Medulloblastomas, the most frequent malignant brain tumours affecting
children, comprise at least 4 distinct clinicogenetic subgroups. Aberrant
sonic hedgehog (SHH) signalling is observed in approximately 25% of tumours
and defines one subgroup. Although alterations in SHH pathway genes (e.g.
PTCH1, SUFU) are observed in many of these tumours,
high throughput genomic analyses have identified few other recurring
mutations. Here, we have mutagenised the Ptch+/- murine tumour model using the Sleeping Beauty transposon
system to identify additional genes and pathways involved in SHH subgroup
medulloblastoma development.
Results
Mutagenesis significantly increased medulloblastoma frequency and identified
17 candidate cancer genes, including orthologs of genes somatically mutated
(PTEN, CREBBP) or associated with poor outcome (PTEN,
MYT1L) in the human disease. Strikingly, these candidate genes were
enriched for transcription factors (p= 2x10-5), the
majority of which (6/7; Crebbp, Myt1L, Nfia, Nfib,
Tead1 and Tgif2) were linked within a single regulatory
network enriched for genes associated with a differentiated neuronal
phenotype. Furthermore, activity of this network varied significantly
between the human subgroups, was associated with metastatic disease, and
predicted poor survival specifically within the SHH subgroup of tumours.
Igf2, previously implicated in medulloblastoma, was the most
differentially expressed gene in murine tumours with network perturbation,
and network activity in both mouse and human tumours was characterised by
enrichment for multiple gene-sets indicating increased cell proliferation,
IGF signalling, MYC target upregulation, and decreased neuronal
differentiation.
Conclusions
Collectively, our data support a model of medulloblastoma development in
SB-mutagenised Ptch+/- mice which involves disruption of a novel transcription
factor network leading to Igf2 upregulation, proliferation of GNPs,
and tumour formation. Moreover, our results identify rational therapeutic
targets for SHH subgroup tumours, alongside prognostic biomarkers for the
identification of poor-risk SHH patients.
Medulloblastoma (MB) is the most common malignant brain tumour of childhood,
accounting for around 10% of paediatric cancer deaths. Despite recent therapeutic
advances, up to 40% of patients still die from their disease and cure is often
associated with disabling therapy-related effects in later life. New therapeutic
approaches based on improved biological understanding of the disease will be
essential to improve outcomes, through strategies such as the delivery of
risk-adapted therapies guided by molecular prognostic biomarkers, and the stratified
use of molecularly-targeted agents (reviewed in [1]).
Four major molecular subtypes of medulloblastoma with distinct biological, clinical
and pathological features are now recognised, and this subgrouping is beginning to
have clinical impact [1–3]. WNT subgroup tumours (~15% of total) are characterised by activation of
the wnt/wingless pathway through CTNNB1 mutations, and appear to originate
from progenitor cells derived from the dorsal brain stem [4]. Patients with WNT-associated tumours have a favourable prognosis and
will receive reduced therapies in forthcoming international clinical trials [1, 5, 6]. The SHH subgroup (~25% of total) is defined by activation of the sonic
hedgehog signalling pathway, and mutations in SHH pathway genes (e.g. PTCH1,
SUFU) arise in a significant subset [2, 3]. All evidence suggests that these tumours originate from cerebellar
granule neuron precursors (cGNPs) within the external granular layer of the
developing cerebellum (reviewed in [7]) or cochlear nuclei of the brainstem [8]. SHH tumours are associated with an intermediate prognosis. Early
clinical trials of SHH pathway inhibitors are underway, although acquired resistance
has been reported [9] and tumours with downstream pathway mutations (e.g. GLI2, SUFU)
are predicted to be insensitive to their action [10]. Group 3 and 4 tumours are more heterogeneous and show overlapping
molecular features such as frequent chromosome 17 defects. However, Group 3 tumours
(~25% of total) have been associated with high-risk features, such as MYC
gene amplification and large-cell/anaplastic (LCA) pathology, and a poor prognosis [2].
Recent genome-wide studies have further underlined the complexity of MB;
heterogeneous mutations targeting processes including histone methylation and
chromatin remodelling have been discovered, but these typically describe limited
subsets of tumours and few additional recurrent mutations targeting specific genes
and/or pathways have been identified [11–15]. Critical genes therefore remain to be uncovered in all MB subgroups and
there is a growing need to identify low-frequency alterations which drive disease
progression, distinguish these from passenger mutations, and determine their
mechanisms of action and clinical significance. Primary tumour data alone may not be
sufficient to achieve these goals.
A number of murine MB models have been created allowing comparative analyses
(reviewed in [16]), including models which recapitulate WNT [4] and Group 3 [17, 18] tumours. However, the most widely studied mouse model is a knockout of
the Shh transmembrane receptor, Ptch
[19], which mimics SHH subgroup tumours. Ptch+/- heterozygotes develop MBs at a frequency which is significantly
influenced by genetic background [20]. Sleeping Beauty ( SB) murine mutagenesis [21, 22], coupled to statistical analysis of insertion site distribution [23, 24], has emerged as a powerful method to identify genes involved in a wide
variety of human cancers [24–29]. Recently the SB11 transposase [22] driven by the Math1 promoter has been used to mutagenise
developing neuronal tissues in both the Ptch+/- and p53 loss-of-function models of MB [30]. This identified a large number of candidate genes potentially involved
in MB progression, and demonstrated that the genetic events observed in metastases
show limited overlap with those in matched primary tumours, supporting a
bicompartmental genetic model of metastatic disease [30, 31].
Here, we report the application of whole-body SB mutagenesis [26] to the Ptch+/- tumour model, and the identification of 17 genes associated with enhanced
medulloblastoma formation. We show that these genes are enriched for neuronal
transcription factors defining a novel gene network which, when mutagenised by SB,
is associated with increased cell proliferation and reduced neuronal
differentiation. Significantly, increased expression of Igf2, a gene known
to be essential for tumour formation in the Ptch+/- model, is associated with insertional mutations within this network.
