Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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9.1. Digital Profiling of GNS Cell Lines Discussion contain a GNS component of relevance for prognosis. Five individual GNS signature genes were significantly associated with survival of glioblastoma patients in both of the two largest data sets: PLS3, HOXD10, TUSC3, PDE1C and the well-studied tumour suppressor PTEN. PLS3 (T-plastin) regulates actin organisation and its over-expression in the CV-1 cell line resulted in partial loss of adherence [26]. Elevated PLS3 expression in GNS cells may thus be relevant for the invasive phenotype. The association between transcriptional up-regulation of HOXD10 and poor survival is surprising, because HOXD10 protein levels are suppressed by a microRNA (miR-10b), which is highly expressed in gliomas, and it has been suggested that HOXD10 suppression by miR-10b promotes invasion [476]. Notably, in our microRNA microar- ray dataset microRNA miR-10b is up-regulated three-fold in GNS cell lines with respect to NS cell lines (Table 6.8; Appendix F) and the HOXD10 mRNA up-regulation we observe in GNS lines also occurs in glioblastoma tumours, as shown by comparison with grade III astrocytoma (Fig 7.4b). Similarly to our findings, miR-10b is present at higher levels in glioblastoma compared to gliomas of lower grade [476]. It is conceivable that HOXD10 transcriptional up-regulation and post-transcriptional suppression is indicative of a regulatory program associated with poor prognosis in glioma. Also, from the survival analysis it was clear that tumours from older patients featured an expression pattern more similar to the GNS signature. One of the genes contributing to this trend, TUSC3, is known to be silenced by promoter methylation in glioblastoma, particularly in patients over 40 years of age [277]. Loss or down-regulation of TUSC3 has been found in other cancers, e.g. of the colon, where its promoter becomes increasingly methylated with age in the healthy mucosa [13]. Taken together, these data suggest that transcriptional changes in healthy aging tissue, such as TUSC3 silencing, may contribute to the more severe form of glioma in older patients. Thus, the molecular mechanisms underlying the expression changes described here are likely to be complex and varied. To capture these effects and elucidate their causes, transcriptome analysis of cancer samples will benefit from integration of diverse genomic data, including structural and nucleotide-level genetic alterations, as well as DNA methylation and other chromatin modifications. To identify expression alterations common to most glioblastoma cases, other studies have profiled tumour biopsies in relation to non-neoplastic brain tissue [196,383,497]. While such comparisons have been revealing, their power 226
9.1. Digital Profiling of GNS Cell Lines Discussion is constrained by discrepancies between reference and tumour samples; for in- stance the higher neuronal content of normal brain tissue compared to tumours. Gene expression profiling of tumour biopsies further suffers from mixed signal due to a stromal cell component and heterogeneous populations of cancer cells, only some of which contribute to tumour progression and maintenance [379]. Part of a recent study bearing a closer relationship to our analysis examined gene expression in another panel of glioma-derived and normal NS cells [299], but included neurosphere cultures which often contain a heterogeneous mix- ture of self-renewing and differentiating cells. We have managed to circumvent these issues by profiling uniform cultures of primary malignant stem cell lines that can reconstitute the tumour in vivo [402], in direct comparison to normal counterparts of the same cell type [107,481]. While the resulting expression patterns largely agree with those obtained from glioblastoma tissues, there are notable differences. For example, we found the breast cancer oncogene LMO4 (discussed above) to be up-regulated in most GNS lines, although its average expression in glioblastoma tumours is low relative to normal brain tissue (Fig 7.4 3a). Similarly, TAGLN and TES were absent or low in most GNS lines, but displayed the opposite trend in glioblastoma tissue compared to normal brain (Fig 7.4c) or grade III astrocytoma (Fig 7.4d). Importantly, both TAGLN and TES have been characterised as tumour suppressors in malignancies outside the brain and the latter is often silenced by promoter hypermethylation in glioblastoma [30,349]. A very interesting hypothesis about the TES gene that we could explore further with specific biochemical assays, is that, assuming the methylation mark observed in glioblastoma literature is maintained in our GNS cell lines, there must be a very robust mechanism to keep this gene silenced. In fact, TES is found on chromosome 7, which is nearly always present in very high copy number, for example in cell line G144, which carries more than 10 copies at late passages (Fig 6.7). If we could verify that the methylation mark in our GNS cell lines was maintained, this would be consistent with TES being silenced very early on in the disease, before the chromosomal gains and ane- uploidy, which may make it a good candidate as a glioblastoma initiating event. In assigning each GNS cell line to one of the four expression signature profiles as defined by the latest study by Verhaak et al [511], each match was reflective of their known histopathological features and this further supported the need for a patient stratification approach in assigning therapies. G166 and G179, both glioblastoma cell lines, were assigned to the mesenchymal signature, asso- 227
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Transcriptional Characterization of
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This dissertation is my own work an
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Acknowledgements I would like to th
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5.9 External Dataset Expression Cor
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Introduction 1
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1.1 Glial Cells in the Central Nerv
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1.2 Glioblastoma Multiforme Introdu
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1.3 Primary and Secondary Glioblast
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1.4 Pathways Involved in Glioblasto
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1.5 Pathway Crosstalk Introduction
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Chapter 2 Neurogenesis Contents 2.1
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2.1 Radial Glia Introduction VZ adj
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2.2 Neural Stem Cells Introduction
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Chapter 3 Brain Cancer Stem Cells C
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3.1 The Cancer Stem Cell Hypothesis
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3.1 The Cancer Stem Cell Hypothesis
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3.2 Brain Cancer Stem Cells Introdu
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3.3 Glioma Culture Systems Introduc
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Chapter 4 The Non-Coding RNA World
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4.1 MicroRNA regulation Introductio
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4.1 MicroRNA regulation Introductio
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4.2 Target Prediction and Validatio
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Methods 93
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5.1 Tag-sequencing Data Processing
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5.1 Tag-sequencing Data Processing
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5.2 Array Comparative Genomic Hybri
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5.4 Quantitative Real Time-PCR Vali
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5.5 Literature Mining Methods Figur
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5.5 Literature Mining Methods How m
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5.6 Differential Isoform Expression
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5.9 External Dataset Expression Cor
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5.10 Glioblastoma Pathway Construct
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5.11 MicroRNA Target Prediction Ana
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Results 119
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6.2 Tag mapping Results Table 6.1:
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6.2 Tag mapping Results 123 Figure
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6.3 Copy Number Aberrations Results
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6.3 Copy Number Aberrations Results
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6.4 Core Differentially Expressed G
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6.5 Large-scale qRT-PCR Validation
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6.6 Literature Mining for Different
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6.7 Isoform Differential Expression
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6.8 Long ncRNA Differential Express
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6.8 Long ncRNA Differential Express
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Chapter 7 Dataset Correlation Analy
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7.1 Enrichment Analysis Results Tab
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7.1 Enrichment Analysis Results Fig
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7.1 Enrichment Analysis Results Tab
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7.2 Glioblastoma Expression Signatu
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7.3 Tumour Expression Correlation R
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7.3 Tumour Expression Correlation R
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D.1 Pathway Interactions Appendix F
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D.2 Pathway Images Appendix Figure
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D.2 Pathway Images Appendix Figure
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E. Exon Array Data Appendix Average
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F. MicroRNA Array Data Appendix Tab
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F. MicroRNA Array Data Appendix GNS
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F. MicroRNA Array Data Appendix GNS
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Ln Natural logarithm LOH Loss of He
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3.3 Schematisation of cell cycle ph
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8.3 Workflows at the core of the pr
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7.1 Selected Gene Ontology terms an
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