NLRP3 activation promotes cGAS/STING signaling and antitumor immunity by colorectal cancer cells
Bibliographic record
Abstract
INTRODUCTION: Colorectal cancer (CRC) is a highly prevalent and deadly disease that is largely refractory to immunotherapy. The only CRC subset that responds to these therapies is characterized by prevalent microsatellite instability (MSI), extensive CD8+ T cell infiltration and high expression of innate immune signaling pathways. Endogenous activation of the cGAS/STING pathway is essential for this CD8+ T cell antitumor response in MSI CRCs, suggesting that activating it in other CRCs could boost immunotherapy response rates. In contrast, activation of the NLRP3 inflammasome is typically associated with tumor-promoting inflammation although this has primarily been studied in immune cells. METHODS: We used a mixture of flow cytometry, activation assays, in vivo orthotopic models and patient-derived organoids to investigate the effect of NLRP3 activation in CRC cells on cGAS/STING-mediated antitumor immunity. RESULTS: Our results show that activation of the NLRP3 inflammasome specifically in CRC cells boosts cGAS/STING signaling in both MSI and non-MSI CRCs and that dual stimulation increases CD8+ T cell-mediated antitumor immunity. The ability of NLRP3 to enhance cGAS/STING signaling was specific and did not occur with activation of other innate immune pathways such as AIM2 or TLRs. Enhancement of cGAS/STING signaling by NLRP3 proceeded via a positive feedback loop that was inflammasome-independent and required early crosstalk between the signaling mediators and regulation of their gene expression. CONCLUSIONS: Activation of NLRP3 specifically in CRC cells could be a promising strategy to boost antitumor immunity in otherwise immunotherapy resistant CRCs.
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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".