{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Laks DR"],"funding":["National Institutes of Neurological Disorders and Stroke (NINDS)","NCATS NIH HHS","NICHD NIH HHS","California Institute of Regenerative Medicine (CIRM) Training Grant","NINDS NIH HHS","NCI NIH HHS","NINDS"],"pagination":["1367-78"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5035518"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(10)"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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 d"],"journal":["Neuro-oncology"],"pubmed_title":["Large-scale assessment of the gliomasphere model system."],"pmcid":["PMC5035518"],"funding_grant_id":["R56 NS052563","R25 NS079198","TG2-01169","NS052563","P30 CA016042","R01 CA179071","UL1 TR000124","R01 NS052563","P30 NS062691","U54 HD087101"],"pubmed_authors":["Garrett MC","Liau LM","Cloughesy TF","Laks DR","Lai A","Sperry J","Shih MY","Gao F","Coppola G","Mottahedeh J","Yong WH","Crisman TJ","Kornblum HI"],"additional_accession":[]},"is_claimable":false,"name":"Large-scale assessment of the gliomasphere model system.","description":"<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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 d","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016 Oct","modification":"2026-05-05T05:43:05.37Z","creation":"2019-03-27T02:25:03Z"},"accession":"S-EPMC5035518","cross_references":{"pubmed":["27116978"],"doi":["10.1093/neuonc/now045"]}}