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Updated: Apr 20, 2026
Employing Digital Droplet PCR to Detect BRAF V600E Mutations in Formalin-fixed Paraffin-embedded Reference Standard Cell Lines
Published on: October 8, 2015
Variation in pre-PCR processing of FFPE samples leads to discrepancies in BRAF and EGFR mutation detection: a
Joshua R Kapp1, Tim Diss2, James Spicer3
1Division of Surgery and Interventional Sciences, University College London, London, UK.
Aims:
Mutation detection accuracy has been described extensively; however, it is surprising that pre-PCR processing of formalin-fixed paraffin-embedded (FFPE) samples has not been systematically assessed in clinical context. We designed a RING trial to (i) investigate pre-PCR variability, (ii) correlate pre-PCR variation with EGFR/BRAF mutation testing accuracy and (iii) investigate causes for observed variation.
Methods:
13 molecular pathology laboratories were recruited. 104 blinded FFPE curls including engineered FFPE curls, cell-negative FFPE curls and control FFPE tissue samples were distributed to participants for pre-PCR processing and mutation detection. Follow-up analysis was performed to assess sample purity, DNA integrity and DNA quantitation.
Results:
Rate of mutation detection failure was 11.9%. Of these failures, 80% were attributed to pre-PCR error. Significant differences in DNA yields across all samples were seen using analysis of variance (p<0.0001), and yield variation from engineered samples was not significant (p=0.3782). Two laboratories failed DNA extraction from samples that may be attributed to operator error. DNA extraction protocols themselves were not found to contribute significant variation. 10/13 labs reported yields averaging 235.8 ng (95% CI 90.7 to 380.9) from cell-negative samples, which was attributed to issues with spectrophotometry. DNA measurements using Qubit Fluorometry demonstrated a median fivefold overestimation of DNA quantity by Nanodrop Spectrophotometry. DNA integrity and PCR inhibition were factors not found to contribute significant variation.
Conclusions:
In this study, we provide evidence demonstrating that variation in pre-PCR steps is prevalent and may detrimentally affect the patient's ability to receive critical therapy. We provide recommendations for preanalytical workflow optimisation that may reduce errors in down-stream sequencing and for next-generation sequencing library generation.
Insights
Pre-PCR processing of formalin-fixed paraffin-embedded (FFPE) samples shows significant variability, impacting mutation detection accuracy. Optimizing pre-PCR workflows is crucial for reliable molecular pathology results and patient care.
Area of Science:
- Molecular Pathology
- Genomic Analysis
- Clinical Diagnostics
Background:
- Mutation detection accuracy is critical for targeted therapies.
- Pre-PCR processing of formalin-fixed paraffin-embedded (FFPE) samples is a key step, yet its variability is understudied in a clinical context.
Purpose of the Study:
- To investigate pre-PCR variability in molecular pathology laboratories.
- To correlate pre-PCR variation with mutation testing accuracy for EGFR/BRAF.
- To identify causes of pre-PCR variation in FFPE sample processing.
Main Methods:
- A RING trial involving 13 laboratories processing 104 blinded FFPE samples.
- Assessment of pre-PCR steps including DNA extraction, purity, integrity, and quantitation.
- Follow-up analysis to investigate sources of variation, including operator error and measurement techniques.
Main Results:
- An 11.9% mutation detection failure rate was observed, with 80% attributed to pre-PCR errors.
- Significant DNA yield variations were detected across laboratories (p<0.0001).
- Spectrophotometry (Nanodrop) overestimated DNA quantity by fivefold compared to fluorometry (Qubit); operator error affected DNA extraction in two labs.
Conclusions:
- Pre-PCR processing variability is prevalent and can negatively impact patient treatment decisions.
- Recommendations are provided for optimizing preanalytical workflows to improve downstream sequencing accuracy.
- Standardization of FFPE pre-PCR steps is essential for reliable next-generation sequencing library generation.
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