DNA-PK inhibition extends the therapeutic effects of Top2 poisoning to non-proliferating cells, increasing activity at a cost
Bibliographic record
Abstract
Type II topoisomerase (Top2) poisoning therapy is used to treat a broad range of cancers via induction of double strand breaks (DSBs) in cells undergoing replication and transcription. Preventing the repair of DSBs via inhibition of DNA-PK, an inhibitor of non-homologous end-joining (NHEJ), increases cell kill with Top2 poisons and has led to the initiation of several clinical trials. To elucidate the cellular mechanisms leading to synergistic activity of dual DNA-PK/Top2 inhibition we looked at their effects in cycling versus non-cycling cells, in 3D spheroids and in xenograft models. Combined DNA-PK/Top2 inhibition was found to not only increase the cell kill in proliferating cells, the cell population that is typically most vulnerable to Top2 poisoning, but also in non-proliferative but transcriptionally active cells. This effect was observed in both cancer and normal tissue models, killing more cells than high concentrations of etoposide alone. The combination treatment delayed tumor growth in mice compared to Top2 poisoning alone, but also led to increased toxicity. These findings demonstrate sensitization of Top2β-expressing, non-cycling cells to Top2 poisoning by DNA-PK inhibition. Expansion of the target cell population of Top2 poison treatment to include non-proliferating cells via combination with DNA damage repair inhibitors has implications for efficacy and toxicity of these combinations, including for inhibitors of DNA-PK currently in clinical trial.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".