Rejection of Leukemic Cells Requires Antigen-Specific T Cells with High Functional Avidity
Bibliographic record
Abstract
In a context where injection of antigen (Ag)-specific T cells probably represents the future of leukemia immunotherapy, identification of optimal target Ags is crucial. We therefore sought to discover a reliable marker for selection of the most potent Ags. To this end, (1) we immunized mice against 8 individual Ags: 4 minor histocompatibility Ags (miHAs) and 4 leukemia-associated Ags (LAAs) that were overexpressed on leukemic relative to normal thymocytes; (2) we assessed their ability to reject EL4 leukemic cells; and (3) we correlated the properties of our Ags (and their cognate T cells) with their ability to induce protective antileukemic responses. Overall, individual miHAs instigated more potent antileukemic responses than LAAs. Three features had no influence on the ability of primed T cells to reject leukemic cells: (1) MHC-peptide affinity; (2) the stability of MHC-peptide complexes; and (3) epitope density at the surface of leukemic cells, as assessed using mass spectrometry. The cardinal feature of successful Ags is that they were recognized by high-avidity CD8 T cells that proliferated extensively in vivo. Our work suggests that in vitro evaluation of functional avidity represents the best criterion for selection of Ags, which should be prioritized in clinical trials of 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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".