Scientific background Allogeneic hematopoietic stem cell transplantation (alloHSCT) is the first cellular immunotherapy developed to cure hematologic malignancies. It is based on the anti-tumor allo-immune response (graft versus tumor effect) induced by the donor immune system also transferred during the transplant process. Despite its efficiency, hematologic malignancies relapse accounts for half of deceases and to date, no biomarker allow to predict whose patient will relapse after allogeneic HSCT and to identify these patients early before relapse. Traditional statistical methods used for biomarker identifications are limited, mostly by their parametric nature, and could benefit from advanced machine learning and optimization techniques to select relevant variables and link them to the relapsing process. This unmet medical need is of critical importance to improve prognosis of patients who are currently treated for a hematologic cancer with allo-HSCT and to adapt their treatment before relapse. Hypothesis Here, we assume that integration of clinical data with immune and metabolic variables could provide metadata for a mathematical model to predict relapse occurrence. Aims To characterize circulating immune subsets and metabolome in the donor and to compare them at 3 months and one year after transplantation in patients with or without relapse To build a calibrated stochastic simulator for the relapsing process, accounting for post-transplant events and integrating clinical data with immune and metabolic variables. Methodology This project will rely on a multicentric cohort of 369 patients who received an alloHSCT. We will use mass cytometry and mass spectrometry to decipher circulating immune subsets and metabolites associated with relapse and other post-transplantation events. We will then create a simulator that model the dynamics of post-transplant events to identify relevant biomarkers using advanced optimization techniques and to generate a tool to predict relapse after alloHSCT. Validation in animal model will finally help to identify relevant new therapeutic targets. Expected results and impact This project will use data from an already constituted large cohort of patients to develop a machine learning tool for clinicians to estimate the probability of relapse based on various clinical and immune-metabolic data.