O001 A novel molecular HLA mismatch algorithm to identify transplant patients at low risk of primary alloimmunity
Bibliographic record
Abstract
Abstract Introduction There is an unmet need for prognostic biomarkers for alloimmune risk stratification to guide personalised recipient care kidney transplantation. Methods We used three molecular Mismatch (molMM) algorithms - Amino-Acid-Mismatch-Score (AAMS), Electrostatic-Mismatch-Score (EMS3D) and NetMHCIIpan in a dataset of patients receiving standardised donor lymphocyte injections to predict de-novo donor-specific-antibody (dnDSA) at the individual HLA level (n=665). Risk thresholds were derived for HLA-DR/DQ mismatch and algorithms combined. External validation was performed in two extensively-phenotyped kidney transplant cohorts (Manitoba, n=856, Denver n=404) and in the NHSBT kidney transplant registry (2000–2020, n=27,028). Results External validation (Manitoba cohort, 10-year follow-up) showed all algorithms predicted dnDSA development using experimentally derived HLA-DR/DQ molMM risk thresholds (HLA-DQ AUC: 0.78-0.80; HLA-DR AUC:0.76-0.81). AAMS and EMS3D had improved risk stratification (low-medium-high risk groups for HLA-DR+DQ dnDSA) vs NetMHCIIpan (p=NS). A combined AAMS+EMS3D molMM algorithm further improved discrimination and identified a large patient cohort (15.4% patients) at very-low risk of Class-II dnDSA (1.3% at 10 years). Multivariate analysis confirmed correlation with primary alloimmune events (dnDSA p<0.001, TCMR p<0.001, ABMR p=0.0049) and all-cause graft loss (p=0.0038). Further external validation of the combined molMM algorithm in an ethnically diverse cohort (Denver) confirmed the dnDSA risk association (p<0.0001), and in the UK registry cohort confirmed significant association with all-cause graft loss (p<0.001). Conclusion We developed and validated a novel molMM algorithm, incorporating information from HLA amino-acid sequence and tertiary structurebthat may be used as a prognostic biomarker of primary alloimmunity risk and to enrich prospective clinical trials in kidney transplantation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".