Careful Decoy Receptor Titering is Required to Inhibit Tumor Angiogenesis While Avoiding Adversely Altering VEGF Bioavailability
Bibliographic record
Abstract
To inhibit tumor-induced angiogenesis, the VEGF signaling pathway was targeted using AAV vectors encoding a VEGF decoy receptor, a truncated, soluble form of the murine VEGF receptor-2 (tsFlk-1). This approach initially had significant anti-neuroblastoma efficacy in murine xenograft models of local and metastatic disease, but when higher circulating levels of tsFlk-1 were established, tumor growth was more aggressive than even in control mice. Part of the mechanism for this apparent tumor resistance was increased human VEGF expression by the tumor cells. However, further investigation revealed that although a greater amount of VEGF could be bound by higher levels of tsFlk-1, more VEGF also existed in an unbound state and was, therefore, available to support angiogenesis. This novel, tumor-independent mechanism for resistance to antiangiogenic strategies suggests that careful titering of angiogenesis inhibitors may be required to achieve maximal antitumor efficacy and avoid therapy resistance mediated, in part, by ligand bioavailability. This has important implications for therapeutic strategies that use decoy receptors and other agents, such as antibodies, to bind angiogenic factors, in an attempt to inhibit tumor neovascularization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".