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The Proneural Molecular Signature Is Enriched in
Oligodendrogliomas and Predicts Improved Survival
among Diffuse Gliomas
Lee A. D. Cooper
1
, David A. Gutman
1
,QiLong
3,4
, Brent A. Johnson
4
, Sharath R. Cholleti
1
, Tahsin Kurc
1
,
Joel H. Saltz
1,2,3
, Daniel J. Brat
1,2,3
, Carlos S. Moreno
1,2,3
*
1 Center for Comprehensive Informatics, Emory University, Atlanta, Georgia, United States of America, 2 Pathology and Laboratory Medicine, Emory University School of
Medicine, Atlanta, Georgia, United States of America, 3 Emory Winship Cancer Institute, Atlanta, Georgia, United States of America, 4 Department of Biostatistics and
Bioinformatics, Emory University, Atlanta, Georgia, United States of America
Abstract
The Cancer Genome Atlas Project (TCGA) has produced an extensive collection of ‘-omic’ data on glioblastoma (GBM),
resulting in several key insights on expression signatures. Despite the richness of TCGA GBM data, the absence of lower
grade gliomas in this data set prevents analysis genes related to progression and the uncovering of predictive signatures. A
complementary dataset exists in the form of the NCI Repository for Molecular Brain Neoplasia Data (Rembrandt), which
contains molecular and clinical data for diffuse gliomas across the full spectrum of histologic class and grade. Here we
present an investigation of the significance of the TCGA consortium’s expression classification when applied to Rembrandt
gliomas. We demonstrate that the proneural signature predicts improved clinical outcome among 176 Rembrandt gliomas
that includes all histologies and grades, including GBMs (log rank test p = 1.16e-6), but also among 75 grade II and grade III
samples (p = 2.65e-4). This gene expression signature was enriched in tumors with oligodendroglioma histology and also
predicted improved survival in this tumor type (n = 43, p = 1.25e-4). Thus, expression signatures identified in the TCGA
analysis of GBMs also have intrinsic prognostic value for lower grade oligodendrogliomas, and likely represent important
differences in tumor biology with implications for treatment and therapy. Integrated DNA and RNA analysis of low-grade
and high-grade proneural gliomas identified increased expression and gene amplification of several genes including GLIS3,
TGFB2, TNC, AURKA, and VEGFA in proneural GBMs, with corresponding loss of DLL3 and HEY2. Pathway analysis highlights
the importance of the Notch and Hedgehog pathways in the proneural subtype. This demonstrates that the expression
signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for low-grade oligodendrogliomas,
and likely represent important differences in tumor biology with implications for treatment and therapy.
Citation: Cooper LAD, Gutman DA, Long Q, Johnson BA, Cholleti SR, et al. (2010) The Proneura l Molecular Signature Is Enriched in Oligodendrogliomas and
Predicts Improved Survival among Diffuse Gliomas. PLoS ONE 5(9): e12548. doi:10.1371/journal.pone.0012548
Editor: Maciej Lesniak, The University of Chicago, United States of America
Received May 24, 2010; Accepted August 11, 2010; Published September 3, 2010
Copyright: ß 2010 Cooper et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provide d the original author and source are credited.
Funding: This work was supported in part by contract S09-094 from the National Cancer Institute (NCI) administered through NCI Science Applications
International Corporation (SAIC) Frederick campus, and the C enter for Comprehensive Informatics, Emory University. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: cmoreno@emory.edu
Introduction
Glioblastoma (GBM) is the most common primary brain tumor,
with 8700 new cases per year in the United States [1]. It is also the
highest grade astrocytoma (WHO grade IV), with a truly dismal
prognosis [2]. Following surgical resection, radiation therapy and
temozolamide chemotherapy–the current therapeutic gold stan-
dard–mean survival is 60 weeks [3]. Perhaps more telling of its
aggressive clinical behavior, survival of patients treated by surgical
resection alone averages 13 weeks [4]. Lower grade gliomas (i.e.
WHO grade II and III) account for an additional 2500 cases/year
and are also ultimately fatal, but have slower growth rates and
longer survival times (3–8 years). With rare exception, patients
with lower grade tumors will eventually die due to progression to
GBM.
