Immunomodulatory and anticancer effects of intra-tumoral co-delivery of synthetic lipid A adjuvant and STAT3 inhibitor, JSI-124
Bibliographic record
Abstract
Ommoleila Molavia*, Zengshuan Maa*, Samar Hamdya*, Afsaneh Lavasanifar*a* & John Samuela*a Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, CanadaDedication: This article is dedicated to the memory of our mentor, colleague, and friend, Prof. John Samuel, who was a distinguished scientist in the field of cancer immunotherapy. Prof. Samuel passed away on April 17, 2007.Address for Correspondence: Afsaneh Lavasanifar, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, 4119 Dent/Pharm Centre, Edmonton, Alberta T6G 2N8, Canada. E-mail: alavasanifar@pharmacy.ualberta.caThe efficiency of cancer immunotherapy strategies is hampered by the existence of an intra-tumoral immunosuppressive environment involving tolerogenic dendritic cells (DCs) and regulatory T (Treg) cells. Hyperactivation of STAT3 in tumor is implicated in the generation of this immunosuppressive environment. The purpose of this study was to test whether simultaneous inhibition of STAT3 in tumor and TLR4 ligand-induced activation of DCs can modulate tumor-induced immunosuppression. For this purpose, the effects of a TLR4 ligand, 7-acyl lipid A, delivered by poly(lactic-co-glycolic acid) nanoparticles (PLGA-NPs) to DCs on the activity of DCs and Treg cells was evaluated in vitro. In addition the immunomodulatory and anticancer effects of 7-acyl lipid A PLGA-NPs in combination with a STAT3 inhibitory agent, JSI-124, in a B16 mouse melanoma model was explored, in vivo. PLGA-NP delivery of 7-acyl lipid A to DCs reduced the suppressive effects of Treg cells on T cells in vitro. Besides, daily Intra-tumoral co-administration of 7-acyl lipid A PLGA-NPs and JSI-124 in C57BL/6 mice bearing B16-F10 tumor for 8 days resulted in a significant increase in the percentage of tumor infiltrated T cells as compared with control group that received PBS and monotherapy groups. The average tumor volume in the tumor-bearing mice that received JSI-124 plus 7-acyl lipid A PLGA-NPs combination therapy was found to be significantly lower than that in PBS and monotherapy groups. Our findings show a potential for the combination of STAT3 inhibition in tumor and TLR4 induced DC activation in increasing the efficacy of cancer 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) | 0.000 | 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".