Abstract 142: Identification of symmetric methylation and hemimethylation patterns with optimal panel design, library preparation, and error correction

Abstract

DNA methylation patterns are epigenetic modifications with direct implications in gene expression and chromatin structure regulation. Altered DNA methylation has been reported in various human cancers. The methylation profile of cell-free DNA (cfDNA) from plasma can be exploited to detect and diagnose tissue pathologies and is therefore of great diagnostic interest. Due to limitations of existing NGS library preparations, we present a new workflow, that integrates Unique Molecular Identifier (UMI) based error correction and optimizes library preparation and target enrichment. This workflow incorporates an engineered mutant ligase and proprietary methylated adapters that together prevent chimeras, suppress dimer-formation, and maximize conversion. Our library preparation provides >80% unique mapping efficiency and high conversion efficiency which creates superior sensitivity and specificity for detecting epigenetic signatures from a wide range of inputs (1-250 ng) and samples, including genomic DNA, FFPE DNA and cfDNA. This workflow is suitable for both genome-wide and targeted methylation sequencing. We implemented the target methyl-seq workflow to distinguish between fully methylation and hemimethylation on only one strand of the DNA by ligating in-line UMIs prior to target enrichment, followed by bisulfite conversion and PCR. Using a custom panel for targeted methylation sequencing, we detected >60% of CpG methylation in normal human samples and >95% of CpG methylation in in vitro methylated HCT116 samples. Employing ultra-deep sequencing to an average target depth of 10,000X followed by double-stranded consensus building analysis enables accurate low frequency methylation and hemimethylation detection. The studies presented here offer tools to advance epigenetics research. Citation Format: Hsiao-Yun Huang, Ushati Das Chakravarty, Kevin Lai, Timothy Barnes, Tzu-Chun Chen, Jessica Sheu, Ramses Lopez, Karissa Scott, Lynette Lewis, Anastasia Potts. Identification of symmetric methylation and hemimethylation patterns with optimal panel design, library preparation, and error correction [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 142.