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DNA Methylation in Ovarian Tumors-a Comparison Between Fresh Tissue and FFPE Samples.

ABSTRACT: Among women, ovarian cancer (OC) is one of the most severe forms of malignancy, accounting for a low 5-year survival rate, of approximately 52%. Early symptoms are unspecific and hence hard to detect. The origin of OC and its subtypes are still unclear, underlying the need for efficient diagnostic biomarkers. In that regard, epigenetics studies are emerging in cancer diagnostics, with encouraging outcomes. Among them, DNA methylation profiling has shown that the origins of the cancer epigenome are associated with molecular factors that are crucial to carcinogenesis, such as regulation of oncogenes and tumor suppressors. Furthermore, those events have been detected in abnormal cell morphology before neoplastic formation, indicating its potential crucial use in the OC diagnostics in the future. Nonetheless, studies are limited, and whether methylation analysis can be performed optimally in formalin-fixed paraffin-embedded (FFPE) preparations of OC cases is still elusive. In the present report, we investigated the performance of DNA methylation analysis in FFPE samples, compared to their matched fresh frozen tissue in a small cohort of OC samples. We found that the overall DNA methylation profile in FFPE tissue showed high concordance to that found in fresh frozen tissue, and accounting for the small cohort size, the differentially methylated sites found primarily in frozen tissue, compared to benign samples, were also reproducible in FFPE. Overall, by using samples from our current clinical setting of tissue preservation, these preliminary observations might provide insights into the clinical use of FFPE tissues in methylation studies without critically compromising the outcome.

SUBMITTER: Oliveira DVNP

PROVIDER: S-EPMC8526488 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundArchived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples.ResultsAs a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit ("QA"), and QIAGEN GeneRead DNA FFPE Kit ("QGR"). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different DNA extraction methods. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range (< 5%), and collectively shared a "C > T|G > A" mutational signature known to be representative of FFPE artifacts resulting from cytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates.ConclusionsThrough this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

Project description:BackgroundEpithelial ovarian carcinoma is a significant cause of cancer mortality in women worldwide and in the United States. Epithelial ovarian cancer comprises several histological subtypes, each with distinct clinical and molecular characteristics. The natural history of this heterogeneous disease, including the cell types of origin, is poorly understood. This study applied recently developed methods for high-throughput DNA methylation profiling to characterize ovarian cancer cell lines and tumors, including representatives of three major histologies.Methodology/principal findingsWe obtained DNA methylation profiles of 1,505 CpG sites (808 genes) in 27 primary epithelial ovarian tumors and 15 ovarian cancer cell lines. We found that the DNA methylation profiles of ovarian cancer cell lines were markedly different from those of primary ovarian tumors. Aggregate DNA methylation levels of the assayed CpG sites tended to be higher in ovarian cancer cell lines relative to ovarian tumors. Within the primary tumors, those of the same histological type were more alike in their methylation profiles than those of different subtypes. Supervised analyses identified 90 CpG sites (68 genes) that exhibited 'subtype-specific' DNA methylation patterns (FDR<1%) among the tumors. In ovarian cancer cell lines, we estimated that for at least 27% of analyzed autosomal CpG sites, increases in methylation were accompanied by decreases in transcription of the associated gene.SignificanceThe significant difference in DNA methylation profiles between ovarian cancer cell lines and tumors underscores the need to be cautious in using cell lines as tumor models for molecular studies of ovarian cancer and other cancers. Similarly, the distinct methylation profiles of the different histological types of ovarian tumors reinforces the need to treat the different histologies of ovarian cancer as different diseases, both clinically and in biomarker studies. These data provide a useful resource for future studies, including those of potential tumor progenitor cells, which may help illuminate the etiology and natural history of these cancers.

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DNA Methylation in Ovarian Tumors-a Comparison Between Fresh Tissue and FFPE Samples.

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