Molecular Pathways: Multimodal Cancer-Killing Mechanisms Employed by Oncolytic Vesiculoviruses
Bibliographic record
Abstract
Cancer is a heterogeneous disease that, for the most part, is not effectively managed with existing therapies. Oncolytic viruses are an attractive class of experimental cancer medicine because, unlike conventional chemotherapeutic and molecularly targeted drugs, they orchestrate tumor cell death in multiple ways simultaneously. In this review, we discuss the numerous cancer-killing "pathways" marshalled by oncolytic vesiculoviruses. From directly infecting and lysing malignant cells, to engaging the host's innate and adaptive anticancer immune responses, to inducing vascular collapse within a tumor, oncolytic vesiculovirus therapy commandeers a coordinated, multipronged assault on cancer that is curative in numerous preclinical models. And as our appreciation of these mechanisms has progressed, so has our capacity to engineer improved outcomes. Notably, efforts to polarize the host's immune system toward the tumor and away from the virus have been particularly effective in immunocompetent murine models, and hold tremendous therapeutic promise for human patients. With a first-in-man phase I trial recently initiated in the United States, the clinical significance of oncolytic vesiculorivus therapy, after nearly 15 years of development, may soon come into focus.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".