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A critical spotlight on the paradigms of FFPE-DNA sequencing

ABSTRACT: Formalin-fixed, paraffin-embedded (FFPE) tissue is the most widely available material for diagnostics and retrospective clinical studies. Reliable sequencing data obtained from such specimens would have enormous potential for basic and clinical research. However, formaldehyde fixation introduces chemical modifications which ultimately can lead to false interpretations. Here we describe a start-to-end approach to mitigate FFPE-problems comprising preanalytical sample specification and quality, repair treatments, analytical sample preparation, and bioinformatic sub-processes. We have also developed an alternative sequential DNA repair technique (IQBErepair) addressing the cause of formalin-induced false positives: too low and uneven coverage. For low-input approaches this technique increased the unique on-target bases by 53% and 80% in two independent centers, respectively. Furthermore, we present other measures to reduce FFPE artifacts: replicate strategies (up to 98% for 1% VAF detection threshold), bioinformatic filtering (>99.9%). Combined with general recommendations for sample quality and preparation this can create reliable data quality, even from low amounts (50 ng) of low-quality (DIN 2.0/5% Q129 bp/Q41 bp) FFPE-DNA.

PROVIDER: EGAS00001005757 | EGA |

REPOSITORIES: EGA

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Project description:Formalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data. We optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics.. Samples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes (11,400 # of detected genes with TPM > 4). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.9 ng/ul and 2.08 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.82 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.848 in predicting QC status (pass/fail) of FFPE samples. We provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.7 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.

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A critical spotlight on the paradigms of FFPE-DNA sequencing

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