Cytotoxic amounts of cisplatin induce either checkpoint adaptation or apoptosis in a concentration‐dependent manner in cancer cells
Bibliographic record
Abstract
BACKGROUND INFORMATION: Checkpoint adaptation (entry into mitosis with damaged DNA) is a process that links arrest at the G2/M cell cycle checkpoint and cell death in cancer cells. It is not known, however, whether cells treated with the genotoxic agent, cisplatin, undergo checkpoint adaptation or if checkpoint adaptation is a major pathway leading to cell death or not. Therefore, we investigated the relationship between treatment with cisplatin and cytotoxicity in cancer cells. RESULTS: Treatment of HT-29 human colorectal adenocarcinoma cells with cisplatin can induce cell death by one of two different mechanisms. Cells treated with a cytotoxic 30 μM amount of cisplatin died after undergoing checkpoint adaptation. Before dying, however, almost all treated cells were positive for histone γH2AX staining and contained high levels of cyclin B1. Rounded cells appeared that were positive for phospho-Ser10 histone H3, with low levels of phospho-Tyr15 cyclin-dependent kinase 1, high levels of cyclin-dependent kinase 1 activity, and checkpoint kinase 1 that was not phosphorylated on Ser345. These cells were in mitosis with damaged DNA. Strikingly, with 30 μM cisplatin, 81% of cells had entered mitosis before dying. By contrast, after treatment with 100 μM cisplatin, nearly all cells died but only 7% of cells had entered mitosis. Instead, these cells died by apoptosis; they were positive for annexin-V staining, contained cleaved caspase 3, cleaved caspase 9 and cleaved PARP and did not contain Mcl-1. CONCLUSIONS: Our data demonstrate that cancer cells treated with cisplatin can undergo one of two modes of cell death depending upon concentration used. These findings suggest that checkpoint adaptation is likely a primary pathway in genotoxic cell death at pharmacological concentrations of cisplatin. SIGNIFICANCE: Checkpoint adaptation might be a common biochemical pathway taken by human cancer cells in response to pharmacologically relevant, cytotoxic amounts of damaged DNA.
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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 | 0.001 |
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".