Itaconate enhances oncolytic virotherapy by multitarget inhibition of antiviral and inflammatory pathways
Bibliographic record
Abstract
Abstract Altered metabolism and defective innate immune responses are hallmarks of tumor cells, creating a niche that can be exploited by viruses with oncolytic properties. However, heterogeneity in responses to oncolytic virotherapy is a barrier to clinical effectiveness. Resistance to oncolytic virotherapy exists and occurs via inhibition of viral spread within the tumor that may result in treatment failures. Here we show that the Krebs cycle-derived metabolite itaconate and its chemical derivatives or natural isomers enhance oncolytic virotherapy with VSVΔ51M in various models including resistant cancer cell lines, murine tumor biopsies, three-dimensional (3D) patient-derived colon tumoroids and organotypic brain tumor slices. The strongest sensitizing effect to VSVΔ51M infection within the itaconate family is elucidated by 4-octyl itaconate (4-OI), a known activator of nuclear factor erythroid 2-related factor 2 (NRF2). Importantly, the sensitization to VSVΔ51M with 4-OI is not observed in non-cancer primary human cells, healthy mouse-derived tissues, and normal human colon organoids. Additionally, 4-OI improves the virus spread within a resistant mouse colon tumor model in vivo. Mechanistically, we found that the effect of 4-OI is not mediated by NRF2 or KEAP1. Instead, we show that 4-OI targets multiple pathways including the RIG-MAVS, the IKKβ-NF-κB and the JAK1-STAT1 pathways through the modification of cysteine residues in MAVS, IKKβ and JAK1, respectively. Here, we propose that the combination of a metabolite-derived drug with an oncolytic virus agent can greatly improve anticancer therapeutic outcomes by direct interference with the type I IFN and NF-κB-mediated antiviral responses.
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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) | 0.005 | 0.001 |
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".