Cancer cells that survive checkpoint adaptation contain micronuclei that harbor damaged DNA
Bibliographic record
Abstract
We have examined the relationship between checkpoint adaptation (mitosis with damaged DNA) and micronuclei. Micronuclei in cancer cells are linked to genomic change, and may induce chromothripsis (chromosome shattering). We measured the cytotoxicity of the cancer drug cisplatin in M059K (glioma fibroblasts, IC50 15 μM). Nearly 100% of M059K cells were positive for histone γH2AX staining after 48 h treatment with a cytotoxic concentration of cisplatin. The proportion of micronucleated cells, as confirmed by microscopy using DAPI and lamin A/C staining, increased from 24% to 48%, and the total micronuclei in surviving cells accumulated over time. Promoting entry into mitosis with a checkpoint inhibitor increased the number of micronuclei in cells whereas blocking checkpoint adaptation with a Cdk inhibitor reduced the number of micronuclei. Interestingly, some micronuclei underwent asynchronous DNA replication, relative to the main nuclei, as measured by deoxy-bromo-uracil (BrdU) staining. These micronuclei stained positive for histone γH2AX, which was linked to DNA replication, suggesting that micronuclei arise from checkpoint adaptation and that micronuclei may continue to damage DNA. By contrast the normal cell line WI-38 did not undergo checkpoint adaptation when treated with cisplatin and did not show changes in micronuclei number. These data reveal that the production of micronuclei by checkpoint adaptation is part of a process that contributes to genomic change.
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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".