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Genome-Wide Molecular Characterisation of Spnet

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Genome-Wide Molecular Characterisation of Spnet Genome-wide molecular characterisation of central nervous system primitive neuroectodermal tumours and pineoblastomas Suzanne Miller Thesis presented for the degree of Doctor of Philosophy The University of Nottingham June 2010 This thesis is dedicated to the children and their families whose lives have been affected by brain tumours ii CONTENTS iii ABSTRACT ix ACKNOWLEDGEMENTS x LIST OF ILLUSTRATIONS xi LIST OF TABLES xvi LIST OF ABBREVIATIONS xx CHAPTER 1: INTRODUCTION 1 1.1 Paediatric brain tumours 2 1.1.1 General background and epidemiology 2 1.1.2 Neuroembryology 5 1.1.3 WHO grading and classification 9 1.1.4 Embryonal CNS tumours 11 1.1.5 The ‘PNET’ entity - A historical perspective 15 1.1.6 Histological subtypes and markers 21 1.1.7 Genetic subtypes and markers 25 1.2 Familial syndromes predisposing to paediatric brain tumours 29 1.2.1 Li-Fraumeni syndrome 31 1.2.2 Turcot syndrome 31 1.2.3 Gorlin syndrome 32 1.2.4 Rhabdoid tumour predisposition 32 1.3 Clinical aspects of CNS PNET and pineoblastoma 34 1.3.1 Patient age 34 1.3.2 Tumour location 35 1.3.3 Metastatic disease 38 1.3.4 Treatments and survival 39 1.4 Cancer genetics 42 1.4.1 Oncogenes 43 1.4.2 Tumour suppressor genes 43 1.4.3 Loss of heterozygosity 44 1.4.4 Epigenetics 47 1.5 Genetic alterations of CNS PNET and pineoblastoma 48 1.5.1 Cytogenetics – CNS PNET and pineoblastoma karyotypes 48 1.5.2 Comparative genomic hybridisation 53 1.5.3 Array CGH 58 1.5.4 SNP arrays 63 1.5.5 Gene mutation 66 iii 1.5.6 Loss of heterozygosity 67 1.5.7 Gene expression 67 1.5.8 Epigenetics 70 1.6 Genetic alterations of medulloblastoma 72 1.7 Direct comparisons of the genetics of CNS PNET and medulloblastoma 73 1.8 Altered pathway activity and telomere dysfunction in intracranial PNETs 75 1.8.1 Shh-Gli pathway 75 1.8.2 The Wnt pathway 77 1.8.3 The Notch-Hes pathway 78 1.8.4 Telomeric alteration 78 1.9 High resolution genetic analysis of CNS PNET and pineoblastoma 79 1.9.1 Project objectives 79 CHAPTER 2: MATERIALS AND METHODS 80 2.1 Clinical samples entered into the SNP array analysis 81 2.2 Sample preparation – ‘tumour’ assessment 81 2.3 DNA extraction 82 2.3.1 DNA extraction from fresh/frozen tumour tissue 82 2.3.2 DNA extraction from blood 83 2.3.3 DNA extraction from cell line pellets 84 2.3.4 DNA extraction - clean up and precipitation of DNA 84 2.4 100K and 500K Affymetrix SNP array protocol 84 2.4.1 100K and 500K SNP array protocol overview 84 2.4.2 Room set up 86 2.4.3 DNA digestion 86 2.4.4 DNA ligation 88 2.4.5 Polymerase chain reaction 89 2.4.6 PCR purification and elution 91 2.4.7 Quantification of purified PCR product 92 2.4.8 Fragmentation and end labeling 92 2.4.9 Hybridisation 94 2.4.10 Array washing and staining 95 2.4.11 Scanning and interpretation of results 97 2.4.12 SNP array analysis – GTYPE 98 2.4.13 SNP array analysis – CNAG 99 2.4.14 SNP array analysis – Spotfire® 100 2.4.15 SNPview 102 2.4.16 aUPD analysis 102 iv 2.5 Validation of SNP array results – real time PCR 103 2.5.1 Primer design 103 2.5.2 Primer optimisation 105 2.5.3 Primer efficiencies 105 2.5.4 Quantification of copy number 109 2.6 Immunohistochemistry 110 2.7 Sequencing 111 2.7.1 Primer optimisation 111 2.7.2 PCR clean up – exoSAP 112 2.7.3 Big dye sequencing reaction 112 2.7.4 Precipitation 113 2.7.5 Sequencing scan 114 2.8 Statistics 114 2.8.1 Associations in two way frequency Tables – Fisher’s exact test 114 2.8.2 Associations with age – independent samples t-test 115 2.8.3 Survival analysis – Kaplan-Meier 115 2.8.4 Extent of chromosome arm imbalance in patients of different ages with CNS PNET and pineoblastoma 115 2.8.5 Real time PCR vs SNP array results – Spearman’s rank correlation coefficient 115 2.8.6 Power calculations 115 CHAPTER 3: CLINICAL ASPECTS OF CNS PNET AND PINEOBLASTOMA 116 3.1 Introduction 117 3.2 CNS PNET and pineoblastoma patient clinical overview 117 3.3 Discussion 120 CHAPTER 4: GENOME-WIDE APPROACH TO CHARACTERISING CNS PNET AND PINEOBLASTOMA 122 4.1 Introduction 123 4.2 Materials and methods 125 4.2.1 100K and 500K SNP array analysis of 46 CNS PNETs and pineoblastomas 125 4.2.2 SNP call rates 127 4.3 Results 131 4.3.1 Chromosome arm imbalance in 46 CNS PNETs and pineoblastoma 131 4.3.2 Unsupervised hierarchical clustering of cytoband copy numbers