Abstract
Abstract 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.
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