Amino acid substitution at peptide-binding pockets of HLA class I molecules increases risk of severe acute GVHD and mortality
Bibliographic record
Abstract
HLA disparity has a negative impact on the outcomes of hematopoietic cell transplantation (HCT). We studied the independent impact of amino acid substitution (AAS) at peptide-binding positions 9, 99, 116, and 156, and killer immunoglobulin-like receptor binding position 77 of HLA-A, B, or C, on the risks for grade 3-4 acute graft-versus-host disease (GVHD), chronic GVHD, treatment-related mortality (TRM), relapse, and overall survival. In multivariate analysis, a mismatch at HLA-C position 116 was associated with increased risk for severe acute GVHD (hazard ratio [HR] = 1.45, 95% confidence interval [CI] = 1.15-1.82, P = .0016). Mismatch at HLA-C position 99 was associated with increased transplant-related mortality (HR = 1.37, 95% CI = 1.1-1.69, P = .0038). Mismatch at HLA-B position 9 was associated with increased chronic GVHD (HR = 2.28, 95% CI = 1.36-3.82, P = .0018). No AAS were significantly associated with outcome at HLA-A. Specific AAS pair combinations with a frequency >30 were tested for association with HCT outcomes. Cysteine to tyrosine substitution at position 99 of HLA-C was associated with increased TRM (HR = 1.78, 95% = CI 1.27-2.51, P = .0009). These results demonstrate that donor-recipient mismatch for certain peptide-binding residues of the HLA class I molecule is associated with increased risk for acute and chronic GVHD and death.
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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 | 0.001 |
| 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".