Antiangiogenic Cancer Therapy with Microencapsulated Cells
Bibliographic record
Abstract
Inhibition of angiogenesis has led to tumor suppression in several cancer models. Although administering purified recombinant antiangiogenic product is effective, alternative approaches through genetic manipulation may be more cost-effective. We propose to implant nonautologous recombinant cells secreting angiostatin for systemic delivery of angiostatin in cancer treatment. These cells are protected from graft rejection in alginate microcapsules to function as "micro-organs" to deliver angiostatin in vivo. This approach was tested by implanting encapsulated mouse myoblast C2C12 cells genetically modified to secrete angiostatin into mice bearing solid tumor. Angiostatin was detected in sera of the treated mice. Efficacy was demonstrated by suppression of palpable tumor growth and improved survival. At autopsy, angiostatin localized to residual tumors and high levels of angiostatic activity were detected in tumor extracts. Tumor tissues showed increased apoptosis and necrosis compared with those from untreated or mock-treated mice. Immunohistochemical staining against von Willebrand factor, an endothelial cell marker, showed that within tumors from the treated mice, the neovasculature was poorly defined by endothelial cells, many of which were undergoing apoptosis. However, the tumors eventually developed neovasculature independent of endothelial cells. Such vascular mimicry would account for the lack of long-term efficacy despite persistent angiostatin delivery. In conclusion, implantation with nonautologous microencapsulated cells is feasible for systemic delivery of angiostatin, resulting in localization of angiostatin to tumors and targeted apoptosis of the endothelial cells. Clinical efficacy was demonstrated by suppression of tumor growth and extension of life span. Although the potential of this cell-based approach for angiostatin-mediated cancer therapy is confirmed, long-term efficacy must take into account the possible escape by some tumors from angiogenesis inhibition.
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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".