Concomitant blockade of A2AR and CTLA‐4 by siRNA‐loaded polyethylene glycol‐chitosan‐alginate nanoparticles synergistically enhances antitumor T‐cell responses
Bibliographic record
Abstract
Inhibitory immune checkpoint (ICP) molecules are important immunosuppressive factors in a tumor microenvironment (TME). They can robustly suppress T-cell-mediated antitumor immune responses leading to cancer progression. Among the checkpoint molecules, cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) is one of the critical inhibitors of anticancer T-cell responses. Besides, the expression of adenosine receptor (A2AR) on tumor-infiltrating T cells potently reduces their function. We hypothesized that concomitant silencing of these molecules in T cells might lead to enhanced antitumor responses. To examine this assumption, we purified T cells from the tumor, spleen, and local lymph nodes of CT26 colon cancer-bearing mice and suppressed the expression of A2AR and CTLA-4 using the small interfering RNA (siRNA)-loaded polyethylene glycol-chitosan-alginate (PCA) nanoparticles. The appropriate physicochemical properties of the produced nanoparticles (NPs; size of 72 nm, polydispersive index [PDI] < 0.2, and zeta potential of 11 mV) resulted in their high efficiency in transfection and suppression of target gene expression. Following the silencing of checkpoint molecules, various T-cell functions, including proliferation, apoptosis, cytokine secretion, differentiation, and cytotoxicity were analyzed, ex vivo. The results showed that the generated nanoparticles had optimal physicochemical characteristics and significantly suppressed the expression of target molecules in T cells. Moreover, a concomitant blockade of A2AR and CTLA-4 in T cells could synergistically enhance antitumor responses through the downregulation of PKA, SHP2, and PP2Aα signaling pathways. Therefore, this combination therapy can be considered as a novel promising anticancer therapeutic strategy, which should be further investigated in subsequent studies.
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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".