CDK1 in Breast Cancer: Implications for Theranostic Potential
Bibliographic record
Abstract
Breast cancer has been identified as one of the main cancer-related deaths among women during some last decades. Recent advances in the introduction of novel potent anti-cancer therapeutics in association with early detection methods led to a decrease in the mortality rate of breast cancer. However, the scenario of breast cancer is yet going on and further improvements in the current anti-cancer therapeutic approaches are needed. Several factors are present in the tumor microenvironment which help to cancer progression and suppression of anti-tumor responses. Targeting these cancer-promoting factors in the tumor microenvironment has been suggested as a potent immunotherapeutic approach for cancer therapy. Among the various tumorsupporting factors, Cyclin-Dependent Kinases (CDKs) are proposed as a novel promising target for cancer therapy. These factors in association with cyclins play a key role in cell cycle progression. Dysregulation of CDKs which leads to increased cell proliferation has been identified in various cancers, such as breast cancer. Accordingly, the development and use of CDK-inhibitors have been associated with encouraging results in the treatment of breast cancer. However, it is unknown that the inhibition of which CDK is the most effective strategy for breast cancer therapy. Since the selective blockage of CDK1 alone or in combination with other therapeutics has been associated with potent anti-cancer outcomes, it is suggested that CDK1 may be considered as the best CDK target for breast cancer therapy. In this review, we will discuss the role of CDK1 in breast cancer progression and treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".