P-glycoprotein (P-gp1) as direct modulator of collateral sensitivity in multidrug resistant tumor cells.
Bibliographic record
Abstract
2285 Resistance to multiple anti-cancer drugs is a major obstacle in cancer treatment. Such multidrug resistance (MDR), often to chemically unrelated compounds is mediated by the action of several membrane transport proteins or ABC transporters. P-glycoprotein (P-gp1) is the most studied MDR-causative protein. P-gp1 mediates the active efflux of drugs in tumor cell lines with a ratio of ATP molecules to drug of 2:1. The P-gp1 ATPase activity modulator verapamil, has been shown to induce collateral sensitivity. In a previous report (Karwatsky, et. al. Biochemistry, 2003) we demonstrated that verapamil and other collateral sensitivity agents cause apoptosis in MDR cells that over-express P-gp1 in a p53 independent manner, through increased reactive oxygen species resulting from hyper-activation of P-gp1 ATPase. Moreover, inhibitors of P-gp1 ATPases (e.g. PSC833 or Ivermectin) reverse Verapamil-induced collateral sensitivity. Collectively, our earlier results provided a strong correlation between P-gp1 and Verapamil induced collateral sensitivity. In this report, using a siRNA approach, we demonstrate a direct link between P-gp1 expression and verapamil induced collateral sensitivity. Our results demonstrate for the first time that down regulation of P-gp1 expression reverses P-gp1 induced collateral sensitivity to verapamil and other collateral sensitivity-inducing agents or drugs. Moreover, and consistent with the molecular mechanism of verapamil-induced collateral sensitivity with respect to the role of P-gp1 and reactive oxygen species, we show a direct synergy between verapamil and inhibitors of electron-transport chain, (eg. Rotenone & Antimycin A). Taken together, these results resolve a very important and outstanding mechanism relating to the role of P-gp1 in collateral sensitivity. In addition, these findings open up the possibility of developing highly specific anti-cancer drugs to MDR tumors.
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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) | 0.002 | 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".