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DNA-methylome-assisted classification of patients with poor prognostic subventricular zone associated IDH-wildtype glioblastoma

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Glioblastoma (GBM) derived from the “stem cell” rich subventricular zone (SVZ) may constitute a therapy-refractory subgroup of tumors associated with poor prognosis. Risk stratification for these cases is necessary but is curtailed by error prone imaging-based evaluation. Therefore, we aimed to establish a robust DNA methylome-based classification of SVZ GBM and subsequently decipher underlying molecular characteristics. MRI assessment of SVZ association was performed in a retrospective training set of IDH-wildtype GBM patients ( n = 54) uniformly treated with postoperative chemoradiotherapy. DNA isolated from FFPE samples was subject to methylome and copy number variation (CNV) analysis using Illumina Platform and cnAnalysis450k package. Deep next-generation sequencing (NGS) of a panel of 130 GBM-related genes was conducted (Agilent SureSelect/Illumina). Methylome, transcriptome, CNV, MRI, and mutational profiles of SVZ GBM were further evaluated in a confirmatory cohort of 132 patients (TCGA/TCIA). A 15 CpG SVZ methylation signature (SVZM) was discovered based on clustering and random forest analysis. One third of CpG in the SVZM were associated with MAB21L2 / LRBA . There was a 14.8% ( n = 8) discordance between SVZM vs. MRI classification. Re-analysis of these patients favored SVZM classification with a hazard ratio (HR) for OS of 2.48 [95% CI 1.35–4.58], p = 0.004 vs. 1.83 [1.0–3.35], p = 0.049 for MRI classification. In the validation cohort, consensus MRI based assignment was achieved in 62% of patients with an intraclass correlation (ICC) of 0.51 and non-significant HR for OS (2.03 [0.81–5.09], p = 0.133). In contrast, SVZM identified two prognostically distinct subgroups (HR 3.08 [1.24–7.66], p = 0.016). CNV alterations revealed loss of chromosome 10 in SVZM– and gains on chromosome 19 in SVZM– tumors. SVZM– tumors were also enriched for differentially mutated genes ( p < 0.001). In summary, SVZM classification provides a novel means for stratifying GBM patients with poor prognosis and deciphering molecular mechanisms governing aggressive tumor phenotypes.
Molecular characterization of SVZ GBM via integrative omics. a A consensus set of 439 SVZM associated differentially methylated probes, intersect between training (HD) and validation (TCGA) cohort, was identified. A significant relative hypomethylation in SVZM+ compared to SVZM- tumors was found (right, mean methylation of all CpGs, test: linear model). b 3456 genes showed an inverse gene expression vs. CpG methylation pattern. c In line with CpG hypomethylation pattern, an enhanced mean gene-expression was found in SVZM+ tumors (selection: t-test, FDR < 0.05. test: linear model). d Rank ordered genes based on the number of differentially methylated CpG sites found in the 439-consensus signature. Of note, MAB21L2/LRBA from the SVZM random forest classifier (15 CpG RF set) are with 9 CpGs among the top ranked genes. e Intersection between different molecular layers showing an inverse relationship between methylation and expression for MAB21L2 (less expressed in SVZ+) and additional genes higher expressed. Expression and high-resolution CNV alterations show overlap for GRK5, NDST2, ZNF559-ZNF117, and ADGRE3. Methylation and CNV show hypomethylation and relative loss for ICAM5 and ONECUT3 (CNV: p < 0.05, Fisher test; methylation: FDR < 0.05, SAM; expression: FDR < 0.05, t test). f LRBA and MAB21L2 methylation (left, n = 132, linear model), expression (middle, n = 47, neg-binomial model, count data, log offset [total counts], one-sided p value, transformation for visualization) and correlation between methylation and expression (n = 47, Pearson). g PROGENy inferred enhanced pathway activity in SVZM+ tumors derived from RNAseq data, linear model analysis
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Acta Neuropathologica (2022) 144:129–142
https://doi.org/10.1007/s00401-022-02443-2
ORIGINAL PAPER
DNA‑methylome‑assisted classification ofpatients withpoor
prognostic subventricular zone associated IDH‑wildtype glioblastoma
SebastianAdeberg1,2,3,4,5 · MaximilianKnoll1,2,3,4,6· ChristianKoelsche7,8· DeniseBernhardt1,9·
DanielSchrimpf7,8· FelixSahm7,8· LailaKönig1,2,3,4,5· SemiBenHarrabi1,2,3,4,5· JulianeHörner‑Rieber1,2,3,4,5·
VivekVerma11· MelanieBewerunge‑Hudler12· AndreasUnterberg1,13,14· DominikSturm10,15· ChristineJungk1,13,14·
ChristelHerold‑Mende1,14· WolfgangWick1,16· AndreasvonDeimling1,7,8· JuergenDebus1,2,3,4,5·
StefanRieken1,2,3,4,5· AmirAbdollahi1,2,3,4,6
Received: 3 January 2022 / Revised: 4 May 2022 / Accepted: 21 May 2022 / Published online: 4 June 2022
© The Author(s) 2022
Abstract
Glioblastoma (GBM) derived from the “stem cell” rich subventricular zone (SVZ) may constitute a therapy-refractory sub-
group of tumors associated with poor prognosis. Risk stratification for these cases is necessary but is curtailed by error prone
imaging-based evaluation. Therefore, we aimed to establish a robust DNA methylome-based classification of SVZ GBM and
subsequently decipher underlying molecular characteristics. MRI assessment of SVZ association was performed in a retro-
spective training set of IDH-wildtype GBM patients (n = 54) uniformly treated with postoperative chemoradiotherapy. DNA
isolated from FFPE samples was subject to methylome and copy number variation (CNV) analysis using Illumina Platform
and cnAnalysis450k package. Deep next-generation sequencing (NGS) of a panel of 130 GBM-related genes was conducted
(Agilent SureSelect/Illumina). Methylome, transcriptome, CNV, MRI, and mutational profiles of SVZ GBM were further
evaluated in a confirmatory cohort of 132 patients (TCGA/TCIA). A 15 CpG SVZ methylation signature (SVZM) was discov-
ered based on clustering and random forest analysis. One third of CpG in the SVZM were associated with MAB21L2/LRBA.
