The human STING agonist E7766 induces immunogenic tumor clearance, independent of tumor-intrinsic STING expression in the <i> KRAS <sup>G12D/+</sup> Trp53 <sup>−/−</sup> </i> murine model of sarcoma
Bibliographic record
Abstract
Soft tissue sarcomas (STS) are aggressive high-fatality cancers that affect children and adults. Most STS subtypes harbor an immunosuppressive tumor microenvironment (TME) and respond poorly to immunotherapy. Therapies capable of dismantling the immunosuppressive TME are needed to improve sensitivity to emerging immunotherapies. Activation of the Stimulator of INterferon Genes (STING) pathway has shown promising anti-tumor effects in preclinical models of carcinoma, but evaluations in sarcoma are lacking. Herein, we sought to examine the immune modulation and therapeutic efficacy of three translational small molecule STING agonists in an immunologically cold model of STS. Three classes of STING agonists, ML RR-S2 CDA, MSA-2, and E7766 were evaluated in an orthotopic KrasG12D/+ Trp53-/- model of STS. Dose titration survival studies, cytokine serology, and tumor immune phenotyping were used to examine STING agonist efficacy following intra-tumoral treatment. All STING agonists significantly increased survival time, however, only E7766 resulted in durable tumor clearance, inducing CD8+ T-cell infiltration and activated lymphocyte transcriptomic signatures in the TME. Antibody depletion was used to assess the dependency of treatment responses on CD8+ T-cells, showing that in their absence, tumor clearance did not occur following E7766 therapy. Using STING deficient mice, and CRISPR/Cas9 gene editing, we demonstrated that STS clearance following STING therapy was dependent on host STING and not tumor-intrinsic STING pathway functionality. E7766 represents a promising candidate able to remodel the TME of murine STS tumors toward an inflamed phenotype independent of tumor-intrinsic STING functionality, and should be considered for potential translation in STS treatment.
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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) | 0.001 | 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".