Heterogeneous multi-omic data integration for biomarker discovery and drug repurposing in acute spinal cord injury
Funded in: 2018, 2019, 2020
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Problem: Effective therapies to promote functional recovery in acute SCI have been elusive
Target: Development of an objective, molecular biomarker that could stratify patients by SCI severity, and predict neurologic recovery.
Goal: The application of data-driven approaches could provide a means to bridge the translational divide in SCI.
Despite the personal impact and economic burden of spinal cord injury (SCI), effective therapies to promote functional recovery in acute SCI have been elusive, in part because conducting even a single clinical trial of a novel SCI therapy can take over a decade and considerable financial resources to complete. The duration and cost of clinical trials for SCI are inflated by reliance on standardized neurological measures for patient enrolment. An objective, molecular biomarker that could stratify patients by SCI severity, and predict neurologic recovery, could dramatically decrease the cost and time required to conduct clinical trials in SCI. This proposal will address the translational divide in acute SCI by analyzing a unique dataset of CSF and serum samples collected from acute SCI patients, which have been subjected to genomic, proteomic, metabolomic, and lipidomic analyses under the leadership of Prof. Brian Kwon. I will evaluate of individual ‘-omics’ datasets, and ultimately perform an integrative, multi-omic analysis, to identify molecular features of acute SCI that predict neurological recovery more accurately than conventional clinical measures. Furthermore, the conservation of these biomarkers in a pig model of SCI will be evaluated to facilitate ‘bedside-back-to-bench’ research, by providing a translatable basis for preclinical development. The application of data-driven approaches to interrogate an unprecedented resource will therefore provide a means to bridge the translational divide in SCI.