IRE1α translational suppression potentiates STING-dependent chemoresistance in pancreatic cancer
Bibliographic record
Abstract
Chemotherapy remains a standard treatment for pancreatic ductal adenocarcinoma (PDAC); however, its effectiveness is limited, and the underlying mechanisms are poorly understood. STING plays diverse and critical roles in cancer, yet the role of PDAC cell-intrinsic STING signaling and its regulation under chemotherapy remain unclear. Here, we report that chemotherapy induces cancer cell-intrinsic STING signaling and that STING deletion in PDAC enhances cell death under chemotherapy while suppressing tumor growth in both immune-deficient and immune-competent mice. Interestingly, chemotherapy selectively inhibits translation of IRE1α, an ER membrane protein and a canonical mediator of ER stress. Loss of IRE1α in PDAC amplifies STING signaling and increases resistance to chemotherapy. Mechanistically, IRE1α interacts with STING via their transmembrane regions, reducing STING stability in PDAC cells. Our study reveals that PDAC cells downregulate IRE1α to reinforce STING-mediated pro-survival response; however, this adaptation also makes them more vulnerable to proteostasis imbalance and ER stress-induced cell death. Notably, we demonstrate that combining ER stress inducers with STING signaling inhibition enhances chemotherapy efficacy both in vitro and in vivo.
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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) | 0.001 | 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".