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Unmasking novel epigenetic mechanisms of
medulloblastoma pathogenesis
by
Xin Wang
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Laboratory Medicine and Pathobiology University of Toronto
© Copyright by Xin Wang 2016
ii
Abstract
Name: Xin Wang
Thesis title: Unmasking novel epigenetic mechanisms of medulloblastoma pathogenesis
Degree: Doctor of Philosophy, 2016
Department: Laboratory Medicine and Pathobiology, University of Toronto
The leading cause of cancer-related death in children is due to brain tumours.
Medulloblastoma is the most common malignant paediatric brain tumour, representing
20% of childhood brain malignancies. Despite current multimodal therapies, there
remains significant treatment-induced morbidity. We therefore require a greater
understanding of medulloblastoma pathogenesis to further stratify those patients that
require aggressive treatment and those in which milder regiments can be implemented.
The aim of this thesis is to unravel the molecular underpinnings of childhood
medulloblastoma.
To date, our lab has re-conceptualized the genetic landscape of medulloblastoma by
demonstrating that it is a heterogeneous disease with multiple distinct molecular
subgroups. These different subgroups are currently treated the same, but have vastly
different prognosis and patient characteristics. In the first half of this thesis, I attempt to
address the question whether medulloblastoma subgroups remain stable between the
primary and metastatic compartments. This has significant implications on clinical
management given the advent of targeted, subgroup-based therapies. This investigation
represents the largest reported primary-metastatic paired cohort profiled to date and
provides a unique opportunity to evaluate subgroup-specific molecular aberrations
within the metastatic compartment. Our findings further support the hypothesis that
medulloblastoma subgroups arise from distinct cells of origin, which are retained during
metastatic progression.
Given the paucity of recurrent somatic mutations found to date using sequencing
technologies, much effort has gone into understanding the epigenetic mechanisms that
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drive medulloblastoma formation. In the latter half of this thesis, I present data to
support the identification of a previously well studied hypermethylated locus on 17p13.3
harbouring a newly described bidirectional promoter. This locus is frequently methylated
and rarely mutated in cancer, suggesting a clonal selection towards hypermethylation.
Further characterization of this locus reveals a novel tumor suppressor microRNA
cluster miR-212/132 in the pathogenesis of medulloblastoma. Re-expression of this
endogenously methylated locus using RNA-guided gene activation with CRISPR-Cas9
technology decreased tumour proliferation in vitro and in vivo; this approach may
represent new therapeutic options in the fight against cancer.
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Acknowledgments
First and foremost, I would like to thank my supervisor Dr. Michael D. Taylor for
mentoring me and showing me what it takes to be a successful clinician-scientist. It truly
has been a privilege working in Michael’s lab and being a part of, and hopefully
contributed to, the grand vision of curing childhood brain tumours. I look forward to
many more collaborations (and ‘Taylorizations’) as future colleagues. Modern science is
no longer a solitary endeavor and witnessing the wealth of collaborations and the fruits
from those efforts is truly an inspiration; I would like to thank everyone in the Brain
Tumour Research Centre and all our national and international collaborators who have
contributed to my time here. Thank you to all the Taylor lab members both past and
present for your insight, troubleshooting advice, scientific discourse, and most of all,
your friendship; I could not have dreamed of a better research environment. Thank you
to my committee members Dr. David Malkin, Dr. Benjamin Alman, and Dr. Ian Scott for
helping me develop this thesis and for your guidance throughout my training. I would
like to thank my friends from both graduate school and medical school who have stuck
with me even when I bailed on get-togethers due to long experiments. Getting to this
point would not have been possible without you supporting and encouraging me along
the way. I am grateful for my better half Dr. Jennie G. Pouget for her unwavering
support and love. Thank you for being the triple-threat and for always being there when I
needed a shoulder to lean on. You have made me a very happy man; thank you for
being in my life. To my parents, Shumin Ma and Fulin Wang, thank you for being the
best parents a son can ask for. Thank you for always encouraging me, you have always
believed in me even when I doubted myself. Your sacrifice and unconditional love is the
only reason I am here today. Lastly, I want to thank my two younger brothers Michael
and James Wang. Both of you have made me unbelievably proud as your older brother,
keep up the amazing things you do and always believe in yourself. Part of my reason for
pursuing this research is from the experience that my brother James went through as a
childhood cancer survivor, James, you are forever an inspiration and role model to me.
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Table of Contents
Acknowledgments ........................................................................................................................ iv
Table of Contents .......................................................................................................................... v
Abbreviations ................................................................................................................................ ix
List of Figures .............................................................................................................................. xii
List of Tables ............................................................................................................................... xiv
Chapter 1 ....................................................................................................................................... 1
1 Introduction ............................................................................................................................... 2
1.1 Medulloblastoma .............................................................................................................. 2
1.1.1 Historical perspective........................................................................................... 2
1.1.2 Epidemiology......................................................................................................... 3
1.1.3 Clinical Presentation and Histopathology ......................................................... 3
1.1.4 Risk stratification .................................................................................................. 5
1.1.5 Treatment and prognosis .................................................................................... 7
1.2 Genomics of medulloblastoma subgroups ................................................................. 14
1.2.1 WNT subgroup medulloblastoma .................................................................... 14
1.2.2 SHH subgroup medulloblastoma ..................................................................... 15
1.2.3 Group 3 medulloblastoma ................................................................................. 15
1.2.4 Group 4 medulloblastoma ................................................................................. 16
1.3 Epigenetics of medulloblastoma .................................................................................. 19
1.3.1 Histone Modification and DNA Methylation.................................................... 19
1.4 Role of microRNAs in development and medulloblastoma ..................................... 23
1.4.1 microRNA biogenesis and role in normal CNS development ..................... 23
1.4.2 Oncogenic and tumor suppressor microRNAs .............................................. 25
Chapter 2 ..................................................................................................................................... 27
vi
2 Thesis Rationale and Hypothesis ....................................................................................... 27
2.1 Study One: Medulloblastoma subgroups remain stable across primary and
metastatic compartments .............................................................................................. 28
2.1.1 Hypothesis ........................................................................................................... 28
2.2 Study Two: Silencing of bidirectional promoters through hypermethylation causes preferential clonal selection in cancer ........................................................... 28
2.2.1 Hypothesis ........................................................................................................... 28
Chapter 3 ..................................................................................................................................... 29
3 Medulloblastoma subgroups remain stable across primary and metastatic
compartments......................................................................................................................... 30
3.1 Abstract ............................................................................................................................ 30
3.2 Introduction...................................................................................................................... 31
3.3 Methods and Materials .................................................................................................. 32
3.3.1 Medulloblastoma tumour specimens............................................................... 32
3.3.2 RNA extraction.................................................................................................... 32
3.3.3 DNA extraction and bisulfite-conversion......................................................... 33
3.3.4 Subgroup assignment........................................................................................ 33
3.3.5 Statistical analysis .............................................................................................. 34
3.4 Results ............................................................................................................................. 35
3.4.1 Cohort description .............................................................................................. 35
3.4.2 Subgroup stability by expression ..................................................................... 35
3.4.3 Subgroup stability by methylation .................................................................... 36
3.5 Discussion ....................................................................................................................... 37
Chapter 4 ................................................................................................................................ 49
4 Silencing of bidirectional promoters through hypermethylation leads to preferential clonal selection in cancer ..................................................................................................... 50
4.1 Abstract ............................................................................................................................ 50
4.2 Introduction...................................................................................................................... 51
vii
4.3 Methods and Materials .................................................................................................. 52
4.3.1 Patients and tumour samples ........................................................................... 52
4.3.2 Luciferase reporter assay ................................................................................. 53
4.3.3 Medulloblastoma cell lines and cell culture, MTS, treatments, and
transfections ........................................................................................................ 53
4.3.4 RNA extraction.................................................................................................... 54
4.3.5 DNA extraction and bisulfite-conversion......................................................... 54
4.3.6 Sequenom MassCleave analysis of primary medulloblastoma .................. 55
4.3.7 Western blot analysis......................................................................................... 55
4.3.8 qRT-PCR ............................................................................................................. 56
4.3.9 Orthotopic xenograft model of patient derived cell lines .............................. 56
4.3.10 Generation of floxed mice ................................................................................. 56
4.3.11 Lentiviral construction and viral preparation .................................................. 57
4.3.12 CRISPR-Cas9 synergistic activation mediators ............................................ 57
4.4 Results ............................................................................................................................. 58
4.4.1 Identification of HIC1 and miR-212/132 as a gene/miR pair regulated by a cancer-specific hypermethylated bidirectional promoter ..................... 58
4.4.2 Subgroup specific correlation of HIC1 and miR-212/132 expression ........ 59
4.4.3 Overexpression of HIC1 and miR-212/132 decreases medulloblastoma
and glioblastoma proliferation in vitro and in vivo ......................................... 60
4.4.4 Role of HIC1 and miR-212/132 in medulloblastoma formation in vivo ...... 61
4.4.5 Reactivation of HIC1 and miR-212/132 using CRISPR SAM ..................... 62
4.5 Discussion ....................................................................................................................... 62
Chapter 5 ................................................................................................................................ 76
5 Conclusion and Future Directions....................................................................................... 77
5.1 Summary of Results....................................................................................................... 77
5.2 Future Directions ............................................................................................................ 78
viii
5.2.1 Distinguishing between driver and passenger mutations ............................ 78
5.2.2 Subgroup specific pre-clinical models............................................................. 81
5.2.3 Unravelling the epigenetic code in medulloblastoma ................................... 82
5.2.4 Targeting the metastatic compartment for translation of new therapies ... 84
Copyright Permissions............................................................................................................... 85
References .................................................................................................................................. 87
ix
Abbreviations
APC adenomatous polyposis coli
ATOH1 atonal homolog 1 (aka MATH1)
ATRT atypical teratoid/ rhabdoid tumor
BCOR BCL6 co-repressor
BLBP brain lipid-binding protein
CCNU Lomustine
CDK6 cyclin-dependent kinase 6
CGI CpG island
cGy centigray
COG Children's Oncology Group
CSF cerebrospinal fluid
CT computed tomography
CTNNB1 β-catenin
CRISPR clustered regularly-interspaced short palindromic repeats
DDX3X ATP-dependent RNA helicase
EGL external granular layer
ETANTR embryonal tumor with abundant neuropil and true rosettes
EZH2 enhancer of zeste homolog 2
GFP green fluorescent protein
GFAP glial fibrillary acidic protein
GNCP granule neuron precursor cells
H3K4me3 histone H3 lysine 4 trimethylation
H3K27me3 histone H3 lysine 27 trimethylation
HART hyperfractionated accelerated radiotherapy
HCL hierarchical clustering
x
HDAC histone deacytylase
HDM histone demethylase
H&E hematoxylin and eosin
HMT histone methyltransferase
HIC1 hypermethylated in cancer 1
IHC immunohistochemistry
ING1 inhibitor of growth 1
INI1 see SMARCB1
KDM6A lysine (K)- specific demethylase 6A
LAD lamina-associated domains
LCA large cell anaplastic
LDB1 LIM domain-binding 1
LDE225 Sonidigeb (smoothened inhibitor)
LOCK large organized chromatin K9 modifications
MBEN medulloblastoma with extensive nodularity
mESC mouse embryonic stem cell
miRNA microRNA
MLL mixed linear leukemia
MLL2 histone-lysine N-methyltransferase 2D
MRI magnetic resonance imaging
MYC v-myc avian myelocytomatosis viral oncogene homolog
N-CoR nuclear receptor co-repressor complex
NMF non-negative matrix factorization
NSC neural stem cell
NSG NOD scid gamma
PB piggyBac
xi
PCA principal component analysis
PCTH1 patched-1
PI3K phosphatidylinositol-4,5-bisphosphate 3-kinase
qRT-PCR quantitative real time polymerase chain reaction
RB1 retinoblastoma 1
REB research ethics board
REST RE1 silencing transcription factor
RL rhombic lip
SAM synergistic activation mediator
SB Sleeping Beauty
SCNV somatic copy number variation
sgRNA single-guide RNA
SHH sonic hedgehog
SIOP International Society of Pediatric Oncology
SIRT1 NAD-dependent deacetylase sirtuin-1
SMARCB1 SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily B member 1
SNV single nucleotide variation
TCAG the centre for applied genomics
TGFB transforming growth factor beta
TP53 tumour protein 53
TSS transcriptional start site
UTR untranslated region
WHO World Health Organization
WNT wingless-related integration site
xii
List of Figures
Figure 1-1: Alterations in the cancer epigenome that leads to a stem-like bivalent
chromatin state able to differentiate into heterochromatin and euchromatin. ....... 23
Figure 1-2: Dichotomous roles of miR-34a, miR-9, and miR-124 in normal neuronal
development and medulloblastoma.................................................................................... 26
Figure 3-1: Expression signatures remain stable between primary and metastatic
medulloblastoma...................................................................................................................... 41
Figure 3-2: Methylation signatures remain stable between primary and metastatic
medulloblastoma...................................................................................................................... 43
Figure 3-3: Immunohistochemical markers of medulloblastmoa subgroups remain
stable between primary and metastatic compartments. ............................................... 44
Figure 3-4: Subgroup specific pathway analysis of the differentially expressed
genes between primary and metastatic medulloblastoma. .......................................... 46
Figure 4-1: Tumor suppressor gene HIC1 is frequently methylated across multiple
cancer types and is never mutated in medulloblastoma. ............................................. 64
Figure 4-2: HIC1 and miR-212/132 is a gene/miR pair regulated by a bidirectional
promoter. .................................................................................................................................... 67
Figure 4-3: Subgroup specific correlation between HIC1 and miR-212/132. ........... 68
Figure 4-4: Expression of HIC1 and miR-212/132 in a large cohort of human
medulloblastoma...................................................................................................................... 69
Figure 4-5: Establishment of dox-inducible stable cell lines overexpressing HIC1
and miR-212/132. ...................................................................................................................... 70
Figure 4-6: Overexpression of HIC1 and miR212/132 increases survival in
xenografts. ................................................................................................................................. 72
xiii
Figure 4-7: Target Scan predicts SIRT1 as a conserved target for both miR-212
and miR-132............................................................................................................................... 73
Figure 4-8: Targeted heterozygote deletion of Hic1 and miR212-132 does not
increase medulloblastoma incidence nor decrease tumour latency as compared
to Ptc+/-....................................................................................................................................... 74
Figure 4-9: Endogenous reactivation of Hic1 and miR212-132 using RNA-guided
Cas9 mediated transcriptional activation.......................................................................... 75
Figure 5-1: Overview of a spatially restricted, temporally inducible insertional
mutagenesis system using a hybrid Sleeping Beauty and piggyBac transposon to
delineate driver/maintenance genes................................................................................... 81
xiv
List of Tables
Table 1-1: Clinical features of different subgroups of medulloblastoma .................. 9
Table 1-2: Molecular features of different subgroups of medulloblastoma ............ 18
Table 3-1: Clinical Characteristics of Medulloblastoma Primary-Metastasis Cohort
........................................................................................................................................ 47
Table 3-2: Medulloblastoma Subgroup Predictions Using Orthogonal
Technologies ................................................................................................................ 48
1
Chapter 1
知己知彼,百戰不殆。 “Know thy self, know thy enemy. A thousand battles, a thousand
victories." - Sun Tzu
*Contents of this chapter have contributed to the following publications:
Wang X, Ramaswamy V, Remke M, et al. (2013) Intertumoral and intratumoral
heterogeneity as a barrier for effective treatment of medulloblastoma. Neurosurgery 60
Suppl 1:57–63. doi: 10.1227/01.neu.0000430318.01821.6f
Chan TYC, Wang X, Spence T, et al. (2015) Pediatric Neuro-oncology. Pediatr Neuro-
oncology. doi: 10.1007/978-1-4939-1541-5
Wang X, Ramaswamy V, Taylor MD (2015) Familial Tumor Syndromes. Neuro-
Oncology: The Essentials. doi: 10.1055/b-0034-97883
Wang X, Mack, S, Taylor MD (2016) Genetics of Pediatric Brain Tumors. Youmans-
Winn Neurological Surgery 7th ed
2
1 Introduction
1.1 Medulloblastoma
1.1.1 Historical perspective
Nearly a century ago, Harvey Cushing and Percival Bailey coined the term
‘medulloblastoma’ [1]. The nosology of the disease reveals a rather fascinating history
of clinical observations and empirical discovery. In order to hope to find a cure, one
must first understand the disease’s classification and behavior. Prior to 1925, there was
little consensus on the nomenclature of posterior fossa tumors. The nature and cell of
origin of this small, blue cell tumor of childhood has thus long evaded surgeons.
Although the discovery of stem cells in the central nervous system was not made for
another 65 years, the primitive appearance of tumour cells convinced Cushing and
Bailey to call the hypothetical multipotent cell a “medulloblast”. Even though no such cell
type has to date been identified, the naming of medulloblastoma remained and spurred
on generations of scientific discoveries. By the 1930s, Cushing completed operations on
over 60 cases of medulloblastoma. His medical acumen and careful documentation led
to the first meticulous description of the disease and its clinical features; his detailed
records noted the male proclivity, predilection for adolescents, the short history of
presenting symptoms, and the midline vermian anatomical location [2]. His work left a
lasting legacy and posed a herculean challenge to future clinicians and surgeons to
match his meticulous study and dedication to improve the lives of his patients. A
hallowed Chinese philosopher once said “Know thy self, know thy enemy. A thousand
battles, a thousand victories." In order to defeat the enemy known as ‘cancer’, one must
‘know’ or characterize cancer’s signatures. In the current era of genomic medicine and
classification of disease into subgroups, the hallmarks of cancer are beginning to be
unraveled and will no doubt herald the introduction of targeted individualized therapy.
The introduction of this thesis will span current classification schemes and treatment
options for medulloblastoma. Following this, recent genomic and epigenetic features of
cancer with a focus on medulloblastoma will be summarized.
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1.1.2 Epidemiology
Medulloblastoma represents up to 20% of all pediatric brain tumours. Medulloblastoma
arises from the cerebellum, and is classified as a World Health Organization (WHO)
grade IV tumor, with a predilection for children, with peak incidence between the age of
3 to 4 and 8 to 9. The annual incidence has been estimated at 1 in every 200 000
children under the age of 15 [3, 4]. Consistent with its classification as an embryonal
tumour, medulloblastoma is rarely seen in adults; in fact, 70% of patients present before
the age of 20. There is a modest male preponderance with a ratio of 1.4:1. A small
proportion of medulloblastoma (<5%) is associated with germline mutations, these
include: Gorlin’s syndrome (also known as nevoid basal cell carcinoma syndrome), that
is caused by mutation in the patched-1 (PTCH1) gene, Turcot syndrome, caused by
inactivating mutations in the adenomatous polyposis coli (APC) gene, and Li-Fraumeni
syndrome, caused by a mutation in TP53 [5]. These familial cancer syndromes-related
mutations provided us with the first clues to biological pathways that underlie
medulloblastoma pathogenesis and reveal some of the first mismatch repair genes
involved in cancer formation.
1.1.3 Clinical Presentation and Histopathology
Patients with medulloblastoma frequently present with signs and symptoms related to
hydrocephalus secondary to fourth ventricle obstruction. Predominant symptoms
include vomiting, headache, and nausea [6]. Due to a localized mass in the posterior
fossa, truncal ataxia, dysmetria, and diplopia secondary to sixth nerve palsy, are often
accompanying symptoms. Preceding symptoms include morning headache with
vomiting, irritability, and lethargy; these subtle clinical signs present difficult diagnostic
challenges. Recent advances in treatment strategies have dramatically increased
survival to upwards of 75 percent [7]. However, the presence of metastatic disease at
diagnosis, and recurrent disease still result in significant mortality.
4
The differential diagnosis for medulloblastoma includes a range of tumours with a
predilection for the cerebellum. These include pilocytic astrocytoma, ependymoma, and
atypical teratoid/rhabdoid tumors (ATRT). ATRT is much rarer than medulloblastoma
[8]. It can be difficult to distinguish these tumour entities through clinical symptoms
alone, however patients with pilocytic astrocytoma tend to have a longer duration of
symptoms. This likely reflects the underlying biology of the tumour. Further, children
with ependymoma often have a history of neck pain or stiffness. Certain imaging
findings can assist with the differential, but the ultimate diagnosis relies on surgical
pathology.
