Synergistic antitumor effects of CpG oligodeoxynucleotide and STAT3 inhibitory agent JSI‐124 in a mouse melanoma tumor model
Bibliographic record
Abstract
One of the major limitations for cancer immunotherapy is related to the frequent existence of an intra-tumoral immunosuppressive environment, to which STAT3 (Signal transducer and activator of transcription-3) activation in tumor and dendritic cells (DCs) are believed to contribute. In this study, we tested the hypothesis that the combination of CpG (a DC activator) and JSI-124 (a STAT3 inhibitor) may generate synergistic antitumor effects compared to CpG or JSI-124 alone. B16-F10, a mouse melanoma cell line that has constitutively active STAT3, was grafted in C57BL/6 mice and then tumor-bearing mice treated intra-tumorally with (a) phosphate buffered saline, (b) 10 microg CpG, (c) 1 mg kg(-1) JSI-124 or (d) 10 microg CpG+1 mg kg(-1) JSI-124. The effects of treatments on tumor growth, survival and antitumor immune responses were evaluated. Although significant antitumor effects were detected with the single-agent treatments, the CpG+JSI-124 treatment resulted in synergistic antitumor effects compared to CpG or JSI-124 alone. Correlating with these findings, the combination therapy resulted in significantly higher intra-tumoral levels of several proinflammatory, TH1-related cytokines (including IL-12, IFN-gamma, TNF-alpha and IL-2), increases in intra-tumoral CD8+ and CD4+ T cells expressing activation/memory markers and NK cells and increases in activated DCs in the tumors and regional lymph nodes (LNs). Concomitantly, the combination therapy led to a significantly decreased level of immunosuppression, as evidenced by lower intra-tumoral level of VEGF and TGF-beta, and decreased number of CD4+CD25+Foxp3+ regulatory T cells in the regional LNs. This study has provided the proof-of-principle for combining CpG and JSI-124 to enhance antitumor immune responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".