Schwarzer, A;
Talbot, SR;
Selich, A;
Morgan, M;
Schott, JW;
Dittrich-Breiholz, O;
Bastone, AL;
... Rothe, M; + view all
(2021)
Predicting genotoxicity of viral vectors for stem cell gene therapy using gene expression-based machine learning.
Molecular Therapy
10.1016/j.ymthe.2021.06.017.
(In press).
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Abstract
Hematopoietic stem cell gene therapy is emerging as a promising therapeutic strategy for many diseases of the blood and immune system. However, several patients that underwent gene therapy in different trials developed hematological malignancies caused by insertional mutagenesis. Preclinical assessment of vector safety remains challenging as 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%. In summary, SAGA is more robust, sensitive and faster than previous assays and reliably predicts a mutagenic risk for vectors that led to leukemic severe adverse events in clinical trials. Therefore, 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.
| Type: | Article |
|---|---|
| Title: | Predicting genotoxicity of viral vectors for stem cell gene therapy using gene expression-based machine learning. |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ymthe.2021.06.017 |
| Publisher version: | https://doi.org/10.1016/j.ymthe.2021.06.017 |
| Language: | English |
| Additional information: | This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Infection, Immunity and Inflammation Dept |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10134820 |
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