The combination of gefitinib and RAD001 inhibits growth of HER2 overexpressing breast cancer cells and tumors irrespective of trastuzumab sensitivity
Bibliographic record
Abstract
BACKGROUND: HER2-positive breast cancers exhibit high rates of innate and acquired resistance to trastuzumab (TZ), a HER2-directed antibody used as a first line treatment for this disease. TZ resistance may in part be mediated by frequent co-expression of EGFR and by sustained activation of the mammalian target of rapamycin (mTOR) pathway. Here, we assessed feasibility of combining the EGFR inhibitor gefitinib and the mTOR inhibitor everolimus (RAD001) for treating HER2 overexpressing breast cancers with different sensitivity to TZ. METHODS: The gefitinib and RAD001 combination was broadly evaluated in TZ sensitive (SKBR3 and MCF7-HER2) and TZ resistant (JIMT-1) breast cancer models. The effects on cell growth were measured in cell based assays using the fixed molar ratio design and the median effect principle. In vivo studies were performed in Rag2M mice bearing established tumors. Analysis of cell cycle, changes in targeted signaling pathways and tumor characteristics were conducted to assess gefitinib and RAD001 interactions. RESULTS: The gefitinib and RAD001 combination inhibited cell growth in vitro in a synergistic fashion as defined by the Chou and Talalay median effect principle and increased tumor xenograft growth delay. The improvement in therapeutic efficacy by the combination was associated in vitro with cell line dependent increases in cytotoxicity and cytostasis while treatment in vivo promoted cytostasis. The most striking and consistent therapeutic effect of the combination was increased inhibition of the mTOR pathway (in vitro and in vivo) and EGFR signaling in vivo relative to the single drugs. CONCLUSIONS: The gefitinib and RAD001 combination provides effective control over growth of HER2 overexpressing cells and tumors irrespective of the TZ sensitivity status.
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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".