There was a 14.8% (n = 8) discordance between SVZM vs. MRI classification. Re-analysis of these patients favored SVZM
classification with a hazard ratio (HR) for OS of 2.48 [95% CI 1.35–4.58], p = 0.004 vs. 1.83 [1.0–3.35], p = 0.049 for MRI
classification. In the validation cohort, consensus MRI based assignment was achieved in 62% of patients with an intraclass
correlation (ICC) of 0.51 and non-significant HR for OS (2.03 [0.81–5.09], p = 0.133). In contrast, SVZM identified two
prognostically distinct subgroups (HR 3.08 [1.24–7.66], p = 0.016). CNV alterations revealed loss of chromosome 10 in
SVZM– and gains on chromosome 19 in SVZM– tumors. SVZM– tumors were also enriched for differentially mutated genes
(p < 0.001). In summary, SVZM classification provides a novel means for stratifying GBM patients with poor prognosis and
deciphering molecular mechanisms governing aggressive tumor phenotypes.
Introduction
Glioblastoma (GBM), the most common primary brain tumor,
is characterized by an infiltrative growth pattern and inherent
refractoriness to therapy. This inevitably leads to local therapy
failure and dismal prognosis, with an overall survival (OS) of
approximately 15months after surgery and chemoradiotherapy
[16], and up to 20.5months with the addition of tumor-treat-
ing fields (TTFields) [46]. The resistance of GBM to therapy
has been attributed to a variety of factors, such as inter- as
well as intra-tumoral heterogeneity, tumor origin, stem cell-
like characteristics and tumor-stroma communication at the
tumor vessel/immune response [1, 16, 19]. Predictive biomark-
ers such as promoter methylation status of the DNA repair
enzyme O6-methylguanine DNA methyltransferase (MGMT)
was causally linked to enhanced vulnerability of GBM cells to
DNA damage inducing agents such as temozolomide chemo-
therapy [20, 53]. Hence, identification of molecular subtypes
has advanced patient selection for ongoing prospective trials,
such as the NCT Neuro Master Match (N2M2), a basket trial
wherein poor prognostic MGMT hypomethylated tumors are
Sebastian Adeberg, Maximilian Knoll, Stefan Rieken and Amir
Abdollahi are the first and senior authors.
* Sebastian Adeberg
Sebastian.adeberg@med.uni-heidelberg.de
Extended author information available on the last page of the article
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130 Acta Neuropathologica (2022) 144:129–142
1 3
molecularly stratified to different targeted therapies adminis-
tered concurrently with radiotherapy [52].
A growing body of data indicates a prognostic impact for
GBM localized to the subventricular zone (SVZ), a 3–5mm-
thick region located adjacent to the lateral ventricles that
harbors neural stem cells (NSCs) throughout adult life [4, 5,
24, 25, 36]. These data examined the hypothesis that tumors
originating from this stem cell-rich niche may conserve NSC
characteristics. Indeed, genetic models have impressively dem-
onstrated that the introduction of a few aberrations selectively
targeted to a few hundred NSCs present in the murine SVZ
region are sufficient to form tumors with human GBM char-
acteristics [28, 31]. Moreover, ablation of this low-cycling cell
population in established tumors was associated with improved
prognosis and outcomes [57]. Accordingly, the association of
GBM in SVZ areas was attributed to a stem cell-like pheno-
type with aggressive clinical behavior and poor survival [3, 4].
Classification of SVZ GBM in clinical studies largely relies
on non-invasive magnetic resonance imaging (MRI). High
interobserver variability and a lack of objective criteria for
defining “central” tumors originating from the SVZ region ver-
sus secondary infiltration of “peripheral” tumors to this region
limits implementation of a robust imaging-based clinical clas-
sifier. Accordingly, part of the poor prognosis in MRI-based
SVZ GBM may be related to selection for a more invasive
and migratory GBM subtype secondarily infiltrating this area
[5]. As such, GBM in contact with the SVZ defined by MRI
are more likely to recur as multifocal disease [5, 30]. There-
fore, reliable molecular classifiers of SVZ GBM are urgently
needed [44] to assist physicians in efforts to circumvent the
uncertainty of current MRI-based methods.
Pioneering studies have led to introduction of DNA
methylome analysis into the clinical realm, revolutionizing
tumor classification in neuropathology [9, 13, 14]. A par-
ticular strength of this method relates to the long-term pres-
ervation of the epigenetic fingerprint, as opposed to more
dynamic transcriptome signatures, thereby better allowing
for determination of the cell of origin. This study reports on
the utilization of DNA methylome analysis for discovery of
a novel molecular SVZ classification principle that could
potentially improve current imaging-based stratification.
The SVZ methylome (SVZM) classifier was subsequently
employed for molecular characterization of these tumors on
multiple chromosomal aberrations (CNV), mutational sig-
natures (exome), and transcriptome levels.
Materials andmethods
Patients andtissue sampling.
This study was approved by the Institutional Review Board
and Ethics Committee (No. S-056/2015) and retrospectively
evaluated 54 isocitrate dehydrogenase-1 (IDH1) wild-type
GBMs with available tissue samples that completed a course
of radiotherapy (RT) in the Department of Radiation Oncol-
ogy at University Hospital Heidelberg from 2005–2013. Of
these patients, radiographic assessment using magnetic
resonance imaging (MRI) was conducted for stratification
into two cohorts (SVZ– and SVZ+). The latter (also labeled
as “central” GBMs) was defined by the contrast-enhanced
lesion having infiltrated the borders of the lateral ventri-
cle and SVZ (5mm margin lateral to the lateral ventricles),
which could be accompanied by subependymal spreading
(tumor-related contrast enhancement spreading along the
ventricle walls). The remainders were categorized as SVZ–,
or “peripheral” GBMs. The SVZ– patients were matched
with the SVZ+ patients according to tumor localization, per-
formance status, age, availability of sufficient pre-radiother-
apeutic tumor tissue samples, and follow-up data.
