Glioma stem cell signaling: therapeutic opportunities and challenges
Bibliographic record
Abstract
The field of cancer research has experienced significant momentum from the discovery that most malignant tumors harbor subpopulations of cancer cells with stem cell features. Consequently, identification and characterization of so-called 'cancer-initiating cells' or 'cancer stem cells' has also provided novel insights into the biology of malignant brain tumors. Despite an ongoing debate regarding the exact role and identity of cancer stem cells, several studies have suggested that this subpopulation is critical for tumor initiation, tumor progression, angiogenesis and resistance to available therapies. The study of signaling pathways critical to normal neural stem and progenitor cells has also increased our understanding of the mechanisms that drive cancer stem cell-associated tumorigenesis and tumor progression. Novel treatment strategies are being developed to selectively target the molecular pathways relevant to cancer stem cells. This review summarizes important signaling pathways employed by both normal and cancer stem cells and highlights promising molecular-targeted therapies interfering with those signaling pathways in malignant gliomas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".