628. Oncogenic Ras Inhibits IRF1 To Promote Viral Oncolysis
Bibliographic record
Abstract
Oncolytic viruses exploit common molecular changes in cancer cells, which are not present in normal cells, to target and kill cancer cells. Ras transformation and defects in type I interferon (IFN)-mediated antiviral responses are known to be the major mechanisms underlying viral oncolysis. Previously, we demonstrated that oncogenic RAS/Mitogen-activated protein kinase kinase (Ras/MEK) activation suppresses the transcription of many IFN-inducible genes in human cancer cells, suggesting that Ras transformation underlies type I IFN defects in cancer cells. Here, we investigated how Ras/MEK downregulates IFN-induced transcription. By conducting promoter deletion analysis of IFN-inducible genes, namely guanylate-binding protein 2 and IFN gamma inducible protein 47 (Ifi47), we identified the IFN regulatory factor 1 (IRF1) binding site as the promoter region responsible for the regulation of transcription by MEK. MEK inhibition promoted transcription of the IFN-inducible genes in wild type mouse embryonic fibroblasts (MEFs), but not in IRF1-/- MEFs, showing that IRF1 is involved in MEK-mediated downregulation of IFN-inducible genes. Furthermore, IRF1 protein expression was lower in RasV12 cells compared with vector control NIH3T3 cells, but was restored to equivalent levels by inhibition of MEK. Similarly, the restoration of IRF1 expression by MEK inhibition was observed in human cancer cells. IRF1 re-expression in human cancer cells caused cells to become resistant to infection by the oncolytic vesicular stomatitis virus strain. Together, this work demonstrates that Ras/MEK activation in cancer cells downregulates transcription of IFN-inducible genes by targeting IRF1 expression, resulting in increased susceptibility to viral oncolysis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".