Integrated Genomic Analyses of Childhood Central Nervous System-Primitive Neuro-ectodermal Tumours (CNS-PNETs)

By

Daniel J. Picard

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Graduate Department of Laboratory Medicine and Pathobiology University of Toronto

2014

© Copyright by Daniel J. Picard

ii 

Integrated Genomic Analyses of Childhood Central Nervous System-Primitive Neuro-ectodermal Tumours (CNS-PNETs)

Daniel J. Picard

Master of Science

Graduate Department of Laboratory Medicine and Pathobiology University of Toronto

2014

Abstract

CNS-PNETs are rare, aggressive, paediatric embryonal brain tumours that are poorly

studied. We recently identified an aggressive sub-group of CNS-PNETs characterized by the

amplification of the C19MC microRNA cluster, however, little is known regarding the features

of other CNS-PNET tumours. This study was designed to define additional molecular sub-groups

of CNS-PNET by interrogating a large cohort of CNS-PNETs.

To elucidate the features of CNS-PNET, we examined transcriptional and copy number

profiles from primary hemispheric CNS-PNETs. We then validated and examined the clinical

significance of novel sub-group markers in 123 primary CNS-PNETs.

This thesis demonstrates that CNS-PNET can be categorized into three molecular sub-

groups that are distinguished by distinct primitive neural, oligo-neural and mesenchymal lineage

gene expression signatures and also correlated with distinct clinical features.

Data from my thesis has generated a substantial body of knowledge to fuel both

biological and clinical investigations of childhood CNS-PNETs. 

iii 

Acknowledgements 

I am thankful to my supervisor, Dr. Annie Huang, and my graduate committee members, Dr.

Cynthia Hawkins and Dr. Herman Yeger, whose guidance and support from the initial to the

final stages of the project have provided a productive research environment and helped me

develop an understanding of the subject. Specifically, I would like to thank Dr. Huang for the

amazing opportunity to be a part of this study.

I wish to express my gratitude to past and present members of the Huang Lab, specifically David

Shih, Tiffany Chan, Patrick Sin-Chan and Jonathon Torchia, for their advice and support during

the course of my research. I am also grateful for helpful discussions with and advice from

members of other labs. I would also like to thank: Suzanne Miller and her supervisor Richard

Grundy for their help and contribution to the study; Pawel Buczkowicz, from Dr. Hawkin’s lab,

for his help with the Partek and SPSS packages and discussions regarding his experience with

bioinformatics; Anath Lionel, from Dr. Steve Scherer’s lab, for help with the copy number

analysis; and Sameer Agnihotri, from Abhijit Guha’s lab, for helpful and insightful scientific

discussions.

The progress of my project was also facilitated by the collaboration with and the generous

sharing of equipment and reagents from other labs; I am grateful to: Dr. Peter Dirks, Dr.

Meredith Irwin, Dr. Michael Taylor, and Dr. Jane McGlade.

I would also like to acknowledge members of the Pathology Department at Sickkids and the

Pathology Research Program at the University Health Network for their wonderful immuno-

histochemistry work and for being so accommodating.

This work was supported in part by a University of Toronto Fellowship.

iv 

Table of Contents

Abstract ........................................................................................................................................... ii

Acknowledgments.......................................................................................................................... iii

List of Tables and Figures............................................................................................................. vii

List of Appendices ....................................................................................................................... viii

Abbreviations ................................................................................................................................. ix

Chapter 1 Introduction .................................................................................................................. 1

1.1. Paediatric brain tumours .......................................................................................................... 2

1.1.1. Classification of childhood brain tumours ..................................................................... 2

1.1.2. Clinical challenges in the treatment of childhood brain tumours .................................. 3

1.2. Childhood CNS-PNETs ........................................................................................................... 4

1.2.1. Clinical characteristics of CNS-PNET .......................................................................... 4

1.2.2. CNS-PNET models........................................................................................................ 5

1.3. Molecular and genetic characteristics of CNS-PNET ............................................................. 5

1.3.1. Review of published data .............................................................................................. 5

1.3.2. Limitations of current knowledge.................................................................................. 8

1.4. Objectives of Thesis ................................................................................................................. 9

1.4.1. Hypothesis ..................................................................................................................... 9

1.4.2. Objectives ...................................................................................................................... 9

Chapter 2 Methods and Materials ................................................................................................ 10

2.1 Clinical cohort and tumour materials ...................................................................................... 11

v 

2.2 Gene expression and DNA copy number profiles .................................................................. 11

2.3 PCR, Fluorescence in-situ hybridization and immuno-histochemical validation studies ....... 12

2.4 Informatics and Statistical Analyses ....................................................................................... 13

Chapter 3 CNS-PNETs comprise 3 molecular sub-groups with distinct gene expression and copy

number features ............................................................................................................................. 15

3.0 Overview ................................................................................................................................. 16

Objective 1 ............................................................................................................................. 16

Hypothesis ............................................................................................................................. 16

3.1 Central review of a multi-centre CNS-PNET cohort .............................................................. 16

3.2 CNS-PNETs arising in the cerebral hemispheres comprise 3 molecular sub-groups ............. 20

3.3 Cell lineage markers, LIN28 and OLIG2, distinguish molecular sub-groups of CNS-PNETs

....................................................................................................................................................... 28

3.4 Molecular sub-groups of CNS-PNET exhibit distinct DNA copy number patterns ............... 29

3.5 Discussion ............................................................................................................................... 36

Chapter 4 LIN28 and OLIG2 correlate with survival and metastatic phenotypes in CNS-PNETs .

....................................................................................................................................................... 38

4.0 Overview ................................................................................................................................. 39

Objective 2 ............................................................................................................................. 39

Hypothesis ............................................................................................................................. 39

4.1 Defining clinical phenotypes of CNS-PNET molecular sub-groups ...................................... 39

4.1.1. Gender ......................................................................................................................... 42

4.1.2. Age............................................................................................................................... 42

vi 

4.1.3. Metastasis .................................................................................................................... 45

4.2 Survival features of CNS-PNET molecular sub-groups ......................................................... 45

4.3 Discussion ............................................................................................................................... 46

Chapter 5 Discussion and future directions ................................................................................. 50

5.0 Discussion ............................................................................................................................... 51

5.1 Clinical relevance of findings ................................................................................................. 52

5.2 Future directions ..................................................................................................................... 53

References ..................................................................................................................................... 55

Appendices .................................................................................................................................... 60

 

vii 

List of Tables and Figures  

Tables

Table 1.1: Molecular genetics of supratentorial PNETs and pineoblastomas ................................ 6

Table 1.2: Molecular and cytogenetic abnormalities of CNS embryonal tumours ......................... 7

Table 2.1: Quantitative qRT-PCR primers and probes ................................................................. 13

Table 4.1: Clinical and molecular features of CNS-PNET ........................................................... 40

Table 4.2: Clinical and molecular features of CNS-PNET in relation to age ............................... 41 

Figures

Figure 3.1 Flow chart of sample analysis ..................................................................................... 34

Figure 3.2 Gene expression profiles define 3 molecular sub-groups of CNS-PNET ................... 18

Figure 3.3 Molecular sub-groups of CNS-PNET exhibit distinct cell lineage ............................. 21

Figure 3.4 Molecular sub-groups of CNS-PNET exhibit distinct signalling signatures ............... 24

Figure 3.5 Validation of sub-group specific gene expression signatures ..................................... 26

Figure 3.6 qRT-PCR and immuno-histochemical analyses of IGF2 ............................................ 30

Figure 3.7 Cell lineage markers, LIN28 and OLIG2, correlate with molecular sub-groups of CNS-PNET ................................................................................................................................... 32

Figure 3.8 Molecular sub-groups of CNS-PNET have distinct DNA copy number alterations ... 34

Figure 4.1 Molecular sub-groups of CNS-PNET exhibit distinct clinical phenotypes ................. 43

Figure 4.2 Molecular sub-groups of CNS-PNET exhibit distinct survival features ..................... 47 

viii 

List of Appendices  

Appendix 1 .................................................................................................................................... 61

Table A-1.1 Clinicopathologic characteristics of tumour samples ............................................... 62

Table A-1.2 Analyses performed on tumour samples .................................................................. 65

Appendix 2 .................................................................................................................................... 69

Manuscript: Markers of survival and metastatic potential in childhood CNS primitive neuro-ectodermal brain tumours: an integrative genomic analysis ......................................................... 70

ix 

Abbreviations ATRT Atypical Teratoid Rhabdoid Tumour BAF47 Synonym of SWI/SNF related, matrix associated, actin dependent regulator of

chromatin, subfamily b, member 1 (SMARCB1) CDKN2A Cyclin-dependent Kinase Inhibitor 2A CDKN2B Cyclin-dependent Kinase Inhibitor 2B CNA Copy Number Aberration CNS Central Nervous System EGFR Epidermal Growth Factor Receptor ETANTR Embryonal Tumour with Abundant Neuropil and True Rosettes FDR False Discovery Rate FFPE Formalin Fixed Paraffin Embedded GBM GlioblastomaMultiforme GFAP Glial Fibrillary Acidic Protein H3.3 Synonym for H3 Histone, Family 3A (H3F3A) HCL Hierarchal Clustering IHC Immunohistochemistry INK4D Synonym of cyclin-dependent kinase inhibitor 2D (CDKN2D, p19) IPA Ingenuity Pathway Analysis MLPA Multiplex Ligation-Dependent Probe Amplification MYC v-myc Avian Myelocytomatosis Viral Oncogene Homolog MYCN v-myc Avian Myelocytomatosis Viral Oncogene Neuroblastoma Derived

Homolog NMF Non-negative Matrix Factorization PARP1 poly (ADP-ribose) polymerase 1 PCA Principal Component Analysis PDGFB Platelet-Derived Growth Factor Beta Polypeptide PDGFRA Platelet-Derived Growth Factor Receptor, Alpha Polypeptide PNET Primitive Neuro-Ectodermal Tumour PNET-NOS Primitive Neuro-Ectodermal Tumour-Not Otherwise Specified RB Retinoblastoma 1 RCAS Replication-Competent ASLV long terminal repeat (LTR) with a Splice acceptor RHOG Ras Homolog Family Member G SMARCB1 SWI/SNF related, matrix associated, actin dependent regulator of chromatin,

subfamily b, member 1 SNP Single Nucleotide Polymorphism SV40 Simian Vacuolating Virus 40 TMA Tissue Micro Array TP53 Tumour Protein p53 tv-a CD320 molecule, avian viral receptor WNT16 Wingless-Type MMTV Integration Site Family, Member 16

1 

Chapter 1

Introduction

 

 

 

 

 

 

 

 

 

2 

1.1.Paediatricbraintumours

1.1.1.Classificationofchildhoodbraintumours

Brain tumours are the most common childhood solid tumour1 and a leading cause of

childhood cancer related morbidity and mortality2. Studies have shown that paediatric and adult

brain tumours are molecularly distinct as exemplified by glioblastoma multiforme, where

amplification of EGFR is detected in >40% of adult glioblastoma multiforme but is rarely

observed in paediatric glioblastoma multiforme3. Of note, many adult cancers are the result of

carcinogenic exposures, whereas aberrations in developmental signalling pathways have been

demonstrated to be an important factor in paediatric brain tumour pathogenesis4. Compared to

adult brain tumours, paediatric brain tumours are rare and until recently, there have been few

studies characterizing the molecular landscape and pathogenesis of paediatric brain tumours.

Historically, embryonal tumours, which comprise a large fraction of paediatric brain

tumours, were grouped under the umbrella term primitive neuroectodermal tumour (PNET)5;

regardless of location in the central nervous system (CNS). There has been controversy over

classification, treatment, and cell of origin of these tumours since their defining feature is a

relatively homogeneous histological appearance consisting of poorly cohesive, undifferentiated

neuroepithelial cells, often with a high mitotic rate6. Due to these common features, embryonal

tumours were thought to arise from a common precursor cell of the subependymal matrix in the

CNS. The tendency for these neoplasms to infiltrate nearby normal tissues and disseminate

through cerebrospinal fluid (CSF) pathways was believed to contribute to their poor outcome.

However, evidence suggests that rather than being comprised of a large uniform group of

tumours, PNETs are a heterogeneous group of WHO grade IV neoplasms which include

medulloblastoma, atypical teratoid/rhabdoid tumour (ATRT), pineoblastoma, and CNS-PNET6.

Evidence of substantial tumour biological diversity across embryonal/PNET tumours is reflected

in patient survival; despite histologic similarities CNS-PNETs have distinctly poorer 5 year

overall survival of 20-50%7,8 as compared to medulloblastoma in which 5 year overall survival

for localized disease of 75-85% is achievable9. In fact, recent global gene expression profiling of

embryonal/PNET neoplasm favour the older concept of a cell type specific origin for distinct

embryonal tumours 10,11, which may be reflected by a combination of tumour location and

histopathologic patterns of differentiation.

3 

1.1.2.Clinicalchallengesinthetreatmentofchildhoodbraintumours

In contrast to intrinsic brain tumours of adults, paediatric brain tumours span a much

wider histologic and biologic spectrum of diseases of which embryonal brain tumours/PNETs

comprise a major category. All embryonal/PNET brain tumours, which include medulloblastoma

and CNS-PNET, currently receive similar types of intensive post-surgical, chemo-

radiotherapeutic treatment regimens. Whole brain and spine irradiation is often applied, as

embryonal/PNET brain tumours, unlike malignant glial tumours, have a propensity to

metastasize within the cranio-spinal axes.

Substantial progress has been made with recent global genomic studies regarding

molecular pathogenesis of medulloblastoma. Amongst the embryonal tumours, medulloblastoma

patients have the best clinical outcomes12-19. However, despite histologic resemblance to

medulloblastoma, patients with CNS-PNETs fare poorly even with intensified high-dose

radiation and chemotherapy regimens designed for patients with metastatic medulloblastoma8,20-

23. Due to the rare incidence of CNS-PNETs, which represent 1/10th of medulloblastoma, effects

of specific intervention on CNS-PNET outcomes has been difficult to evaluate, as most clinical

data has been often been reported together with medulloblastoma. Furthermore, clinical studies

of CNS-PNETs have often been based on anatomical categorization to include all tumours

arising above the tentorium, including hemispheric CNS-PNETs and pineoblastomas as one

therapeutic entity. However, pineoblastomas exhibit distinct histo-pathologic features from CNS-

PNETs arising in the cerebral hemispheres24,25 and appear to respond differently to therapy as

compared to hemispheric CNS-PNETs6, and thus should probably be considered separately in

therapeutic trials.

The molecular and cellular make-up of CNS-PNET remain largely unknown24, hence

current tumour treatments are empiric and largely ineffective26. Tragically, even with the advent

of new intensive treatments, the prognosis for these patients diagnosed with CNS-PNETs is

dismal and often leads to debilitating and severe late effects, such as decreased intelligence in the

small proportion of survivors. In order to advance CNS-PNET therapeutics, it is important to

delineate the cellular and molecular pathogenesis of CNS-PNET to better inform diagnosis,

prognosis, and tumour specific treatment design.

4 

1.2.ChildhoodCNS‐PNETs

1.2.1.ClinicalcharacteristicsofCNS‐PNET

CNS-PNETs are rare, predominantly hemispheric tumours comprising ~3-5% of all

paediatric brain tumours. Age at presentation is from birth to 20 years for paediatric CNS-

PNETs, however rare adult cases have been reported27,28. To date, 65% of CNS-PNETs are

reported in patients less than 5 years of age and no gender bias has been observed29. Patients

present with a variety of symptoms which are related to tumour location; however, increased

intracranial pressure is the most common symptom. CNS-PNETs can be visualized by computed

tomography (CT) and magnetic resonance imaging (MRI), but look similar regardless of location

or to other hemispheric lesions6. Although the 5 year disease-free survival is approximately 37%,

many patients suffer recurrences21.

