Systemic NK4 gene therapy inhibits tumor growth and metastasis of melanoma and lung carcinoma in syngeneic mouse tumor models
Bibliographic record
Abstract
Hepatocyte growth factor (HGF) promotes malignant development of cancer cells by enhancing invasion and metastasis. NK4, a competitive antagonist for HGF, is a bifunctional molecule that acts as a HGF antagonist and angiogenesis inhibitor. Although successful tumor inhibition by NK4 gene expression in tumor models has been demonstrated, the effects of systemic NK4 gene introduction are yet to be addressed. Here we show that systemic administration of a replication-defective adenovirus expressing NK4 (Ad.NK4) inhibits tumor growth and lung metastasis of B16F10 melanoma and Lewis lung carcinoma in syngeneic mice. Single tail-vein injection of Ad.NK4 achieved therapeutic levels of NK4 in the circulation and in multiple organs. Despite NK4 expression that was highest in the liver, toxicity in the liver was minimal. Ad.NK4-mediated growth inhibition was associated with decreased blood vessel density and increased apoptosis in tumor tissues, which suggests that NK4 suppressed tumor growth as an angiogenesis inhibitor. Metastasis of B16F10 melanoma and Lewis lung carcinoma cells to the lung was potently inhibited by systemic Ad.NK4-administration. Our results demonstrated that the adenovirus-mediated induction of high levels of circulating NK4 significantly inhibited in vivo tumor growth and distant metastasis without obvious side effects. NK4 gene therapy is thus a safe and promising strategy for the treatment of cancer patients, and further validation in clinical trials is needed.
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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) | 0.001 | 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".