Targeting the DNA Damage Response for Cancer Therapy by Inhibiting the Kinase Wee1
Bibliographic record
Abstract
Cancer cells typically heavily rely on the G2/M checkpoint to survive endogenous and exogenous DNA damage, such as genotoxic stress due to genome instability or radiation and chemotherapy. The key regulator of the G2/M checkpoint, the cyclin-dependent kinase 1 (CDK1), is tightly controlled, including by its phosphorylation state. This posttranslational modification, which is determined by the opposing activities of the phosphatase cdc25 and the kinase Wee1, allows for a more rapid response to cellular stress than via the synthesis or degradation of modulatory interacting proteins, such as p21 or cyclin B. Reducing Wee1 activity results in ectopic activation of CDK1 activity and drives premature entry into mitosis with unrepaired or under-replicated DNA and causing mitotic catastrophe. Here, we review efforts to use small molecule inhibitors of Wee1 for therapeutic purposes, including strategies to combine Wee1 inhibition with genotoxic agents, such as radiation therapy or drugs inducing replication stress, or inhibitors of pathways that show synthetic lethality with Wee1. Furthermore, it become increasingly clear that Wee1 inhibition can also modulate therapeutic immune responses. We will discuss the mechanisms underlying combination treatments identifying both cell intrinsic and systemic anti-tumor activities.
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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 | 0.001 | 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) | 0.004 | 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".