Decitabine Response in Breast Cancer Requires Efficient Drug Processing and Is Not Limited by Multidrug Resistance
Bibliographic record
Abstract
Dysregulation of DNA methylation is an established feature of breast cancers. DNA demethylating therapies like decitabine are proposed for the treatment of triple-negative breast cancers (TNBC) and indicators of response need to be identified. For this purpose, we characterized the effects of decitabine in a panel of 10 breast cancer cell lines and observed a range of sensitivity to decitabine that was not subtype specific. Knockdown of potential key effectors demonstrated the requirement of deoxycytidine kinase (DCK) for decitabine response in breast cancer cells. In treatment-naïve breast tumors, DCK was higher in TNBCs, and DCK levels were sustained or increased post chemotherapy treatment. This suggests that limited DCK levels will not be a barrier to response in patients with TNBC treated with decitabine as a second-line treatment or in a clinical trial. Methylome analysis revealed that genome-wide, region-specific, tumor suppressor gene-specific methylation, and decitabine-induced demethylation did not predict response to decitabine. Gene set enrichment analysis of transcriptome data demonstrated that decitabine induced genes within apoptosis, cell cycle, stress, and immune pathways. Induced genes included those characterized by the viral mimicry response; however, knockdown of key effectors of the pathway did not affect decitabine sensitivity suggesting that breast cancer growth suppression by decitabine is independent of viral mimicry. Finally, taxol-resistant breast cancer cells expressing high levels of multidrug resistance transporter ABCB1 remained sensitive to decitabine, suggesting that the drug could be used as second-line treatment for chemoresistant patients.
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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 | 0.001 |
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".