Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples

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Authors

ZHANG Yifan BLOMQUIST Thomas M. KUSKO Rebecca STETSON Daniel ZHANG Zhihong YIN Lihui SEBRA Robert GONG Binsheng LOCOCO Jennifer S MITTAL Vinay K NOVORADOVSKAYA Natalia YEO Ji-Youn DOMINIAK Nicole HIPP Jennifer RAYMOND Amelia QIU Fujun ARIB Hanane SMITH Melissa L BROCK Jay E FARKAS Daniel H CRAIG Daniel J CRAWFORD Erin L LI Dan MORRISON Tom TOM Nikola XIAO Wenzhong YANG Mary MASON Christopher E RICHMOND Todd A JONES Wendell JR Donald J Johann SHI Leming TONG Weida WILLEY James C XU Joshua

Year of publication 2022
Type Article in Periodical
Magazine / Source GENOME BIOLOGY
MU Faculty or unit

Central European Institute of Technology

Citation
Web https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02709-8
Doi http://dx.doi.org/10.1186/s13059-022-02709-8
Keywords Cancer genomics; Next-generation sequencing; FFPE; Preanalytics; Precision medicine; Oncopanel sequencing
Description Background Clinical laboratories routinely use formalin-fixed paraffin-embedded (FFPE) tissue or cell block cytology samples in oncology panel sequencing to identify mutations that can predict patient response to targeted therapy. To understand the technical error due to FFPE processing, a robustly characterized diploid cell line was used to create FFPE samples with four different pre-tissue processing formalin fixation times. A total of 96 FFPE sections were then distributed to different laboratories for targeted sequencing analysis by four oncopanels, and variants resulting from technical error were identified. Results Tissue sections that fail more frequently show low cellularity, lower than recommended library preparation DNA input, or target sequencing depth. Importantly, sections from block surfaces are more likely to show FFPE-specific errors, akin to "edge effects" seen in histology, while the inner samples display no quality degradation related to fixation time. Conclusions To assure reliable results, we recommend avoiding the block surface portion and restricting mutation detection to genomic regions of high confidence.
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