CDK4/6 Inhibition Enhances Oncolytic Virus Efficacy by Potentiating Tumor-Selective Cell Killing and T-cell Activation in Refractory Glioblastoma
Bibliographic record
Abstract
Glioblastoma (GBM) is among the most aggressive human cancers. Although oncolytic virus (OV) therapy has been proposed as a potential approach to treat GBM, it frequently fails because GBM cells are usually nonpermissive to OV. Here, we describe a dual-step drug screen for identifying chemical enhancers of OV in GBM. From a high-throughput screen of 1416 FDA-approved drugs, an inhibitor of CDK4/6 was identified as the top enhancer, selectively increasing potency of two OV strains, VSVΔ51 and Zika virus. Mechanistically, CDK4/6 inhibition promoted autophagic degradation of MAVS, resulting in impaired antiviral responses and enhanced tumor-selective replication of VSVΔ51 in vitro and in vivo. CDK4/6 inhibition cooperated with VSVΔ51 to induce severe DNA damage stress and amplify oncolysis. In GBM xenograft models, combined treatment with CDK4/6 inhibitor and VSVΔ51 significantly inhibited tumor growth and prolonged the survival of tumor-bearing mice. Further investigation revealed that CDK4/6 inhibitor and VSVΔ51 synergistically induced immunogenic cell death and boosted antitumor immunity. Together, this study features a promising approach of treating aggressive GBM through the combination of CDK4/6 inhibitor with OV. SIGNIFICANCE: This study proposes inhibition of cyclin-dependent kinases as a clinically translatable combinatorial strategy to enhance the efficacy of oncolytic virotherapy in GBM.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".