Abstract 3571: Precise somatic mutation prediction in the absence of matching normal DNA

Accurate identification of somatic mutations is an essential first step for many cancer studies. It is usually done by comparing the genome of the tumour to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a number of possible scenarios in which matched normal tissues might be not available for comparisons. It is most commonly encountered when performing analysis on retrospective studies with human tissues from clinical trials or pathology archives when normal samples were not collected in the first place or patient consent precludes examination of normal tissue or germline variants. Another common scenario is the use of a cancer cell line as an experimental model, many of which have no information on the donor’s normal genome. In this work, we describe an algorithm to identify somatic single nucleotide variants (SNVs) in Next Generation Sequencing (NGS) data in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1,600 samples, including cell lines, fresh frozen tissues, and formalin-fixed paraffin-embedded (FFPE) tissues. In addition, our algorithm was tested with both deep targeted sequencing and whole exome sequencing strategies. The algorithm correctly classified between 95% and 98% of somatic mutations with F1-measure ranges from 75.9% to 98.6% depending on the tumour type. We have released the algorithm as a software package called ISOWN (Identification of SOmatic mutations Without matching Normal tissues), which is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN. Citation Format: Irina Kalatskaya, Quang Trinh, Melanie Spears, John Bartlett, Lincoln Stein. Precise somatic mutation prediction in the absence of matching normal DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3571. doi:10.1158/1538-7445.AM2017-3571