Moreover, we show that in human disease, network activity predicts poor survival
specifically within the SHH tumour subgroup. Together, these findings provide
important novel insights into the molecular mechanisms of medulloblastoma
pathogenesis, and identify exploitable therapeutic targets and prognostic biomarkers
for development towards improved therapy.
Results
The incidence of MB but not RMS is enhanced in mutagenised Ptch+/- mice
A total of 243 mutagenised Ptch+/- animals (Ptch+/-;
SB11+/-;T2Onc+/-) and 195 control littermates were aged for up to 15 months and
monitored for tumour development. Mortality in mutagenised Ptch+/-
animals was approximately 90% after 1 year, significantly higher than in the
predisposition only (Ptch+/-;T2Onc+/-) or transposition only
(T2Onc+/-;SB11+/-) control genotypes (Figure 1a and
Additional file 1: Table S1). Approximately ~28% of
mutagenised Ptch+/- animals aged for more than 6 months succumbed to
haematological neoplasms, which usually presented as large thoracic tumours
and/or hepatosplenomegaly. A low frequency of parenchymal brain lesions
consistent with glial tumours (2%) was also observed. These malignancies
occurred at comparable frequencies within the transposition controls, have
previously been reported using the same transposase/transposon combination [26, 29], and are not analysed further here.
Figure 1
Mortality in experimental/control cohorts and tumour pathology.
a-c. Kaplan Meier survival curves. All p-values are from
log-rank tests. a. Mortality due to all causes in experimental
cohort and controls. b. Medulloblastoma (MB) arising from
cerebellum (C). Scale bar = 500 μm. c. High resolution image
showing typical cytomorphology of small, polygonal or slightly elongated
cells. Perivascular pseudorosetting and occasional necrotic foci were
also seen in some tumours. Scale bar = 50 μm.
d. Mortality due to medulloblastoma alone. The difference
between predisposition and transposition controls was not significant
(p=0.118). e. Typical appearance of RMS consisting of
variable numbers of small, round mitotically active cells together with
large more well-differentiated rhabdomyoblasts arranged in interlacing
fascicles. Scale bar = 200 μm. f. High resolution
image showing large rhabdomyoblasts with characteristic
cross-striations, accompanied by smaller less well differentiated cells.
Scale bar = 50 μm. g. Mortality due to RMS alone.
h. Hepatocellular adenoma containing mixed pattern
(micro- and macrovesicular) steatosis and leukaemic infiltration. Scale
bar = 50 μm. i. Low power view of a well demarcated
hepatocellular carcinoma with leukaemic infiltration (T=tumour, L=liver
parenchyma). Scale bar = 500 μm. j. High resolution image of
hepatocellular carcinoma showing tumour cells (right-hand side of image)
with eosinophilic cytoplasmic inclusions. Scale bar = 50 μm. All
sections are stained with Haematoxylin and Eosin.
Despite this mutagen-specific burden, significant mortality within mutagenised
Ptch+/- animals was related to the Ptch genotype. Large
exophytic and/or invasive MBs (Figure 1b and c)
developed in ~23% of mutagenised Ptch+/- animals aged for over 6
months, with no macroscopic meningeal masses being observed (typical of the
Ptch model [19]). Tumours with indistinguishable pathology were observed in the
predisposition controls but at a much lower frequency (6%), with survival
analysis providing clear evidence that SB mutagenesis enhanced the
predisposition of Ptch+/- mice to MB (p<0.0001, Figure 1d). Slow growing and locally invasive rhabdomyosarcomas
(RMSs), also typical of the Ptch+/- model [32], developed in ~22% of experimental animals aged over 6 months
(Figure 1e and f). However, mutagenesis did not
significantly alter RMS related mortality relative to predisposition controls
(Figure 1g, p=0.31). Finally, multiple liver
tumours (Figure 1h-j), morphologically similar to
those generated using a conditional SB11 screen for hepatocellular carcinoma [27], were observed in 19 mutagenised Ptch+/- mice but not in
predisposition controls. Although not previously reported in whole body SB
mutagenesis screens, liver tumours were also observed at low frequency in
transposition only controls (Additional file 1: Table
S1), suggesting that they can be induced by SB mutagenesis alone.
To identify genes responsible for the impact of mutagenesis upon MB incidence,
transposon insertion sites within MBs and control cerebellar tissues were
recovered using Splinkerette PCR, sequenced, and their distribution analysed
using Gaussian kernel convolution (GKC [23]) to identify common insertion sites (CISs, see Methods). A total of
17 genes were identified within 20 CISs recovered [median p-value = 0.008
(Table 1)]. The majority were also recovered
using Monte Carlo simulation analysis [24], and these are highlighted in bold in Table 1. Three of the CISs target known tumourigenic genes
(Crebbp, Nfib and Pten), mutations within
orthologs of two (Crebbp and Pten) are somatically mutated at
low frequency in human MB [11, 33], and six (Crebbp, Nfia, Nfib,
Pten, Sfi1, and Tead1) have recently been
identified as MB CISs in a tissue-specific mutagenesis screen [30]. Strikingly, ontological analysis established that six of the 17 CIS
genes have transcription factor activity (Nfia, Tead1,
Tgif2, Nfib, Myt1l and L3mbtl4), a
highly significant excess relative to expectation (FDR corrected p-value =
2×10-5). Furthermore, seven genes (Tgif2,
Pten, Nfia, Nfib, Myt1l, Slit3
and Fgf13) are implicated in neuronal biological processes. In
contrast, only three genic CISs were identified in control tissue, all with
modest p-values (0.01-0.05, Table 1). All of these
observations are consistent with insertional mutations at CISs contributing to
increased penetrance of the tumour phenotype. As all SB insertions within
Tgif2 mapped to a single ~4 kb intron, these were analysed in
detail using both genomic DNA and cDNA templates. This both validated our
sequence data and confirmed the inferred upregulation of this gene by SB
insertion (Additional file 2: Figure S1).
