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

使用基于基因表达的机器学习预测干细胞基因治疗病毒载体的基因毒性

阅读:6
作者:Adrian Schwarzer, Steven R Talbot, Anton Selich, Michael Morgan, Juliane W Schott, Oliver Dittrich-Breiholz, Antonella L Bastone, Bettina Weigel, Teng Cheong Ha, Violetta Dziadek, Rik Gijsbers, Adrian J Thrasher, Frank J T Staal, Hubert B Gaspar, Ute Modlich, Axel Schambach, Michael Rothe0

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。