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Genomic and Epigenetic Characterisation of Childhood Ependymoma

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Genomic and Epigenetic Characterisation of Childhood Ependymoma GENOMIC AND EPIGENETIC CHARACTERISATION OF CHILDHOOD EPENDYMOMA John-Paul Kilday, MBChB MRCPCH Thesis submitted to the University of Nottingham for the degree of Doctor of Philosophy December 2011 i This thesis is dedicated to the children and their families whose lives have been affected by brain tumours ii ABSTRACT iv ACKNOWLEDGEMENTS v CONTENTS vi LIST OF FIGURES xii LIST OF TABLES xix ABBREVIATIONS (AND NOTE) xxiv iii ABSTRACT Paediatric ependymomas remain a clinical management challenge, with a relatively poor prognosis when compared to other childhood tumours of the central nervous system. An improved understanding of underlying ependymoma biology may identify new correlates of outcome and potential therapeutic targets. To address this, AffymetrixTM 500K SNP arrays were used to establish the nature and range of genomic imbalances in 63 paediatric ependymomas (42 primary and 21 recurrent). Over 80 % of tumours were analysed against patient-matched constitutional DNA. In addition, the Illumina® GoldenGate® Cancer Panel I array was used to identify differences in methylation profile across 98 paediatric ependymomas (73 primary and 25 recurrent). While collective assessment revealed the most common anomalies, specific aberrations were characteristic of certain ependymoma subgroups, particularly those relating to tumour location, patient age, disease recurrence and patient prognosis. The genomic imbalance of 15 selected candidate genes (NSL1, DNAJC25, NAV1, CDKN2A, CHI3L1, HOXA5, TXN, BNIPL, and PRUNE) were confirmed by quantitative Polymerase Chain Reaction. Genomic gain involving regions of chromosome 1q were associated with an unfavourable patient outcome, such as the focal locus on 1q21.3 encompassing PRUNE. The genomic gain of PRUNE correlated with an increased encoded protein expression, as assessed by immunohistochemistry (IHC). This adverse prognostic association with 1q was upheld in the subsequent part of this work. Fluorescent in situ hybridisation and IHC were used to evaluate a panel of six putative prognostic markers (1q25 gain, PRUNE, Tenascin-C, Nucleolin, Ki-67 and NAV1 expression) across a paediatric intracranial ependymoma tissue microarray cohort of 107 primary tumours treated within the confines of two aged defined clinical trials (UK CCLG 1992 04 and SIOP 1999 04). Within the younger UK CCLG 1992 04 cohort, copy number gain of chromosome 1q25 and PRUNE overexpression were independently associated with an increased risk of disease progression, while strong PRUNE expression was also an independent marker of worse overall survival. In addition, increased Tenascin-C expression correlated with a reduced overall survival on univariate analysis. For older children in the SIOP 1999 04 cohort, strong PRUNE expression in ependymomas was again identified as an adverse prognostic marker, correlating with increased mortality on univariate assessment. iv ACKNOWLEGDEMENTS I would like to thank numerous people for their help, advice and support throughout this PhD. Particular thanks must go to principal supervisor Professor Richard Grundy for allowing me to work as one of his PhD students. His encouragement, insight, advice and guidance were much invaluable and much appreciated. I am also extremely grateful to my second supervisor Dr. Beth Coyle for being a source of knowledgeable support. I am extremely grateful to Dr. Richard Gilbertson and his laboratory (St. Jude’s Hospital, Memphis, Tennessee, USA) for their collaborative support in providing a proportion of the SNP array data. I must also thank Dr. Steve Clifford and Dr. Ed Schwalbe (Northern Institute for Cancer Research) for their guidance with the methylation array data analysis, Dr. Sarah Nicholson (CCLG) for TMA construction and assisting me retrieve patient clinical information and Dr. Felipe Andreiuolo (Institut Gustav-Roussy) for his collaborative expertise with Tenascin-C IHC staining and scoring. All the members of staff at the Children’s Brain Tumour Research Centre have helped me at some stage throughout this work and I am extremely thankful to you all. Special thanks must go to Dr. Hazel Rodgers and Dr. Suzanne Miller for listening to my numerous questions and supporting me with the thesis presentation. Others that must be mentioned for their technical support and scientific advice include Dr. Martyna Adamowicz, Dr. Ruman Rahman, Dr. Jennifer Barrow, Mr. Stuart Smith, Biswaroop Mistra, Dr. Lisa Storer, Professor James Lowe, Dr. Keith Sibson and Lee Ridley. Thanks also to Dr Alain Pitiot and Francois Morvillier at the Brain and Body centre for allowing me to use the SNPview computer program. A special thank you must also be reserved for my family. Their