The analysis of gene expression patterns in GBMs suggests that
this histologic category may include distinct subtypes. Several
groups have developed approaches for subtyping GBMs by gene
expression signatures [5,6]. Verhaak et al [6] used data from The
Cancer Genome Atlas (TCGA) to describe four distinct subtypes
of GBM (Proneural, Neural, Classic, and Mesenchymal) that are
defined by gene expression signatures following unsupervised
hierarchical clustering. TCGA gene expression subtypes have
moderate similarity to those described by Phillips et al [7], who
defined three transcriptional subtypes (proneural, proliferative,
and mesenchymal). Although the TCGA analysis did not reveal
differences in survival between subtypes, this may reflect the
overall short survival period for this highly malignant neoplasm
[8]. The TCGA data at this point does not contain any lower-
grade gliomas. To assess whether the gene expression patterns
uncovered by TCGA data could be used to segregate or predict
survival of lower-grade gliomas, we applied these gene signatures
to the NCI Repository for Molecular Brain Neoplasia Data
(Rembrandt) dataset, which includes infiltrative gliomas with
diverse histologies and includes grades II, III, and IV. The
Rembrandt dataset used here contains 176 samples with long-term
PLoS ONE | www.plosone.org 1 September 2010 | Volume 5 | Issue 9 | e12548
survival data, including 32 astrocytomas (WHO grades II and III),
and 43 oligodendrogliomas (WHO grades II and III). Here, we
demonstrate that the Proneural gene expression signature as
defined by TCGA is enriched in gliomas with oligodendrogliomal
differentiation. This signature also predicts improved outcome for
oligodendrogliomas. Moreover, we have compared the expression
patterns and DNA copy number of low-grade and high-grade
proneural gliomas and identified genes and pathways associated
with progression of this disease. Genes increased in proneural
GBMs (PN-GBM) at both the DNA and RNA level included
GLIS3, TGFB2, TNC, AURKA, and VEGFA. Network analysis
of these changes in gene expression identified several regulatory
hubs, including GLI1, RUNX2, MYC, BMP2, and NOTCH1.
Materials and Methods
Ethics Statement
All human subjects data was publicly available de-identified
data from The Cancer Genome Atlas project and the Rembrandt
database, and thus, not designated as human subjects research. No
Institutional Review Board approval was required.
Patient Datasets
Microarray and clinical data were acquired in un-normalized
form from the Rembrandt [9] public data repository (https://
caintegrator.nci.nih.gov/rembrandt/) using data available on
November 11, 2009. Clinical data were derived from contributing
center institutional diagnoses at Henry Ford Hospital, UCSF, Lee
Moffitt Cancer Center, Dana Farber Cancer Center, University of
Wisconsin, and NCI and are available as Table S1. Affymetrix
HGU133 Plus 2.0 CEL files were normalized using the robust
multi-array average (RMA) method [10] with default parameters
from the Matlab Bioinformatics Toolbox. Of the 296 samples
downloaded, 176 had associated survival data, 75 were oligoden-
drogliomas or astrocytomas with grades II or III, and 101 were
GBMs. Copy number data was derived using Affymetrix 100K
SNP arrays available from the Rembrandt public data repository.
Of the 176 samples with survival data, 147 have associated SNP
arrays for tumor tissue (85 GBMs, 34 Oligodendrogliomas grades
II and III, and 28 Astrocytomas grades II and III).
Data Analysis
To classify Rembrandt samples within the TCGA classification
schema, Rembrandt data for the probes from the Affymetrix U133
Plus 2.0 GeneChip were mapped to the TCGA class signature
genes using HUGO gene symbol and Entrez gene ID number.
This comparison yielded an intersection of 1486 Affymetrix probe
sets. Classification of the Rembrandt samples was then performed
using prediction analysis for microarrays using the signature gene
class centroids [11].
Comparisons of survival between different classes were
performed using the log-rank test [12] as implemented in
GenePattern 3.0 [13]. Survival was compared between each
expression subtype and all others for all 176 samples, all grade II
and III gliomas, and the subsets of the 32 astrocytomas, and 43
oligodendrogliomas. Unsupervised hierarchical clustering was
performed using uncentered correlation and average linkage with
gene and array centering and normalization using cluster [14] and
javatreeview [15].
Differences in gene expression between subtypes were determined
using the comparative selection marker module of GenePattern 3.0.
Cutoffs for statistical significance were a Benjamini- Hochberg
corrected False Discovery Rate (FDR) ,0.05 and a minimum fold
change .1.8. Statistical significance of Gene Ontology overrepresen-
tation were determined by hypergeometric distribution using the
DAVID database [16] and Ingenuity Pathway Analysis [17]. Gene
Set Enrichment Analysis used the GSEA software [18] with an FDR
cutoff of 0.25. Classes were analyzed using the curated c2.biocar-
ta.v2.5.symbols and c2.kegg.v2.5.symbols databases and the gene
ontology c5.all.v2.5.symbols and c5.bp.v2.5.symbols databases from
the Molecular Signatures Database (MSigDB) at the Broad Institute
[18]. Literature Lab software [19] version 2.9 (Acumenta, Inc.,
Boston, MA) was used to identify statistically significant associations of
gene lists with Pathways and MESH terms, and to compare gene lists
against one another.
Cox proportional hazards (PH) models were used to examine
the association between patient survival and four subtypes after
adjusting for patient age at diagnosis as well as 1p/19q status
whenever applicable. We note that 1p/19q deletion is present only
in Oligodendrogliomas; hence, 1p/19q deletion status was not
adjusted for in cases with Astrocytoma or GBM.
Results
Unsupervised hierarchical cluster analysis of gene expression
data derived from TCGA GBM samples resulted in four distinct
gene expression subtypes: Neural, Proneural, Classical, and
Mesenchymal. This classification utilized an integrated analysis
of three gene expression platforms to identify a set of genes that is
consistently and variably expressed among the TCGA samples.
