Discovering optimal targets for adoptive T-cell immunotherapy of leukemia (P4346)
Bibliographic record
Abstract
Abstract The main barrier in allogeneic hematopoietic cell transplantation is the risk of developing graft-versus-host disease. This can be prevented by the injection of CD8 T cells targeted to leukemia associated antigens (LAAs) or minor histocompatibility antigens (MiHAs). Several studies in humans have established the value of LAAs and MiHAs as target for immunotherapy on solid tumors and leukemia, respectively. Importantly, their therapeutic efficacy has never been assessed on a per antigen basis. Thus, our goal is to directly compare the therapeutic efficacy of T-cells targeted to LAAs or MiHAs expressed on EL4 cells. We selected 4 MiHAs and 4 LAAs and confirmed their immunogenicity by in vitro cytotoxicity assays. We evaluated the anti-leukemic activity of antigen-specific CD8 T cells in vivo. Notably, mice immunized against MiHAs showed enhanced survival compared to mice immunized against LAAs. We found that decreased survival of EL4 bearing mice cannot be explained by weaker MHC I/Peptide interactions. Interestingly, we showed that T cells specific for 4 LAAs and 1 MiHA are undetectable by flow cytometry and that the abundance of tetramer positive cells correlates strongly with survival of EL4 bearing mice. We propose that CD8 T cells, mainly targeted to LAAs, bind weakly to their MHC I/Peptide complexes, leading to decreased immunogenicity. We believe that the insights gained from our studies will serve as guide for selecting the best antigens for future clinical trials.
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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 | 0.001 | 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".