Role of promoter methylation in the induction of ABCB1 gene expression in anthracycline-resistant and paclitaxel-resistant breast tumor cells
Bibliographic record
Abstract
4291 A major obstacle in the successful treatment of breast cancer is the acquisition of multidrug resistance (MDR), which in vitro often involves the elevated expression and activity of the ABCB1 drug transporter. While the exact mechanism responsible for increased ABCB1 expression in drug-resistant cancer cells remains unclear, it has been proposed that changes in the methylation status of a CpG island within the ABCB1 promoter may be involved. A novel system to study the role of epigenetics in the acquisition of drug resistance within breast cancer cells has been developed within our laboratory. Three panels of drug-resistant MCF-7 cell lines were established by selection in increasing concentrations of various chemotherapy agents. Resistance to doxorubicin, epirubicin and paclitaxel was acquired at specific threshold doses (29.1 nM, 31.5 nM and 3.66 nM respectively) and resistance levels continued to increase with higher selection doses. Gene expression analysis of the drug-resistant cell lines in comparison to MCF-7 co-cultured control cells by quantitative-PCR (Q-PCR) revealed that ABCB1 expression was dramatically higher in epirubicin-resistant (MCF-7EPI) and paclitaxel-resistant (MCF-7TAX-2) cell lines at or above the threshold dose [X2(4)=11.067,p
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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".