APOBEC3 Mediates Resistance to Oncolytic Viral Therapy
Bibliographic record
Abstract
Tumor cells frequently evade applied therapies through the accumulation of genomic mutations and rapid evolution. In the case of oncolytic virotherapy, understanding the mechanisms by which cancer cells develop resistance to infection and lysis is critical to the development of more effective viral-based platforms. Here, we identify APOBEC3 as an important factor that restricts the potency of oncolytic vesicular stomatitis virus (VSV). We show that VSV infection of B16 murine melanoma cells upregulated APOBEC3 in an IFN-β-dependent manner, which was responsible for the evolution of virus-resistant cell populations and suggested that APOBEC3 expression promoted the acquisition of a virus-resistant phenotype. Knockdown of APOBEC3 in B16 cells diminished their capacity to develop resistance to VSV infection in vitro and enhanced the therapeutic effect of VSV in vivo . Similarly, overexpression of human APOBEC3B promoted the acquisition of resistance to oncolytic VSV both in vitro and in vivo . Finally, we demonstrate that APOBEC3B expression had a direct effect on the fitness of VSV, an RNA virus that has not previously been identified as restricted by APOBEC3B. This research identifies APOBEC3 enzymes as key players to target in order to improve the efficacy of viral or broader nucleic acid-based therapeutic platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".