VSV∆M51 drives CD8+ T cell-mediated tumour regression through infection of both cancer and non-cancer cells
Bibliographic record
Abstract
Oncolytic viruses (OV) are designed to selectively infect and kill cancer cells, while simultaneously eliciting antitumour immunity. The mechanism is expected to originate from infected cancer cells. However, recent reports of tumour regression unaccompanied by cancer cell infection suggest a more complex mechanism of action. Here, we engineered vesicular stomatitis virus (VSV)ΔM51-sensitive and VSVΔM51-resistant tumour lines to elucidate the role of OV-infected cancer and non-cancer cells. We found that, while cancer cell infections elicit oncolysis and antitumour immunity as expected, infection of non-cancer cells alone can also contribute to tumour regression. This effect is partly attributed to the systemic production of cytokines that promote dendritic cell (DC) activation, migration and antigen cross-presentation, leading to magnified antitumour CD8+ T cell activation and tumour regression. Such OV-induced antitumour immunity is complementary to PD-1 blockade. Overall, our results reveal mechanistic insights into OV-induced antitumour immunity that can be leveraged to improve OV-based therapeutics. The role of non-cancer cells infected by oncolytic viruses (OV) in cancer regression remains elusive. Here the authors engineer OV-sensitive and OV-resistant cancer cell lines and show that OV infection of non-cancer cells can elicit effective antitumour immunity via enhancing DC function and CD8+ T cell activation.
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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".