Interleukin 12 Gene Transfer into Skin Distant from the Tumor Site Elicits Antimetastatic Effects Equivalent to Local Gene Transfer
Bibliographic record
Abstract
We have reported that particle-mediated interleukin 12 (IL-12) gene transfer into the skin overlying the local tumor inhibits systemic metastases. To further characterize this effect, we compared the antitumor and antimetastatic effects of IL-12 cDNA delivered at the local tumor site versus at a site distant from the primary tumor, in a spontaneous metastasis model of LLC-F5 tumor. Local IL-12 gene delivery into the skin overlying the intradermal tumor (local IL-12 treatment) on days 7, 9, and 11 after tumor implantation resulted in the most suppression of the growth of the primary LLC-F5 tumor, whereas IL-12 gene transfer into the skin distant from the tumor (distant IL-12 treatment) was less effective. In contrast, both local IL-12 and distant IL-12 treatment, followed by tumor excision, inhibited lung metastases to a similar extent, resulting in significantly extended survival of test mice. The results of in vivo studies using depleting anti-asialo GM1 antibody and anti-CD4/anti-CD8 monoclonal antibodies, or neutralizing anti-interferon gamma (IFN-gamma) monoclonal antibody demonstrated that natural killer (NK) cells, CD8(+) T cells, and IFN-gamma contributed to the antimetastatic effects in both treatment groups. Furthermore, the levels of mRNA expression of vascular endothelial growth factor and matrix methalloproteinase 9 at the tumor microenvironment were suppressed after both local and distant IL-12 treatment. These results suggest that the current particle-mediated IL-12 gene delivery in the spontaneous LLC-F5 metastasis model can confer antimetastatic activities, irrespective of the gene transfection site, via a combination of several mechanisms involving CD8(+) T cells, NK cells, IFN-gamma, and antiangiogenesis.
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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".