Eliminating Donor Availability Disparities in Hematopoietic Stem Cell Transplantation with AI-based Prediction of Permissive HLA Mismatches in the Context of Posttransplant Cyclophosphamide

PROJECT SUMMARY
Allogeneic hematopoietic stem cell transplantation (HCT) is a potentially curative treatment for various
hematologic malignancies and severe blood disorders. However, patients from ethnic minority groups
encounter significant health disparities in HCT utilization and outcomes. These patients experience lower
donor availability and suboptimal transplant outcomes due to their underrepresentation in donor
registries and the polymorphic nature of human leukocyte antigen (HLA) genes. Our goal is to eliminate
this critical health disparity by improving the safety and usability of HLA-mismatched transplants. We
are developing the first machine learning model designed to predict permissive HLA mismatches in the
context of posttransplant cyclophosphamide (PTCy)-based graft-versus-host disease (GVHD)
prophylaxis, the current standard of care in HLA-mismatched transplants. Permissive HLA mismatches
are those donor-recipient HLA mismatches that do not significantly increase the risk of transplant
complications. The need for a PTCy-specific permissive HLA mismatch model arises because existing
models fail to accurately predict outcomes in the PTCy setting. By predicting permissive HLA
mismatches, transplant centers can optimize mismatched donor selection for ethnic minority patients,
thereby improving HCT utilization and outcomes. To achieve this, we have designed a Cox proportional
hazards neural network model that incorporates domain-specific knowledge and employs a streamlined
parameter set focused on learning the importance of polymorphic amino acid positions to alloreactivity.
Our approach reduces the data required for training and enhances the model’s generalizability to unseen
data. Successful completion of this project has the potential to eliminate donor availability disparities in
HCT.

Grant Number: 1R43HL179155-01
NIH Institute/Center: NIH
Principal Investigator: Lidio Marx Carvalho Meireles