From sorting to sequencing in the molecular era: the evolution of the cancer stem cell model in medulloblastoma
Bibliographic record
Abstract
The cancer stem cell (CSC) model posits that tumors contain subpopulations that display defining features of normal stem cells including self-renewal capacity and differentiation. Tumor cells exhibiting these features are now considered to be responsible for tumor propagation and drug resistance in a wide variety of cancers. Therefore, the identification of robust CSC markers and characterization of CSC-specific molecular signatures may lead to the identification of novel therapeutics that selectively abolish this clinically relevant cell population while preserving normal tissue. Brain tumor researchers have been at the forefront of the CSC field. From initial in vitro cell sorting experiments to the sophisticated bioinformatic technologies that now exquisitely resolve primary brain tumors at a single-cell level, recent glioma and medulloblastoma (MB) studies have integrated developmental state with genomic and transcriptome data to identify the spectrum of cell types that may drive tumor progression. This review will examine the last two decades of CSC studies in the field. Seminal discoveries, emerging controversies, and outstanding questions will be covered with a particular focus on MB, the most common malignant primary brain tumor in children.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".