Expression measurements from the Affymetrix HT-HG-U133A,
the Affymetrix Human Exon 1.0 ST, and a custom Agilent array,
were combined by mapping to a transcript database 11,861 total
genes. Of these, 1740 were reliably expressed across platforms,
with 840 identified as class signature genes by ClaNC analysis
[20]. The full details of the TCGA cluster analysis are available
elsewhere [8].
Total gene expression and clinical survival data were downloaded
from the Rembrandt website (https://caintegrator.nci.nih.gov/
rembrandt/). To map the 176 Rembrandt samples with survival
data to the relevant TCGA molecular subtypes, we extracted the
gene symbols from the 840 gene centroid developed previously [6].
Gene symbols and corresponding entrez gene ID numbers were
used to filter the Rembrandt Affymetrix U133Plus2.0 GeneChip
data, resulting in a total of 1486 probes corresponding to 819 genes.
These data were analyzed by supervised hierarchical clustering
(Figure 1) and visual inspection indicated that the four subtypes are
readily discernible in the Rembrandt dataset. A composite dataset
corresponding to these 819 genes was then assembled including
both the TCGA and Rembrandt datasets. A shrunken centroid
model was trained on the TCGA data using PAM software [11] and
tested by leave-one-out-cross-validation (Table 1, 148/160 cor-
rect = 92.5% classification accuracy). We next applied the centroid
model to the Rembrandt dataset to classify each of the samples to
one of the four molecular subtypes (Table 2 and Figure 1A).
Classification of GBM samples in the Rembrandt dataset mirrored
those previously observed in the TCGA analysis. We observed that
the proneural class dominated the oligodendrogliomas grade II and
III samples (32/43 = 74%), and that the mesenchymal class was
completely absent in the oligodendrogliomas (Figure 1A). Although
the proneural class was also enriched somewhat in astrocytomas, it
was less dramatic, and all gene expression subtypes, including
mesenchymal, were represented. The classic signature was more
common in GBM samples than in grade II and III samples.
Classifications, class probabilities, pathology, and survival data for
all 176 samples included in this analysis are provided in Table S2.
The complete set of clinical data for all of the Rembrandt samples
used in this analysis downloaded in November, 2009 is given in
Proneural Glioma Signature
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Table S1. Unsupervised hierarchical clustering of these 176 samples
using these 819 genes shows that they cluster into the four
representative subtypes identified in the TCGA GBM dataset
(Figure 1B).
To examine if there were any differences in survival for the
entire set of 176 classified Rembrandt samples, we performed a
Kaplan-Meier analysis (Figure 1C). The 74 samples in the
Proneural class had significantly better outcome by the log rank
test (p = 1.16e-6). The other three gene expression classes did not
show appreciable survival differences. We next examined whether
gene expression class was predictive of outcome in grade II and
grade III gliomas, including both oligodendrogliomas and
astrocytomas in the analysis. We found that the proneural subtype
predicted significantly better outcome in lower grade gliomas
(p = 0.000265, Figure S1). To determine whether the prognostic
value of the Proneural subtype was independent of histology and
grade (GBM, oligodendroglioma, or astrocytoma), we performed a
similar analysis on each of these subsets. The Proneural subtype
demonstrated improved outcome for oligodendrogliomas (grades
II and III, p = 1.26e-4), but not astrocytomas (grades II and III,
p = 0.3) or GBM (grade IV, p = 0.588). Noushmehr et al [21] has
recently identified a specific subtype of diffuse glioma based on its
CpG methylation pattern that is enriched in the proneural
subtype, in oligodendrogliomas, and in all low-grade gliomas,
which has significantly improved outcome, corroborating our
findings.
We next performed a multivariate Cox Proportional Hazard
analysis of expression subtypes, adjusting for age and 1p/19q
deletion status (Table 3). Only 152 samples could be included in
this analysis, as 24 samples lacked SNP/copy number data for
assessing 1p/19q status. As expected, the classic (HR = 6.25;
p = 0.0100) and neural (HR = 23.28; p = 0.0001) subtypes of
oligodendrogliomas had significantly worse outcome compared
to the proneural subtype. For astrocytomas, the classic subtype
had significantly worse outcome than proneural cases (HR = 8.48;
p = 0.0059), but the mesenchymal and neural subtypes did not
exhibit significantly different outcome from proneural samples.
To make sure that none of the classic or neural oligodendro-
gliomas were misclassified small cell GBMs, we carefully examined
patient age, chromosome 10 loss, and EGFR amplification status.
Of the six classic and five neural oligodenrogliomas, none of the
neural and only two of the classic oligodendrogliomas had high
patient age (.70), chr10 loss, and/or EGFR amplification. We
reanalyzed the survival data excluding these two cases (HF1150
and HF0510), and none of the statistical findings were affected,
except that the p-value for HR comparing classic and proneural
oligodendrogliomas increased to 0.0595 (marginally significant)
from 0.0100 (significant), which is not too surprising given that the
sample size of the oligodendrogliomas was reduced by two.
