Treatment of HER-2/<b> <i>neu</i> </b> Overexpressing Breast Cancer Xenograft Models with Trastuzumab (Herceptin) and Gefitinib (ZD1839): Drug Combination Effects on Tumor Growth, HER-2/<b> <i>neu</i> </b> and Epidermal Growth Factor Receptor Expression, and Viable Hypoxic Cell Fraction
Bibliographic record
Abstract
PURPOSE: The purpose of this research was to assess the effects of single agent and combination treatment with trastuzumab and gefitinib on tumor growth and tumor microenvironment in two HER-2/neu overexpressing breast xenograft models, MDA-MB-435/LCC6(HER-2) (LCC6(HER-2); estrogen receptor negative) and MCF-7(HER-2) (estrogen receptor positive). EXPERIMENTAL DESIGN: LCC6(HER-2) and MCF-7(HER-2) cells, both in tissue culture and xenografts grown in SCID-Rag 2M mice, were treated with trastuzumab and gefitinib, alone or in combination. The rate of tumor growth was determined. In addition, tumor HER-2/neu and epidermal growth factor receptor expression, cell viability, cell cycle distribution, and proportion of viable hypoxic cells were determined by flow cytometric analyses of single tumor cell suspensions. RESULTS: Both tumor models were very sensitive to trastuzumab and moderately sensitive to gefitinib in vivo. The combination resulted in therapeutic effects, as judged by inhibition of tumor growth, which was greater (albeit not statistically significant) than that observed with trastuzumab administered as a single agent. Trastuzumab was effective in down-regulating HER-2/neu, and gefitinib mediated a reduction in epidermal growth factor receptor expression on tumor cells. In LCC6(HER-2) tumors, trastuzumab significantly reduced tumor cell viability, which was not improved by the addition of gefitinib. Gefitinib dramatically reduced the proportion of viable hypoxic cells in LCC6(HER-2) and MCF-7(HER-2) tumors. This effect was abrogated by the addition of trastuzumab. CONCLUSIONS: Although in vivo efficacy studies in two HER-2/neu overexpressing breast xenograft models showed that the combination of trastuzumab and gefitinib was effective, analyses of various cellular parameters failed to reveal beneficial effects and argue that this drug combination may not be favorable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".