Characterization of a novel metabolic strategy used by drug‐resistant tumor cells
Bibliographic record
Abstract
ABSTRACT Acquired or inherent drug resistance is the major problem in achieving successful cancer treatment. However, the mechanism(s) of pleiotropic drug resistance remains obscure. We have identified and characterized a cellular metabolic strategy that differentiates drug‐resistant cells from drug‐sensitive cells. This strategy may serve to protect drug‐resistant cells from damage caused by chemotherapeutic agents and radiation. We show that drug‐resistant cells have low mitochondrial membrane potential, use nonglucose carbon sources (fatty acids) for mitochondrial oxygen consumption when glucose becomes limited, and are protected from exogenous stress such as radiation. In addition, drug‐resistant cells express high levels of mitochondrial uncoupling protein 2 (UCP2). The discovery of this metabolic strategy potentially facilitates the design of novel therapeutic approaches to drug resistance.—Harper, M.‐E., Antoniou, A., Villalobos‐Menuey, E., Russo, A., Trauger, R., Vendemelio, George, A. M., Bartholomew, R., Carlo, D., Shaikh, A., Kupperman, J., Newell, E. W., Bespalov, I. A., Wallace, S. S., Liu, Y., Rogers, J. R., Gibbs, G. L., Leahy, J. L., Camley, R. E., Melamede, R., Newell, M. K. Characterization of a novel metabolic strategy used by drug‐resistant tumor cells. FASEB J . 16, 1550–1557 (2002)
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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 | 0.001 |
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".