Adeno-Associated Virus–Mediated Antiangiogenic Gene Therapy with Thrombospondin-1 Type 1 Repeats and Endostatin
Bibliographic record
Abstract
PURPOSE: Recombinant adeno-associated virus (rAAV)-mediated antiangiogenic gene therapy offers a powerful strategy for cancer treatment, maintaining sustained levels of antiangiogenic factors with coincident enhanced therapeutic efficacy. We aimed to develop rAAV-mediated antiangiogenic gene therapy delivering endostatin and 3TSR, the antiangiogenic domain of thrombospondin-1. EXPERIMENTAL DESIGN: rAAV vectors were constructed to express endostatin (rAAV-endostatin) or 3TSR (rAAV-3TSR). The antiangiogenic efficacy of the vectors was characterized using a vascular endothelial growth factor (VEGF)-induced mouse ear angiogenesis model. To evaluate the antitumor effects of the vectors, immunodeficient mice were pretreated with rAAV-3TSR or rAAV-endostatin and received orthotopic implantation of cancer cells into the pancreas. To mimic clinical situations, mice bearing pancreatic tumors were treated with intratumoral injection of rAAV-3TSR or rAAV-endostatin. RESULTS: rAAV-mediated i.m. gene delivery resulted in expression of the transgene in skeletal muscle with inhibition of VEGF-induced angiogenesis at a distant site (the ear). Local delivery of the vectors into the mouse ear also inhibited VEGF-induced ear angiogenesis. Pretreatment of mice with i.m. or intrasplenic injection of rAAV-endostatin or rAAV-3TSR significantly inhibited tumor growth. A single intratumoral injection of each vector also significantly decreased the volume of large established pancreatic tumors. Tumor microvessel density was significantly decreased in each treatment group and was well correlated with tumor volume reduction. Greater antiangiogenic and antitumor effects were achieved when rAAV-3TSR and rAAV-endostatin were combined. CONCLUSIONS: rAAV-mediated 3TSR and endostatin gene therapy showed both localized and systemic therapeutic effects against angiogenesis and tumor growth and may provide promise for patients with pancreatic cancer.
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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) | 0.001 | 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".