Maintenance of primary tumor phenotype and genotype in glioblastoma stem cells
Bibliographic record
Abstract
The clinicopathological heterogeneity of glioblastoma (GBM) and the various genetic and phenotypic subtypes in GBM stem cells (GSCs) are well described. However, the relationship between GSCs and the corresponding primary tumor from which they were isolated is poorly understood. We have established GSC-enriched neurosphere cultures from 15 newly diagnosed GBM specimens and examined the relationship between the histopathological and genomic features of GSC-derived orthotopic xenografts and those of the respective patient tumors. GSC-initiated xenografts recapitulate the distinctive cytological hallmarks and diverse histological variants associated with the corresponding patient GBM, including giant cell and gemistocytic GBM, and primitive neuroectodermal tumor (PNET)-like components. This indicates that GSCs generate tumors that preserve patient-specific disease phenotypes. The majority of GSC-derived intracerebral xenografts (11 of 15) demonstrated a highly invasive behavior crossing the midline, whereas the remainder formed discrete nodular and vascular masses. In some cases, GSC invasiveness correlated with preoperative MRI, but not with the status of PI3-kinase/Akt pathways or O(6)-methylguanine methyltransferase expression. Genome-wide screening by array comparative genomic hybridization and fluorescence in situ hybridization revealed that GSCs harbor unique genetic copy number aberrations. GSCs acquiring amplifications of the myc family genes represent only a minority of tumor cells within the original patient tumors. Thus, GSCs are a genetically distinct subpopulation of neoplastic cells within a GBM. These studies highlight the value of GSCs for preclinical modeling of clinically relevant, patient-specific GBM and, thus, pave the way for testing novel anti-GSC/GBM agents for personalized therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".