The initial diagnosis of medulloblastoma is usually made on non-contrast computed
tomography (CT) followed by magnetic resonance imaging (MRI), and later confirmed
with pathology. The classical CT finding is a hyperattenuating midline mass on an
unenhanced study that markedly enhances after the administration of contrast medium
[9]. It is important to note that by CT, medulloblastoma may be missed and cannot be
easily distinguished from other posterior fossa tumours; MRI with gadolinium
enhancement is the preferred imaging modality. Unlike medulloblastoma, pilocytic
astrocytomas are typically cystic with a mural nodule. Ependymomas often grow to fill
the ventricles and fill the foraminal extensions; this is much less common in
medulloblastoma. ATRT can have similar appearance to medulloblastoma on MRI, but
they more frequently involve lateral hemispheres and contain intratumoral hemorrhage.
A diagnosis of medulloblastoma has long been based on histological examination.
Microscopically, medulloblastoma is often referred to as a “small round blue cell tumor”,
given the characteristics of densely packed cells with prominent nuclei surrounded by
scant cytoplasm under H&E staining. In accordance to the World Health Organization
Classification of Tumors of the Central Nervous System, there exist five major
histological variants in medulloblastoma: classic, desmoplastic, large-cell, anaplastic,
and medulloblastoma with extensive nodularity (MBEN). Clinically, histological
examination is used, along with other patients’ clinical presentations, to predict clinical
outcome and to guide therapeutic decisions. For instance, large cell and anaplastic
variants often show the poorest outcome while the best survival outcome is observed in
5
desmoplastic and MBEN lesions. However, tumour heterogeneity and subjectivity in the
pathology lab can confound the stratification of patients.
The pathological differential diagnosis for medulloblastoma consists of ATRT and
embryonal tumor with abundant neuropil and true rosettes (ETANTR) [10]. ATRT should
be part of the differential for all suspected medulloblastoma patients under the age of 5
and careful attention is required to look for a rhabdoid component upon histological
examination. Loss of INI1/hSNF5, a molecular characteristic of ATRT, is commonly
used in diagnostic assessment for infants diagnosed with medulloblastoma. Thus,
negative immunostaining is indicative as ATRT while medulloblastoma tumors show
positive reactivity. Major pathological features of ETANTR include ependymoblastic
rosettes, neuronal differentiation on a neuropil background, and molecular
characteristics of focal amplification at 19q13.42. Other posterior fossa tumors including
pilocytic astrocytoma and ependymomas. These can be ruled out by the presence of
solitary cystic nodules and foraminal extensions, respectively.
1.1.4 Risk stratification
Traditionally, patients are assigned into different risk groups according to their clinical
presentations, based on standard diagnostic features, which includes age, extent of
resection and the presence of metastasis at time of diagnosis. High-risk patients are
defined as <3 years of age, those with more than 1.5cm of residual tumour after surgery
or with evidence of metastasis at presentation [11, 12]. As up to one third of
medulloblastoma patients present with leptomeningeal metastasis to the brain and/or
spinal canal, MRI imaging of the entire craniospinal axis should be performed pre- and 2
weeks post-operatively. Presence of metastatic and/or residual tumour is indicative of
high-risk disease and impacts clinical management. Furthermore, cerebrospinal fluid
(CSF) cytology is also used to test for evidence of tumor dissemination. Patients with
non-metastatic disease and with little or no postoperative residual tumour, who are
greater than 36 months of age, are stratified as standard-risk. Although this schema has
6
been in place for many years, assigning patients to these risk groups remains
subjective.
Multimodality treatment protocols are assigned to patients according to the risk
stratification schema. It has been argued that the current staging system fails to define
the true extent of disease leading to frequent over-/under-treating patients. The
devastating acute and long-term sequelae have been a major concern due to the
substantial effect on patient quality of life. In spite of the aggressive therapies, around
one quarter of patients eventually succumb to disease.
Recent studies have highlighted the genetic heterogeneity of medulloblastoma [13–18].
Consensus in the field now acknowledges that medulloblastoma comprises at least 4
distinct molecular variants, termed Wingless (WNT), Sonic Hedgehog (SHH), Group 3
and Group 4. These subgroups have highly disparate cytogenetics and gene expression
signatures in addition to divergent clinical phenotypes such as tumor cell histology and
patient outcome (refer to Table 1-1). There are also marked clinical differences between
WNT and SHH subgroups of medulloblastoma, whereby patients with WNT tumours
exhibiting the best prognosis (greater than 95% survival). These tumors typically occur
in older children, and exhibit classic histology. Meanwhile, SHH medulloblastoma
represents an intermediate prognosis subgroup with overall survival ranging from 60%
to 80%. SHH subgroup is found predominantly in infants and young adults, and
desmoplastic histology is almost exclusively restricted to this subgroup [19]. A more
detailed molecular description of medulloblastoma subgroups will be covered in section
1.2.
Over the past few decades, with advances in surgical techniques, radiotherapy and
chemotherapy, overall survival for patients with a diagnosis of medulloblastoma has
reached nearly 75%. Despite this, the treatment-induced toxicity, especially that of
radiation on a developing central nervous system, results in significant morbidity and
profound negative consequences to the quality of life among patients. The availability of
tools to segregate molecular subgroups will profoundly alter the design of
medulloblastoma clinical trials. A significant challenge involves development of robust
diagnostic assays that can be used in clinical trials to reliably distinguish
7
medulloblastoma subgroups with high sensitivity and specificity. Most consistent
subtyping data has involved screening for nuclear CTNNB1 immuno-positivity and/or
monosomy 6 which identifies WNT medulloblastoma and MYC amplifications which
identifies group 3 medulloblastoma. Assay to reliably identify SHH and group 4
medulloblastoma are lacking. Promising assays for medulloblastoma subgrouping using
focused transcriptional and methylation assays are rapid and accurate, but remain to be
validated. A recent 22-gene nanoString expression signature has been reported and
able to assign up to 88% of formalin-fixed paraffin embedded specimens with up to 98%
accuracy [20]. In addition to enhanced risk stratification for current conventional
treatment regimens, molecular subtyping of medulloblastoma will enable concerted
investigations of novel therapy tailored to subgroup specific biology. The inclusion of
molecular analyses with traditional histo-clinical examination will be the standard of care
in establishing diagnosis and treatment stratification of medulloblastoma in the near
future.
1.1.5 Treatment and prognosis
Patients with medulloblastoma often present with significant secondary hydrocephalous
due to occupying mass impeding the fourth ventricle. As such, the management of
symptoms related to increased intracranial pressure is a common priority. Often,
surgery alone is sufficient to treat hydrocephalus; as such pre-operative management of
increased intracranial pressure is done to alleviate symptoms. Patients are typically
managed with corticosteroids to alleviate tumour edema. In most cases, placement of a
CSF diversion may also be indicated. A multi-modal approach involving maximal safe
surgical resection, radiation therapy to the primary tumour site and craniospinal axis, as
well as systemic adjuvant chemotherapy represent the current standard of care [21].
8
Tumor Resection
Maximal safe resection of the posterior fossa mass is a key component for patients with
medulloblastoma. Resection confirms the diagnosis, relieves hydrocephalus and
significantly improves survival. The primary goal of resection is gross-total resection
where it is safe to do so. As medulloblastoma is sensitive to both chemotherapy and
radiation, residual tumour can still be managed with other treatment modalities. A post-
operative MRI is always indicated 72 hours following surgery to determine the extent of
resection. Residual disease may be associated with poorer outcome, and re-resection
for large residual tumour should be considered unless the surgeon stopped the initial
resection due to excessive vascularity or invasion of critical structures. With modern
surgical techniques, gross total resection can be achieved in a majority of patients.
9
Table 1-1: Clinical features of different subgroups of medulloblastoma
WNT SHH Group 3 Group 4
Clinical features
Frequency 10% 30% 25% 35%
Gender (M:F) 1:1 3:2 2:1 3:1
Age distribution Children, rarely adult
Infant, adult, infrequently children
Infant, children Children, adult
Histology Classic (rarely LCA)
Desmoplastic/ MBEN/ Classic/ LCA
Classic/ LCA Classic/ LCA
Frequency of metastasis
Rare Uncommon Very common Common
5-year survival 95% 75% 50% 75%
IHC markers Nuclear β-catenin, DKK1
GAB1, SFRP1 NPR3 KCNA1
10
Radiation Therapy
Radiation therapy remains a critical part of a multimodal approach for the initial
management of medulloblastoma, and should be initiated within one month of surgical
resection. The goal of radiotherapy is to control for both residual tumour at the primary
site and to treat leptomeningeal spread along the cranialspinal axis that is otherwise not
amenable to surgical resection. Due to the severe toxicity to the developing nervous
system, craniospinal radiation is often avoided or delayed in patients under the age of 3.
For average risk patients, where gross total resection has been achieved, the
craniospinal axis is treated with 2340cGy with a boost to the posterior fossa for a total
dose of 5400-5580cGy. For patients who present with disseminated disease, the
dosage to the craniospinal axis is raised to 3600cGy with a boost in the posterior fossa
to 4500cGy [22]. Radiation therapy has tremendous toxicity to the developing normal
brain, and efforts are currently being made to improve intensity modulated radiation
therapy and explore proton based radiation therapy in addition to radiosensitizers to try
and minimize normal tissue radiation.
Radiation therapy is typically never administered alone, but rather in combination with
adjuvant cisplatin based chemotherapy. This is done to reduce the dosage of radiation
and to improve overall survival. Survival for patients with average risk disease have
peaked at 85%, while those with high-risk disease has seen survival rates range from
30-65%. Despite significant improvements in survival with radiotherapy, there are
severe adverse effects, particularly a high incidence of neurologic complications. In
addition to significant cognitive impairment, ototoxicity, thyroid dysfunction, growth
failure, endocrine abnormalities, and radiation necrosis are all side effects of radiation
therapy [23–28]. As such concomitant chemotherapy has led the way in evolution of
therapeutic approaches with the goal of minimizing late toxicity.
11
Chemotherapy
In both high risk and average risk patients, adjuvant chemotherapy plays an important
role for the management of medulloblastoma. In all cases, chemotherapy is used with
the intent to lower radiation doses. Especially in young children, chemotherapy is used
after surgical resection. This will help delay or even avoid irradiating the developing
central nervous system, thereby eluding the extreme progressive cognitive decline seen
in young patients who have been irradiated.
For children under the age of three, therefore, chemotherapy is the only option following
surgery. Currently, three approaches have been reported in the literature with 5-year
overall survival ranging between 50-70%. These include i) systemic administration of
chemotherapy followed by myeloablative therapy with autologous stem cell support
followed by radiotherapy for local recurrence, ii) concomitant systemic and
intraventricular chemotherapy, and iii) systemic chemotherapy with conformal local
radiotherapy [29–31]. Due to the ongoing nature of chemotherapeutic advances,
patients are frequently enrolled in large clinical trials. This places further emphasis on
proper risk stratification with the most important factors being extent of dissemination
and risk for treatment toxicity.
For average risk patients over the age of three, several multimodal approaches have led
to overall survival exceeding 80%. In the Children’s Oncology Group study, 4-9 cycles
of cisplatin based adjuvant chemotherapy is frequently administered concomitantly with
radiation therapy. Patients received weekly vincristine during radiation therapy followed
by eight cycles of chemotherapy with one of two regimens (vincristine, cisplatin, and
CCNU, or vincristine, cisplatin and cyclophosphamide); five-year overall survival in this
case reached 86% [32]. In the St. Jude-Medulloblasotma-96 protocol, where patients
received 2340cGy of radiation followed by four cycles of cyclophosphamide-based,
dose-intensive chemotherapy, 5-year overall survival was 85% [33].
Patients with high-risk disease frequently present with metastasis, which significantly
decreases survival. Several approaches have been studied in this population, including
high-dose chemotherapy with autologous hematopoietic cell transplantation following
radiation therapy. In the Milan Strategy, patients received postoperative methotrexate,
12
etoposide, cyclophosphamide, and carboplatin in a 2-month schedule, followed by
hyperfractionated accelerated radiotherapy (HART). This regiment resulted in a 5-year
overall survival of 73% [34]. In the COG study, patients were given 15 to 30 doses of
the radiosensitizer carboplatin along with vincristine during radiation, followed by
randomization into two regimens. In the regimen with the highest overall survival at
82%, patients received 6 months of maintenance chemotherapy with cyclophosphamide
[35].
Due to the increased incidence of secondary malignancies from both radiation and
chemotherapy as well as the monitoring of treatment complications and disease
recurrence, periodic surveillance with brain and spine MRIs, as well as regular
neuropsychological and medical examinations, are indicated. Recurrent disease, which
occurs in approximately 25% of patients, remains a significant clinical challenge. Most
relapses tend to occur within the first 3 years post-diagnosis and long-term survival in
this population remains poor. High-dose chemotherapy with autologous hematopoietic
cell transplantation has been studied with long-term survival in 20-25% of patients.
Molecular Therapeutic Targets
Early insight into medulloblastoma biology from the identification of subtypes have
transformed future clinical trial design. It is expected that, in the near future, patients will
be stratified and treated based on the biological makeup of their disease, which will
hopefully lead to improved patient outcomes with less adverse effects. In general, one
main goal is to reduce morbidity of current treatment regimens while maintaining long-
term survival. Due to the favorable outcome characteristics observed in WNT subgroup,
reducing chemotherapy and craniospinal irradiation could be one tangible approach that
will minimize late effects [36].
To date, multiple pharmacological inhibitors against SHH signaling have been designed
and shown to have promising anti-tumour effects in SHH medulloblastoma models.
Some of these are being studied in multicenter clinical trials [37, 38]. Despite promising
preclinical studies in the mouse, drug resistance was often observed during the course
13
of treatment in humans. Although it is better to use a specific small molecule inhibitor in
order to minimize adverse effects, often times targeting one pathway may not be
enough to kill all cancer cells, thereby contributing to treatment failure. Therefore, multi-
targeting approach, for example targeting Shh signaling combined with other key
molecular pathways such as AKT, NOTCH, TGF-β, could represent a promising
strategy for personalized therapy. The feasibility of combinational therapy has been
assessed in medulloblastoma preclinical models. For instance, the combination of
retinoic acid, a metabolite of Vitamin A that induces apoptosis of cancer cells, with
histone deacetylase inhibitor, showed success in xenograft and transgenic mice models
[39]. Also the combination of LDE225 (Sonidegib) with PI3K inhibitors markedly delayed
development of resistance, suggesting the importance of multiple pathway inhibitions for
sustainable cancer suppression [40].
More recently it appears that activation of PI3K/AKT signaling is also implicated in
medulloblastoma, especially in the most aggressive Group 3 tumours, making inhibition
of this pathway an attractive therapeutic approach. Small inhibitors of PI3K/AKT have
already been shown to suppress medulloblastoma cell line growth in vitro, as well as
tumorigenesis of Myc-driven mouse models. As MYC over-expression confers
aggressive and metastatic behavior in medulloblastoma, targeting MYC function has
always been desirable. Intriguingly, recent studies using myeloma and lymphoma
models in vivo have showed effective use of BET bromo-domain inhibitors to suppress
MYC expression. More interestingly, synthetic lethal targets for MYC-driven cancer also
provide for new therapeutic opportunities, such as the core SUMOlyation machinery [41]
and eukaryotic initiation factor complex assembly that are required to support a MYC
oncogenic state [42]. All of these therapeutic strategies could potentially be used to treat
MYC amplified medulloblastoma. Future biological and clinical studies should additional
novel therapeutic targets with the aim of maximizing cure rates and minimizing
sequelae.
14
1.2 Genomics of medulloblastoma subgroups
Medulloblastoma was first linked to abnormalities in the Wingless (WNT) and Sonic
Hedgehog (SHH) developmental signaling pathways based on observed association of
medulloblastoma with Turcot syndrome and Gorlin syndrome, as well as demonstration
of alterations respectively in the APC and PTCH genes in some medulloblastoma. Early
small cohort studies also suggested that specific genetic alterations, notably, MYC gene
amplification and CTNNB1 mutations, had prognostic correlations in medulloblastoma.
Next-generation sequencing highlighted subgroup-specific classification of the disease.
These studies have also validated previously identified mutations, while unveiling
previously unappreciated novel drivers [43–45]. An examination of somatic copy
number aberrations (SCNAs), across a large assembly of medulloblastoma samples
revealed several novel actionable targets in a subgroup-specific manner [46]. A
summary of the molecular features of the different subgroups of medulloblastoma can
be found in Table 1-2.
1.2.1 WNT subgroup medulloblastoma
WNT subgroup medulloblastoma are genomically bland, with an absence of focal
recurrent SCNAs. The most common somatic mutation is CTNNB1, which confirms the
importance of WNT signaling through β-catenin [16, 47]. Other recurrent somatic
mutations found within this subgroup include genes coding for TP53, the DEAD-box
RNA helicase DDX3X, involved in cellular growth and division, and chromatin-modifier
SMARCA4. The developmental origin of WNT tumors is thought to be progenitor cells of
the lower rhombic lip. A mouse model harboring activated Ctnnb1 in Blbp expressing
radial glial cells in a Trp53 heterozygote background with activated PI3K signaling
(Blbp-cre; Ctnnb1+/lox(Ex3); Trp53+/flx; Pik3caE545K) develop highly penetrant WNT
medulloblastoma [48].
15
1.2.2 SHH subgroup medulloblastoma
Activation of Sonic Hedgehog signaling has long been known to play a pathogenic role
in medulloblastoma [49]. Somatic mutations targeting the SHH receptor PTCH1 are only
found in this subgroup. Other somatically mutated genes include TP53 and MLL2, in
14% and 12% of patients, respectively [50]. SHH medulloblastoma also exhibit frequent
SCNAs, frequently including loci involved in PI3K signaling [46]. This finding is notable
as inhibitors of the PI3K pathway are widely available, and may be used in combination
with SHH inhibitors for treatment of this disease variant [38, 40, 51]. Several mouse
models of this subgroup have been developed by targeting cerebellar granule neuron
precursors (CGNP), as well as neural stem cells (NSC) located in the subventricular
zone [52–54]. Specifically, inactivation of patched in either the Atoh1 (marks CGNP
cells) or GFAP (marks NSCs) compartments lead to medulloblastoma development
[55–57].
1.2.3 Group 3 medulloblastoma
Poor prognostic Group 3 medulloblastomas frequently harbor genomic instability and
show high-level amplification of c-MYC [16]. Somatic mutations in this subgroup appear
to converge on regulators of the epigenome, mainly SMARCA4 and MLL2, validating
previous work done using traditional Sanger based sequencing [58]. More interesting is
the identification of a novel PVT1-MYC fusion, the first recurrent gene fusion identified
in medulloblastoma [46]. This discovery offers new insights into the complex function of
MYC in driving tumorigenesis. Recent work has also identified overexpression of growth
factor independent 1 family proto-oncogenes (in a subset of Group 3 and Group 4
tumours), GFI1 and GFI1B through structural variants that place enhancers upstream of
either locus [59]. In vivo, these oncogenes cooperate with MYC to promote
medulloblasomta formation in mice. Further, the finding that greater than 20% of Group
3 tumors have aberrant TGF-β signaling provides a new opportunity for therapeutic
intervention in these aggressive tumors. Two recent publications describe the first
16
mouse models for Group 3 disease, both using orthotopic transplantation [60–62]. In
CGNP and NSC populations, activation of Myc with concomitant p53 inactivation were
able to generate these pre-clinical models for Group 3 medulloblastoma. Current
research is aimed at developing transgenic models of Group 3 tumours that better
represent the heterogeneity and microenvironment of tumours.
1.2.4 Group 4 medulloblastoma
Group 4 disease is characterized by amplification of MYCN and isochromosome 17q.
Prior gene expression studies have reported that Group 4 tumours have a neuronal
expression signature [15, 63]. Recurrent somatic mutations, similar to Group 3, seem to
converge on epigenetic regulators, particularly histone modifiers [64–67]. For example,
mutations in KDM6A, MLL3, and ZMYM3 are common. These genes may play a role in
maintaining cells in an undifferentiated state paralleling their role in normal stem cell
function; further research is needed to elucidate this pathogenic mechanism. One of the
recurrently altered genes involves a tandem duplication of the SNCAIP gene, which is
mutated in a subset of Parkinson’s disease patients [68]. The biological relevance of
this duplication needs to be functionally characterized. One of the remaining hurdles for
this subgroup is the lack of an animal model. Recent evidence suggests that a MYCN-
driven mouse model, independent of SHH signaling, resembles a Group 4
medulloblatsoma; although its specific subgroup affiliation remains to be definitively
defined [61, 69].
