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Universitat Aut`Onoma De Barcelona Towards Objective Human Brain

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Universitat Aut`Onoma De Barcelona Towards Objective Human Brain Universitat Aut`onoma de Barcelona Departament de Bioqu´ımica i Biologia Molecular Towards Objective Human Brain Tumours Classification using DNA microarrays Dissertation for the degree of Doctor of Biochemistry and Molecular Biology presented by Xavier Castells Domingo This work was performed at the Department of Biochemistry and Molecular Biology of the Universitat Aut`onoma de Barcelona under the supervision of Dr. Carles Ar´us Caralt´o, Dr. Joaqu´ın Ari˜no Carmona and Dr. Anna Barcel´o Vernet Undersigned by Dr. Carles Ar´us Caralt´o Dr. Joaqu´ın Ari˜no Carmona Dr. Anna Barcel´oVernet Xavier Castells Domingo Cerdanyola del Vall`es, 7 May 2009 En agra¨ıment a totes les persones que van decidir donar un tros de bi`opsia al noste grup. Especialment vull dedicar aquesta tesi a la mem`oriade les persones que han mort durant el transcurs de la meva tesi, i de les quals he tingut el gran honor de poder extreure RNA de les seves bi`opsies. Contents Abbreviations xvii 1 INTRODUCTION1 INTRODUCTION1 1.1 Overview on human brain tumours (HBT) . .3 1.1.1 Incidence and mortality of HBT . .3 1.1.2 Description of HBT . .3 1.1.2.1 Diagnosis of HBT in current clinical practice . .3 1.1.2.2 Overview on HBT classification . .5 1.1.3 World Health Organization (WHO) classification criteria . .5 1.1.3.1 Historical overview . .5 1.1.3.2 Entities, variants and patterns . .6 1.1.3.3 Malignancy grade schemes . .7 1.1.3.4 Differences between classification schemes . .7 1.1.3.5 Survival of patients suffering from HBT . .9 1.2 HBT targets for genetic diagnosis implementation . 10 1.2.1 Glialtumours .......................... 10 1.2.1.1 Overview on gliomas . 10 1.2.1.2 Malignancy grades . 10 1.2.1.3 Molecular genetics . 12 1.2.1.4 Treatment of gliomas . 14 1.2.2 Meningeal tumours . 15 1.2.2.1 Overview on meningiomas . 15 1.2.2.2 Malignancy grades . 15 1.2.2.3 Genetic alterations . 16 1.2.2.4 Treatment of meningiomas . 17 1.2.3 Mouse models to study HBT . 17 viii CONTENTS 1.2.3.1 Xenograft tumour models . 17 1.2.3.2 Genetically engineered mice (GEM) . 18 1.2.3.3 Application of mice xenografts to model ischaemia in HBT . 18 1.3 Microarray technology . 19 1.3.1 An insight on microarray technology . 19 1.3.1.1 Historical remarks . 19 1.3.1.2 Principle of the technology . 19 1.3.1.3 Impact of gene-expression microarrays in research . 19 1.3.1.4 Validation of gene-expression microarrays . 21 1.3.1.5 Type of microarray experiments . 21 1.3.2 Spotted-based microarrays . 22 1.3.2.1 Introductory insights . 22 1.3.2.2 Manufacturing . 22 1.3.2.3 Sample labelling, hybridisation and image scanning 22 1.3.3 In situ synthesized-based microarrays . 23 1.3.3.1 Introductory insights . 23 1.3.3.2 Manufacturing of Affymetrix microchips . 23 1.3.3.3 Manufacturing of Agilent technologies microchips . 24 1.3.3.4 Sample labelling, hybridisation and image scanning 25 1.3.3.5 Other in situ synthesis-based microchips . 26 1.4 Microarray data analysis . 27 1.4.1 Rlanguage ........................... 27 1.4.1.1 Language definition . 27 1.4.1.2 Advantages . 27 1.4.1.3 Use of R . 27 1.4.1.4 The Bioconductor repository . 28 1.4.2 Data pre-processing . 28 1.4.2.1 Spotted-based microarrays image processing . 28 1.4.2.2 Affymetrix GeneChip image processing . 31 1.4.2.3 Alternative methods for image processing for Affy- metrix GeneChips . 35 1.4.3 Data normalisation . 35 1.4.3.1 Scope of normalisation . 35 1.4.3.2 Global or scale normalisation . 36 CONTENTS ix 1.4.3.3 Local weighted and scatterplot smoothing (lowess) normalisation . 36 1.4.3.4 Quantile normalisation . 37 1.4.3.5 Non-linear normalisation . 37 1.4.4 Feature selection . 37 1.4.4.1 Introductory remarks . 37 1.4.4.2 Fold-change ratio . 39 1.4.4.3 Statistical significance analysis . 39 1.4.4.4 Multiple-test adjustment . 39 1.4.4.5 Principal component analysis (PCA) . 40 1.4.5 Classification methods . 41 1.4.5.1 Introductory remarks . 41 1.4.5.2 Unsupervised classification . 41 1.4.5.3 Supervised classification . 41 1.4.5.4 Procedure to estimate the classification accuracy . 43 1.5 Implementation of microarray-data results in clinical practise . 47 1.5.1 RNA stability in human biopsies . 47 1.5.1.1 RNA quality . 47 1.5.1.2 RNA integrity . 47 1.5.1.3 Studies on RNA integrity . 49 1.5.2 Improvement of diagnosis and prognosis of HBT by microarray- based data . 