Despite major advances in heart transplant patient management, 1 and 5-year survival rates for heart allografts have remained static over the last decade. Advances, and cost reduction, in various genomic, transcriptomic, proteomic, metabolomics and other omic technologies over the last decade, and the scaling of deep phenotyping and electronic health records affords unique opportunities for precision medicine. In this proposal we outline a number of key multiomic molecular studies and integrative analyses to bridge the genome and dynamic physiology in cardiac transplant patients.
We aim to diagnose and prognosticate acute allograft rejection and to assess the impact of biomarkers of post-transplantation complications including acute rejection, from the various omics across using ?integrative personal omic profiling? (iPOP) developed by investigators in our team. We outline a transformation advance in the molecular diagnoses of acute cardiac allograft rejection within the formalin-fixed paraffin embedded (FFPE) heart allograft biopsy samples, which may change the current standard-of-care which uses conventional histopathology grading alone. We will also assess how genetic polymorphisms impact other omic profiles in the same-, and in subsequent-, timepoints from the same individuals through to post-transplantation complex phenotypes such as acute rejection.
We aim to investigate how genetic variants in the HLA and minor histocompatibility (mHA) regions impact clinically relevant post-transplantation outcomes including acute rejection and patient survival.
Our overarching aim is to use multi-omics profiling, which spans genomics, transcriptomics, proteomics and metabolomics profiling of biospecimens collected at prospective timepoints from cardiac patient?s post-transplantation, for discovery and validation of prognostic and diagnostic biomarkers of post-transplantation complications. We utilize existing whole transcriptomics of endomyocardial biopsies (EMBs) and formalin fixed paraffin embedded (FFPE) EMBs, to discover and validate diagnostic and prognostic signatures of acute rejection. We will also leverage genomic, RNAseq, protein and metabolite datasets from equivalent blood timepoints from additional prospectively collected samples to validate these biological findings.