These findings, identified using modern genomic technologies uncover an astoundingly
small number of somatic mutations in medulloblastoma, on the order of 10-12,
compared with hundreds to thousands in solid tumors of adulthood [50, 70]. This
suggests that Group 3 and 4 medulloblastoma may be copy number driven diseases
with the pathogenic process converging on a few key molecular processes. Further, the
preponderance of aberrations in epigenetic regulators suggest that medulloblastoma
may be driven by epigenetic mechanisms targeting the histone code. Over the coming
years, with the establishment of pre-clinical models recapitulating subgroup-specific
17
diseases, these models should enable rapid testing of targeted therapeutics for clinical
translation.
18
Table 1-2: Molecular features of different subgroups of medulloblastoma
WNT SHH Group 3 Group 4
Genomic features
Cytogenetics: Gain
n/a 3q (27%) 17q (62%), 1q (35%), 7 (55%), 8q (22%), 12q (17%), 18 (26%)
17q (73%), 7 (47%), 12q (20%), 18 (16%)
Cytogenetics: Loss
6 (85%) 9q (47%), 17p (25%), 10q (26%)
9q (21%), 17p (42%), 8p (33%), 10q (49%), 16q (50%)
17p (63%), 8p (41%), 10q (15%)
Recurrent somatic mutations/ focal SCNAs
CTNNB1 (90.6%), MYC (16.7), DDX3X (50%), SMARCA4 (26.3%), MLL2
(12.5%), TP53 (12.5%)
PTCH1 (28%), TP53 (13.6%), MLL2 (12.9%), DDX3X (11.7%), MYCN (8.2%), BCOR (8%), LDB1 (6.9%), TCF4 (5.5%), GLI2 (5.2%)
MYC (16.7%), PVT1 (11.9%), SMARCA4 (10.5%), OTX2 (7.7%), CTDNEP1 (4.6%), LRP1B (4.6%), MLL2 (4%)
KDM6A (13%), SNCAIP (10.4%), MYCN (6.3%), MLL3 (5.3%), CDK6 (4.7%), ZMYM3 (3.7%)
Expression signature
WNT signaling SHH signaling Retinal signature, MYC signature
Neuronal signature
Selected animal models
-Blbp–Cre;
Ctnnb1+/lox(Ex3)
;
Trp53+/flx
;
Pik3caE545K
-Atoh1–Cre;
Ptch1flx/flx
-GFAP–Cre;
Ptch1flx/flx
-Atoh1–SB11; T2Onc;
Ptch1+/−
-Prom1+Lin−
NSCs infected
with MycT58A
plus DNp53 retroviruses
-Atoh1–GFP;
Trp53−/−
CGNPs infected with Myc-RFP retroviruses
n/a
Purported cell-of-origin
Lower rhombic lip of progenitor cells
CGNPs of the EGL and cochlear nucleus; NSCs of the SVZ
Prominin1+, lineage- NSCs; CGNPs of the EGL
Unknown
19
1.3 Epigenetics of medulloblastoma
1.3.1 Histone Modification and DNA Methylation
Studies over the last few decades have begun to reveal the influence of the epigenome
on cancer formation. Early experiments looking at DNA methylation demonstrated that
samples of human colorectal cancer have widespread hypomethylation involving
approximately one-third of single copy genes [71]. Further studies show
hypermethylation of promoters, such as that of tumor suppressor RB1 [72]. One of the
recurrently hypermethylated genes in medulloblastoma is hypermethylation in cancer 1
(HIC1). HIC1 is a tumor suppressor gene methylated across multiple tumour types,
whose protein product regulate SIRT1 to modulate p53-dependent DNA damage
response [73]. Reports in mouse studies indicate that methylation of this gene may work
in concert with Ptch1 mutations to promote tumorigensis [74]. Large-scale
hypomethylation with focal hypermethylation of individual genes appear to be an
underlying mechanism for tumorigenesis in many contexts [75]. These hypomethylated
blocks contain the most differentially expressed genes in tumours compared with
normal tissue [76]. Several studies have shown that these methylation changes occur
as a result of histone modifications [77, 78]. Tissue specific differentially methylated
regions often reside outside of CpG islands in regions known as shores. During cellular
differentiation, large organized chromatin K9 modifications (LOCKs) are established as
barriers against dedifferentiation. These regions of methylation, normally sharply
demarcated, are lost in the context of cancer and can lead to altered gene expression
[76]. Gene body hypomethylation to drive active transcription is another novel
mechanism that has been shown in medulloblastoma [79].
Recent sequencing studies on pediatric malignancies found few somatic mutations,
suggesting that epigenetic derangements can be drivers. For example, pediatric
rhabdoid tumours harbor biallelic loss of the chromatin remodeler gene SMARCB1, part
of the SWI/SNF family of chromatin regulators. Remarkably, no other recurrent
mutations were found [80]. Similarly, hindbrain ependymomas have no recurrent
somatic mutations, but instead show a CpG island methylator phenotype leading to
20
transcriptional silencing of Polycomb repressive complex 2 targets [81]. It has now been
proposed that environmental stresses converge on epigenetic modulators that
restructure the epigenome to allow for permissive expression of epigenetic mediators
overlapping with stem-like genes [82, 83].
One class of chromatin modifiers are histone deacytylases (HDACs). Histone tail
acetylation attenuates DNA binding and compaction and allows transcription to take
place. HDACs has the opposite effect of encouraging histone-DNA binding. Several
HDACs have been found to be upregulated in high-risk medulloblastoma, including
HDAC5 and HDAC9 [84]. The Sirtuin family of histone deacetylases is important in
regulation of neural stem cells. One member of this family, SIRT1 is overexpressed in
medulloblastoma [85]. Several HDAC inhibitors have found their way to pre-clinical
studies including the HDAC inhibitor SAHA.
Modulation of chromatin through covalent histone modifications represents a key
mechanism to facilitate coordinated gene expression, DNA replication and DNA repair
[86]. Histone modifications allow for manipulation of nucleosome structures to recruit
downstream ‘reader’ or ‘effector’ proteins. DNA methylation is a common mechanism
employed by tumor cells to silence tumor suppressor genes. Furthermore, the
epigenetic theory of tumorigenesis postulates that epigenetic alterations involving
histone modifications lead to dysregulation of gene expression and consequently
tumorigenesis. DNA methylation and histone modification have crucial roles in cell fate
determination, and help to establish tumour-initiating cell populations in early
tumorigenesis [82]. Several methyltransferases are mutated in medulloblastoma. In fact,
genes regulating chromatin modification are mutated in up to 33% of medulloblastoma
cases [50].
Histone methylation marks most associated with oncogenesis include histone H3 lysine
4 (H3K4) and H3 lysine 27 (H3K27). Homeostasis at these sites is mediated by
antagonizing groups of enzymes, histone methylation ‘writers’ and ‘erasers’, which
install and remove histone methylation marks. Methylation of histones can occur at both
lysine and arginine residues and is a reversible process, offering potential for
therapeutic intervention. H3K4 trimethylation (H3K4me3) is strongly associated with
21
transcriptional competence and activation (highest near transcriptional start sites), while
H3K27 trimethylation (H3K27me3) is frequently associated with gene silencing
(especially of unwanted differentiation programmes during lineage specification) [87,
88]. Histone lysine demethylases (HDMs), especially those that remove methylation on
H3K4 and H3K27 are found mutated or deregulated in human cancer. Upon DNA
damage, the H3K4me3 mark recruits and/or stabilizes the inhibitor of growth 1 (ING1).
ING2 repressive complexes then bind to genes responsible for cell proliferation such as
MYC and cyclins, leading to gene repression and the halt of cell cycle progression. A
subset of cancer-associated somatic mutations in ING1 interfere with binding to
H3K4me3/2 and response to DNA damage [89].
H3K4 methylation is established by SET1 and mixed lineage leukemia (MLL) family of
histone methyltransferases (HMTs) [90], and removed by the lysine-specific histone
demethylase 1 (LSD1) as well as Jumonji AT-rich interactive domain 1 (JAIRD1) family
histone demethylases [91]. HMTs play an important role in tumorigenesis. Of note, MLL
gene rearrangement, is one of the most common chromosomal abnormalities in human
leukemia, accounting for around 80% of infant leukemia and 5-10% of adult acute
myeloid leukemia [92, 93]. In the context of solid tumours, inactivating mutations in
MLL2 and MLL3 are some of the earliest alterations in the medulloblastoma genome
[58].
Arguably the most important repressive histone mark is H3K27me3. H3K27-specific
demethylase UTX (ubiquitously transcribed tetratricopeptide repeat X chromosome) is a
HDM gene frequently inactivated in solid tumours [94]. These histone modifiers often
coordinate in oncogenesis. UTX can bind to MLL2, forming a complex which erases
H3K27me3 and writes H3K4me3 at target chromatin [95]. These modifications appear
to silence the tumor suppressor locus INK4B-ARF-INK4A. Enhancer of zeste homolog 2
(EZH2) is an H3K27-specific methyltransferase, and as expected, overexpression of
EZH2 is found in various solid tumours, including medulloblastoma (Group 3 and 4).
Recent work demonstrates that EZH2 behaves as a writer of H3K27me leading to
transcription repression of key tumor suppressors such as INK4A and CDKN1B [96].
22
The dichotomous nature of histone ‘writers’ and ‘erasers’ enables clonal selection
through alternative gene mutations to converge on the same pathway (Figure 1-1). In
fact, in the recent medulloblasotma sequencing studies, KDM6A, a histone H3K27
demethylase on chromosome Xp11.3 was found to be the most frequently mutated
gene in Group 4 medulloblasotma, present in 12% of tumours [43]. These mutations are
nonsense mutations and implicate this gene as a tumor suppressor. Interestingly, EZH2
(an H3K27 methyltransferase), which functions to oppose the effect of KDM6A (which
promotes differentiation), helps maintain cell in an undifferentiated state when
overexpressed. These mechanisms are mutually exclusive approaches to disrupt the
chromatin code. Similarly, SHH subgroup medulloblastoma frequently exhibit mutations
in the nuclear receptor co-repressor (N-CoR) complex, BCL6 co-repressor (BCOR) and
LIM domain-binding 1 (LDB1). The N-CoR complex is associated with HDAC activity
and is thought to mediate transcription repression by influencing deacetylation [97].
These histone modifications shifts the balance from active euchromatin to an inactive
heterochromatin state thereby silencing expression of tumor suppressor genes.
Convergence on the histone code underlies the importance of epigenetic deregulation in
medulloblastoma.
It will now be important to determine whether mutations in epigenetic modifiers
represent ‘drivers’, as expected, or mere ‘passengers’ of oncogenesis. The causality of
deregulation remains unknown and whether these marks are in fact maintaining tumor
progression or simply a remnant of cell lineage specification requires further research.
Perhaps the most appealing aspect of cancer epigenetics is that unlike the genetic
code, many histone-modifying enzymes are ideal targets for therapy as their enzymatic
activity is ‘druggable’ [98]. However, caution must be taken as these factors are also
involved in various crucial processes including regulation of normal stem cell
differentiation.
23
Figure 1-1: Alterations in the cancer epigenome that leads to a stem-like bivalent
chromatin state able to differentiate into heterochromatin and euchromatin.
Adapted from Timp et al., 2013.
1.4 Role of microRNAs in development and medulloblastoma
1.4.1 microRNA biogenesis and role in normal CNS development
MicroRNAs (miRNAs) are short 18-25 nucleotide non-coding RNAs that act to regulate
gene expression post-transcriptionally [99]. Since the discovery and identification of
miRNAs, it has revolutionized our understanding of gene regulation. Through
complementary sequences within target mRNA, miRNAs have been shown to regulate
thousands of genes. This complex network of gene regulation is now accepted to be a
24
critical process of normal development and function. Due to miRNA’s wide-reaching
effects on large-scale gene regulation, their aberrant expression has been associated
with a variety of different pathological states, including cancer.
Biogenesis of miRNA follows an intricate process of transcription and post-
transcriptional modification. The canonical pathway for this process initiates in the
nucleus where RNA polymerase II transcribes nascent primary-miRNA (pri-miRNA).
These transcripts are similar to protein-coding transcripts, which are usually long
nucleotide sequences with 5’ cap and poly-adenylated 3’ tails. Due to the specific stem-
loop secondary structures of pri-miRNA, they are recognized and processed by the
nuclear ribonuclease Drosha and its partmer DGCR8, resulting in a shorter precursor
miRNA called pre-miRNA. Following this, pre-miRNA is transported to the cytoplasm via
XPO5 and undergoes further processing by DICER1 ribonuclease [100]. Commonly,
the miRNA undergoes strand separation where a mature miRNA is incorporated into the
RNA-induced silencing complex (RISC) and the passenger miRNA is degraded. The
mature miRNA and RISC complex is then able to regulate gene expression through
binding of complementary sequences generally in the 3’ untranslated regions of the
target mRNA [99].
During early central nervous system development, neural stem cells show a high
proliferative rate, dividing symmetrically to expand their progeny [101]. This is achieved
through activation of proliferation (e.g. SHH), stemness maintenance (e.g. NOTCH),
and repression of neuronal differentiation programs (e.g. RE1 silencing transcription
factor, REST). Once the number of neural stem cells is optimal, they switch to
asymmetric division giving rise to immature neurons that undergo differentiation
(through REST inactivation) [102]. This switch coincides with activation of miRNA
expression that helps fascilitate neuronal differentiation. miR-34a [103–105], miR-9
[106, 107], and miR-124 [108, 109], are among the most important regulations in this
context.
One of the most studied regulatory loops involved in neural stem cell differentiation
involves the REST-SCP1 complex. This complex normally silences neuronal genes in
non-neuronal cells by suppressing miR-9 and miR-124. During neuronal differentiation a
25
negative feedback loop is activated, whereby miR-124 suppresses the activity of SCP1
and thus inhibits the REST complex allowing for neuronal specification [110]. miR-124
also appears to promote the transition of neuronal precursors to more mature neurons
through inhibition of SOX9 [111]. Differentiation from a stem-like to mature state
involves coordinated activation of neuronal genes while activating cell cycle exit.
Interestingly, miR-9 deletion in the hindbrain results in an increase in cell proliferation
due to indirect downregulation of the cell cycle inhibitor p27 [112].
1.4.2 Oncogenic and tumor suppressor microRNAs
Tumorigenesis has long been thought to occur through alterations in normal
development. As miRNAs play such a pivotal role in CNS development, their
involvement in cancer is well-linked, but so far poorly understood. During differentiation,
miRNA expression increases. As cancer is often a disease characterized by global
dedifferentiation, studies have shown that miRNA expression is globally downregulated
in several brain tumours. As expected, one of the targets of these downregulated
microRNAs, REST, which represses differentiation, is frequently upregulated in
medulloblastoma [108, 109]. REST acts as a driver in medulloblastoma mouse models
and its expression is considered a poor prognostic factor. miR-34a is a downstream
effector of the p53 signaling pathway that inhibits cell cycle and trigger apoptosis. miR-
34a expression is activated by TP53, which is recurrently inactivated in
medulloblastoma by p53 deletions, transcriptional repression, or inactivating mutations,
leading to deregulated proliferation [104, 105]. miR-34a re-expression in
medulloblastoma cell lines functions to inhibit CDK6, promotes p53-independent
apoptosis. miR-34a also reduces the resistance to chemotherapeutic agents in
medulloblastoma cell lines with p53 inactivating mutations. In addition, mir-34a activates
p53 expression by targeting factors involved in p53 epigenetic repression, such as
MAGE-A and SIRT1, establishing a positive auto-regulatory loop (Figure 1-2).
26
Figure 1-2: Dichotomous roles of miR-34a, miR-9, and miR-124 in normal neuronal
development and medulloblastoma. In normal neurons, activation of differentiation
programs induce miRNAs to repress cell cycle progression. In medulloblastoma,
repression of several miRNAs, including miR-34a, miR-9, and miR-124 leads to
unregulated cell proliferation.
27
Chapter 2
2 Thesis Rationale and Hypothesis
Many groups, including our own, have profiled large cohorts of medulloblastoma using
copy number, exome sequencing, and parallel technologies to understand the genetic
basis of disease formation. These studies have shown that primary medulloblastoma is
comprised of four unique subgroups, with subgroup specific genetic alterations [19, 43–
46, 58]. Although advances in genetic technologies have enabled us to characterize the
primary disease at the molecular level, the metastatic compartment, which is the leading
cause of mortality, remains poorly studied.
One of the key findings revealed from genetic studies of medulloblastoma is a paucity of
recurrent somatic mutations. Of the 189 tumours sequenced using next-generation
sequencing, there appears to be very few recurrent SNVs or in-dels [43–45]. These
observations have pointed to structural variations such as copy number aberrations and
epigenetics as culprits for medulloblastoma pathogenesis [50, 113]. In order to address
these challenges, I hypothesize that epigenetics may be able to explain the rare number
of recurrent mutations seen in medulloblastoma.
Bidirectional promoters are highly pervasive in the genome, representing up to 20% of
genes [114]. Interestingly, studies have shown that bidirectional promoters are
especially common in DNA repair genes, representing up to 40% of this class [115].
Hypermethylation is a common mechanism in tumorigenesis leading to the silencing of
tumor suppressor genes. Hypermethylation of bidirectional promoters have been shown
in colon cancer and acute lymphoid leukemia to inhibit two tumor suppressor genes
simultaneously. As medulloblastoma exhibit very sparse somatic mutations, this
epigenetic phenomenon whereby a single event targeting a bidirectional promoter can
disable two tumor suppressor genes is an appealing tumorigenetic mechanism.
Whether this epigenetic mechanism occurs in medulloblastoma is currently unknown.
28
2.1 Study One: Medulloblastoma subgroups remain stable across
primary and metastatic compartments
2.1.1 Hypothesis
While the genomic era of medulloblastoma has substantially improved our
understanding of the primary disease, due to its ease of accessibility from surgical
resection, it is imperative that we investigate metastatic disease as this is the leading
cause of mortality in patients. Previous reports have shown that recurrent disease
retains their subgroup signature [116], I therefore hypothesize that the metastatic
compartment at the time of diagnosis retains their subgroup affiliation.
2.2 Study Two: Silencing of bidirectional promoters through
hypermethylation causes preferential clonal selection in
cancer
2.2.1 Hypothesis
The lack of somatic mutations identified using reverse genetics to date, in significant
cohorts of medulloblastoma, suggests alternative mechanisms targeting epigenetics.
Hypermethylation of a newly identified bidirectional promoter on 17p13.3 results in
epigenetic silencing of tumor suppressor gene HIC1 and its upstream partner miR-
212/132. I therefore hypothesize that the associative silencing of gene pairs through
bidirectional promoter hypermethylation is a novel mechanism that is clonally selected
for in medulloblastoma.
29
Chapter 3
“The histogenesis of the brain furnishes the indispensable background for an
understanding of its tumors” - Bailey and Cushing, 1926
*Contents of this chapter have contributed to the following publications:
Wang X, Dubuc AM, Ramaswamy V, et al. (2015) Medulloblastoma subgroups remain
stable across primary and metastatic compartments. Acta Neuropathol 449–457. doi:
10.1007/s00401-015-1389-0
Kahn S, Wang X et al. Notch1 promotes Group 3 medulloblastoma metastasis
(Manuscript under review at Nature, contributed to the bioinformatics analysis)
30
3 Medulloblastoma subgroups remain stable across
primary and metastatic compartments
3.1 Abstract
Medulloblastoma comprises four distinct molecular variants with distinct genetics,
transcriptomes, and outcomes. Subgroup affiliation has been previously shown to
remain stable at the time of recurrence, which likely reflects their distinct cells of origin.
However, a therapeutically relevant question that remains unanswered is subgroup
stability in the metastatic compartment. We assembled a cohort of 12-paired primary-
metastatic tumors collected in the MAGIC consortium, and established their molecular
subgroup affiliation by performing integrative gene expression and DNA methylation
analysis. Frozen tissues were collected and profiled using Affymetrix gene expression
arrays and Illumina methylation arrays. Class prediction and hierarchical clustering were
performed using existing published datasets. Our molecular analysis, using consensus
integrative genomic data, establishes the unequivocal maintenance of molecular
subgroup affiliation in metastatic medulloblastoma. We further validated these findings
by interrogating a non-overlapping cohort of 19-pairs of primary-metastatic tumors from
the Burdenko Neurosurgical Institute using an orthogonal technique of
immunohistochemical staining. This investigation represents the largest reported
primary-metastatic paired cohort profiled to date and provides a unique opportunity to
evaluate subgroup-specific molecular aberrations within the metastatic compartment.
Our findings further support the hypothesis that medulloblastoma subgroups arise from
distinct cells of origin, which are carried forward from ontogeny to oncology.