Of note, because these patients were treated as early
as 2005, not all patients received concurrent and adjuvant
temozolomide (TMZ), and a variety of RT dose/fractiona-
tion regimens were utilized. RT in all patients was deliv-
ered as three-dimensional conformal RT. Target volume
delineation was based on T1-weighted MRI, and included
the primary tumor region, resection cavity (if applicable),
and T2-hyperintense areas. A clinical target volume (CTV)
margin of up to 2cm was added on the gross tumor vol-
ume (GTV) and resection cavity to account for microscopic
tumor spread, respecting anatomic borders. The planning
target volume (PTV) was made from the CTV+ a 5mm mar-
gin for setup inaccuracies.
Tumor samples, obtained before commencing RT, were
archived in the Department of Neuropathology at the Uni-
versity Hospital of Heidelberg’s Institute of Pathology. As
part of contemporary diagnostic assessment based on molec-
ular factors, all tissue samples were re-evaluated based on
histopathological criteria, immunohistochemical staining,
and molecular analyses according to the 2016 World Health
Organization classification of central nervous system tumors
[33]. O6-methylguanine DNA methyltransferase (MGMT)
promoter status was determined as previously described
[10].
DNA extraction andmolecular analysis
Tumor DNA was extracted from formalin-fixed, paraffin-
embedded (FFPE) material. Representative tumor areas with
high tumor content (> 80%) were utilized. DNA extraction
was performed using the automated Maxwell system (Pro-
mega, Madison, WI, USA).
DNA methylation analysis was carried out in the
Genomics and Proteomics Core Facility at the German
Cancer Research Center (DKFZ, Heidelberg) according
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131Acta Neuropathologica (2022) 144:129–142
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to the manufacturer’s instructions as previously described
[21]. Genomic positions refer to the hg19 assembly.
GBMs have previously been reported to cluster in
distinct subgroups: RTKI, RTKII, H3.3 K27, and H3.3
G34 mutated, IDH mutated, and mesenchymal (Suppl.
Figure2A and B, online resource) [13]. We additionally
selected representative reference cases of each subgroup
(n = 10 RTK1, n = 11 RTKII, n = 15 H3.3 G34 mut., n = 19
H3.3 K27 mut., n = 11 IDH mut., n = 16 mesenchymal) and
compared DNA methylation patterns between SVZ and
SVZ + GBMs via unsupervised hierarchical clustering
analyses of 30,000 probes showing the highest median
absolute deviation (MAD) in beta values (Suppl. Fig-
ure2A, online resource).
CNV data were extracted from methylation arrays uti-
lizing the minfi package. Downstream processing (identi-
fication of segments, and automatic threshold selection for
the identification of gains/losses) was performed with the
cnAnalysis450k package, using references for normal tissue
as described earlier [27].
For methylome analysis, idat files were processed with
the minfi R package [8, 18]. Data were filtered for probes
mapping to chromosome X and Y, as well as single nucleo-
tide polymorphisms (SNPs) and repetitive sequences. Data
were funnorm normalized, and M-values were used for
analysis. Identification of differentially methylated CpGs
between SVZ+ and SVZ– tumors is outlined in Suppl. Fig-
ure1, online resource.
Lastly, for DNA sequencing, the customized SureSelect
kit (Agilent), encompassing 130 brain tumor relevant genes,
was used for target-enrichment and sequenced on a NextSeq
500 machine (Illumina) as previously described [40]. Muta-
tional data were obtainable for 45 patients, but the DNA
quantity was insufficient for the remaining samples.
Validation dataset
To validate the findings herein using an independent dataset,
the GBM data collection (TCGA-GBM) from The Cancer
Genome Atlas (TCGA) was utilized. Retrieved data from
the TCGA data portal included level 2 Illumina 450k meth-
ylation data, RNASeq data, whole exome sequencing data
(level 3), and clinical data (follow_up_v1.0). RNASeq was
rlog transformed prior to analysis. Silent mutations were
filtered. Enrichment tests between groups were performed
using Barnard’s tests per gene and any type of mutation
vs no/silent mutation. IDH1 mutant samples (WXS data)
were excluded from analyses. Matching MRI data for the
TCGA-450k cohort were retrieved from The Cancer Imag-
ing Archive (TCIA) and classified as SVZ+ or SVZ– by two
independent raters. Patient characteristics of the respective
cohorts are shown in Suppl.-Tbl. 1, online resource.
Survival analysis
To examine the association of SVZ apposition (and correla-
tion with molecular/genetic factors) with clinical outcomes,
evaluation of survival was conducted. As a prerequisite to
this analysis, all patients were followed up clinically and
with MRI 4weeks after RT, followed by 3month intervals
until recurrence/progression or death. Tumor localization
and progression were assessed by an experienced radiolo-
gist based on the RANO criteria [51]. Overall survival (OS)
was calculated using the Kaplan–Meier method as the time
between the date of commencing RT until the date of death,
or censored at last contact.
Statistical analyses
Statistics were carried out in R v3.6.1 R Core Team, [47] and
utilized a two-sided α of 0.05 unless stated otherwise. For
categorical tests, the chi-squared, Fisher’s, or Barnard tests
were used. Survival was additionally assessed using Cox
proportional hazard models or parametric survival regres-
sions with the survival package [48]. Random forest analyses
were conducted with the randomForest package [29]. The
psych and irr packages were used to calculate intraclass cor-
relations. Pathway activity estimation from expression data
was performed with PROGENy [43].
