Anti-Thymocyte Globulin Prophylaxis Induces a Decrease in Naive Th Cells to Inhibit the Onset of Chronic Graft-versus-Host Disease: Results from the Canadian Bone Marrow Transplant Group (CBMTG) 0801 Study
Bibliographic record
Abstract
Anti-thymocyte globulin (ATG) is an established approach to decrease chronic GVHD (cGVHD), yet the exact mechanism is uncertain. To better understand the mechanism of action of ATG in preventing cGVHD, we evaluated the day 100 immune reconstitution of known cGVHD cellular biomarkers using patients from the randomized Canadian Bone Marrow Transplant Group (CBMTG) 0801 trial, which demonstrated a significant impact of ATG on cGVHD. In a separate companion biology study, we evaluated the impact of ATG prophylaxis on cGVHD cellular markers at day 100 in 40 CBMTG 0801 patients. Analysis focused on previously identified cGVHD cellular biomarkers, including naive helper T (Th) cells, recent thymic emigrant (RTE) Th cells, CD21 low B cells, CD56 bright NK reg cells, and T reg cells ST2, osteopontin, soluble B-cell activating factor (sBAFF), Interleukin-2 receptor alpha (sCD25), T-cell immunoglobulin and mucin domain-3 (TIM-3), matrix metallopeptidase 3, ICAM-1, C-X-C motif chemokine 10 (CXCL10), and soluble aminopeptidase N. The ATG-treated group had a >10-fold decrease in both RTE naive Th and naive Th cells ( P < .0001) and a 10-fold increase in CD56 bright NK reg cells ( P < .0001). T reg cells, conventional Th cells, CD21 low B cells, and all plasma markers were not affected. In the populations most affected by ATG, changes in naive Th cells were associated with the later development of cGVHD. This analysis suggests that ATG primarily impacts on cGVHD through suppression of naive Th cell expansion after transplantation. These associations need to be validated in additional studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".