Pre-Transplant Multiomic Profiling to Quantify The Risk of Rejection Following Heart Transplantation

PROJECT SUMMARY______________________________________________________________________ One-third of heart transplant recipients will develop acute rejection of their new heart within the first post- transplant year. These episodes can cause worsening heart failure, acceleration of chronic rejection, and

decrease post-transplant survival. Although immunosuppression decreases the risk of rejection, its long-term use is associated with infection, cancer, and worsening kidney disease, all of which may limit the lifespan of a patient after transplant. The ability to safely minimize immunosuppression without increasing the risk of

rejection would improve heart transplant recipients' outcomes. Calculating the risk of rejection for a given patient remains a clinical challenge, as age, sex, and immunologic mismatch between the donor and recipient are incomplete predictors of risk. While several non-invasive tests are available to diagnose rejection after

transplant, there is not currently an assay that can be used before transplant to quantify whether a recipient is more prone to infection or rejection. If we could identify heart transplant candidates at low risk for rejection, we could safely minimize immunosuppression and its toxicities without increasing the risk of graft loss. The

objective of the proposed study is to leverage a large, well-phenotyped cohort with existing biospecimens and multi-omic technologies to identify novel pre-transplant biomarkers associated with clinically significant rejection within the first post-transplant year. First, we will perform proteomic profiling to validate three

biomarkers (FGF-2, SPRTY-2, IRAK-1) we identified in preliminary studies as associated with rejection. We will then expand our profiling on the Olink platform to include additional panels of proteins reporting on innate and adaptive immune activation. We will test whether biomarkers provide incremental predictive utility, beyond a

set of prespecified clinical variables. Next, we propose to perform bulk RNA sequencing on peripheral blood mononuclear cells prospectively collected before transplant & compare differential gene expression of transcripts related to the protein biomarkers identified between recipients with and without rejection. We will

also perform whole transcriptomic profiling and pathway analysis to identify relevant biological processes reporting on rejection risk. Finally, we will leverage novel machine learning approaches to identify an integrated omics signature of rejection risk. The results of the current study will support the submission of future grants to

prospectively evaluate the use of these pre-transplant biomarkers in the clinical care of heart transplant recipients.