CNS-PNETs comprise a diverse histologic group which exhibit variable neuronal,

astrocytic, or ependymal differentiation and can be challenging to diagnose by routine histo-

pathology30. Presently, CNS–PNETs are sub-categorized based on location and specific

histological features. The most common sub-category is CNS-PNET not otherwise specified

(NOS), previously known as supratentorial-PNET (sPNET), a histologically poorly differentiated

tumour arising in the cerebrum. Rarer histologic sub-categories include cerebral neuroblastomas,

ganglioneuroblastoma, medulloepitheliomas, ependymoblastomas and ‘embryonal tumour with

abundant neuropil and true rosettes’ (ETANTR). Medulloepitheliomas are tumours characterized

by the presence of neural tube formation, whereas ependymoblastic rosettes are the defining

feature of ependymoblastomas31. ETANTRs are a recently described CNS-PNETs sub-group32,

with histological features similar to neuroblastoma and ependymoplastoma, characterized by true

and pseudo-rosettes on a background of abundant neuropil. However, CNS-PNET sub-

classification is under debate33 as emerging data suggest many of the histologic sub-classes may

represent closely related biological and molecular entities.

5 

1.2.2.CNS‐PNETmodels 

Studies of CNS-PNET in mouse models are limited since the cell of origin is unknown;

there are also few xenograft models of CNS-PNETs34. Specific genetic models of CNS-PNETs

remain to be described. However, hemispheric tumours resembling CNS-PNETs have been

described in mouse models of medulloblastoma with defects in p53, pRB and the CDKN2A

tumour suppressor pathways. Transgenic mice universally expressing the SV40 large T-antigen,

which inhibits both p53 and RB, develop CNS-PNETs with an incidence of 27%35. In addition,

mice with null mutations in PARP1 and p53, as well as INK4D and p53, develop CNS-PNET

with an incidence of 2% and 10%, respectively36,37. In 2008, Momota et al. applied the RCAS

somatic transgenic system to model CNS-PNET tumour formation38. These investigators

generated a ~30% incidence of CNS-PNET-like tumours in a p53 knockout mouse with over-

expression of c-Myc or c-Myc with -catenin using an RCAS somatic transgenic system.

Collectively, these murine studies indicate that multiple abnormalities are likely to be required

for tumour initiation and progression of CNS-PNETs.

1.3.MolecularandgeneticcharacteristicsofCNS‐PNET

1.3.1.Reviewofpublisheddata

Due to the absence of specific animal models and limited studies of primary tumours,

genetic features of CNS-PNET remain largely unknown. Studies of small number of CNS-

PNETs (total of 33 CNS-PNETs reported prior to 2009) indicate diverse copy number

aberrations (CNAs) in most CNS-PNET but only recurrent CNAs in <10-20% of tumours.

Importantly, these data indicate that CNS-PNETs have distinct CNAs from medulloblastoma

(Tables 1.1, 1.2)24,38. Specifically, they have rare iso-chromosome 17q (i17q), which is present

in 30- 40% of medulloblastoma24,39-41, and more frequent CNAs of chromosome 1p12-22.1, 9p41,

13q40, 14q42, 19p39,41 and 19q42. To date, MYCN, PDGFB, and PDGFRA amplification and

deletions of CDKN2A/2B39-41 have been detected in CNS-PNET. However, the identity of genes

underlying most CNAs, the relevance of observed CNAs and genetic alterations to the biology

and clinical attributes of CNS-PNETs remain largely unexplored.

6 

Table1.1:MoleculargeneticsofsupratentorialPNETsandpineoblastomas

Reproduced from Li et al 24 with copyright permission from Neurosurgical Focus 

 

 

 

7 

Table1.2:MolecularandcytogeneticabnormalitiesofCNSembryonaltumours

Reproduced from Inda et al 39 with copyright permission from Histopathology 

 

 

8 

In 2009, Li et al 43 reported the first large cohort study of 40 CNS-PNET using global gene

expression and copy number profiling. They discovered amplification of a novel oncogenic

miRNA cluster, C19MC, in 25% of hemispheric CNS-PNETs by copy number analyses and

demonstrated that the C19MC marker identified a sub-group of CNS-PNETs with variant

histology that shared the same transcriptional signatures. Significantly, the C19MC amplicon

was not restricted to a specific histologic sub-class of CNS-PNETs, but was detected in

ETANTR, medulloepithelioma and ependymoblastoma samples as well as a proportion of CNS-

PNET-NOS. Korshunov et al44 confirmed the presence of C19MC amplicon in CNS-PNETs

variants with histologic features of rosette formation. Both studies showed that the C19MC

tumours had poor survival compared to other CNS-PNETs. To date, no specific histologic

features which correlate with non-C19MC amplified tumours have been identified. Interestingly,

a recent study suggested that a specific mutation of the H3.3 gene (H3.3G34R), which is seen in

about 25% of glioblastoma, is also found in subset of CNS-PNETs45. However, the H3.3

mutation K27M46 which is seen at high frequency in glioblastoma and diffuse intrinsic pontine

glioma, is not present in CNS-PNETs45. These findings highlight longstanding challenges with

histologic diagnosis of CNS-PNET and suggest that molecular features of some tumours

diagnosed as CNS-PNETs and glioblastoma may overlap.

1.3.2.Limitationsofcurrentknowledge

Although the study by Li et al43 led to the identification of one distinct sub-group of CNS-

PNETs, little is known regarding the molecular features of the other CNS-PNET tumours which

lack the C19MC marker. In order to define the full molecular spectrum of CNS-PNETs,

concerted molecular analyses of a substantial numbers of primary CNS-PNETs, which can only

be enabled by multi-institutional, international collaboration, is necessary. In my thesis, I

undertook a study designed to integrate gene expression, copy number, and immuno-

histochemical analyses in order to characterize 254 primary hemispheric CNS-PNETs collected

through a multi-institutional collaborative network. Integrated studies of such a large cohort

should enable the identification of molecular markers for additional CNS-PNET sub-groups and

help to define additional molecular correlates of clinical phenotypes in CNS-PNETs which will

be instrumental in informing prospective therapeutic studies.

9 

1.4.ObjectivesofThesis

1.4.1.Hypothesis

CNS-PNETs comprise additional molecular sub-groups which may be defined by

integrated genomic analyses of a substantial CNS-PNET cohort.

1.4.2.Objectives

1. To comprehensively define molecular features of a substantial CNS-PNET cohort

using integrated global gene expression and copy number profile analyses.

2. To correlate molecular with clinico-pathologic features of CNS-PNETs in order to

define clinically relevant sub-groups of CNS-PNETs.

10 

Chapter 2

Methods and Materials  

These materials and methods were described in part in Picard D, Miller S, Hawkins CE, Bouffet E, Rogers HA, Chan TS, Kim SK, Ra YS, Fangusaro J, Korshunov A et al: Markers of survival and metastatic potential in childhood CNS primitive neuro-ectodermal brain tumours: an integrative genomic analysis. Lancet Oncol 2012, 13(8):838-848 and have been reproduced with copyright permission from Lancet Oncology.

11 

2.1Clinicalcohortandtumourmaterials  

Tumour materials and clinical information used in this study were collected through an

international collaborative network. Tumour samples and patient clinical information were

collected with consent as per protocols approved by the Hospital Research Ethics Board at

participating institutions including Children’s Cancer and Leukemia Group (CCLG) registered

centers in the UK, and from the Cooperative Human Tissue Network (CHTN, Columbus, Ohio

USA). CNS-PNET tissue microarrays used in this study were constructed at the Hospital for Sick

Children, University of Nottingham25, and Institute of Cancer Research in Sutton, UK. All

collected tumour samples were re-reviewed blindly by Dr. Cynthia Hawkins, and tested for loss

of SMARCB1/BAF47/INI1/SNF5 protein expression or SMARCB1 alterations by sequencing or

MLPA analyses to rule out misdiagnosed ATRT. Only hemispheric tumours diagnosed as CNS-

PNET according to the 2007 WHO CNS tumour classification criteria6 and without alterations of

SMARCB1 were included. Patient and tumour information are listed in Appendix 1 Table A-1.1.

2.2GeneexpressionandDNAcopynumberprofiles

DNA and RNA were extracted using standard methods from 77 and 51 primary CNS-

PNET samples respectively and analysed using Illumina Omni 2.5M SNP and Illumina HT-12

v4 gene expression arrays (http://www.illumina.com) to generate gene expression and DNA

copy number profiles. DNA and RNA hybridizations were performed at The Centre of Applied

Genomics (TCAG), Hospital for Sick Children, Toronto (http://www.tcag.ca/), according to the

manufacturer’s protocol. For clinical correlative analyses, only 59 of the 77 tumours with copy

number profiles that had complete clinical information were included. Details of molecular

analyses performed on individual tumour samples are shown in Appendix 1, Table A-1.2; all

data are deposited in the Wellcome Trust, European Genome-phenome Archive

(www.EBI.AC.UK; Accession number:EGAS00000000116).

12 

2.3PCR,Fluorescencein‐situhybridizationandimmuno‐histochemicalvalidationstudies

For gene-specific qRT-PCR validation of array data, 10 ng cDNA synthesized from 1µg

of RNA (TaqMan® Reverse Transcription Kit, Applied Biosystems) was amplified using

specific TaqMan probes/primer sets (Table 2.1) and mRNA expression levels were determined

relative to actin using the ΔCt method. All qRT-PCR assays were performed in triplicate.

Immuno-histochemical analyses on tumour tissue microarray or FFPE tumour slides were

performed by the Pathology Research Program core laboratory (http://www.uhnres.utoronto.ca).

All tissue sections were treated with heat-induced epitope retrieval and blocked for endogenous

peroxidase and biotin. Antibodies used in this study included: anti-LIN28 (Cell Signalling

Technology, Boston, USA), anti-OLIG2, (Immuno-Biological Laboratories, Minneapolis, USA),

GFAP (DAKO, Burlington, CA), and anti-SYNAPTOPHYSIN (Millipore, Massachusetts,

USA). Antibody reactions were visualized using a Biogenix detection kit (BioGenix

Laboratories, San Ramon, USA). Immuno-reactivity for LIN28, GFAP and SYNAPTOPHYSIN

were scored manually based on intensity (1=low, 2=mod, 3=high) and distribution of stains

(1=≤10%, 2=10-50%, 3>50%). OLIG2 immuno-stains were quantified using the Aperio

Scanscope (Aperio, Vista CA, USA) system and the ImageScope software nuclear IHC

algorithm. For tumours on TMA, IHC values were determined based on average staining score of

at least 2 tissue cores, while tumours with FFPE slides were scored based on the extent of

staining in relation to the entire tumour section. Normal testicular tissue (human and murine) and

oligodendroglioma tumour tissue were used respectively as positive controls for LIN28 and

OLIG2 immuno-stains; samples processed in parallel without primary antibodies served as

negative controls. All IHC stains were scored blindly by myself and Tiffany Chan, and reviewed

by Dr. Annie Huang and Dr. Cynthia Hawkins. FISH was performed on FFPE TMA or

individual slides using established protocols. Test MYCN (2p24) and p16 (9p21) specific

PlatinumBright550 probe with corresponding LAF (2q11) and 9q21 PlatinumBright495 control

probes (Kreatech, Stretton Scientific, Stretton, UK) were used.

13 

Table2.1:QuantitativeqRT‐PCRprimersandprobes

Gene Probe # ASCL1 Hs00269932_m1 COL1A1 Hs00164004_m1 COL1A2 Hs00164099_m1 COL5A1 Hs00609088_m1 CRABP1 Hs00171635_m1 ERBB3 Hs00176538_m1 FOXJ1 Hs00230964_m1 GFAP Hs00909233_m1 GLI2 Hs01119974_m1 GLI3 Hs00609233_m1 IGF2 Hs00171254_m1 LIN28A Hs00702808_s1 LIN28B Hs01013729_m1 MSX1 Hs00427183_m1 NCAM2 Hs00189850_m1 NES Hs00707120_s1 NEUROG2 Hs00702774_s1 NKX6-2 Hs00752986_s1 OLIG1 Hs00744293_s1 OLIG2 Hs00377820_m1 PDGFRA Hs00998018_m1 SMO Hs01090242_m1 TGFB3 Hs01086000_m1 TGFBR3 Hs01114253_m1 TUBB3 Hs00801390_s1 ZIC2 Hs00600845_m1

14 

2.4InformaticsandStatisticalAnalyses

CNS-PNETs were classified into molecular sub-groups by unsupervised hierarchical

clustering (HCL), Non-negative Matrix Factorization (NMF)47 and principle component (PCA)

analyses of genes with the highest co-efficient of variation using the Partek Genomics Suite

(Partek, St Louis, MO). Genes enriched within tumour sub-groups were determined using a

supervised T-test adjusted for multiple hypotheses testing using the FDR method. Ingenuity

Pathway Analyses were performed on supervised gene sets to identify canonical signalling

pathways in each tumour sub-group. To determine regions of copy number gains and losses,

inferred copy number data was generated using the Illumina Genome Studio software and was

imported into Partek for CNA partitioning/segmentation analyses using a SNP window of 150.

Significance of CNAs in tumour sub-groups was then determined using Fisher’s exact test. Log

rank analysis using the Kaplan Meier method and Chi-square analyses were used respectively to

compare survival time and proportion of survivors across tumour sub-groups, while ANOVA

was used to determine significance of tumour sub-group in relation to age. To analyse the

significance of molecular sub-groups in relation to gender and metastatic status at diagnosis,

features in an individual molecular sub-group were compared to a pooled cohort of the other two

molecular sub-groups using Fisher’s exact test. Adjustment for multiple testing was not

performed as number of patients with complete information available for each clinical parameter

varied.

15 

Chapter 3

CNS-PNETs comprise 3 molecular sub-groups with distinct gene expression and copy number features

Data presented in this chapter has been published in Picard D, Miller S, Hawkins CE, Bouffet E, Rogers HA, Chan TS, Kim SK, Ra YS, Fangusaro J, Korshunov A et al: Markers of survival and metastatic potential in childhood CNS primitive neuro-ectodermal brain tumours: an integrative genomic analysis. Lancet Oncol 2012, 13(8):838-848 and have been reproduced with copyright permission by Lancet Oncology.

Acknowledgements: Statistical analyses in this study were performed in consultation with Dr. Pingzhao Hu. Immunohistochemical analyses were performed by Daniel Picard and received from Drs Cynthia E Hawkins and Gino Somers. Fluorescence in-situ hybridization analyses were performed by Dr Suzanne Miller and Hazel A. Rogers.

16 

3.0Overview 

Biological studies of CNS-PNETs have to date been limited to low resolution genomic

studies on very small cohorts. In this chapter, I applied an integrated high resolution genomic

approach to analyse one of the largest CNS-PNET collection assembled to date. As diagnostic

criteria for CNS-PNETs remains in flux, I collaborated with Dr. Cynthia Hawkins to perform

detailed histologic and focused molecular characterization of all samples assembled through our

international study network to eliminate other diagnoses including rhabdoid brain tumours which

may share significant overlap in morphologic and clinical characteristics with paediatric CNS-

PNETs. In section 3.2 and 3.4, I describe high resolution gene expression analyses, which

identify molecular sub-groups of CNS-PNETs, and the association of characteristic CNAs with

distinct molecular sub-groups of CNS-PNETs.

Objective1

To comprehensively define molecular features of CNS-PNETs using global gene

expression and copy number profile analyses.

Hypothesis

CNS-PNETs comprise a genetically heterogeneous group of tumours that may be

classified into distinct molecular sub-groups.

3.1Centralreviewofamulti‐centreCNS‐PNETcohort

We received 254 malignant brain tumours with an institutional histopathologic diagnosis of

CNS-PNET from 20 international medical centres. In order to confirm that tumour samples

received exhibited histologic and molecular characteristic compatible with CNS-PNETs, we

performed a histologic review of all tumour samples using the WHO CNS tumour classification

for CNS-PNETs (Figure 3.1). In addition, immuno-staining was performed on all samples to

assess for SMARCB1 protein expression, an immuno-marker which is characteristic and

diagnostic for CNS rhabdoid tumours6. Using such an approach, only 56% of tumours (142/254)

in our cohort met the diagnostic criteria for primary CNS-PNETs thus underscoring the

significant diagnostic challenge posed by CNS-PNETs. In total we identified 108 samples with

17 

Figure3.1Flowchartofsampleanalyses 254 Samples received with an institutional diagnosis of CNS-PNET were centrally reviewed.

112 samples (44%) which did not meet the WHO CNS tumour classification for primary CNS-

PNET diagnostic criteria were excluded. Material available for each case varied: 77 and 51,

respectively, samples had biomaterial avalilable for copy number and gene expression analysis,

and 95 FFPE tissue cases were available for immunohistochemical analysis.

