Hypermethylation of the ABCB1 downstream gene promoter accompanies ABCB1 gene amplification and increased expression in docetaxel-resistant MCF-7 breast tumor cells
Bibliographic record
Abstract
Drug transporters have been implicated in resistance of solid and non-solid tumors to a variety of chemotherapeutic agents. Higher expression of the ABCB1 drug transporter is often observed in drug-resistant tumor cells, although the precise mechanism remains unclear. During selection of MCF-7 cells for survival in increasing concentrations of docetaxel (MCF-7TXT cells), we observed in this study a temporal correlation between the acquisition of docetaxel resistance at selection dose 9 and the increased expression of ABCB1. Both the magnitude of docetaxel resistance and the level of ABCB1 expression then rose as the selection dose was further elevated. We also observed through bisulfite sequencing experiments that the ABCB1 downstream promoter became increasingly methylated following the acquisition of drug resistance (selection doses 10-12). Transcription was solely attributed to the upstream ABCB1 promoter within MCF-7TXT cells at the highest selection dose suggesting that hypermethylation caused a shift in promoter usage. The hypermethylation was also accompanied by regional amplification of chromosome 7 containing the ABCB1 gene and its neighbor ABCB4 but not DBF-4. The amplification of the ABCB1 gene correlated positively both with the hypermethylation of the ABCB1 downstream promoter (r=0.90) and the increased expression of ABCB1 (r=0.78). Moreover demethylation of the ABCB1 downstream promoter induced by 5-aza-2A'deoxycytidine treatment decreased the expression of ABCB1 mRNA in MCF-7TXT cells. Taken together, our findings suggest that the increased expression of ABCB1 upon acquisition of docetaxel resistance in breast tumor cells can be multifactorial, involving both epigenetic changes in promoter usage and regional chromosome amplification.
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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".