Table 1
Common Insertion Sites (CISs) in Medulloblastomas and Cerebellum
Controls
Gene
Chr
CIS width
N
I
GKC p-value
Other genes
Inferred MOA
CGC
MB- Mut
MB- CIS
A. Medulloblastomas
Nfia
4
28.6
20
27
<1E-14
-
Loss
-
-
Ptch
Atxn2
5
25
6
6
<1E-14
-
?
-
-
-
Tead1
7
19.1
6
7
3.9E-14
-
Gain
-
-
Ptch
Tgif2
2
0.8
3
4
0.0006
-
Gain
-
-
-
Crebbp
16
37.1
5
5
0.0009
-
Loss
S
Y
Ptch
Dscr3
16
18.3
5
6
0.0009
-
Loss
-
-
-
CIS13:72336914
13
44.3
5
6
0.0042
n/a
n/a
n/a
n/a
n/a
Pten*
19
50.2
4
5
0.0046
-
Loss
S/G
Y
Both
Itgbl1
14
43.4
5
5
0.0062
Fgf14
?
-
-
-
Nfib
4
24.7
7
9
0.0071
-
Gain
S
-
Ptch
Myt1l
12
67.2
8
10
0.0089
Pxdn,Tpo,Sntg2
Loss
-
-
-
Ankrd5
2
1071.3
8
15
0.0092
Plcb4, Pak7,Snap25
?
-
-
-
Slit3
11
494.3
8
12
0.012
-
Loss
-
-
-
Tmem45b
9
58.4
4
5
0.019
-
?
-
-
-
Sfi1
11
51
5
6
0.021
-
Loss
-
-
Both
CIS15:70979306
15
20.3
4
4
0.028
n/a
n/a
n/a
n/a
n/a
Fgf13
X
23.5
3
4
0.026
-
Gain
-
-
-
CIS3:147532546
3
140.5
4
7
0.043
n/a
n/a
n/a
n/a
n/a
L3mbtl4
17
221.6
7
7
0.043
Tmem200c
Loss
-
-
-
Adcy5
16
127.1
4
6
0.044
Ptblb
Loss
-
-
-
B. Cerebellum controls
Faf1
4
41.7
4
4
0.01
-
?
l7Rn6
7
10
3
3
0.018
Ccdc81
?
CIS6:31346217_240k
6
844
6
8
0.045
n/a
n/a
Ric3
7
99
4
4
0.035
Tub
?
CIS9:68415409_240k
9
1738
9
11
0.016
n/a
n/a
Genes associated with CISs, the chromosomes to which they map, and
the genomic extent of each CIS are shown. GKC p-values are from the
Genome Kernel Convolution analysis (see Methods). The number of
tumours (N) and T2Onc insertions (I) which
define each GKC CIS, together with other genes within the CIS
interval, are also shown. The position and orientation of SB
insertions can be used to infer the mode of action (MOA) of
insertion (e.g. [28]). Most of the CISs have insertions distributed throughout
the genes they identify, and in both orientations, suggesting loss
of function (Loss). Both of the CIS genes which have proven
tumour suppressor activity (Pten and Crebbp)
show this pattern. An excess of inserts in the 5’ end of a
gene, and in a +ve orientation with respect to transcription,
implies upregulation of expression (Gain) through SB
enhancer function [34]. CGC – Genes known to cause cancer due
to somatic (S) or germline (G) mutations from the
Cancer Genome Census
(http://www.sanger.ac.uk/genetics/CGP/Census/).
MB-Mut – Somatic mutations previously
identified in MB [11, 12, 14, 15]. MB-CIS – previously identified as an MB CIS [30] in either the Ptch model (Ptch) or the Ptch and
p53 model (both). *Pten, was also identified as a
CIS within haematological tumours (data not shown) but is included
here as it is a known MB gene. For details, see Methods.
Association of CIS genes with survival and focal copy number alterations
To investigate the association between CIS gene expression and survival, log-rank
(Mantel-Cox) tests were performed on median split microarray expression data
from human tumours [35]. Reduced expression of two genes, PTEN (a tumour suppressor
previously implicated in MB) and MYT1L, was associated with poor
outcome (Figure 2). This is consistent with the
inferred mode of action of the SB insertions that target these genes
(Table 1). Furthermore, the association of
MYT1L expression with survival remained significant within a
Cox-regression model incorporating high-risk clinical features using the data
from Cho, Tsherniak et al
[35], even after exclusion of the good prognosis WNT subgroup (p=0.011,
see Additional file 3: Table S2).
Figure 2
Expression ofMYT1LandPTENcorrelates with survival. Kaplan Meier curves
showing survival in high and low expressing groups in data of [35] split by median of expression in all MB regardless of
subtype. a. MYT1L, b. PTEN. Numbers at
risk and log-rank test p-values are shown.
To investigate the relationship between CIS genes and regions of chromosomal loss
or gain defined within primary tumours [35, 36], a GISTIC analysis was also performed [37]: PTEN maps within a well-established region of common
chromosomal loss on 10q associated with SHH tumours [38], and both NFIB and TMEM45B were found to be present
within peak regions of localised copy number gain (Additional file 4: Figure S2). While this is consistent with the mode of
action inferred for PTEN and NFIB, the mode of action of
TMEM45B remains unclear from insert data alone (Table 1).
CIS genes are differentially expressed in MB clinico- genetic subgroups
To establish whether the CIS genes are relevant specifically to the SHH subgroup
of tumours, we used published data sets [38, 39] to compare expression of human orthologs in SHH subgroup tumours with
expression in all other subgroups. Of the 17 CIS genes, 9 show significant
differential expression when SHH subgroup tumours are compared to all others
(including MYT1L and PTEN discussed above), and 15 show
differential expression in one or more clinicogenetic subgroups
(Table 2). However, only two (ITGBL1 and
L3MBTL4) show clear differential expression in the SHH subgroup
alone. Some show marked differential expression in a single non-SHH subgroup,
such as NFIA (Group 3 tumours) and FGF13 (Group 4 tumours), or
in more than one subgroup (e.g. TGIF2 and MYT1L), suggesting
that these genes may be relevant to MB in general. We also investigated
expression with respect to the presence/absence of metastatic disease, and 7
genes show a significant association, most notably genes with extreme expression
values in Group 3 and 4 tumours where metastatic disease is common
(Table 2).
