Chemotherapy and Oncolytic Virotherapy: Advanced Tactics in the War against Cancer
Bibliographic record
Abstract
Cancer is a traitorous archenemy that threatens our survival. Its ability to evade detection and adapt to various cancer therapies means that it is a moving target that becomes increasingly difficult to attack. Through technological advancements, we have developed sophisticated weapons to fight off tumor growth and invasion. However, if we are to stand a chance in this war against cancer, advanced tactics will be required to maximize the use of our available resources. Oncolytic viruses (OVs) are multi-functional cancer-fighters that can be engineered to suit many different strategies; in particular, their retooling can facilitate increased capacity for direct tumor killing (oncolytic virotherapy) and elicit adaptive antitumor immune responses (oncolytic immunotherapy). However, administration of these modified OVs alone, rarely induces successful regression of established tumors. This may be attributed to host antiviral immunity that acts to eliminate viral particles, as well as the capacity for tumors to adapt to therapeutic selective pressure. It has been shown that various chemotherapeutic drugs with distinct functional properties can potentiate the antitumor efficacy of OVs. In this review, we summarize the chemotherapeutic combinatorial strategies used to optimize virally induced destruction of tumors. With a particular focus on pharmaceutical immunomodulators, we discuss how specific therapeutic contexts may alter the effects of these synergistic combinations and their implications for future clinical use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".