Correlation between Multi-Drug Resistance-Associated Membrane Transport in Clonal Cancer Cells and the Cell Cycle Phase
Bibliographic record
Abstract
Multidrug resistance driven by ABC membrane transporters is one of the major reasons for treatment failure in human malignancy. Some limited evidence has previously been reported on the cell cycle dependence of ABC transporter expression. However, it has never been demonstrated that the functional activity of these transporters correlates with the cell cycle position. Here, we studied the rate of intrinsic ABC transport in different phases of the cell cycle in cultured MCF-7 breast cancer cells. The rate was characterized in terms of the efflux kinetics from cells loaded with an ABC transporter substrate. As averaging the kinetics over a cell population could lead to errors, we studied kinetics of ABC transport at the single-cell level. We found that the rate of ABC transport in MCF-7 cells could be described by Michaelis-Menten kinetics with two classical parameters, V(max) and K(M). Each of these parameters showed similar unimodal distributions with different positions of maxima for cell subpopulations in the 2c and 4c states. Compared to the 2c cells, the 4c cells exhibited greater V(max) values, indicating a higher activity of transport. They also exhibited a greater V(max)/K(M) ratio, indicating a higher efficiency of transport. Our findings suggest that cell cycle-related modulation of MDR may need to be taken into account when designing chemotherapy regimens which include cytostatic agents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".