BACKGROUND. In patients with limited response to conventional therapeutics, repositioning of already approved drugs can bring new, more effective options. Current drug repositioning methods, however, frequently rely on retrospective computational analyses and genetic testing — time consuming methods that delay application of repositioned drugs. Here, we show how proteomic analysis of liquid biopsies successfully guided treatment of neovascular inflammatory vitreoretinopathy (NIV), an inherited autoinflammatory disease with otherwise poor clinical outcomes. METHODS. Vitreous biopsies from NIV patients were profiled by an antibody array for expression of 200 cytokine-signaling proteins. Non-NIV controls were compared with NIV samples from various stages of disease progression. Patterns were identified by 1-way ANOVA, hierarchical clustering, and pathway analysis. Subjects treated with repositioned therapies were followed longitudinally. RESULTS. Proteomic profiles revealed molecular pathways in NIV pathologies and implicated superior and inferior targets for therapy. Anti-VEGF injections resolved vitreous hemorrhages without the need for vitrectomy surgery. Methotrexate injections reversed inflammatory cell reactions without the side effects of corticosteroids. Anti–IL-6 therapy prevented recurrent fibrosis and retinal detachment where all prior antiinflammatory interventions had failed. The cytokine array also showed that TNF-α levels were normal and that corticosteroid-sensitive pathways were absent in fibrotic NIV, helping explain prior failure of these conventional therapeutic approaches. CONCLUSIONS. Personalized proteomics can uncover highly personalized therapies for autoinflammatory disease that can be timed with specific pathologic activities. This precision medicine strategy can also help prevent delivery of ineffective drugs. Importantly, proteomic profiling of liquid biopsies offers an endpoint analysis that can directly guide treatment using available drugs.
Gabriel Velez, Alexander G. Bassuk, Diana Colgan, Stephen H. Tsang, Vinit B. Mahajan
This article was first published December 21, 2017. Usage data is cumulative from December 2017 through January 2018.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.