loading page

Benchmarking and optimization of a Next Generation Sequencing based method for transgene Sequence Variant Analysis in Biotherapeutic Cell Line Development
  • +9
  • 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

Corresponding Author:[email protected]

Author Profile
Yizhou Zhou
Biogen Inc
Author Profile
Eric Marshall
Biogen Inc
Author Profile
Thomas Carlile
Biogen Inc
Author Profile
Patrick Cullen
Biogen Inc
Author Profile
Dongdong Lin
Biogen Inc
Author Profile
Chongfeng Xu
Biogen
Author Profile
Justin Crisafulli
Biogen Inc
Author Profile
Chao Sun
Biogen Inc
Author Profile
Fergal Casey
Biogen Inc
Author Profile
Baohong Zhang
Biogen Inc
Author Profile
Christina Alves
Biogen Inc
Author Profile

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.
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