bioRxivCancer BiologyPreprint
Zika virus oncolytic signaling reverts immunosuppressive and angiogenic glioblastoma microenvironment in vitro and in vivo
Gabriella Pinheiro A. de FreitasiD, Jefferson Henrique Quintanilla, José Marcos Janeiro, Oleksandra Brechko, Romy Weinstock, Gabriel Cardoso Atella, Matheus Villanueva Andrade, Jéssica C. C. G. FerreiraiD, Janaina M.de Vasconcelos, João Vianez, Maria BellioiD, Amílcar TanuriiD, Luiz Gustavo DuboisiD, Flavia R. S. Lima, Vivaldo Moura‐NetoiD, Luiza M. HigaiD, Patricia Pestana GarceziD
Abstract
Glioblastoma (GBM), the most aggressive primary brain tumor, remains refractory to conventional therapies, characterized by high relapse rates and poor clinical prognosis. Oncolytic viruses represent a promising therapeutic frontier; specifically, Zika virus (ZIKV) has shown potent oncolytic activity against GBM in vitro and in vivo. However, its impact on the tumor microenvironment (TME) remains poorly understood. Here, we demonstrate that ZIKV modulates pivotal TME components, triggering paracrine anti-angiogenic signaling that impairs endothelial stabilization and reprogramming microglia toward an anti-tumoral phenotype. These findings were corroborated in vivo in GBM-bearing mice. Using unbiased RNA-Seq, we identified type III interferon (IFN) signaling as the primary mediator of this ZIKV-induced TME remodeling. Crucially, analysis of human glioma datasets revealed that the type III IFN receptor complex is highly expressed in patients, identifying a high-risk population equipped with the molecular machinery to respond to this axis. Exogenous treatment with type III IFN in vitro and in vivo successfully mimicked ZIKV's anti-tumoral effects, reducing angiogenesis and promoting immune activation. Our results demonstrate that ZIKV acts as an immunotherapeutic agent capable of shifting the TME toward an anti-tumoral state, highlighting the type III IFN pathway as an actionable vulnerability and a promising strategy for patients failing conventional therapies.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
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