Results
SVZ classification based onMRI
A retrospective cohort of 54 GBM patients classifiable by
MRI for SVZ association were denoted as the training set.
Among them, tumors of 30 (55.6%) patients were classified
as SVZ– and 24 (44.4%) were SVZ + GBMs as discerned by
MRI features. Table1 demonstrates clinical characteristics
of both cohorts.
The vast majority of patients (≥ 90%) underwent resection
prior to RT, the median dose of which was 60Gy (range:
40.1–60). The MRI classified SVZ– group was associated
with better OS than the SVZ + patients (p = 0.05, LRT,
Fig.1A). All 54 patients were IDH1/2 wt as determined by
panel sequencing.
Development ofaDNA‑methylation‑based SVZ
classifier
The MRI classification was utilized to identify the most
relevant differentially methylated probes (DMP, 225 CpGs,
defined with p < 0.001) using Illumina 450K microarrays.
Hierarchical clustering (HCL) for identification of the
least stable classification error between the two modalities
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132 Acta Neuropathologica (2022) 144:129–142
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and the most relevant DMP among these 188 CpGs were
selected by random forest analysis (details in Suppl. Fig-
ure1, online resource). This analysis resulted in a 15
CpG DNA methylation signature distinguishing between
SVZ+ and SVZ– tumors (SVZM, Fig.1B, Suppl, Fig.11,
online resource, upper row). This separation was also seen
on global epigenetic level (non-selected CpGs, Suppl. Fig-
ure11, online resource, bottom row). Among the selected
CpGs, 5 of the 15 DMP were associated with the LRBA
gene. Heatmap and HCL of the SVZM classified training
cohort combined with other relevant parameters (i.e., MRI
evaluation, IDH1, CIMP, and MGMT status) are provided
in Fig.1C. SVZM clearly separated the training cohort into
prognostic subgroups (p = 0.003, LRT). No enrichment
of SVZ assigned tumors to previously discovered glioma
methylation subtypes was found based on hierarchical clus-
ter analysis of most variant probes (Suppl. Figure2, online
resource), using the v11b4 neuropathology classifier [13]
revealed that all subtypes were present in SVZM+ and
negative tumors (Suppl. Figure2C, online resource). A
previously observed trend between MRI classification and
degree of resection (p = 0.08, chi-squared test) was weaker
for SVZM (p = 0.26, Suppl. Figure3A, online resource). In
concordance, Goodman Kruskal’s lambda showed smaller
values for SVZM, however the 95% CI included 0 (no asso-
ciation) for all performed comparisons (Suppl. Figure3A,
online resource). Finally, multivariable survival analysis of
the extent of resection and SVZ classification method did
not show a significant contribution of adding the extent of
resection in either case (SVZM: p = 0.47 and MRI: p = 0.43,
Suppl. Figure3B, online resource).
Reassessment ofmisclassified tumors
As compared to MRI-based classification, eight (14.8%)
tumors were reclassified following molecular SVZM
assessment. Interestingly, SVZM was able to separate these
patients into two distinct prognostic subgroups confirmed on
survival analysis (p = 0.02 by Weibull distribution, Fig.2A,
updated survival data after extended follow-up). Representa-
tive MRI images of tumors with mismatching radiographic
and molecular classifications illustrate current difficulties
associated with discrimination of SVZ-driven central GBM
from secondary infiltration of peripheral tumors into the
SVZ. Most MRI SVZ-/SVZM + tumors showed FLAIR
signal reaching the SVZ (Fig.2B, Suppl. Figure13, online
resource). FLAIR signal for classification, however, is only
useful if the SVZ region is not reached (Suppl. Figure14,
online resource).
Univariable analysis for OS identified SVZM (hazard
ratio HR 2.48, 95% CI [1.35–4.58] p = 0.004), MRI SVZ
(HR 1.83 [1.00–3.35] p = 0.049), age (HR 1.04 [1.01–1.07]
Table 1 Patient characteristics
Chi-squared test for categorical data, t test for continuous data [median, range]
SVZ subventricular zone, TMZ Temozolomide, MGMT O6-methylguanine-DNA methyltransferase,
MGMT-STP27 classifier; TMZ+ adjuvant + concomitant Temozolomide treatment
Patient characteristics [MRI classification] SVZ+, n = 24 (%) SVZ–, n = 30 (%) p value
Gender 0.43
Male 17 (70.8) 17 (56.7)
Female 7 (29.2) 13 (43.3)
Age at start RT, year 57.9 [39–81] 59.7 [39–81] 0.78
Karnofsky Performance Status 0.54
> = 80 14 21
< 80 10 9
RT dose [Gy] 60 [40.1–60] 60 [45–60] 0.27
Temozolomide 0.33
Yes 14 (58.3) 22 (73.3)
No 9 (37.5) 8 (26.7)
Unsure 1 (4.2) 0 (0)
Surgery 0.08
Subtotal resection 16 (66.7) 11 (36.7)
Gross total resection 6 (25.0) 16 (53.3)
Biopsy 2 (8.3) 3 (10.0)
MGMT promoter 0.53
Hypermethylated 11 (45.8) 10 (33.3)
Hypomethylated 12 (50.0) 17 (56.7)
Unsure 1 (4.2) 3 (10.0)
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133Acta Neuropathologica (2022) 144:129–142
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p = 0.013), performance status (HR 0.24 [0.12–0.48]
p < 0.001), use of TMZ (HR 0.25 [0.13–0.5] p < 0.001),
Radio-Chemotherapy (HR 0.19, [0.06–0.38], p < 0.001) and
MGMT promoter methylation status (HR 2.04 [1.03–4.04]
p = 0.041) as significant covariates (Fig.2C). Comparative
multivariable analysis for MRI-based versus SVZM with
clinical (age, performance status) and other molecular/
treatment associated covariates (MGMT promoter methyla-
tion, Radiochemotherapy) showed a significant independent
contribution only for the SVZM-based classification with
Cox-PH models (Suppl. Figure4, online resource). After
parametric models were applied to compensate for relatively
low n, MGMT and Radiochemotherapy also showed a sta-
tistically significant contribution (Suppl. Figure12, online
resource).
