Down-regulation of ABCB1 by collateral sensitivity drugs reverses multidrug resistance and up-regulates enolase I
Bibliographic record
Abstract
The emergence of drug resistance remains an obstacle in the clinical treatment of cancer. Recent developments in the studies of drug resistance have identified compounds such as verapamil and tamoxifen that specifically target ABCB1-expressing multidrug-resistant (MDR) cells, through an ATP-dependent ROS-generating mechanism. In this report, we demonstrate that treatment of ABCB1-expressing MDR cells (CHORC5 or MDA-Doxo400) or individual clones of the latter with sub-lethal concentrations of tamoxifen or verapamil down-regulates ABCB1 protein and mRNA expression in surviving clones. Consequently, tamoxifen- and verapamil-treated cells show increased sensitivity to chemotherapeutic drugs (e.g., colchicine and doxorubicin) and decreased sensitivity to collateral sensitivity drugs (e.g., verapamil and tamoxifen). Importantly, we show for the first time that down-regulation of ABCB1 expression resulting from tamoxifen treatment and CRISPR-knockout of ABCB1 expression up-regulate α-enolase (enolase I) protein levels and activity. These findings demonstrate a possible effect of ABCB1 expression on the metabolic homeostasis of MDR cells. Moreover, given the use of tamoxifen to prevent the recurrence of oestrogen receptor-positive breast cancer, the findings of this study may be clinically important in modulating activity of other drugs.
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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 | 0.001 |
| 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".