KBMA Listeria monocytogenes is an effective vector for DC-mediated induction of antitumor immunity
Bibliographic record
Abstract
Vaccine strategies that utilize human DCs to enhance antitumor immunity have yet to realize their full potential. Approaches that optimally target a spectrum of antigens to DCs are urgently needed. Here we report the development of a platform for loading DCs with antigen. It is based on killed but metabolically active (KBMA) recombinant Listeria monocytogenes and facilitates both antigen delivery and maturation of human DCs. Highly attenuated KBMA L. monocytogenes were engineered to express an epitope of the melanoma-associated antigen MelanA/Mart-1 that is recognized by human CD8+ T cells when presented by the MHC class I molecule HLA-A*0201. The engineered KBMA L. monocytogenes induced human DC upregulation of costimulatory molecules and secretion of pro-Th1 cytokines and type I interferons, leading to effective priming of Mart-1-specific human CD8+ T cells and lysis of patient-derived melanoma cells. KBMA L. monocytogenes expressing full-length NY-ESO-1 protein, another melanoma-associated antigen, delivered the antigen for presentation by MHC class I and class II molecules independent of the MHC haplotype of the DC donor. A mouse therapeutic tumor model was used to show that KBMA L. monocytogenes efficiently targeted APCs in vivo to induce protective antitumor responses. Together, our data demonstrate that KBMA L. monocytogenes may be a powerful platform that can both deliver recombinant antigen to DCs for presentation and provide a potent DC-maturation stimulus, making it a potential cancer vaccine candidate.
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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 | 0.001 |
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".