Impact of the HLA Immunopeptidome on Survival of Leukemia Patients After Unrelated Donor Transplantation
Bibliographic record
Abstract
PURPOSE Immunopeptidome divergence between mismatched HLA-DP is a determinant of T-cell alloreactivity and clinical tolerability after fully HLA-A, -B, -C, -DRB1, -DQB1 matched unrelated donor hematopoietic cell transplantation (UD-HCT). Here, we tested this concept in HLA-A, -B, and -C disparities after single class I HLA-mismatched UD-HCT. PATIENTS AND METHODS We studied 2,391 single class I HLA-mismatched and 14,426 fully HLA-matched UD-HCT performed between 2008 and 2018 for acute leukemia or myelodysplastic syndromes. Hierarchical clustering of experimentally determined peptide-binding motifs (PBM) was used as a proxy for immunopeptidome divergence of HLA-A, -B, or -C disparities, allowing us to classify 1,629/2,391 (68.1%) of the HLA-mismatched UD-HCT as PBM-matched or PBM-mismatched. Risks associated with PBM-matching status were assessed by Cox proportional hazards models, with overall survival (OS) as the primary end point. RESULTS Relative to full matches, bidirectional or unidirectional PBM mismatches in graft-versus-host (GVH) direction (PBM-GVH mismatches, 60.7%) were associated with significantly lower OS (hazard ratio [HR], 1.48; P < .0001), while unidirectional PBM mismatches in host-versus-graft direction or PBM matches (PBM-GVH matches, 39.3%) were not (HR, 1.13; P = .1017). PBM-GVH mismatches also had significantly lower OS than PBM-GVH matches in direct comparison (HR, 1.32; P = .0036). The hazards for transplant-related mortality and acute and chronic graft-versus-host disease but not relapse increased stepwise from full HLA matches to single PBM-GVH matches, and single PBM-GVH mismatches. A webtool for PBM-matching of single class I HLA-mismatched donor-recipient pairs was developed. CONCLUSION PBM-GVH mismatches inform mortality risks after single class I HLA-mismatched UD-HCT, suggesting that prospective consideration of directional PBM-matching status might improve outcome. These findings highlight immunopeptidome divergence between mismatched HLA as a driver of clinical tolerability in UD-HCT.
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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 | 0.002 |
| 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 | 0.000 |
| 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".