Anti-tumor immunity in a model of acute myeloid leukemia
Bibliographic record
Abstract
Whole-cell vaccines allow the induction of anti-tumor immune responses without the need to define tumor antigens. We wished to directly compare, for the first time, the capacity of B7-1, B7-2 and 4-1BB ligand (4-1BBL) costimulatory molecules to convert murine and human acute myeloid leukemia (AML) cells into whole vaccines. 32Dc-kit is a murine myeloid cell line, which develops an AML-like disease over a protracted period, emulating human AML disease development. 32Dc-kit cells were modified to express elevated levels of B7-1, B7-2 or 4-1BBL, and each led to tumor rejection, although only mice injected with 32Dc-kit/B7-2 cells were able to reject subsequent parental tumor cell challenge. T-cell deficient nude mice were able to reject the 32Dc-kit variants, but they could not reject parental cell challenge; however, we found no evidence of cytotoxic T lymphocyte or natural killer (NK) activity ex vivo suggesting that tumor cell killing was mediated by an immune response that could not be recapitulated using purified NK or T cells as lone effectors. In human allogeneic mixed lymphocyte reactions (MLRs), we found no single costimulatory molecule was more effective, suggesting that the induction of a universal anti-tumor response will require a combination of costimulatory molecules.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".