Gene Expression Analysis
To investigate mechanisms underlying the difference in survival
associated with the Proneural subtype, we performed several
differential gene expression analyses. First we compared the set of
proneural lower grade gliomas (both oligodendrogliomas and
astrocytomas; PN-OA) to proneural GBMs (PN-GBM). In
addition, because of the markedly improved survival of proneural
oligodendrogliomas (PN-Oligo), we compared the gene expression
of PN- Oligo to PN-GBM, proneural astrocytomas (PN-Astro),
neural oligodendrogliomas (N-Oligo), and classic oligodendrogli-
Figure 1. The proneural subtype is enriched in oligodendrogli-
omas and has longer survival. (A) Uns upervised hierarchical
clustering of 176 Rembrandt samples using TCGA classification genes
identifies the four major subtypes: Proneural (cyan), Neural (blue),
Mesenchymal (magenta), and Classic (orange). (B) Kaplan-Meier survival
samples shows the Proneural subtype has significantly better survival for
all cases (n = 176, p = 1.16e-6), and oligodendrogliomas (n = 43, p = 1.26e-
4), but not for astrocytomas (n = 32, p = 0.3), or GBM cases (n = 101,
p = 0.588).
doi:10.1371/journal.pone.0012548.g001
Proneural Glioma Signature
PLoS ONE | www.plosone.org 3 September 2010 | Volume 5 | Issue 9 | e12548
omas (C-Oligo). We also compared proneural oligodendrogliomas
to the combined set of classic and neural oligodendrogliomas.
Using minimum fold-change cutoff of 1.8 and a Benjamini-
Hochberg corrected False Discovery Rate (FDR) ,0.05, we
identified 779 probes differentially regulated between PN-Oligo
and PN-GBM (Table S3), and 576 probes differentially regulated
between PN-OA and PN-GBM (Table S4). Venn diagram analysis
of these two gene sets determined that 508 probes were in
common, 68 were unique to the PN-OA vs PN-GBM comparison,
and 271 were unique to the PN-Oligo vs PN-GBM comparison.
Thus, the union of these comparisons resulted in 847 differentially
expressed probes. A hierarchical clustering of the PN-Oligo, PN-
Astro, and PN-GBM samples using these 847 differentially
expressed probes is shown in Figure 2A. With identical false
discovery and fold-change criteria, we found no significant probes
differentially expressed between PN-Oligo and PN-Astro subtypes.
However, we did observe 1508 probes differentially expressed
between PN-Oligo and N-Oligo (Table S5), 2118 probes
differentially expressed between PN-Oligo and C-Oligo (Table
S6), and 190 probes differentially expressed between PN-Oligo
and the combined set of N-Oligo and C-Oligo (Table S7).
Complete results for these comparisons providing data on all
probes is also available in Tables S3, S4, S5, S6.
Copy Number Analysis
DNA Copy number data for the Rembrandt samples was
calculated using GenePattern version 3.2.0. Segmentation of raw
copy numbers was calculated using the GLAD module (version 2).
Significance analysis of amplifications and deletions was per-
formed using the GISTIC method [22] (version 3), with
amplification/deletion threshold 0.1 and minimum segment size
of four markers.
We identified a number of regions with significant amplifica-
tions or deletions in the PN-GBM and PN-Oligo subtypes
(Figure 2B). It is worth noting that the PN-Oligo samples had
highly significant frequency of co-deletion of chromosomes 1p36
and 19q13. While there were relatively few neural (n = 3) and
classic (n = 5) oligodendrogliomas with SNP data, and no
mesenchymal oligodendrogliomas, we did note that they generally
contained either 1p deletion or 19q deletion, but not both
deletions in any given tumor sample.
Integrated Copy Number and Gene Expression Analysis
To gain insight into the progression of proneural samples, we
performed an integrated analysis of DNA copy number and
mRNA gene expression within the proneural subtype of the
Rembrandt samples, specifically examining those changes between
PN-OA and PN-GBM samples. We observed 66 probe sets
corresponding to 52 genes that showed significant amplification or
deletion, as well as significant differences in gene expression
between these subtypes. A comprehensive list of the mRNA probes
that intersect with GISTIC copy number analysis is given in Table
S8, and a summary of the loci with both expression and copy
number alterations between PN-GBM and PN-OA samples is
provided in Table 4. Among these genes, of particular interest,
were the Hedgehog pathway transcription factor GLIS3 (amplified
at 9p23 and increased in PN-GBM), Meningioma 1 (MN1), RAS-
like 10A (RASL10A), Src homology 2 containing transforming
protein D (SHD), and Dishevelled associated activator of
morphogenesis 2 (DAAM2), all amplified in PN-OA samples
and increased in PN-OA relative to PN-GBM. Genes deleted in
PN-OA samples and downregulated relative to PN-GBM included
Epithelial membrane protein 3 (EMP3), Secreted phosphoprotein
1/Osteopontin (SPP1), and integrin-binding sialoprotein (IBSP).
