Aurora kinase B phosphorylates ZBP1 to drive PANoptosis following treatment with PARP and ATR inhibitors combination
Bibliographic record
Abstract
The combination of DNA damage response inhibitors (DDR inhibitors) has emerged as a promising strategy for anticancer therapy. Herein, we demonstrate that the combined administration of poly (ADP-ribose) polymerase (PARP) and ataxia telangiectasia and Rad3-related (ATR) inhibitors elicits PANoptosis across diverse cellular lineages, including non-cancerous cell populations. The induction of PANoptosis is dependent on mitotic entry and ZBP1-dependent PANoptosome (ZBP1-RIPK1-Caspase8-Caspase6). We identify the Aurora kinase B (AURKB) as the upstream regulator, essential for phosphorylation of ZBP1 which impacts ZBP1-dependent PANoptosome assembly and activation. The Trp53-/- Brca1-/- model of ID8 and two patient-derived xenograft (PDX) models further confirm the occurrence of PANoptosis following combination administration in vivo. The toxicity is mitigated in ZBP1-knockout mice. This study unveils a mechanism that dictates cell fate during DDR inhibitors-induced aberrant mitosis, emphasizing the critical balance between efficacy and safety in optimizing DDR inhibitors combination therapies. DNA damage response inhibitors are a promising cancer therapy. Here, the authors show that combined PARP and ATR inhibition triggers ZBP1-dependent PANoptosis in cancer and normal cells, revealing a mechanism to balance efficacy and toxicity in DDR inhibitor therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".