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Comparison of MicroRNA deep sequencing of matched formalin-fixed paraffin-embedded and fresh frozen cancer tissues

ABSTRACT: MicroRNAs regulate several aspects of tumorigenesis and cancer progression. Most cancer tissues are archived formalin-fixed and paraffin-embedded (FFPE). While microRNAs are a more stable form of RNA thought to withstand FFPE-processing and degradation there is only limited evidence for the latter assumption. We examined whether microRNA profiling can be successfully conducted on FFPE cancer tissues using SOLiD ligation based sequencing. Tissue storage times (3-9 years) appeared to not affect the number of detected microRNAs in FFPE samples compared to matched frozen samples (paired t-test p>0.7). Correlations of microRNA expression values were very high across microRNAs in a given sample (Pearson’s r=0.71-0.95). Higher variance of expression values among samples was associated with higher correlation coefficients between FFPE and frozen tissues. One of the FFPE samples in this study was degraded for unknown reasons with a peak read length of 17 nucleotides compared to 21 in all other samples. The number of detected microRNAs in this sample was within the range of microRNAs detected in all other samples. Ligation-based microRNA deep sequencing on FFPE cancer tissues is feasible and RNA degradation to the degree observed in our study appears to not affect the number of microRNAs that can be quantified.

ORGANISM(S): Homo sapiens

PROVIDER: GSE45740 | GEO | 2013/04/15

SECONDARY ACCESSION(S): PRJNA196169

REPOSITORIES: GEO

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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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Comparison of MicroRNA deep sequencing of matched formalin-fixed paraffin-embedded and fresh frozen cancer tissues

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