Beyond eplet counts: machine learning integration of evolutionary distance and electrostatic divergence improves prediction of HLA-DQ donor-specific antibody formation after kidney transplantation

Background. The emergence of donor-specific antibodies (DSA) against HLA-DQ reduces graft survival and limits future transplant options. Current practice predicts DSA development from HLA antigen mismatches or unique eplet counts (HLAMatchmaker), but predictive accuracy remains limited. Improving the prediction accuracy is crucial for solid organ transplants. Methods. We analysed a retrospective cohort of 240 kidney transplants (480 donor-DQ samples) in which the donor DQ allele targeted by each post-transplant DSA was identified. We compared the predictive accuracy of unique eplets (HLAMatchmaker), total eplets, amino acid mismatches, PAM genetic distance, and electrostatic mismatch score (EMS), individually and in combination. DSA emerge through the response of two donor and two patient DQ heterodimers. We evaluate the appropriate combination method of patient and donor alleles. Results. Total eplet or amino acid mismatch counts outperformed unique eplets as used in HLAMatchmaker (AUC 0.79 vs. 0.73). Combining eplets with PAM and EMS distances in an XGBoost classifier achieved AUC 0.84, with a hazard ratio above 7. The most informative features were PAM distance and a subset of eplets distributed across the HLA-DQ alpha and beta chains. When extending prediction from a single donor DQ allele to the donor as a whole, the maximum of the two per-allele scores outperformed a probabilistic combination. Conclusions. The unique-eplet approach is suboptimal for predicting anti-DQ DSA in this cohort; combining total eplets with genetic and biochemical distance metrics yields substantially better prediction, essential for the reduction of DSA emergence. External validation in independent cohorts is the necessary next step.