The Glioblastoma Cell Culture Resource

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Executive summary
This article focusses on a collection of human glioblastoma cell lines that have been produced and examined in great detail by scientists and clinicians from Uppsala University and their collaborators. The cells are made available to the research community to facilitate our understanding of the basic mechanisms of disease as well as to identify targets for novel drugs.

Background
In vitro cell culture models are the mainstay of research into basic biology, pathological mechanisms and the discovery of novel therapies, their use in glioblastoma research being no exception. Identifying the cells that most accurately mimic this human brain cancer in situ is particularly challenging for the following reasons:

  1. GBM cells show extensive heterogeneity, even within the same patient.1
  2. Cells cultured in standard serum-containing media differ from the originating tumour in several ways, including depletion of the stem cells which are thought to drive cancer progression.2,3
  3. Tumour cell lines injected into mouse xenograft models often fail to develop the defining morphological features of GBM.4
  4. Detailed information on the donor patient is often lacking, making it impossible to correlate molecular features of the cell line with clinical outcome.

The above considerations apply to many of the commonly used cell lines that have been used extensively in GBM research for many years, including discovery workhorses such as U87 and U251.
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To address these issues and to provide a resource for the scientific and clinical community, members of the Neuro-Oncology Division of the Department of Immunology, Genetics and Pathology at Uppsala University, Sweden have created the HGCC Biobank.5 This resource consists of 48 sustainable GBM lines derived from Swedish patients between 2009 and 2012. A detailed analysis of these publicly-available cell lines has been published by Xie et al6 and is the subject of this Window on Glioblastoma article.
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Generation of the HGCC cell lines
114 tumour explants were dissociated and cultured in defined serum-free media as neurospheres for 5-7 days before transfer to laminin-coated dishes for further propagation as adherent monolayer cultures. Growth patterns of cells from different patients varied extensively, and most of the primary cell cultures formed smaller aggregates of cells rather than neurospheres. Cell lines were considered to be sustainable if they continued to proliferate after 8-10 passages. 48 stable cell lines were produced and subjected to further analysis.
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Molecular subtyping of the HGCC cell lines
– based on gene expression
The Cancer Genome Atlas Research Network (TCGA) has defined four molecular subtypes of GBM cells based on their genetic and transcriptional profiles7 which are listed in the table below, along with the characteristic molecular features described in reference 6.

Subset Defining features
Proneural Higher frequency of PDGFRA or IDH1 mutations, plus glioma-CpG island methylator phenotype subgroup
Neural No distinctive mutations
Classical High rates of EGFR amplification and homozygous deletions of CDKN2A
Mesenchymal Often harbour hemizygous deletions of NF1


mRNA expression in the 48 HGCC cell lines of 765 classifier genes7 was measured by hybridisation to the Affymetrix Human Exon 1.0 ST GeneChip. Data were compared with that derived from the TCGA collection of 529 tissue samples. This confirmed a clear separation of the Classical, Mesenchymal and Proneural TCGA subtypes in the latter samples, with good representation of these subtypes by the 48 HGCC cell lines (see Figure, below left). This analysis is illustrated using Isomap (see Figure, below right). IsoMap is a dimensionality reduction technique applicable to the grouping of cancer samples.8

