Abstract 5454: A data driven pipeline to detect somatic mutations in oncology

Abstract

Abstract The analysis of Next Generation Sequencing (NGS) data relies on workflows that integrate different bioinformatics tools. Designing workflows that are assay agnostic and have a robust computational framework for result generation in a timely manner is particularly important for clinical applications of NGS. These technologies play an important role in classifying a patient's tumor at a molecular level however, there is still a need to develop workflows that generate consistent mutation profiles for oncology. We developed a bioinformatics tool to analyze DNA and RNA sequencing data in a single pipeline and to specifically detect somatic mutations in oncology clinical workflows. Our automated pipeline report contains extensive quality metrics for each sample and generates a robust mutation annotation and filtration module vital for clinical use. We use data driven filters and algorithms that predict the effectiveness of mutations to filter out mutations of poor quality or mutations without clinical significance. We applied our pipeline to standard cell lines and various cancer FFPE samples acquired from the Intermountain Healthcare Biorepository. Our pipeline successfully processes different mutation classes including Single Nucleotide Variants (SNVs), small (<20 bp) and large (>21 bp) insertions and deletions (InDels), gene fusions, and exon skipping mutations. In addition, we established a reference set that allows us to call Copy Number Variants (CNVs) in a gene panel designed to detect clinically relevant mutations in lung cancers. Comparison of this pipeline to a validated external workflow yield high concordance of clinically significant mutations. We conclude that our pipeline offers a sensitive and specific methodology to analyze DNA and RNA sequencing data to clinically relevant results from low input amplicon-based assays for various solid tumor cancer types. Citation Format: Katherine Shortt, Christopher Johnson, Archana Ramesh, Tom Neuwerth, Chris Giauque, Sharanya Raghunath. A data driven pipeline to detect somatic mutations in oncology [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 5454.