Introduction: The use of “liquid biopsies”, where limited or no tumor tissue is available, is increasingly important for molecular demographics, diagnostics and pharmacodynamic monitoring of patients during therapy.  The ICE COLD-PCR (ICP) technique preferentially amplifies sequence alterations in samples having either vast excesses of wild-type sequence or when sample DNA quantity is sub-optimal. ICP delivers unbiased, high-level enrichment of gene regions enabling determination of point mutations and insertions/deletions using Sanger sequencing, Next Generation Sequencing (NGS) or droplet digital PCR (ddPCR). This is especially important when sample DNA, e.g. from circulating free DNA, exosomes and circulating tumor cells (CTCs), is insufficient for multiplexed analysis. A critical limitation of mutational analysis of such samples is the need for increasing amounts of DNA for detecting very low-level mutations. A range of 100 to330 ng of substrate DNA is usually needed for reliable detection of alterations present at 0.01% in the sample DNA; this is not feasible with the limited quantities of blood/plasma/serum from clinical trials. ICP's ability to enrich alterations can provide a ≥100-fold increase in Sanger, NGS and ddPCR sensitivity. Materials and Methods: To increase throughput, address the limiting amounts of DNA present in these samples and provide enriched amplification from many different gene regions in a single DNA sample, a multiplex ICP approach has been developed (MX-ICP). This MX-ICP method provides enrichment of any alteration present in all targeted genes from a single sample of DNA. When MX-ICP products are analyzed by Sanger, NGS or ddPCR, lower quantities of sample DNA can be used for detection of mutations at ≤0.01%. We compared detection of low-level of mutations in limiting amounts of DNA, with or without the use of MX-ICP prior to NGS and ddPCR, using digitally verified chromosomal DNA mixtures from Horizon Diagnostics. The alterations analyzed were from (1) CTC and NSCLC patients’ plasma, (2) longitudinal sampling of melanoma patients and (3) CTCs isolated from NSCLC patients. In all cases, use of MX-ICP, prior to analysis using NGS or ddPCR, enabled very sensitive detection with low amounts of input DNA. Conclusion: MX-ICP is a key component of procedures for sensitive detection and monitoring of genetic alterations in multiple targets using a single DNA sample. Coupling MX-ICP with platforms such as NGS and ddPCR enables the use of these powerful technologies for high sensitivity detection and monitoring of liquid biopsies from cancer patients. The combination of MX-ICP with NGS and ddPCR platforms means that they can be used efficiently for detection of alterations at ≤0.01% in samples with <100 ng DNA. This enables monitoring and detection of alterations in the low volumes of liquid biopsies obtained from patients and clinical trials. Citation Format: Katherine Anne Richardson, Sarah Statt, Grant Wu, Karissa Scott, Erin Montagne, Sheena Jensen, Courtney Cubrich, Phil Krzycki, Jason Stoddard, Amy Kruempel, Emily McCutchen, Stephanie Veys, Kylee Baughman, Sarah Cherubin, Vicki Rosendale, Jaclyn Pope, Paula Bartlett, Phil Eastlake, Stephanie Peterson, Benjamin Legendre. Multiplexed ICE COLD-PCR coupled to NGS and ddPCR enables enhanced detection of low-level DNA mutations in tissues and liquid biopsies. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5438. doi:10.1158/1538-7445.AM2015-5438

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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.

Discovery and identification of new, targetable biomarkers is driven by comprehensive tumor profiling using next generation sequencing (NGS). However, converting tissue samples into NGS libraries is often challenging due to the low quantity and quality of DNA in such samples. Here we present sensitive and accurate detection of low-frequency variants by combining a novel library preparation optimized for low-input and degraded samples with IDT's xGen hybridization capture. The library preparation is enabled by an engineered mutant ligase and proprietary sequencing adapters that together prevent chimeras, suppress dimer-formation, enable double strand consensus calling, and maximize conversion. We demonstrate the performance of these reagents with archived tissue samples collected from five individuals with lung cancer. These matched samples consisted of DNA isolated from formalin-fixed paraffin-embedded (FFPE) tumor, DNA isolated from fresh-frozen normal tissue, and cell-free DNA (cfDNA) isolated from plasma. Sequencing libraries were prepared from 250 ng of tumor and healthy samples and 10 ng of cfDNA. Libraries were captured using a pan-cancer panel designed to simultaneously detect copy number variations, indels, rearrangements, and microsatellite instability across 532 oncogene targets. For each individual, tumor-associated mutations were identified using single-stranded consensus calling of variants found in tumor but not normal samples. As cfDNA has been correlated to disease progression, we wanted to determine if tumor-associated variants could be identified in the matched cfDNA samples. Custom capture panels were designed and delivered within 5 business days to target each individual's tumor-associated mutations. Relevant cfDNA samples were captured with these custom panels, sequenced, and variants were called after double stranded consensus calling. Mutations called from both FFPE tumor and cfDNA samples were verified by droplet digital PCR. By combining a high conversion library preparation with efficient hybridization capture and double stranded consensus calling, we have demonstrated an effective approach to extract information from difficult samples. Citation Format: Karissa Scott, Ushati Das Chakravarty, Hsiao-Yun Huang, Timothy Barnes, Kevin Lai, Jessica Sheu, Tzu-Chun Chen, Ramses Lopez, Lynette Lewis, Anastasia Potts, Steven Henck. Enabling personalized biomarker discovery in challenging oncology samples by coupling a novel library preparation chemistry with hybridization capture [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 1988.