Seize the engine: Emerging cell cycle targets in breast cancer
Bibliographic record
Abstract
Breast cancer arises from a series of molecular alterations that disrupt cell cycle checkpoints, leading to aberrant cell proliferation and genomic instability. Targeted pharmacological inhibition of cell cycle regulators has long been considered a promising anti-cancer strategy. Initial attempts to drug critical cell cycle drivers were hampered by poor selectivity, modest efficacy and haematological toxicity. Advances in our understanding of the molecular basis of cell cycle disruption and the mechanisms of resistance to CDK4/6 inhibitors have reignited interest in blocking specific components of the cell cycle machinery, such as CDK2, CDK4, CDK7, PLK4, WEE1, PKMYT1, AURKA and TTK. These targets play critical roles in regulating quiescence, DNA replication and chromosome segregation. Extensive preclinical data support their potential to overcome CDK4/6 inhibitor resistance, induce synthetic lethality or sensitise tumours to immune checkpoint inhibitors. This review provides a biological and drug development perspective on emerging cell cycle targets and novel inhibitors, many of which exhibit favourable safety profiles and promising activity in clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".