Oncolytic viruses: a step into cancer immunotherapy
Bibliographic record
Abstract
Jonathan G Pol, Julien Rességuier, Brian D LichtyMcMaster Immunology Research Centre, Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, CanadaAbstract: Oncolytic virotherapy is currently under investigation in phase I–III clinical trials for approval as a new cancer treatment. Oncolytic viruses (OVs) selectively infect, replicate in, and kill tumor cells. For a long time, the therapeutic efficacy was thought to depend on the direct viral oncolysis (virocentric view). The host immune system was considered as a brake that impaired virus delivery and spread. Attention was paid primarily to approaches enhancing virus tumor selectivity and cytotoxicity and/or that limited antiviral responses. Thinking has changed over the past few years with the discovery that OV therapy was also inducing indirect oncolysis mechanisms. Among them, induction of an antitumor immunity following OV injection appeared to be a key factor for an efficient therapeutic activity (immunocentric view). Indeed, tumor-specific immune cells persist post-therapy and can search and destroy any tumor cells that escape the OVs, and thus immune memory may prevent relapse of the disease. Various strategies, which are summarized in this manuscript, have been developed to enhance the efficacy of OV therapy with a focus on its immunotherapeutic aspects. These include genetic engineering and combination with existing cancer treatments. Several are currently being evaluated in human patients and already display promising efficacy.Keywords: oncolytic virus, cancer immunotherapy, tumor antigen, cancer vaccine, combination strategies
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".