi Sakai (Clinical Laboratory, Kariya Toyota General Hospital, Kariya, Japan) and Mr Hideaki
Maruse, Mr Takafumi Fukui and Mr. Yosuke Furui (FALCO Biosystems, Kyoto, Japan) for wonderful technical assistance.

Disclosure Statement
Kazuya Motomura was supported by a Grant-in-Aid (B) for Scientific
Research from the Ministry of Health, Labor, and Welfare, Japan.

doi: 10.1111/j.1349-7006.2012.02377.x© 2012 Japanese Cancer Association

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Supporting Information
Additional Supporting Information may be found in the online version of this article:
Table S1. Antibodies and immunostaining conditions.
Table S2. Scoring system for immunohistochemical positivity used in this study.
Table S3. Clinical characteristics of the four GBM subtypes.
Table S4. Scores of all immunohistochemical analysis and genetic/epigenetic data lists for all the analyses.
Table S5. Significant pairwise correlation coefficients derived from the Kruskal–Wallis rank test of the expressions of 16 proteins in the four
GBM subgroups.
Table S6. Significant pairwise correlation coefficients derived from the Kruskal–Wallis rank test of the genetic and epigenetic alterations in thefour GBM subgroups.
Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries(other than missing material) should be directed to the corresponding author for the article.

Motomura et al.

Cancer Sci | 2012 | 9© 2012 Japanese Cancer Association