Palbociclib and dsRNA sensor co-operate to enhance anti-cancer effects through ER stress and modulation of immune evasion
Bibliographic record
Abstract
Cytoplasmic pattern recognition receptors (PRR) for double-stranded RNA, such as RIG-I/MDA5, are key mediators of anti-viral responses. Here we screen for synergistic drug-virotherapy combinations and find that the reovirus type III Dearing strain (Rt3D)-palbociclib combination augments oncolytic virus-induced stress responses and increases interferon production and signaling. Data from RIG-I agonist and ER stress-inducing agents further confirms the crosstalk between RNA-sensing and ER stress in inducing cancer cell death and interferon production. Combined Rt3D-palbociclib also increases innate immune activation and IFN-induced HLA expression within tumor cells, with accompanying alterations in the epigenetic landscape and endogenous retroviral (ERV) elements. Analysis of the immunopeptidome in treated cells further reveals changes to HLA-captured peptides, including altered expression of peptides from cancer or testis antigens and ERVs. Our findings thus highlight the crosstalk between stress signaling and PRR activation for mediating enhanced anti-cancer efficacy. Pattern recognition receptors (PRR) critically modulate innate immunity. Here the authors show in cancer cells that interferon responses and anti-tumor immunity activated by dsRNA-induced PRR signaling is enhanced by palbociclib-induced ER stress, with epigenetic changes and altered antigen presentation potentially contributing to this effect.
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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) | 0.003 | 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".