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

Yifan Zhang, Thomas M. Blomquist, Rebecca Kusko, Daniel Stetson, Zhihong Zhang, Lihui Yin, Robert Sebra, Binsheng Gong, Jennifer S. Lococo, Vinay K. Mittal, Natalia Novoradovskaya, Ji Youn Yeo, Nicole Dominiak, Jennifer Hipp, Amelia Raymond, Fujun Qiu, Hanane Arib, Melissa L. Smith, Jay E. Brock, Daniel H. FarkasDaniel J. Craig, Erin L. Crawford, Dan Li, Tom Morrison, Nikola Tom, Wenzhong Xiao, Mary Yang, Christopher E. Mason, Todd A. Richmond, Wendell Jones, Donald J. Johann, Leming Shi, Weida Tong, James C. Willey, Joshua Xu

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number141
JournalGenome Biology
Issue number1
StatePublished - Dec 2022


  • Cancer genomics
  • FFPE
  • Next-generation sequencing
  • Oncopanel sequencing
  • Preanalytics
  • Precision medicine


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