Antitumor activity of motesanib alone and in combination with cisplatin or docetaxel in multiple human non–small-cell lung cancer xenograft models
Bibliographic record
Abstract
BACKGROUND: Non-small-cell lung cancer (NSCLC) is categorized into various histologic subtypes that play an important role in prognosis and treatment outcome. We investigated the antitumor activity of motesanib, a selective antagonist of vascular endothelial growth factor receptors (VEGFR) 1, 2, and 3, platelet-derived growth factor receptor, and Kit, alone and combined with chemotherapy in five human NSCLC xenograft models (A549, Calu-6, NCI-H358, NCI-H1299, and NCI-H1650) containing diverse genetic mutations. RESULTS: Motesanib as a single agent dose-dependently inhibited tumor xenograft growth compared with vehicle in all five of the models (P < 0.05). When combined with cisplatin, motesanib significantly inhibited the growth of Calu-6, NCI-H358, and NCI-H1650 tumor xenografts compared with either single agent alone (P < 0.05). Similarly, the combination of motesanib plus docetaxel significantly inhibited the growth of A549 and Calu-6 tumor xenografts compared with either single agent alone (P < 0.05). In NCI-H358 and NCI-H1650 xenografts, motesanib with and without cisplatin significantly decreased tumor blood vessel area (P < 0.05 vs vehicle) as assessed by anti-CD31 staining. Motesanib alone or in combination with chemotherapy had no effect on tumor cell proliferation in vitro. CONCLUSIONS: These data demonstrate that motesanib had antitumor activity against five different human NSCLC xenograft models containing diverse genetic mutations, and that it had enhanced activity when combined with cisplatin or docetaxel. These effects appeared to be mediated primarily by antiangiogenic mechanisms.
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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".