Intravenous RNA Interference Gene Therapy Targeting the Human Epidermal Growth Factor Receptor Prolongs Survival in Intracranial Brain Cancer
Bibliographic record
Abstract
PURPOSE: The human epidermal growth factor receptor (EGFR) plays an oncogenic role in solid cancer, including brain cancer. The present study was designed to prolong survival in mice with intracranial human brain cancer with the weekly i.v. injection of nonviral gene therapy causing RNA interference (RNAi) of EGFR gene expression. EXPERIMENTAL DESIGN: Human U87 gliomas were implanted in the brain of adult scid mice, and weekly i.v. gene therapy was started at day 5 after implantation of 500000 cells. An expression plasmid encoding a short hairpin RNA directed at nucleotides 2529-2557 within the human EGFR mRNA was encapsulated in pegylated immunoliposomes. The pegylated immunoliposome was targeted to brain cancer with 2 receptor-specific monoclonal antibodies (MAb), the murine 83-14 MAb to the human insulin receptor and the rat 8D3 MAb to the mouse transferrin receptor. RESULTS: In cultured glioma cells, the delivery of the RNAi expression plasmid resulted in a 95% suppression of EGFR function, based on measurement of thymidine incorporation or intracellular calcium signaling. Weekly i.v. RNAi gene therapy caused reduced tumor expression of immunoreactive EGFR and an 88% increase in survival time of mice with advanced intracranial brain cancer. CONCLUSIONS: Weekly i.v. nonviral RNAi gene therapy directed against the human EGFR is a new therapeutic approach to silencing oncogenic genes in solid cancers. This is enabled with a nonviral gene transfer technology that delivers liposome-encapsulated plasmid DNA across cellular barriers with receptor-specific targeting ligands.
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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".