Discovery and validation of multi-omic plasma biomarkers for Adrenomyeloneuropathy

PROJECT SUMMARY/ABSTRACT The goal of this project is to utilize omics approaches in conjunction with machine learning tools to develop novel prognostic and monitoring biomarkers for Adrenomyeloneuropathy (AMN), the most common adult neurologic variant of X-linked adrenoleukodystrophy (X-ALD). X-ALD is an inherited disorder due to mutations in the ABCD1

gene. All males with a mutation and half of all females who carry a mutation in ACBD1 develop progressive neurological symptoms affecting gait, and bowel/bladder function, and there is currently no existent therapy. While several therapeutic strategies are rapidly emerging, the implementation of clinical trials is severely

challenged by slow and variable disease course and lack of validated monitoring and prognostic biomarkers. Building on our prior research, cutting edge omics techniques the overall hypothesis of this new proposal is that by generating and integrating evaluating metabolites and micro RNAs in patient blood plasma samples, it is

possible to identify both monitoring biomarker(s) that correlate with disease severity and prognostic biomarker(s) that either individually or in aggregate as a biomarker signature precede clinical changes allowing the prediction of AMN disease progression. We will test this hypothesis by in two phases in this milestone driven project. During

the initial R61 Phase, we plan to identify monitoring and prognostic biomarkers signature in longitudinal plasma samples obtained from a cohort of AMN subjects whose blood and clinical information has already been collected by the investigators. The identified miRNA and metabolite signature along with clinical outcome measures will

be used to create a machine learning based prediction tool. Then if successful, in the next phase, the R33 Phase, we will validate clinical utility of monitoring and prognostic AMN biomarkers and the machine learning tool in a new prospective multicenter cohort of AMN patients. We will accomplish our goals through a multi-disciplinary

approach which will bring together molecular biologists, neuroscientists, clinical leukodystrophy experts, statistical experts and computer engineers with machine learning expertise. If successful, the outcome of this research is expected to represent a necessary vertical advancement to the field and we will have (1) a clinically

assessed miRNA and metabolomic biomarker platform that is able to verify AMN disease severity as compared to age and sex matched controls and (2) a novel machine learning based prediction tool of disease progression in AMN.