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Updated: Mar 22, 2026
A 3D Spheroid Model for Glioblastoma
Published on: April 9, 2020
Large-scale assessment of the gliomasphere model system
Dan R Laks1, Thomas J Crisman1, Michelle Y S Shih1
1Department of Biological Chemistry, University of California, Los Angeles, California (D.R.L.); Department of Psychiatry and Biobehavioral Sciences and Semel Institute for Neuroscience & Human Behavior, University of California, Los Angeles, California (T.J.C., M.Y.S.S., J.M., F.G., M.C.G., G.C., H.I.K.); Department of Pharmacology, University of California, Los Angeles, California (J.S.); Department of Pathology, University of California, Los Angeles, California (W.H.Y.); Department of Neurology, University of California, Los Angeles, California (T.F.C., A.L., G.C.); Department of Neurosurgery, University of California, Los Angeles, California (L.M.L.); Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, California (H.I.K.); The Jonsson Comprehensive Cancer Center, University of California, Los Angeles, California (W.H.Y., T.F.C., L.M.L., A.L., H.I.K.).
Background:
Gliomasphere cultures are widely utilized for the study of glioblastoma (GBM). However, this model system is not well characterized, and the utility of current classification methods is not clear.
Methods:
We used 71 gliomasphere cultures from 68 individuals. Using gene expression-based classification, we performed unsupervised clustering and associated gene expression with gliomasphere phenotypes and patient survival.
Results:
Some aspects of the gene expression-based classification method were robust because the gliomasphere cultures retained their classification over many passages, and IDH1 mutant gliomaspheres were all proneural. While gene expression of a subset of gliomasphere cultures was more like the parent tumor than any other tumor, gliomaspheres did not always harbor the same classification as their parent tumor. Classification was not associated with whether a sphere culture was derived from primary or recurrent GBM or associated with the presence of EGFR amplification or rearrangement. Unsupervised clustering of gliomasphere gene expression distinguished 2 general categories (mesenchymal and nonmesenchymal), while multidimensional scaling distinguished 3 main groups and a fourth minor group. Unbiased approaches revealed that PI3Kinase, protein kinase A, mTOR, ERK, Integrin, and beta-catenin pathways were associated with in vitro measures of proliferation and sphere formation. Associating gene expression with gliomasphere phenotypes and patient outcome, we identified genes not previously associated with GBM: PTGR1, which suppresses proliferation, and EFEMP2 and LGALS8, which promote cell proliferation.
Conclusions:
This comprehensive assessment reveals advantages and limitations of using gliomaspheres to model GBM biology, and provides a novel strategy for selecting genes for future study.
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