Effect of RasGAP N2 Fragment–Derived Peptide on Tumor Growth in Mice
Bibliographic record
Abstract
Peptides that interfere with the natural resistance of cancer cells to genotoxin-induced apoptosis may improve the efficacy of anticancer regimens. We have previously reported that a cell-permeable RasGAP-derived peptide (TAT-RasGAP(317-326)) specifically sensitizes tumor cells to genotoxin-induced apoptosis in vitro. Here, we examined the in vivo stability of a protease-resistant D-form of the peptide, RI.TAT-RasGAP(317-326), and its effect on tumor growth in nude mice bearing subcutaneous human colon cancer HCT116 xenograft tumors. After intraperitoneal injection, RI.TAT-RasGAP(317-326) persisted in the blood of nude mice for more than 1 hour and was detectable in various tissues and subcutaneous tumors. Tumor-bearing mice treated daily for 7 days with RI.TAT-RasGAP(317-326) (1.65 mg/kg body weight) and cisplatin (0.5 mg/kg body weight) or doxorubicin (0.25 mg/kg body weight) displayed reduced tumor growth compared with those treated with either genotoxin alone (n = 5-7 mice per group; P = .004 and P = .005, respectively; repeated measures analysis of variance [ANOVA, two-sided]). This ability of the RI.TAT-RasGAP(317-326) peptide to enhance the tumor growth inhibitory effect of cisplatin was still observed at peptide doses that were at least 150-fold lower than the dose lethal to 50% of mice. These findings provide the proof of principle that RI.TAT-RasGAP(317-326) may be useful for improving the efficacy of chemotherapy in patients.
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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".