Active growth signalling promotes cancer cell sensitivity to the CDK7 inhibitor ICEC0942
Bibliographic record
Abstract
CDK7 has a central role in promoting cellular proliferation, through the activation of the mitotic CDKs, and by driving global gene expression, through targeting RNA polymerase II. Several recently developed CDK7 inhibitors (CDK7i) have been shown to be non-toxic and to limit tumour growth for a number of cancer cell types and are now in Phase I/II clinical trials. However, the mechanisms underlying the sensitivity of particular cancer cells to CDK7 inhibition remain largely unknown. To improve the outcome of individual patients and increase the chances of successful CDK7i approval, we assessed which fundamental cellular processes determine sensitivity to CDK7 inhibition, using the highly specific CDK7 inhibitor ICEC0942. Our data shows that selective CDK7 inhibition acutely arrests cells in the G1 phase of the cell cycle, which over time leads to senescence. Through a genome-wide CRISPR knock-out chemogenetic screen we identified active mTOR (mammalian target of rapamycin) signalling, as an important determinant of ICEC0942-induced senescence and show that a cancer-associated mutation that promotes cell growth can increase sensitivity to ICEC0942. Our work indicates that cellular growth is an important predictive marker for sensitivity to CDK7i.
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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) | 0.003 | 0.001 |
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".