Table 2
CIS gene expression according to clinicogenetic groups and presence
of metastatic disease
Clinicogenetic groups
Metastatic disease
SHH v Other Subgroups
WNT (N=15)
SHH (N=24)
Group 3 (N=24)
Group 4 (N=48)
Mets (N=32)
No Mets (n=75)
Mean SHH
Mean others
t
p-val
Mean
SE
Mean
SE
Mean
SE
Mean
SE
p-val
Mean
SE
Mean
SE
p-val
NFIA
22497_at
11.25
10.80
-1.31
11.41
±
0.18
11.25
±
0.30
9.44
±
0.42
11.32
±
0.16
**
10.78
±
0.32
11.03
±
0.15
ATXN2
202622_s_at
7.41
7.60
2.00
7.50
±
0.11
7.41
±
0.08
7.36.
±
0.11
7.75
±
0.06
**
7.68
±
0.08
7.47
±
0.05
*
TEAD1
224955_at
10.80
9.85
-6.34
**
11.10
±
0.12
10.80
±
0.12
10.13
±
0.15
9.33
±
0.07
**
9.68
±
0.13
10.11
±
0.11
*
TGIF2
216262_s_at
7.61
5.02
-8.98
***
7.75
±
0.10
7.61
±
0.23
5.48
±
0.28
3.97
±
0.10
**
4.46
±
0.22
5.88
±
0.22
***
CREBBP
202160_at
9.93
9.80
-0.85
9.95
±
0.10
9.93
±
0.10
9.13
±
0.32
10.09
±
0.07
**
9.76
±
0.13
9.82
±
0.12
DSCR3
203635_at
7.08
6.78
-3.19
**
6.84
±
0.18
7.08
±
0.08
6.69
±
0.09
6.80
±
0.06
**
6.82
±
0.08
6.82
±
0.06
PTEN
225363_at
10.31
11.12
4.63
***
11.16
±
0.20
10.31
±
0.15
10.12
±
0.15
11.62
±
0.06
**
11.03
±
0.14
10.94
±
0.10
ITGBL1
214927_at
6.49
3.90
-4.47
***
4.27
±
0.56
6.49
±
0.55
3.16
±
0.25
4.16
±
0.25
**
3.91
±
0.33
4.85
±
0.27
*
NFIB
209290_s_at
12.92
12.67
-0.66
10.46
±
0.48
12.92
±
0.32
12.18
±
0.38
13.58
±
0.09
**
12.81
±
0.28
12.85
±
0.16
MYT1L
210016_at
8.27
10.24
2.80
**
6.06
±
0.70
8.27
±
0.64
9.20
±
0.43
12.02
±
0.11
**
11.28
±
0.22
9.41
±
0.36
**
ANKRD5
220144_s_at
4.63
4.75
0.54
4.51
±
0.28
4.63
±
0.19
5.46
±
0.21
4.46
±
0.10
**
4.65
±
0.16
4.77
±
0.11
SLIT3
203813_s_at
3.01
2.98
-0.17
2.90
±
0.09
3.01
±
0.14
2.82
±
0.05
3.09
±
0.10
2.98
±
0.10
2.94
±
0.06
TMEM45B
226226_at
2.37
2.44
1.57
2.34
±
0.04
2.37
±
0.04
2.52
±
0.04
2.44
±
0.03
*
2.45
±
0.03
2.44
±
0.02
SFI1
36545_s_ar
5.96
5.19
-2.68
5.74
±
0.24
5.96
±
0.25
4.42
±
0.23
5.42
±
0.19
**
5.24
±
0.20
5.36
±
0.16
FGF13
205110_s_at
5.88
8.10
6.66
*
5.33
±
0.86
5.88
±
0.54
6.81
±
0.49
9.58
±
0.16
**
8.73
±
0.39
7.01
±
0.32
**
L3MBTL4
228557_at
6.85
3.73
-7.43
***
4.28
±
0.28
6.85
±
0.40
4.82
±
0.22
3.01
±
0.10
**
3.76
±
0.21
4.59
±
0.24
*
ADCY5
228182_at
3.50
3.70
0.66
***
3.52
±
0.27
3.50
±
0.27
2.73
±
0.16
4.25
±
0.21
**
3.65
±
0.22
3.60
±
0.16
Genes and associated Affymetrix probe IDs are shown. All values are
mean log2 expression levels from [38] and [39]. Standard errors (SE) are shown. SHH v Other Subgroups,
and presence/absence of metastatic disease, were analysed using
t-tests. Clinicogenetic groups [2] were analysed using ANOVA. *p<0.05, **p<0.01,
***p<0.001.
For 13 genes, a comparison of expression in human tumours and normal cerebellum
was also possible (using data from Cho, Tsherniak et al. [35]) and a total of 10 genes show significantly different expression
between cerebellum and either SHH subgroup tumours alone or all tumours
(Additional file 5: Table S3), consistent with
dysregulation of expression during tumorigenesis. Furthermore, the direction of
expression change observed is generally consistent with the predicted mode of
action of each CIS. For instance, expression levels of FGF13,
NFIB, TEAD1 and TGIF2 are all increased versus
normal cerebellum whilst expression of MYT1L, SFI1, and
SLIT3 is appropriately reduced (Table S2) in line with the inferred
mechanism of action (Table 1).
