Optimizing bone marrow transplantation – Prof. David Gfeller (UNIL) and Prof. Jean Villard (HUG)

Optimized Predictions of Patient-Donor Immune Compatibility for Hematopoietic Stem Cell Transplantation

The primary goal of the project is to optimize the prediction of recipient-donor genetic compatibility for hematopoietic stem cell transplantation (HSCT) in hematological malignancies. Recipient-donor compatibility is one of the best predictors of the success of HSCT. By improving prediction accuracy, the project seeks to reduce the incidence of graft-versus-host disease and other immune-related complications. This advance could potentially increase the survival rates and quality of life for patients undergoing this common and key practice in the clinic.

Prof. Gfeller and Prof. Villard will capitalize on a combination of state-of-the-art immunopeptidomics data, machine learning algorithms and clinical data to develop their predictor of recipient-donor genetic compatibility. This model is designed to learn from past transplantation outcomes, thereby continuously improving its predictive capability. The team plans to integrate their results with current medical knowledge to develop a robust tool that can be used directly in clinical settings to assist in making more informed donor selection decisions.