Effect of extracellular vesicles in remodeling the tumor microenvironment by DNMT1 downregulation for enhanced cancer immunotherapy
Bibliographic record
Abstract
BACKGROUND: The efficacy of immunotherapy is often hindered by the suppression of immune responses via the tumor microenvironment (TME). The presence of cancer cells forces other proximal non-cancerous cells to support tumor growth and persistence. A clear example of this cancerous-to-non-cancerous communication is represented by the accumulation of myeloid-derived suppressor cells (MDSCs) within the TME. Several studies have convergently shown that the overexpression of DNA-methyl-transferase-1 (DNMT1) in these cells results in protection from necroptosis and enhanced accumulation in vivo. Conversely, targeting DNMT1 through hypo-methylating agents has shown promising therapeutic potential by not only reducing the levels of MDSCs but also enhancing cancer immunogenicity and the efficacy of immune checkpoint inhibitors (ICI). METHODS: Murine 4T1 (triple-negative breast cancer (TNBC)) and CT26 (colon carcinoma) cell lines were cultured under standard conditions and used to generate tumor models in BALB/c mice. An oncolytic adenovirus expressing a DNMT1-targeting short hairpin RNA (OAd.shDNMT1) was engineered and validated for DNMT1 knockdown and genome-wide methylation reduction. Small extracellular vesicles (sEVs) were isolated from virus-infected cancer cells and characterized for RNA content and uptake by MDSCs. MDSC differentiation and suppressive function were assessed in vitro using flow cytometry and co-culture assays with murine splenocytes. In vivo, tumor-bearing mice received intratumoral OAd.shDNMT1, systemic decitabine, or immune checkpoint inhibitors (anti-Programmed cell Death protein-1), and tumor growth, immune infiltration, and systemic MDSC levels were evaluated. RESULTS: In this study, we report that, by using virally infected TNBC murine cells as a source for shDNMT1-loaded sEVs, OAd.shDNMT1 successfully reduced MDSC levels in vitro and in vivo. Furthermore, the co-administration with ICI resulted in a significant tumor growth reduction in mice bearing poorly immunogenic TNBC 4T1 cells. Also, our treatment promoted antitumor immunity, prolonged survival, and complete tumor eradication in modestly immunogenic colon CT26 cancer cells. CONCLUSION: This multifaceted strategy, based on OV-mediated immune stimulation and reduction of MDSC levels via sEVs, may improve clinical outcomes and the success of immuno-based regimens for patients facing MDSC-rich and highly aggressive cancer subtypes.
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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) | 0.001 | 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".