Immunotherapy with STING and TLR9 agonists promotes synergistic therapeutic efficacy with suppressed cancer-associated fibroblasts in colon carcinoma
Bibliographic record
Abstract
The innate immune sensing of nucleic acids using effective immunoadjuvants is critical for increasing protective immune responses against cancer. Stimulators of interferon genes (STING) and toll-like receptor 9 (TLR9) agonists are considered promising candidates in several preclinical tumor models with the potential to be used in clinical settings. However, the effects of such treatment on tumor stroma are currently unknown. In this study, we investigated the immunotherapeutic effects of ADU-S100 as a STING agonist and CpG ODN1826 as a TLR9 agonist in a preclinical model of colon carcinoma. Tumor-bearing mice were treated intratumorally on days 10 and 16 post-tumor inoculation with ADU-S100 and CpG ODN1826. Cytokine profiles in the tumor and spleen, tumor cell apoptosis, the infiltration of immune cells, and cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) were evaluated to identify the immunological mechanisms after treatment. The powerful antitumor activity of single and combination treatments, the upregulation of the expression of pro-inflammatory cytokines in the tumor and spleen, and the recruitment and infiltration of the TME by immune cells revealed the synergism of immunoadjuvants in the eradication of the colon carcinoma model. Remarkably, the significant downregulation of CAFs in the TME indicated that suppression of tumorigenesis occurred after immunoadjuvant therapy. The results illustrate the potential of targeting the STING and TLR9 pathways as powerful immunoadjuvants in the treatment of preclinical colon carcinoma and the possibility of harnessing these pathways in future therapeutic approaches.
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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".