STING agonist enhances type I interferon-driven GITRL co-stimulatory functions in monocytic lineage cells for durable and effective anti-tumor CD8+ T cell responses 3684
Bibliographic record
Abstract
Abstract Description Opimizing T cell-based therapies requires mechanistic insights into T cell activation, which involves Ag/MHC (signal 1), co-stimulation (signal 2), and cytokinessuch as type I interferons (IFN-I) (signal 3). Our group previously showed that the co-stimulatory receptor GITR provides a post-priming survival signal, “signal 4,” enhancing T cell accumulation during viral infections. GITR ligand (GITRL), mainly induced on inflammatory monocytic lineage cells (infMC) by IFN-I, is not well-studied in tumors where IFN-I arises from the cGAS-STING pathway. To examine GITRL-mediated signal 4 in cancer, we used a STING agonist to induce IFN-I via the cGAS-STING pathway. Treatment with one dose of STING agonist was used to examine the rapidly induced GITRL signaling on effector T cells, while three doses were used to study its impact on tissue resident memory T cell (TRM). STING agonist treatment resulted in IFN-I-dependent GITRL upregulation on infMCs in tumors. We also showed that conditional deletion of GITRL on CCR2+ infMCs impairs STING agonist-induced tumor control. Using GITR+/+: GITR-/- mixed bone marrow chimeras, we showed that GITR+/+ CD8+ T cells outcompete GITR-/- counterpart upon STING agonist treatment. Similarly, we found that GITR+/+ CD8+ skin TRM forms locally and have a competitive advantage over GITR-/- counterpart. Together, these data showed that STING agonist promotes GITR/GITRL co-stimulation, enhancing effector and TRM accumulation for improve tumor control. Funding Sources This research was funded by Canadian Cancer Society grant #706759, Canadian Institutes of Health Research grants # FDN-143250, PJ4-185649 and PJT 190128 (all to THW) and a CHIR Canada graduate scholarships doctoral award (MG). THW holds the Canada research chair in anti-viral immunity at the University of Toronto Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)
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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".