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 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".