Promoting antigen escape from dendritic cell endosomes potentiates anti-tumoral immunity
Bibliographic record
Abstract
Background: Dendritic cells (DCs) are usually very efficient at activating CD8 T cells. However, their capacity to cross-present soluble antigens can be limited by non-specific degradation of captured antigens during endosome maturation, which would lead to the destruction of immunogenic epitopes. To bypass this limitation, we tested a new antigen formulation using AccumTM, a technology originally designed to enhance accumulation of antibody-drug conjugate in target cells by promoting endosome-to-cytosol escape.Methods: Following chemical linking and characterization of the Accum-antigen complex, a series of in vitro antigen presentation assays were conducted to assess the T-cell activation potency of the antigen formulation. The generated cellular product was then tested in vivo for its capacity to elicit anti-tumoral responses in both prophylactic and therapeutic vaccination settings. Finally, allogeneic DCs pulsed with tumor lysate linked to AccumTM were characterized for their therapeutic potency. Results: Linking the Accum moiety to the experimental xenoantigen ovalbumin caused endosomal damages resulting in enhanced intracellular protein processing by primary DCs. As a result, production of pro-inflammatory cytokines and chemokines was enhanced by responding T cells. When evaluated for their anti-tumoral properties, Accum-ovalbumin-pulsed DCs elicited potent effector and central memory CD8 T cells, which explains the complete and long-lasting protection observed in vaccinated mice despite multiple EG.7 T-cell lymphoma challenges. When combined with the anti-PD-1 immune checkpoint inhibitor, therapeutic vaccination using both syngeneic and allogeneic DCs pulsed with Accum-linked ovalbumin triggered potent anti-tumoral responses; an observation further enhanced using Accum-linked lymphoma tumor lysate proteins.Conclusion: These highly favorable therapeutic effects highlight the promising potential of AccumTM as a distinct and potent technology platform suitable for the design of next generation cell cancer vaccines.
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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".