Inhibiting Wee1 and ATR kinases produces tumor-selective synthetic lethality and suppresses metastasis
Bibliographic record
Abstract
We used the cancer-intrinsic property of oncogene-induced DNA damage as the base for a conditional synthetic lethality approach. To target mechanisms important for cancer cell adaptation to genotoxic stress and thereby to achieve cancer cell-specific killing, we combined inhibition of the kinases ATR and Wee1. Wee1 regulates cell cycle progression, whereas ATR is an apical kinase in the DNA-damage response. In an orthotopic breast cancer model, tumor-selective synthetic lethality of the combination of bioavailable ATR and Wee1 inhibitors led to tumor remission and inhibited metastasis with minimal side effects. ATR and Wee1 inhibition had a higher synergistic effect in cancer stem cells than in bulk cancer cells, compensating for the lower sensitivity of cancer stem cells to the individual drugs. Mechanistically, the combination treatment caused cells with unrepaired or under-replicated DNA to enter mitosis leading to mitotic catastrophe. As these inhibitors of ATR and Wee1 are already in phase I/II clinical trials, this knowledge could soon be translated into the clinic, especially as we showed that the combination treatment targets a wide range of tumor cells. Particularly, the antimetastatic effect of combined Wee1/ATR inhibition and the low toxicity of ATR inhibitors compared with Chk1 inhibitors have great clinical potential.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".