Collateral Damage of Nonhematopoietic Tissue by Hematopoiesis-Specific T Cells Results in Graft-versus-Host Disease During an Ongoing Profound Graft-versus-Leukemia Reaction
Bibliographic record
Abstract
After allogeneic stem cell transplantation (allo-SCT), donor T cells may recognize minor histocompatibility antigens (MiHA) specifically expressed on cells of the recipient. It has been hypothesized that T cells recognizing hematopoiesis-restricted MiHA mediate specific graft-versus-leukemia (GVL) activity without inducing graft-versus-host disease (GVHD), whereas T cells recognizing ubiquitously expressed MiHA induce both GVL and GVHD reactivity. It also has been hypothesized that alloreactive CD4 T cells are capable of mediating specific GVL reactivity due to the hematopoiesis-restricted expression of HLA class II. However, clinical observations suggest that an overt GVL response, associated with expansion of T cells specific for hematopoiesis-restricted antigens, is often associated with GVHD reactivity. Therefore, we developed in vitro models to investigate whether alloreactive T cells recognizing hematopoiesis-restricted antigens induce collateral damage to surrounding nonhematopoietic tissues. We found that collateral damage to MiHA-negative fibroblasts was induced by misdirection of cytotoxic granules released from MiHA-specific T cells activated by MiHA-positive hematopoietic cells, resulting in granzyme-B-mediated activation of apoptosis in the surrounding fibroblasts. We demonstrated that direct contact between the activated T cell and the fibroblast is a prerequisite for this collateral damage to occur. Our data suggest that hematopoiesis-restricted T cells actively participate in an overt GVL response and may contribute to GVHD via induction of collateral damage to nonhematopoietic targets.
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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 | 0.000 | 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".