Adenovirus-Mediated Expression of the p14 Fusion-Associated Small Transmembrane Protein Promotes Cancer Cell Fusion and Apoptosis In Vitro but Does Not Provide Therapeutic Efficacy in a Xenograft Mouse Model of Cancer
Bibliographic record
Abstract
Adenoviruses (Ads) are used in numerous preclinical and clinical studies for delivery of anti-cancer therapeutic genes. Unfortunately, Ad has a poor ability to distribute throughout a tumor mass after intratumoral injection, and infects cells primarily within the immediate area of the injection tract. Thus, Ad-encoded transgene expression is typically limited to only a small percentage of cells within the tumor. One method to increase the proportion of the tumor impacted by Ad is through expression of fusogenic proteins. Infection of a single cell with an Ad vector encoding a fusogenic protein should lead to syncytium formation with adjacent cells, effectively spreading the effect of Ad and Ad-encoded therapeutic transgenes to a greater percentage of the tumor mass. Moreover, syncytium formation can be cytotoxic, suggesting that such proteins may be effective sole therapeutics. We show that an early region 1 (E1)-deleted Ad expressing reptilian reovirus p14 fusion-associated small transmembrane (FAST) protein caused extensive cell fusion in the replication-permissive 293 cell line and at high multiplicity of infection in non-permissive human lung adenocarcinoma A549 cells in vitro. FAST protein expression in the A549 cancer cell line led to a loss of cellular metabolic activity and membrane integrity, which correlated with induction of apoptosis. However, in an A549 xenograft CD-1 nude mouse cancer model, Ad-mediated FAST gene delivery did not induce detectable cell fusion, reduce tumor burden nor enhance mouse survival compared to controls. Taken together, our results show that, although AdFAST can enhance cancer cell killing in vitro, it is not effective as a sole therapeutic in the A549 tumor model in vivo.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".