Lipid Nanoparticles Delivering Constitutively Active STING mRNA to Stimulate Antitumor Immunity
Bibliographic record
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 have shown that STING is silenced in many human cancers, including pancreatic ductal adenocarcinoma (PDAC) and Merkel cell carcinoma (MCC). In this study, we demonstrated that mRNA-lipid nanoparticle (LNP) technology could be used to efficiently deliver naturally occurring constitutively active STING mutant STINGR284S into these cancer cells to reactivate STING antitumor immunity and trigger robust killing of tumor cells. STING agonists are being actively pursued as cancer immunotherapies. However, traditional STING agonists can induce T cell cytotoxicity, counteracting the desired antitumor immune response. In addition, the antitumor efficacy of traditional STING agonists obligatorily depends on STING expression and does not work in STING-silenced cancers. Importantly, we found that STINGR284S mRNA-LNP does not introduce T cell cytotoxicity. Our studies demonstrated that mRNA-LNP delivery of STINGR284S can reactivate the antitumor response without introducing antiproliferative effects in lymphocytic immune cells, overcoming the toxicity and limitations of conventional STING agonists. Our work therefore identifies a novel therapeutic tool for reactivating antitumor immunity in an array of STING-silenced immunologically “cold” tumors that are refractory to current therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".