Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

Abstract
Tumours affecting the glial portion of brain parenchyma are termed gliomas and consti-
tute the most frequent and lethal cancers affecting the central nervous system. Glioblastoma
multiforme is the most aggressive glioma in adults and a World Health Organisation clas-
sified grade IV astrocytoma, characterised by widespread intra-tumoural heterogeneity. A
recent advance in the study of gliomas has been the establishment of glioma-derived neural
stem (GNS) cell lines that may represent the glioma cell of origin. While these cell lines
show many similarities to normal neural stem (NS) cells, an important difference is their
capacity to give rise to authentic glioma-like tumours when xenografted into subventricular
strata of immunocompromised mice.
Here I describe an in-depth characterisation of the transcriptome of GNS cells, to identify
differences in the gene expression between normal and glioma-derived cell lines that may
underlie tumorigenesis. Analyses were carried out at the levels of gene expression, molecular
signature profiling, transcript isoform detection and the quantitation of small non-coding
RNAs, taking genetic alterations into account at both the karyotype and mutational level.
Importantly, the cell lines studied were established from tumours with differing histology,
allowing us to sample the breadth of the disease rather than focus on the differences between
unhealthy versus healthy counterparts.
We identified a large cohort of significantly differentially-expressed genes and a smaller
subset of strictly up- and down-regulated ones, including several known glioma oncogenes
as well as novel candidates. An extensive glioblastoma pathway was manually curated
to show the expression of our dataset on the known and unknown glioblastoma-affected
pathways. Interestingly, gene set enrichment analysis revealed a consistent up-regulation of
inflammatory genes in the GNS lines belonging to the MHC class II family, suggesting an
immune-evasion phenotype that has been noted in a number of early glioma studies.
Gliomas have been classified into a small number of subtypes on the basis of patient
survival and response to therapy. We found that the expression signatures of GNS cell lines
closely resembled the mesenchymal and proneural subtypes, as well as reflecting their known
histopathological features. To characterise genes correlating with patient survival time, we
tested for the association between survival time and gene expression in publicly available
glioma and glioblastoma data sets and found four genes to be strongly positively correlated
with patient survival time and patient age. Together these studies provide an in-depth
analysis of a model of glioma pathology driven by an aberrant population of NS cells.
Finally, a package for the performance evaluation of eight leading microRNA target pre-
diction algorithms was built using exon array and microRNA array data from the same GNS
cell lines. This data was used to validate experimentally the target prediction algorithms
that were assessed in their performance as single and combinations of them. The combi-
natorial weight analysis allowed us to conclude that (i) tissue specificity bears a non-trivial
weight in predicting what set of genes a certain microRNA regulates and, therefore, should
be included in future versions of these algorithms, and that (ii) the ElMMO prediction
algorithm fares better than any other combination of prediction algorithms.