Suppression of the growth of human colorectal cancer cells by therapeutic stem cells expressing cytosine deaminase and interferon‐β via their tumor‐tropic effect in cellular and xenograft mouse models
Bibliographic record
Abstract
Genetically engineered stem cells (GESTECs) exhibit a potent therapeutic efficacy via their strong tumor tropism toward cancer cells. In this study, we introduced the human parental neural stem cells, HB1.F3, with the human interferon beta (IFN-β) gene which is a typical cytokine gene that has an antitumor effect and the cytosine deaminase (CD) gene from Escherichia coli (E. coli) that could convert the non-toxic prodrug, 5-fluorocytosine (5-FC), to a toxic metabolite, 5-fluorouracil (5-FU). Two types of stem cells expressing the CD gene (HB1.F3.CD cells) and both the CD and human IFN-β genes (HB1.F3.CD.IFN-β) were generated. The present study was performed to examine the migratory and therapeutic effects of these GESTECs against the colorectal cancer cell line, HT-29. When co-cultured with colorectal cancer cells in the presence of 5-FC, HB1.F3.CD and HB1.F3.CD.IFN-β cells exhibited the cytotoxicity on HT-29 cells via the bystander effect. In particular, HB1.F3.CD.IFN-β cells showed the synergistic cytotoxic activity of 5-FU and IFN-β. We also confirmed the migration ability of HB1.F3.CD and HB1.F3.CD.IFN-β cells toward HT-29 cells by a modified migration assay in vitro, where chemoattractant factors secreted by HT-29 cells attracted the GESTECs. In a xenograft mouse model, the volume of tumor mass was decreased up to 56% in HB1.F3.CD injected mice while the tumor mass was greatly inhibited about 76% in HB1.F3.CD.IFN-β injected mice. The therapeutic treatment by these GESTECs is a novel strategy where the combination of the migration capacity of stem cells as a vector for therapeutic genes towards colorectal cancer and a synergistic antitumor effect of CD and IFN-β genes can selectively target this type 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 | 0.001 |
| 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".