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1000 Titel
  • Predicting genotoxicity of viral vectors for stem cell gene therapy using gene expression-based machine learning
1000 Autor/in
  1. Schwarzer, Adrian |
  2. Talbot, Steven |
  3. Selich, Anton |
  4. Morgan, Michael |
  5. Schott, Juliane W. |
  6. Dittrich-Breiholz, Oliver |
  7. Bastone, Antonella L. |
  8. Weigel, Bettina |
  9. Ha, Teng Cheong |
  10. Dziadek, Violetta |
  11. Gijsbers, Rik |
  12. Thrasher, Adrian |
  13. Staal, Frank |
  14. Gaspar, Hubert |
  15. Modlich, Ute |
  16. Schambach, Axel |
  17. Rothe, Michael |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-12-01
1000 Erschienen in
1000 Quellenangabe
  • 29(12):3383-3397
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1016/j.ymthe.2021.06.017 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636173/ |
1000 Ergänzendes Material
  • https://www.cell.com/molecular-therapy-family/molecular-therapy/fulltext/S1525-0016(21)00323-3?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1525001621003233%3Fshowall%3Dtrue#supplementaryMaterial |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Sacherschließung
lokal gene therapy
lokal insertional mutagenesis
lokal Gentherapie
lokal integrating viral vectors
lokal preclinical risk assessment
lokal in vitro assay
lokal genotoxicity
lokal safety assay gene therapy
lokal machine learning
lokal gene expression
lokal support vector machine
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0001-5684-2280|https://orcid.org/0000-0002-9062-4065|https://orcid.org/0000-0003-2584-7841|https://frl.publisso.de/adhoc/uri/TW9yZ2FuLCBNaWNoYWVsIA==|https://frl.publisso.de/adhoc/uri/IFNjaG90dCwgSnVsaWFuZSBXLiAgICA=|https://orcid.org/0000-0003-4587-5022|https://frl.publisso.de/adhoc/uri/QmFzdG9uZSwgQW50b25lbGxhIEwuIA==|https://orcid.org/0000-0003-1684-9087|https://orcid.org/0000-0001-6945-0462|https://frl.publisso.de/adhoc/uri/IER6aWFkZWssIFZpb2xldHRhIA==|https://orcid.org/0000-0003-0191-3904|https://orcid.org/0000-0002-6097-6115|https://orcid.org/0000-0003-1588-8519|https://orcid.org/0000-0001-6700-7213|https://orcid.org/0000-0001-8018-4118|https://orcid.org/0000-0003-2743-0070|https://orcid.org/0000-0002-6813-4705
1000 Label
1000 Förderer
  1. Deutsche Forschungsgemeinschaft |
  2. Niedersächsisches Ministerium für Wissenschaft und Kultur |
  3. National Centre for the Replacement, Refinement and Reduction of Animals in Research |
  4. Horizon 2020 |
  5. European Commission |
  6. Medizinischen Hochschule Hannover |
  7. Else Kröner-Fresenius-Stiftung |
  8. Joachim Herz Stiftung |
  9. Wellcome Trust |
1000 Fördernummer
  1. RO 5102/1-1; Exc 62/2
  2. -
  3. NC/C015102/1
  4. 755170
  5. 261387
  6. -
  7. 2017_A74
  8. -
  9. 090233/Z/09/Z
1000 Förderprogramm
  1. Cluster of excellence REBIRTH; SFB738
  2. R2N
  3. CRACK-IT initiative
  4. -
  5. FP7 CELL-PID
  6. Clinician-Scientist Program “Young Academy”
  7. -
  8. -
  9. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Deutsche Forschungsgemeinschaft |
    1000 Förderprogramm Cluster of excellence REBIRTH; SFB738
    1000 Fördernummer RO 5102/1-1; Exc 62/2
  2. 1000 joinedFunding-child
    1000 Förderer Niedersächsisches Ministerium für Wissenschaft und Kultur |
    1000 Förderprogramm R2N
    1000 Fördernummer -
  3. 1000 joinedFunding-child
    1000 Förderer National Centre for the Replacement, Refinement and Reduction of Animals in Research |
    1000 Förderprogramm CRACK-IT initiative
    1000 Fördernummer NC/C015102/1
  4. 1000 joinedFunding-child
    1000 Förderer Horizon 2020 |
    1000 Förderprogramm -
    1000 Fördernummer 755170
  5. 1000 joinedFunding-child
    1000 Förderer European Commission |
    1000 Förderprogramm FP7 CELL-PID
    1000 Fördernummer 261387
  6. 1000 joinedFunding-child
    1000 Förderer Medizinischen Hochschule Hannover |
    1000 Förderprogramm Clinician-Scientist Program “Young Academy”
    1000 Fördernummer -
  7. 1000 joinedFunding-child
    1000 Förderer Else Kröner-Fresenius-Stiftung |
    1000 Förderprogramm -
    1000 Fördernummer 2017_A74
  8. 1000 joinedFunding-child
    1000 Förderer Joachim Herz Stiftung |
    1000 Förderprogramm -
    1000 Fördernummer -
  9. 1000 joinedFunding-child
    1000 Förderer Wellcome Trust |
    1000 Förderprogramm -
    1000 Fördernummer 090233/Z/09/Z
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6432238.rdf
1000 Erstellt am 2022-03-15T11:02:58.453+0100
1000 Erstellt von 323
1000 beschreibt frl:6432238
1000 Bearbeitet von 317
1000 Zuletzt bearbeitet Thu Apr 07 10:41:09 CEST 2022
1000 Objekt bearb. Thu Apr 07 10:40:57 CEST 2022
1000 Vgl. frl:6432238
1000 Oai Id
  1. oai:frl.publisso.de:frl:6432238 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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