CIS genes define a neuronal transcription factor network in human MBs
The significant enrichment for transcription factor (TF) activity within the MB
CIS genes raised the possibility that they could be present within co-ordinated
signalling or developmental pathways. ARACNE [40, 41] is a method which uses gene-gene co-regulation measures, and
elimination of indirect relationships, to infer TF-target interactions within
expression data. It has successfully been used to identify novel oncogenes in
expression datasets from glioma [42] and acute lymphoblastic leukaemia [43]. We used ARACNE to infer regulatory networks within publicly
available MB gene expression data ([38, 39] see Methods). Strikingly, seven CIS genes, including four of the five
CISs with the highest GKC p-values, were linked within a single network either
directly or via nearest neighbours (Figure 3). This
cluster of CIS nodes is highly significant (p=0.006 using 1000 randomly
re-sampled networks) and consists of 6 genes with transcription factor/cofactor
activity (CREBBP, MYT1L, NFIA, NFIB, TEAD1
and TGIF2) and one neuronal growth factor (FGF13). Gene
ontology analysis (see Methods) established that the extended network is
enriched both for transcription factors/regulators (p=0.0026/0.0035, Additional
file 6: Table S4, yellow in Figure 3) and for genes with ontologies relating to cellular components of
differentiated neurons (p=0.019-0.0042, Additional file 6: Table S4, green in Figure 3). This
suggests that the network consists primarily of neuronal transcription factors
and their targets.
Figure 3
A neuronally enriched transcription factor network is defined by MB
CIS genes. MB CIS genes (enlarged) linked via nearest neighbours
are shown. The topological arrangement of connected CIS nodes is highly
significant (p<0.006) as the average minimum path distance between
the 7 CISs is lower than in 994/1000 randomly resampled networks. All
network edges are supported with a bootstrapped p-value of
<1x10-8. Genes identified as biallelically mutated MB
CISs by Wu et al. [30] are shown as diamonds. Genes with transcription regulator
activity (GO ID 30528) are shown in yellow. Genes with enriched
ontologies associated with differentiated neurons (GO IDs 45202, 44456,
45211, 43005, 43198) are shown in green. Other genes with a known role
in neuronal development are shown with a green border. For details of
network construction, see Additional file 7.
For details of enriched ontologies see Additional file 6: Table S4.
To investigate the relative activity of genes within these enriched ontologies in
human tumours, expression heatmaps of CIS genes, transcription factors, and
neuronal genes within the network were generated (Figure 4a). There are clear expression differences between clinicogenetic
subgroups, consistent both with the ANOVA analysis of CIS gene expression across
subgroups (Table 2) and with the presence of genes
previously shown to be highly expressed in Group 3 and 4 tumours (e.g.
NEUROD2, GABBR2
[38]). However, most striking are the neuronal genes which include
neurotransmitter receptors and synaptic scaffold/matrix proteins, the vast
majority of which show low expression in the SHH and WNT tumours.
Figure 4
Network activity correlates with tumour subgroup, metastasis and
survival. a. Gene expression heatmaps of CIS genes and network
genes with enriched ontologies in all 4 clinicogenetic subgroups are
shown. Where more than one probe per gene was present in the network,
the probe with the highest mean expression level across all samples is
shown. b. Box and whisker plot showing log of metagene expression
in MB clinicogenetic subgroups. ANOVA p-value is shown. Boxes represent
the 25th to 75th percentile, with median values
shown as solid lines. Whiskers represent 95th percentile, and
outliers are shown individually. c. Log of metagene expression in
tumours with metastases and those without. T-test p-value is shown.
d. Kaplan Meier curve of SHH subgroup tumours relative to
Metagene score.
Four of the seven networked CIS genes have recently been identified as MB CISs in
a tissue-specific SB mutagenesis screen of primary tumours generated using the
Ptch model (Crebbp, Nfia, Nfib and
Tead1
[30]), and orthologs of a further three genes within our network
(Dip2c, Edil3 and Erbb4) were identified as MB
CISs in the same screen. Strikingly, with the exception of Tead1, both
alleles of all of these genes were targeted by inserts in primary tumours in
this screen [30], indicative of key tumour promoting events. We therefore analysed the
distribution of all 17 biallelic events identified in this tissue-specific study
and found them to be significantly enriched within our network (6/90, 7%)
compared to outside our network (11/5823, 0.19%; Fisher’s Exact Test p
<0.00001). This provides evidence that the TF network identified here was
also targeted in an independent SB screen.
Network activity correlates with advanced disease and survival in SHH
tumours
To assess network activity within human tumour datasets further, we generated a
single “metagene” metric to summarise the expression of CIS network
genes. The expression of each gene was signed according to direction of
correlation with other CIS genes, such that a single score reflected the unified
action of all genes (see Additional file 7). As a
result, the expression of genes where CISs are inferred to cause loss of
function was positively correlated with metagene score (e.g. MYT1L),
whereas the expression of genes where CISs are inferred to cause gain of
function (e.g. TGIF2, Additional file 2:
Figure S1) was negatively correlated. Metagene activity was then investigated
with respect to MB subgroups and clinical features. As expected from the
expression heatmaps (Figure 4a), the activity of the
network metagene differs significantly between MB clinicogenetic subgroups (F =
62.8 p<0.0001 Figure 4b), with the highest network
activity being observed in Group 4 tumours. Metagene activity is also higher in
tumours presenting with metastatic disease when compared to those that do not
(bootstrapped t = 2.388; p<0.013 Figure 4c). This
is likely to reflect the high metagene expression in Group 4 tumours where
metastases are frequently observed. A subgroup specific analysis of available
expression data which has associated survival information [35], however, showed that network activity correlates significantly with
survival in SHH subgroup tumours (log-rank 8.03; p <0.005, Figure 4d), but not in other subgroups (data not shown).
