Multidrug resistance proteins MRP3, MRP1, and MRP2 in lung cancer: correlation of protein levels with drug response and messenger RNA levels.
Bibliographic record
Abstract
Previously (L. C. Young et al., Clin. Cancer Res., 5: 673-680, 1999), we found, in a panel of 23 lung cancer cell lines that had not been selected for in vitro drug resistance, that the mRNA levels of MRP3 and MRP1, two members of the ATP-binding cassette superfamily of transport proteins, correlated with resistance to doxorubicin, vincristine, VP-16, and cis-diamminedicholoroplatinum(II). To extend these studies, we measured multidrug resistance protein (MRP)1, MRP2, and MRP3 protein levels in a panel of 30 lung cancer cell lines that included the original 23 cell lines as well as an additional 7 unselected lung cancer cell lines. In the case of MRP3, a polyclonal antibody was developed that was found to be a sensitive reagent for the detection of MRP3 by Western blot analysis. We found good agreement in the original 23 cell lines between the cognate mRNA and protein levels for MRP1, MRP2, and, especially, MRP3 (r, 0.852), supporting the use of semiquantitative PCR to predict MRP1, MRP2, and MRP3 protein levels in patient samples. There were also strong correlations between the mRNA and protein levels of MRP3 and MRP1, which suggested that these genes might be expressed in a coordinate manner. MRP3, MRP1, and MRP2 protein levels were higher in the non-small cell lung cancer (NSCLC) than in the SCLC cell lines and, in addition, MRP3 and MRP2 were detected almost exclusively in the NSCLC cell lines. Finally, we found that both MRP3 and MRP1, but not MRP2, protein levels correlated with decreased sensitivity of these lung cancer cell lines to doxorubicin, VCR, VP-16, and cis-diamminedicholoroplatinum(II). These findings are consistent with our hypothesis that both MRP3 and MRP1 are components of the multifactorial multidrug resistance phenotype of lung cancer and that MRP3 contributes to the intrinsic resistance of NSCLC cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".