Also of note was the fact that multiple genes associated with the
complement cascade (C1S, C1R, C1RL, F13A1, and CFI) were
within the set of genes showing both copy number and expression
changes between PN-OA and PN-GBM samples. All of these
complement cascade genes showed higher expression in PN-GBM
samples relative to PN-OA samples.
Secondly, to gain insight into differences in histologic differen-
tiation and survival, we also performed an integrated analysis of
DNA copy number and mRNA gene expression between PN-
Oligo samples and PN-GBM samples in the Rembrandt dataset.
We observed 323 probe sets corresponding to 240 genes that
showed significant amplification or deletion, as well as significant
differences in gene expression between these subtypes. A
comprehensive list of the mRNA probes that intersect with
GISTIC copy number analysis is given in Table S9. Among these
genes, of particular interest, were CD44 (amplified at 11p13 and
increased in PN-GBM), CDKN2C and CDC42 (both amplified in
Table 1. Classification accuracy of subtype predictor on the training set of TCGA samples generated using Prediction Analysis of
Microarrays (PAM) software.
CV Confusion Matrix (Threshold = 2.66891)
True\Predicted Classic Mesenchymal Neural Proneural Class Error rate
Classic 43 4 0 0 0.085
Mesenchymal 2 40 1 0 0.070
Neural 0 1 28 1 0.067
Proneural 1 0 2 37 0.075
Overall prediction accuracy was 148/160 = 92.5% correct.
doi:10.1371/journal.pone.0012548.t001
Table 2. Classification of Rembrandt samples using the TCGA
molecular subtype gene signatures derived from the PAM
classifier.
Subtype GBM Oligodendroglioma Astrocytoma All
Classic 42 6 3 51
Mesenchymal 21 0 5 26
Neural 12 5 8 25
Proneural 26 32 16 74
Total 101 43 32 176
The Proneural subtype dominates oligodendriogliomas and there are no
mesenchymal subtype in the oligodenroglioma samples.
doi:10.1371/journal.pone.0012548.t002
Proneural Glioma Signature
PLoS ONE | www.plosone.org 4 September 2010 | Volume 5 | Issue 9 | e12548
PN-GBM and deleted in PN-Oligo subtypes at 1p36), TGFB2,
TNC, AURKA, and VEGFA, all amplified in PN-GBM, and
SOX8 and Noggin (NOG) amplified in PN-Oligo samples.
Pathway Analysis
We performed four separate types of pathway analysis using gene
set enrichment analysis (GSEA), the Database for Annotation,
Visualization and Integrated Discovery (DAVID), Literature Lab
Analysis (LLA), and Ingenuity Pathway Analysis (IPA). The
comparison of PN-GBM to PN-OA subtypes identified a number
of gene ontologies and pathways that were significantly enriched in
multiple analyses (Table 5). Pathways, ontologies, and biological
processes that were differential between the PN-GBM and PN-OA
subtypes included several annotations associated with increased
vasculature, including blood coagulation, the complement cascade,
vasculature development, wound healing, immune response, and the
IKK-NFkB pathway. In addition, IPA identified significant overrep-
resentation of genes associated with cancer, cell death, proliferation,
glioblastomas, and astrocytomas. An overview of gene expression
differences between PN-OA and PN-GBM samples is shown in a
network diagram (Figure 3A) in which genes increased in PN-GBM
relative to PN-OA are red, and genes decreased in PN-GBM relative
to PN-OA are green. LLA of this gene set identified the
‘Thrombospondin-1 Induced Apoptosis in Microvascular Endothe-
lial Cells’ pathway as the most strongly associated (p = 0.0009) with
the differences between PN-GBM and PN-OA samples.
In addition, to investigate molecular mechanisms underlying
improved survival of the PN-Oligo subtype relative to other types of
oligodendrogliomas, we performed pathway analysis of the 129 genes
corresponding to the 190 probe sets differentially expressed between
PN-Oligo and the Classic and Neural Oligodendrogliomas. As
expected, the differential genes were indicative of the proneural
subtype, with some of the most significant annotations including
Differentiation of Astrocytes, Neurogenesis, Cell Death, Develop-
ment of Blood Vessels, and Benign Tumor (Table S10). A Network
analysis of this set of 129 genes revealed several hubs including GLI1,
RUNX2, MYC, BMP2, and NOTCH1 (Figure 3B). These data
suggest that differences in signaling in the Hedgehog and Notch
pathways likely play an important role in the improved outcomes of
PN-Oligo cases. Moreover, Literature Lab analysis of the gene sets
derived from the integrated expression and copy number analysis
comparing PN-Oligo to PN-GBM identified both the Notch
(p = 0.0098) and Hedgehog (p = 0.0434) pathways as significantly
associated with progression of the proneural subtype.
