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Leveraging locus-specific epigenetic heterogeneity to improve the performance of blood-based DNA methylation biomarkers
Clinical Epigenetics ( IF 5.7 ) Pub Date : 2020-10-20 , DOI: 10.1186/s13148-020-00939-w
Brendan F Miller 1 , Thomas R Pisanic Ii 2 , Gennady Margolin 1 , Hanna M Petrykowska 1 , Pornpat Athamanolap 3 , Alexander Goncearenco 1 , Akosua Osei-Tutu 4 , Christina M Annunziata 4 , Tza-Huei Wang 2, 3, 5 , Laura Elnitski 1
Affiliation  

Variation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass ) that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification. We developed and tested the classifier using reduced representation bisulfite sequencing (RRBS) data derived from ovarian carcinoma tissue DNA and controls. We used these data to perform in silico simulations using methylation density profiles from individual epiallelic copies of ZNF154, a genomic locus known to be recurrently methylated in numerous cancer types. From these profiles, we predicted the performance of the classifier in liquid biopsies for the detection of epithelial ovarian carcinomas (EOC). In silico analysis indicated that EpiClass could be leveraged to better identify cancer-positive liquid biopsy samples by implementing precise thresholds with respect to methylation density profiles derived from circulating cell-free DNA (cfDNA) analysis. These predictions were confirmed experimentally using DREAMing to perform digital methylation density analysis on a cohort of low volume (1-ml) plasma samples obtained from 26 EOC-positive and 41 cancer-free women. EpiClass performance was then validated in an independent cohort of 24 plasma specimens, derived from a longitudinal study of 8 EOC-positive women, and 12 plasma specimens derived from 12 healthy women, respectively, attaining a sensitivity/specificity of 91.7%/100.0%. Direct comparison of CA-125 measurements with EpiClass demonstrated that EpiClass was able to better identify EOC-positive women than standard CA-125 assessment. Finally, we used independent whole genome bisulfite sequencing (WGBS) datasets to demonstrate that EpiClass can also identify other cancer types as well or better than alternative methylation-based classifiers. Our results indicate that assessment of intramolecular methylation density distributions calculated from cfDNA facilitates the use of methylation biomarkers for diagnostic applications. Furthermore, we demonstrated that EpiClass analysis of ZNF154 methylation was able to outperform CA-125 in the detection of etiologically diverse ovarian carcinomas, indicating broad utility of ZNF154 for use as a biomarker of ovarian cancer.

中文翻译:

利用基因座特异性表观遗传异质性提高基于血液的 DNA 甲基化生物标志物的性能

细胞间甲基化模式的变化会使甲基化生物标志物在临床诊断应用(如基于血液的癌症检测)中的使用复杂化。在这里,我们描述了一种称为 EpiClass(可从 https://github.com/Elnitskilab/EpiClass 下载)的甲基化密度二元分类方法的开发和验证,该方法可用于预测和优化甲基化生物标志物的性能,特别是在具有挑战性的,异质样本,如液体活检。这种方法基于利用单分子样品甲基化密度分布的统计差异来确定样品分类的理想阈值。我们使用源自卵巢癌组织 DNA 和对照的简化代表性亚硫酸氢盐测序 (RRBS) 数据开发并测试了分类器。我们使用这些数据使用来自 ZNF154 的单个表等位基因拷贝的甲基化密度谱进行计算机模拟,ZNF154 是一种已知在多种癌症类型中反复甲基化的基因组位点。根据这些配置文件,我们预测了分类器在用于检测上皮性卵巢癌 (EOC) 的液体活检中的性能。计算机分析表明,通过对循环无细胞 DNA (cfDNA) 分析得出的甲基化密度谱实施精确的阈值,可以利用 EpiClass 更好地识别癌症阳性液体活检样本。使用 DREAMing 对从 26 名 EOC 阳性和 41 名无癌症女性获得的一组低容量 (1-ml) 血浆样本进行数字甲基化密度分析,通过实验证实了这些预测。随后在来自 8 名 EOC 阳性女性的纵向研究的 24 份血浆样本和来自 12 名健康女性的 12 份血浆样本的独立队列中验证 EpiClass 性能,灵敏度/特异性为 91.7%/100.0%。CA-125 测量值与 EpiClass 的直接比较表明,与标准 CA-125 评估相比,EpiClass 能够更好地识别 EOC 阳性女性。最后,我们使用独立的全基因组亚硫酸氢盐测序 (WGBS) 数据集来证明 EpiClass 还可以识别其他癌症类型,甚至优于其他基于甲基化的分类器。我们的结果表明,评估从 cfDNA 计算的分子内甲基化密度分布有助于将甲基化生物标志物用于诊断应用。此外,
更新日期:2020-10-20
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