Abstract 4348: DNA-based fusion detection using a pan-cancer tumor profiling 532-oncogene panel

Abstract

Abstract Large-scale cancer profiling using next generation sequencing (NGS) has become instrumental to the discovery and identification of new, targetable cancer alterations. A comprehensive set of 532 oncogene targets were combined to create the new xGen® Pan-Cancer Panel V2 for hybrid capture sequencing. This xGen panel covers 2.2 Mb of the human genome, and allows for the simultaneous detection of copy number variations (CNVs), insertions and deletions (indels), gene rearrangements, and microsatellite instability across a wide range of sample types, inputs, and quality. Using a new library prep workflow optimized for low quality samples and low input, panel performance was first evaluated with 30 ng of input DNA using libraries built from matched samples [formalin-fixed paraffin-embedded (FFPE) tumor gDNA, frozen adjacent normal tissue gDNA, and cell-free DNA (cfDNA)] from five lung cancer donors (n = 15). Sample quality ranged from a mean DIN of 4.4 ±1.1 to 8.3 ±0.9 for FFPE tumor gDNA and frozen normal gDNA, respectively. After subsampling to 200X mean target coverage, 96% of target bases had at least 40X coverage for all libraries. Comparative hierarchal clustering analysis was then used to identify lung cancer mutations shared in all tumor samples. NGS gDNA reference standards from Horizon Discovery (HD753, HD798, HD799, and HD803) with verified CNVs, single nucleotide variations (SNVs), amplifications, and fusions, and were used to evaluate detection rates at different library input masses down to 1 ng. cDNA libraries were also prepared from RNA extracted from FFPE 5-Fusion RNA Multiplex Reference Standards (HD796, HD783). We identified all possible gene fusion events in the positive control using the structural variant caller, LUMPY (https://github.com/arq5x/lumpy-sv). The xGen Pan-Cancer Panel V2 enables a cost-efficient and time-saving approach for the detection of multiple oncogene targets. Citation Format: Katharine Dilger, Yongming Sun, Kevin Lai, Ushati Das Chakravarty, Nicole Sponer, Kristina Giorda, Patrick Lau, Yu Wang. DNA-based fusion detection using a pan-cancer tumor profiling 532-oncogene panel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4348.