Discussion
Here we demonstrate that the Proneural gene expression signature
as defined by TCGA is enriched in gliomas with oligodendrogliomal
differentiation. This signature also predicts improved outcome for
oligodendrogliomas. Analysis of the copy number data from the
Rembrandt dataset demonstrated a high frequency of large losses of
chromosomes 1p and 19q in oligodendrogliomas, but not in
astrocytomas or GBMs. Twelve of the 42 oligodendrogliomas
showed co-deletion of at least 85% of chromosomes 1p and 19q,
and eleven of those twelve samples were PN-oligos. The other sample
with co-deletion of 1p/19q was a classic oligodendroglioma.
Nevertheless, our findings regarding the improved outcome of
proneural subtype remained significant in multivariate analyses
controlling for 1p/19q status and age at diagnosis. Multivariate
analysis also showed that the proneural subtype of astroctyoma has
significantly better outcome than the classic subtype of astrocytoma,
suggesting that the proneural gene expression signature carries
prognostic significance across histologic types in the diffuse gliomas.
Our integrated analysis of expression patterns and DNA copy
number of low-grade and high-grade proneural gliomas in the
Rembrandt dataset identified genes and pathways associated with
progression of this disease. Genes increased in high-grade
proneural GBMs (PN-GBM) at both the DNA and RNA level
included GLIS3, TGFB2, TNC, AURKA, and VEGFA. Network
analysis of these gene expression changes identified several
regulatory hubs, including GLI1, RUNX2, MYC, BMP2, and
NOTCH1. The GLI transcription factors are effectors of the
Hedgehog pathway and have been strongly implicated as key
regulators of glioblastoma behavior since their discovery. GLI1
regulates stem cell renewal and tumorigenicity of gliomas [23],
and targeted inhibition of the Hedgehog pathway results in partial
tumor regression in animal models [24]. Tenascin-C (TNC) is
Table 3. Cox proportional hazards (PH) models were used to examine the association between patient survival and four subtypes
after adjusting for patient age at diagnosis as well as 1p/19q status whenever applicable.
All Cases GBM Astrocytomas Oligodendrogliomas
Number of cases 152 101 32 36
Subtype
Classic 1.96 (CI: 1.23, 3.13);
p = 0.0045
1.17 (CI: 0.69, 1.96); p = 0.563 8.48 (CI: 1.85, 38.85); p = 0.0059 6.25 (CI: 1.55, 25.19); p = 0.0100
Mesenchymal 2.68 (CI: 1.51, 4.76);
p = 0.0008
1.56 (CI: 0.85, 2.85); p = 0.150 1.04 (CI: 0.29, 3.76); p = 0.9537 N/A
Neural 2.38 (CI: 1.38, 4.11);
p = 0.0018
1.57 (CI: 0.78, 3.17); p = 0.207 1.91 (CI: 0.72, 5.07); p = 0.1963 23.28 (CI: 4.63, 116.98); p = 0.0001
Age ($5vs,55) 3.14 (CI: 2.12, 4.64);
p
,
0.0001
2.90 (CI: 1.84, 4.55); p
,
0.0001 6.79 (CI: 2.77, 16.66); p
,
0.0001 4.97 (CI: 1.58, 15.70); p = 0.0062
1p/19q (deletion vs. no deletion) 0.42 (CI: 0.16, 1.05);
p = 0.0634
N/A N/A 2.23 (CI: 0.59, 8.46); p = 0.2380
Shown are Hazard Ratios (HRs) plus 95% confidence intervals (CI) and associated p-values. Significant p-values (,0.05) are shown in bold font. For the Cox PH models,
Proneural was used as the reference group for the subtype variable, patient age was dichotomized as ,55 or $55 with ,55 as the reference group, and no deletion in
1p/19q status was used as the reference group. Cox PH multivariate analysis was performed for all cases, cases with Astrocytoma, cases with GBM and cases with
Oligodendrogliomas, respectively. We note that 1p/19q deletion is present only in Oligodendrogliomas; hence, 1p/19q deletion status was not adjusted for in cases with
Astrocytoma or GBM.
doi:10.1371/journal.pone.0012548.t003
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induced by TGFb signaling [25,26], and promotes invasion of
glioma cells through matrix metalloproteinase 12 [27].
Both Ingenuity Pathway Analysis and Literature Lab Analysis
identified the Notch pathway as being differentially regulated in low
grade and high-grade proneural gliomas. Several component s of the
Notch pathway, including DLL3 and HEY2 are reduced in PN-
GBM, while NOV/CCN3, which is associated with Notch inhibition
[28,29] was increased. These data suggest that Notch pathway
activity decreases as proneural gliomas progress, which could account
for the abnormal angiogenesis observed in high-grade gliomas, since
Notch activity is essential for coordination with VEGF-stimulated
sprouting [30]. We also observed several genes associated with
angiogenesis and oxidative stress in the analysis of low and high-grade
proneural gliomas. These genes included thrombospondin-1
(THBS1), vascular endothelial growth factor A (VEGFA), angiopoie-
tin 2 (ANGPT2), and mitochondrial superoxide dismutase 2 (SOD2).
Finally, we observed reduced expression of the pro-apoptotic Bcl2
family member, BH3 interacting domain death agonist (BID) in high-
grade proneural gliomas, suggesting that loss of BID expression
promotes survival of these high-grade tumors.
