Unmasking novel epigenetic mechanisms of medulloblastoma ... · Further characterization of this...

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.

iv

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.

v

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


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


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.

3

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


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.

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.

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


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,

http://crispr.mit.edu/

58

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

61

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.

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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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).

73

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).

74

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.

http://crispr.mit.edu/

76

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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