enduring patience, love and support were much appreciated – thanks Mum, Dad, Anne-Marie and Laura. Finally I would like to express my gratitude to the James Tudor Foundation for funding this research. v CHAPTER 1: INTRODUCTION 1 1.1 Paediatric brain tumours 2 1.1.1 Background 2 1.1.2 Neural development 3 1.1.3 Brain tumour stem cells 6 1.2 Clinical and pathological aspects of paediatric ependymoma 7 1.2.1 Epidemiology 7 1.2.2 Tumour location 8 1.2.3 Aetiology and predisposing syndromes 8 1.2.4 Histology and ependymoma classification 9 1.2.5 Treatment of paediatric intracranial ependymoma 13 1.2.6 Survival and clinicopathological prognostic markers 16 1.3 Genetics of cancer 17 1.3.1 Oncogenes and tumour suppressor genes 17 1.3.2 Loss of heterozygosity 19 1.3.3 Epigenetics 20 1.4 Increasing resolution of genomic profiling 22 1.4.1 Comparative genomic hybridisation 22 1.4.2 Array comparative genomic hybridisation 24 1.4.3 Single nucleotide polymorphism arrays 25 1.5 Biological aspects of paediatric ependymoma 27 1.5.1 Cytogenetic studies – ependymoma karyotypes 27 1.5.2 Comparative genomic hybridisation meta-analysis 29 1.5.2.1 Paediatric versus adult ependymomas 30 1.5.2.2 Paediatric ependymomas from different CNS locations 35 1.5.3 Higher resolution genomic analyses of paediatric ependymoma 35 1.5.4 Methylation analyses of ependymoma 38 1.5.5 Ependymoma-initiating cells 39 1.5.6 Cell signalling pathways 41 1.5.7 Biological prognostic markers for paediatric ependymoma 43 1.6 Summary and aims 55 CHAPTER 2: MATERIALS AND METHODS 56 2.1 DNA extraction 57 2.1.1 Frozen ependymoma cohort 57 2.1.2 Tumour tissue assessment 57 2.1.3 DNA extraction from frozen tumour tissue 59 2.1.4 DNA extraction from blood 59 2.1.5 DNA quantification and quality control 60 vi 2.2 The 500K AffymetrixTM SNP array 61 2.2.1 500K SNP array protocol overview 61 2.2.2 SNP array scanning and initial data interpretation 62 2.2.3 SNP array analysis – GTYPE 63 2.2.4 SNP array analysis – CNAG 64 2.2.5 SNP array analysis – data visualisation software 66 2.3 Illumina® GoldenGate® Cancer Panel assay for methylation 67 2.3.1 GoldenGate® Cancer Panel assay protocol overview 67 2.3.2 Methylation array scanning 70 2.3.3 Methylation array quality control 71 2.3.4 Methylation array initial data interpretation 74 2.4 Real time quantitative Polymerase Chain Reaction 75 2.4.1 Primer optimisation 77 2.4.2 Primer efficiencies 78 2.4.3 PCR reactions on tumour and blood samples 82 2.4.4 Quantification of target gene copy number in tumour samples 82 2.5 Fluorescent in situ hybridisation 83 2.6 Immunohistochemistry 85 2.7Statistics 87 2.7.1 Associations of variables in two way tables 88 2.7.2 Associations with patient age 88 2.7.3 Correlation of SNP array and qPCR results 88 2.7.4 Correlation of methylation array technical replicates 89 2.7.5 Kappa measure of agreement 89 2.7.6 Survival analysis 89 2.7.7 Differential methylation analysis between clinical subgroups 90 CHAPTER 3: GENOME-WIDE CHARACTERISATION OF GENOMIC 91 AND EPIGENETIC ABERRATIONS IN PAEDIATRIC EPENDYMOMA 3.1 Introduction 92 3.2 Materials and methods 93 3.2.1 500K SNP array analysis of 63 paediatric ependymomas 93 3.2.1.1 The sample cohort 93 3.2.1.2 Global genomic imbalance data analysis 97 3.2.1.3 SNP array call rates 98 3.2.1.4 FISH validation of SNP array findings 98 3.2.2 Methylation array analysis of 98 paediatric ependymomas 98 3.2.2.1 The sample cohort 98 3.2.2.2 Methylation array data analysis 103 3.2.2.3 Assessment of methylation intra-assay reproducibility 103 3.2.3 Statistical analysis 104 vii 3.3 Results 104 3.3.1 Clinical associations: the SNP array primary tumour cohort 104 3.3.2 Clinical associations: the methylation array primary tumour cohort 105 3.3.3 Chromosomal arm imbalance in 63 paediatric ependymomas 106 3.3.4 Association of chromosomal arm imbalances with clinical subgroups 111 3.3.5 Association of cytoband imbalances with clinical subgroups 114 3.3.6 FISH validation of chromosome 1q gain results from the SNP array 120 3.3.7 Methylation cluster analysis identifies clinically relevant subgroups 122 3.4 Discussion 126 3.4.1 Aberrations in paediatric ependymomas from different CNS locations 128 3.4.2 Aberrations in ependymomas from younger and older children 131 3.4.3 Aberrations in potential prognostic groups 133 3.4.3.1 Cytoband imbalance group two / chromosome 1q gain 133 3.4.3.1.1 Refining the SNP definition of 1q gain 135 3.4.3.2 Cytoband imbalance group three 136 3.4.3.3 Other aberrations in recurrent intracranial ependymomas 137 3.5 Summary 138 CHAPTER 4: PUTATATIVE CANDIDATE GENES IN THE 140 PATHOGENESIS OF PAEDIATRIC EPENDYMOMA 4.1 Introduction 141 4.2 Materials and methods 144 4.2.1 500K SNP array analysis of 45 paediatric ependymomas 144 4.2.1.1 The sample cohort 144 4.2.1.2 Genomic imbalance data analysis – gene list formation 145 4.2.1.3 aUPD data analysis
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