CDK4/6 inhibition and dsRNA sensor agonism co-operate to enhance anti-cancer effects through ER stress and immune modulation of tumour cells
Bibliographic record
Abstract
Abstract Cytoplasmic pattern recognition receptors (PRRs) for double-stranded RNA (RIG-I/MDA5) are key mediators of anti-viral responses. PRR agonists, such as dsRNA oncolytic Reovirus type 3 Dearing (Rt3D), potently activate RNA sensors. We used an unbiased cytotoxicity screen to reveal synergistic drug-virotherapy combinations and found potent effects of Rt3D combined with the CDK4/6 inhibitor, palbociclib. The combination augmented oncolytic virus-induced endoplasmic reticulum (ER) stress/unfolded protein response (UPR) and the expression and activation/signaling of RNA sensors. Combined Rt3D-palbociclib treatment potently increased interferon production and signaling, and knockdown studies implicated key UPR proteins and the RNA sensor, RIG-I, as essential to the phenotype observed. Further experiments, using canonical RIG-I agonists and an ER stress inducer, thapsigargin, confirmed cross-talk between RNA sensing and ER stress pathways that augmented cancer cell death and interferon production. Combined Rt3D-palbociclib also increased innate immune activation within tumour cells and IFN-induced HLA expression. Analysis of the immunopeptidome revealed changes to HLA-captured peptides with Rt3D-palbociclib, including altered expression of peptides from cancer/testis antigens (CTA) and endogenous retroviral elements (ERVs). Our findings highlight cross-talk between UPR signaling and RNA-mediated PRR activation as a means of enhancing anti-cancer efficacy with potential pro-immunogenic consequences. This has implications for future clinical development of PRR agonists and oncolytic viruses, and broadens the therapeutic remit of CDK4/6 inhibitors to include roles as both ER stress and dsRNA PRR sensitizers.
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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 | 0.001 |
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".