Inhibition of Pyruvate Dehydrogenase Kinase Enhances the Antitumor Efficacy of Oncolytic Reovirus
Bibliographic record
Abstract
Abstract Oncolytic viruses (OV) such as reovirus preferentially infect and kill cancer cells. Thus, the mechanisms that dictate the susceptibility of cancer cells to OV-induced cytotoxicity hold the key to their success in clinics. Here, we investigated whether cancer cell metabolism defines its susceptibility to OV and if OV-induced metabolic perturbations can be therapeutically targeted. Using mass spectrometry–based metabolomics and extracellular flux analysis on a panel of cancer cell lines with varying degrees of susceptibility to reovirus, we found that OV-induced changes in central energy metabolism, pyruvate metabolism, and oxidative stress correlate with their susceptibility to reovirus. In particular, reovirus infection accentuated Warburg-like metabolic perturbations in cell lines relatively resistant to oncolysis. These metabolic changes were facilitated by oxidative stress–induced inhibitory phosphorylation of pyruvate dehydrogenase (PDH) that impaired the routing of pyruvate into the tricarboxylic acid cycle and established a metabolic state unsupportive of OV replication. From the therapeutic perspective, reactivation of PDH in cancer cells that were weakly sensitive for reovirus, either through PDH kinase (PDK) inhibitors dichloroacetate and AZD7545 or short hairpin RNA–specific depletion of PDK1, enhanced the efficacy of reovirus-induced oncolysis in vitro and in vivo. These findings identify targeted metabolic reprogramming as a possible combination strategy to enhance the antitumor effects of OV in clinics. Significance: This study proposes targeted metabolic reprogramming as a valid combinatorial strategy to enhance the translational efficacy of oncolytic virus–based cancer therapies.
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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".