Cancer chemoresistance: The relationship between p53 and multidrug transporters
Bibliographic record
Abstract
Extensive studies indicate that both p53 and multidrug transporters play important roles in chemoresistance. Since the initial reports a decade ago demonstrating a transcriptional dependence of the ABCB1 gene (MDR) promoter by p53, much data have been accumulated. However, despite being the subject of intense study, this p53-MDR relationship remains unclear in human cancers. The data are confounded by variable and contrasting results when considering the in vitro regulation and attempting to draw parallels in tissue specimens. The original model suggested that wild-type p53 downregulates the ABCB1 promoter, whereas mutant p53 increases expression of ABCB1. This review summarizes the data for and against this hypothesis. What emerges from these studies is a complex picture, where data have been obtained in support of this hypothesis, but there are also many circumstances where it is not supported. Taken together, these data suggest that the relationship between p53 and multidrug transporters is conditional. It is dependent on cellular environment, the drug used, and the nature of the p53 mutation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".