Sarcoma IL-12 overexpression facilitates NK cell immunomodulation
Bibliographic record
Abstract
Interleukin-12 (IL-12) is an inflammatory cytokine that has demonstrated efficacy for cancer immunotherapy, but systemic administration has detrimental toxicities. Lentiviral transduction eliciting IL-12-producing human sarcoma for autologous reintroduction provides localized delivery for both innate and adaptive immune response augmentation. Sarcoma cell lines and primary human sarcoma samples were transduced with recombinant lentivirus engineering expression of human IL-12 (hu-IL-12). IL-12 expressing sarcomas were assessed in vitro and in vivo following implantation into humanized NSG and transgenic human IL-15 expressing (NSG.Tg(Hu-IL-15)) murine models. Lentiviral transduction (LV/hu-IL-12) of human osteosarcoma, Ewing sarcoma and rhabdomyosarcoma cell lines, as well as low-passage primary human sarcomas, engendered high-level expression of hu-IL-12. Hu-IL-12 demonstrated functional viability, eliciting specific NK cell-mediated interferon-γ (IFN-γ) release and cytotoxic growth restriction of spheroids in vitro. In orthotopic xenograft murine models, the LV/hu-IL-12 transduced human sarcoma produced detectable IL-12 and elicited an IFN-γ inflammatory immune response specific to mature human NK reconstitution in the NSG.Tg(Hu-IL-15) model while restricting tumor growth. We conclude that LV/hu-IL-12 transduction of sarcoma elicits a specific immune reaction and the humanized NSG.Tg(Hu-IL-15) xenograft, with mature human NK cells, can define in vivo anti-tumor effects and systemic toxicities. IL-12 immunomodulation through autologous tumor transduction and reintroduction merits exploration for sarcoma treatment.
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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 | 0.000 |
| 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".