Multi-modal Potentiation of Oncolytic Virotherapy by Vanadium Compounds
Bibliographic record
Abstract
Oncolytic viruses (OV) are an emerging class of anticancer bio-therapeutics that induce antitumor immunity through selective replication in tumor cells. However, the efficacy of OVs as single agents remains limited. We introduce a strategy that boosts the therapeutic efficacy of OVs by combining their activity with immuno-modulating, small molecule protein tyrosine phosphatase inhibitors. We report that vanadium-based phosphatase inhibitors enhance OV infection in vitro and ex vivo, in resistant tumor cell lines. Furthermore, vanadium compounds increase antitumor efficacy in combination with OV in several syngeneic tumor models, leading to systemic and durable responses, even in models otherwise refractory to OV and drug alone. Mechanistically, this involves subverting the antiviral type I IFN response toward a death-inducing and pro-inflammatory type II IFN response, leading to improved OV spread, increased bystander killing of cancer cells, and enhanced antitumor immune stimulation. Overall, we showcase a new ability of vanadium compounds to simultaneously maximize viral oncolysis and systemic anticancer immunity, offering new avenues for the development of improved immunotherapy strategies. Oncolytic viruses (OV) are an emerging class of anticancer bio-therapeutics that induce antitumor immunity through selective replication in tumor cells. However, the efficacy of OVs as single agents remains limited. We introduce a strategy that boosts the therapeutic efficacy of OVs by combining their activity with immuno-modulating, small molecule protein tyrosine phosphatase inhibitors. We report that vanadium-based phosphatase inhibitors enhance OV infection in vitro and ex vivo, in resistant tumor cell lines. Furthermore, vanadium compounds increase antitumor efficacy in combination with OV in several syngeneic tumor models, leading to systemic and durable responses, even in models otherwise refractory to OV and drug alone. Mechanistically, this involves subverting the antiviral type I IFN response toward a death-inducing and pro-inflammatory type II IFN response, leading to improved OV spread, increased bystander killing of cancer cells, and enhanced antitumor immune stimulation. Overall, we showcase a new ability of vanadium compounds to simultaneously maximize viral oncolysis and systemic anticancer immunity, offering new avenues for the development of improved immunotherapy strategies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".