Human Breast Cancer Cells Selected for Resistance to Trastuzumab <i>In vivo</i> Overexpress Epidermal Growth Factor Receptor and ErbB Ligands and Remain Dependent on the ErbB Receptor Network
Bibliographic record
Abstract
PURPOSE: We have investigated mechanisms of acquired resistance to the HER2 antibody trastuzumab in BT-474 human breast cancer cells. EXPERIMENTAL DESIGN: BT-474 xenografts established in athymic nude mice were eliminated by trastuzumab. Continuous cell lines (HR for Herceptin resistant) were generated from tumors that recurred in the presence of continuous antibody therapy. RESULTS: The isolated cells behaved resistant to trastuzumab in culture as well as when reinjected into nude mice. They retained HER2 gene amplification and trastuzumab binding and were exquisitely sensitive to peripheral blood mononuclear cells ex vivo in the presence of the antibody. The HR cells exhibited higher levels of phosphorylated epidermal growth factor receptor (EGFR) and EGFR/HER2 heterodimers. Phosphorylation of HER2 in HR cells was inhibited by the EGFR tyrosine kinase inhibitors erlotinib and gefitinib. Gefitinib also inhibited the basal association of p85 with phosphorylated HER3 in HR cells. Both inhibitors as well as the dual EGFR/HER2 inhibitor, lapatinib, induced apoptosis of the HR cells in culture. Growth of established HR5 xenografts was inhibited by erlotinib in vivo. In addition, the HR cells overexpressed EGFR, transforming growth factor alpha, heparin-binding EGF, and heregulin RNAs compared with the parental trastuzumab-sensitive cells. CONCLUSIONS: These results are consistent with the inability of trastuzumab to block the heterodimerization of HER2 and suggest that amplification of ligand-induced activation of ErbB receptors is a plausible mechanism of acquired resistance to trastuzumab that should be investigated in primary mammary cancers.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".