HER2/neu Oncogene and Sensitivity to the DNA-Interactive Drug Doxorubicin
Bibliographic record
Abstract
Breast tumor cells overexpressing the proto-oncogene HER2/neu are known to be less responsive to certain DNA-binding chemotherapeutic agents. The current study specifically investigates the correlation between chemosensitivity to the DNA-binding drug doxorubicin and cellular HER2/neu protein levels in a panel of eight breast cancer cell lines (HS-578, BT-474, MDA-MB-453, MDA-MB-231, MDA-MB-175, MCF-7, ZR-75-1 and T47D). The IC50 (the drug concentration required to inhibit cell growth by 50%) values for the cell lines were determined by the sulforhodamine B assay. IC50 values were correlated with HER2/neu protein levels determined by Western blotting. An almost linear relationship between IC50 and HER2/neu protein level for seven cell lines (p = 0.02, r2 = 0.680) was found, with protein levels increasing as resistance increased. The findings suggest that overexpression of HER2/neu correlates with increased resistance to doxorubicin in seven of eight breast cancer cell lines studied. The observation that, in one cell line (MDA-MB-175), doxorubicin IC50 did not correlate with HER2/neu levels, suggests that in these cells, an as-of-yet unidentified factor contributes to resistance. If the observed correlation, which was present in seven of eight cell lines, is confirmed in a larger sample size, increased HER2/neu levels may be implemented as a predictor of breast tumor sensitivity to doxorubicin.
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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 | 0.000 |
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".