Genetic and environmental imprints on T cell receptor repertoires as predictors of graft-versus-host disease
Bibliographic record
Abstract
Abstract Hematopoietic cell transplantation (HCT) as potentially curative treatment for patients with hematologic malignancies relies on T cells to mediate the potentially curative graft-versus-tumor (GVT) effect, which may be associated with graft-versus-host disease (GVHD), a potentially life-threatening complication. T cells recognize peptides presented by Human Leukocyte Antigen (HLA) molecules. It is commonly believed that matching HLA alleles between donors and recipients ensures similarity in their T cell receptor (TCR) repertoires, thereby reducing the risk of GVHD and graft rejection. However, TCR repertoires are shaped by multiple factors beyond HLA genetics, including sex, age, and immune history. The extent to which genetic variation and past infections influence TCR repertoire composition and GVHD risk remains unclear. Here, we show that while HLA haplotypes contribute to broad TCR repertoire differences, recent viral infections significantly impact TCR composition and influence GVHD risk. Analyzing 401 patients who were uniformly transplanted from healthy HLA-identical sibling HCT donors, we introduced HLA-TCR coherence , a metric that quantifies the extent to which an individual’s TCR repertoire reflects their HLA haplotype. We find that higher TCR-HLA coherence is associated with greater HLA heterozygosity and an increased incidence of severe acute GVHD (grade 3-4) in transplant recipients. Furthermore, in silico identification of virus-associated TCRs (vaTCRs) using TCR sequencing and viral serology reveal specific vaTCRs predictive of either increased or decreased GVHD risk. These findings suggest that beyond HLA allele matching, donor-specific immune history and repertoire characteristics are critical determinants of GVHD risk. Thus, a more systematic integration of donor immune history may offer a complementary avenue for refining donor selection and potentially improving transplant outcomes. One Sentence Summary While genetics shape the potential of a donor’s T cell repertoire, factors like infections and HLA diversity influence its composition and impact on transplant outcomes, including GVHD risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".