Abacavir enhances the efficacy of doxorubicin via inhibition of histone demethylase KDM5B in breast cancer
Bibliographic record
Abstract
KDM5B, a lysine-specific histone demethylase, is widely upregulated in breast cancer. The current study investigated the role of KDM5B in breast cancer and explored the repurposing potential of the antiviral drug abacavir (ABC). The cytotoxic effects and the effect of ABC sensitization on doxorubicin (DOX) efficacy were evaluated using 2-D and 3-D cell culture models. KDM5B expression was elevated in breast cancer tissues compared to normal breast tissues. In vitro studies demonstrated that ABC treatment reduced KDM5B expression in breast cancer cells and increased their sensitivity towards DOX. ABC induced late apoptosis and S-phase arrest, while the ABC + DOX combination led to S/G2 phase arrest, late apoptosis, and cell death. Data generated from patient-derived breast tumoroids corroborated the 2-D cell culture-based findings. Additionally, molecular docking studies indicated that the active drug metabolite carbovir triphosphate (CBV-TP) could interact with the DNA polymerase β-DNA complex, suggesting its potential mechanism to be incorporated into the DNA synthesis cycle, leading to cell cycle arrest in tumor cells. Our findings highlight the repurposing potential of ABC to target KDM5B in breast cancer. This approach enhanced the efficacy of DOX, which could allow further dose reduction and reduced side effects, offering a promising therapeutic strategy.
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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) | 0.002 | 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".