18 

Figure3.2Geneexpressionprofilesdefines3molecularsub‐groupsofCNS‐PNET

19 

Figure 3.2 Gene expression profiles defines 3 molecular sub-groups of CNS-PNETMultiple unsupervised analyses were performed on human HT-12v4 expression array (Illumina)

data from 51 primary CNS-PNET samples. Cluster patterns were derived re-iteratively using an

initial set of 1000 genes identified by the highest co-efficient of variation to determine the most

stable tumour group clusters achievable with a minimal gene set. Three molecular sub-groups of

CNS-PNETs were independently indicated by A. Unsupervised Hierarchical Cluster (HCL)

analyses, B. Non-negative Matrix Factorization (NMF) and C. Principal Component Analysis

(PCA). Individual tumours corresponding to sub-groups 1, 2 and 3 are respectively indicated by

green, blue and purple coloured boxes or spheres.

20 

snap frozen or paraffin embedded tumour material suitable for further molecular or immuno-

based studies which are described in sections 3.2, 3.3 and 4.1

3.2CNS‐PNETsarisinginthecerebralhemispherescomprise3molecularsub‐groups

In prior studies, our lab identified a sub-group of CNS-PNETs arising in the cerebral

hemispheres with highly primitive gene expression signature that were distinguished from other

CNS-PNETs by recurrent amplification of the C19MC miRNA cluster43. However, the molecular

spectrum and composition of the other sub-groups of CNS-PNETs could not be fully elucidated

due to limited tumour cohort size. In order to better define the molecular spectrum of CNS-

PNETs, I performed global gene expression analyses on an expanded cohort of 51 primary CNS-

PNET originating in the cerebral hemispheres. Gene expression data sets were analysed using

multiple unsupervised cluster algorithms including HCL (Partek), NMF (GenePattern) and PCA

(Partek) to define molecular sub-groups of CNS-PNETs. Clustering of gene expression data

performed iteratively using 200-1000 genes by HCL analyses revealed CNS-PNET comprises

three distinct molecular sub-groups. Consistent with HCL analyses, NMF analysis using the

same re-iterative approach revealed the highest co-phenetic co-efficient (0.9711) with a 200

genes data set that correlated with an optimal k value of 3 which also indicated the strongest

statistical support for 3 molecular classes of CNS-PNETs (Figure 3.2 A-B). Analyses of gene

expression data with PCA, a three dimensional orthogonal approach to clustering, showed sub-

group 1 tumours clearly segregated as a distinct molecular entity, while sub-group 2 and 3

tumours showed a closer spatial relationship and overlap in transcriptomic features not defined

(Figure 3.2 C) by HCL and NMF clustering, and highlight the importance of applying multiple

parallel analyses to define tumour sub-groups by transcriptome analyses.

To uncover enriched genes or pathways that characterize and correlate with each CNS-

PNET sub-group, I performed supervised analyses based on relative inter-group comparison to

identify the most highly differentially expressed gene sets between sub-groups which were then

subject to further analyses with gene and pathway enrichment tools. Using the Ingenuity

Pathway Analysis tool, I observed that the 3 molecular sub-groups of CNS-PNETs exhibited

21 

Figure3.3Molecularsub‐groupsofCNS‐PNETexhibitdistinctcelllineage

22 

Figure 3.3 Molecular sub-groups of CNS-PNET exhibit distinct cell lineage

Molecular sub-groups of CNS-PNETs independently indicated by all three unsupervised

analyses methods (Figure 3.2) were further subjected to supervised analyses. Three molecular

sub-groups of CNS-PNET with primitive neural, oligo-neural and mesenchymal gene expression

signatures identified using supervised analyses are shown in relation to a hierarchical cluster

map. Cell lineage genes enriched in each tumour sub-group were determined using a moderated

t-test adjusted for multiple testing (FDR≤0.05). Magnitude and significance of cell lineage genes

most significantly up-regulated in tumour sub-groups are denoted respectively by fold change

and p-values.

23 

significant differences in expression patterns of neural lineage and differentiation genes (Figure

3.3). Expression profiles of sub-group 1 were most significantly enriched for genes associated

with embryonic or neural stem cells which include MEIS1/2, SOX3 and the HOX 2/B3/B4/B5/C4

genes. The RNA binding protein LIN28 and the retinoic acid–binding protein CRABP148, which

are implicated in stem cell pluripotency, were amongst the top over-expressed genes with nearly

20-30 fold greater expression in sub-group 1 tumours which have recurrent C19MC

amplification, as compared to sub-group 2 and 3 CNS-PNETs. In sub-group 2 tumours,

OLIG1/2, SOX8/10 and BCAN, which are markers of oligo-neural differentiation49, were the

most highly up-regulated genes. Additional genes with up-regulated expression in sub-group 2

CNS-PNETs included PDGFRA, ERBB3, NCAM2, members of the AKT/ERK/mTOR signalling

pathway, and the C1orf61 locus which encodes hsa-miR-9, a miRNA locus with functions in

neural fate determination. Sub-group 3 tumours exhibited limited expression of neural

differentiation genes, but were characterized by significant up-regulation of genes implicated in

epithelial and mesenchymal differentiation including COL1A, COL5A, FOXJ150, MSX151 and

IGF pathway genes, IGF2 and LHX2.

In addition to differential enrichment of cell lineage genes, CNS-PNET sub-groups also

exhibited significant differences in expression of canonical, developmental signalling pathway

components (Figure 3.4). IPA analyses revealed enrichment of WNT and SHH signalling genes

in sub-group 1, in which WNT and FZD family members, as well as PTCH1 and GLI2, and

SFRP1/2, an inhibitory WNT ligand, were up-regulated. Collectively these findings suggested

the SHH and/or non-canonical WNT signalling pathways were up-regulated in sub-group 1

tumours. In contrast gene and pathway enrichment analyses of sub-group 2 tumours revealed

down-regulation of the WNT, FZD and GLI gene families, as well as SMO and PTCH1 genes,

thus indicating down-modulation of the WNT and SHH signalling pathways in these tumours.

Our analyses did not identify specific canonical signalling pathways that were up-regulated in

sub-group 2 tumours. In sub-group 3 tumours, genes involved in the TGF pathway (TGFB3,

TGFBR2, BMP4 and SMAD6), as well as PTEN signalling pathway (BCL2 and FGFR1/L1),

were up-regulated. Of note, genes implicated in the epithelial to mesenchymal transition, which

24 

 

Figure3.4Molecularsub‐groupsofCNS‐PNETexhibitdistinctsignallingsignatures

25 

Figure 3.4 Molecular sub-groups of CNS-PNET exhibit distinct signalling signatures

 

Signalling pathways enriched in each tumour sub-group were determined by Ingenuity Pathways

Analyses (www.ingenuity.com) of sub-group-specific gene sets derived from supervised

analyses. Most significantly altered canonical pathways determined from analyses of 343, 276

and 325 genes respectively in sub-groups 1, 2 and 3 are represented in relation to tumour sub-

group. Proportion of up- or down-regulated genes within each category are respectively indicated

in red and green.

26 

Figure3.5Validationofsub‐groupspecificgeneexpressionsignatures

27 

Figure3.5Validationofsub‐groupspecificgeneexpressionsignaturesQuantitative RT-PCR analysis was performed to confirm gene expression patterns observed from

supervised analyses of microarray data; gene expression levels (∆Ct) determined relative to actin

are represented. A. Expression levels of select qRT-PCR validated genes significantly enriched

within each tumour sub-group (p≤0.05) is shown in a global skyline plot; mean value of n=3 is

plotted/sample. B. Mean expression levels of individual specific lineage/signalling genes (n=3

replicas) with most robust and significant over-expression and correlation with each tumour sub-

group are represented with SEM for specific mRNA (horizontal bars). Sub-group 1, 2 and 3

specific genes are indicated by green spheres, blue squares and purple triangles respectively.

28 

included JAG2, SNAI2, TWIST1 and genes that overlap with TGF and PTEN, were prominently

enriched in this sub-group.

In summary, our collective analyses of CNS-PNETs gene expression characteristics

reveal CNS-PNETs comprise three molecular sub-groups with distinct transcriptomic features

differentially enriched for cell lineage genes and signalling pathways. In order to evaluate the

clinical relevance and significance of these molecular sub-groups, it will be important to

establish robust markers that can be used to study larger archived patient cohorts.

3.3CelllineagemarkersLIN28andOLIG2distinguishmolecularsub‐groupsofCNS‐PNETs

CNS-PNETs frequently express a spectrum of neural (MAP2; synatophysin) or glial

(Nestin, GFAP) differentiation markers, which are conventionally used in diagnostic histo-

pathology to distinguish CNS-PNETs from other malignant tumours arising in the cerebrum.

However, we observed that gene expression patterns of these conventional markers did not

correlate with molecular sub-grouping of CNS-PNET. Based on the differential enrichment of

cell lineage genes in transcriptional signatures of sub-group 1 and 2 CNS-PNETs, I investigated

whether other lineage specific markers identified by gene expression analyses could serve to

distinguish CNS-PNET molecular sub-groups.

I performed qRT-PCR analyses to first validate gene enrichment patterns seen in the sub-group

specific expression profiles and to identify genes that were most consistently expressed across

individual tumours within sub-groups (Figure 3.5). I selected 8 (sub-group 1), 9 (sub-group 2),

and 15 (sub-group 3) sub-group enriched genes that exhibited at least a 2 fold change in gene

expression with a value <0.05 q for qRT-PCR validation. These analyses revealed the LIN28,

OLIG2 and IGF2 loci to be most highly differentially and consistently expressed respectively

across tumours within CNS-PNET sub-groups 1, 2 and 3 tumours. To validate LIN28, OLIG2

and IGF2 expression at the protein level, I optimized and performed IHC analyses of LIN28,

OLIG2 and IGF2 on control normal tissues and an initial test cohort of 22 tumour samples also

analysed by qRT-PCR. IGF2 protein expression could not be reliably scored on normal or

tumour tissues (Figure 3.6), however IHC expression patterns for LIN28 and OLIG2 were

robust and correlated quantitatively with gene expression levels determined by gene expression

29 

arrays and qRT-PCR analyses (Figure 3.7 A). Although a subset of sub-group 1 tumours also

exhibited variable levels of OLIG2 expression, LIN28 and OLIG2 expression correlated most

robustly with gene expression data in the three different molecular sub-groups, and a

combination of LIN28 and OLIG2 protein expression status reliably distinguished the three

molecular classes of CNS-PNETs. Cytoplasmic LIN28 and/or nuclear OLIG2 positivity defined

sub-group 1, nuclear OLIG2 immuno-positivity alone identified sub-group 2, whereas LIN28

and OLIG2 double negative stained tumours were considered sub-group 3 (Figure 3.7 B).

I further examined LIN28 and OLIG2 IHC in an additional 72 primary CNS-PNETs in

order to assess the distribution of CNS-PNET sub-groups. 15/72 samples with poor IHC quality

were excluded from further analyses. Based on sub-grouping analyses of 108 primary CNS-

PNETs by gene expression and/or IHC analyses for LIN28 or OLIG2 expression, we determined

that sub-group 1, 2 and 3 tumours respectively comprised 27%, 33% and 40% of all CNS-PNETs

analysed (Figure 3.7 C).

3.4Molecularsub‐groupsofCNS‐PNETexhibitdistinctDNAcopynumberpatterns

Copy number analyses can drive gene expression signatures and are known to be

important pathogenetic drivers in other paediatric brain tumours13,15. To investigate whether

specific CNA patterns were associated with molecular sub-groups of CNS-PNETs, we examined

genomic DNA from CNS-PNETs using ultra-high resolution Illumina Omni Quad genomic

arrays which interrogates 2.5 million SNPs. With the exception of the C19MC miRNA amplicon

previously identified in our lab43, there were few other recurrent high level copy number gains or

amplification observed in a cohort of 77 CNS-PNETs. Focal MYCN and CDK4 amplification

was detected in isolated tumours. Deletions centred on CDKN2A/2B (10 of 77 tumours) and

gains of Chr1q (11 of 77 tumours) were the most frequent CNAs observed (Figure 3.8). I

analyzed CNA patterns of 59 CNS-PNETs in relation to tumour sub-groups established by gene

expression and/or IHC analyses as well as based on genomic analyses of the C19MC locus by

FISH and/or qRT-PCR. In addition to C19MC amplification (15/19), gains of chr 2 (13/19) and 3

(5/19) were also significantly associated with sub-group 1 CNS-PNETs (p≤0.05). Gains of chr

8p (4/16), 13 (4/16) and 20 (4/16) were significantly more frequently in sub-group 3 tumours as

compared to sub-group 1 and 2 tumours (p≤0.05). Notably, frequent chr 9p loss centred on the

30 

Figure3.6qRT‐PCRandimmuno‐histochemicalanalysesofIGF2

31 

Figure 3.6 qRT-PCR and immuno-histochemical analyses of IGF2

 

A. Quantitative RT-PCR analysis was performed to confirm gene expression patterns observed

from supervised analyses of microarray data; gene expression levels (∆Ct) determined relative to

actin are represented. Mean expression levels of IGF2 (n=3 replicas) is represented with SEM

(horizontal bars). Sub-group 1, 2 and 3 specific genes are indicated by green spheres, blue

squares and purple triangles respectively. B-E. IGF2 immunohistochemistry was performed on

positive control placental tissue. Trophoblastic cells in the placenta are known to express high

levels of IGF2 and exhibit cytoplasmic and membranous immuno-staining for IGF2

(www.proteinatlas.org). IHC analyses for IGF2 were performed as described in methods using

polyclonal IGF2 antibodies (Santa Cruz, California). IGF2 staining patterns of placenta tissue

treated with primary antibody at 1:50 (B), 1:75 (C), 1:100 (D) dilutions and no primary antibody

as negative control (E) are shown.

32 

Figure3.7Celllineagemarkers,LIN28andOLIG2,correlatewithmolecularsub‐groupsofCNS‐PNET

33 

1 2 3Total

samples

GeneExpression

14 22 15 51

IHC 15 14 28 57

Total Samples /Group

29 36 43 108

% / Group 27% 33% 40% 100%

Tumour Sub-groupC

 

Figure 3.7 Cell lineage markers, LIN28 and OLIG2, correlate with molecular sub-groups of CNS-PNET

A. To validate OLIG2 and LIN28 as sub-group specific markers, immuno-histochemical

analyses were performed on 22 CNS-PNET samples with molecular sub-grouping information.

LIN28 protein expression was restricted to sub-group 1 samples while OLIG2 protein expression

was predominantly restricted to sub-group 2 samples with the exception of PNET3, a C19MC

amplified tumour, which was immuno-positive for both LIN28 and OLIG2. Sub-group 3 tumours

did not exhibit immunostaining for LIN28 or OLIG2. B. Representative immuno-histochemical

staining pattern for sub-group 1, 2 and 3 molecular sub-groups of CNS-PNET (60X

magnification). Top panel shows a hematoxylin and eosin (H&E) stain, middle panel shows

characteristic, strong cytoplasmic reactivity for LIN28 in a sub-group 1 tumour but limited to no

reactivity in a sub-group 2 and 3 tumour. Lower panel shows characteristic, strong OLIG2

nuclear immuno-reactivity in a sub-group 2 tumour, but limited to no OLIG2 expression in sub-

group 1 and 3 tumours. Corresponding tissue microarray core is shown at low magnification in

inset. C. Summary of molecular sub-grouping of 108 CNS-PNETs determined by IHC and/or

gene expression analyses.

34 

 

Figure3.8Molecularsub‐groupsofCNS‐PNEThavedistinctDNAcopynumberalterations

35 

Figure 3.8 Molecular sub-groups of CNS-PNET have distinct DNA copy

number alterations A. DNA copy number profiles of primary CNS-PNET were generated

using the Human Omni 2.5 Quad SNP array and recurrent copy number patterns established

using CNA partition (Partek) analyses. Significant enrichment of specific copy number

abnormalities (CN lesion) in tumour sub-groups was determined based on 59 primary samples

with known molecular sub-grouping. Heat map shows select CN lesions which correlate

significantly, as determined using Chi-square analyses, with specific CNS-PNET sub-groups. B-

D Recurrent focal copy number abnormalities (CNAs) in CNS-PNETs are shown by heat map

and copy number profiles of specific CNAs generated using dChIP and plotted relative to

chromosome ideograms. B. Heat map and copy number plots of recurrent chr2 gains in sub-

group 1 CNS-PNET. C. Heat map, copy number plot and FISH validation of focal CDKN2A/B

loss on chr9p21 seen in 10 primary sub-group 2 and 3 CNS-PNET. D. Heat map and copy

number plots of recurrent focal C19MC locus amplification on chr19q13.41 in sub-group 1

tumours.

