CD133, CD15/SSEA-1, CD34 or side populations do not resume tumor-initiating properties of long-term cultured cancer stem cells from human malignant glio-neuronal tumors
Bibliographic record
Abstract
BACKGROUND: Tumor initiating cells (TICs) provide a new paradigm for developing original therapeutic strategies. METHODS: We screened for TICs in 47 human adult brain malignant tumors. Cells forming floating spheres in culture, and endowed with all of the features expected from tumor cells with stem-like properties were obtained from glioblastomas, medulloblastoma but not oligodendrogliomas. RESULTS: A long-term self-renewal capacity was particularly observed for cells of malignant glio-neuronal tumors (MGNTs). Cell sorting, karyotyping and proteomic analysis demonstrated cell stability throughout prolonged passages. Xenografts of fewer than 500 cells in Nude mouse brains induced a progressively growing tumor. CD133, CD15/LeX/Ssea-1, CD34 expressions, or exclusion of Hoechst dye occurred in subsets of cells forming spheres, but was not predictive of their capacity to form secondary spheres or tumors, or to resist high doses of temozolomide. CONCLUSIONS: Our results further highlight the specificity of a subset of high-grade gliomas, MGNT. TICs derived from these tumors represent a new tool to screen for innovative therapies.
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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) | 0.002 | 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".