Validation oftheSVZM andmolecular
characterization
For validation of the SVZM by an independent cohort,
TCGA methylome data of 132 GBM patients were uti-
lized. An overview of the available levels of molecular-/
imaging data is shown in Fig.3A. Patients were assigned to
SVZM + vs. SVZM– groups via HCL of the 15 DMP of the
SVZM signature (Fig.3B). Two main clusters could be iden-
tified, which showed significant differences in OS (p = 0.04,
LRT). To further validate the proposed SVZM, available
matched TCIA MRI imaging data was assessed by a sin-
gle independent observer yielding an intraclass correlation
(ICC) of 0.51 (Fig.3C). Of note, for 15 out of 39 patients
(38%), no consensus classification could be determined by
MRI classification. Among the patients with a consensus
MRI-based classification, SVZM performed better (SVZM
HR 3.08 [CI 1.24–7.66], p = 0.016) compared to MRI (HR
2.03 [CI 0.81–5.09], p = 0.13) (Fig.3C).
Segmental CNV alterations revealed loss of chromo-
some 10 in SVZM– tumors and gains on chromosome 19
in SVZM– tumors (Fig.4A). Segmental and gene/tran-
script level CNVs are shown in Suppl. Figure6A and B,
online resource. On whole exome sequencing data (WES,
TCGA cohort), evaluation of variant calls from four pipe-
lines revealed differentially enriched mutations mostly
confined to SVZM– tumors (Fig.4B), whereas the total
number of non-silent mutations did not differ between
the two classes (Suppl. Figure7A, online resource). More
specifically, frame shift deletions/insertions, in-frame
deletions, mutations in splice regions, and splice sites
were enriched in SVZM + tumors (Fig.4D). EPHA1,
DECAF12L2 and ADCY5 mutations were detected exclu-
sively in SVZM + tumors, whereas CNTNAP2, AHNAK2
and ITIH6 mutations were among the most significantly
enriched in SVZ– tumors (p < 0.01) (Fig.4B). Deep panel
sequencing of the Heidelberg cohort revealed no mutational
enrichments as a function of SVZM classification; at 10%
FDR for the exclusive presence of ARID1B1 and BRCA2
in SVZ+, and VHL in SVZM– tumors. Cross-comparison
of mutational readouts between the Heidelberg and TCGA
cohorts was limited by differences in methodology (e.g.,
sequencing depth and consequently VAF cut-off criteria
applied to define mutations). Therefore, it was not surpris-
ing that repetitive elements difficult to detect with WES such
as TERTp mutations were found in 46 (85%) samples of
the Heidelberg cohort but not reported in the TCGA study
(Fig.4C). Accordingly, we failed to confirm an exclusive
enrichment of mutations in SVZ+ tumors identified in the
Heidelberg cohort with TCGA WES data (Suppl. Figure7C,
online resource).
A consensus set of 439 CpGs was identified as being
differentially methylated in both cohorts as a function of
SVZM status (FDR < 0.05 by SAM with 500 permutations,
Fig.5A and Suppl. Figure5A, online resource). DMPs were
significantly hypomethylated in SVZM + vs. SVZM– tumors
in both studied cohorts (p < 0.001). Global alterations on
methylome and gene expression levels revealed an inverse
relationship between DMPs and differentially expressed
genes (DEG). The mean expression of DEGs was signifi-
cantly increased in SVZ + tumors (Fig.5B), showing a hypo-
methylation of 430 CpGs and higher expression of 3456
genes (total regulated genes: 3456 + 55, n = 55 being in aver-
age less expressed in SVZM + tumors, t test, FDR < 0.05 for
selection of differential genes, Fig.5C).
With 9 CpGs, MAB21L2, LRBA, and ZNF177 were the
most frequently abundant in the consensus 439 SVZ associ-
ated DMPs (Fig.5D, Suppl. Figure5C, online resource).
The LRBA and MAB21L2 CpGs are located on chromo-
some 4 on overlapping positions, and ZNF177 is located on
chromosome 19 (Suppl. Figure5C+D, online resource). A
paralog of ZNF177 on chromosome 19 (hypomethylated in
SVZ +), ZNF559-ZNF177, showed a CN loss in SVZ+ and
was less expressed, whereas ZNF177 did not show a sig-
nificant difference in expression (Suppl. Figure8, online
resource). Five of the 9 LRBA/MAB21L2 annotated and dif-
ferentially methylated CpGs were part of the SVZM signa-
ture (Suppl. Figure5B, online resource). The next highest
ranked (≥ 6CpGs) were ENPP4, SLC32A1 and FAM43A.
NETO1, KCNH1 and BMP3 were present with > 3 CpGs.
Additional overlaps between all DMPs and 15CpG RF sig-
nature were detected for CPSF1 and EHHADH (Suppl. Fig-
ure5B, online resource).
Pair-wise co-alterations of SVZM differential genes
(methylation [M], expression [E], copy number [CN]) are
shown in Fig.5E. In SVZ+ , co-regulation analysis between
expression and methylation identified MAB21L2 (high E/
low M), and for low E/high M MSC, RNASE11, RNASE12,
PTGER2 and WNK4. Expression vs copy number analy-
sis identified GRK5 and NDST2 with high E/CN gain,
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134 Acta Neuropathologica (2022) 144:129–142
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135Acta Neuropathologica (2022) 144:129–142
1 3
ZNF599-ZNF177 low E/CN loss, and ADGRE3 high E/CN
loss. Methylation and copy number co-alteration showed low
M/CN loss for ONECUT3 and ICAM5.