36 

CDKN2A/2B locus were seen only in sub-groups 2 (6/22) and 3 (4/19) tumours. Collectively

our findings indicate that molecular sub-groups of CNS-PNET exhibit specific patterns of CNAs

and suggest that in addition to the C19MC amplicon other sub-group-specific CNAs may

underlie the distinct gene expression patterns seen in CNS-PNETs.

3.5Discussion

Our gene expression and copy number analyses were based on samples collected from

over 20 independent institutes. As described in section 3.1, there was substantial discordance

between institutional and repeat histo-pathology review performed by Dr. Cynthia Hawkins.

Most significantly, after appropriate immuno-histochemical or molecular testing for SMARCB1,

11% of our cohort was removed from the study as potential ATRTs. These findings underscore

the substantial challenge in current histo-pathologic classification of CNS-PNETs and the need

for unbiased molecular analyses to augment traditional histopathologic methods used for tumour

diagnoses.

A potential limitation of obtaining tissues from multiple different centres is the

heterogeneous methods of tissue handling that may confound molecular analyses. However,

given the substantial heterogeneity of tumour samples, an institutional bias was not observed

after application of conventional methods to correct for batch effects as described in methods. In

cluster analyses as described in section 3.2, I did not detect any significant difference in samples

based on their institutional origin (data not shown) suggesting that batch effects or institutional

origin was not a significant factor confounding our analyses.

Our analyses (Figure 3.4) revealed that CNS-PNETs differed in enrichment of cell

lineage and canonical signalling pathways thus suggesting different cells of origin and oncogenic

pathways may underlie the observed tumour sub-groups. A limitation of our cluster analyses is

that we performed inter-sub-group comparison with the goal of better defining tumour

heterogeneity amongst tumours diagnosed as CNS-PNETs at the molecular level. In contrast to

sub-group 1 and 3 tumours, we did not observe significant enrichment or activation of known

oncogenic pathways in sub-group 2 tumours. The nature of our analyses limits identification of a

candidate common pathway that is seen across all three sub-groups, such as the RAS pathway,

which might be the underlying driving pathway for sub-group 2. A more in depth comparison to

37 

relevant normal fetal or childhood brain tissue may reveal up-regulated canonical or non-

canonical signalling pathways which specifically drive sub-group 2 tumourigenesis. However, as

CNS-PNET can arise in various sites within the hemisphere and at various ages, selecting the

appropriate control tissue may be difficult. It remains possible that pathway activation in sub-

group 2 samples might arise from post-translational mechanisms and thus will not be detected by

the genetic analyses performed.

Unlike sub-group 1 and 2 tumours, I could not identify a robust immuno-histochemical

marker for sub-group 3 tumours (Figure 3.6). Thus, further attempts at identifying other

potential markers for sub-group 3 tumours that can be tested using immuno-histochemical

methods remains to be undertaken. Candidate genes include ZIC2 or LHX2 which are highly

enriched in sub-group 3 tumours. Of note PCA analyses indicated some overlap in samples

between sub-group 2 and 3 tumours (Figure 3.2 C), thus suggesting that markers in addition to

OLIG2 may be needed to precisely distinguish sub-group 2 and 3 tumours.

Experiments performed in this chapter have provided some of the first insights into the

biology of this rare paediatric brain tumour. Specifically gene expression and copy number

analyses reveal 3 distinct sub-group of CNS-PNETs distinguished by different cell lineage gene

expression and signalling patterns which has provided an essential, initial molecular framework

for classification of CNS-PNET and will enable clinical correlative associations to be undertaken

relative to CNS-PNET molecular sub-grouping. The results of these studies are described in

Chapter 4.

38 

Chapter 4

LIN28 and OLIG2 correlate with survival and metastatic phenotypes in CNS-PNETs

Data from this chapter was published in part in Picard D, Miller S, Hawkins CE, Bouffet E, Rogers HA, Chan TS, Kim SK, Ra YS, Fangusaro J, Korshunov A et al: Markers of survival and metastatic potential in childhood CNS primitive neuro-ectodermal brain tumours: an integrative genomic analysis. Lancet Oncol 2012, 13(8):838-848. and have been reproduced with copyright permission by Lancet Oncology.

Acknowledgments: Statistical analyses was performed with the help of Drs Pingzhao Hu and Derek Stephens.

39 

4.0Overview 

Until recently, biological studies of CNS-PNET have only been performed on small

cohorts (<10 samples per study) of tumours. Based on the first global analysis of a substantial

cohort of 41 CNS-PNETs, Li et al43 reported the identification of C19MC as a distinguishing

feature of one sub-group of CNS-PNETs. However, these studies, which were confirmed by

Korshunov et al44, were not powered to define other molecular classes of CNS-PNETs. In this

chapter, I applied the molecular sub-grouping established from studies described in Chapter 3 to

determine how molecular features of CNS-PNET sub-classes correlate with clinic-pathologic

features of an extended cohort of CNS-PNET.

Objective2

To correlate clinico-pathologic features with molecular sub-classes established for CNS-

PNETs.

Hypothesis

Molecular sub-groups of CNS-PNET will be associated with distinct clinico-pathologic

phenotypes.

4.1DefiningclinicalphenotypesofCNS‐PNETmolecularsub‐groups

We obtained 180 samples of CNS-PNETs with archived FFPE tissue for analyses. A

subset of 108 samples that had relevant clinical information and for which molecular sub-group

assignment was possible based on gene expression, IHC or CAN analyses were further examined

for clinco-pathologic and molecular correlation. I analyzed the clinical demographic data for

gender (n=107), age (n=100), and tumour stage (n=66) in section 4.1. (Table 4.1, 4.2, Appendix

1 Table A-1.1). In section 4.2, I analyzed survival time (n=58), as a whole, and in relation to

patient age.

40 

Table4.1:ClinicalandmolecularfeaturesofCNS‐PNET 

˜ Pearson Chi-Square ǂ Fisher's Exact Test * Log Rank (Mantel-Cox) test ¥ Feature in one individual group was compared to a pooled cohort of the other two groups Note: some patients were not included in analyses due to lack of specific clinical data - details of all patients in cohort are listed in Appendix 1 Table A-1.1

41 

Table4.2:ClinicalandmolecularfeaturesofCNS‐PNETinrelationtoage

˜ Pearson Chi-Square ǂ Fisher's Exact Test * Log Rank (Mantel-Cox) test ¥ Feature in one individual group was compared to a pooled cohort of the other two groups Note: some patients were not included in analyses due to lack of specific clinical data - details of all patients in cohort are listed in Appendix 1 Table A-1.1

42 

4.1.1. Gender

Gender bias has been observed in embryonal tumours such as medulloblastoma and

ATRTs, where there is a predominance of males, but no such bias has been reported in CNS-

PNET to date6. In this study, I analyzed information available from 107 sub-grouped patients (29

sub-group 1, 36 sub-group 2 and 42 sub-group 3) and I observed that sub-group 1 exhibit a

higher female to male ratio (18 females: 11 males) and a reverse trend (16 females: 26 males) in

sub-group 3 patients. However, no gender bias for sub-group 2 patients was observed (16

females: 20 males). As shown in Figure 4.1 A (Table 4.1), the female to male ratios between the

3 sub-groups, 1.64, 0.8 and 0.62 for sub-group 1, 2 and 3 respectively, are significantly different

(p=0.04).

4.1.2.Age

There is a well-known association of specific histologic categories of paediatric brain

tumours with patient age. Recently, an age-specific association for molecular sub-groups of

medulloblastoma has been reported52. Similar to that observed in medulloblastoma, my analyses

revealed an age specific distribution of CNS-PNET sub-groups. Sub-group 1 and 2 patients

exhibited a bi-modal age distribution with peaks age of diagnosis between 0-2 years for sub-

group 1 and 4-6 years for sub-group 2 CNS-PNETs, in contrast sub-group 3 patients had a single

age peak between 4-6 years. Overall sub-group 1 patients were significantly younger (median

age 2.9 years; 95% CI:2.4-5.2, p=0.005) as compared to sub-group 2 (median age 7.9; 95% CI:6-

9.7) and 3 (median age 5.9; 95% CI:4.9-7.8) patients (Table 4.1). Historical data suggest that

CNS-PNET is predominantly a disease of very young children (<3-4 years), and indeed we

observed a median age of 1.75 years for all patients in our cohort (n=100). However young

patients were significantly over-represented in sub-group 1 as compared to sub-group 2 and 3

(p=0.001) CNS-PNETs. Specifically, 77% (20/26) of sub-group 1 as compared to 28% (9/32) of

sub-group 2 and 43% (18/42) of 3 patients were ≤4 years of age at diagnosis (Figure 4.1 B,

Table 4.1). These observations suggest that CNS-PNETs presenting children ≤4 years are more

likely to have sub-group 1 tumours.

43 

Figure4.1Molecularsub‐groupsofCNS‐PNETexhibitdistinctclinicalphenotypes

44 

Figure 4.1 Molecular sub-groups of CNS-PNET exhibit distinct clinical phenotypes

Demographic and clinical information available on 108 primary CNS-PNET (Table 4.1, 4.2 and

Appendix 1 Table A-1.1) were examined to determine tumour sub-group correlation with: A-

Gender, B- Age at diagnosis, C- Metastatic status at diagnosis, D- Age and metastatic status at

diagnosis. Significance was determined using ANOVA (gender), Chi-square (age), and 2-sided

Fisher’s exact test (gender and metastatic status at diagnosis). Number of patients in each

category is indicated within bar graphs.

45 

4.1.3.Metastasis

Metastasis is a clinical feature linked to poorer outcomes in medulloblastoma and other

embryonal brain tumours and represents an important factor in determining if a patient will

receive cranio-spinal radiation. Molecular sub-groups of CNS-PNET exhibited significant

differences in incidence of tumour metastases. Sub-group 3 tumours demonstrated the highest

incidence of tumour metastases at diagnosis: 53% (10/19) sub-group 3 patients were metastatic

(M+) at diagnosis while only 26% (5/19) sub-group 1 and 15% (3/20) sub-group 2 patients were M+

at diagnosis (p=0.037) (Figure 4.1 C). Proportion of localized (M0) to metastatic (M+) tumours

in sub-groups 1, 2 and 3 were respectively 2.8, 5.67 and 0.9 (p=0.03 (Table 4.1)).

Interestingly, although metastatic disease is reported to be more frequent in younger

children with embryonal brain tumours, analyses performed with the previous stratification for

age (≤ or > 4 years) showed no significant difference in frequency of metastases based on age.

This may be due to the small numbers found in the ≤4 category; sub-group 1 (n=15), sub-group 2

(n=6) and sub-group 3 (n=4). However, incidence of tumour metastases remained significantly

different amongst CNS-PNET sub-groups diagnosed in older children (Figure 4.1 D, Table 4.2).

A majority (9/15; 60%) of sub-group 3 patients > 4 years of age at diagnosis had metastatic

presentation as compared to only 1/4 (25%) of sub-group 1 and 2/14 (14%) sub-group 2 patients.

Proportion of M0 to M+ tumours, which were respectively 3, 6 and 0.67 for sub-group 1, 2 and 3

CNS-PNETs in children > 4 years, differed significantly in comparison of sub-group 3 to a

combined cohort of sub-group 1 and 2 patient (p=0.033). Since sub-group 1 consisted of 4

patients, I compared the incidence of metastases in patients >4 years between sub-group 2 and 3

alone and saw that incidence of metastases in sub-group 3 remained significant (p=0.014)

(Figure 4.1 D, Table 4.2). Notably, the incidence of metastases across sub-groups did not

correlate with patient outcomes, specifically although sub-group 3 tumours exhibited the highest

incidence of metastases; sub-group 1 patients exhibited the worst survival.

4.2SurvivalfeaturesofCNS‐PNETmolecularsub‐groups

Li et al43 and Korshunov et al44 have shown that C19MC amplified tumours are more

aggressive than other CNS-PNETs, however, specific molecular sub-grouping of the other non-

C19MC amplified CNS-PNETs had not yet been established. I compared survival features of all

46 

3 molecular sub-groups of patients. Log rank analysis confirmed overall survival for group 1 was

significantly less than group 2 and 3 patients. Median survival for sub-group 1, 2 and 3 were

respectively 0.8 years (95% CI:0.47-1.2), 1.8 years (95% CI:1.4-2.3) and 4.3 years (95% CI:

0.82-7.8) p= 0.019 (Figure 4.2 A, Table 4.1). With the exception of two longer term survivors

which were sub-grouped by LIN28 IHC, all sub-group 1 patients were deceased within 4.2 years

of diagnosis.

As the majority of sub-group 1 tumours arise in younger children who are often treated

heterogeneously with radiation sparing therapeutic approaches due to concerns of neuro-

cognitive damage7,8, I examined whether the poor prognostic association of LIN28 expression in

sub-group 1 tumours held true for older children who are conventionally prescribed intensified

treatment regimens with higher dose cranio-spinal irradiation. We had very limited treatment

information for our patients, therefore, as most infant brain tumour protocols enroll patients up to

3-4 years of age7,8,23, we stratified patients by age ≤ or >4 years, to remove potential treatment

biases based on age. As shown in Figure 4.2 B and Table 4.2, while overall survival for all

young patients was similarly dismal, older children with LIN28 sub-group 1 tumours fared

significantly worse (median survival of 0.5 years, 95% CI: 0-1, p=0.004) than patients in sub-

group 2 (median survival of 1.8 years, 95% CI:1.5-2.2) and 3 (median survival of 4.8 years, 95%

CI:1.6-8). These findings indicate that sub-group 1 patients, identified by gene expression or

immuno-positivity for LIN28, describe a particularly high risk sub-group of CNS-PNETs across

all ages.

4.3Discussion

Collectively my analyses demonstrate distinct clinico-pathologic features of molecular

sub-groups of CNS-PNETs. This information provides valuable diagnostic and prognostic tools

for in depth biological studies of disease mechanisms specific to CNS-PNET sub-groups and for

informing prospective treatment trials.

Our findings suggest that current treatment regimens for sub-group 1 CNS-PNETs are

largely ineffective and that such children should be considered for novel therapeutic approaches.

Older children with CNS-PNETs represented in the sub-group 2 and 3 patients routinely received

higher dose cranio-spinal irradiations. Interestingly, while overall survival for all young

47 

 

Figure4.2Molecularsub‐groupsofCNS‐PNETexhibitdistinctsurvivalfeatures

48 

Figure 4.2 Molecular sub-groups of CNS-PNET exhibit distinct survival features

Demographic and clinical information available on 66 primary CNS-PNET (Table 4.1, 4.2 and

Appendix Table A-1.1) were examined to determine tumour sub-group correlation with A-

Overall survival, and B- Overall survival and age. Significance was determined using log-rank

(survival) analyses.

49 

patients was similarly dismal, sub-group 3 patients aged > 4 years of age trended (p=0.089)

towards better survival than sub-group 2 or sub-group 1, which had the worst overall survival.

This is particularly interesting as sub-group 3 tumours exhibited the highest incidence of

metastases which is generally considered a high risk feature of embryonal brain tumours. Thus

this observation potentially has significant implications for risk stratification for CNS-PNET

patients. Future studies with larger cohorts will clearly be important for validating this early

observation. We observed that sub-group 2 tumours, which are predominantly localized tumours,

also expressed high levels of OLIG2, a marker associated with glial lineage. These findings

reflect long standing challenges in distinguishing a sub-set of malignant gliomas arising in the

cerebral hemispheres with poorly differentiated features from CNS-PNETs45. The extent to

which sub-group 2 CNS-PNETs overlap with malignant glial entities remains to be established

with direct comparative studies of histologically verified glioblastoma.

