Signaling interactions of rapamycin combined with erlotinib in cervical carcinoma xenografts
Bibliographic record
Abstract
Clinical trials using rapamycin analogues or HER1/epidermal growth factor receptor (EGFR) inhibitors show that each class of agent has activity against a range of human solid tumors. Because blockade of mitogen-activated protein kinase signaling occurs following HER1/EGFR inhibition in some cell types, we tested the combination of rapamycin and erlotinib in SiHa, Me180, and CaSki human cervical carcinomas xenografts in severe combined immunodeficient mice. In tissue culture, all three cell lines showed decreased phosphorylated S6 ribosomal protein and decreased phosphorylated extracellular signal-regulated kinase (ERK) following treatment with rapamycin and erlotinib, respectively. In SiHa tumors, suppression of phosphorylated S6 was induced by either drug alone, whereas phosphorylated ERK decreased with erlotinib, and enhancement of these effects was obtained with the combination. Continuous treatment of xenografts for 3 weeks led to significant tumor growth delay compared with vehicle control for rapamycin as single agent (P = 0.003) and greater for the combination (P = 0.04 versus rapamycin). Significant antiangiogenic effect was obtained in SiHa xenografts using the drugs together (measured by microvascular density and vascular endothelial growth factor plasma levels) but not for the single agents. Me180 and CaSki xenografts showed significant growth delay with rapamycin but not with erlotinib. Erlotinib treatment resulted in decreased phosphorylated ERK, associated with enhanced suppression of phosphorylated S6 and improved growth delay in Me180 but not in CaSki tumors. These results support the further clinical investigation of rapamycin and EGFR inhibitor combinations in anticancer therapy but highlight the problem of intertumoral heterogeneity in the prediction of in vivo response.
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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".