Computational Analysis of HLA-presentation of Non-synonymous Recipient Mismatches Indicates Effect on the Risk of Chronic Graft-vs.-Host Disease After Allogeneic HSCT
Bibliographic record
Abstract
Genetic mismatches in protein coding genes between allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipient and donor can elicit an alloimmunity response via peptides presented by the recipient HLA receptors as minor histocompatibility antigens (mHAs). While the impact of individual mHAs on allo-HSCT outcome such as graft-vs.-host and graft-vs.-leukemia effects has been demonstrated, it is likely that established mHAs constitute only a small fraction of all immunogenic non-synonymous variants. In the present study, we have analyzed the genetic mismatching in 157 exome-sequenced sibling allo-HSCT pairs to evaluate the significance of polymorphic HLA class I associated peptides on clinical outcome. We applied computational mismatch estimation approaches based on experimentally verified HLA ligands available in public repositories, published mHAs, and predicted HLA-peptide affinites, and analyzed their associations with chronic graft-vs.-host disease (cGvHD) grades. We found that higher estimated recipient mismatching consistently increased the risk of severe cGvHD, suggesting that HLA-presented mismatching influences the likelihood of long-term complications in the patient. Furthermore, computational approaches focusing on estimation of HLA-presentation instead of all non-synonymous mismatches indiscriminately may be beneficial for analysis sensitivity and could help identify novel mHAs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".