SVZ± population-based testing for differences in LRBA
and MAB21L2 expression revealed decreased expression
in SVZ+ for both genes. Moreover, a negative correlation
between methylation pattern and gene-expression was found
(Fig. 5F, – 0.76 for MAB21L2, -0.42 for LRBA, Pearson
p < 0.01). Detailed methylation analysis of MAB21L2 and
LRBA showed significant hypermethylation in 14 out of 16
MAB21L2 annotated CpGs (88%), and 19 CpGs located in
the body region of LRBA (Fig.5F, Suppl. Figure5C, online
resource). Potential sources of LRBA might be T-cells and/
or microglia (Suppl. Figure10 [50]), a direct link to tumor
cells seems less likely (Suppl. Figure9 [38, 56]).
Finally, we performed more global pathway activity esti-
mation from expression data which revealed higher inferred
activity for TNFα, NFκB, TGFβ, estrogen, p53, and hypoxia
in SVZM + tumors (p < 0.05, Fig.5G).
Discussion
This study reports on the discovery of a novel molecular
classifier of SVZ-driven GBM based on DNA methylome
analysis – the SVZM. Based on the growing implementa-
tion of DNA methylome analysis in neuropathology [13, 14,
24, 55], the existence of such classifiers could be of utmost
relevance for designing prospective studies where SVZM
complement current MRI-based classification for a more
accurate and robust stratification of patients. In the training
cohort, where patients with a clear consensus MRI-based
SVZ association were selected to guide methylome clas-
sifier development, clustering based on the SVZM signa-
ture showed superior prognostic performance. Moreover,
detailed re-analysis of MRI data based on SVZM assign-
ment in SVZM vs. MRI discordant cases provided a plausi-
ble explanation for a possible erroneous MRI classification
of peripheral tumors with secondary infiltration to the SVZ
region or contact to the SVZ on T2 sequence data. This is
also consistent with the improved performance of SVZM on
multivariable analysis and its ability to discriminate prog-
nostic subgroups among the discordantly classified patients.
In addition to secondary infiltration as a source for the MRI
classification error, heterogeneity in treatment and clinical
variables as well as known prognostic subgroups such as
enrichment for mutant IDH/G-CIMP in peripheral tumors
might contribute to differential outcomes attributed to SVZ
status [25]. Therefore, IDH-mut/G-CIMP positive patients
were excluded in our study and clinically relevant param-
eters were well balanced in our training cohort. Moreover,
for detection of the tumor cell of origin, preservation of the
epigenetic fingerprint by DNA-methylome analysis may
pose advantages over more dynamic molecular readouts
such as transcriptome analysis. Together, MRI classifica-
tion bias with potential enrichment for invasive tumors in
the SVZ group (secondary infiltration of peripheral tumors),
heterogeneity and unintended enrichment for prognostic sub-
groups as well as contamination of stroma cell signatures by
analysis of tumor bulk might provide plausible explanations
for previous failure to molecularly characterize SVZ GBM
as a distinct biological subgroup [37]. Consequently, recent
attempts to reduce the influence of the aforementioned varia-
bles (e.g., by excluding IDHmut tumors) reported successful
characterization of SVZ GBM as a distinct gene-expression
subtype with enrichment of cancer stem cell-like markers
(e.g., CD133) and increased expression of genes associated
with Notch and DNA-repair pathways [24, 45].
Male abnormal 21 (MAB21) homolog protein MAB21L2
(Mab-21 Like 2) and LRBA (lipopolysaccharide-
LPS–responsive vesicle trafficking, beach- and anchor-con-
taining) build a nested gene pair (embedding of one gene in
another), which is a unique evolutionarily conserved feature
reaching back to C. elegans [49]. MAB21 like protein fam-
ily members are linked to cell fate determination, neuronal
development, and increasingly functionally connected to the
immune response. In addition to involvement in key immune
pathways such as TGF signaling described for MAB21L2,
their nucleotidyltransferase activity has been recently stud-
ied and compared with another prominent MAB21 family
member, cGAS (cyclic GMP-AMP synthase, also known as
MAB21 domain-containing protein 1–MB21D1), which is
a pivotal cytosolic DNA sensor and activator of the innate
immune system [15]. Intriguingly, 14/16 SVZM+ DMPs
and 1/3 of the classifier was dominated by probes related
to MAB21L1/LRBA, all demonstrating a hypermethyla-
tion pattern further correlated with decreased expression
in SVZM+ tumors. Mab21L2 expression is considered for
classification of medulloblastoma subtypes (is among the
Nanostring signature genes [39]), differentially expressed
in brain vs. bone metastases of breast cancer [26], and low
expression of lnc-MAB21L2-1 correlated with resistant
to neoadjuvant chemoradiotherapy in rectal cancer [17].
Fig. 1 Subventricular zone positive “central” (SVZ+) and subven-
tricular zone negative “peripheral” (SVZ–) glioblastoma differ in
their epigenomic signatures. a Imaging-based (MRI) classification
of GBM patients with poor prognosis SVZ+ tumors (Kaplan–Meier,
Cox model likelihood ratio test, LRT). b Identification of SVZ spe-
cific DNA methylome signature (SVZM) consisting of 15 CpGs.
Left: random forest derived rank order of single CpGs according to
their relevance to differentiate SVZ state are shown (left red dots,
right CpG annotations and importance score). c SVZM Classifica-
tion separates the training cohort into two main clusters (heatmap, hcl
with Euclidean distance and complete linkage). Molecular (MGMT,
G-CIMP) and MRI classifications are also provided. An inferior prog-
nosis of GBM patients with SVZM+ tumors was found by Kaplan–
Meier analysis of patient survival
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136 Acta Neuropathologica (2022) 144:129–142
1 3
Silencing of MAB21L2, as a TGFβ transcriptional repres-
sor, was shown to induce an immune-suppressive micro-
environment in myelodysplastic syndrome (MDS) [41].
