Neural Stem Cell–Mediated Enzyme/Prodrug Therapy for Glioma: Preclinical Studies
Bibliographic record
Abstract
High-grade gliomas are extremely difficult to treat because they are invasive and therefore not curable by surgical resection; the toxicity of current chemo- and radiation therapies limits the doses that can be used. Neural stem cells (NSCs) have inherent tumor-tropic properties that enable their use as delivery vehicles to target enzyme/prodrug therapy selectively to tumors. We used a cytosine deaminase (CD)-expressing clonal human NSC line, HB1.F3.CD, to home to gliomas in mice and locally convert the prodrug 5-fluorocytosine to the active chemotherapeutic 5-fluorouracil. In vitro studies confirmed that the NSCs have normal karyotype, tumor tropism, and CD expression, and are genetically and functionally stable. In vivo biodistribution studies demonstrated NSC retention of tumor tropism, even in mice pretreated with radiation or dexamethasone to mimic clinically relevant adjuvant therapies. We evaluated safety and toxicity after intracerebral administration of the NSCs in non-tumor-bearing and orthotopic glioma-bearing immunocompetent and immunodeficient mice. We detected no difference in toxicity associated with conversion of 5-fluorocytosine to 5-fluorouracil, no NSCs outside the brain, and no histological evidence of pathology or tumorigenesis attributable to the NSCs. The average tumor volume in mice that received HB1.F3.CD NSCs and 5-fluorocytosine was about one-third that of the average volume in control mice. On the basis of these results, we conclude that combination therapy with HB1.F3.CD NSCs and 5-fluorocytosine is safe, nontoxic, and effective in mice. These data have led to approval of a first-in-human study of an allogeneic NSC-mediated enzyme/prodrug-targeted cancer therapy in patients with recurrent high-grade glioma.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".