Enhanced immunogenicity of leukemia-derived exosomes via transfection with lentiviral vectors encoding costimulatory molecules
Bibliographic record
Abstract
Abstract Background: Tumor cell-derived exosomes (TEXs) have been widely used to induce antitumor immune responses in animal models and clinical trials. Similarly, leukemia cell-derived exosomes (LEXs) can induce antileukemia immune responses in animal models. However, the antileukemia immunity induced by LEXs is less effective, which may be due to an inadequate costimulatory capacity. Methods: In this study, we transduced L1210 leukemia cells with a lentiviral vector encoding two B7 costimulatory molecules (CD80, CD86) and obtained LEXs that highly expressed CD80 and CD86. The antileukemia immune response derived from these LEXs was examined in vitro and in vivo in animal models. Results: We found that B7 gene-modified LEXs, including LEX-CD80, LEX-CD86, and LEX-8086, could significantly boost the expression of CD80 and CD86 in dendritic cells (DCs) and promote the secretion of functional cytokines such as TNF-α and IL-12. Moreover, these B7 gene-modified LEXs, particularly LEX-CD8086, could effectively induce CD4+T cell proliferation, Th1 cytokine secretion, and an antigen-specific anti-leukemia cytotoxic T lymphocyte (CTL) response. Additional animal studies indicated that immunization with B7 gene-modified LEXs, in particular LEX-CD8086, could significantly retard tumor growth compared to the control LEXnull group. Conclusions:This study sheds light on the feasibility of obtaining LEXs that overexpress costimulatory molecules via genetically modified leukemia cells, thereby enhancing their anti-leukemia immunity and providing a potential therapeutic strategy that contributes to leukemia 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 | 0.001 |
| 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".