Abstract 5349: Vesicular stomatitis virus oncolysis is potentiated by impairing mTORC1-dependent type-I IFN production
Bibliographic record
Abstract
Abstract Oncolytic viruses constitute a promising therapy against malignant gliomas (MGs). However, virus-induced type-I interferon (IFN) greatly limits its clinical application. The kinase mTOR (mammalian target of rapamycin) stimulates type-I IFN production via phosphorylation of its effector proteins, 4E-BPs and S6Ks. Here we show that both mouse embryonic fibroblasts (MEFs) and mice lacking S6K1 and S6K2, are susceptible to Vesicular Stomatitis Virus (VSV) infection due to an impaired type-I interferon response. We used this knowledge to employ a pharmacoviral approach to treat MGs. The highly specific inhibitor of mTOR, rapamycin, in combination with an IFN-sensitive VSV-mutant strain (VSVΔM51), dramatically increased the survival of immunocompetent rats bearing MGs. More importantly, VSVΔM51 selectively killed tumor, but not normal cells, in MG-bearing rats, which were treated with rapamycin. These results demonstrate that reducing type-I IFNs through inhibition of mTOR is an effective strategy to augment the therapeutic activity of VSVΔM51. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5349.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.009 | 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".