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 this gene expression signature during the course of in vitro immortalization. On a set of benchmark vectors with known genotoxic potential, SAGA achieved an accuracy of 90.9%. SAGA is more robust and sensitive and faster than previous assays and reliably predicts a mutagenic risk for vectors that led to leukemic severe adverse events in clinical trials. Our work provides a fast and robust tool for preclinical risk assessment of gene therapy vectors, potentially paving the way for safer gene therapy trials.

Keywords: gene expression; gene therapy; genotoxicity; in vitro assay; insertional mutagenesis; integrating viral vectors; machine learning; preclinical risk assessment; safety assay gene therapy; support vector machine.