In summary, our findings demonstrate for the first time that differential expression of cell

lineage markers LIN28 and OLIG2 distinguishes 3 molecular sub-groups of CNS-PNET and

identifies disease sub-groups with specific clinical features. Although our studies are limited by

the lack of complete clinical information for all patients, it represents a unique and highly

valuable study for this rare tumour entity. Our findings that LIN28 and OLIG2 respectively

identify CNS-PNETs with very different rates of treatment failures and distinct propensity for

metastasis has potential far reaching implications for future treatment of CNS-PNETs.

50 

Chapter 5

Discussion and future directions

51 

5.0Discussion 

Advances in paediatric CNS-PNET research have been limited due to rare tumour

incidence6, lack of information regarding clinical and biological features of this disease and lack

of genetic markers for more precise histo-pathological diagnosis24,30. In this integrative study, I

examined the largest cohort of primary hemispheric CNS-PNET and have observed that CNS-

PNET comprise 3 distinct molecular and clinical sub-groups. By exploiting distinct molecular

expression signatures for each molecular sub-group, I was able to identify immuno-

histochemical markers that can be used to identify CNS-PNET sub-groups.

In my thesis, I have also further characterized the C19MC amplicon associated molecular

sub-group, which our group previously described2, and confirmed that these are distinctly

aggressive tumours and occur almost exclusively in children < 4 years of age. I showed that

these sub-group 1 tumours express high levels of the pluripotentcy gene LIN28, suggesting an

important oncogenic role for LIN28 in the pathogenesis of the sub-group 1 CNS-PNETs.

Interestingly, recent studies from our lab have demonstrated that C19MC amplified tumours with

or without high LIN28 expression span several histologic classes of CNS-PNETs including

ETANTR, medulloepithelioma and ependymoblastoma, as well as a proportion of “classic”

CNS-PNET53. Furthermore studies from our lab54 show that the LIN28/let-7 miRNA axis, which

is important in regulation of stemness, cellular metabolism and disease state55-58, is functional in

C19MC amplified/LIN28+ CNS-PNETs. Importantly, these studies have shown that LIN28

drives aberrant IGF/PI3K/mTOR signalling and that tumour cell lines with C19MC amplification

or high LIN28 expression are sensitive to Rapamycin, a pharmacologic mTOR antagonist which

represents a novel, candidate therapeutic for sub-group 1 CNS-PNETs.

LIN28 and OLIG2 are cell lineage markers expressed at various times during normal

development. LIN28 is an early precursor/primitive cell marker which has been reported to be

highly expressed in the developing germ cells and in germ cell associated tumours. To date, no

direct genetic alteration has been observed in our genomics data to account for high LIN28

expression. Ongoing epigenomic/methylation profiling studies in our lab indicate that LIN28

may be regulated at the epigenetic level in these tumours, thus suggesting that the association of

high LIN28 expression in sub-group 1 tumours may reflect an early lineage tumour cell of origin.

High expression of OLIG2, which has been reported in both neural and glial cell lineage

52 

precursor cell types, was observed in sub-group 2 tumours which suggest an oligodendroglial

precursor cell of origin. Notably, OLIG2 is known to be co-expressed with ASCL1, a primitive

neural marker in oligodendroglial precursor cells (OPCs). Current data suggest that upon loss of

ASCL1 in the sub ventricular zone, OPCs differentiate into an astrocytic lineage59, expression of

the NKX2-2 and SOX10 gene are associated with a down-regulation of the myelination and

astrocytic commitment program by OPCs60,61. In our study, we observed variable amounts of

OLIG2 expression in sub-group 2 tumours, which was associated with high levels of SOX10.

Notably, as a subset of OLIG2 tumours also expressed high levels of ASCL1 and NKX2-2, these

findings suggest OLIG2+ve sub-group 2 CNS-PNETs exhibit a range of neural/glial features. As

OLIG2 is expressed in many tumours of glial lineage, it is possible that a subset of sub-group 2

CNS-PNET may have overlapping molecular features with malignant glial tumours of childhood.

However, H3.3 mutations, a defining feature of malignant childhood gliomas46, have not been

reported in CNS-PNETs. In contrast to sub-group 1 and 2 tumours, we observed that sub-group 3

tumours were enriched for genes associated with mesenchymal differentiation. Although the

potential cellular origin of sub-group 3 CNS-PNETs could not be determined based on our gene

expression data, we did observe enrichment ZIC262 and LHX263 which are expressed in early

neural precursors and have functions in brain patterning. Our study is limited by relatively small

numbers of tumour with substantial heterogeneity. Therefore, future in depth analyses of CNS-

PNET genomes and transcriptomes by global sequencing methods will better elucidate the

genetic spectrum of sub-group 2 and 3 CNS-PNETs.

5.1Clinicalrelevanceoffindings

Prior clinical studies of CNS-PNETs have often grouped tumours from various

anatomical locations including pineoblastoma as a supratentorial PNET and have also included

cohorts of medulloblastoma. Therefore patient features and survival trends have difficult to

interpret. My study represents the first study on a large cohort of primary CNS-PNET restricted

to only the cerebral hemisphere. My data indicates that hemispheric CNS-PNETs comprise three

molecular and clinically distinct sub-groups of CNS-PNET with characteristic age, gender,

metastasis and survival features.

Current treatment options for CNS-PNET are largely based on histologic similarities to

medulloblastoma, a more common malignant childhood brain tumour. However, current

53 

approaches to CNS-PNETs treatment are often ineffective and are limited by lack of biological

studies to inform patient treatments. To date, only 2 biological studies2,44 of modest cohorts of

CNS-PNET have been undertaken, confirming an association of C19MC with more aggressive

features than other CNS-PNETs. In this study, we integrated molecular and genomic analyses

with immunohistochemistry of a large cohort of primary hemispheric CNS-PNETs and

demonstrated that cell lineage markers correlate with and reveal additional clinically relevant

sub-groups of CNS-PNETs. Data from my study suggest that LIN28 and OLIG2 represent

promising and readily applied markers that should be further explored in prospective patient

cohorts.

5.2Futuredirections

I have shown that chr 3q with a minimal region of overlap (MRO) centered on IGF2BP2,

is gained in group 1 tumours; interestingly, IGF2BP2 is significantly up-regulated in group 1

tumours. Indeed, although I have shown differences in CNAs between molecular sub-groups, I

observed many additional alterations that span sub-groups (e.g chr 1q, and MROs centered on

WNT16 (chr 7q) and RHOG (chr 11p) (Figure 3.8). The nature of genes altered by these CNAs,

and how they contribute to gene expression patterns and CNS-PNET tumourigenesis, remains to

be determined by ongoing and future sequencing studies.

Cell lines for CNS-PNETs are rare. Therefore, CNS-PNET cell lines which recapitulate

the histology and expression patterns of primary tumours are important tools. To further study

sub-group 2/3 tumours, it is imperative to generate such reagents. Recently a sub-group 3 cell

line and intra-cranial xenograft model was reported by Zhigang Liu et al64. Furthermore

preliminary reports of genetic models resembling sub-group 2 CNS-PNET has also been reported

in mice and zebra fish65. Cross-species analysis of these models with human CNS-PNETs will be

very powerful, not only for elucidating the cell of origin, but also for determining important

driver genes necessary for the formation of CNS-PNETs.

The biological relationship of CNS-PNETs arising in different anatomical locations

remains unclear. As my study comprised a substantial cohort of tumours restricted to the

hemispheres, data generated from my analyses will enable comparative studies of large tumour

cohorts across CNS-PNETs. Specifically these molecular and clinical data sets will be important

54 

for determining the relationship of CNS-PNETs to pineoblastomas, as well as other hemispheric

tumours. Such studies will help refine and generate more powerful diagnostic markers that will

help distinguish malignant childhood brain tumours such as CNS-PNETs and other malignant

hemispheric tumours for which treatment approaches are substantially different.

In summary, data from my thesis has generated a substantial body of knowledge to fuel

both biological and clinical investigations of childhood CNS-PNETs and other related tumours.

Specifically, I have identified promising candidate disease markers that have the potential to be

highly informative diagnostic and prognostic markers which will help advance the therapeutic

challenges posed by patient with CNS-PNETs.

55 

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56 

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63.  Hoch, R.V., Rubenstein, J.L. & Pleasure, S. Genes and signaling events that establish regional patterning of the mammalian forebrain. Semin Cell Dev Biol 20, 378‐86 (2009). 

64.  Liu, Z. et al. A Patient Tumor‐derived Orthotopic Xenograft Mouse Model Replicating the Group 3 

Supratentorial Primitive Neuroectodermal Tumor in Children. Neuro‐Oncology (2013). 65.  2013 Pediatric Neuro‐Oncology Basic and Translational Research Conference. Neuro Oncol 15, 

i1‐i51 (2013). 

60 

Appendix

 

 

 

 

 

 

 

 

 

Appendix 1-2 were published in Picard D, Miller S, Hawkins CE, Bouffet E, Rogers HA, Chan TS, Kim SK, Ra YS, Fangusaro J, Korshunov A et al: Markers of survival and metastatic potential in childhood CNS primitive neuro-ectodermal brain tumours: an integrative genomic analysis. Lancet Oncol 2012, 13(8):838-848. Reproduced with copyright permission from Lance Oncology 

61 

Appendix 1

 

 

 

 

 

 

 

 

62 

TableA‐1.1Clinicopathologiccharacteristicsoftumoursamples

Annotation Sub-

group Gender Diagnosis

Age at Diagnosis

(years) Treatment Metastasis

Status Survival Status

Survival Time

(years)