31
3.2 Introduction
Medulloblastoma is the most common malignant pediatric brain tumor [3]. Despite multi-
modal treatments of maximal-safe surgical resection, radiation, and chemotherapy,
there remains a significant portion of patients who succumb to their disease[118].
Recent integrative genomics have identified four distinct subgroups of medulloblastoma,
these include WNT, SHH, Group 3 and Group 4 [14, 17, 18, 119, 120]. These four
subgroups have disparate demographics, clinical features, and genetics. Previous work
demonstrates that clinical parameters used to risk stratify patients are largely attributed
to molecular subgroup differences. For example, WNT patients have the best prognosis,
whereas Group 3 patients often present with metastatic disease and have the worst
prognosis [16, 50, 121]. As patient mortality and high-risk disease are characterized by
the presence of metastatic lesions, there is significant interest in unraveling the role of
subgroup affiliation between the primary and metastatic compartments.
Previous study comparing primary and recurrent medulloblastoma has demonstrated
the maintenance of subgroup affiliation at recurrence, using a 22-gene nanoString
probe-set [116]. The stability of tumour subgroups largely deviate from other tumours,
such as glioblastoma multiforme, where molecular subclass switching has been
identified, both temporally and spatially [122–124]. What remains unknown is whether
medulloblastoma maintain subgroup identity between the primary and metastatic
compartment. As inclusion/exclusion schemas for many clinical trials already
necessitates molecular subtyping, the establishment of molecular subgroup in both the
primary and metastatic compartments remains of critical importance [125]. Whether
molecular subgroups play a significant prognostic and biological role in the metastatic
compartment remains to be seen. Moreover, future trials will likely evaluate patients with
relapsed/recurrent and metastatic disease, highlighting the need to identify molecular
subgroup identity in both the primary and metastatic disease.
32
3.3 Methods and Materials
3.3.1 Medulloblastoma tumour specimens
Our integrative molecular and clinical analysis comprised of two non-overlapping
cohorts. Cohort 1 (discovery) consisted of all patients with metastatic medulloblastoma
with either frozen or formalin-fixed paraffin-embedded (FFPE) material along with
clinical variables and survival data from 10 different centres (Johns Hopkins University
School of Medicine, Baltimore, MD, USA; Virginia Commonwealth University,
Richmond, VA, USA; New York University Langone Medical Center, New York, NY,
USA; Children’s Hospital of Minnesota, Minneapolis, MN, USA; Stanford University
School of Medicine, Stanford, CA, USA; Emory University, Atlanta, GA, USA; Texas
Children’s Cancer Center, Houston, TX, USA; Weill Medical College of Cornell
University, New York, NY, USA; Brain Tumour Tissue Bank, London, ON, Canada;
Hospital for Sick Children, Toronto, ON, Canada). Cohort 2 (validation) consisted of
samples from patients with metastatic medulloblastoma obtained at the NN Burdenko
Neurosurgical Institute (Moscow, Russia).
The research ethics boards at all participating centres approved the study and all
samples and clinical information were obtained with consent in accordance with the
research ethics board at the Hospital for Sick Children and collaborating centres.
3.3.2 RNA extraction
Matched samples from primary and metastatic samples were extracted using TRIzol
RNA (Life Technologies) reagent as suggested by the manufacturer. Quantification was
performed using a Nanodrop ND-1000 Spectrophotometer. Verification of RNA
concentration and assessment of RNA quality was performed by the TCAG using
Agilent 2100 Bioanalyzer. Samples that passed internal TCAG quality control standards
hybridized to Affymetrix Human Exon 2.0ST arrays and RMA analyzed.
33
3.3.3 DNA extraction and bisulfite-conversion
Matched samples from primary and metastatic samples were extracted using a
Phenol:Chloroform extraction protocol. In brief, fresh frozen samples were pulverized
using mortar and pestle in liquid nitrogen. Powder was resuspended in 1 mL Lysis
Buffer (10mM Tris, 0.1M EDTA, 0.5% w/v SDS, 20 μg/mL DNase-free pancreatic
RNase), spiked with Proteinase K to a final concentration of 100 μg/mL. Samples were
incubated for 3-hours at 55°C, and agitated at 1000 RPMs on an Eppendorf
Thermomixer. One-volume of 0.1M Tris-Cl, pH 8.0 equilibrated Phenol:Chloroform (1:1)
was added. Phase separation was achieved by centrifugation at 3000 RPMs for 10-
minutes at room temperature (25°C). DNA was precipitated using 0.2 volumes 10 M
ammonium acetate and 2 volumes of ethanol. Following centrifugation at 13,000 RPMs
for 5-minutes at room temperature, DNA pellets were washed three times in 75%
ethanol. Pellets were dissolved in 100 μL H2O and quantified using a Nanodrop ND-
1000 Spectrophotometer. Samples were bisulfite (BS) converted using an EZ-DNA
methylation kit (ZymoResearch) following manufacturer’s instructions.
3.3.4 Subgroup assignment
Subgroup determination was established using gene expression profiling, nanoString
targeted gene-expression profiling, as well as 450k DNA methylation, as previously
described, in all cases, where available, from cohort 1 [14, 20, 126]. Subgroup affiliation
for cohort 2 was completed by immunohistochemistry employing the four-antibody
approach, as previously described (WNT=nuclear β-catenin, SHH=SFRP1, Group
3=NPR3, Group 4=KCNA1) [13, 14, 127]. For SFRP1 and NPR3, we detected
membranous-cytoplasmic staining and most of the tumor cells were stained with these
markers. For KCNA1 we detected cytoplasmic and nuclear staining with wide
extensions in the group 4 tumors. Antibodies against the following antigens were used:
34
β-catenin (1:100; BD Transduction Laboratories), SFRP1 (1:2,000; Abcam), NPR3
(1:200; Abcam), and KCNA1 (1:2,000; Abcam).
3.3.5 Statistical analysis
Whole genome expression was generated using the Affymetrix GeneChip Human Gene
2.0 ST Array. Samples were normalized using RMA as part of the R/Bioconductor oligo
package (version 1.26.6) [128]. DNA methylation was generated using the Illumina
Infinium HumanMethylation450 BeadChip array (450k array). Samples were normalized
using the SWAN as part of the R/Bioconductor minfi package (version 1.12.0).
Assessment of differential expression between primary and metastatic samples was
conducted using the generalized linear model with empirical Bayes adjustment using
the limma package from R (version 3.0.2). Unsupervised hierarchical clustering (HCL)
using the Pearson correlation metric and non-negative matrix factorization (NMF)
consensus analysis for whole genome expression and DNA methylation were
completed using the top 1,000 differentially expressed genes and top 10,000
differentially methylated probes, respectively. We used the cophenetic coefficient as a
measure of correlation between the sample distances induced by the consensus matrix
[129]. The red circle is the evidence for the number of clusters resulting in the highest
similarity between samples. Principle component analysis was done in the Partek
Genomic Suite and HCL and NMF was done using MultiExperiment Viewer (version
10.2). Class prediction was done using prediction analysis of microarrays (PAM) as
previously described [130], using the expression training data as reported by Northcott
et al [14]. (Gene Expression Omnibus accession No. GSE 21140) and methylation
training data as reported by Hovestadt et al [131]. (Gene Expression Omnibus
accession No. GSE 54880). Raw and normalized whole genome expression and 450k
DNA methylation data were deposited to Gene Expression Omnibus under accession
number GSE 63670.
35
3.4 Results
3.4.1 Cohort description
Biopsies of metastatic lesions of medulloblastoma are not routinely taken; as such very
few primary-metastatic pairs have been analyzed. We set out and collected a relatively
large cohort of primary-metastatic pairs to our knowledge and performed integrative
genetic analysis to determine subgroup affiliation. Table I shows the demographics of all
patients in this study. Due to limitation and rarity of patient samples with matched
primary and metastasis, 9 patient samples were subjected to gene expression profiling
and 11 patient samples were profiled using high resolution genome wide methylation
arrays. Eight out of the 12 patients have both gene expression and 450k DNA
methylation data; this cohort of patients will thus be referred to as the discovery cohort.
We have also conducted immunohistochemistry on a non-overlapping cohort of patient
samples obtained from the Burdenko Neurosurgical Institute; this cohort of patients will
be referred to as the validation cohort. Both the discovery and validation cohort have
similar age, with the vast majority of patients between the ages of 5-18. The cohorts are
comparable in terms of gender and histology. A summary of the two cohorts can be
found in Table 3-1. Using a previously validated 22-nanoString probe-set for subgroup
determination[20], the most enriched subgroup is Group 4, followed by Group 3 (Figure
3-1a). We did not have any WNT patients, which is likely a reflection of the largely local
and non-metastatic nature of these tumours. Using an established cohort of 103
patients with known subgroup affiliation as the training set, we further used Prediction
Analysis of Microarrays (PAM) prediction to assign subgroup to the primary and
metastases pairs (Table 3-2).
3.4.2 Subgroup stability by expression
Using gene expression signatures (Affymetrix GeneChip Human Gene 2.0 ST Array)
from 9 pairs of primary-metastasis pairs, we show the subgroup affiliation is stable
36
between the primary and metastatic compartment. Unsupervised hierarchical clustering
using the top 1,000 differentially expressed genes is able to recapitulate the subgroups
despite the low sample number. In all 9 pairs, the primary and metastatic samples
clustered with the same subgroup and furthermore clustered with the same patient,
even in cases with multiple metastases (Figure 3-1b). We further demonstrate using
NMF-consensus clustering that in all but one case (patient 4), primary and metastatic
samples are more alike to each other, with the highest support for 3 subgroups (k=3,
cophenetic coefficient=0.87) (Figure 3-1c). The similarity and stability of subgroup
between the primary and metastatic compartment was also demonstrated using
Principal Components Analysis (PCA) (Figure 3-1d). The primary (pink) consistently
cluster with the matched metastasis (purple). Individual patients also cluster more
closely together to each other. Using three orthogonal methods, we demonstrate that
primary and metastasis from the same patients cluster together.
Using Gene Set Enrichment Analysis, pathway signatures were determined at the
transcriptional level using the top 1,000 differentially expressed genes. Comparisons of
primary versus metastasis compartment show enrichment for gene sets involving
extracellular matrix and cell surface receptor linked signal transduction (Figure 3-4).
Interestingly, one of the pathways enriched in the metastatic compartment is MAPK,
which have effective MEK inhibitors for therapeutic application.
3.4.3 Subgroup stability by methylation
To further demonstrate the subgroup stability between primary and metastasis, we
performed Illumina 450k DNA methylation array (Infinium HumanMethylation450
BeadChip) on 11 patient pairs. Unsupervised hierarchical clustering using the top
10,000 most differentially methylated probes as calculated by the Kruskal-Wallis test,
demonstrates maintenance of subgroup between primary and metastatic pairs. In all
cases, the primary (pink) clustered together with the metastases (purple) (Figure 3-2a).
NMF consensus analysis further provides statistical support for the three-
medulloblastoma subgroups that remain stable between patient pairs (k=3, cophenetic
37
coefficient=1.0) (Figure 3-2b). Using PCA, the methylation of the primary and metastatic
samples cluster together (Figure 3-2c) and are more alike to each other than to other
patients in the same subgroup. Using a publically available dataset of 100 primary
medulloblastoma samples with subgroup affiliation as determined through 450k DNA
methylation array, we further validated the stability of subgroup between primary and
metastases using PAM prediction (Table 3-2). Using integrative genetic analysis looking
at gene expression signatures and 450k DNA methylation, we demonstrate the
maintenance and stability of medulloblastoma subgroups between the primary and
metastatic compartments. Using an orthogonal technique of immunohistochemistry on a
non-overlapping cohort of 19 primary and metastases patient pairs, we further validated
the maintenance of subgroup affiliation between primary and metastatic compartments
(Figure 3-3a). Table 3-2 shows a summary of the subgroup calls using different
platforms and statistical tests. We observed a total of 4/28 misclassified samples using
3 different strategies comprising of both gene expression and DNA methylation data for
subgrouping totaling 168 tests, thus comprising only a very small disconcordance rate
(2.98%). Currently the gold standard is considered consensus clustering using Illumina
Infinium HumanMethylation450 arrays. Using consensus clustering by high-density
methylation arrays, the primary and metastatic samples uniformly share subgroup
affiliation. We therefore conclude, using multiple experimental approaches examining
the levels of gene expression, DNA methylation, and protein expression, that
medulloblastoma subgroups remain stable across both primary and metastatic
compartments.
3.5 Discussion
Herein we demonstrate that medulloblastoma subgroup affi liation remains stable in both
the primary and metastatic compartments. Using a multi-modal validation strategy
integrating molecular - both gene expression and methylation analysis – and
immunohistochemistry tools, we evaluated two non-overlapping cohorts of
medulloblastoma. This study, to our knowledge, represents the largest study to date
38
designed to evaluate matched primary and metastasis samples with detailed subgroup
information. Metastatic and primary disease from the same subgroup will always cluster
together, further highlighting their similarity, and strengthening the notion that
medulloblastoma subgroups are distinct entities.
Our finding that subgroup affiliation is stable between the primary and metastatic
compartments further reinforces the stability of medulloblastoma subgroups. Indeed,
this finding further suggests that medulloblastoma subgroups arise from distinct cells of
origin [48, 60, 116, 132]. The maintenance of subgroup affiliation between the two
compartments reflects the primary and metastatic compartments sharing a distinct cell
of origin. However, our previous work suggests that the metastatic compartment is
distinct form the primary. Clinically, Group 3 and 4 patients fail almost exclusively with
metastatic dissemination suggesting a therapy resistant subclone drives relapse [116].
This coupled with our previous cross species genomic studies suggest that in both
murine and human medulloblastoma, the primary and metastatic compartments are
genomically distinct [133]. This current work suggests that although the cell of origin
between the primary and metastatic compartments are retained, the two compartments
are distinct within the context of a preserved subgroup affiliation.
It is of interest to note that despite subgroup affiliation being preserved between the
primary and metastasis compartments, metastasis often cluster closer to each other
than to their primary disease. Although this evidence is preliminary given our limited
number of samples with multiple metastases, this finding suggests the intriguing
possibility that clonal evolution has given rise to divergent populations in the metastatic
compartment. Previous evidence from murine medulloblastoma indeed shows that the
primary and metastatic compartments are biologically distinct and harbor different driver
events [133]. This observation may have significant clinical implications, therapies
aimed at targeting disease subgroups may be more efficacious than targeting single
genetic aberrations, which may or may not be present in the metastatic compartment or
at recurrence. Recent work looking at Notch1 signaling implicate the pathway to be a
driver of Group 3 metastasis. Sorted patient xenograft cells show increased NOTCH1
expression and abrogation of this pathway leads to tumour regression in vivo (data not
shown).
39
Treatment for metastatic medulloblastoma has led to survival rate approaching 70% [34,
35, 134, 135]. However, the requirement for 36Gy of craniospinal irradiation results in
devastating neurocognitive sequelae. In order to further increase survival and improve
quality of life, targeted therapies aimed at the metastatic compartment are urgently
required. Future clinical trials, which are often conducted in the setting of metastatic or
relapsed patients, need to prioritize on targets that are present in metastatic lesions. To
better understand the metastatic compartment, sampling of the metastatic disease
needs to be considered if possible. However, sampling for the sole purpose of
subgrouping is unwarranted and based on the findings of this paper unnecessary and
should rather be extrapolated from the primary disease. Prospective multi -centered
longitudinal studies of metastatic medulloblastoma need to be conducted in a subgroup-
specific fashion to increase our understanding of metastatic progression. Further
studies using high-resolution platforms, such as RNA-sequencing and next generation
whole genome sequencing comparing both primary and matched metastases will guide
therapeutic development.
40
41
Figure 3-1: Expression signatures remain stable between primary and metastatic
medulloblastoma
(a) Heatmap of relative gene expression of 22 nanoString probe-set (normalized with
ACTB, GAPDH, LDHA) on 17 samples (6 matched primary-metastasis patients). (b)
Non-negative matrix factorization (NMF) consensus analysis provides strong statistical
support for three subgroups (k=2, cophenetic coefficient=0.86; k=3, cophenetic
coefficient=0.87; k=4, cophenetic coefficient=0.77). (c) Unsupervised hierarchical
clustering of human 2.0 exon array (Affymetrix GeneChip Human Gene 2.0 ST Array)
expression data from 22 medulloblastoma samples (9 matched primary-metastasis
patients) using 1,000 most differentially expressed genes. (d) Principle component
analysis (PCA) of the primary and metastatic medulloblastoma samples described in (a)
using the same 1000 most differentially expressed genes. Coloured ellipsoids
(red=SHH, yellow=Group 3, green=Group 4) represent 1.5 SDs of the data distribution
for each subgroup. Individual primary samples are indicated with magenta colour and
metastatic samples are indicated with purple colour.
42
43
Figure 3-2: Methylation signatures remain stable between primary and metastatic
medulloblastoma.
(a) Unsupervised hierarchical clustering of 450k DNA methylation (Infinium
HumanMethylation450 BeadChip Kit) data from 27 medulloblastoma samples (11
matched primary-metastasis patients) using 10,000 most differentially methylated
probes. (b) Non-negative matrix factorization (NMF) consensus analysis provides strong
statistical support for three subgroups (k=2, cophenetic coefficient=1.0; k=3, cophenetic
coefficient=1.0; k=4, cophenetic coefficient=0.85). (c) Principle component analysis
(PCA) of the primary and metastatic medulloblastoma samples described in (a) using
the same 10,000 most differentially methylated probes. Coloured ellipsoids (red = SHH,
yellow = Group 3, green = Group 4) represent 1.5 SDs of the data distribution for each
subgroup. Individual primary samples are indicated with magenta colour and metastatic
samples are indicated with purple colour.
44
Figure 3-3: Immunohistochemical markers of medulloblastmoa subgroups remain
stable between primary and metastatic compartments.
(a) Immunohistochemistry of 19 matched primary-metastasis patient samples in our
validation cohort (SHH=SFRP, Group3=NPR3, Group4=KCNA1) provides additional
support using orthogonal technique the maintenance of molecular subgroups between
primary and metastatic compartments.
45
A
B
C
Cell Cycle
Transmembrane
Receptor
Extracellular
Matrix
MAP Kinase
Ion Channels
Mitochondrion
ActivityTransmembrane
Receptor
Cell Cycle
Extracellular
Matrix
Immune
Response Organnelle
Membrane
Extracellular
Matrix
Transmembrane
Receptor
Supplementary Figure 1
46
Figure 3-4: Subgroup specific pathway analysis of the differentially expressed
genes between primary and metastatic medulloblastoma.
(a) Gene Set Enrichment Analysis (GSEA) comparing (a) all, (b) Group 3, and (c) Group
4 metastasis (red) against primary (blue) medulloblastoma, illustrating distinct pathways
and biological processes between both compartments (3.5 % FDR, P=0.05). Ingenuity
pathway analysis was used to look for enriched curated pathways (P=0.1). Cytoscape
and Enrichment Map were used for visualization of the GSEA results. Nodes represent
enriched gene sets, which are grouped and annotated by their similarity according to
related gene sets. Enrichment results were mapped as a network of gene sets (nodes).
Node size is proportional to the total number of genes within each gene set. Proportion
of shared genes between gene sets is represented as the thickness of the green line
between nodes.
47
Table 3-1: Clinical Characteristics of Medulloblastoma Primary-Metastasis
Cohort
HuGene2.0
Gene Expression (n=9)
450K
DNA Methylation (n=11)
Validation
Tissue Microarray (n=19)
Variable No. % No. % No.
