The farnesyltransferase inhibitor R115777 (tipifarnib) in combination with tamoxifen acts synergistically to inhibit MCF-7 breast cancer cell proliferation and cell cycle progression <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Cross-talk between receptor tyrosine kinases and estrogen receptor is at least partly responsible for the development of acquired resistance to endocrine therapies. Hence, targeting receptor tyrosine kinases and their downstream partners with inhibitors/antagonists may reverse this resistance. Although ras mutations are rare in breast cancer (2%), aberrant function of Ras signal transduction pathways is common. We therefore investigated the efficacy of the farnesyltransferase inhibitor (FTI) R115777 (tipifarnib) in combination with tamoxifen in MCF-7 human breast cancer models both in vitro and in vivo. There was a synergistic antiproliferative interaction between R115777 and 4-hydroxy-tamoxifen in vitro as calculated by median effect analysis. The combination resulted in a significantly greater G(1) arrest than either drug alone and this was associated with marked inhibition of cyclin D1 and induction of the cell cycle inhibitor p27(kip1). Combining R115777 with either tamoxifen or estrogen withdrawal in vivo produced a significantly greater inhibition of tumor growth and lower xenograft cell proliferation than either therapy alone. These results suggest that the combination of this FTI with endocrine therapy may be of therapeutic benefit in the treatment of breast cancer. Enhanced G1 arrest due to modulation of cell cycle regulatory proteins may be the underlying mechanism for the positive interaction between FTIs and tamoxifen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".