AI-26 * OVERCOMING RESISTANCE TO VEGF-BLOCKADE BY TARGETING THE ANGIOPOIETIN/TIE2 AXIS
Bibliographic record
Abstract
Angiogenesis inhibitors have evolved in the past decade as one of the most promising biology based therapeutic strategies. Pre-clinical studies (published in 1993 and 1994) provided proof of principle that blocking of VEGF dramatically inhibits tumor growth. These observations led to the successful development of inhibitors for VEGF and VEGF receptors by various pharmaceutical companies. Bevacizumab, a monoclonal antibody neutralizing VEGF, was the first-in-class drug to achieve FDA approval for the treatment of colorectal carcinoma in 2004 and later on in other cancer types, including glioblastoma. Anti-VEGF therapy led to an increase in progression free survival in recurrent and primary GBM, but its definite role in first-line-treatment is less clear. Importantly, several preclinical studies suggested that resistance to anti-angiogenic therapy might evolve in the course of the treatment due to an infiltration of specially polarized myeloid cells. In order to define new therapeutic options for anti-angiogenic therapy we investigated the potential role of Tie2/Angiopoietin (Ang) signaling pathway in human and murine glioblastomas. We here show by means of 1) transgenic mice overexpressing Angiopoietin-1 (GFAPtet/Ang-1), 2) transgenic mice overexpressing Angiopoietin-2 (Tie-1tet/Ang-2), 3) application of synthetic activators of the Tie2 receptor tyrosine kinase, 4) application of peptibodies targeting Ang-1 and Ang-2 and 5) application of monoclonal antibodies blocking Ang-2, that targeting of the Tie2/Angiopoietin signaling pathway leads to vascular normalization, diminished influx of myeloid cells and prolonged overall survival in mice pretreated with VEGF-blockers. These findings suggest that targeting of the Tie2/Angiopoietin pathway, even alone or in combination with VEGF inhibition, might be a potential therapeutic option for glioblastoma therapy.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".