Carriers Multimerize STING Protein Fragments to Activate Type I Interferon Signaling in STING-Deficient Cancer Cells
Bibliographic record
Abstract
Therapeutic activation of the stimulator of interferon genes (STING) innate immune pathway shows promise for cancer immunotherapy; however, frequent loss of STING expression in cancer cells renders these cells unresponsive to existing agonists. We report that cytosolic delivery of a soluble STING protein fragment bypasses this challenge by interacting directly with downstream signaling molecules TANK-binding kinase 1 (TBK1) and interferon regulatory factor 3 (IRF3) to activate a Type I interferon response, even in STING-deficient cells. Prior work demonstrated that this same STING fragment was not capable of activating signaling when overexpressed, leading us to investigate how the cytosolic delivery of the protein enables activity. Our results suggest that in addition to facilitating transport across the cell membrane, complexation by delivery vehicles can promote multimerization of the STING fragment, allowing it to remain in a multimeric active state even after escape from the cytosol. Activity remains even after truncation or unfolding of STING's crystallizable domain containing the interface of the protein involved in full-length STING oligomerization, showing that multimerization of STING fragments can proceed by nontypical means. Finally, we demonstrate that this strategy can induce a type I interferon response in multiple STING-proficient and -deficient cancer cell lines. Overall, this work shows how delivery vehicles can be used to modify a protein therapeutic into an active state and provides a proof of concept motivating further development of STING fragment delivery as an immunotherapy.
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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".