Bioinformatics and DNA-extraction strategies to reliably detect genetic variants from FFPE breast tissue samples
Bioinformatics and DNA-extraction strategies to reliably detect genetic variants from FFPE breast tissue samples
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DOI：
10.1186/s12864-019-6056-8
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发表时间：
2019-09-02
期刊：
影响因子：
4.4
通讯作者：
Wang, Chen
中科院分区：
文献类型：
--
作者：
Bhagwate, Aditya Vijay;Liu, Yuanhang;Wang, Chen
背景：经存档的福尔马林固定石蜡包埋标本(FFPE)是检查临床相关形态学特征和研究遗传学变化的宝贵临床资源。然而，FFPE样本的DNA质量和数量往往是次优的，由此产生的基于NGS的遗传学变异检测容易出现假阳性。需要对湿实验室和生物信息学方法进行评估，以优化FFPE样本中的变异检测。结果作为一项先导性研究，我们设计了受试者内的三份DNA样本，这些样本来自成对的FFPE和新鲜的冷冻乳腺组织，以突出FFPE特有的伪影。对于FFPE样本，我们测试了两种FFPE DNA提取方法，以确定湿实验室程序对变体调用的影响：QIAGEN QIAAmp DNA Mini Kit(“QA”)和QIAGEN GeneRead DNA FFPE Kit(“QGR”)。我们还使用了阴性对照(NA12891)和阳性对照样本(Horizon Discovery参考标准FFPE)。所有的DNA样本库都是根据QIAseq人类乳腺癌靶向DNA面板方案为NGS准备的，并在HiSeq 4000上进行测序。使用QIAGEN基因全球数据门户进行变量调用和筛选。进行了详细的变异一致性比较和突变特征分析，以考察FFPE样本与配对的新鲜冷冻样本以及不同的DNA提取方法的效果。在这项研究中，我们发现，即使在应用了供应商推荐的分子条形码纠错和默认生物信息学过滤之后，FFPE样本调用的变体也是其配对的新鲜冰冻组织样本的五倍或更多。我们还发现，QGR作为一种优化的FFPE-DNA提取方法，使得成对的新鲜冷冻样品和FFPE样品之间的不一致变异要少得多。大约92%的唯一命名的FFPE变异体是低等位基因频率范围(<；5%)，并且共同拥有一个已知的代表胞嘧啶脱氨基导致的FFPE人工产物的突变特征。基于对照样本和FFPE冻结的副本，我们推导了一个有效的过滤策略和相关的经验假发现估计。结论通过这项研究，我们证明了使用分子条形码、优化的DNA提取和结合基因组学背景的生物信息学方法(如突变特征和变异等位基因频率)相结合的策略从FFPE组织样本中调用和筛选遗传变异的可行性。
Background Archived 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. Results As 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. Conclusions Through 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.