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Cerebral cavernous malformations (CCMs), one of the most common microvascular malformations in the capillary beds of the brain, are susceptible to hemorrhagic stroke. As an autosomal dominant disorder with incomplete penetrance, the majority of CCM gene mutation carriers are largely asymptomatic but when symptoms occur, the disease has typically reached the stage of focal hemorrhage with irreversible brain damage. Currently, the invasive neurosurgery removal of CCM lesions is the only treatment option, despite the recurrence of the symptoms after surgery. Therefore, there is a grave need for prognostic/monitoring biomarkers as risk predictors for stroke prevention. The objective of the proposal is to develop a set of blood prognostic/monitoring biomarkers as precise risk indicators for stroke prevention. In this project, the plan is to validate the novel serum biomarkers identified in Ccms animal models and human CCMs patients, and utilize these biomarkers with statistical algorithms for risk prediction of hemorrhagic CCMs. This proposal has been formulated based on recent findings of five serum etiological biomarkers associated with disruption of the Blood-Brain Barrier (BBB), the first step for hemorrhagic CCMs in Ccm mice models. This work will lay the groundwork for larger human trials for final validation and revolutionary potential clinical applications.
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Cerebral cavernous malformations (CCMs), one of the most common vascular malformations, are characterized by abnormally dilated intracranial capillaries resulting in increased susceptibility to hemorrhagic stroke. As an autosomal dominant disorder with incomplete penetrance, the majority of CCMs gene mutation carriers are largely asymptomatic but when symptoms occur, the disease has typically reached the stage of focal hemorrhage with irreversible brain damage. Currently, the invasive neurosurgery removal of CCM lesions is the only treatment option, despite the recurrence of symptoms after surgery. Therefore, there is a grave need for prognostic/monitoring biomarkers as risk predictors for stroke prevention. The goal is to develop a set of blood prognostic biomarkers as risk predictors for stroke prevention. The objective of this project is to validate novel serum biomarkers identified in Ccm animal models in human blood that could predict the risk of hemorrhagic events. The central hypothesis is that quantitative detection of certain serum biomarkers can be utilized to predict the timing of hemorrhagic events. The hypothesis has been formulated based on the recent findings of five serum etiological biomarkers associated with disruption of the Blood-Brain Barrier (BBB), which could lead to hemorrhagic events. Taking advantage of the research team's expertise in Ccm pathology and serum biomarkers, the central hypothesis will be tested to achieve the study goal by pursuing the following three revolutionary aims in two phases, starting from the phase 1 with the first two biomarker discovery aims (1, 2):
Phase-2 Aim: 3) Improve clinical utility of validated prognostic/monitoring biomarkers with optimized algorithms in larger cohorts in preparation for human clinical trials. The working hypothesis is that clinical sensitivity and specificity of blood biomarkers can be further improved through a blindly test-retest approach in larger independent human cohorts. This aim will provide sufficient proof that the clinical utility of blood biomarkers can be improved and utilized to predict the risk of hemorrhagic stroke, laying the groundwork for future clinical trials and possible future revolutionary clinical applications. The research team will continue to 3a). Improve clinical utility by optimizing the precision of prognostic/monitoring algorithms of validated biomarker(s)/panel(s) using larger cohorts; 3b). Further improve clinical utility by confirming reliability of validated biomarker(s)/panel(s) in a larger cohort; and 3c). Confirm validated prognostic/monitoring biomarker(s)/panel(s) are ready for future clinical trials.
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12 participants in 2 patient groups
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Johnathan S Abou-Fadel, PhD; Jun Zhang, PhD
Data sourced from clinicaltrials.gov
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