As gliomas progress from lower grade (grades II and III) to
GBM, hypoxia and necrosis develop centrally, while angiogenesis
emerges peripherally. These processes are related, with hypoxia-
inducible factors (e.g. VEGFA) secreted by hypoxic, perinecrotic
tumor cells, resulting in the development of new vessels. It has
been suggested that vascular pathology, including vascular
endothelial apoptosis, vascular occlusion and thrombosis, initiates
the development of central hypoxia and necrosis. Angiopoetin 2
has been implicated in initiating endothelial apoptosis by Tie2
receptor in this setting [31,32,33]. THBS-1, a protein that inhibits
angiogenesis by inducing apoptosis via activation of CD36 in
microvascular endothelial cells, may be relevant to these
mechanisms as well, as we found this pathway to be markedly
upregulated in GBMs compared to lower grade gliomas. The non-
receptor tyrosine kinase FYN is activated by THBS-1 through
CD36, activating the apoptosis inducing proteases like caspase-3
Figure 2. RNA and DNA copy number analysis of low- and high-grade proneural gliomas. (A) Supervised hierarchical clustering of
Proneural GBM, Proneural Oligodendrogliomas, and Proneural Astrocytomas using 847 probes significantly different between these subtypes based
on differential marker analysis (FDR ,5%, 1.8 Fold change). (B) Amplification and Deletion GISTIC Analysis of Proneural GBM and Proneural
Oligodendrogliomas. Affymetrix SNP 6.0 data from the Rembrandt dataset was analyzed for copy number gain and loss using the GISTIC algorithm in
GenePattern 3.0. A total of 26 Proneural GBM and 32 Proneural Oligodendroglioma samples were included in the analysis. No significant copy
number changes were identified in the set of 14 Proneural Astrocytomas.
doi:10.1371/journal.pone.0012548.g002
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Table 4. Integrated analysis of differences in gene expression and copy number between Proneural GBM and Proneural
Oligodendroglioma/Astrocytoma samples.
Alteration Locus GISTIC q value Gene Symbols
GBM-Amp 12p13.32 1.21E-03 C1R, C1RL, C1S
GBM-Amp 9p23 1.34E-02 GLIS3, HAUS6
GBM-Amp 9p23 1.34E-02 PLIN2
GBM-Amp 6p21.31 4.30E-02 CLIC1, F13A1
GBM-Amp 6p21.31 4.30E-02 HISTH1C, HISTH2BK, HLA-DQA1, HLA-DQB1, HLA -DRA, HLA-DRB1
OA-Amp 22q11.1 4.69E-04 BCR
OA-Amp 22q11.1 4.69E-04 MN1, RASL10A, SEZ6L
OA-Amp 22q11.1 4.69E-04 SLC25A18
OA-Amp 8q24.22 8.23E-04 FAM84B
OA-Amp 19p13.3 3.10E-03 SHD, SLC1A6
OA-Amp 6p21.2 8.78E-03 DAAM2
OA-Del 19q13.31 1.19E-06 EMP3
OA-Del 4q21.23 8.58E-03 CCDC109B, CFI
OA-Del 4q21.23 8.58E-03 ENPEP, HERC5, IBSP, MLF1IP, SEC24D, SPP1, TDO2
Loci with amplification or deletion and corresponding changes in gene expression in Proneural GBM or Proneura l Oligodendrogliomas/Astrocytomas are shown.
doi:10.1371/journal.pone.0012548.t004
Table 5. Significant Gene Ontology and Pathway Annotations identified for 576 probes corresponding to 395 unique genes
differentially expressed between Proneural Oligodendrogliomas/Astrocytomas and Proneural GBM.
DAVID DAVID GSEA GSEA IPA IPA
GO Term Count FDR Count FDR Count p-value
Response To Wounding 47 1.89E-13 11 5.97E-05
Collagen Fibril Organization 14 1.02E-11 10 1.88E-10
Extracellular Matrix Organization 19 8.33E-09 23 0.16288991 3 1.86E-03
Skeletal System Development 29 2.58E-07 30 1.59E-09
Inflammatory Response 28 1.90E-06 120 1.37E-06
Cell Adhesion 40 3.90E-05 43 1.11E-05
Vasculature Development 21 5.88E-04 16 7.30E-09
Regulation Of Cell Proliferation 38 0.00586091 109 6.41E-11
ECM_Receptor_Interaction 79 0.0247256
Cell_Communication 107 0.0645318 5
Blood_Coagulation 38 0.20890287
Regulation_Of_MAPKKK_Cascade 18 0.14662187
Cancer 166 1.78E-27
Apoptosis 61 2.54E-10
Development 30 1.59E-09
Invasion 37 1.75E-09
Brain Cancer 25 4.04E-08
Cell Cycle Progression 51 5.69E-08
Mitosis 29 1.12E-06
Glioma 13 3.05E-05
Astrocytoma 8 4.23E-04
Proneural GBM samples have increased expression of genes associated with enhanced vascularization, proliferation, invasion, and inflammation. Count = number of
genes identified with each annotation by the three analyses. FDR = false discovery rate. P-value for IPA is based on the hyperge ometric distribution.
doi:10.1371/journal.pone.0012548.t005
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and p38 protein kinases. p38 is a mitogen-activated kinase that
also induces apoptosis in some conditions, perhaps through AP-1
activation and the activation of genes that lead to apoptosis.