PNET3 1 female PNET/EP 4.08 Chemo M0 Dead 0.5

PNET4 2 female PNET 10.75 Chemo/XRT M0 Dead 0.67

PNET5 1 female PNET/EP 1.67 Chemo M0 Dead 0.58

PNET6 1 female PNET/EP 2.42 Chemo M0 Dead 1

PNET7 2 male PNET n/a n/a n/a n/a n/a

PNET8 3 male PNET 6 n/a M+ Alive n/a

PNET9 3 male PNET 4.08 n/a M+ Alive 0.42

PNET10 n/a n/a PNET n/a n/a n/a n/a n/a

PNET11 n/a n/a PNET n/a n/a n/a n/a n/a

PNET15 1 male PNET/ME 0.42 None M0 Alive n/a

PNET17 3 male PNET 1.83 Chemo/XRT M0 Dead 6.33

PNET20 3 female PNET 11 n/a n/a Dead 1

PNET22 2 female PNET 9.67 n/a n/a Dead 2

PNET25 3 male PNET 2 n/a n/a n/a n/a

PNET28 3 male PNET 4 n/a n/a n/a n/a

PNET30 2 male PNET 12 n/a n/a n/a n/a

PNET31 2 female PNET n/a n/a n/a n/a n/a

PNET36 2 male PNET 10 Chemo/XRT n/a Alive 4

PNET37 3 female PNET 8 Chemo/XRT n/a Alive 3.92

PNET40 1 male PNET 2.5 None M0 Dead 0.01

PNET42 1 female PNET 3.17 Chemo/XRT M0 Dead 0.83

PNET43 1 male PNET 1.5 Chemo M+ Dead 0.83

PNET44 2 male PNET 11 Chemo M0 Dead 1.75

PNET47 2 female PNET 2.58 Chemo/XRT M+ Alive 11.17

PNET48 3 female PNET 1 Chemo M0 Dead 2.33

PNET49 n/a male PNET 17 n/a n/a n/a n/a

PNET51 n/a female PNET 3 n/a n/a n/a n/a

PNET52 n/a female PNET 8 n/a n/a n/a n/a

PNET53 n/a female PNET 3 n/a n/a n/a n/a

PNET54 1 male PNET/EP 3 n/a M0 n/a n/a

PNET55 n/a female PNET 2 n/a n/a n/a n/a

PNET56 1 female PNET/ETNATR 1.67 n/a M0 Dead 0.25

PNET59 3 n/a PNET n/a n/a n/a n/a n/a

PNET61 3 female PNET 0.67 n/a n/a Dead n/a

PNET62 3 female PNET 1 n/a n/a Dead n/a

PNET64 3 male PNET 16 n/a n/a Alive n/a

PNET65 3 male PNET 10 n/a n/a Alive n/a

PNET66 2 female PNET 6 n/a n/a Dead n/a

PNET67 1 male PNET/ETNATR 3 n/a n/a Dead n/a

63 

PNET68 3 male PNET 2 n/a n/a Alive n/a

PNET71 2 male PNET 7 n/a n/a Alive n/a

PNET72 3 male PNET 0.06 n/a n/a Dead n/a

PNET75 3 male PNET 0.92 n/a n/a Dead n/a

PNET78 3 male PNET 4 n/a n/a Alive n/a

PNET79 3 male PNET/ME 4 n/a n/a Dead n/a

PNET81 1 female PNET/ETNATR 3 n/a n/a Alive n/a

PNET82 1 female PNET/ME 12 n/a n/a Alive n/a

PNET91 1 female PNET n/a n/a n/a n/a n/a

PNET93 1 female PNET n/a n/a n/a n/a n/a

PNET94 1 female PNET 15 n/a n/a n/a n/a

PNET95 3 female PNET 3.08 n/a n/a n/a n/a

PNET96 3 male PNET 8 n/a n/a n/a n/a

PNET97 2 male PNET 12 n/a n/a n/a n/a

PNET99 n/a male PNET 6 n/a M+ Dead 1.25

PNET100 n/a female PNET 11 n/a M+ Dead 8.7

PNET101 n/a male PNET 3 n/a M0 Alive 6.6

PNET105 n/a female PNET n/a n/a n/a n/a n/a

PNET106 n/a male PNET n/a n/a n/a n/a n/a

PNET109 1 female PNET 1.58 n/a n/a Dead n/a

PNET111 1 male PNET 2.33 n/a n/a Dead n/a

PNET112 2 male PNET 2 n/a n/a Dead n/a

PNET113 3 male PNET 7 n/a n/a Dead n/a

PNET114 2 male PNET n/a n/a n/a n/a n/a

PNET116 2 male PNET 14.2 Chemo/XRT M0 Dead 1.79

PNET118 1 male PNET 2.9 XRT M0 Dead 0.38

PNET119 2 male PNET 17.9 Chemo/XRT M0 Dead 0.58

PNET122 3 male PNET 16.7 Chemo/XRT M0 Dead 4.33

PNET123 2 male PNET 17.9 Chemo M+ Dead 0.88

PNET126 2 male PNET 3 n/a n/a n/a n/a

PNET129 2 male PNET 8 n/a n/a n/a n/a

PNET131 3 female PNET 0.83 n/a n/a n/a n/a

PNET132 3 male PNET 4 n/a n/a n/a n/a

PNET135 1 male PNET n/a n/a n/a Dead 0

PNET138 1 male PNET 2.83 n/a M0 Dead 1.58

PNET139 n/a n/a PNET n/a n/a n/a n/a n/a

PNET140 n/a n/a PNET n/a n/a n/a n/a n/a

PNET141 n/a n/a PNET n/a n/a n/a n/a n/a

PNET142 n/a n/a PNET n/a n/a n/a n/a n/a

PNET143 2 female PNET 1.92 n/a M0 Dead 0.75

PNET146 3 male PNET 10.58 n/a M+ Alive 3.58

PNET148 1 female PNET 2.2 n/a M0 Alive 3.6

PNET149 2 male PNET 6.42 n/a M0 Dead 1.25

64 

PNET157 3 male PNET 2.25 n/a M0 Dead 8.17

PNET158 2 male PNET 0.03 n/a M0 Dead 6.08

PNET160 3 male PNET 5.75 XRT M+ Alive 1.75

PNET161 1 female PNET 1.75 Chemo/XRT M0 Dead 3.17

PNET163 2 female PNET 1.42 Chemo M0 Dead 0.08

PNET164 1 female PNET 8.92 n/a M+ Dead 1.33

PNET166 n/a female PNET 0.06 n/a M0 Dead 0.25

PNET167 3 female PNET 1.08 n/a n/a Dead 2.71

PNET169 2 female PNET 4.08 n/a n/a Dead 0.17

PNET170 2 female PNET n/a None n/a n/a n/a

PNET171 2 male PNET 11.17 Chemo/XRT M+ Dead 4.42

PNET172 3 female PNET 3.5 Chemo/XRT M0 Alive 2.17

PNET173 2 male PNET 13.17 Chemo/XRT M0 Dead 1.83

PNET174 2 male PNET 7.83 Chemo/XRT M0 Dead 2.33

PNET187 2 female PNET 15.83 Chemo/XRT M0 Alive 2.92

PNET188 2 male PNET 1.67 Chemo M0 Dead 3.42

PNET190 3 male PNET 11.75 XRT M+ Dead 2.5

PNET191 2 female PNET 4.42 Chemo M0 Alive 5.17

PNET196 2 female PNET 1.67 None M0 Dead 0

PNET197 3 male PNET 10.17 Chemo/XRT M0 Alive 7.5

PNET199 2 female PNET 9.67 Chemo/XRT M0 Dead 2.08

PNET200 3 female PNET 7 Chemo/XRT M+ Dead 1.67

PNET226 1 female PNET 3.4 n/a n/a Dead 0

PNET255 1 female PNET 1.92 Chemo/XRT M+ Alive 4.67

PNET256 2 female PNET 13 Chemo/XRT M0 Dead 1.17

PNET258 3 male PNET 7.5 Chemo/XRT M+ Alive 2.08

PNET259 1 male PNET 3.67 Chemo/XRT M+ Dead 0.67

PNET260 2 female PNET 8.5 XRT M0 Alive 7.75

PNET265 n/a female PNET 1.33 Chemo M+ Dead 0.5

PNET266 3 male PNET 8.92 XRT M+ Dead 5.92

PNET267 n/a female PNET 4.25 Chemo/XRT M0 Dead 2

PNET268 n/a male PNET 2.75 None M0 Dead 0

PNET269 n/a male PNET 1.75 None M0 Dead 0

PNET270 n/a female PNET 0.42 Chemo M+ Dead 1.17

PNET271 n/a female PNET 5.92 XRT M0 Dead 0.58

PNET272 1 female PNET 0.83 Chemo M+ Dead 3.42

PNET273 1 female PNET 8.25 Chemo/XRT M0 Dead 0.67

PNET274 3 male PNET 15.5 Chemo/XRT M0 Alive 21.33

PNET276 n/a male PNET 10.25 Chemo/XRT M0 Dead 1.33

PNET277 n/a female PNET 3.58 Chemo/XRT M0 Alive 22

PNET279 n/a female PNET 4.17 None M0 Dead 0.17

PNET281 3 female PNET 11.75 Chemo/XRT M+ Dead 4.83

PNET282 3 female PNET 7.08 Chemo/XRT M0 Alive 9.33

65 

PNET283 3 male PNET 11.83 None M0 Dead 0

PNET284 3 male PNET 4.92 Chemo/XRT M+ Dead 0.75

PNET285 n/a female PNET 2 Chemo M0 Dead 0.25

PNET286 1 male PNET 5.17 Chemo M0 Dead 3.17

PNET287 n/a male PNET 0.5 Chemo M0 n/a 0

PNET288 1 male PNET 2.25 None M0 Dead 3.17

PNET289 n/a male PNET 7 Chemo/XRT M+ Dead 1.67

PNET290 3 male PNET 0.17 n/a n/a Dead 0

PNET291 1 female PNET 3.75 Chemo/XRT n/a Dead 1

PNET293 2 female PNET 1.75 n/a n/a n/a n/a

PNET294 3 female PNET 10 n/a n/a n/a n/a

PNET295 2 male PNET 5.33 n/a n/a n/a n/a

PNET296 n/a female PNET 9.17 n/a n/a n/a n/a

PNET297 3 female PNET 15.33 Chemo/XRT n/a Dead 2.08

PNET300 3 female PNET 5.67 n/a n/a n/a n/a

PNET301 1 female PNET 3.25 Chemo M0 Alive 13.75

PNET302 n/a female PNET 8.75 Chemo/XRT M0 Dead 1.75 n/a = not available

Chemo = Chemotherapy

XRT = Radation therapy

PNET/ME: PNET with features of medulloepithelioma

PNET/EP: PNET with features of ependymal or ependymoblastic differentiation

PNET/ETNATR: PNET variant with excess neuropil and true rosettes

65 

TableA‐1.2Analysesperformedontumoursamples

Annotation Illumina HT-12 v4 OmniQuad 2.5 SNP Immunohistochemistry

PNET3 √ √ √

PNET4 √ √ √

PNET5 √ √ √

PNET6 √ √ √

PNET7 √ √ no material

PNET8 no material √ √

PNET9 √ √ √

PNET10 no material √ no material

PNET11 no material √ no material

PNET15 √ √ no material

PNET17 √ √ no material

PNET20 √ √ √

PNET22 √ √ √

PNET25 √ √ √

PNET28 no material √ √

PNET30 √ √ √

PNET31 √ √ no material

PNET36 √ √ no material

PNET37 √ √ no material

PNET40 √ √ no material

PNET42 √ √ no material

PNET43 √ √ no material

PNET44 √ √ no material

PNET47 √ √ no material

PNET48 √ √ no material

PNET49 no material √ no material

PNET51 no material √ no material

PNET52 no material √ no material

PNET53 no material √ no material

PNET54 no material √ no material

PNET55 no material √ no material

PNET56 no material no material √

PNET59 no material no material √

PNET61 no material no material √

PNET62 no material no material √

PNET64 no material no material √

PNET65 no material no material √

PNET66 no material no material √

PNET67 no material no material √

PNET68 no material no material √

66 

PNET71 no material no material √

PNET72 no material no material √

PNET75 no material no material √

PNET78 no material no material √

PNET79 no material no material √

PNET81 no material no material √

PNET82 no material no material √

PNET91 √ √ no material

PNET93 √ √ no material

PNET94 √ √ no material

PNET95 √ √ no material

PNET96 √ √ no material

PNET97 √ √ no material

PNET99 no material √ no material

PNET100 no material √ no material

PNET101 no material √ no material

PNET105 no material √ no material

PNET106 no material √ no material

PNET109 √ √ √

PNET111 √ √ √

PNET112 no material no material √

PNET113 no material no material √

PNET114 √ √ no material

PNET116 no material no material √

PNET118 no material no material √

PNET119 no material no material √

PNET122 no material no material √

PNET123 no material no material √

PNET126 no material no material √

PNET129 no material no material √

PNET131 no material no material √

PNET132 √ no material √

PNET135 √ √ no material

PNET138 √ √ √

PNET139 no material √ no material

PNET140 no material √ no material

PNET141 no material √ no material

PNET142 no material √ no material

PNET143 √ √ no material

PNET146 √ √ no material

PNET148 no material √ no material

PNET149 √ √ no material

PNET157 √ √ no material

67 

PNET158 √ √ √

PNET160 no material no material √

PNET161 no material no material √

PNET163 no material no material √

PNET164 no material no material √

PNET166 no material no material √

PNET167 no material no material √

PNET169 no material no material √

PNET170 √ √ no material

PNET171 √ √ √

PNET172 √ √ √

PNET173 √ √ √

PNET174 √ √ √

PNET187 √ √ √

PNET188 √ √ no material

PNET190 √ √ √

PNET191 √ √ √

PNET196 √ √ √

PNET197 √ √ √

PNET199 √ √ no material

PNET200 √ √ no material

PNET226 no material √ no material

PNET255 no material no material √

PNET256 no material no material √

PNET258 no material no material √

PNET259 no material no material √

PNET260 no material no material √

PNET265 no material no material √

PNET266 no material √ √

PNET267 no material no material √

PNET268 no material no material √

PNET269 no material no material √

PNET270 no material no material √

PNET271 no material √ √

PNET272 no material √ √

PNET273 no material no material √

PNET274 no material no material √

PNET276 no material no material √

PNET277 no material no material √

PNET279 no material no material √

PNET281 no material no material √

PNET282 no material no material √

PNET283 no material √ √

68 

PNET284 no material √ √

PNET285 no material √ √

PNET286 no material no material √

PNET287 no material no material √

PNET288 no material no material √

PNET289 no material no material √

PNET290 no material no material √

PNET291 no material no material √

PNET293 no material no material √

PNET294 no material no material √

PNET295 no material no material √

PNET296 no material no material √

PNET297 no material no material √

PNET300 no material no material √

PNET301 no material no material √

PNET302 no material no material √

n/a = not available 

69 

Appendix 2

 

 

 

 

 

 

 

 

 

838 www.thelancet.com/oncology Vol 13 August 2012

Articles

IntroductionBrain tumours are the most common paediatric solid neoplasms1 and a leading cause of cancer-related morbid-ity and mortality in children.2 Embryonal tumours are the largest group of malignant paediatric brain tumours and include medulloblastoma, atypical rhabdoid teratoid tumour, and CNS primitive neuro-ectodermal brain tumours (PNETs). Despite histological resemblance to medullo blastoma, patients with CNS PNETs fare poorly even with intensifi ed therapy designed for patients with metastatic medulloblastoma.3,4 By contrast with this disease, in which substantial progress has been made in molecular understanding5,6 and clinical outcomes,7 the molecular and cellular make-up of CNS PNET is largely unknown8 and tumour treatments are often ineff ective. To improve outcomes from CNS PNET, delineation of the cellular and molecular pathogenesis of CNS PNET will be important to inform diagnosis, prognosis, and design of tumour-specifi c treatments.

CNS PNETs are predominantly hemispheric tumours and make up about 3–5% of all paediatric brain tumours. Such cancers are histologically heterogeneous with variable neuronal, ependymal, or glial diff erentiation9 and can be challenging to diagnose by routine histopath ology.10 Although diagnostic techniques and molecular-based tumour classifi cations have improved for atypical rhabdoid teratoid tumours11 and medulloblastoma, the working classifi cation for CNS PNET is not settled and thus therapeutic and molecular studies can be chal-lenging. In recent studies, our research groups identifi ed a distinctly aggressive subgroup of CNS PNETs that showed fre quent amplifi cation of an oncogenic miRNA cluster (C19MC).12,13 However, the molecular composition of most CNS PNETs is unknown. Although genomic studies suggest sub stantial heterogeneity in DNA copy number profi les,8,12,14 the signifi cance of these fi ndings in relation to clinical phenotypes is unclear. Similarly, gene-expression studies of small cohorts12,15 have yielded few

Markers of survival and metastatic potential in childhood CNS primitive neuro-ectodermal brain tumours: an integrative genomic analysisDaniel Picard*, Suzanne Miller*, Cynthia E Hawkins, Eric Bouff et, Hazel A Rogers, Tiff any S Y Chan, Seung-Ki Kim, Young-Shin Ra, Jason Fangusaro, Andrey Korshunov, Helen Toledano, Hideo Nakamura, James T Hayden, Jennifer Chan, Lucie Lafay-Cousin, Pingzhao Hu, Xing Fan, Karin M Muraszko, Scott L Pomeroy, Ching C Lau, Ho-Keung Ng, Chris Jones, Timothy Van Meter, Steven C Cliff ord, Charles Eberhart, Amar Gajjar, Stefan M Pfi ster, Richard G Grundy†, Annie Huang†

SummaryBackground Childhood CNS primitive neuro-ectodermal brain tumours (PNETs) are very aggressive brain tumours for which the molecular features and best treatment approaches are unknown. We assessed a large cohort of these rare tumours to identify molecular markers to enhance clinical management of this disease.

Methods We obtained 142 primary hemispheric CNS PNET samples from 20 institutions in nine countries and examined transcriptional profi les for a subset of 51 samples and copy number profi les for a subset of 77 samples. We used clustering, gene, and pathway enrichment analyses to identify tumour subgroups and group-specifi c molecular markers, and applied immuno histochemical and gene-expression analyses to validate and assess the clinical signifi cance of the subgroup markers.

Findings We identifi ed three molecular subgroups of CNS PNETs that were distinguished by primitive neural (group 1), oligoneural (group 2), and mesenchymal lineage (group 3) gene-expression signatures with diff erential expression of cell-lineage markers LIN28 and OLIG2. Patients with group 1 tumours were most often female (male:female ratio 0·61 for group 1 vs 1·25 for group 2 and 1·63 for group 3; p=0·043 [group 1 vs groups 2 and 3]), youngest (median age at diagnosis 2·9 years [95% CI 2·4–5·2] for group 1 vs 7·9 years [6·0–9·7] for group 2 and 5·9 years [4·9–7·8] for group 3; p=0·005), and had poorest survival (median survival 0·8 years [95% CI 0·5–1·2] in group 1, 1·8 years [1·4–2·3] in group 2 and 4·3 years [0·8–7·8] in group 3; p=0·019). Patients with group 3 tumours had the highest incidence of metastases at diagnosis (no distant metastasis:metastasis ratio 0·90 for group 3 vs 2·80 for group 1 and 5·67 for group 2; p=0·037).

Interpretation LIN28 and OLIG2 are promising diagnostic and prognostic molecular markers for CNS PNET that warrant further assessment in prospective clinical trials.

Funding Canadian Institute of Health Research, Brainchild/SickKids Foundation, and the Samantha Dickson Brain Tumour Trust.

Lancet Oncol 2012; 13: 838–48

Published OnlineJune 11, 2012

http://dx.doi.org/10.1016/S1470-2045(12)70257-7

See Comment page 753

*Authors contributed equally

†Joint lead authors

Division of Hematology-Oncology, Arthur and Sonia

Labatt Brain Tumour Research Centre, Department of

Pediatrics (D Picard BSc, Prof E Bouff et MD,

T S Y Chan BSc, A Huang MD), Department of Pathology (C E Hawkins MD), and The

Centre for Applied Genomics (P Hu PhD), Hospital for Sick

Children, University of Toronto, Toronto, ON, Canada;

Children’s Brain Tumour Research Centre, Queen’s

Medical Centre, University of Nottingham, Nottingham, UK

(S Miller PhD, H A Rogers PhD, Prof R G Grundy MD);

Department of Neurosurgery, Seoul National University Children’s Hospital, Seoul, South Korea (S-K Kim MD);

Department of Neurosurgery, Asan Medical Center, Seoul,

South Korea (Prof Y-S Ra MD); Division of Pediatric

Hematology/Oncology and Stem Cell Transplantation,

Children’s Memorial Hospital, Chicago, IL, USA

(J Fangusaro MD); Clinical Cooperation Unit

Neuropathology, German Cancer Research Center,

Heidelberg, Germany (A Korshunov MD); Oncology

Department, Schneider Hospital, Petach Tikva, Israel

(H Toledano MD); Department of Neurosurgery, Kumamoto

University, Kumamoto, Japan (H Nakamura MD); Northern

Institute for Cancer Research, Newcastle University,

Newcastle Upon Tyne, UK

Articles

www.thelancet.com/oncology Vol 13 August 2012 839

(J T Hayden MD, Prof S C Cliff ord PhD); Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada (J Chan MD); Department of Pediatric Oncology, Alberta Children’s Hospital, Calgary, AB, Canada (L Lafay-Cousin MD); Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, USA (X Fan MD, Prof K M Muraszko MD); Department of Neurology, Children’s Hospital Boston, Boston, MA, USA (Prof S L Pomeroy MD); Texas Children’s Cancer Center, Baylor College of Medicine, Houston, TX, USA (C C Lau MD); Department of Anatomical and Cellular Pathology, Chinese University of Hong Kong, Hong Kong, China (Prof H-K Ng MD); Department of Paediatric Molecular Pathology, Institute of Cancer Research, Sutton, UK (C Jones PhD); Department of Pediatrics, Virginia Commonwealth University, Richmond, VA, USA (T Van Meter PhD); Division of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA (Prof C Eberhart MD); Neuro-oncology Division, St Jude Children’s Research Hospital, Memphis, TN, USA (A Gajjar MD); and German Cancer Research Centre, and Paediatric, Haematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany (S M Pfi ster MD)

Correspondence to:Dr Annie Huang, Division of Hematology-Oncology, Arthur and Sonia Labatt Brain Tumour Research Centre, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada M5G 1X8annie.huang@sickkids.ca

See Online for appendix

insights into the clinical diversity of CNS PNETs. In this study, we undertook a multicentre, international col-laboration with the aim of providing a concerted molecular analysis of a substantial number of primary CNS PNETs. To assess clinical signifi cance of potential CNS PNET molecular subgroups, we examined whether subgroups diff ered in patient characteristics and outcome.

MethodsParticipants and study designWe obtained 254 samples with an institutional diagnosis of CNS PNET from participating institutions including six registered Children’s Cancer and Leukaemia Group centres in the UK and the Cooperative Human Tissue Network in Columbus, OH, USA (centres listed in the appendix).