Microarray expression analysis of mutagenised tumours identifies
Igf2 as a key network associated gene
To identify specific genes whose expression might be altered by network
mutations, we generated murine expression data using Illumina bead arrays (see
Methods) from 30 SB induced tumours, 6 non-mutagenised tumours from
Ptch+/- animals, and 6 normal cerebella. We first validated the
mutagenised murine model in terms of gene expression as follows: Four metagenes
were generated from human tumour expression data using NMF (see Methods) to
define the four MB biological subgroups. These metagenes were then projected
across the mouse tumour data using all available orthologous probes
(Figure 5a), and the subgroup identity of the
mouse tumours was then tested with a Support Vector Machine (SVM) using the
metagene scores for the human data as the training set and the mouse tumours as
the test set. All human tumours were trained correctly with zero errors. The
mouse SB tumours were also correctly predicted to be SHH tumours in 29/30 (96%)
of cases (Figure 5b) and 6/6 (100%) of non-transposon
PTCH MB controls, establishing that gene expression in SB induced mouse tumours
is similar to expression in human SHH tumours.
Figure 5
Ptch+/-MBs are SHH subgroup tumours andIgf2is a key network associated gene.
a-b. Projection of four sub-group specific metagenes
derived from 108 human primary MB expression profiles (Training Set)
onto MB mouse model expression profiles (Test Set) using Non-negative
Matrix Factorisation (NMF). a. Heatmaps showing expression
of metagenes across human tumours (training; left panel) and expression
of metagenes across murine tumours (test; right panel).
b. Pseudo-plot of NMF projections coloured according to
confident MB sub-group classification of murine tumours using metagene
expression values. This unsupervised analysis shows that the majority of
Ptch mouse model samples recapitulate the expression
profiles of primary SHH MB. c. Box and whisker plots
showing relative Igf2 expression assayed by Real Time PCR
(for details, see Methods): Left Panel - tumours with inserts in 1 or
more network CIS gene versus tumours with no inserts; Right Panel -
Igf2 expression in tumours with 1 or more insertions
in Nfia versus all other tumours with inserts in at least
1 network CIS gene. Boxes represent the 25th to
75th percentile, with median values shown as solid
lines. Whiskers represent 95th percentile, and outliers are
shown individually. d-e. GSEA plots for human tumours
ranked by metagene score (left panels) and mouse tumours ranked by fold
change of expression in tumours with network hits compared to
predisposition control MBs not exposed to mutagenesis (right panels).
ES-Enrichment Score, RM-Ranked list Metric.
d. Lein_Neuron_Markers. Human NES=2.15 p<0.001, Mouse
NES=-2.24 p<0.001. e. KEGG_Ribosome. Human NES=-3.00
p<0.001, Mouse NES=2.59 p<0.001.
We then looked for genes differentially expressed between tumours with hits in
CIS network genes and tumours with no hits in these genes. This identified the
Insulin-like growth factor 2 (Igf2) as the most differentially
expressed gene with a mean fold change of 3.58 (p=0.002, Additional file 8: Table S5). To validate the association between network
insertions and Igf2 expression, we quantified Igf2 expression
in all of our SB-induced MB tumours for which RNA was available using Real Time
PCR (See Methods). This confirmed that Igf2 was expressed at a
significantly higher level in tumours with one or more insertions in a network
CIS gene, than in tumours with no insertion in a network CIS gene (p<0.0001,
Figure 5c). Furthermore, we also established that
tumours with hits in Nfia, the CIS gene most frequently affected by SB
insertion, expressed Igf2 at higher levels than network tumours with no
insert in Nfia (Figure 5c).
CIS related Network activity is associated with proliferation and reduced
differentiation
Finally, to gain insight into biological processes which may be affected by the
network, we also performed a Gene Set Enrichment Analysis (GSEA) which uses gene
ranking to test for enrichment of predefined genesets [44]. This was performed both in mouse tumours (ranking genes by fold
change of expression in SB-induced tumours with network hits compared to
Ptch+/- MBs not exposed to mutagenesis) and in primary human MBs
(ranking by association with metagene score).
GSEA revealed a broad picture of increased cell proliferation and reduced
differentiation associated with network hits in mice and low network metagene
activity in human tumours (the concordant gene expression pattern), as
demonstrated by significant enrichment of multiple genesets (see Additional file
9: Table S6). For instance, genesets indicative of
neuronal differentiation, such as “Cahoy_Neuronal” and
“Lein_Neuron_Markers”, are significantly enriched in human MBs with
high metagene expression and in MBs from murine PTCH controls with no
transposition (e.g. Figure 5d). A similar effect is
seen for genesets describing genes containing CREB and cAMP responsive elements
(e.g. Additional file 10: Figure S3A). In contrast,
genesets denoting proliferation and elevated cell growth are significantly
enriched in human MB with low network metagene activity and mouse PTCH
MBs with CIS network hits. These include genesets linked to mitosis and cell
cycle, MYC targets (Additional file 10: Figure S3B),
and ribosome biogenesis (Figure 5e).
Consistent with increased Igf2 expression in MBs from mice with network
hits, enrichment of IGF-related genesets is also observed in human and mouse
tumours; e.g. enrichment of Pacher_Targets_of_IGF1 _and_IGF2 _up
in mouse tumours with network hits (Additional file 10: Figure S3C), and of Boudoukha_Bound_by_IGF2BP2 in
human tumours with a low Metagene score (Additional file 10: Figure S3D). In addition, the genesets Lee_Targets_of
PTCH1 _and_SUFU “ _up” and “_dn”,
indicative of SHH dependent murine tumorigenesis [45], show consistent differential enrichment; upregulated targets are
enriched in mouse network tumours and human tumours with a low Metagene score
(Additional file 10: Figure S3E), and downregulated
targets are enriched in mouse tumours with no mutagenesis and human tumours with
a high Metagene score (Additional file 10: Figure
S3F). Collectively, the broad concordance of human and murine datasets indicates
conservation of CIS mutational function across species, and substantiates the
concerted action of CIS network genes as a tumourigenic process promoting cell
proliferation and inhibiting neuronal differentiation.
