16 Combining an oncolytic vaccinia virus with image-guided radiotherapy: a multi-modal therapeutic approach for treating glioma
Bibliographic record
Abstract
Malignant gliomas (MG) are highly invasive and aggressive brain tumors. Despite the current standard of care, the prognosis for patients with MG is abysmal– highlighting the need for novel, more effective treatment options to combat this aggressive disease. Oncolytic virus (OV) therapy is an advancing treatment option that harnesses tumor-selective viruses to kill cancer cells while simultaneously facilitating a systemic anti-tumor immune response. Many studies have noted synergistic effects when OV’s are combined with radiotherapy in preclinical cancer models, warranting further investigation of this multi-modal approach. Image-guided radiotherapy (IGRT) uses computer-modulated imaging techniques to precisely deliver ionizing radiation to treat cancer. Despite the precision IGRT offers, cancer cells can still be ‘missed’ due to tumor microextensions or radioresistant cell populations– such as glioma stem cells or therapy-induced senescent cancer cells –and may contribute to recurrence or progression. Here we propose to combine our mCherry-tagged mutant vaccinia virus (deltaF4L-deltaJ2R-mCherry), which exhibits tumor-selectivity due to mutations in key viral nucleotide biosynthesis genes, with IGRT executed using state-of-the-art Small Animal Radiation Research Platform (SARRP) technology. We hypothesize that combining deltaF4L-deltaJ2R-mCherry with IGRT will produce better tumor control than either modality alone, by generating additive or synergistic effects in which IGRT destroys the majority of the tumor mass while our OV seeks out and targets any remaining cancer cells that have been missed or are resistant to radiotherapy.
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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 | 0.001 |
| 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".