Myt1 overexpression mediates resistance to cell cycle and DNA damage checkpoint kinase inhibitors
Bibliographic record
Abstract
Cell cycle checkpoint kinases serve as important therapeutic targets for various cancers. When they are inhibited by small molecules, checkpoint abrogation can induce cell death or further sensitize cancer cells to other genotoxic therapies. Particularly aberrant Cdk1 activation at the G2/M checkpoint by kinase inhibitors causing unscheduled mitotic entry and mitotic arrest was found to lead to DNA damage and cell death selectively in cancer cells. Promising drugs inhibiting kinases like Wee1 (Adavosertib), Wee1+Myt1 (PD166285), ATR (AZD6738) and Chk1 (UCN-01) have been developed, but clinical data has shown variable efficacy for them with poorly understood mechanisms of resistance. Our lab recently identified Myt1 as a predictive biomarker of acquired resistance to the Wee1 kinase inhibitor, Adavosertib. Here, we investigate the role of Myt1 overexpression in promoting resistance to inhibitors (PD166285, UCN-01 and AZD6738) of other kinases regulating cell cycle progression. We demonstrate that Myt1 confers resistance by compensating Cdk1 inhibition in the presence of these different kinase inhibitors. Myt1 overexpression leads to reduced premature mitotic entry and decreased length of mitosis eventually leading to increased survival rates in Adavosertib treated cells. Elevated Myt1 levels also conferred resistance to inhibitors of ATR or Chk1 inhibitor. Our data supports that Myt1 overexpression is a common mechanism by which cancer cells can acquire resistance to a variety of drugs entering the clinic that aim to induce mitotic catastrophe by abrogating the G2/M checkpoint.
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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 | 0.000 |
| 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".