Radiation-Induced Cellular Senescence Reduces Susceptibility of Glioblastoma Cells to Oncolytic Vaccinia Virus
Bibliographic record
Abstract
Glioblastoma (GBM) is a malignant brain cancer refractory to the current standard of care, prompting an extensive search for novel strategies to improve outcomes. One approach under investigation is oncolytic virus (OV) therapy in combination with radiotherapy. In addition to the direct cytocidal effects of radiotherapy, radiation induces cellular senescence in GBM cells. Senescent cells cease proliferation but remain viable and are implicated in promoting tumor progression. The interaction of viruses with senescent cells is nuanced; some viruses exploit the senescent state to their benefit, while others are hampered, indicating senescence-associated antiviral activity. It is unknown how radiation-induced cellular senescence may impact the oncolytic properties of OVs based on the vaccinia virus (VACV) that are used in combination with radiotherapy. To better understand this, we induced cellular senescence by treating GBM cells with radiation, and then evaluated the growth kinetics, infectivity, and cytotoxicity of an oncolytic VACV, ∆F4LΔJ2R, as well as wild-type VACV in irradiated senescence-enriched and non-irradiated human GBM cell lines. Our results show that both viruses display attenuated oncolytic activities in irradiated senescence-enriched GBM cell populations compared to non-irradiated controls. These findings indicate that radiation-induced cellular senescence is associated with antiviral activity and highlight important considerations for the combination of VACV-based oncolytic therapies with senescence-inducing agents such as 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 | 0.000 |
| 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".