Lipid nanoparticles delivering constitutively active STING mRNA as a novel anti-cancer therapeutic approach
Bibliographic record
Abstract
Abstract Treating immunosuppressive tumors represents a major challenge in cancer therapies. Activation of STING signaling has shown remarkable potential to invigorate the immunologically ‘cold’ tumor microenvironment (TME). However, we and others have shown that STING is silenced in many cancers, including pancreatic ductal adenocarcinoma (PDAC) and Merkel cell carcinoma (MCC), both of which are associated with an immune-dampened TME. In this study, we applied mRNA lipid nanoparticles (LNP) to deliver a permanently active gain-of-function STING R284S mutant into PDAC and MCC cells. Expression of STING R284S induces cytokines and chemokines crucial for promoting intratumoral infiltration of CD8 + T cells and, importantly, also leads to robust cancer cell death while avoiding T cell entry and toxicity. Our studies demonstrated that mRNA-LNP delivery of STING R284S could be explored as a novel therapeutic tool to reactivate antitumor response in an array of STING-deficient cancers while overcoming the toxicity and limitations of conventional STING agonists.
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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) | 0.001 | 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".