In summary, our in silico analysis of available molecular datasets
for low and high-grade gliomas has provided new insights into the
survival and progression of the proneural subtype. These findings
demonstrate the power and importance of publicly available
molecular datasets and the potential for future discoveries from
The Cancer Genome Atlas project.
Supporting Information
Table S1 Complete clinical data downloaded from the Re-
mbrandt public data repository (https://caintegrator.nci.nih.gov/
rembrandt/) using data available on November 11, 2009. Clinical
data were derived from contributing center institutional diagnoses
at Henry Ford Hospital, UCSF, Lee Moffitt Cancer Center, Dana
Farber Cancer Center, University of Wisconsin, and NCI.
Found at: doi:10.1371/journal.pone.0012548.s001 (0.15 MB
XLS)
Table S2 Survival and PAM subtype classification data for
Rembrandt samples used in the survival analysis.
Found at: doi:10.1371/journal.pone.0012548.s002 (0.05 MB
PDF)
Table S3 Complete Comparative Marker Selection Results
generated from Rembrandt data comparing low- and high-grade
proneural samples. Data were generated using GenePattern 3.0.
Found at: doi:10.1371/journal.pone.0012548.s003 (7.50 MB
TXT)
Table S4 Complete Comparative Marker Selection Results
generated from Rembrandt data comparing proneural oligoden-
droglioma and proneural GBM samples. Data were generated
using GenePattern 3.0.
Found at: doi:10.1371/journal.pone.0012548.s004 (7.50 MB
TXT)
Table S5 Complete Comparative Marker Selection Results
generated from Rembrandt data comparing proneural oligoden-
droglioma and neural oligodendroglioma samples. Data were
generated using GenePattern 3.0.
Found at: doi:10.1371/journal.pone.0012548.s005 (7.50 MB
TXT)
Table S6 Complete Comparative Marker Selection Results
generated from Rembrandt data comparing proneural oligoden-
droglioma and classic oligodendroglioma samples. Data were
generated using GenePattern 3.0.
Found at: doi:10.1371/journal.pone.0012548.s006 (7.50 MB
TXT)
Table S7 Significant Comparative Marker Selection Results
generated from Rembrandt data comparing proneural oligoden-
droglioma and all other oligodendroglioma samples. Data were
generated using GenePattern 3.0.
Found at: doi:10.1371/journal.pone.0012548.s007 (0.15 MB
XLS)
Figure 3. Network analysis of genes differentially expressed between low- and high-grade proneural gliomas identifies regulatory
hubs. (A) Protein interaction network generated by Ingenuity Pathway Analysis (IPA) of the 576 probes differentially expressed between Proneural
GBM and Proneural Oligodendrogliomas/Astrocytomas. Proteins marked in red are increased in Proneural GBM samples, and those in green are
decreased, relative to Proneural low grade gliomas. Solid lines represent direct interactions, and dashed lines represent indirect interactions. All
interactions are derived on the Ingenuity Knowledgebase, which is extracted from the PubMed literature. (B ) Protein interaction network based on
IPA analysis of 129 genes differentially expressed between PN-Oligodendrogliomas and Classic and Neural Oligodendrogliomas. Network hubs are
readily discernible around the Notch receptor, Myc oncogene, and the GLI1 transcription factor.
doi:10.1371/journal.pone.0012548.g003
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Table S8 Integrated RNA and DNA Copy number analysis
comparison of low- and high-grade proneural gliomas.
Found at: doi:10.1371/journal.pone.0012548.s008 (0.19 MB
XLS)
Table S9 Integrated RNA and DNA Copy number analysis
comparison of proneural oligodendrogliomas and proneural GBM
samples.
Found at: doi:10.1371/journal.pone.0012548.s009 (0.15 MB
XLS)
Table S10 Gene Ontology analysis of the 129 genes differen-
tially expressed between proneural oligodendrogliomas and the all
other oligodendrogliomas.
Found at: doi:10.1371/journal.pone.0012548.s010 (0.02 MB
XLS)
Figure S1 Kaplan-Meier Analysis of Low-grade gliomas.
Found at: doi:10.1371/journal.pone.0012548.s011 (0.13 MB TIF)
Acknowledgments
The authors recognize the support of the Emory In Silico Center of
Research Excellence (ISCRE) for performance of these studies.
Author Contributions
Conceived and designed the experiments: TK JHS DB CSM. Performed
the experiments: LC. Analyzed the data: LC DAG QL BAJ SRC DB
CSM. Contributed reagents/materials/analysis tools: JHS. Wrote the
paper: LC DB CSM.
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