Combined Treatment of a Murine Breast Cancer Model with Type 5 Adenovirus Vectors Expressing Murine Angiostatin and IL-12: A Role for Combined Anti-Angiogenesis and Immunotherapy
Bibliographic record
Abstract
In this study, we used intratumor delivery of adenoviral vectors to induce a selective anti-tumor response by combining the potent angiogenesis inhibitor murine angiostatin (adenovirus (Ad)-angiostatin) with the powerful immune simulator and angiostatic cytokine murine IL-12 (Ad-IL-12). In a murine model of breast carcinoma, intratumor injection of Ad-angiostatin delayed mean tumor growth, as compared with control virus with an initial regression of tumor growth, in 65% of treated animals. However, all treated animals eventually succumbed to the tumors. Mice injected with Ad-IL-12 alone responded with an initial regression in 20% of treated animals, with only 13% developing a total regression. Coinjection of the vectors resulted in 96% of the treated animals developing an initial regression, with 54% undergoing a total regression of the tumor. These mice were resistant to tumor rechallenge and developed a strong CTL response. Frozen tumor sections were stained for microvessel density using an Ab against murine CD31, an endothelial cell marker. Automated image analysis revealed the mean microvessel density following the administration of Ad-angiostatin and Ad-IL-12 alone or in combination was significantly reduced compared with the control-treated tumor. In summary, we have shown that a short-term course of antiangiogenic therapy combined with immunotherapy can effectively shrink a solid tumor and vaccinate the animal against rechallenge. The rationale for this therapy is to limit the tumor size by attacking the vasculature with angiostatin, thereby allowing IL-12 to mount a T cell-specific response against the tumor AG:
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 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) | 0.001 | 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".