Effects of chemical manipulation of mitotic arrest and slippage on cancer cell survival and proliferation
Bibliographic record
Abstract
Microtubule-targeting cancer therapies interfere with mitotic spindle dynamics and block cells in mitosis by activating the mitotic checkpoint. Cells arrested in mitosis may remain arrested for extended periods of time or undergo mitotic slippage and enter interphase without having separated their chromosomes. How extended mitotic arrest and mitotic slippage contribute to subsequent cell death or survival is incompletely understood. To address this question, automated fluorescence microscopy assays were designed and used to screen chemical libraries for modulators of mitotic slippage. Chlorpromazine and triflupromazine were identified as drugs that inhibit mitotic slippage and SU6656 and geraldol as chemicals that stimulate mitotic slippage. Using the drugs to extend mitotic arrest imposed by low concentrations of paclitaxel led to increased cell survival and proliferation after drug removal. Cells arrested at mitosis with paclitaxel or vinblastine and chemically induced to undergo mitotic slippage underwent several rounds of DNA replication without cell division and exhibited signs of senescence but eventually all died. By contrast, cells arrested at mitosis with the KSP/Eg5 inhibitor S-trityl-L-cysteine and induced to undergo mitotic slippage were able to successfully divide and continued to proliferate after drug removal. These results show that reinforcing mitotic arrest with drugs that inhibit mitotic slippage can lead to increased cell survival and proliferation, while inducing mitotic slippage in cells treated with microtubule-targeting drugs seems to lead to protracted cell death.
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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".