%
Age, years ≤ 5 1 11 1 9 5 26 5-18 5 56 6 55 11 58
≥ 18 1 11 1 9 3 16 Unknown 2 22 3 27 0 0
Sex
Male 3 33 4 36 10 53 Female 4 44 4 36 9 47 Unknown 2 22 3 28 0 0
Subgroup
WNT 0 0 0 0 0 0
SHH 1 11 1 9 4 21
Group 3 2 22 3 27 8 42
Group 4 6 67 7 64 7 37
Unknown 0 0 0 0 0 0
Histology Classic 3 22 3 27 N/A
Desmoplastic 0 0 0 0 N/A Large Cell Anaplastic 1 11 1 9 N/A Unknown 5 67 7 64 N/A
Died of disease No 1 11 2 18 4 21 Yes 4 44 3 27 15 79 Unknown 4 44 6 55 0 0
*8/12 samples have both gene expression and 450K DNA methylation data
48
Table 3-2: Medulloblastoma Subgroup Predictions Using Orthogonal
Technologies
HuGene2.0
Gene Expression (n=9)
450K DNA Methylation
(n=11)
Subgroup Consensus
Patient ID
HCL NMF PAM HCL NMF PAM
1-P 4 4 4 4 4 4 4
1-M 4 4 4 4 4 4 4
2-P 3 3 3 3 3 3 3
2-Ma 3 3 3 3 3 3 3
2-Mb 3 3 3 3 3 3 3
3-P 4 4 4 4 4 4 4
3-Ma 4 4 4 4 4 4 4
3-Mb 4 4 4 4 4 4 4
4-P 3 3 3 3 3 3 3
4-Ma 3 3 3 3 3 3 3
4-Mb 3 3 SHH 3 3 3 3
4-Mc 3 4 3 3 3 3 3
5-P 4 4 4 4 4 4 4
5-M 4 4 4 4 4 4 4
6-P 4 4 4 4 4 4 4
6-M 4 4 4 4 4 4 4
7-P 4 4 4 N/A N/A N/A 4
7-M 4 4 4 N/A N/A N/A 4
8-P 4 4 4 4 4 4 4
8-M 4 4 4 4 4 4 4
9-P SHH SHH SHH SHH SHH WNT SHH
9-M SHH SHH 3 SHH SHH WNT SHH
10-P N/A N/A N/A 3 3 3 3
10-M N/A N/A N/A 3 3 3 3
11-P N/A N/A N/A 3 3 3 3
11-M N/A N/A N/A 3 3 3 3
12-P N/A N/A N/A 4 4 4 4
12-M N/A N/A N/A 4 4 4 4
49
Chapter 4
“It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change.” - Charles Darwin
*Contents of this chapter have contributed to the following publications:
Wang, X. et al., Silencing of bidirectional promoters through hypermethylation is a
result of preferential clonal selection in cancer (Manuscript in preparation)
50
4 Silencing of bidirectional promoters through
hypermethylation leads to preferential clonal selection
in cancer
4.1 Abstract
Abnormal gene expression in cancer is mediated through genetic and epigenetic
mechanisms. Bidirectional promoters are prevalently represented in the human
genome. We demonstrate a novel mechanism in pediatric medullloblastoma, whereby a
hypermethylated locus on 17p13.3 is clonally selected due to the presence of a newly
identified bidirectional promoter. The abundant DNA methylation in this locus results in
the silencing of HIC1 and a newly implicated tumor suppressor microRNA cluster miR-
212/132. Expression profiling of both HIC1 and miR-212/132 in human medulloblastoma
revealed a significant correlation between their respective expressions. When looking at
the expression in a subgroup-specific manner, it is evident that the expression of HIC1
and miR-212/132 is highest in normal cerebellum and lowest in groups 3 and 4 tumours,
which are subgroups with the poorest prognosis. Inducible overexpression of Hic1 and
miR-212/132 decreases medulloblastoma proliferation in vitro and extend survival in
vivo. Knockout mice harbouring floxed alleles of either Hic1 or miR-212/132 cluster did
not increase tumour incidence in a Ptc+/- background, indicating that the driver event is
likely further upstream. Re-expression of this endogenously methylated locus using
RNA-guided gene activation with CRISPR-Cas9 significantly reduces tumour
proliferation and represent a new therapeutic window. This novel mechanism of
epigenetic regulation of bidirectional promoters may be relevant to a wide array of
cancers and can be applied to identify novel cancer genes.
51
4.2 Introduction
In order to attain abnormal gene expression in cancer, epigenetic dysregulation is often
orchestrated with genetic aberrations. Certain genes are mutated or deleted to obtain
the loss-of-function phenotype, whereas others are epigenetically silenced to stifle
expression [136]. However, it is unclear why certain genes harbor inactivating mutations
while others are silenced from epigenetic mechanisms. DNA methylation is often
associated with large organized chromatin lysine modifications (LOCKs) and lamina-
associated domains (LADs), which together form heterochromatin. These tightly
regulated regions are lost in cancer and leads to a stem-cell like state characterized by
permissive euchromatin [76]. This general disorganization leads to an increased
epigenetic flexibility and heterogeneity that is amenable for clonal selection and tumour
progression. The loss of boundary stability of methylation at CpG islands leads to CpG
island hypermethylation, often a prominent feature marking promoters of tumor
suppressor genes. Conversely, boundaries outside of these islands are often shifted,
leading to hypomethylated CpG shores and altered three-dimensional organization of
chromatin [137]. Recent genome-wide DNA methylation study of human
medulloblastoma point to extensive hypomethylation driving aberrant gene expression
in a subgroup specific manner [131].
One of the earliest tumor suppressor genes reported to be effected by de novo
methylation is the CpG island of RB1 in retinoblastoma [138]. It is now accepted that
DNA methylation at CpG islands plays a vital role in carcinogenesis. However, there
remain important questions as to the causality and mechanism of this ectopic
methylation. Specifically, methylation at a specific locus may be an epimutation that is a
result of upstream dysregulation leading to transcriptional inactivity. Given the
heterogeneous epigenetic landscape of the early tumor initiating cell, regions of
methylation-associated silencing are then clonally selected for and confer a growth
advantage over their non-silenced counterpart.
Earlier work by Stephen Baylin’s group have demonstrated Hypermethylated in Cancer
1 (HIC1) as an important tumor suppressor gene involved in medulloblastoma and other
52
solid tumours [139]. Mutations in HIC1 is exceedingly rare, while methylation of this
gene is widespread in both solid and hematologic malignancies. When crossed with
Ptch1 heterozygote mice, these animals have an increased penetrance of
medulloblastoma. It is interesting to note that the allele harbouring HIC1 deletion retain
the hypermethylation which points to additional regulatory elements that are silenced by
DNA methylation. This prompted us to look closer at the genes involved in this locus.
We were able to identify a microRNA cluster miR-212/132 co-regulated by the
hypermethylated promoter of HIC1. We report the identification of a novel bidirectional
promoter co-regulating HIC1 and miR-212/132. Through both in vitro and in vivo
studies, we show methylation of this bidirectional promoter confers a growth advantage
to medulloblastoma and activation of this gene pair leads to growth suppression in
medulloblastoma xenografts compared with either gene alone. This work implicates
miR-212/132 as novel tumour suppressor microRNAs in medulloblastoma. We propose
a new mechanism of tumor suppressor silencing in the form of preferential clonal
selection of hypermethylated bidirectional promoters. These observations highlight the
need to identify these methylated regions targeting bidirectional promoters as they offer
a potential therapeutic avenue; activating these silenced regions of the cancer
epigenome can reactivate latent tumor suppressor genes and induce growth arrest.
4.3 Methods and Materials
4.3.1 Patients and tumour samples
Tumour samples and clinical information were processed in approval from the Hospital
for Sick Children Research Ethics Board (REB) with local ethics board approval as
previously published [140]. Patients included in this study in both the gene expression
and methylation cohorts represent only primary samples for analysis. Tumour
subgrouping was based on gene expression profiling or nanoString method, previously
validated and published [20]. Diagnosis was confirmed by histopathologic assessment.
53
4.3.2 Luciferase reporter assay
~4000bp upstream of the HIC1 transcriptional start site was cloned in 3 pieces into the
pGL3 luciferase vector (Promega). The 3 pieces transect the promoter into equidistant
regions separated by restriction endonucleases XhoI and SalI (NEB). Independent
regions were cloned in both the forward and reverse orientation. Cloned constructs and
empty vector was transfected in 293T, DAOY, and D283 cell lines using X-tremeGene 9
DNA Transfection Reagent (Sigma-Aldrich). Cells were collected at 48 hours after
transfection with 0.25% Trypsin-EDTA at 37oC (Thermo Fisher Scientific) and
centrifuged. Luciferase readings were done using the Dual-Luciferase Reporter Assay
Kit (Promega).
4.3.3 Medulloblastoma cell lines and cell culture, MTS, treatments, and
transfections
Long-term medulloblasotma cells lines were obtained from ATCC and in collaboration
with Dr. Darrell Bigner at Duke University. Cell lines (D283, D341, D425, DAOY,
Med8A, and Ons76) were grown in DMEM (Life Sciences) supplemented with 10%
Fetal Bovine Serum (FBS, Life Sciences) with 100x antibiotic-antimycotic (Life
Sciences). D283, D341, and D425 are grown in suspension T25-T75 flasks (Sarstedt),
while DAOY, Med8A, and Ons76 are grown as a monolayer on10cm plates (Falcon),
passage at 80-90% confluence. Primary brain tumour cells were isolated from patients
and cultured in neural stem cell media to establish short term cultures (M137, M486,
MB002 = medulloblastoma, G498, G551 = glioblastoma, E479, E520 = ependymoma).
Short term cultures are grown in suspension T25 flasks (Sarstedt) in Neurobasal media
(Invitrogen) consisting of N2 (Invitrogen), GlutaMax (Invitrogen), BSA (Sigma), heparin
(Sigma), human EGF (Invitrogen), human basic FGF (Invitrogen), and LIF (Leukemia
inhibitory factor, Sigma Aldrich). Cell viability assays were performed in 96wells using
the MTS Aqueous One assay (Promega) according to manufacturer’s instructions. 5-
aza-2’-deoxycytidine (decitabine-Sigma) was dissolved to a stock concentration of 2mM
54
in PBS and stored in aliquots at -20 C. DAC was prepared fresh and added to treatment
media on a daily basis at the appropriate final concentration (5μM, for a total of 7 days).
Cell transfections was done using X-tremeGENE 9 for 293T cells or HP for all other cell
lines according to manufacturer’s instructions, transfection reagent concentrated was
added at 3:1 ratio to amount of plasmid.
4.3.4 RNA extraction
Frozen cell pellets are pulverized using a bead homogenizer and suspended in 1mL
TRIzol RNA (Life Technologies) reagent as suggested by the manufacturer. Samples in
TRIzol were then purified using Direct-Zol RNA Kit (Zymo Research). Quantification was
performed using a Nanodrop ND-1000 Spectrophotometer. Verification of RNA
concentration and assessment of RNA quality was performed by the TCAG using
Agilent 2100 Bioanalyzer. Samples that passed internal TCAG quality control standards
hybridized to Affymetrix Human Exon 1.0ST arrays and RMA analyzed. Tumour
subgrouping was based on gene expression profiling or nanoString method, previously
validated and published [20]. Statistical analysis was done on GraphPad prism.
4.3.5 DNA extraction and bisulfite-conversion
Fresh frozen samples were pulverized using mortar and pestle in liquid nitrogen and
extracted using a Phenol:Chloroform extraction protocol. In brief, powder was
resuspended in 1 mL Lysis Buffer (10mM Tris, 0.1M EDTA, 0.5% w/v SDS, 20 μg/mL
DNase-free pancreatic RNase), spiked with Proteinase K to a final concentration of 100
μg/mL. Samples were incubated for 3-hours at 55°C, and agitated at 1000 RPMs on an
Eppendorf Thermomixer. One-volume of 0.1M Tris-Cl, pH 8.0 equilibrated
Phenol:Chloroform (1:1) was added. Phase separation was achieved by centrifugation
at 3000 RPMs for 10-minutes at room temperature (25°C). DNA was precipitated using
0.2 volumes 10 M ammonium acetate and 2 volumes of ethanol. Following
55
centrifugation at 13,000 RPMs for 5-minutes at room temperature, DNA pellets were
washed three times in 75% ethanol. Pellets were dissolved in 100 μL H2O and
quantified using a Nanodrop ND-1000 Spectrophotometer. Samples were bisulfite (BS)
converted using an EZ-DNA methylation kit (ZymoResearch) following manufacturer’s
instructions.
4.3.6 Sequenom MassCleave analysis of primary medulloblastoma
Primers spanning the HIC1 promoter sequence (from TSS to ~4,000bp upstream) were
designed using Sequenom: EpiDesigner and tested on bisulfite-treated universally
methylated DNA (Invitrogen) by standard PCR (Qiagen) followed by Sanger
Sequencing. For bisulphite converted tumour samples, following PCR amplification,
amplicons were sent to Genome Quebec for quantification using Sequenom Mass
Spectrometry.
4.3.7 Western blot analysis
Tumour cell lines were lysed in 1x RIPA lysis buffer containing deoxycholate and
protease inhibitor. SDS-PAGE analysis was performed in a 10% gel, loading 20 ug of
protein, as quantified by BCA (Pierce). Membranes were blocked with 5% skim milk
(Roche) diluted in TBST. Western blot antibodies were used at the following
concentrations in overnight incubations (5% BSA): HIC1 (ab33029, Abcam, 1:1,000),
SIRT1 (#2310, Cell Signaling Technologies, 1:1,000), and Alpha Tubulin (Cell Signaling:
#2148, 1:20,000). Secondary antibodies were used at a concentration of 1:5000 for all
primary antibodies, and 1:20,000 for alpha-tubulin.
56
4.3.8 qRT-PCR
Extracted RNA from methods above was converted to cDNA (1µg RNA) using
Invitrogen’s Superscript III First Strand Synthesis kit (Invitrogen). microRNA synthesis
was done using TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems)
according to manufacturer’s instructions. Real-time RT-PCR was performed using
TaqMan probe-based gene expression with TaqMan Universal MasterMix II according
to manufacturer’s instructions (Applied Biosytems). The TaqMan probes used are: HIC1
(human: Hs00359611_s1, mouse: Mm03058120_m1), GAPDH (human:
Hs02758991_g1, mouse: Mm99999915_g1), miR-212 (human: 000515, mouse:
002551), miR-132 (human/mouse: 000457), RNU6B (001093). Cycling was performed
as follows as according to TaqMan conditions (Thermo Fisher Scientific). Samples were
amplified in triplicate and data analyzed using the ΔΔCT method.
4.3.9 Orthotopic xenograft model of patient derived cell lines
50,000 cells were xenografted by stereotactic injection into posterior fossa of
immunodeficient NOD scid gamma mice of 5-8 weeks old. Tumours were allowed to
develop. Doxycycline chow was introduced on Day 1 at a concentration of 2g/kg. Mice
were then observed to end point, survival of mice was visualized using a Kaplan-Meier
curve and quantified using a log rank test.
4.3.10 Generation of floxed mice
Floxed miR-132/212 mice were generous gifts from the Goodman lab [141]. The Hic1
floxed mice were generous gifts from the Korinek lab [142]. Hic1prom floxed mice were
generated in collaboration with the Hospital for Sick Children Stem Cell facility. In short,
an ~10kb targeting vector was generated using recombineering bacteria technology to
allow for sufficient homology arms. As part of the LoxP arm, a PGK-Neo-pA selection
57
cassette was included for clonal selection. This cassette was immediately flanked by
flippase recognition target (FRT) sites. The linearized targeting vector was transfected
into G4 embryonic stem cells, which are derived from a C57BL6 mouse. Founders were
selected based on high degree of chimerism (coat colour). To screen for correct
integration of the targeting construct, we designed primers to detect the novel cassette
insertion. Mice used in this study had a mixed background of C57Bl6 and FVB but had
been backcrossed into C57Bl6/J mice for at least three generations. Mice were housed
in accordance with the Toronto Centre for Phenogenomics Animal Care Committee
(TCP ACC) regulations.
4.3.11 Lentiviral construction and viral preparation
Expression plasmids for miR-212/132 were ordered from System Biosciences (SBI).
The gene of interest is then subcloned into the Lenti-X Tet-ON 3G Inducible Expression
System (Clontech). Replication-incompetent lentivirus was produced by co-transfection
of the expression vector and along with packaging mix consisting of VSV-G and
Gag/Pol into HEK 293T cells. Media was changed after 8 hours, viral supernatant was
harvested 48h after transfection, filtered through a 0.45-mm filter and concentrated
using Lenti-X concentrator (Cat#631232, Clontech). Tumour cell lines were transduced
with pLVX-Tet3G and pLVX-TRE3G and selected for 5 passages. Concentrations for
selection agents were determined using a kill curve: G418: 400 µg/mL; Puromycin: 0.5
µg/mL; Zeocin 400 µg/mL; Blasticidin: 10 µg/mL; and Hygromycin: 600 µg/mL.
4.3.12 CRISPR-Cas9 synergistic activation mediators
Plasmids used were obtained from Addgene: lenti-MS2-P65-HSF1_Hygro (Addgene
61426), lenti-dCAS9-VP64_Blast (Addgene 61425), and lenti-sgRNA(MS2)_Puro
(Addgene 73795). sgRNA for activation were designed using http://crispr.mit.edu/. Full
detailed protocol on the design, transfection and transduction of Cas-9 based activators
is published elsewhere [143]. In short, transfection protocol can be found in 4.3.3,
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transduction protocol can be found in 4.3.11. The primer sequences used for the design
of sgRNA are: SAM1 (5-GAAGCAATGAGGGCTTGAGGagg-3), SAM2 (5-
TTCTGCCGTCACACCCCGCAagg-3), and SAM3 (5-
TCCCGGAGACCAGAATTGGCagg-3).
4.4 Results
4.4.1 Identification of HIC1 and miR-212/132 as a gene/miR pair regulated
by a cancer-specific hypermethylated bidirectional promoter
HIC1 is a frequently hypermethylated gene in the context of a variety of different
cancers. Located on chromosome locus 17p13.3, loss of heterozygosity in this region is
the most frequent event in medulloblastoma [74]. HIC1 is a tumour suppressor gene
involved in the p53 apoptotic pathway, the gene acts as a transcriptional repressor for
downstream targets including ATOH1 and SIRT1 [73, 139]. In animal models of Hic1+/-
knockouts, there is an increased incidence of tumorigenesis where it is observed that
the wildtype allele becomes rapidly methylated. Interestingly, HIC1 is rarely mutated in
the context of cancer, and even more surprising, in the context of Hic1+/- knockouts, the
promoter region of the deleted allele is also methylated [144]. Recent cancer genomics
data compiled on cBioPortal cataloguing all known somatic mutations in cancer reports
that there are 0 mutations in HIC1 in medulloblastoma (n=189), furthermore, across all
major cancer sequencing studies completed, HIC1 remains one of the least mutated
genes (Figure 4-1B). Furthermore, this gene is among the most hypermethylated genes
across different cancers (Figure 4-1A). This raises the intriguing possibility that this
region is preferentially selected over the course of clonal selection due to the
associative silencing of another nearby tumour suppressor gene.
When we explored this region using the UCSC genome browser, there is a miR-
212/132 cluster ~4kb upstream of the HIC1 transcriptional start site (TSS) on the
antisense strand, its function in cancer is poorly defined [145]. A single CpG island
59
spans this entire region (Figure 4-1C). To assay whether this locus harbours a
bidirectional promoter, this region was cloned into a luciferase vector (Figure 2A).
Expression of luciferase can be seen in both the forward and reverse direction in
multiple cell lines; 293T, D283, and Daoy (Figure 4-2B). Furthermore, when the plasmid
is subjected to in vitro methylation, the expression of luciferase decreases. This was
further repeated in low-passage human-derived cultures (data not shown). Based on
these findings, we report that the HIC1 promoter have bidirectional activity. In a panel of
cancer cell lines from low to long-term passage medulloblastoma, as well as glioma and
ependymoma, both HIC1 and its upstream miR cluster miR-212/132 are expressed at
very low levels as compared to normal control (n=3; Figure 4-2C). To determine
whether a demethylating agent can rescue the expression of both gene pairs, cells were
treated with 5-aza-2'-deoxycytidine (5μM) and show re-expression of both HIC1 and
miR-212/132 in a panel of brain tumour low-passage human-derived cultures (normal
fetal brain control; n=2, medulloblastoma; n=3, GBM; n=2, ependymoma; n=2; Figure 4-
2D). This further supports the presence of a novel bidirectional promoter that is
epigenetically regulated.
4.4.2 Subgroup specific correlation of HIC1 and miR-212/132 expression
We then profiled the expression of HIC1 and miR-212/132 in the MAGIC database and
found a significant correlation in each of the four subgroups of medulloblastoma, which
support their co-regulation (Figure 4-3A); the pearson correlations and significance can
be found in Figure 4-3B. When looking at the expression in a subgroup-specific manner,
it is evident that the expression of HIC1 and miR-212/132 is highest in normal
cerebellum and show decreased expression in medulloblastoma across all subgroups. It
is interesting that the lowest expression of both HIC1 and miR-212/132 resides in group
4 tumours, this may be caused by a higher frequency of 17p loss (i17q) in this subgroup
(Figure 4-4A). I then performed a sequenom mass-spectrometry based analysis on
twenty unique medulloblastoma samples and demonstrate strong methylation in patient
samples and patient derived cell lines as compared with both fetal and adult normal
cerebellum controls (Figure 4-4B).