CNS PNET tissue microarrays used in this study were constructed at the Hospital for Sick Children (Toronto, ON, Canada),12 University of Nottingham (Nottingham, UK),14 and the Institute of Cancer Research (Sutton, UK). All collected samples were initially reviewed for age of patient, location, and primary tumour occurrence

(fi gure 1) and then subject to histopathologic review by CEH, who was masked to fi ndings of previous assessments at the other centres. Samples were tested for loss of INI1 immunoreactivity or changes in INI1 by sequencing or multiplex ligation-dependent probe amplifi cation ana lyses to rule out misdiagnosed atypical rhabdoid teratoid tumours. We included only hemispheric tumours diag nosed as CNS PNET according to the 2007 WHO CNS tumour classifi cation criteria9 without mutations in INI1. For correlative analyses with clinical characteristics, we included only tumours with complete clinical information (full details of the patients and tumour information is listed in the appendix). We obtained tumour samples and clinical information with consent as per protocols approved by the hospital research ethics boards at participating institutions.

ProceduresAll tumour samples confi rmed to be CNS PNET and with snap frozen tumour material were processed for gene expression or DNA array analyses to initially establish tumour molecular subgroups. Tumour

77 included in copy-number analyses 95 included in immunohistochemical analyses

142 samples confirmed as paediatric primary hemispheric CNS PNET as per WHO CNS classification

18 no gene expression, immunohistochemistry, or clinical data

15 incomplete immunohistochemistry

59 samples with copy number and molecular subgroup information

80 included in immunohistochemical analyses

51 included in gene-expression analyses

23 duplicate samples

108 independent primary tumours with molecular grouping by gene expression and/or immunohistochemistry and complete clinical data

254 samples received

112 excluded 5 located in the posterior fossa 5 patients aged >18 years 11 duplicate samples 25 recurrent CNS-PNET 29 atypical teratoid rhabdoid tumours 15 ependymoma 2 glioblastoma 11 pineoblastoma 9 non-CNS tumours

Figure 1: Sample analysisPNET=primitive neuro-ectodermal brain tumour.

Articles

840 www.thelancet.com/oncology Vol 13 August 2012

0 5 10 15 20

Axonal guidance

WNT

SHH

0 5 10 15 20

Axonal guidance

0 5 10 15

Axonal guidance

PTEN signaling

TGFβ signaling

Group 1 Group 2 Group 3

*

*

**

*

*

*

**

**

**

**

**

**

**

**

Pluripotency

Pluripotency Pluripotency

SHH

WNT

WNT

Gene p value

NKX6-2 7 <0·0001

HOXA2 5 0·0008

HOXB3 3 <0·0001

HOXB4 4 <0·0001

HOXB5 6 <0·0001

HOXC4 5 <0·0001

SALL4 13 <0·0001

SOX3 13 <0·0001

PROM1 3 <0·0001

<0·0001

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BCAN 7 <0·0001

NCAM2 5 <0·0001

COL1A2 4 0·0034

COL5A1 6 <0·0001

COL21A1 2 0·0008

LHX2 10 <0·0001

ZIC2 11 <0·0001

MSX1 7 <0·0001

SNAI2 3 0·0012

TWIST1 3 0·202

SHISA2 4 0·0040

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OLIG1/2 15

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Figure 2: Molecular subgroups of CNS primitive

neuro-ectodermal brain tumours

(A) Heat map of highly expressed cell lineage genes in

each subgroup identifi ed using a supervised t test adjusted for

multiple testing (false discovery rate ≤0·05) relative

to a hierarchical cluster map of all tumours; magnitude (fold

change) and signifi cance (p value) of cell lineage genes

upregulated in each tumour subgroup is shown.

(B) Signifi cant changes in canonical pathways are

presented from analyses of 343 genes in group 1, 276 genes in group 2,

and 325 genes in group 3.

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grouping for samples with only formalin-fi xed paraffi n-embedded (FFPE) materials available were determined with immunohistochemical analyses. All tumour samples with established molecular grouping in for-mation and clinical data for patients’ demographics, metastatic status, and survival were then examined to determine whether molecular subgroups correlated with specifi c CNS PNET phenotypes.

To assess gene expression and DNA copy number profi les, we extracted RNA from 51 primary CNS PNET samples and DNA from 77 samples with standard methods. We used Illumina Omni 2.5M single-nucleo tide polymorphism (SNP) for ultra-high resolution copy number analyses (interrogating 2·5 million SNPs) and Illumina HT-12.v4 gene-expression arrays (San Diego, CA, USA) to generate DNA copy number and gene expression profi les. We did DNA and RNA hybridisations at the Centre of Applied Genomics Facility at the Hospital for Sick Children, according to the manufacturer’s protocol.

For the gene expression profi les, we did multiple unsupervised analyses to identify molecular subgroups of CNS PNETs. To defi ne genes or pathways that charac-terise each CNS PNET subgroup, we then did supervised analyses of each subgroup relative to the others, and examined the most highly diff erentially expressed gene sets between subgroups for gene and pathway enrich-ment. To assess the clinical signifi cance of identifi ed molecular subgroups, we sought markers of each sub-group that could be examined by immunohistochem istry on a larger cohort of clinically well characterised tumours. We did quantitative RT-PCR analyses to validate group-specifi c gene clusters identifi ed by supervised analyses and examined expression levels of individual genes across groups to identify the most robust, upregulated loci that can distinguish tumour subgroups.

To determine the relationship of copy number changes to molecular subgroups, we included 59 (77%) of 77 tumours with copy number profi les that had established molecular grouping.

The appendix shows details of molecular analyses done on individual tumour samples. All data are deposited in the Wellcome Trust, European Genome-Phenome Archive (accession number EGAS00000000116).

For gene-specifi c quantitative RT-PCR validation of array data, we amplifi ed 10 ng cDNA synthesised from 1 μg of RNA (TaqMan Reverse Transcription Kit, Applied Biosystems, Burlington, ON, Canada) by use of specifi c TaqMan probes-primer sets (see appendix) and deter-mined mRNA expression levels relative to actin with the ΔCt method. We did all RT-PCR assays in triplicate. Immunohistochemical analyses of tumour tissue micro-array or FFPE tumour slides were done by the Pathology Research Program laboratory at the University Health Network (Toronto, ON, Canada). We treated all tissue sections with heat-induced epitope retrieval and blocked for endogenous peroxidase and biotin. We assessed expression of markers for primitive neural, glial (nestin,

glial fi brillary acidic protein [GFAP]) or neuronal (synaptophysin) diff erentiation—which are measure-ments conventionally used in histopathological diagnosis of CNS PNET—for all tumours. The anti bodies used in this study were anti-LIN28 (Cell Signalling Technology, Boston, MA, USA), OLIG2 (Immuno-Biological Laboratories, Minneapolis, MN, USA), GFAP (DAKO, Burlington, CA, USA), and synaptophysin (Millipore, MA, USA). Antibody reactions were visual ised with a Biogenix detection kit (BioGenex Laboratories, San Ramon, CA, USA). Immunoreactivity for LIN28, GFAP, and synaptophysin were scored manually on the basis of intensity (1 was low, 2 was moderate, and 3 was high) and distribution of stains (1 was ≤10%, 2 was 10–50%, and 3 was >50%). OLIG2 immunostains were quantifi ed with the Aperio Scanscope (Aperio, Vista, CA, USA) system and the ImageScope software nuclear immunohistochemistry

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Figure 3: Cell lineage markers of molecular subgroups of CNS PNETs(A) Quantitative RT-PCR analyses of 51 primary CNS PNETs profi led by gene expression arrays (appendix) showing enriched mean expression levels of LIN28 (group 1), OLIG2 (group 2), and IGF2 (group 3; three replicas) are shown with standard errors of mean (bars) and transcript levels shown as circles, squares, and triangles. (B) Characteristic immunohistochemical analyses from the validation of 72 samples of CNS PNET; LIN28 and OLIG2 immunostains (20× magnifi cation) are shown in relation to a haematoxylin and eosin stain; insets (1× magnifi cation) show corresponding tissue microarray cores. PNET=primitive neuro-ectodermal brain tumour.

For the European Genome-Phenome Archive see https://www.ebi.ac.uk/ega/

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algorithm. For tumours on tissue microarray, we established immunohistochemistry values on the basis of average staining score of at least two tissue cores, while tumours with FFPE slides were scored on the basis of the extent of staining in relation to the entire tumour section. Normal testicular tissue (human and mouse) was used as a positive control for LIN28 and oligodendroglioma tumour tissue was used as a positive control for OLIG2 immunostains; samples processed in parallel without primary antibodies were used as negative controls. DP and TC scored all immuno histochemistry stains while masked to cancer status, which were reviewed by AH and CEH. FISH was done on FFPE tissue microarrays or individual slides with established protocols. To confi rm robustness of LIN28 and OLIG2 immunohistochemistry for subgrouping, we tested an initial cohort of 22 samples with subgroups established by gene expression studies for LIN28 and OLIG2 expression by immunohistochemistry (appendix) We used MYCN (2p24) and p16 (9p21) specifi c PlatinumBright550 probe with corresponding LAF (2q11) and 9q21 PlatinumBright495 control probes (Kreatech, Stretton Scientifi c, Stretton, UK).

Statistical analysisWe classifi ed CNS PNETs into molecular subgroups by unsupervised hierarchical clustering, non-negative matrix factorisation,16 and principal component analyses of genes with the highest coeffi cient of variation with the Partek Genomics Suite version 6.5 (Partek, St Louis, MO, USA). We assessed genes enriched within tumour sub groups with a supervised t test adjusted for multiple hypotheses testing with the false-discovery-rate method. Ingenuity pathway analyses were done on supervised gene sets to identify canonical signalling pathways in each tumour subgroup. To establish regions of copy number gains and losses, inferred copy number data were generated with the Illumina Genome studio software and were imported into Partek for copy number variation partitioning-segmentation analyses with a SNP window of 150. We then determined signifi cance of copy number alterations in tumour subgroups with Fisher’s exact test. We used the log-rank analysis with the Kaplan-Meier method to compare survival times and χ² analyses to compare the proportion of survivors across tumour subgroups, whereas ANOVA was used to assess signifi cance of tumour subgroups in relation to age. To analyse the signifi cance of molecular subgroups in relation to sex and metastatic status at diagnosis, we compared features in an individual molecular subgroup to a pooled cohort of the other two molecular subgroups with Fisher’s exact test. Adjustment for multiple testing was not done because patients with complete infor-mation available for every clinical parameter varied. A p value of less than 0·05 was regarded as signifi cant for all analyses. All statistical analyses were done with SPSS version 19·0.

Role of the funding sourceThe sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. DP, SM, RGG, and AH had access to the raw data. The corresponding author had full access to all the data in the study and had fi nal responsibility for the decision to submit for publication.

ResultsUnsupervised hierarchical and non-nega tive matrix factorisation clustering with 200–1000 genes consistently identifi ed three distinct molecular sub groups of CNS PNET, with non-negative matrix factorisation analyses suggesting a strongest cophenetic coeffi cient at k=3 (fi gure 2, appendix). Principle com ponent analyses suggested that group 1 tumours, which have frequent C19MC locus amplifi cation, segregated distinctly, whereas group 2 and 3 tumours showed greater proximity and some overlap (appendix).

Supervised analyses revealed that the three subgroups showed signifi cant diff erences in neural lineage and diff erentiation genes (fi gure 2). Expression profi les of group 1 were most signifi cantly enriched for genes

Subgroup 1 (n=29)

Subgroup 2 (n=36)

Subgroup 3 (n=43)

p value Comparison

Sex

n 29 36 42

Male 11 20 26

Female 18 16 16

Ratio 0·61 1·25 1·63 0·043* Group 1 vs groups 2 and 3

Age at diagnosis

n 26 32 42

Median, years 2·9 7·9 5·9 0·005† Groups 1 vs 2 vs 3

95% CI 2·4–5·2 6·0–9·7 4·9–7·8

≤4 years 20 9 18

>4 years 6 23 24

Ratio 3·33 0·39 0·75 0·001‡ Groups 1 vs 2 vs 3

Metastasis status

n 19 20 19

M0 14 17 9

M+ 5 3 10

Ratio 2·80 5·67 0·90 0·037* Group 3 vs groups 1 and 2

Status

n 26 26 34

Dead 21 20 20

Alive 5 6 14

Ratio 4·20 3·33 1·43 0·13‡ Groups 1 vs 2 vs 3

Survival time

n 20 23 23

Median, years 0·8 1·8 4·3 0·019§ Groups 1 vs 2 vs 3

95% CI 0·5–1·2 1·4–2·3 0·8–7·8

Some patients were not included in analyses because of a lack of specifi c clinical data; details of all patients are shown in the appendix. *Fisher’s exact test. †ANOVA. ‡Pearson’s χ². §Log-rank (Mantel-Cox) test.

Table 1: Clinical and molecular characteristics of children with CNS primitive neuro-ectodermal brain tumours, by molecular subgroup

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associated with embryonic or neural stem cells. Notably, LIN28 and CRABP1,17 which are implicated in stem-cell pluripotency, were among the most overexpressed genes with nearly 20–30 fold greater expression in group 1 as compared with group 2 and group 3. In group 2 tumours, OLIG1/2, SOX8/10, and BCAN (which are markers of oligoneural diff erentiation18) were the most upregulated genes, whereas group 3 tumours showed reduced ex pression of neural diff erentiation genes but upregulation of epithelial and mesenchymal diff erentiation genes including COL1A2, COL5A, FOXJ1,19 and MSX1.20

Pathway enrichment analyses also suggested signifi -cant diff erences in the signalling gene profi les of every tumour subgroup (fi gure 2). Consistent with diff erential enrichment of lineage related genes in tumour sub groups, we noted signifi cant diff erences in expression of axonal guidance genes among the CNS PNET subgroups. Genes involved in WNT signalling were upregulated in group 1 tumours and those involved in SHH signalling were downregulated in group 2 tumours, whereas TGF-β and PTEN signalling pathway genes were specifi cally upregulated in group 3 tumours.

Immunohistochemistry and quantitative RT-PCR analyses showed that LIN28, OLIG2, and IGF2 were highly diff erentially expressed in CNS PNET group 1, group 2, and group 3 tumours, respectively (fi gure 3, appendix). IGF2 protein expression could not be reliably scored on tumour samples (appendix); however, immuno-histochemical analyses for LIN28 and OLIG2 were robust and correlated with gene-expression levels as established by arrays and quantitative RT-PCR analyses. Immunohistochemical analyses on a test cohort of 22 tumours indicated that extent of cytoplasmic LIN28 and nuclear OLIG2 immunostaining also correlated with tumour subgroup assignment based on gene-expression profi les (appendix). LIN28 was expressed at high levels and OLIG2 was expressed at low levels in group 1 tumours, whereas group 2 tumours had high OLIG2 and little LIN28 immunopositivity. LIN28 and OLIG2 protein expression was low or absent in group 3 tumours (fi gure 3).

In the LIN28 and OLIG2 immunohistochemical analyses of an additional 72 primary CNS PNETs with only FFPE samples available for analyses, 15 tumours had inconclusive immuno histochemical analyses (appendix). Overall, we were able to assign 108 of 142 primary CNS PNETs to molecular subgroups on the basis of gene expression or immunohistochemical analyses of LIN28 or OLIG2 protein expression (or both measures; tables 1, 2, appendix). We classifi ed 29 (27%) tumours as group 1, 36 (33%) as group 2, and 43 (40%) as group 3 (appendix). Group 1 tumours with high LIN28 expression generally also expressed high levels of nestin, but had little to no expression of GFAP. GFAP and synaptophysin expression varied substantially between each of the tumour groups and did not consistently correlate with LIN28 or OLIG2 expression. Notably, quantitative RT-PCR

and expression analyses suggested that expression of other neuronal diff erentiation genes also do not diff er signifi cantly among the molecular subgroups of CNS PNETs (appendix). These fi ndings collectively suggest the limitations of conven tional markers to capture the molecular diversity of CNS PNETs.

