Nuanced role for dendritic cell intrinsic IRE1 RNase in the regulation of antitumor adaptive immunity
Bibliographic record
Abstract
ABSTRACT The IRE1/XBPls axis of the unfolded protein response (UPR) plays divergent roles in dendritic cell (DC) biology in steady state versus tumor contexts. Whereas tumor associated DCs show dysfunctional IRE1/XBP1s activation that curtails their function, the homeostasis of conventional type 1 DCs (cDC1) in tissues requires intact IRE1 RNase activity. Considering that cDC1s are key orchestrators of antitumor immunity, it is relevant to understand the functional versus dysfunctional roles of IRE1/XBP1s in tumor DC subtypes. Here, we show that cDC1s constitutively activate IRE1 RNase within subcutaneous B16 melanoma and MC38 adenocarcinoma tumor models. Mice lacking XBP1s in DCs display increased melanoma tumor growth, reduced T cell effector responses and accumulation of terminal exhausted CD8 + T cells. Transcriptomic studies revealed that XBP1 deficiency in tumor cDCls decreased expression of mRNAs encoding XBPls and regulated IRE1 dependent decay (RIDD) targets. Finally, we find that the dysregulated melanoma growth and impaired T cell immunity noticed in XBP1 deficient mice are attributed to RIDD induction in DCs. This work indicates that IREl RNase activity in melanoma/MC38-associated DCs fine tunes aspects of antitumor immunity independently of XBP1s, revealing a differential role for the UPR axis that depends on the DC subtype and cancer model.
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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".