for 25 primary CNS PNETs and pineoblastomas 142 4.4 Discussion 145 v CHAPTER 5: MAINTAINED AND ACQUIRED ALTERATIONS IN PRIMARY AND RECURRENT CNS PNET PAIRS 154 5.1 Introduction 155 5.2 Materials and methods 157 5.3 Results 157 5.3.1 Chromosome arm imbalance identified in 5 primary and recurrent CNS PNET pairs 157 5.3.2 Common regions of maintained copy number alteration in 5 primary and recurrent CNS PNET pairs 159 5.3.3 Common regions of acquired copy number alterations in 5 primary and recurrent CNS PNET pairs 163 5.4 Discussion 166 CHAPTER 6: REGIONS ENCOMPASSING CANDIDATE GENES POTENTIALLY INVOLVED IN THE PATHOGENESIS OF CNS PNET AND PINEOBLASTOMA 169 6.1 Introduction 170 6.2 Materials and methods 171 6.3 Results 171 6.3.1 Gene copy number imbalance in CNS PNET and pineoblastoma 171 6.3.1.1 Genomic regions encompassing candidate gene gain in primary CNS PNETs analysed using 100K SNP arrays 171 6.3.1.2 Genomic regions encompassing candidate gene loss in primary CNS PNETs analysed using 100K SNP arrays 175 6.3.1.3 Genomic regions encompassing candidate gene gain in primary CNS PNETs analysed using 500K SNP arrays 178 6.3.1.4 Genomic regions encompassing candidate gene loss in primary CNS PNETs analysed using 500K SNP arrays 182 6.3.1.5 Genomic regions encompassing candidate gene gain in recurrent CNS PNETs analysed using 100K SNP arrays 186 6.3.1.6 Genomic regions encompassing candidate gene loss in recurrent CNS PNETs analysed using 100K SNP arrays 190 6.3.1.7 Genomic regions encompassing candidate gene gain in primary pineoblastomas analysed using 100K SNP arrays 193 6.3.1.8 Genomic regions encompassing candidate gene loss in primary pineoblastomas analysed using 100K SNP arrays 196 6.3.1.9 Genomic regions encompassing candidate gene gain in Recurrent pineoblastomas analysed using 100K SNP arrays 198 6.3.1.10 Genomic regions encompassing candidate gene loss in Recurrent pineoblastomas analysed using 100K SNP arrays 202 6.3.1.11 Identfication of candidate regions of high level gain/amplification in CNS PNET and pineoblastoma 202 6.3.1.12 Identification of candidate regions of homozygous loss in CNS PNETs and pineoblastomas 205 vi 6.3.2 Combination of copy number and LOH results to identify regions of aUPD in 15 paired CNS PNETs 208 6.4 Discussion 214 CHAPTER 7: CONFIRMATION OF CHROMOSOMAL REGIONS AND GENES OF INTEREST 222 7.1 Introduction 223 7.2 Materials and methods 224 7.2.1 aCGH and SNP array result comparison 224 7.2.2 Real time qPCR validation 224 7.2.3 p15INK4B immunohistochemistry 224 7.2.4 INI1 immunohistochemistry 227 7.2.5 INI1 sequencing 228 7.3 Results 228 7.3.1 aCGH comparison of CNS PNETs analysed using the Affymetrix SNP array platform 228 7.3.2 Real time qPCR validation of SNP array results 230 7.3.2.1 Real time qPCR validation of PCDHGA3 gain 230 7.3.2.2 Real time qPCR validation of FAM129A gain 230 7.3.2.3 Real time qPCR validation of PDGFRA amplification 233 7.3.2.4 Real time qPCR validation of MYCN amplification 233 7.3.2.5 Real time qPCR validation of OR4C12 loss 236 7.3.2.6 Real time qPCR validation of CADPS loss 236 7.3.2.7 Real time qPCR validation of SALL1 loss 236 7.3.2.8 Real time qPCR validation of CDKN2A loss 240 7.3.2.9 Real time qPCR validation of CDKN2B loss 240 7.3.3 Statistical associations identified between SNP array and real time qPCR derived gene copy number alterations and patient clinical characteristics 243 7.3.4 Immunohistochemistry of CDKN2B (p15INK4B) 245 7.3.5 Immunohistochemistry of INI1 247 7.3.6 INI1 sequencing 249 7.4 Discussion 253 CHAPTER 8: SUMMARY AND CONCLUSIONS 259 8.1 Final discussion 260 8.1.1 CNS PNETs arising in patients of different ages are genetically distinct 263 8.1.2 CNS PNETs and pineoblastomas are genetically distinct 264 8.1.3 Characterising the genetic alterations of recurrent CNS PNETs will identify genes involved in tumour progression and biologically adverse behaviour 266 8.1.4 Characterising the genetic alterations of metastatic CNS PNETs will identify genes involved in the development of metastatic disease 267 8.1.5 CNS PNETs and medulloblastomas are distinct entities at the vii genetic level 268 8.1.6 Paediatric embryonal brain tumours are a spectrum of diseases and not clearly defined entities 271 8.2 Study limitations 272 8.3 Future work 275 BIBLIOGRAPHY 279 APPENDIX 297 viii ABSTRACT CNS PNET and pineoblastomas are highly malignant embryonal brain tumours of poor prognosis.
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