CNS PNET subgroups have distinct DNA copy number patterns. Apart from the C19MC miRNA amplicon that we previously identifi ed,12 we noted few other recurrent high level copy number gains or amplifi cation. Focal MYCN and CDK4 amplifi cation was detected in isolated tumours. Deletions centred on CDKN2A/2B were the most frequent copy number aberration noted (10 [12%] of 77 tumours; appendix). To establish whether there were characteristic copy number aberrations within the CNS PNET subgroups, we analysed the copy number patterns

Subgroup 1 Subgroup 2 Subgroup 3 p value Comparison

Age ≤4 years

n 20 9 18

Metastasis status

n 15 6 4

M0 11 5 4

M+ 4 1 0

Ratio 2·75 5·00 4·00 0·48* Groups 1 vs 2 vs 3

Status

n 20 7 13

Dead 16 6 10

Alive 4 1 3

Ratio 4·00 6·00 3·33 0·90* Groups 1 vs 2 vs 3

Survival time

n 15 6 6

Median survival, years 1·0 0·8 2·7 0·70† Groups 1 vs 2 vs 3

95% CI 0·7–1·3 0–4·8 1·9–3·5

Age >4 years

n 6 23 24

Metastasis status

n 4 14 15

M0 3 12 5

M+ 1 2 10

Ratio (analysis 1) 3·00 6·00 0·50 0·033* Group 3 vs groups 1 and 2

Ratio (analysis 2) ·· 6·00 0·50 0·014‡ Group 2 vs group 3

Status 6 19 21

Dead 5 14 10

Alive 1 5 11

Ratio (analysis 1) 5·00 2·80 0·91 0·13*

Ratio (analysis 2) ·· 2·80 0·91 0·087‡ Group 2 vs group 3

Survival time

n 5 17 17

Median survival, years 0·5 1·8 4·8 0·004 Groups 1 vs 2 vs 3

95% CI 0·0–1·0 1·5–2·2 1·6–8·0

Some patients were not included in analyses because of a lack of specifi c clinical data; details of all patients are shown in the appendix. *Pearson χ². †Log-rank (Mantel-Cox) test. ‡Fisher’s exact test.

Table 2: Clinical and molecular characteristics of children with CNS primitive neuro-ectodermal brain tumours, by molecular subgroup and age

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of a subset of 59 tumours that could be subgrouped on the basis of LIN28 and OLIG2 gene or protein expression (appendix). Copy number analyses showed that, in addition to chr19q13·41 amplifi cation and chromosome 2 gains, group 1 tumours had frequent gains of chromo-some 3. Group 2 tumour copy number aberration profi les were characterised by more frequent gains of chromosome 8p (p=0·027), 13 (p=0·009), and 20 (p=0·039) compared with group 1 and 2 tumours. Notably, only group 2 and 3 tumours had frequent chromosome 9p loss centred on the CDKN2A/2B locus. Moreover, group 3 tumours showed frequent loss of chromosome 14 (p=0·009). Thus, CNS PNET subgroups correlate with distinct gene expression as well as genomic profi les.

To determine the clinical signifi cance of CNS PNET molecular subgroups, we examined whether subgroups diff ered in patient characteristics and outcome. Of 108 patients for which tumour subgrouping could be established, demographic data on sex, age, survival, and tumour stage were available for 107, 100, 58 and 66 cases, respectively (tables 1 and 2; appendix). Molecular subgroups were associated with distinct clinical pheno-types. Sex and age distribution diff ered between the three molecular CNS PNET subgroups. Group 1 tumours were

more often noted in female patients than were group 2 and group 3 tumours (fi gure 4, table 1). Patients with group 1 and group 2 tumours had bimodal age distributions with peak incidence at opposite age spectra, whereas patients with group 3 tumours had a single peak between 4–8 years. Patients with group 1 tumours were younger than were those with group 2 or group 3 tumours (table 1). 47 (47%) of 100 patients with data for age were 4 years old or younger; however young patients were signifi cantly over-represented in group 1 as compared with group 2 and group 3 (p=0·001; fi gure 4, table 1).

Molecular subgroups of CNS PNET also had signifi cant diff erences in incidence of tumour metastases. Patients with group 3 tumours had the highest incidence of disseminated disease at diagnosis (fi gure 4, table 1). Although metastatic disease is reportedly more frequently in younger children with embryonal brain tumours, analyses done with stratifi cation for age (≤4 years vs >4 years) showed the incidence of tumour metastases diff ered signifi cantly in CNS PNET subgroups diagnosed in older children (fi gure 4, table 2). More patients aged older than 4 years at diagnosis in group 3 presented with metastatic disease than did those in group 1 or group 2 (table 2). The proportion of

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Figure 4: Clinical phenotypes of molecular subgroups of CNS PNETs(A) Sex-specifi c and (B) age-specifi c correlations with tumour subgroup in 108 primary CNS PNET tumours (tables 1, 2, appendix). (C) Metastatic status at diagnosis (58 patients); p value from the two-sided Fisher’s exact test (group 1 vs groups 2 and 3). (D) Metastatic status at diagnosis, stratifi ed by age (58 patients); p values from Pearson’s χ² (group 1 vs group 2 vs group 3). PNET=primitive neuro-ectodermal brain tumour.

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non-metastatic to metastatic tumours in this age group diff ered signifi cantly in comparisons of group 3 to a combined cohort of group 1 and group 2 patients and to group 2 patients alone (fi gure 4, table 2).

Log-rank analysis of all tumour age groups showed that overall survival for patients in group 1 was signifi cantly shorter than it was for patients in group 2 and group 3 (fi gure 5, table 1). With the exception of two longer term survivors, all patients in group 1 were deceased within 4·2 years of diagnosis. Because most group 1 tumours arise in younger children who are often treated hetero-geneously with radiation-sparing thera peutic approaches due to worries of neurocognitive damage,3,21 we examined whether the poor prognostic association of LIN28 expression in group 1 tumours held true for older children who are conventionally prescribed intensifi ed treatment regimens with higher dose craniospinal irradiation. Moreover, because most infant brain tumour protocols enrol patients up to 3–4 years of age,3,4,21 we stratifi ed patients by age at the cutoff of 4 years, to remove age and potential treatment biases on survival. Although overall survival for all young patients was similarly poor, children older than 4 years of age with LIN28 group 1 tumours fared signifi cantly worse (median survival of 0·5 years, 95% CI 0·0–1·0; p=0·004) than did patients older than 4 years of age in group 2 (1·8 years, 1·5–2·2) and group 3 (4·8 years, 1·6–8·0). These fi ndings suggest that immunopositivity for LIN28 identifi es a particularly high risk group of CNS PNET across ages.

DiscussionAdvances in treatment for childhood CNS PNET have been diffi cult because of the low incidence of the disease,9 incomplete understanding of the clinical and biological spectra of disease, and an absence of specifi c markers to aid histopathological diagnoses (panel).8,10 In this study, we aimed to integrate gene expression, copy number, and immuno histochemical analyses to characterise 142 primary hemispheric CNS PNETs. Diff erential expres sion of cell lineage markers, LIN28 and OLIG2, distinguishes three molecular subgroups of CNS PNET and identifi es CNS PNET subgroups at very high risk of metastases and treatment failures and distinct demographic features. Primitive neural group 1 tumours, with frequent C19MC amplifi cation and high LIN28 expression, are distinctly aggressive tumours arising in young children. Oligoneural group 2 tumours, which have high OLIG2 expression, arise in older children and are frequently localised. Mesenchymal group 3 tumours, which have low LIN28 and OLIG2 expression, are associated with a high incidence of metastases and occur at all ages. Group 1 tumours were more frequently in females, whereas group 2 and group 3 tumours arose more frequently in males. These markers are promising molecular identifi ers for childhood CNS PNET that could be applied to refi ne tumour diagnosis, classifi cation, and treatment risk stratifi cation.

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Figure 5: Survival of molecular subgroups of primary CNS primitive neuro-ectodermal brain tumours(A) Overall survival (66 patients). Overall survival, stratifi ed by age 4 years or younger (B; 27 patients) and age older than 4 years (C; 39 patients). p values from the log-rank test (group 1 vs group 2 vs group 3).

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Comprehensive clinical and biological data for a large cohort of primary CNS PNETs are not available. Previous molecular studies have often included a spectrum of CNS PNETs including rare variants, tumours arising in diff erent anatomical locations such as pineoblastomas, and medulloblastoma.8 Because the biological relation of CNS PNET arising in diff erent anatomical locations is unclear, we restricted our study to tumours arising in the cerebral hemispheres, making up most childhood CNS PNET.

Although CNS PNETs are generally regarded as mainly a disease of younger children, analysis of our data suggests that more than 50% of CNS PNETs arise in older children (>4 years) and that CNS PNETs have an age-dependent distribution of molecular subgroups. The strong association of lineage-specifi c gene-expression signatures and age with specifi c tumour subgroups suggests molecular subgroups of hemispheric CNS PNET might derive from diff erent precursor cell stages or type. Specifi cally, transcriptional signatures of group 1

tumours (enriched for CD133, CRABP1, LIN28, and ASCL1) and group 2 tumours (enriched for OLIG1/2) suggest that the former arose from early neural progenitors and the latter from oligoneural progenitors. The possible cellular origin of group 3 CNS PNETs, which are enriched for mesenchymal diff erentiation genes including ZIC222 and LHX2,23 is less clear.

Studies of human brain tumours and brain tumour models suggest that cell lineage-related gene-expression signatures often correlate with and underlie clinical and biological heterogeneity in a spectrum of CNS tumours including malignant gliomas,24 ependymoma,25 and medulloblastoma.26 In addition to age and sex, we noted signifi cant diff erences in survival and metastatic tendency between the three CNS PNET subgroups. We noted poorest survival in the primitive neural group 1 tumours identifi ed on the basis of LIN28 expression, irrespective of age or metastatic status. Together with previous fi ndings that link C19MC amplifi cation with a distinctly aggressive CNS PNET phenotype,12,13 our study further emphasises CNS PNET with C19MC amplifi cation and/or LIN28 expression as a unique clinicopathological entity and suggests LIN28 immuno-histochemistry could be an important, new diagnostic tool for this distinct group of embryonal brain tumours.

Overall survival for group 2 and group 3 tumours, which more commonly presented in older children, did not diff er signifi cantly (p=0·087), but there was some suggestion that children older than 4 years with group 3 mesenchymal lineage tumours had better survival (11 [52%] of 21 children were alive at last assessment) than did those with group 2 tumours (fi ve [26%] of 19 were alive at last assessment; table 2). This fi nding is surprising, because group 3 tumours had the highest incidence of metastases at diagnosis, which is linked to poorer outcomes in medulloblastoma and other embryonal brain tumours. These observations suggest greater sensitivity of group 3 tumours to medulloblastoma-type drugs, which are usually prescribed to older children with CNS PNET, and might suggest greater biological relatedness of group 3 CNS PNETs to medulloblastoma. However, treatment designed for high-risk medullo-blastoma with higher-dose craniospinal irradiation, which is usually prescribed to older children with CNS PNET, might not off er additional therapeutic benefi t for most group 2 CNS PNETs. Our fi ndings underscore the clinical hetero geneity of CNS PNET arising in the cerebral hemisphere and suggest that group 2 and group 3 CNS PNET need diff erent therapeutic approaches tailored to their specifi c biology.

Our study confi rms the documented poor overall outcome of CNS PNETs across age groups and emphasises the need to seek new treatment strategies for this aggressive disease. Pathway enrichment analyses suggested that the non-canonical WNT pathway pre-dominates in group 1 tumours and thus could be an attractive target for treatment. Group 1 tumours also

Panel: Research in context

Systematic reviewWe searched PubMed and Google Scholar for molecular studies of childhood CNS primitive neuro-ectodermal brain tumours (PNETs) published in English between Jan 1, 1985, and Dec 31, 2011, with the search terms “childhood PNET”, “CNS-PNET”, “supratentorial PNET”, and “embryonal brain tumours” (because CNS PNETs were often included in molecular studies of medulloblastoma).

InterpretationPresent treatment strategies for CNS PNETs are largely designed based on the cancer’s close histological similarities to medulloblastoma, although such therapy is not as effi cacious in CNS PNETs. A tailored treatment for CNS PNET is needed to exploit their distinct biology; however, molecular studies of CNS PNETs have been restricted by rare disease incidence and scarcity of robust diagnostic markers. Apart from two recent studies12,14 by our group and a previous review,8 molecular studies of CNS PNETs have been restricted to small cohorts of CNS PNETs from various anatomical sites and diagnoses made on various histopathological criteria. Our integrated genomic analyses of primary CNS PNET tumour samples were restricted to the cerebral hemispheres and tumours that met the 2007 WHO CNS classifi cation criteria for CNS PNET and were confi rmed with genetic methods to be non-rhabdoid tumours. In keeping with previous clinical observations, we show that CNS PNET makes up a heterogeneous spectrum of tumours and defi nes three molecular subtypes of CNS PNETs with distinct survival and metastatic features. Our study provides the fi rst molecular prognostic markers for CNS PNET, and is a substantial advance towards biology-driven treatment strategies for this disease.

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showed signifi cant upregulation of the SHH signalling pathway, suggesting that novel SHH pathway inhibitors in clinical trials27 might be attractive new drugs for this subgroup. Of note, CRABP1, a retinoid-binding protein known to change retinoic metabolism and confer all-trans retinoic acid resistance,28 is very highly expressed in the group 1 CNS PNET. Thus retinoic acid, which is being tested in high-risk medulloblastoma and CNS PNET cooperative group clinical trials for older children, might be of little therapeutic benefi t in group 1 CNS PNETs.

By contrast with group 3 tumours that showed upregu-lation of several canonical pathways, the oligoneural group 2 tumours showed downregulation of both SHH and WNT signalling pathways. Although we noted higher expression of PDGFRA and ERBB3 in this subgroup (appendix), pathway analyses did not show signifi cant global enrichment of receptor tyrosine sig nalling pathways. However, these potential therapeutic pathways might emerge with studies of larger cohorts, and further delineation of CNS PNET subgroups. Consistent with the higher incidence of metastases noted in group 3 tumours, we identifi ed substantial activation of semaphorin signalling genes in this tumour group. In addition to activated TGFβ signalling, group 3 tumours showed upregulation of PTEN signalling and IGF2 expression, thus making these pathways and genes attractive targets for potential subgroup specifi c therapies.

Our data show that hemispheric tumours diagnosed as CNS PNET in children can be diff erentiated into subgroups with distinct survival and metastatic charac-teristics on the basis of lineage markers, LIN28 and OLIG2. Because our study was restricted to hemispheric CNS PNETs, assessment of the signifi cance of these molecular groupings to non-hemispheric CNS PNETs, such as pineoblastoma, will be important. Our study was limited by sample size and the absence of an independent validation cohort due to the rare incidence of CNS PNET. Nonetheless, we anticipate that LIN28 and OLIG2, which are the fi rst molecular markers reported for CNS PNET to date, will help identify high-risk group 1 tumours for new therapies, allow tailoring of chemoradiotherapy for patients with group 2 and group 3 tumours (which diff er strikingly in metastatic potential), and will help establish a working classifi cation of CNS PNETs. Our report under scores the importance of concerted, collaborative eff orts to study large retrospective cohorts of tumours and patients to accelerate biological and ultimately therapeutic studies of rare tumours.

ContributorsAH, DP, RGG designed the study. AH and RGG procured fi nancial

support. CH, RGG, AG, SMP, EB, AK, JC, LL-C, CE, HT, JF, S-KK, Y-SR,

TVM, CCL, SLP, H-KN, CJ, SCC, JTH, XF, KMM, HN, and AH provided

study materials or patients. DP, AH, SM, CH, and PZH analysed and

interpreted the data. DP, AH and SM wrote the report; AH, SM, SMP

and RGG reviewed and edited the fi nal report. All authors approved the

fi nal manuscript.

Confl icts of interestWe declare that we have no confl icts of interest.

AcknowledgmentsFunding was received from the Canadian Institute of Health Research

(grant number 102684) and Brainchild (AH), and the Samantha Dickson

Brain Tumour Trust, grant number 17/53 (RG). We are grateful for the

assistance of clinicians from the Children’s Cancer Leukaemia Group

centres and Biological studies committee, the neuropathological review

by Keith Robson and James Lowe, Jennifer Ward for FISH studies

(Children’s Brain Tumour Research Centre, University of Nottingham,

Nottingham, UK), statistical consultations from Derek Stephen

(Statistical Support Unit, Hospital for Sick Children, Toronto, ON,

Canada), and technical help from Jonathon Torchia (Hospital for Sick,

Children, Toronto, ON, Canada).

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