In our manuscript Predicting genotoxicity of integrating viral vectors for stem cell gene therapy using gene expression-based machine learning, we report an improved in vitro test to determine the risk of insertional mutagenesis of integrating vectors for gene therapy. SAGA builds on the well accepted cell culture protocol of the In Vitro IMmortalization (IVIM) assay but screens for the deregulation of oncogenic gene expression signatures. We demonstrate a new bioinformatic approach to correctly classify the mutagenic potential of retroviral vectors used in previous and current clinical trials.
We provide the R scripts and workspace files to reproduce major analysis steps and figures described in Schwarzer et al., 2020.
We provide two scripts and supplementary data table 1 of the current rebuttal phase of the manuscript
We provide the SAGA analysis pipeline as a freely available R-package, which is hosted and maintained on GitHub by Steven R. Talbot.