Predicting genotoxicity of viral vectors for stem cell gene therapy using gene expression-based machine learning.

ABSTRACT: Hematopoietic stem cell gene therapy is emerging as a promising therapeutic strategy for many diseases of the blood and immune system. However, several individuals who underwent gene therapy in different trials developed hematological malignancies caused by insertional mutagenesis. Preclinical assessment of vector safety remains challenging because there are few reliable assays to screen for potential insertional mutagenesis effects in vitro. Here we demonstrate that genotoxic vectors induce a unique gene expression signature linked to stemness and oncogenesis in transduced murine hematopoietic stem and progenitor cells. Based on this finding, we developed the surrogate assay for genotoxicity assessment (SAGA). SAGA classifies integrating retroviral vectors using machine learning to detect

SUBMITTER: Schwarzer A 

PROVIDER: S-EPMC8636173 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature