Human Neural Stem Cells Target Experimental Intracranial Medulloblastoma and Deliver a Therapeutic Gene Leading to Tumor Regression
Bibliographic record
Abstract
PURPOSE: Medulloblastoma, a malignant pediatric brain tumor, is incurable in about one third of patients despite multimodal treatments. In addition, current therapies can lead to long-term disabilities. Based on studies of the extensive tropism of neural stem cells (NSC) toward malignant gliomas and the secretion of growth factors common to glioma and medulloblastoma, we hypothesized that NSCs could target medulloblastoma and be used as a cellular therapeutic delivery system. EXPERIMENTAL DESIGN: The migratory ability of HB1.F3 cells (an immortalized, clonal human NSC line) to medulloblastoma was studied both in vitro and in vivo. As proof-of-concept, we used HB1.F3 cells engineered to secrete the prodrug activating enzyme cytosine deaminase. We investigated the potential of human NSCs to deliver a therapeutic gene and reduce tumor growth. RESULTS: The migratory capacity of HB1.F3 cells was confirmed by an in vitro migration assay, and corroborated in vivo by injecting chloromethylbenzamido-Dil-labeled HB1.F3 cells into the hemisphere contralateral to established medulloblastoma in nude mice. In vitro studies showed the therapeutic efficacy of HB1.F3-CD on Daoy cells in coculture experiments. In vitro therapeutic studies were conducted in which animals bearing intracranial medulloblastoma were injected ipsilaterally with HB1.F3-CD cells followed by systemic 5-flourocytosine treatment. Histologic analyses showed that human NSCs migrate to the tumor bed and its boundary, resulting in a 76% reduction of tumor volume in the treatment group (P<0.01). CONCLUSION: These studies show for the first time the potential of human NSCs as an effective delivery system to target and disseminate therapeutic agents to medulloblastoma.
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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".