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The study was extended to a further 22 samples by comparing their transcriptional profiles with the paired originating tumour tissue. Since the RNA samples were too degraded for use with microarrays, the nCounter gene expression system (NanoString Technology) which captures and counts individual mRNA transcripts9 was employed instead. In 10 out of 24 cases, tissue and the cell lines derived from it had the same GBM subtype. Mismatches in the remaining samples were probably due in part to tumour heterogeneity and preferential clonal outgrowth during culture.
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– based on genomic features
In addition to comparing gene expression patterns between the HGCC lines and the more extensive TCGA collection, Xie et al analysed copy number variation using Affymetrix Cytoscan HD arrays containing more than 2.6 million probes. This allowed them to show that changes such as chromosome 7 gains and chromosome 10 losses, which are characteristic of GBM, occurred in 70% of the cell lines and were similar to the TCGA samples. Other areas of the genome were amplified in broadly the same way, with a few discrepancies (favouring HGCC cells) at a modest level of significance.
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– based on expression of lineage markers
27 cell lines were examined by immunostaining for the expression of SOX2, NESTIN, GFAP, OLIG2, S100B and TUBB3. The presence of SOX2 and NESTIN in most of the cell lines indicated that an immature stem cell-like population of GBM cells had been propagated, but expression of the other markers was variable. It is interesting to note the low expression of OLIG2 on some lines, since this transcription factor is a potential therapeutic target for GBM.10
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In vivo experiments
Investigations were also undertaken by Xie et al6 to determine the tumorigenicity of the cell lines as well as their lineage stability in the brain after passage in mouse (NOD-SCID) xenografts. Out of 30 lines used, most, but not all, produced macroscopic tumours, further indicating the heterogeneous nature of GBM cells. However, individual tumours derived from the same cell line exhibited highly similar histopathology indicating that their distinct properties can be maintained both in vitro and in vivo. In summary, the xenografted tumours had the hallmarks of high-grade gliomas but did not always display the same histopathology.
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Transcriptional stability
Three different HGCC lines were implanted intra-cranially into NOD-SCID mice and re-cultured from the resulting tumour explant. Transcript profiling revealed that the pro-neural lines remained pro-neural throughout. One mesenchymal line changed to the classical subtype in the xenograft, retaining this phenotype in explanted cells. The other mesenchymal line changed to the pro-neural phenotype in the xenograft but reverted to mesenchymal in explanted cells. These observations are consistent with the findings that pro-neural GCs are more transcriptionally stable than mesenchymal GBM cells when transferred from culture to xenograft.11
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Correlation of molecular properties with clinical observations
A great advantage of the HGCC resource compared with other GBM cell collections lies in the detailed clinical information associated with each cell line. This has allowed Xie et al6 to show a significant correlation between successful establishment of a cell line from a patient’s tumour and a shorter patient survival time. Age at the time of diagnosis is a major determinant of GBM patient survival12 and this was confirmed in the Xie et al cohort, where younger GBM patients survived significantly longer.
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Indeed, age alone seemed to be a significant predictor of survival, not the ability of excised tumour cells to propagate in culture which was associated with older patients. This could suggest that shorter survival in the older population is due to inherently more aggressive tumour cells rather than factors such as differences in the extent of resection or type of treatment, although the influence of changes in the micro-environment due to aging cannot be excluded.
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Conclusions
The HGCC Biobank resource from Uppsala University contains cell lines that have been rigorously examined for properties of GBM cells in terms of molecular features and tumourigenicity. They have been assigned to distinct subtypes and associated with specific clinical outcomes. Their utility in GBM research has already been demonstrated in studies of basic biology, drug screening and drug discovery, as well as gene therapy vector development.13-16
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References

  1. Patel et al. (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344:1396–1401.
  2. Lee et al. (2006) Tumor stem cells derived from glioblastomas cultured in bFGF and EGF more closely mirror the phenotype and genotype of primary tumors than do serum-cultured cell lines. Cancer Cell 9:391–403.
  3. Window on Glioblastoma: Glioblastoma Stem Cells and Drug Discovery. October 15th, 2017
  4. Westermark et al. (1973) Determinants for the establishment of permanent tissue culture lines from human gliomas. Acta Pathol. Microbiol. Scand. A 81:791–805.
  5. http://www.hgcc.se
  6. Xie et al. (2015) The Human Glioblastoma Cell Culture Resource: validated cell models representing all molecular subtypes. EBioMedicine 2:1351-1363.
  7. Verhaak et al. (2010) Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17:98-110.
  8. Nilsson et al. (2004) Approximate geodesic distances reveal biologically relevant structures in microarray data. Bioinformatics 20:874-80.
  9. Geiss et al. (2008) Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol 26:317-325.
  10. Window on Glioblastoma: Drugs for targeting transcription factors in GBM: OLIG2 as a case study. 1st November 2017.
  11. Bhat et al. (2013) Mesenchymal differentiation mediated by NF-kappaB promotes radiation resistance in glioblastoma. Cancer Cell 24:331-346.
  12. Brennan et al. (2013) The somatic genomic landscape of glioblastoma. Cell 155:462-477.
  13. Babateen et al. (2013) Etomidate, propofol and diazepam potentiate GABA-evoked GABAA currents in a cell line derived from human glioblastoma. Eur.J. Pharmacol. 748:101-107.
  14. Schmidt et al. (2013). Comparative drug pair screening across multiple glioblastoma cell lines reveals novel drug–drug interactions. Neuro Oncol. 15:1469-1478.
  15. Kitambi et al. (2014) Vulnerability of glioblastoma cells to catastrophic vacuolization and death induced by a small molecule. Cell 157:313-328.
  16. Yu et al. (2013) Adenovirus serotype 5 vectors with Tat-PTD modified hexon and serotype 35 fiber show greatly enhanced transduction capacity of primary cell cultures. PLoS One 8:e54952.
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