51 1.5.3 Developed clinical trials based on microarray-data . 52 1.5.3.1 Relevance of clinical trials . 52 1.5.3.2 Steps in clinical trials . 52 1.5.3.3 Standardization of microarray data prior to clinical trials.......................... 53 1.5.3.4 Reported clinical trials based on microarray data . 54 1.5.4 Contribution of eTUMOUR, HealthAgents and MEDIVO2 projects to improve diagnosis and prognosis of HBT . 54 1.5.4.1 The eTUMOUR project . 54 1.5.4.2 The HealthAgents project . 55 1.5.4.3 The MEDIVO2 project . 55 2 OBJECTIVES 57 3 MATERIALS AND METHODS 59 x CONTENTS MATERIALS AND METHODS 61 3.1 Collection, storage and histopathology analysis of samples . 61 3.1.1 Collection and storage of samples . 61 3.1.2 Histopathologycal analysis of samples . 61 3.1.3 Storage of data at the eTUMOUR and HealthAgents databases 61 3.2 RNAisolation.............................. 62 3.2.1 Isothiocyanate-based RNA isolation (Qiagen) . 62 3.2.2 Acid-Phenol:Chloroform-based RNA isolation (Ambion) . 63 3.2.3 Evaluation of RNA quality . 63 3.3 Labelling and scanning . 64 3.3.1 Single-Cy3 cDNA microarray labelling . 64 3.3.2 Affymetrix labelling . 65 3.4 Prediction of Gbm and Mm using single-labe-lling cDNA microar- raysdata................................. 66 3.4.1 Foreword ............................ 66 3.4.2 Data pre-processing . 66 3.4.3 Feature selection and sample classification . 67 3.4.4 Functional analysis of gene signatures . 67 3.4.5 RT-PCR validation . 68 3.5 Exploratory analysis of meningioma and glial tumours using Affy- metrixdata ............................... 68 3.5.1 Data pre-processing . 68 3.5.1.1 Background correction and data normalisation . 68 3.5.2 Generation of prediction models . 69 3.5.2.1 Grouping of samples . 69 3.5.2.2 Statistical analysis . 69 3.5.3 Glioblastoma subtypes . 72 3.5.3.1 Assessment of statistically significance of clusters . 72 3.5.3.2 Data pre-processing of NMR data . 72 3.6 Generation of a murine glial tumour model to simulate ex vivo is- chaemia at normal body temperature in brain tumour biopsies . 73 3.6.1 Animals and cells . 73 3.6.2 Inoculation of the mice brain with GL261 tumour glial cells 73 3.6.3 Experimental procedure to simulate ex vivo ischaemia at nor- mal body temperature of brain tumour samples . 74 3.6.3.1 Animal sacrifice and encephalon removal . 74 CONTENTS xi 3.6.3.2 Dissection of the tumour mass . 74 3.6.3.3 Simulation of ex vivo ischaemia at normal body temperature . 75 3.7 Simulation of ex vivo ischaemia at normal body temperature in C6 cells ................................... 76 3.7.1 Culture and harvesting of C6 cells . 76 3.7.2 Simulation of ex vivo normal body temperature ischaemia . 76 4 RESULTS AND DISCUSSION 77 RESULTS AND DISCUSSION 79 4.1 Discrimination of Gbm and Mm using cDNA-microarrays data . 79 4.1.1 Results.............................. 79 4.1.1.1 Collection of biopsies . 79 4.1.1.2 Pre-processing and prediction results . 79 4.1.1.3 Molecular characterization of Gbm and Mm biopsy cases . 83 4.1.1.4 RT-PCR expression results . 87 4.1.1.5 Expression level of GFAP, PTPRZ1, GPM6B and PRELP in Affymetrix-based hybridisation cases . 88 4.1.1.6 Verification of the formula robustness by prediction of Affymetrix-based hybridisation cases . 90 4.1.2 Discussion . 91 4.1.2.1 Development of an automated predictor based on gene signatures of brain tumours . 91 4.1.2.2 Molecular signature characteristics of Gbm and Mm 93 4.2 Exploratory analysis of meningioma and glial tumours using Affy- metrixdata ............................... 96 4.2.1 Results and Discussion . 96 4.2.1.1 Collection of biopsies . 96 4.2.1.2 Selection of cases . 96 4.2.1.3 Grouping of samples . 96 4.2.1.4 Evaluation of RNA integrity of collected biopsies . 97 4.2.1.5 Data pre-processing . 102 4.2.1.6 Data prediction . 105 4.2.1.7 Molecular signature characteristics of meningeal and glialtumours ..................... 110 xii CONTENTS 4.3 Exploratory analysis of molecular subtypes of glioblastoma . 114 4.3.1 Determination of clusters of glioblastoma . 114 4.3.2 Correlation with in vivo and ex vivo 1H-magnetic resonance data ............................... 128 4.3.3 HRMAS data . 128 4.3.4 In vivo NMR data . 130 4.4 Simulation of ex vivo ischaemia at normal body temperature in brain tumour samples of mice and C6 cells . 135 5 CONCLUSIONS 139 6 ANNEXES 159 ANNEXES 159 A-1 Collection of clinical and histopathological data in eTUMOUR . 162 A-2 Description of the eTUMOUR database .
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