Tissue distribution of target antigen has a decisive influence on the outcome of adoptive cancer immunotherapy
Bibliographic record
Abstract
Adoptive transfer of allogeneic T cells has unmatched efficacy to eradicate leukemic cells. We therefore sought to evaluate in kinetic terms interactions between T cells and allogeneic leukemic cells. T cells primed against the model B6(dom1) minor histocompatibility antigen were adoptively transferred in irradiated B10 (B6(dom1)-positive) and congenic B10.H7(b) (B6(dom1)-negative) recipients, some of which were also injected with EL4 leukemia/lymphoma cells (B6(dom1)-positive). A key finding was that the tissue distribution of the target epitope dramatically influenced the outcome of adoptive cancer immunotherapy. Widespread expression of B6(dom1) in B10 recipients induced apoptosis and dysfunction of antigen-specific T cells. Furthermore, in leukemic B10 and B10.H7(b) hosts, a massive accumulation of effector/memory B6(dom1)-specific T cells was detected in the bone marrow, the main site of EL4 cell growth. The accumulation of effector/memory cells in recipient bone marrow was EL4 dependent, and its kinetics was different from that observed in recipient spleen. We conclude that strategies must be devised to prevent apoptosis of adoptively transferred T cells confronted with a high antigen load and that local monitoring of the immune response at the site of tumor growth may be mandatory for a meaningful assessment of the efficacy of adoptive 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 | 0.000 | 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 | 0.000 |
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".