Accordingly, we found increased TGFβ pathway activity in
SVZM+ tumors. MAB21L2 hypermethylation was associ-
ated with chemotherapy resistance in gastric cancer [34],
could discriminate between different thyroid tumors [35],
was among the top hypermethylated DMP in pathology-free
regions of multiple sclerosis-affected brains [22], associated
with neurogenesis [55], as well as linked to neural differen-
tiation and human hippocampal neurogenesis in Alzheimer’s
disease [6]. Additionally, LRBA was shown to be involved in
trafficking key immune checkpoints (e.g., CTLA-4), is known
to contribute to immune dysregulation [32], and is correlated
with both disease mortality and recurrence in breast can-
cer [7]. The source cell of LRBA, however, remains elusive
[50, 56]. Together with the epigenetic and transcriptional
silencing of MAB21L2/LRBA in SVZM+ tumors found in
our study, these data provide a plausible explanation for
a prognostic and potentially functional relevance of these
genes at the tumor immune microenvironment interface
contributing to the observed inferior clinical outcome in
SVZM+ GBM. This hypothesis is further supported by the
presence of probes associated with IL-6 immune signaling
and collagen-18, a precursor to the endogenous angiogenesis
inhibitor endostatin [2], in the SVZM classifier and warrants
further investigation.
Multiscale molecular characterization of SVZM+ tumors
further revealed a global hypomethylation (98% of SVZ asso-
ciated DMP) and increased gene expression in SVZM+ vs.
SVZM– tumors as well as segmental CNV alterations and
a significant enrichment for differentially mutated genes in
SVZ– tumors. Differential regulation patterns as a function
of SVZM status was found on two or more levels, such as in
Fig. 2 Discordance between
SVZM vs. MRI-based clas-
sifications. a Overall survival of
all differently assigned patients
indicating an inferior outcome
in patients with SVZM+ but
according to MRI SVZ negative
tumors. Kaplan–Meier curves
and parametric survival model
(Weibull distribution, dashed
line, LRT). b Tumor localiza-
tion of representative patients
with discordant classification
highlights the difficulty to
distinguish between second-
ary invasion to the SVZ
region and tumors originat-
ing from this region solely
by the imaging method. c A
significantly increased hazard
ratio (HR: 2.48, p < 0.004) for
SVZM by univariable survival
analysis (Cox model) versus
other parameter including
classifications based on MRI
SVZ+ vs SVZ–; female vs male;
performance status (KPS ≥ 80
vs < 80); multi- vs unifocal
presentation; chemotherapy
(TMZ: adjuvant/concurrent vs.
incomplete treatment); radiation
dose (≥ 60Gy vs < 60Gy),
surgery: subtotal vs total and
MGMT status
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137Acta Neuropathologica (2022) 144:129–142
1 3
the ZNF599-ZNF177 locus (CNV, methylation and expres-
sion). Interestingly, ARID1B1 and BRCA2 mutations were
found to be SVZM+ tumor exclusive by deep NGS of the
Heidelberg cohort as well as EPHA1, DECAF12L2, and
ADCY5 by WES in the TCGA cohort. Neither ARID1B1
nor BRCA2 were detected in the unselected original Heidel-
berg GBM cohort evaluated for this panel [40], suggesting
that MR-based enrichment of the cohort for SVZ GBM was
relevant for this discovery. Moreover, our gene panel consti-
tuted relevant mutations in adult as well as pediatric neuro-
oncology. Therefore, the presence of mutations such as
ARID1B1 in 3/21 (~ 14%) of SVZM+ tumors was a relatively
surprising finding. ARID1B mutations are known to appear
in ~ 10% of neuroblastoma patients and are correlated with
poor clinical outcome. ARID1B mutations and alterations are
also believed to serve as a driver of tumorigenesis in a small
fraction of medulloblastoma and other solid tumors such as
breast and ovarian cancers [42]. Exclusive association of
BRCA2 mutations in ~ 14% of SVZM+ provide another inter-
esting target for therapeutic targeting such as synthetic lethal
interactions with DNA-damage repair inhibitors and radio-
therapy as the cornerstone of postoperative therapy. Among
the SVZM+ exclusive mutations identified in the TCGA
cohort, the ephrin family member EPHA1 mutation may
Fig. 3 Performance of SVZM
vs. MRI in the validation
cohort. a Overview of multiple
layer of data that were cor-
related with the SVZM state
in training/validation cohorts.
Methylome and copy number
variation (CNV) analysis by
450K microarrays, T1 contrast
enhanced (CE) MRI, muta-
tional profile by whole exome
sequencing (WXS), deep
“panel” NGS and RNAseq. b
SVZ± assignment of the valida-
tion cohort by cluster analy-
sis of the 15 CpG signature
(maximum distance, ward.D)
and prognostic evaluation
(Kaplan–Meier, Cox model,
and LRT). c Heterogeneity of
SVZ classification by MRI in
the validation cohort. Manual
rating of patients to SVZ
classes based on MRI shows
discordance between the three
observations for a fraction of
patients (intraclass correlation,
ICC). Comparative univariable
survival analyses (bottom) for
the 24 most consistently rated
tumors by MRI vs. SVZM (Cox
model)
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138 Acta Neuropathologica (2022) 144:129–142
1 3
provide an interesting therapeutic target as it was recently
attributed to an improved response to anti-PD-L1 immune
checkpoint inhibition in lung cancer [11]. Furthermore,
DECAF12L2 could be an attractive biomarker for further
investigation as it is among the top 15 driver mutations of
GBM (Suppl. Figure7C, online resource).
