WEE1 inhibitors trigger GCN2-mediated activation of the integrated stress response
Bibliographic record
Abstract
The WEE1 kinase negatively regulates CDK1/2 to control DNA replication and mitotic entry. Genetic factors that determine sensitivity to WEE1 inhibitors (WEE1i) are largely unknown. A genome-wide insertional mutagenesis screen revealed that mutation of EIF2A, a translation regulator, sensitized to WEE1i. Additionally, a genome-wide CRISPR-Cas9 screen revealed that inactivation of integrated stress response (ISR) kinase GCN2 or its co-factor GCN1 rescued WEE1i-mediated cytotoxicity. Conversely, loss of the collided ribosome sensor ZNF598 increased sensitivity to WEE1i. Mechanistically, WEE1i induced paradoxical GCN2 activation, ATF4 upregulation, and altered ribosome dynamics. ISR activation was independent of WEE1 presence, pointing at off-target GCN2 engagement by multiple chemically distinct WEE1i. ISR activation was observed in cancer cells as well as non-transformed cells, and required GCN1 and ongoing translation. Consequently, WEE1i induce multiple independent cellular effects: DNA damage, premature mitotic entry and sensitization to DNA-damaging chemotherapeutics in an ISR-independent fashion, as well as ISR activation independently of CDK1/2 activation. Importantly, low-dose WEE1 inhibition did not induce ISR activation, while it still synergized with PKMYT1 inhibition. Taken together, WEE1i trigger toxic ISR activation and translational shutdown, which can be prevented by low-dose or combination treatments, while retaining the cell cycle checkpoint-perturbing effects.
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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) | 0.000 | 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) | 0.002 | 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".