Abstract 5784: Overcoming FFPE hurdles to enable high quality hybrid capture libraries and somatic mutation detection in matched tumor-normal patient samples

Abstract

Abstract In cancer genomics, a common source of DNA is formalin-fixed, paraffin-embedded (FFPE) tissue from patient surgical samples, where in most cases high quality fresh or frozen tissue samples are not available. FFPE DNA poses many notable challenges for preparing NGS libraries, including low input amounts and highly variable damage from fixation, storage, and extraction methods. Due to the high cost of sequencing and variability of coverage, regions of interest are often specifically enriched using hybrid capture-based approaches, but these methods require a high input of diverse, uniform DNA library to achieve the coverage required for somatic mutation identification in tumor samples.We applied a multi-faceted approach to improving the yield, quality, and coverage from libraries generated from cancer patient sample sets. Using tumor-normal pairs, we evaluated the impact of our method on the sensitivity and accuracy of somatic variant detection. Matched frozen tissue provided a truth set of somatic variants for a particular tumor as well as a gold standard by which we could assess the quality of our FFPE hybrid capture libraries. Combining DNA damage repair and a novel enzymatic fragmentation mix upstream of library preparation not only reduced the false positive rate in somatic variant detection by repairing damage-derived mutations but also improved the library yield, complexity, coverage uniformity, and hybrid capture library quality metrics. The use of unique molecular identifier (UMI)-containing adaptors ensured accurate duplicate marking. Finally, a new PCR master mix boosts the library yield without compromising library quality in FFPE samples ranging from very poor quality to high quality. This combinatorial approach allows even the poorest quality FFPE samples to achieve high quality libraries with sufficient input for hybrid capture in our study. Citation Format: Margaret R. Heider, Jian Sun, Brittany S. Sexton, Bradley W. Langhorst, Andrew Gray, Lauren Higgins, Lixin Chen, Lynne Apone, Thomas C. Evans, Nicole M. Nichols, Eileen T. Dimalanta, Theodore B. Davis, Pingfang Liu. Overcoming FFPE hurdles to enable high quality hybrid capture libraries and somatic mutation detection in matched tumor-normal patient samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5784.