Selective antitumor effect of neural stem cells expressing cytosine deaminase and interferon-beta against ductal breast cancer cells in cellular and xenograft models
Bibliographic record
Abstract
Due to their inherent tumor-tropic properties, genetically engineered stem cells may be advantageous for gene therapy treatment of various human cancers, including brain, liver, ovarian, and prostate malignancies. In this study, we employed human neural stem cells (HB1.F3; hNSCs) transduced with genes expressing Escherichia coli cytosine deaminase (HB1.F3.CD) and human interferon-beta (HB1.F3.CD.IFN-β) as a treatment strategy for ductal breast cancer. CD can convert the prodrug 5-fluorocytosine (5-FC) to its active chemotherapeutic form, 5-fluorouracil (5-FU), which induces a tumor-killing effect through DNA synthesis inhibition. IFN-β also strongly inhibits tumor growth by the apoptotic process. RT-PCR confirmed that HB1.F3.CD cells expressed CD and HB1.F3.CD.IFN-β cells expressed both CD and IFN-β. A modified transwell migration assay showed that HB1.F3.CD and HB1.F3.CD.IFN-β cells selectively migrated toward MCF-7 and MDA-MB-231 human breast cancer cells. In hNSC-breast cancer co-cultures the viability of breast cancer cells which were significantly reduced by HB1.F3.CD or HB1.F3.CD.IFN-β cells in the presence of 5-FC. The tumor inhibitory effect was greater with the HB1.F3.CD.IFN-β cells, indicating an additional effect of IFN-β to 5-FU. In addition, the tumor-tropic properties of these hNSCs were found to be attributed to chemoattractant molecules secreted by breast cancer cells, including stem cell factor (SCF), c-kit, vascular endothelial growth factor (VEGF), and VEGF receptor 2. An in vivo assay performed using MDA-MB-231/luc breast cancer mammary fat pad xenografts in immunodeficient mice resulted in 50% reduced tumor growth and increased long-term survival in HB1.F3.CD and HB1.F3.CD.IFN-β plus 5-FC treated mice relative to controls. Our results suggest that hNSCs genetically modified to express CD and/or IFN-β genes can be used as a novel targeted cancer gene therapy.
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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".