Drs. Cohen-Freue (Statistics) and Blydt-Hansen (Pediatrics) were awarded funding for their project on applying statistical models (i.e., classification) to identify a metabolomic signature for personalized risk assessment in pediatric patients receiving a transplated kidney. The funds from the DSI Postdoctoral Matching Fund Program will enable the team to apply and extend various machine learning methods to build a metabolomics classifer to predict long-term outcome (i.e., allograft survival). They will also integrate other molecular signatures (pre- and post-transplant) to further increase the robustness of their predicitve models.
Kidney transplantation is the most effective treatment for end-stage kidney failure and improves both survival and quality of life. It is not, however, a cure and most young people will experience complications that precipitate allograft failure. At present, children are all treated with a standard protocol for immune suppression, which ignores the wide heterogeneity in both immune responses and susceptibility to complications. As a result, some children suffer complications for excessive immune suppression whereas others may suffer rejection from insufficient immunosuppression. We aim to study how the metabolism state of the kidney recipient affects the evolution of the immune response to the allograft after transplant. Our goal is to identify a metabolomic signature using pre-transplant serum samples and machine learning techniques to support a precision-medicine approach to immunosuppressive treatment that can be tailored to the alloimmune risk-characteristics of each patient. Providing a personalized risk assessment would permit tailoring of treatment to optimize management of immunosuppression and avoid complications related to unnecessary treatment.