Combined Immunotherapy and Antiangiogenic Therapy of Cancer with Microencapsulated Cells
Bibliographic record
Abstract
An alternative form of gene therapy involves immunoisolation of a nonautologous cell line engineered to secrete a therapeutic product. Encapsulation of these cells in a biocompatible polymer serves to protect these allogeneic cells from host-versus-graft rejection while recombinant products and nutrients are able to pass by diffusion. This strategy was applied to the treatment of cancer with some success by delivering either interleukin 2 or angiostatin. However, as cancer is a complex, multifactorial disease, a multipronged approach is now being developed to attack tumorigenesis via multiple pathways in order to improve treatment efficacy. A combination of immunotherapy with angiostatic therapy was investigated by treating B16-F0/neu melanoma-bearing mice with intraperitoneally implanted, microencapsulated mouse myoblasts (C2C12) genetically modified to deliver angiostatin and an interleukin 2 fusion protein (sFvIL-2). The combination treatment resulted in improved survival, delayed tumor growth, and increased histological indices of antitumor activity (apoptosis and necrosis). In addition to improved efficacy, the combination treatment also ameliorated some of the undesirable side effects from the individual treatments that have led to the previous failure of the single treatments, for example, inflammatory response to IL-2 or vascular mimicry due to angiostatin. In conclusion, the combination of immuno- and antiangiogenic therapies delivered by immunoisolated cells was superior to individual treatments for antitumorigenesis activity, not only because of their known mechanisms of action but also because of unexpected protection against the adverse side effects of the single treatments. Thus, the concept of a "cocktail" strategy, with microencapsulation delivering multiple antitumor recombinant molecules to improve efficacy, is validated.
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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) | 0.001 | 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".