Protocol for the derivation of primary cancer stem cell lines from human ependymal tumors
Bibliographic record
Abstract
Cancer stem cells (CSCs) established from surgical biopsies closely mimic the human context and can be used to investigate disease mechanisms, genetic fitness, and therapeutic evaluation. Here, we present a protocol for the derivation of primary patient-derived CSC lines from ependymal tumors. We describe the necessary steps, from surgical intervention and biopsy to the dissociation of ependymomas to derive cultures. We then detail procedures for cell line propagation and define the characteristics of these primary cancer cell lines. For complete details on the use and execution of this protocol, please refer to Michealraj et al. 1 • Protocol to generate patient-derived cancer stem cell lines from ependymal tumors • Steps for the expansion and maintenance of primary ependymoma cell lines • Profiling and characterization of infratentorial/supratentorial ependymoma cell lines Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Cancer stem cells (CSCs) established from surgical biopsies closely mimic the human context and can be used to investigate disease mechanisms, genetic fitness, and therapeutic evaluation. Here, we present a protocol for the derivation of primary patient-derived CSC lines from ependymal tumors. We describe the necessary steps, from surgical intervention and biopsy to the dissociation of ependymomas to derive cultures. We then detail procedures for cell line propagation and define the characteristics of these primary cancer cell lines.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.040 |
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".