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4.4.3 Overexpression of HIC1 and miR-212/132 decreases
medulloblastoma and glioblastoma proliferation in vitro and in vivo
To assess the functional role of HIC1 and mir-212/132, we expressed HIC1, miR-132
alone, miR-212 alone, and mir-212/132 cluster using a lentiviral based system. The
empty vector with zsGreen is used as control. Transduction efficiency was extremely
low (~0.5%), this may be due to lethality as a result of the overexpression of the gene of
interest. MTS assay and cell count revealed that overexpression of these vectors
induced decreased proliferation (data not shown). In order to induce a more robust
response, we used a Tet-inducible lentiviral system to bypass toxicity related to the
expressed gene of interest. Stable lines were generated overexpressing the control
mCherry vector, HIC1-IRES-mCherry, mCherry-miR212/132, and both HIC1 &
miR212/132. These stable lines were made in five independent patient derived short-
term cultures (medulloblastoma n=3; M486, D425, MB002, glioblastoma multiforme
n=2; M441, M471) (Figure 4-5A). The expression of the genes of interest in tightly
regulated by the presence of DOX (Figure 4-5B/C). An MTS experiment further
demonstrated that there is a significant decrease in cell proliferation following the
overexpression of HIC1 and miR-212/132 (P < 0.001; Figure 4-5D).
As tumour growth is often dependent on their microenvironment, we proceeded to inject
these stable cells in NOD scid gamma (NSG) mice to determine the growth in an in vivo
xenograft model. In the presence of DOX in the chow, mice survived significantly longer
when cells were overexpressing both HIC1 and miR212/132 compared with empty
vector control (n=5, MB002, P = 0.0116; n=5, G498, P = 0.0004; Figure 4-6). One
potential mechanism could be the convergence on the p53 apoptotic pathway. Using
the miRBase target identification tool, we observed that SIRT1 is a potential target for
miR-212/132 degradation (Figure 4-7A). This coincides with HIC1’s role in mediating
SIRT1 transcriptional repression. Overexpression experiments show that this appears to
play a role in the pathogenic process by repressing the levels of SIRT1 gene (Figure 4-
7B) and protein expression (Figure 4-7C). In collaboration with Rajeev Vibhakar, we are
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conducting HITS-CLIP (High-throughput sequencing of RNA isolated by crosslinking
immunoprecipitation) to determine protein–RNA binding sites in an unbiased manner to
determine the targets of miR-212/132 (data not shown) in medulloblastoma.
4.4.4 Role of HIC1 and miR-212/132 in medulloblastoma formation in vivo
The ultimate demonstration of the functional role of the miR-212/132 cluster and
importance of the hypermethylated bidirectional promoter is an increase in increased
tumorigenesis in a mouse model. Using a recombineering protocol for introducing large
vectors into bacteria and selecting for homologous recombinants, we designed targeting
vectors to introduce loxp sites flanking the bidirectional promoter. Floxed mice were
generated using mES cell technology and clones have been selected with the proper
targeting integrant. Three rounds of mES cell targeting were conducted, of which
multiple positive clones were selected to proceed with diploid microinjection. Clone #18
has to date yielded pups with germline transmission of the promoter floxed allele. These
floxed mice have then been crossed with Nestin:Cre and Ptc+/- to look for increase in
tumour penetrance or decrease of tumour latency. We have obtained Hic1fl/fl and
miR212/132fl/fl mice from our collaborators and have crossed these mice to Nestin:Cre
and Ptc+/- (Figure 4-8A). Mice were aged to 300 days and survival does not appear to
differ between heterozygote knockout of Hic1, miR-212/132, and promoter knockout,
compared with Ptc+/- alone (Figure 4-8B). Histological examination of these tumours
revealed typical classical histology reminiscent of patient tumours (Figure 4-8C). This
result aligns with the notion that the methylation event of Hic1 or miR-212/132 is an
early event that may be driven by upstream signaling disruptions and is not a driver of
this disease. Upon closer examination, this explanation is supported at the expression
level. Across existing transgenic models of medulloblastoma, the expression of both
gene partners is decreased compared with normal cerebellum (n=3; Figure 4-8D). This
suggests that the hypermethylation of this locus is likely an early clonally selected
event.
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4.4.5 Reactivation of HIC1 and miR-212/132 using CRISPR SAM
In order to impress the importance of this hypermethylated promoter, we sought to
demonstrate that this hypermethylation is reversible and leads to decreased cell
proliferation in established tumours. Given the recent development of CRISPR
technology, we used a recently engineered Cas9-VP64 SAM (Synergistic Activation
Mediator) system to recruit transcription machinery to activate silenced genes. This
engineered Cas9 has no endonuclease activity, but rather mediates transcriptional
activation at silenced genomic loci. We designed 12 single-guide RNAs (sgRNA)
targeting the bi-directional promoter between HIC1 and miR212/132 (Figure 4-9A). We
selected the top 3 sgRNAs based on transient transfection and qRT-PCR and
generated lentiviral-mediated stable cell lines (Figure 4-9B). Stable lines show
overexpression of HIC1 and miR212/132 using qRT-PCR. MTS assay show significant
decrease in cell proliferation that is directly correlated with the level of overexpression
(Figure 4-9C). This reversible silencing may represent a potential therapeutic
application by targeting the endogenous epigenetic silencing machinery.
4.5 Discussion
Recent genomic studies have pointed to the paucity of genetic mutations identified in
pediatric malignancies, this observation alludes to alternative epigenetic mechanisms
underlying pathogenesis [43–45, 146]. Herein, this chapter reports the identification of a
novel bidirectional promoter at the locus containing tumor suppressor gene HIC1. We
show that this novel bidirectional promoter regulates both the expression of HIC1 and
miR-212/132, which has previously not been implicated in medulloblastoma. Through
both in vitro and in vivo studies, we show methylation of this bidirectional promoter
confers a growth advantage to medulloblastoma and activation of this gene pair leads to
growth suppression in medulloblastoma xenografts compared with either gene alone.
This work implicates miR-212/132 as novel tumour suppressor microRNAs in
medulloblastoma that contributes to the p53-SIRT1 pathway. Further studies will be
63
needed to elucidate the full spectrum of targets for miR-212/132 and its precise role in
medulloblastoma pathogenesis.
The demonstration that knockout of Hic1 and miR212/132 did not increase tumour
incidence in a Ptc+/- background suggest that the methylation of this locus may be due
to broader epigenetic silencing mechanisms that increase the global DNA methylation
patterns across broad regions. This suggest that these tumor suppressor genes are not
driver events in medulloblastoma formation. It is of interest that in all medullobalstoma
and glioblastoma samples studied so far, both in transgenic animal models and patient
derived cell lines, the bidirectional promoter of HIC1 and miR212/132 is strongly
methylated. This means that the upstream event that led to the hypermethylation
phenotype exerts a strong selective advantage in the course of tumour progression.
Reactivation of such loci in the cancer genome may be a potential mechanism to
reactive tumor suppressor genes.
Using the recently developed RNA-guided Cas9 transcriptional activation system, we
demonstrate the feasibility of endogenously reactivating a hypermethylated locus. This
led to abrogation of tumour growth in vitro. Further in vivo studies will be needed to
address the safety and efficacy in patients. However, the concept of activating silenced
regions of the cancer epigenome to induce growth arrest has tremendous potential for
cancer therapeutics.
64
Methylation FrequencyA
B
C1 kb hg19
1,954,000 1,955,000 1,956,000 1,957,000 1,958,000 1,959,000 1,960,000 1,961,000 1,962,000 1,963,000
4.88
-4.5
0 -
MIR132MIR212 HIC1
Sequenom
Methylation
Luciferase
Promoter
Reporters
Conensus
Minimal
Promoters
Floxed Mouse
Lines
UCSC Tract
Conservation
CpG Island
1c 1b1a(Wales et al., 1995)
(Guerardel et al., 2001)
(Pinte et al., 2004)(Remenyi et al., 2010)
1F/R
2F/R 3F/R
Figure1: Tumor suppressor gene HIC1 is frequently methylated across multiple cancer types
and is never mutated in medulloblastoma.
65
Figure 4-1: Tumor suppressor gene HIC1 is frequently methylated across multiple
cancer types and is never mutated in medulloblastoma.
(a) Using the PubMeth database, 68% of medulloblastoma displays hypermethylation in
the Hic1 loci compared with control. This phenomenon is seen across a variety of
cancers. (b) Using the cBioPortal database, Hic1 is never mutated in medulloblastoma
and ranks among one of the lowest genes reported to display single nucleotide
variations. (c) USCS tracks featuring HIC1 and mir-212/132 cluster ~4kb upstream on
the reverse strand. CpG track reveals a large CpG island between the two regions.
66
67
Figure 4-2: HIC1 and miR-212/132 is a gene/miR pair regulated by a bidirectional
promoter.
(a) Design of bidirectional promoter luciferase constructs. (b) Luciferase assay reveals
cloned promoter is able to drive transcription of luciferase in both the forward and
reverse orientation, furthermore in vitro methylation of the plasmid abrogated the
expression signifying an epigenetically regulated mechanism. (c) Across a panel of
cancer cell lines, HIC1 and miR-212/132 display very low expression when compared
with normal cerebellum. (d) Cell lines were treated with 5-aza-2'-deoxycytidine (5μm) for
5 days and show re-expression of both HIC1 and miR-212/132 in a panel of brain
tumour low-passage human-derived cultures (normal fetal brain control; n=2,
medulloblastoma; n=3, GBM; n=2, ependymoma; n=2).
68
A
B
miR
-212
miR
-132
Figure 4-3: Subgroup specific correlation between HIC1 and miR-212/132.
(a) There is a positive correlation between HIC1 expression and miR-212 (P < 0.0001),
and HIC1 expression and miR-132 (P < 0.0001). Patients were segmented into 4
categories according to their subgroup, yielding 58 WNT, 224 SHH, 166 Group 3, and
274 Group 4 patients. Patients were arranged according to increasing HIC1 expression,
and a linear model fit was applied to the miR-132 and miR-212 expression values,
respectively. (b) The pearson correlation and p-value for the graphed are summarized.
69
Figure 4-4: Expression of HIC1 and miR-212/132 in a large cohort of human
medulloblastoma.
(a) Subgroup-specific analysis shows decreased expression of HIC1 and miR-212/132
in MBs compared with normal CB control and the expression is lowest in Grp4 (n=356,
**P<0.01) (b) Sequenom analysis reveals promoter hypermethylation spanning the
region from miR-212/132 to HIC1.
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71
Figure 4-5: Establishment of dox-inducible stable cell lines overexpressing HIC1
and miR-212/132.
(a) Four independent constructs were designed overexpressing mCherry, HIC1-IRES-
mCherry, mCherry-miR212/132, and both HIC1 & miR-212/132. (b) qRT-PCR results
show robust overexpression of Hic1 and miR212/132 only in the presence of
doxycycline in representative patient derived medulloblastoma samples. (c)
Flourescence microscopy shows tight regulation of mCherry only in the presence of
doxcycyclin (2μm, MB002 and G498). (d) MTS assay shows significantly decreased
proliferation over the course of 9 days (P < 0.001).
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Figure 4-6: Overexpression of HIC1 and miR212/132 increases survival in
xenografts.
(a) Dox-inducible stable cell lines were implanted in the cerebellum of NSG mice and
aged to end point. (b) Kaplan-Meier survival curves show increased survival in tumour
cells overexpressing both HIC1 and miR-212/132 (MB002 n = 5; P = 0.0116, G498 n =
5; P = 0.0011, log rank test).
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Figure 4-7: Target Scan predicts SIRT1 as a conserved target for both miR-212
and miR-132.
(a) Target Scan [147] predicts SIRT1 as a conserved target for miR-212 and miR-132
targeting the 3’ UTR of SIRT1 at position 1680-1686. (b) qRT-PCR showing
downregulation of SIRT1 with HIC1 (P = 0.049) and miR-212/132 (P = 0.011)
overexpression (n=2, t-test). (c) Western blot showing downregulation at the protein
level of SIRT1 with HIC1 and miR-212/132 overexpression, representative blot and
densitometry shown (P = 0.038, t-test).
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Figure 4-8: Heterozygote deletion of Hic1 and miR212-132 does not increase
medulloblastoma incidence nor decrease tumour latency as compared to Ptc+/-.
(a) Loxp sites flanking the bidirectional promoter mice were generated using
recombineering technology. In addition to Hic1fl/fl and miR212/132fl/fl mice from our
collaborators, the breeding strategy is to cross these mice to Nestin:Cre and Ptc+/-. (b)
Mice were aged for 300 days and no statistically significant difference was observed for
Hic1fl/wt and miR212/132fl/wt as compared to Ptc background alone. (c) Examination of
these tumours reveals a histology reminiscent of patient tumours. (d) Across all
transgenic mouse models of medulloblastoma Hic1 and miR212/132 is poorly
expressed as compared to normal cerebellum (n=3; * = P<0.05, ** = P<0.01, *** =
P<0.001, two-tailed t-test).
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Figure 4-9: Endogenous reactivation of Hic1 and miR212-132 using RNA-guided
Cas9 mediated transcriptional activation.
(a) Design of 12 sgRNAs using http://crispr.mit.edu/; transient expression in HEK-293T
cells show 3 sgRNAs with the highest transcriptional activation. (b) Stable lentiviral
mediated expression of sgRNAs with SAM (synergistic activation mediator) show robust
overexpression compared with sgRNA targeted to GFP using qRT-PCR. (c) Stable
overexpression of sgRNA with the highest transcriptional activation leads to decreased
proliferation in MB002.
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Chapter 5
“Down to their innate molecular core, cancer cells are hyperactive, survival-endowed,
scrappy, fecund, inventive copies of ourselves.” The Emperor of all Maladies – Siddhartha Mukherjee
77
5 Conclusion and Future Directions
5.1 Summary of Results
The ultimate goal of this body of work is to elucidate novel mechanisms of
medulloblastoma pathogenesis. Recent characterization of medulloblastoma into four
unique subgroups that differ in their biology and clinical behavior have transformed the
study and management of this disease. The major clinical challenge in the treatment of
medulloblastoma is the presence of leptomeningeal metastasis. This not only confers a
poor prognosis, but the vast majority of our knowledge of medulloblastoma comes from
studying surgical resections of the primary tumour. This sets up the work in chapter 3 of
this thesis, where we have collected the largest primary-metastasis paired cohort in the
world. This was an undertaking that involved international collaboration. We show using
unsupervised hierarchical clustering of both gene expression and methylation data that
the metastatic compartment retains the subgroup signature of their primary disease.
This has several implications. First, although treatments currently geared towards
primary disease are refractory in metastasis, designing subgroup targeted therapy that
specifically destroy a certain cell type may be an appealing approach. Second, the
stability of the subgroup structure suggests that subgroup identify may be a marker for a
common cell of origin; this idea will be further discussed in section 5.2.2. We have since
made this valuable dataset publically available. We are currently working with
collaborators to validate the Notch signaling pathway as drivers of metastatic
dissemination (under review at Nature). In chapter 4 of this thesis, we have identified a
novel bidirectional promoter regulating a commonly studied tumor suppressor gene,
HIC1. We show that the hypermethylation of this promoter also silences microRNAs
miR-212 and miR-132. To our knowledge, we are the first group to implicate this
microRNA’s tumor suppressor role in medulloblastoma. Reactivation of these robustly
silenced loci can decrease proliferation of tumour cells. Using RNA-guided Cas9
technology, this may be developed into targeted therapies to activate endogenous
transcriptional programs. While this thesis highlights the ever increasing role of
78
epigenetics in medulloblastoma, this work only begins to delve into how genetics and
epigenetics interact to alter cellular growth and homeostasis.
5.2 Future Directions
5.2.1 Distinguishing between driver and passenger mutations
In chapter 4 of this thesis, I presented the identification of a novel bidirectional promoter
that regulates tumor suppressor gene HIC1 and a newly characterized tumor
suppressor microRNA cluster miR212/132. Overexpression of this gene pair in the
context of patient derived xenografts led to decreased proliferation. One of the
limitations of this work was the observation that knockout mice harbouring either Hic1+/-
or miR-212/132+/- did not show increased tumour penetrance nor decreased latency in a
Ptc+/- background, counter to previous publications on HIC1 [139]. Part of the
discrepancy of these findings may relate to the background of these mice. Another
possibility is the nature of the knockouts. The knockout used in this study is conditional
and cell-lineage restricted to the Nestin neural progenitor cells, whereas the line used in
Briggs et al. paper is a constitutive knockout. The increased tumour penetrance may be
a result of disruption in stromal cells and may affect the niche of the tumour
microenvironment rather than the tumour cells themselves. Furthermore, the sequential
order of genetic and epigenetic alterations is often difficult to discern from bulk
heterogeneous tissue. This heterogeneity is further compounded by the countless
passenger mutations, estimated of upwards of 99.9% of the mutational burden in cancer
[148]. The holy grail in cancer genomics is to develop a tool to distinguish between
passenger and driver mutations. It is only by identifying the few drivers that actively
contribute to tumorigenesis that we are able to design targeted therapies against them.
Functional genomics platforms to date have begun to unravel the key genetic players in
cancer. Insertional mutagenesis platforms have been used extensively in the past to
study hematologic and solid tumours in the mouse. More recently, the Sleeping Beauty
(SB) transposon has been developed to take a functional genomic approach to studying
79
other types of solid malignancies [149–151]. The SB system utilizes a DNA element that
can randomly mobilize through the genome when in the presence of an activating
enzyme, transposase, ultimately driving tumorigenesis through overexpression of
oncogenes and silencing of tumour suppressor genes. Our lab has previously
developed a SB driven mouse model on the Ptch+/- background, where we were able to
generate a 100% penetrance model of disseminated medulloblastoma that allowed us,
using deep sequencing technology, to identify genetic events shared by the primary and
its matched metastases [133]. A major challenge remaining in these type of sleeping
beauty screens is similar to large-scale human genome sequencing studies, in that we
are still unable to distinguish driver insertions from passenger insertions. Another
project that I have been leading, outside of the scope of the current thesis, is to develop
an insertional mutagenesis system that can be spatially and temporally controlled. This
hybrid transposon which has elements of both SB and piggyBac (PB) elements takes
advantage of the mobile transposons to enrich for maintenance events required for
tumour progression. To date, we have been able to produce a highly penetrant model of
medulloblasoma as shown through histology and molecular profiling using this model
(Tg[Nestin-cre/Ptc+/-/Nestin-lucSB100/Rosa26-LSL-mPB-ERT2/Lazy Piggy]). We have
collected matched littermate mice for conducting the screen. We are currently in the
process of analyzing the common insertion sites from this experiment. This model is the
first immunocompetent functional genomics model that will allow us to discriminate
driver events and passenger events, which will ultimately increase our understanding of
the factors leading to tumour progression and reveal actionable targets .
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Figure 5-1: Overview of a spatially restricted, temporally inducible insertional
mutagenesis system using a hybrid Sleeping Beauty and piggyBac transposon to
delineate driver/maintenance genes.
(a) Schematic of Lazy Piggy (LP) transposon for enrichment of maintenance insertions.
Sleeping Beauty (SB) insertions drive medulloblastoma initiation. Administration of
tamoxifen induces piggyBac (PB) remobilization of LP transposon, following tumour
initiation, and selects for insertion events that enriches for maintenance genes.
5.2.2 Subgroup specific pre-clinical models
The advent of the genomic revolution has begun to shed light on the biological basis of
histologically similar but molecularly different disease subtypes. Using gene expression
profiling, the discovery of the sub-architecture of medulloblastoma has had profound
implications in understanding the cellular origins of these diverse brain tumours. It has
been suggested that the different molecular signatures of the different subgroups of
medulloblastoma is a reflection of the distinct cell of origin from which they arose. In line
of this thinking, cross-species genomics revealed a that SHH subtype medulloblastoma
likely arise from committed cerebellar granule neuron precursor cells (GNPCs) [55, 56].
Surprisingly, WNT subtype medulloblastoma showed high concordance with neural
precursor cells of the lower rhombic lip and embryonic dorsal brainstem [48], marking a
very distinct cell type from SHH subtype. The cells of origin for Group 3 and 4 diseases
are still quite contentious. As no transgenic model of these subgroups exist, several
groups have attempted to use orthotopic transplantation models. From these
experiments, the overexpression of myc with concomitant p53 inactivation in mouse
neural stem cells (marked by Prominin+ve and Math1-ve) appear to cluster with human
Group 3 tumors when looking at gene expression patterns [60, 132]. These efforts are
vital given the lack of preclinical models of these subgroups. Recent studies using drug
libraries have identified potential alternative combinational treatments for these high-risk
82
patients [152]. At the start of 2016, using enhancer mapping, active enhancers and
transcriptional programs are beginning to reveal potential cellular origins for Group 4
tumours, this will be highlighted in chapter 5.2.4.
