Directed evolution as a means of identifying novel oncolytic Semliki Forest virus genotypes

Abstract

Glioblastoma (GBM) is the most common, aggressive form of primary malignant brain cancer in adults. While patient life expectancy with many other cancers has improved in recently, GBM prognosis remains poor. Current standard therapies (surgical resection, radiotherapy and temozolomide) grant GBM patients a median survival of 14.6 months. Alternative therapies are necessary to improve patient outcomes. Oncolytic virus (OV) therapy, the use of viruses to selectively kill cancer cells and stimulate anti-tumor immune responses, shows promise for many cancers. Several OVs have been approved worldwide, demonstrating improved patient survival without significant systemic disease. However, many candidate anti-GBM OV clinical trials do not achieve stable outcomes. More effective OVs are needed to improve outcomes. Semliki Forest virus (SFV; Family: Togaviridae; Genus Alphavirus) strain A774 (SFV-A774) is safe in mammals, crosses the blood-brain barrier (BBB) – a major roadblock for GBM therapies – and shows promise in treating some preclinical GBM models. However, while SFV kills GBM cells in vitro, it falls short in immunocompetent mouse models. To improve SFV oncolysis, we developed SFV strains with increased cytotoxicity to GBM cells without increasing cytotoxicity to healthy brain cells. We used directed evolution, repeatedly passaging SFV to allow for adaptation using two GBM models: GL-261 murine glioma cells, which are refractory to SFV in vivo, and a 3D patient-derived human glioma tumor microenvironment (TME) model containing glioma stem-like cells, healthy astrocytes, and microglia, to identify mutations that increase SFV's oncolytic efficacy. Following virus passaging, we evaluated oncolysis by the adapted populations by measuring cell death of the target cancer cells, identifying a population of interest from each model. To identify oncolytic mutations, we sequenced the populations, finding a non-synonymous mutation in each model. GL-261 passage produced a mutation in the viral E1 protein, D327G. 3D TME model passage produced a mutation in nsP2, A614T. We constructed these mutants with reverse genetics. These mutants increased cell death and potential immune stimulation in multiple glioma models. Mechanistically, SFV E1 D327G increased binding to GL-261 cells compared to WT SFV-A774, suggesting increased cancer targeting. SFV nsP2 A614T led to reduced interferon-β release, suggesting that this mutant may antagonize antiviral responses more efficiently. In future studies, we will test mutant efficacy in immunocompetent GBM mouse models and in patient-derived human glioma TME models. Overall, we identified SFV variants with improved oncolytic efficacy, underscoring directed evolution's potential to generate novel GBM therapeutics.