Discussion
We have shown that whole body SB mutagenesis of Ptch+/- mice significantly
enhances MB frequency without affecting latency, and does not induce these tumours
in wild type mice. The majority of candidate genes identified have either been
implicated in neuronal development, differentiation and/or migration, have been
linked to SHH signalling, or have been shown to be mutated in SHH subgroup tumours
in humans. Furthermore, we found that one gene identified from our screen,
MYT1L, is of prognostic value within a multivariate analysis of human
MB survival data. These genes, therefore, warrant individual assessment as potential
therapeutic targets and/or biomarkers for the improved stratification and treatment
of medulloblastoma.
Notably, 7 of the genes (6 transcription factors/cofactors and 1 growth factor) show
significant associations with each other within a novel MB expression network,
implying a previously unidentified functional relationship which does not map to
established canonical pathways. This network is enriched for transcriptional
regulators and genes with neuronal ontologies, and links genes with roles in stem
cell/neuron migration, neurite growth and neuronal cell cycle progression, to genes
which encode structural and functional elements of mature neurons (See Additional
file 11: Table S7 for known gene functions). This
suggests that the network comprises transcription factors involved in the
proliferation and differentiation of cGNPs, the cell of origin for SHH MB [7], and their targets. Interestingly, lineage commitment to cGNP identity is
a pre-requisite for SHH tumour development [46, 47].
The relationships identified here between murine network activity, metagene activity
in human tumour subtypes, and individual CIS gene expression, are summarised in
Figure 6. This highlights the variable network
activity within SHH tumours and Group 3 tumours, and identifies CIS genes with
similar, or wholly divergent, expression patterns relative to the metagene. Of
these, TGIF2 and MYT1L are of particular interest as they
influence neuronal SHH expression and development, respectively: A conditional
Tgif1/Tgif2 double knock-out has recently been shown to reduce
Shh expression in the developing brain and to recapitulate
holoprosencephaly [a human condition caused by SHH and TGIF1
mutations (OMIM# 142945 and 142946)], while the transcriptional repressor
MYT1L can contribute to the re-programming of human fibroblasts into
neurons [48, 49]. As neither gene has been implicated in MB development to date, both are
prime targets for further investigation.
Figure 6
Integrated summary of metagene expression, associated clinical features,
and GSEA. The schematic relationship between mouse and human tumours
in terms of network genes is shown in the top panel. Expression heatmaps
showing the network metagene and CIS genes in 108 MBs by subgroup [38, 39], together with the incidence of metastatic disease, are shown in
the middle panel. For all genes, data from the most highly expressed
Affymetrix probe are shown. In the metastases track, red = presence, green =
absence, grey = no data. In the histological subtype track, magenta =
classic, green = desmoplasia, blue = LCA. A schematic of key processes
relevant to tumour biology identified by GSEA is shown in the bottom panel.
For details of GSEA, see text and Additional file 9: Table S6.
Importantly, network activity has clinical relevance, as high activity is associated
with advanced disease in all tumours and low activity is associated with poor
survival specifically in SHH subgroup tumours (Figure 6).
These associations appear incongruous, but the former is likely due to the high
incidence of metastases in Group 4 tumours where network activity is uniformly high.
In contrast, the SHH subgroup-specific association with outcome may reflect
clinically important variation in the developmental status of individual tumours,
and highlights the potential utility of network activity as a prognostic biomarker
for the prediction of outcome within the SHH subgroup.
The GSEA analysis in mouse and human tumours demonstrates very clearly a role for
this network in inhibiting neuronal differentiation and promoting cell
proliferation. Consistent with this, several common functional pathways were
identified in both species of potential relevance to disease. Of these, the MYC and
IGF-dependent signalling pathways are of particular interest, the
latter having recently been highlighted in an independent SB screen [30]. Furthermore, our analysis of gene expression in SB mutagenized mouse
tumours identified Igf2 upregulation as a key output of SB-induced network
perturbation. Igf2 is already known to be required for MB development in
the Ptch+/- model as no tumours are observed in Igf2 null; Ptch+/- mice [50], over-expression of Igf2 in Ptch+/- mice increases the frequency of MBs generated by Shh
transfection of cerebellar neural progenitors [51], and at the cellular level Igf2 acts synergistically with
Shh to increases murine cGNP cell proliferation 10 fold [52].
The results presented here suggest that network mutations converge to inhibit
differentiation and upregulate Igf2. This extends the existing model of MB
formation in SB mutagenised Ptch+/- mice by identifying genes underpinning the upregulation of
Igf2 which leads to the persistence of Ptch+/--induced cerebellar proliferative lesions and progression to MB [53]. Consistent with this model, several network CIS genes, or genes which
they bind/modulate, have already been implicated in Igf2 expression or
activity, including Tead1
[54], Nfia and Nfib
[55, 56], and Crebbp
[57]. There is an unmet clinical need for the development of SHH
pathway-independent targeted therapies for SHH subgroup tumours, particularly in
view of the predicted acquired or intrinsic resistance to current SMO inhibitors [9, 10]. The implication of insulin-dependent signalling in human and mouse SHH
tumours strongly support its development as a therapeutic target for SHH subgroup
tumours.
The application of SB mutagenesis to additional murine MB models [17, 18] could identify genes relevant to other tumour subgroups. However, our
results contrast sharply with a recent Math1 driven tissue-restricted
screen of a more penetrant Ptch+/- model [30] where tumour latency was reduced from 8 to 2.5 months, a high frequency
of metastases (80%) was observed, and divergent primary and metastatic insertional
mutation signatures were defined. Notably, we did not observe metastases following
whole-body mutagenesis in this study. These two models are, therefore, not directly
comparable and suggest that penetrance of the tumour predisposition, and the power
of the mutagen, are likely to determine the nature of genes identified in future
screens.
