Abstract A10: Combined blockade of VEGF and Ang2 promotes dramatic tumor necrosis and growth inhibition of colorectal cancer xenografts.
Bibliographic record
Abstract
Abstract Background: VEGF and Angiopoietin-2 (Ang2) are secreted growth factors that are elevated in many human malignancies and function to regulate critical yet distinct aspects of tumor angiogenesis. While blockade of VEGF in combination with standard chemotherapy regimens has been demonstrated to improve survival in patients with metastatic colorectal cancer, the clinical benefit is transient, with most patients experiencing disease progression. We hypothesized that a combination treatment with agents that block the VEGF and Ang2 signaling pathways would provide more potent inhibition of tumor angiogenesis than single agent therapies, thereby resulting in more pronounced and/or durable effects on tumor growth. Methods: We assessed the antitumor effects of aflibercept (VEGF Trap) as a single agent or in combination with REGN910, a fully-human monoclonal antibody that specifically binds and potently inhibits Ang2 (each agent dosed at 15 mg/kg q4dx3). We employed whole tissue section mapping and multiplexed immunostaining techniques in Colo205 human tumor xenografts to assess changes in vessel density (CD31), hypoxia (pimonidazole) and tumor cell death (TUNEL). Results: The combination treatment of REGN910 plus aflibercept provided significantly greater tumor regression than single agent aflibercept treatment. No additive gross toxicities or body weight loss were observed following the combination treatment. Consistent with a more potent effect on tumor growth, the combination treatment caused a more pronounced decrease in tumor vessel density and a greater increase in tumor hypoxia at 24 hr post-treatment than either single agent. Finally, the combination treatment promoted much more dramatic tumor cell death at 72 hr than either single agent. Conclusions: Combined inhibition of VEGF and Ang2 using aflibercept plus REGN910 provides much more pronounced antitumor effects than either single agent, suggesting that combining these agents in the clinic is an attractive strategy to extend the benefit of anti-VEGF therapies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A10.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".