RESUMO
Age-related macular degeneration is the main cause of irreversible vision in developed countries, and intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections are the current gold standard treatment today. Although anti-VEGF treatment results in important improvements in the course of this disease, there is a considerable number of patients not responding to the standardized protocols. The knowledge of how a patient will respond or how frequently retreatment might be required would be vital in planning treatment schedules, saving both resource utilization and financial costs, but today, there is not an ideal biomarker to use as a predictive response to ranibizumab therapy. Whole blood and blood mononuclear cells are the samples most studied; however, few reports are available on other important biofluid samples for studying this disease, such as aqueous humor. Moreover, the great majority of studies carried out to date were focused on the search for SNPs in genes related to AMD risk factors, but miRNAs, proteomic and metabolomics studies have rarely been conducted in anti-VEGF-treated samples. Here, we propose that genomic, proteomic and/or metabolomic markers could be used not alone but in combination with other methods, such as specific clinic characteristics, to identify patients with a poor response to anti-VEGF treatment to establish patient-specific treatment plans.
RESUMO
Age-related macular degeneration (AMD) is an incurable disease associated with aging that destroys sharp and central vision. Increasing evidence implicates both systemic and local inflammation in the pathogenesis of AMD. Intravitreal injection of anti-vascular endothelial growth factor (VEGF) agents is currently the first-line therapy for choroidal neovascularization in AMD patients. However, a high number of patients do not show satisfactory responses to anti-VEGF treatment after three injections. Predictive treatment response models are one of the most powerful tools for personalized medicine. Therefore, the application of these models is very helpful to predict the optimal treatment for an early application on each patient. We analyzed the transcriptome of peripheral blood mononuclear cells (PBMCs) from AMD patients before treatment to identify biomarkers of response to ranibizumab. A classification model comprised of four mRNAs and one miRNA isolated from PBMCs was able to predict the response to ranibizumab with high accuracy (Area Under the Curve of the Receiver Operating Characteristic curve = 0.968), before treatment. We consider that our classification model, based on mRNA and miRNA from PBMCs allows a robust prediction of patients with insufficient response to anti-VEGF treatment. In addition, it could be used in combination with other methods, such as specific baseline characteristics, to identify patients with poor response to anti-VEGF treatment to establish patient-specific treatment plans at the first visit.