Abstract 320: Development and automation of a streamlined targeted enrichment method for cancer mutation detection

Abstract

Abstract Next Generation Sequencing (NGS) of DNA is a powerful tool for the detection of genetic variations, including single nucleotide polymorphisms, copy number variation, and small insertions/deletions. Target enrichment technologies enhance DNA sequencing by enabling users to analyze specific regions of interest only, which helps to effectively increase sequencing depth and sample throughput while minimizing cost. However, many targeted DNA library construction workflows are not optimal for routine genetic testing needs due to inconsistent, lengthy workflow. This poses significant challenge to application like cancer mutation profiling that requires detection sensitivity down to 1% or below due to heterogeneous nature of tumor tissue. In addition, high throughput testing with minimal hands on time is desirable with the rapid advance of precision medicine. Magnetic bead separations are commonly used between the individual reactions of targeted DNA library preparation workflows for cleanup and size selection. These bead clean up reactions are not only labor-intensive and tedious procedures but can also greatly contribute to sample-to sample variation especially when employing automated library preparation procedures. With the sophisticated QIAseq Targeted DNA Pro, we developed a novel streamlined targeted DNA workflow that greatly reduces handling variation by replacing the majority of bead clean up steps with enzymatic reactions, which now can be finished in as little as six hours. In combination with the newly developed QIAseq Normalizer kit, the protocol has been successfully implemented on the Hamilton NGS Star liquid handling platform resulting in up to 96 highly uniform, complex and ready-to-sequence libraries that can be effortless generated in one run. This complete automated solution saves researcher up to four hours of hands-on time. When using commercial reference samples, cancer mutations down to 1% were consistently detected with this automated workflow. The presented data demonstrate that this integrated and streamlined workflow does not only provide an automation friendly workflow but also allows to reliably identify low frequent cancer mutations. The applications presented here are for research use only. Not for use in diagnostic procedures. Citation Format: Zhong Wu, Michelle Baird, Cornelia Mechlen, Peter Hahn, Jonathan M. Shaffer. Development and automation of a streamlined targeted enrichment method for cancer mutation detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 320.