Development and Characterization of an Oncolytic Human Adenovirus-Based Vector Co-Expressing the Adenovirus Death Protein and p14 Fusion-Associated Small Transmembrane Fusogenic Protein
Bibliographic record
Abstract
Human adenovirus (HAdV)-based oncolytic vectors, which are designed to preferentially replicate in and kill cancer cells, have shown modest efficacy in human clinical trials in part due to poor viral distribution throughout the tumor mass. Previously, we showed that expression of the p14 fusion-associated small transmembrane (FAST) fusogenic protein could enhance oncolytic HAdV efficacy and reduce tumor growth rate in a human xenograft mouse model of cancer. We now explore whether co-expression of the adenovirus death protein (ADP) with p14 FAST protein could synergize to further enhance oncolytic vector efficacy. ADP is naturally encoded within the early region 3 (E3) of HAdV, a region which is frequently removed from HAdV-based vectors, and functions to enhance cell lysis and progeny release. We evaluated a variety of approaches to achieve optimal expression of the two proteins, the most efficient method being insertion of an expression cassette within the E3 deletion, consisting of the coding sequences for p14 FAST protein and ADP separated by a self-cleaving peptide derived from the porcine teschovirus-1 (P2A). However, the quantities of p14 FAST protein and ADP produced from this vector were reduced approximately 10-fold compared to a similar vector-expressing only p14 FAST protein and wildtype HAdV, respectively. Compared to our original oncolytic vector-expressing p14 FAST protein alone, reduced expression of p14 FAST protein and ADP from the P2A construct reduced cell-cell fusion, vector spread, and cell-killing activity in human A549 adenocarcinoma cells in culture. These studies show that a self-cleaving peptide can be used to express two different transgenes in an armed oncolytic HAdV vector, but also highlight the challenges in maintaining adequate transgene expression when modifying vector design.
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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) | 0.000 | 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".