Cancer genetics and response to oncolytic virus treatment for ovarian cancer
Bibliographic record
Abstract
Ovarian cancers (OCs) are often defined as poorly immunogenic tumors that have low response rates to current immunotherapies and frequently develop resistance to chemotherapies. Oncolytic viruses (OVs) are an emerging therapeutic approach that is favored due to its multifactorial mechanism of action; OVs aim to enhance immune cell recovery and infiltration into the tumor, in addition to assisting the immune system to identify and target evasive tumors. While many different OVs have been studied, this review focuses on the four that have been extensively tested in preclinical models and clinical trials with OC patients: vaccinia viruses, vesicular stomatitis virus, herpes simplex 1, and adenoviruses. We will first explore how these viruses have been developed, modified and tested as monotherapies in OCs, with limited success. The various combinatorial approaches involving OVs that are currently being investigated to improve the outcomes for OC patients will then be addressed. Attention will be given to how the genetics of OC cells may influence response to OVs and how that has led to genetic modifications of OVs that improve the cancer specificity and efficacy of these therapies.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".