Bibliographic record
Abstract
In the 1990s, adenovirus became one of the first virus types to be genetically engineered to selectively destroy cancer cells. In the intervening years, the field of "oncolytic viruses" has slowly progressed and culminated in 2015 with the FDA approval of Talimogene laherparepvec, a genetically engineered herpesvirus, for the treatment of metastatic melanoma. Despite the slower progress in translating oncolytic adenovirus to the clinic, interest in the virus remains strong. Among all the clinical trials currently using viral oncolytic agents, the largest proportion of these are using recombinant adenovirus. Many trials are currently underway to use oncolytic virus in combination with immune checkpoint inhibitors (ICIs), and early results using oncolytic adenovirus in this manner are starting to show promise. Many of the existing strategies to engineer adenoviruses were designed to enhance selective tumor cell replication without much regard to interactions with the immune system. Adenovirus possesses a wide range of viral factors to attenuate both innate anti-viral pathways and immune cell killing. In this review, we summarize the strategies of oncolytic adenoviruses currently in clinical trials, and speculate how the mutational backgrounds of these viruses may impact upon the efficacy of these agents in oncolytic and immunotherapy. Despite decades of research on human adenoviruses, the interactions that these viruses have with the immune system remains one of the most understudied aspects of the virus and needs to be improved to rationally design the next generation of engineered viruses.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".