Targeting Angiogenesis and HGF Function Using an Adenoviral Vector Expressing the HGF Antagonist NK4 for Cancer Therapy
Bibliographic record
Abstract
Hepatocyte growth factor (HGF) affects tumor growth/invasion and tumor neovascularization. A proposed HGF antagonist, NK4 (an amino-terminal kringle-domain peptide of HGF), inhibits tumor growth/invasion through the competition of HGF binding to its receptor, c-Met, and acts as an angiogenesis inhibitor. To investigate the in vivo effect of NK4 gene transfer, we constructed an adenovirus vector expressing human NK4 (AdCMV.NK4). Human lung cancer cell lines (A549 and H358) infected in vitro with AdCMV.NK4 yielded NK4 protein without a change in the cell growth rate. In contrast, direct injection of AdCMV.NK4 (1 x 10(9) pfu, twice) into established subcutaneous tumors in BALB/c nu/nu mice resulted in suppression of the tumors by 64% for A549 or by 91% for H358 compared with controls (P<0.02 or P<0.01, respectively). Counting of the tumor vessels revealed suppressed vascularity by 57% in H358 tumors when using AdCMV.NK4 (P<0.0001). Furthermore, systemic NK4 delivery by intraperitoneal injection of AdCMV.NK4 effectively suppressed both angiogenesis in the Matrigel assay (86% reduction, P<0.032), subcutaneous tumor growth in vivo (by 65% for H358, P<0.001), and hematogenous lung metastases without obvious side effects. These results indicate that NK4 elicits tumor-growth suppression in vivo through its anti-angiogenic activity and anti-HGF activity and that NK4 gene transfer can be an effective tool in the treatment of cancer.
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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".