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Benchmarking and optimization of a Next Generation Sequencing based method for transgene Sequence Variant Analysis in Biotherapeutic Cell Line Development
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  • Joost Groot,
  • Yizhou Zhou,
  • Eric Marshall,
  • Thomas Carlile,
  • Patrick Cullen,
  • Dongdong Lin,
  • Chongfeng Xu,
  • Justin Crisafulli,
  • Chao Sun,
  • Fergal Casey,
  • Baohong Zhang,
  • Christina Alves
Joost Groot
Biogen Inc
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Yizhou Zhou
Biogen Inc
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Eric Marshall
Biogen Inc
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Thomas Carlile
Biogen Inc
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Patrick Cullen
Biogen Inc
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Dongdong Lin
Biogen Inc
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Chongfeng Xu
Biogen
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Justin Crisafulli
Biogen Inc
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Chao Sun
Biogen Inc
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Fergal Casey
Biogen Inc
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Baohong Zhang
Biogen Inc
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Christina Alves
Biogen Inc
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Abstract

In recent years Next-Generation Sequencing (NGS) based methods to detect mutations in biotherapeutic transgene products have become a key quality step deployed during the development of manufacturing cell line clones. Previously we reported on a higher throughput, rapid mutation detection method based on amplicon sequencing (targeting transgene RNA) and detailed its implementation to facilitate cell line clone selection. By gaining experience with our assay in a diverse set of cell line development programs, we improved the computational analysis as well as experimental protocols. Here we report on these improvements as well as on a comprehensive benchmarking of our assay. We evaluated assay performance by mixing amplicon samples of a verified mutated antibody clone with a non-mutated antibody clone to generate spike-in mutations from ~60% down to ~0.3% frequencies. We subsequently tested the effect of 16 different sample and NGS library preparation protocols on the assay’s ability to quantify mutations and on the occurrence of false-positive background error mutations (artifacts). Our evaluation confirmed assay robustness, established a high confidence limit of detection of ~0.6%, and identified protocols that reduce error levels thereby significantly reducing a source of false positives that bottlenecked the identification of low-level true mutations.

Peer review status:ACCEPTED

27 Oct 2020Submitted to Biotechnology Journal
29 Oct 2020Submission Checks Completed
29 Oct 2020Assigned to Editor
29 Oct 2020Reviewer(s) Assigned
07 Jan 2021Editorial Decision: Revise Major
07 Apr 20211st Revision Received
07 Apr 2021Submission Checks Completed
07 Apr 2021Assigned to Editor
07 Apr 2021Reviewer(s) Assigned
26 Apr 2021Editorial Decision: Revise Minor
16 May 20212nd Revision Received
17 May 2021Submission Checks Completed
17 May 2021Assigned to Editor
18 May 2021Reviewer(s) Assigned
18 May 2021Editorial Decision: Accept