The understanding of cellular origins will be crucial to the generation of robust mouse
models to phenocopy patient disease and offer a platform for preclinical testing. Much of
the current understanding of medulloblastoma arose from early work modeling Ptch
heterozygosity in patients with Gorlin syndrome [153]. These models provided a
foundation for preclinical testing and led to the identification of various SHH pathway
inhibitors [38]. The search is on for mouse models of Group 3 and 4 disease. There are
several limitation however of using transgenic mice for preclinical testing. In short, the
species barrier offers different pharmacokinetics and pharmacodynamics that will limit
ease of translation. Furthermore, the time and scalability of mouse models remain
insurmountable challenges even with the advent of CRISPR-Cas9 technology, which
has significantly sped up the generation of new mouse models. In work outside the
scope of this thesis, I am collaborating with Dr. Ian Scott to develop subgroup specific
zebrafish medulloblastoma models, taking advantage of the zebrafish’s fecundity and
ease for drug screening. Using the UAS-GAL4 system, we have builg several models by
overexpressing subgroup-specific drivers. Another potential solution to this is the use of
cerebral organoids, which has recently been developed to model the three-dimensional
architecture of human organ systems [154, 155]. Lancaster et al. described the
generation of cerebral organoids using 3D cultures that display the transcriptional,
organizational, and functional programs of discrete brain regions [154]. This will be an
attractive model to rapidly test new biologically-relevant, subgroup-specific, mutations
and pathways implicated in medulloblastoma.
5.2.3 Unravelling the epigenetic code in medulloblastoma
Given the largely bland medulloblastoma genomes with a paucity of recurrent somatic
mutations, aberrations in the epigenome, targeting disruption of DNA methylation and
chromatin architecture, may explain the pathogenic process. Methylation profiling using
83
a limited number of CpG sites is sufficient to recapitulate the four subgroup structure of
medulloblastoma [156]. This highlights the remarkable stability of the epigenome and
may be a better reflection of the cell of origin of these different tumour subgroups. Using
whole-genome bisulphite-sequening from 34 patient tumours, Volker et al. characterized
the methylome of medulloblastoma [131]. Prevalent regions of active transcription were
driven by gene-body hypomethylation. These regions are also associated with active
chromatin marks and may represent a novel mechanism of gene activation.
Some of the most common recurrent somatic mutations in medulloblastoma involve
chromatin remodeling factors. These mutations include MLL2, MLL3, SMARCA4 and
KDM6A (also known as UTX). MLL2 and MLL3 are H3K4-methyltransferase, working to
establish transcriptionally active chromatin marks. KDM6A H3-lys27-demethylase. It’s
role has been proposed to promote differentiation. As such it is no stretch of the
imagination that mutations affecting KDM6A are inactivating, while its counter
methyltranserase EZH2 is often overexpressed, maintaining cells at an undifferentiated
state. Recent evidence, using H3K27ac and BRD4 chromatin immunoprecipitation to
survey enhancer marks have revealed subgroup-specific enhancer elements. Lin et al.
show that LMX1A enhancer appear to be highly discriminatory for Group 4 tumours and
may point to the cell of origin for Group 4 disease. LMX1A is expressed in the cerebellar
upper rhombic lip (uRL) and cre drivers are available for further functional validation.
This, in combination with subgroup specific pathways and mutations, such as
overexpression of SNCAIP or deregulation of H3K27 machinery, may lead to the first
transgenic model of Group 4 medulloblastoma. Further studies profiling the whole
spectrum of histone marks will be necessary to fully elucidate the contribution of the
epigenome in oncogenesis. It is also important to note that chromatin architecture often
involves interactions with the nuclear lamina [76], as such the three-dimensional nature
of chromatin conformations will also need to be studied. This can be done with Hi -C
chromosome conformation capture to look for long-range interactions of the genome.
84
5.2.4 Targeting the metastatic compartment for translation of new therapies
As previous publications and current clinical management of human medulloblastoma
assume that the primary tumor and its matched metastases respond to therapy in a
similar manner, this notion may be jarringly false. In fact, a recent publication by Wu et
al. offers support for a bi-compartmental model whereby the primary and matched
metastatic disease are inherently different [133]. Failure to study the leptomeningeal
disease as an important and separate entity may result in the ineffectiveness of targeted
therapies. We show in chapter 3 of this thesis that medulloblasomta subgroups remain
stable between primary and metastatic lesions. This, we hypothesize, is due to a shared
cellular origin between the primary and metastasis. However, it is crucial to appreciate
that the metastatic cells likely only retain an echo of the transcriptional signature
retained in the cell of origin, while significant clonal diversion has caused new distinct
driver mutations. Recently, a SB system has been used to model a highly metastatic
subset of medulloblastoma . Comparing the primary and matched tumors revealed that
although different metastases are genetically very similar to each other, they markedly
deviate from the primary tumor. Certain genetic events in the metastatic lesions were
not present in the matched primary tumour while other genetic events were completely
restricted to the primary tumour. This finding aligns with the hypothesis that metastases
arise from a restricted subclone of the primary tumor that has been selected in the
metastatic tumor niche. This complex pattern of genetic variance may explain the
existence of therapy-resistant clones and thus underscore the difficulty in treating
patients with metastatic disease. Looking at the genome of tumour recurrences, recent
sequencing study show that <12% of events observed at diagnosis are present at
recurrence. This will be a significant clinical challenge as targeted therapies discovered
from pre-treatment surgical resections do not reflect the recurrent disease. Using a
recurrent mouse model, Morrissy et al. show that convergent pathways selected at
relapse target TP53, Chr14q loss and DYNC1H1, a gene involved in intracellular motility
and transport [157]. The implications of these findings remain to be fully elucidated with
functional modeling.
85
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References
1. Cushing H (1930) Experiences with the cerebellar medulloblastomas. Acta Pathol.
Microbiol. Scand.
2. Rutka JT, Hoffman HJ (1996) Medulloblastoma: A historical perspective and
overview. J Neurooncol 29:1–7. doi: 10.1007/BF00165513
3. Dolecek T a, Propp JM, Stroup NE, Kruchko C (2012) CBTRUS Statistical Report:
Primary Brain and Central Nervous System Tumors Diagnosed in the United States
in 2005-2009. Neuro Oncol 14 Suppl 5:v1–v49. doi: 10.1093/neuonc/nos218
4. McKean-Cowdin R, Razavi P, Barrington-Trimis J, et al. (2013) Trends in childhood
brain tumor incidence, 1973-2009. J Neurooncol. doi: 10.1007/s11060-013-1212-5
5. Wang X, Ramaswamy V, Taylor MD (2015) Familial Tumor Syndromes. Neuro Oncol.
doi: 10.1055/b-0034-97883
6. Packer RJ, Cogen P, Vezina G, Rorke LB (1999) Medulloblastoma: clinical and
biologic aspects. Neuro Oncol 1:232–50.
7. Johnston DL, Keene D, Kostova M, et al. (2015) Survival of children with
medulloblastoma in Canada diagnosed between 1990 and 2009 inclusive. J
Neurooncol 0–6. doi: 10.1007/s11060-015-1831-0
8. Chan TYC, Wang X, Spence T, et al. (2015) Pediatric Neuro-oncology. Pediatr
Neuro-oncology. doi: 10.1007/978-1-4939-1541-5
9. Tortori-Donati P, Fondelli MP, Rossi a, et al. (1996) Medulloblastoma in children: CT
and MRI findings. Neuroradiology 38:352–9.
10. Louis DN, Ohgaki H, Wiestler OD, et al. (2007) The 2007 WHO classification of
tumours of the central nervous system. Acta Neuropathol 114:97–109. doi:
10.1007/s00401-007-0243-4
88
11. Gajjar A, Hernan R, Kocak M, et al. (2004) Clinical, histopathologic, and molecular
markers of prognosis: toward a new disease risk stratification system for
medulloblastoma. J Clin Oncol 22:984–93. doi: 10.1200/JCO.2004.06.032
12. Packer RJ, Rood BR, MacDonald TJ (2003) Medulloblastoma: Present Concepts of
Stratification into Risk Groups. Pediatr Neurosurg 39:60–67. doi:
10.1159/000071316
13. Remke M, Hielscher T, Korshunov A, et al. (2011) FSTL5 Is a Marker of Poor
Prognosis in Non-WNT/Non-SHH Medulloblastoma. J Clin Oncol 29:3852–61. doi:
10.1200/JCO.2011.36.2798
14. Northcott PA, Korshunov A, Witt H, et al. (2011) Medulloblastoma comprises four
distinct molecular variants. J Clin Oncol 29:1408–14. doi:
10.1200/JCO.2009.27.4324
15. Cho Y-J, Tsherniak A, Tamayo P, et al. (2011) Integrative genomic analysis of
medulloblastoma identifies a molecular subgroup that drives poor clinical outcome.
J Clin Oncol 29:1424–30. doi: 10.1200/JCO.2010.28.5148
16. Kool M, Korshunov A, Remke M, et al. (2012) Molecular subgroups of
medulloblastoma: an international meta-analysis of transcriptome, genetic
aberrations, and clinical data of WNT, SHH, Group 3, and Group 4
medulloblastomas. Acta Neuropathol. doi: 10.1007/s00401-012-0958-8
17. Thompson MC, Fuller C, Hogg TL, et al. (2006) Genomics identifies
medulloblastoma subgroups that are enriched for specific genetic alterations. J Clin
Oncol 24:1924–31. doi: 10.1200/JCO.2005.04.4974
18. Taylor MD, Northcott P a, Korshunov A, et al. (2012) Molecular subgroups of
medulloblastoma: the current consensus. Acta Neuropathol 123:465–72. doi:
10.1007/s00401-011-0922-z
19. Taylor MD, Northcott P a, Korshunov A, et al. (2011) Molecular subgroups of
medulloblastoma: the current consensus. Acta Neuropathol. doi: 10.1007/s00401-
89
011-0922-z
20. Northcott P a, Shih DJH, Remke M, et al. (2012) Rapid, reliable, and reproducible
molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathol
123:615–26. doi: 10.1007/s00401-011-0899-7
21. Gilbertson RJ (2004) Reviews Medulloblastoma : signalling a change in treatment.
5:209–218.
22. Rieken S, Mohr A, Habermehl D, et al. (2011) Outcome and prognostic factors of
radiation therapy for medulloblastoma. Int J Radiat Oncol Biol Phys 81:e7–e13. doi:
10.1016/j.ijrobp.2010.12.042
23. Schreiber JE, Gurney JG, Palmer SL, et al. (2014) Examination of risk factors for
intellectual and academic outcomes following treatment for pediatric
medulloblastoma. Neuro Oncol 16:1129–36. doi: 10.1093/neuonc/nou006
24. Tsui K, Gajjar A, Li C, et al. (2014) Subsequent neoplasms in survivors of childhood
central nervous system tumors: risk after modern multimodal therapy. Neuro Oncol
1–9. doi: 10.1093/neuonc/nou279
25. Chemaitilly W, Sklar C a (2010) Endocrine complications in long-term survivors of
childhood cancers. Endocr Relat Cancer 17:R141-59. doi: 10.1677/ERC-10-0002
26. Brodin NP, Vogelius IR, Maraldo M V, et al. (2012) Life years lost-comparing
potentially fatal late complications after radiotherapy for pediatric medulloblastoma
on a common scale. Cancer 118:5432–40. doi: 10.1002/cncr.27536
27. Ullrich NJ, Embry L (2012) Neurocognitive dysfunction in survivors of childhood
brain tumors. Semin Pediatr Neurol 19:35–42. doi: 10.1016/j.spen.2012.02.014
28. Pietilä S, Korpela R, Lenko HL, et al. (2012) Neurological outcome of childhood
brain tumor survivors. J Neurooncol. doi: 10.1007/s11060-012-0816-5
29. Rutkowski S, Bode U, Deinlein F, et al. (2005) Treatment of early childhood
medulloblastoma by postoperative chemotherapy alone. N Engl J Med 352:978–86.
90
doi: 10.1056/NEJMoa042176
30. Grill J, Sainte-rose C, Jouvet A, et al. (1996) Treatment of medulloblastoma with
postoperative chemotherapy alone : an SFOP prospective trial in young children.
573–580. doi: 10.1016/S1470-2045(05)70252-7
31. Geyer JR, Sposto R, Jennings M, et al. (2005) Multiagent chemotherapy and
deferred radiotherapy in infants with malignant brain tumors: a report from the
Children’s Cancer Group. J Clin Oncol 23:7621–31. doi: 10.1200/JCO.2005.09.095
32. Packer RJ, Gajjar A, Vezina G, et al. (2006) Phase III study of craniospinal radiation
therapy followed by adjuvant chemotherapy for newly diagnosed average-risk
medulloblastoma. J Clin Oncol 24:4202–8. doi: 10.1200/JCO.2006.06.4980
33. Gajjar A, Chintagumpala M, Ashley D, et al. (2006) Risk-adapted craniospinal
radiotherapy followed by high-dose chemotherapy and stem-cell rescue in children
with newly diagnosed medulloblastoma (St Jude Medulloblastoma-96): long-term
results from a prospective, multicentre trial. Lancet Oncol 7:813–20. doi:
10.1016/S1470-2045(06)70867-1
34. Gandola L, Massimino M, Cefalo G, et al. (2009) Hyperfractionated accelerated
radiotherapy in the Milan strategy for metastatic medulloblastoma. J Clin Oncol
27:566–71. doi: 10.1200/JCO.2008.18.4176
35. Jakacki RI, Burger PC, Zhou T, et al. (2012) Outcome of children with metastatic
medulloblastoma treated with carboplatin during craniospinal radiotherapy: a
Children’s Oncology Group Phase I/II study. J Clin Oncol 30:2648–53. doi:
10.1200/JCO.2011.40.2792
36. Ellison DW, Kocak M, Dalton J, et al. (2011) Definition of disease-risk stratification
groups in childhood medulloblastoma using combined clinical, pathologic, and
molecular variables. J Clin Oncol 29:1400–7. doi: 10.1200/JCO.2010.30.2810
37. LoRusso PM, Rudin CM, Reddy JC, et al. (2011) Phase I trial of hedgehog pathway
inhibitor vismodegib (GDC-0449) in patients with refractory, locally advanced or
91
metastatic solid tumors. Clin Cancer Res 17:2502–11. doi: 10.1158/1078-
0432.CCR-10-2745
38. Rudin CM, Hann CL, Laterra J, et al. (2009) Treatment of medulloblastoma with
hedgehog pathway inhibitor GDC-0449. N Engl J Med 361:1173–8. doi:
10.1056/NEJMoa0902903
39. Spiller S, Ditzler S, Pullar B, Olson J (2008) Response of preclinical
medulloblastoma models to combination therapy with 13-cis retinoic acid and
suberoylanilide hydroxamic acid (SAHA). J Neurooncol 87:133–141.
40. Buonamici S, Williams J, Morrissey M, et al. (2010) Interfering with resistance to
smoothened antagonists by inhibition of the PI3K pathway in medulloblastoma. Sci
Transl Med 2:51ra70. doi: 10.1126/scitranslmed.3001599
41. Kessler JD, Kahle KT, Sun T, et al. A SUMOylation-dependent transcriptional
subprogram is required for Myc-driven tumorigenesis. Science (80- ) 335:348–353.
doi: science.1212728 [pii]10.1126/science.1212728
42. Prochownik E V, Vogt PK (2010) Therapeutic Targeting of Myc. Genes cancer
1:650–659. doi: 10.1177/1947601910377494
43. Robinson G, Parker M, Kranenburg T a, et al. (2012) Novel mutations target distinct
subgroups of medulloblastoma. Nature 488:43–48. doi: 10.1038/nature11213
44. Pugh TJ, Weeraratne SD, Archer TC, et al. (2012) Medulloblastoma exome
sequencing uncovers subtype-specific somatic mutations. Nature 1–5. doi:
10.1038/nature11329
45. Jones DTW, Jäger N, Kool M, et al. (2012) Dissecting the genomic complexity
underlying medulloblastoma. Nature 0–5. doi: 10.1038/nature11284
46. Northcott P a., Shih DJH, Peacock J, et al. (2012) Subgroup-specific structural
variation across 1,000 medulloblastoma genomes. Nature 1:2–9. doi:
10.1038/nature11327
92
47. Pei Y, Brun SN, Markant SL, et al. (2012) WNT signaling increases proliferation and
impairs differentiation of stem cells in the developing cerebellum. Development
139:1724–33. doi: 10.1242/dev.050104
48. Gibson P, Tong Y, Robinson G, et al. (2010) Subtypes of medulloblastoma have
distinct developmental origins. Nature 468:1095–9. doi: 10.1038/nature09587
49. Wang X, Venugopal C, Manoranjan B, et al. (2011) Sonic hedgehog regulates Bmi1
in human medulloblastoma brain tumor-initiating cells. Oncogene 187–199. doi:
10.1038/onc.2011.232
50. Northcott P a., Jones DTW, Kool M, et al. (2012) Medulloblastomics: the end of the
beginning. Nat Rev Cancer 12:818–834. doi: 10.1038/nrc3410
51. Dijkgraaf GJP, Alicke B, Weinmann L, et al. (2011) Small molecule inhibition of
GDC-0449 refractory smoothened mutants and downstream mechanisms of drug
resistance. Cancer Res 71:435–44. doi: 10.1158/0008-5472.CAN-10-2876
52. Hallahan AR, Pritchard JI, Hansen S, et al. (2004) The SmoA1 mouse model
reveals that notch signaling is critical for the growth and survival of sonic
hedgehog-induced medulloblastomas. Cancer Res 64:7794–800. doi:
10.1158/0008-5472.CAN-04-1813
53. Grammel D, Warmuth-Metz M, von Bueren AO, et al. (2012) Sonic hedgehog-
associated medulloblastoma arising from the cochlear nuclei of the brainstem. Acta
Neuropathol 123:601–14. doi: 10.1007/s00401-012-0961-0
54. Oliver TG, Read TA, Kessler JD, et al. (2005) Loss of patched and disruption of
granule cell development in a pre-neoplastic stage of medulloblastoma.
Development 132:2425–39. doi: 10.1242/dev.01793
55. Schüller U, Heine VM, Mao J, et al. (2008) Acquisition of granule neuron precursor
identity is a critical determinant of progenitor cell competence to form Shh-induced
medulloblastoma. Cancer Cell 14:123–34. doi: 10.1016/j.ccr.2008.07.005
56. Yang Z-J, Ellis T, Markant SL, et al. (2008) Medulloblastoma can be initiated by
93
deletion of Patched in lineage-restricted progenitors or stem cells. Cancer Cell
14:135–45. doi: 10.1016/j.ccr.2008.07.003
57. Northcott P a, Fernandez-L A, Hagan JP, et al. (2009) The miR-17/92 polycistron is
up-regulated in sonic hedgehog-driven medulloblastomas and induced by N-myc in
sonic hedgehog-treated cerebellar neural precursors. Cancer Res 69:3249–55. doi:
10.1158/0008-5472.CAN-08-4710
58. Parsons DW, Li M, Zhang X, et al. (2011) The genetic landscape of the childhood
cancer medulloblastoma. Science 331:435–9. doi: 10.1126/science.1198056
59. Northcott P a., Lee C, Zichner T, et al. (2014) Enhancer hijacking activates GFI1
family oncogenes in medulloblastoma. Nature. doi: 10.1038/nature13379
60. Pei Y, Moore CE, Wang J, et al. (2012) An Animal Model of MYC-Driven
Medulloblastoma. Cancer Cell 21:155–167. doi: 10.1016/j.ccr.2011.12.021
61. Swartling FJ, Savov V, Persson AI, et al. (2012) Distinct neural stem cell
populations give rise to disparate brain tumors in response to N-MYC. Cancer Cell
21:601–13. doi: 10.1016/j.ccr.2012.04.012
62. Dang C V (2012) MYC on the path to cancer. Cell 149:22–35. doi:
10.1016/j.cell.2012.03.003
63. Korshunov A, Remke M, Kool M, et al. (2012) Biological and clinical heterogeneity
of MYCN-amplified medulloblastoma. Acta Neuropathol 123:515–27. doi:
10.1007/s00401-011-0918-8
64. Dubuc AM, Mack S, Unterberger A, et al. (2012) Cancer Epigenetics. Methods. doi:
10.1007/978-1-61779-612-8
65. Baylin SB, Jones P a. (2011) A decade of exploring the cancer epigenome —
biological and translational implications. Nat Rev Cancer 11:726–734. doi:
10.1038/nrc3130
66. Northcott P a, Nakahara Y, Wu X, et al. (2009) Multiple recurrent genetic events
94
converge on control of histone lysine methylation in medulloblastoma. Nat Genet
41:465–72. doi: 10.1038/ng.336
67. Dubuc AM, Remke M, Korshunov A, et al. (2013) Aberrant patterns of H3K4 and
H3K27 histone lysine methylation occur across subgroups in medulloblastoma.
