Tumor therapy in mice via antigen targeting to a novel, DC-restricted C-type lectin
Bibliographic record
Abstract
The mouse CD8alpha+ DC subset excels at cross-presentation of antigen, which can elicit robust CTL responses. A receptor allowing specific antigen targeting to this subset and its equivalent in humans would therefore be useful for the induction of antitumor CTLs. Here, we have characterized a C-type lectin of the NK cell receptor group that we named DC, NK lectin group receptor-1 (DNGR-1). DNGR-1 was found to be expressed in mice at high levels by CD8+ DCs and at low levels by plasmacytoid DCs but not by other hematopoietic cells. Human DNGR-1 was also restricted in expression to a small subset of blood DCs that bear similarities to mouse CD8alpha+ DCs. The selective expression pattern and observed endocytic activity of DNGR-1 suggested that it could be used for antigen targeting to DCs. Consistent with this notion, antigen epitopes covalently coupled to an antibody specific for mouse DNGR-1 were selectively cross-presented by CD8alpha+ DCs in vivo and, when given with adjuvants, induced potent CTL responses. When the antigens corresponded to tumor-expressed peptides, treatment with the antibody conjugate and adjuvant could prevent development or mediate eradication of B16 melanoma lung pseudometastases. We conclude that DNGR-1 is a novel, highly specific marker of mouse and human DC subsets that can be exploited for CTL cross-priming and tumor therapy.
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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) | 0.001 | 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 | 0.001 | 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".