Unfortunately, we were not able to validate our deep
panel NGS finding with TCGA WES data. This might be
due to the limited depth of WES and prefiltering criteria
leading to selection of genes with VAF > 50% and other
technical issues. For example, TERT promoter (pTERT)
mutations were rarely found in the TCGA analysis; from
291 GBM patients with WES data 42 had whole genome
NGS but only 25 samples had adequate coverage (read
count > 10) of the TERT promoter for mutational analy-
sis [12]. In contrast, by inclusion of intronic/non-coding
regions to cover the TERT promoter with an average cov-
erage of 550-fold, we found pTERT mutations in 85%
(n = 46) of the Heidelberg GBM cohort. The depth of
reads might also have been advantageous for detection
of genes with large exons like BRCA1 and its association
with SVZM+ in our training cohort. These limitations not-
withstanding, these findings warrant further validation in
well-powered prospective cohorts and may have ramifica-
tions for improved diagnostic and therapeutic tailoring of
SVZ+ GBM.
Fig. 4 Differential CNV and mutational profile of SVZ GBM. a Seg-
mental CNV alterations indicate a relative loss of chromosome 10
in SVZM– and gains on chromosome 19 in SVZM– tumors in both
training and validation cohorts. b Among differentially mutated genes
identified by WXS in the validation cohort, a significantly lower
number of mutations in SVZ+ compared to SVZ– tumors was found
(left, p < 0.001 by Wilcoxon test). Most significantly enriched muta-
tions as a function of SVZ state are shown as heatmap (right, Bar-
nard’s test). Scale bar of the heatmap correspond to non-silent vari-
ants, identified as differential between SVZ± in 3 out of 4 mutation
calling pipeline datasets. #calls indicate the number of pipelines iden-
tifying a mutation in the respective sample. c Differentially enriched
mutations as a function of SVZ state identified by ultra-deep panel
NGS of the training HD-Cohort. p value: Barnard’s test for associa-
tions between mutational enrichment in SVZM or SVZ-MRI classi-
fied groups, respectively. d Interactions between the type of muta-
tion and SVZM status using a linear mixed model (random factor
variant calling method) indicate significant association between
SVZM+ frameshift (insertion/deletion), in frame deletions and splice
region/sites mutations (black bars)
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139Acta Neuropathologica (2022) 144:129–142
1 3
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00401- 022- 02443-2.
Funding Open Access funding enabled and organized by Projekt
DEAL. National Center for Tumor diseases, NCT-PRO-2015-21,
Amir Abdollahi, Deutsche Forschungsgemeinschaft, UNITE SFB13-
89, Deutsches Krebsforschungszentrum.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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142 Acta Neuropathologica (2022) 144:129–142
1 3
Authors and Aliations
SebastianAdeberg1,2,3,4,5 · MaximilianKnoll1,2,3,4,6· ChristianKoelsche7,8· DeniseBernhardt1,9·
DanielSchrimpf7,8· FelixSahm7,8· LailaKönig1,2,3,4,5· SemiBenHarrabi1,2,3,4,5· JulianeHörner‑Rieber1,2,3,4,5·
VivekVerma11· MelanieBewerunge‑Hudler12· AndreasUnterberg1,13,14· DominikSturm10,15· ChristineJungk1,13,14·
ChristelHerold‑Mende1,14· WolfgangWick1,16· AndreasvonDeimling1,7,8· JuergenDebus1,2,3,4,5·
StefanRieken1,2,3,4,5· AmirAbdollahi1,2,3,4,6
1 German Cancer Consortium (DKTK), Core Center
Heidelberg, Heidelberg, Germany
2 National Center forTumor Diseases (NCT), Heidelberg
University Hospital (UKHD) andGerman Cancer Research
Center (DKFZ), Heidelberg, Germany
3 Department ofRadiation Oncology, Heidelberg
University Hospital (UKHD), Im Neuenheimer Feld 400,
69120Heidelberg, Germany
4 Heidelberg Institute forRadiation Oncology (HIRO),
National Center forRadiation Research inOncology
(NCRO), UKHD andDKFZ, Im Neuenheimer Feld 400,
69120Heidelberg, Germany
5 Clinical Cooperation Unit Radiation Oncology, German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280,
69120Heidelberg, Germany
6 Clinical Cooperation Unit Translational Radiation Oncology,
German Cancer Research Center (DKFZ), Im Neuenheimer
Feld 460, 69120Heidelberg, Germany
7 Department ofNeuropathology, University Hospital
ofHeidelberg, Im Neuenheimer Feld 224, 69120Heidelberg,
Germany
8 Clinical Cooperation Unit Neuropathology, German Cancer
Consortium (DKTK), German Cancer Research Center
(DKFZ), Im Neuenheimer Feld 280, 69120Heidelberg,
Germany
9 Department ofRadiation Oncology, TUM, Ismaninger
Str. 22, 81675Munich, Germany
10 Division ofPediatric Glioma Research, German Cancer
Research Center (DKFZ), Im Neuenheimer Feld 280,
69120Heidelberg, Germany
11 Department ofRadiation Oncology, University ofTexas
M.D. Anderson Cancer Center Houston, Houston, TX, USA
12 Genomics andProteomics Core Facility, German Cancer
Research Center (DKFZ), Im Neuenheimer Feld 580,
69120Heidelberg, Germany
13 Department ofNeurosurgery, University Hospital
ofHeidelberg, Im Neuenheimer Feld 400, 69120Heidelberg,
Germany
14 Division ofExperimental Neurosurgery, Department
ofNeurosurgery, University Hospital ofHeidelberg, Im
Neuenheimer Feld 400, 69120Heidelberg, Germany
15 Department ofPediatric Oncology, Hematology,
Immunology andPulmonology, Angelika Lautenschläger
Children’s Hospital, University Medical Center
forChildren andAdolescents, Im Neuenheimer Feld 430,
69120Heidelberg, Germany
16 Department ofNeurooncology, University Hospital
Heidelberg, Im Neuenheimer Feld 400, 69120Heidelberg,
Germany
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