Finally, this is the first time to our knowledge that mutagenesis data from a murine
cancer model have been integrated with human expression networks to explore
biological mechanisms of tumourigenesis. The identification of novel and
biologically relevant candidate genes linked within a single expression network, the
activity of which correlates with disease state and survival within the subgroup of
tumours being modelled, illustrates the utility of this cross-species approach.
Clarification of the interactions between network genes identified here, their roles
in the pathways highlighted by our GSEA analysis, and establishment of their
therapeutic relevance will, however, require extensive functional analyses of
multiple genes both individually and in concert.
Conclusions
Here, we have used SB mutagenesis to define a novel neuronal transcription factor
network involved in medulloblastoma formation within the Ptch+/- model, and provide evidence that disruption of this network
upregulates Igf2, critical for proliferation of GNPs and tumour formation.
Moreover, we have identified rational therapeutic targets for SHH subgroup tumours,
alongside prognostic biomarkers for the identification of poor-risk SHH patients,
supporting the further development of these findings as a basis for improved and
individualised therapy. Our results also suggest that the integration of mutagenesis
data and expression network analysis may help to unravel key events in other cancers
which disrupt complex developmental programmes, for which murine models are
available.
Methods
Mice strains
The following strains were used: B6 Ptch1tm1Mps/J mice [19]; CBA wild type (Charles River laboratories, Margate, UK); T2Onc line
76 [58] and SB11 Rosa26 [22]. All animal work adhered to UK Home Office guidelines and was
performed under Project Licence PLL/60/3621. Animal numbers, together with
tumour incidence in each genotype, are given in Additional file 1: Table S1.
Sample processing, insertion site mapping and CIS identification
Tumours and other abnormal tissue identified upon post-mortem examination were
collected for histological examination and DNA/RNA isolation. Insertion sites
were identified using splinkerette PCR [21] coupled to GS-FLX amplicon sequencing [59] and sequence reads were mapped to the mouse genome (NCBI37/mm9) as
described previously [60]. Common insertion sites (CISs) were identified using Gaussian kernel
convolution (GKC) [23]. The raw p-value of each CIS peak was corrected for the total number
of CIS peaks on the chromosome to which it maps, with a cut off of p<0.05.
Monte Carlo simulation methods [24] were also used for comparative purposes.
Illumina expression analysis and Real Time PCR
200
ng of each RNA was amplified and biotin labelled using the Illumina TotalPrep RNA amplification kit (Applied Biosystems, Foster City, CA. USA). cRNA size distribution was assessed using an Agilent Bioanalyser. Approximately 750 ng of each cRNA was hybridised to the Illumina Mouse8 Reference Array (Illumina, Essex, UK) according to the manufacturer’s recommended protocols by the Wellcome Trust Clinical Research Facility (Edinburgh, UK). Real Time PCR was performed using the 5’ nuclease assay on the ABI PRISM™ 7700 Sequence detector (Perkin-Elmer, Applied Biosystems, Foster City, CA, USA). Oligonucleotides were designed using Primer Express software (v3.0, PE Biosystems, Foster City, CA, USA), and were designed to span an intron to avoid amplification from genomic DNA. Mean Ct values were normalised against the average expression of the endogenous control gene β-Actin. Relative gene expression was calculated by the 2-ΔΔCt method [61] using the control cerebellar RNA which showed the highest expression level as measured by the Illumina microarray data. For primer sequences see Additional file 7.
Bioinformatic Analyses
Expression profiles comprising 119 Affymetrix HGU133p2 arrays were taken from
published studies [38, 39]. CEL files were processed using the Bioconductor RMA package [62]. The ARACNE network was constructed using the aracne2 standalone
software package according to authors instructions [40]. Ontology analyses were performed using the Bingo 2.44 Cytoscape plug
in [63], with Benjamini and Hochberg FDR-corrected hypergeometric tests and
the whole annotation set as background. The topological arrangement of 7 network
genes was significance tested by calculating the mean shortest path distance to
the nearest connecting CIS gene and permuting 10,000 times with 7 randomly
selected genes in order to create a null distribution.
GISTIC analyses [37] were performed using the module provided in Genepattern [64], with Affymetrix SNP Chip .CEL files [35, 36] being processed using the Aroma package [65] and segmented using the CBS algorithm [66]. GSEA [44] was performed using the standalone package
(http://www.broadinstitute.org/gsea/) and genesets were taken
from the MsigDB library [67]. NMF (Non-Negative Matrix Factorisation) was performed using a script
adapted from [68]. All other statistical tests were performed using R [69]. For further details of procedures and analyses, see Additional file
7.
Availability of supporting data
Microarray gene expression data from this study have been deposited in the Gene
Expression Omnibus as submission GSE43994. Insertion site data in the form of BED
files are provided as Additional files 12 and 13.
Declarations
Acknowledgements
The financial support of Cancer Research UK (grants C7679/A7311 and C8464/A13457)
is gratefully acknowledged. DJA is supported by Cancer Research UK and the
Wellcome Trust (grant 0980051). LC is supported by Cancer Research UK (grant
C34648/A12054), Samantha Dickson Trust (grant SDR004X) and Christopher’s
Smile (grant CXC002H). H.H.A-B. was supported by a studentship from the
Government of Kuwait. H.A-A. was supported by a studentship from the Government
of Saudia Arabia.
Authors’ Affiliations
(1)
Institute of Genetic Medicine, Newcastle University
(2)
NewGene Limited, Bioscience Building, International Centre for Life
(3)
Northern Institute for Cancer Research, Newcastle University
(4)
Faculty of Medical Sciences, William Leech Building, Newcastle University
(5)
Division of Clinical Studies and Cancer Therapeutics, The Institute of Cancer
Research & The Royal Marsden NHS Trust
(6)
Experimental Cancer Genetics, Wellcome Trust Sanger Institute
(7)
Department of Pathology, Children’s Memorial Health Institute
(8)
Centre for Molecular Oncology, Barts Cancer Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London
(9)
School of Medicine, Faculty of Health Sciences, University of Adelaide
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