The PARP inhibitor talazoparib synergizes with reovirus to induce cancer killing and tumour control in vivo in mouse models
Bibliographic record
Abstract
Reovirus type 3 Dearing (RT3D) is an oncolytic, double-stranded RNA virus. To identify potential RT3D drug-viral sensitizer, here we use a high-throughput screen of therapeutic agents and find a PARP-1 inhibitor, talazoparib, as a top hit. RT3D interacts with retinoic acid-induced gene-1 (RIG-I) and activates PARP-1, with consequent PARylation of components of the extrinsic apoptosis pathway. Pharmacological or genetic inhibition of PARP-1 abrogates this PARylation and enhances extrinsic apoptosis, NF-kB signalling and pro-inflammatory cell death. Interaction between PARP-1 and RIG-I induced by treating RT3D-infected cells with talazoparib activates downstream IFN-β and TNF/TRAIL production to amplify the therapeutic effect through positive feedback. Furthermore, the effect of RT3D-talazoparib combination is phenocopied by non-viral ds-RNA therapy and RIG-I agonism. In vivo, mouse tumour model results show that RT3D/talazoparib combination regimen induces complete control of inoculated tumour as well as protection from subsequent tumour rechallenge with the, with accompanied innate and adaptive immune activation. Oncolytic virus holds potential as a cancer therapy, but further optimization is desirable. Here the authors screen existing drug to find talazoparib, a PARP inhibitor, synergizing with reovirus type 3 Dearing (RT3D) to induce cancer cell apoptosis, anti-tumor immunity, as well as control of primary and rechallenge tumor in mouse models.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".