Acta Neuropathol 125:373–84. doi: 10.1007/s00401-012-1070-9
68. Eyal A, Engelender S (2006) Synphilin Isoforms and the Search for a Cellular Model
of Lewy Body Formation in Parkinson ’ s Disease ND SC RIB. 2082–2086.
69. Wu X, Northcott PA, Croul S, Taylor MD (2011) Mouse models of medulloblastoma.
Chin J Cancer 30:442–9. doi: 10.5732/cjc.011.10040
70. Cancer T, Atlas G (2012) Comprehensive molecular portraits of human breast
tumours. Nature 490:61–70. doi: 10.1038/nature11412
71. Feinberg AP, Vogelstein B (1983) Hypomethylation distinguishes genes of some
human cancers from their normal counterparts. Nature 301:89–92.
72. Sakai T, Toguchida J, Ohtani N, et al. (1991) Allele-specific hypermethylation of the
retinoblastoma tumor-suppressor gene. Am J Hum Genet 48:880–8. doi:
10.1007/BF02890404
73. Chen WY, Wang DH, Yen RC, et al. (2005) Tumor suppressor HIC1 directly
regulates SIRT1 to modulate p53-dependent DNA-damage responses. Cell
123:437–48. doi: 10.1016/j.cell.2005.08.011
74. Rood BR, Zhang H, Weitman DM, et al. (2002) Hypermethylation of HIC-1 and 17p
Allelic Loss in Medulloblastoma. Cancer 3794–3797.
75. Hansen KD, Timp W, Bravo HC, et al. (2011) Increased methylation variation in
epigenetic domains across cancer types. Nat Genet. doi: 10.1038/ng.865
76. Hansen KD, Timp W, Bravo HC, et al. (2011) Increased methylation variation in
epigenetic domains across cancer types. Nat Genet 43:768–75. doi:
10.1038/ng.865
95
77. Bachman KE, Park BH, Rhee I, et al. (2003) Histone modifications and silencing
prior to DNA methylation of a tumor suppressor gene. Cancer Cell 3:89–95.
78. Sproul D, Nestor C, Culley J, et al. (2011) Transcriptionally repressed genes
become aberrantly methylated and distinguish tumors of different lineages in breast
cancer. Proc Natl Acad Sci U S A 108:4364–9. doi: 10.1073/pnas.1013224108
79. Hovestadt V, Jones DTW, Picelli S, et al. (2014) Decoding the regulatory landscape
of medulloblastoma using DNA methylation sequencing. Nature 510:537–41. doi:
10.1038/nature13268
80. Lee RS, Stewart C, Carter SL, et al. (2012) A remarkably simple genome underlies
highly malignant pediatric rhabdoid cancers. J Clin Invest 122:2983–8. doi:
10.1172/JCI64400
81. Mack SC, Witt H, Piro RM, et al. (2014) Epigenomic alterations define lethal CIMP-
positive ependymomas of infancy. Nature. doi: 10.1038/nature13108
82. Feinberg AP, Ohlsson R, Henikoff S (2006) The epigenetic progenitor origin of
human cancer. Nat Rev Genet 7:21–33. doi: 10.1038/nrg1748
83. Feinberg AP, Koldobskiy MA, Göndör A (2016) Disease mechanisms: Epigenetic
modulators, modifiers and mediators in cancer aetiology and progression. Nat Rev
Genet 17:284–299. doi: 10.1038/nrg.2016.13
84. Milde T, Oehme I, Korshunov A, et al. (2010) HDAC5 and HDAC9 in
medulloblastoma: novel markers for risk stratification and role in tumor cell growth.
Clin Cancer Res 16:3240–52. doi: 10.1158/1078-0432.CCR-10-0395
85. Ma J-X, Li H, Chen X-M, et al. (2013) Expression patterns and potential roles of
SIRT1 in human medulloblastoma cells in vivo and in vitro. Neuropathology 33:7–
16. doi: 10.1111/j.1440-1789.2012.01318.x
86. Chi P, Allis CD, Wang GG (2010) Covalent histone modifications--miswritten,
misinterpreted and mis-erased in human cancers. Nat Rev Cancer 10:457–69. doi:
10.1038/nrc2876
96
87. Bernstein BE, Mikkelsen TS, Xie X, et al. (2006) A bivalent chromatin structure
marks key developmental genes in embryonic stem cells. Cell 125:315–26. doi:
10.1016/j.cell.2006.02.041
88. Barski A, Cuddapah S, Cui K, et al. (2007) High-resolution profiling of histone
methylations in the human genome. Cell 129:823–37. doi:
10.1016/j.cell.2007.05.009
89. Baker LA, Allis CD, Wang GG (2008) PHD fingers in human diseases: disorders
arising from misinterpreting epigenetic marks. Mutat Res 647:3–12. doi:
10.1016/j.mrfmmm.2008.07.004
90. Ruthenburg AJ, Allis CD, Wysocka J (2007) Methylation of lysine 4 on histone H3:
intricacy of writing and reading a single epigenetic mark. Mol Cell 25:15–30. doi:
10.1016/j.molcel.2006.12.014
91. Klose RJ, Zhang Y (2007) Regulation of histone methylation by demethylimination
and demethylation. Nat Rev Mol Cell Biol 8:307–18. doi: 10.1038/nrm2143
92. Krivtsov A V, Armstrong SA (2007) MLL translocations, histone modifications and
leukaemia stem-cell development. Nat Rev Cancer 7:823–33. doi: 10.1038/nrc2253
93. Nakamura T, Mori T, Tada S, et al. (2002) ALL-1 is a histone methyltransferase that
assembles a supercomplex of proteins involved in transcriptional regulation. Mol
Cell 10:1119–28.
94. van Haaften G, Dalgliesh GL, Davies H, et al. (2009) Somatic mutations of the
histone H3K27 demethylase gene UTX in human cancer. Nat Genet 41:521–3. doi:
10.1038/ng.349
95. Agger K, Cloos PAC, Christensen J, et al. (2007) UTX and JMJD3 are histone
H3K27 demethylases involved in HOX gene regulation and development. Nature
449:731–4. doi: 10.1038/nature06145
96. Bracken AP, Helin K (2009) Polycomb group proteins: navigators of lineage
pathways led astray in cancer. Nat Rev Cancer 9:773–84. doi: 10.1038/nrc2736
97
97. Oberoi J, Fairall L, Watson PJ, et al. (2011) Structural basis for the assembly of the
SMRT/NCoR core transcriptional repression machinery. Nat Struct Mol Biol
18:177–84. doi: 10.1038/nsmb.1983
98. Simon JA, Lange CA (2008) Roles of the EZH2 histone methyltransferase in cancer
epigenetics. Mutat Res 647:21–9. doi: 10.1016/j.mrfmmm.2008.07.010
99. Winter J, Jung S, Keller S, et al. (2009) Many roads to maturity : microRNA
biogenesis pathways and their regulation. Rev. Lit. Arts Am. 11:
100. Czech B, Hannon GJ (2011) Small RNA sorting: matchmaking for Argonautes. Nat
Rev Genet 12:19–31. doi: 10.1038/nrg2916
101. Mizutani K, Yoon K, Dang L, et al. (2007) Differential Notch signalling distinguishes
neural stem cells from intermediate progenitors. Nature 449:351–5. doi:
10.1038/nature06090
102. Su X, Kameoka S, Lentz S, Majumder S (2004) Activation of REST / NRSF Target
Genes in Neural Stem Cells Is Sufficient To Cause Neuronal Differentiation.
Society 24:8018–8025. doi: 10.1128/MCB.24.18.8018
103. de Antonellis P, Medaglia C, Cusanelli E, et al. (2011) MiR-34a targeting of Notch
ligand delta-like 1 impairs CD15+/CD133+ tumor-propagating cells and supports
neural differentiation in medulloblastoma. PLoS One 6:e24584. doi:
10.1371/journal.pone.0024584
104. Weeraratne SD, Amani V, Neiss A, et al. (2011) miR-34a confers chemosensitivity
through modulation of MAGE-A and p53 in medulloblastoma. Neuro Oncol 13:165–
75. doi: 10.1093/neuonc/noq179
105. Fan YN, Meley D, Pizer B, Sée V (2014) Mir-34a Mimics Are Potential Therapeutic
Agents for p53-Mutated and Chemo-Resistant Brain Tumour Cells. PLoS One
9:e108514. doi: 10.1371/journal.pone.0108514
106. Fiaschetti G, Abela L, Nonoguchi N, et al. (2013) Epigenetic silencing of miRNA-9
is associated with HES1 oncogenic activity and poor prognosis of medulloblastoma.
98
110:636–647. doi: 10.1038/bjc.2013.764
107. Ferretti E, De Smaele E, Po A, et al. (2009) MicroRNA profiling in human
medulloblastoma. Int J Cancer 124:568–77. doi: 10.1002/ijc.23948
108. Pierson J, Hostager B, Fan R, Vibhakar R (2008) Regulation of cyclin dependent
kinase 6 by microRNA 124 in medulloblastoma. J Neurooncol 90:1–7. doi:
10.1007/s11060-008-9624-3
109. Li KKW, Pang JC, Ching AK, et al. (2009) miR-124 is frequently down-regulated in
medulloblastoma and is a negative regulator of SLC16A1. Hum Pathol 40:1234–43.
doi: 10.1016/j.humpath.2009.02.003
110. Shenoy A, Blelloch RH (2014) Regulation of microRNA function in somatic stem
cell proliferation and differentiation. Nat Rev Mol Cell Biol 15:565–576. doi:
10.1038/nrm3854
111. Cheng L-C, Pastrana E, Tavazoie M, Doetsch F (2009) miR-124 regulates adult
neurogenesis in the subventricular zone stem cell niche. Nat Neurosci 12:399–408.
doi: 10.1038/nn.2294
112. Bonev B, Pisco A, Papalopulu N (2011) MicroRNA-9 reveals regional diversity of
neural progenitors along the anterior-posterior axis. Dev Cell 20:19–32. doi:
10.1016/j.devcel.2010.11.018
113. Northcott P a, Shih DJH, Peacock J, et al. (2012) Subgroup-specific structural
variation across 1,000 medulloblastoma genomes. Nature 488:49–56. doi:
10.1038/nature11327
114. Yang MQ, Koehly LM, Elnitski LL (2007) Comprehensive annotation of bidirectional
promoters identifies co-regulation among breast and ovarian cancer genes. PLoS
Comput Biol 3:e72. doi: 10.1371/journal.pcbi.0030072
115. Shu J, Jelinek J, Chang H, et al. (2006) Silencing of Bidirectional Promoters by
DNA Methylation in Tumorigenesis. 5077–5085. doi: 10.1158/0008-5472.CAN-05-
2629
99
116. Ramaswamy V, Remke M, Bouffet E, et al. (2013) Recurrence patterns across
medulloblastoma subgroups: an integrated clinical and molecular analysis. Lancet
Oncol 14:1200–7. doi: 10.1016/S1470-2045(13)70449-2
117. Wang X, Dubuc AM, Ramaswamy V, et al. (2015) Medulloblastoma subgroups
remain stable across primary and metastatic compartments. Acta Neuropathol
449–457. doi: 10.1007/s00401-015-1389-0
118. Polkinghorn WR, Tarbell NJ (2007) Medulloblastoma: tumorigenesis, current
clinical paradigm, and efforts to improve risk stratification. Nat Clin Pract Oncol
4:295–304. doi: 10.1038/ncponc0794
119. Kool M, Koster J, Bunt J, et al. (2008) Integrated genomics identifies five
medulloblastoma subtypes with distinct genetic profiles, pathway signatures and
clinicopathological features. PLoS One 3:e3088. doi:
10.1371/journal.pone.0003088
120. Cho, Y.J., Tsherniak, A., Tamayo, P., Santagata, S., Ligon, A., Greulich, H.,
Berhoukim, R., Amani V, Goumnerova, L., Eberhart, C.G. et al. (2011) Integrative
genomic analysis of medulloblastoma identifies a molecular subgroup that drives
poor clinical outcome. J Clin Oncol 1424–1430.
121. Ramaswamy V, Northcott P a, Taylor MD (2011) FISH and chips: the recipe for
improved prognostication and outcomes for children with medulloblastoma. Cancer
Genet 204:577–88. doi: 10.1016/j.cancergen.2011.11.001
122. Johnson BE, Mazor T, Hong C, et al. (2014) Mutational analysis reveals the origin
and therapy-driven evolution of recurrent glioma. Science 343:189–93. doi:
10.1126/science.1239947
123. Sottoriva A, Spiteri I, Piccirillo SGM, et al. (2013) Intratumor heterogeneity in
human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci U S
A 110:4009–14. doi: 10.1073/pnas.1219747110
124. Patel AP, Tirosh I, Trombetta JJ, et al. (2014) Single-cell RNA-seq highlights
100
intratumoral heterogeneity in primary glioblastoma. Science 344:1396–401. doi:
10.1126/science.1254257
125. Wang X, Ramaswamy V, Remke M, et al. (2013) Intertumoral and intratumoral
heterogeneity as a barrier for effective treatment of medulloblastoma. Neurosurgery
60 Suppl 1:57–63. doi: 10.1227/01.neu.0000430318.01821.6f
126. Hovestadt V, Remke M, Kool M, et al. (2013) Robust molecular subgrouping and
copy-number profiling of medulloblastoma from small amounts of archival tumour
material using high-density DNA methylation arrays. Acta Neuropathol 125:913–6.
doi: 10.1007/s00401-013-1126-5
127. Remke M, Hielscher T, Northcott P a, et al. (2011) Adult medulloblastoma
comprises three major molecular variants. J Clin Oncol 29:2717–23. doi:
10.1200/JCO.2011.34.9373
128. Irizarry R a. (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic
Acids Res 31:15e–15. doi: 10.1093/nar/gng015
129. Brunet J-P, Tamayo P, Golub TR, Mesirov JP (2004) Metagenes and molecular
pattern discovery using matrix factorization. Proc Natl Acad Sci U S A 101:4164–9.
doi: 10.1073/pnas.0308531101
130. Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer
types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A
99:6567–72. doi: 10.1073/pnas.082099299
131. Hovestadt V, Jones DTW, Picelli S, et al. (2014) Decoding the regulatory
landscape of medulloblastoma using DNA methylation sequencing. Nature
510:537–41. doi: 10.1038/nature13268
132. Kawauchi D, Robinson G, Uziel T, et al. (2012) A Mouse Model of the Most
Aggressive Subgroup of Human Medulloblastoma. Cancer Cell 21:168–180. doi:
10.1016/j.ccr.2011.12.023
133. Wu X, Northcott P a., Dubuc A, et al. (2012) Clonal selection drives genetic
101
divergence of metastatic medulloblastoma. Nature 482:529–33. doi:
10.1038/nature10825
134. Tarbell NJ, Friedman H, Polkinghorn WR, et al. (2013) High-risk medulloblastoma:
a pediatric oncology group randomized trial of chemotherapy before or after
radiation therapy (POG 9031). J Clin Oncol 31:2936–41. doi:
10.1200/JCO.2012.43.9984
135. Packer RJ, Gajjar A, Vezina G, et al. (2006) Phase III study of craniospinal
radiation therapy followed by adjuvant chemotherapy for newly diagnosed average-
risk medulloblastoma. J Clin Oncol 24:4202–8. doi: 10.1200/JCO.2006.06.4980
136. You JS, Jones P a (2012) Cancer genetics and epigenetics: two sides of the same
coin? Cancer Cell 22:9–20. doi: 10.1016/j.ccr.2012.06.008
137. Berman BP, Weisenberger DJ, Aman JF, et al. (2012) Regions of focal DNA
hypermethylation and long-range hypomethylation in colorectal cancer coincide
with nuclear lamina-associated domains. Nat Genet 44:40–46. doi: 10.1038/ng.969
138. Greger V, Passarge E, Höpping W, et al. (1989) Epigenetic changes may
contribute to the formation and spontaneous regression of retinoblastoma. Hum
Genet 83:155–8.
139. Briggs KJ, Corcoran-Schwartz IM, Zhang W, et al. (2008) Cooperation between the
Hic1 and Ptch1 tumor suppressors in medulloblastoma. Genes Dev 22:770–85. doi:
10.1101/gad.1640908
140. Northcott P a, Shih DJH, Peacock J, et al. (2012) Subgroup-specific structural
variation across 1,000 medulloblastoma genomes. Nature 488:49–56. doi:
10.1038/nature11327
141. Magill ST, Cambronne X a, Luikart BW, et al. (2010) microRNA-132 regulates
dendritic growth and arborization of newborn neurons in the adult hippocampus.
Proc Natl Acad Sci U S A 107:20382–7. doi: 10.1073/pnas.1015691107
142. Pospichalova V, Tureckova J, Fafilek B, et al. (2011) Generation of two modified
102
mouse alleles of the Hic1 tumor suppressor gene. Genesis 49:142–51. doi:
10.1002/dvg.20719
143. Konermann S, Brigham MD, Trevino AE, et al. (2014) Genome-scale
transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 2–11.
doi: 10.1038/nature14136
144. Chen WY, Zeng X, Carter MG, et al. (2003) Heterozygous disruption of Hic1
predisposes mice to a gender-dependent spectrum of malignant tumors. Nat Genet
33:197–202. doi: 10.1038/ng1077
145. Wanet A, Tacheny A, Arnould T, Renard P (2012) miR-212/132 expression and
functions: within and beyond the neuronal compartment. Nucleic Acids Res 1–12.
doi: 10.1093/nar/gks151
146. Lee RS, Stewart C, Carter SL, et al. (2012) A remarkably simple genome underlies
highly malignant pediatric rhabdoid cancers. J Clin Invest 122:2983–8. doi:
10.1172/JCI64400
147. Agarwal V, Bell GW, Nam J-W, Bartel DP (2015) Predicting effective microRNA
target sites in mammalian mRNAs. Elife. doi: 10.7554/eLife.05005
148. Stratton MR, Campbell PJ, Futreal PA (2009) The cancer genome. Nature
458:719–24. doi: 10.1038/nature07943
149. Takeda H, Wei Z, Koso H, et al. (2015) Transposon mutagenesis identifies genes
and evolutionary forces driving gastrointestinal tract tumor progression. Nat Genet
47:142–150. doi: 10.1038/ng.3175
150. Mann MB, Black MA, Jones DJ, et al. (2015) Transposon mutagenesis identifies
genetic drivers of Braf(V600E) melanoma. Nat Genet 47:486–495. doi:
10.1038/ng.3275
151. Collier LS, Carlson CM, Ravimohan S, et al. (2005) Cancer gene discovery in solid
tumours using transposon-based somatic mutagenesis in the mouse. Nature
436:272–6. doi: 10.1038/nature03681
103
152. Morfouace M, Shelat A, Jacus M, et al. (2014) Pemetrexed and gemcitabine as
combination therapy for the treatment of Group3 medulloblastoma. Cancer Cell
25:516–29. doi: 10.1016/j.ccr.2014.02.009
153. Musani V, Gorry P, Basta-Juzbasic A, et al. (2006) Mutation in exon 7 of PTCH
deregulates SHH/PTCH/SMO signaling: possible linkage to WNT. Int J Mol Med
17:755–9.
154. Lancaster M a, Renner M, Martin C-A, et al. (2013) Cerebral organoids model
human brain development and microcephaly. Nature 501:373–9. doi:
10.1038/nature12517
155. Li X, Nadauld L, Ootani A, et al. (2014) Oncogenic transformation of diverse
gastrointestinal tissues in primary organoid culture. Nat Med 20:769–77. doi:
10.1038/nm.3585
156. Schwalbe EC, Williamson D, Lindsey JC, et al. (2013) DNA methylation profiling of
medulloblastoma allows robust subclassification and improved outcome prediction
using formalin-fixed biopsies. Acta Neuropathol 125:359–71. doi: 10.1007/s00401-
012-1077-2
157. Morrissy AS, Garzia L, Shih DJH, et al. (2016) Divergent clonal selection
dominates medulloblastoma at recurrence. Nature. doi: 10.1038/nature16478