Revisiting CDK Inhibitors for Treatment of Glioblastoma Multiforme
Bibliographic record
Abstract
Despite extensive efforts and continual progress in research and medicine, outcomes for patients with high-grade glioma remain exceptionally poor. Over the past decade, research has revealed a great deal about the complex biology behind glioma development, and has brought to light some of the major barriers preventing successful treatment. Glioblastoma multiforme (GBM) (stage 4 astrocytoma) is a highly dynamic tumour and one of the most extreme examples of intratumoural heterogeneity, making targeting with specific therapeutics an inefficient and highly unpredictable goal. The cancer stem cell hypothesis offers a new view on the possible mechanisms dictating the heterogeneous nature of this disease and contributes to our understanding of glioma resistance and recurrence. Revealing cell division characteristics of initiating cell populations within GBM may represent novel treatment targets and/or the effective repurposing of existing therapies. In this review, we discuss the potential role of targeting the cyclin-dependent kinases (CDKs) driving this specific population. We also describe developments using multi-omic approaches that may aid in stratifying patient populations for CDK inhibitor 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".