Targeting the cell cycle in breast cancer: towards the next phase
Bibliographic record
Abstract
Deregulation of the cell cycle is a hallmark of cancer that enables limitless cell division. To support this malignant phenotype, cells acquire molecular alterations that abrogate or bypass control mechanisms in signaling pathways and cellular checkpoints that normally function to prevent genomic instability and uncontrolled cell proliferation. Consequently, therapeutic targeting of the cell cycle has long been viewed as a promising anti-cancer strategy. Until recently, attempts to target the cell cycle for cancer therapy using selective inhibitors have proven unsuccessful due to intolerable toxicities and a lack of target specificity. However, improvements in our understanding of malignant cell-specific vulnerabilities has revealed a therapeutic window for preferential targeting of the cell cycle in cancer cells, and has led to the development of agents now in the clinic. In this review, we discuss the latest generation of cell cycle targeting anti-cancer agents for breast cancer, including approved CDK4/6 inhibitors, and investigational TTK and PLK4 inhibitors that are currently in clinical trials. In recognition of the emerging population of ER+ breast cancers with acquired resistance to CDK4/6 inhibitors we suggest new therapeutic avenues to treat these patients. We also offer our perspective on the direction of future research to address the problem of drug resistance, and discuss the mechanistic insights required for the successful implementation of these strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".