Abstract 48: Detecting and controlling for physical contamination in next-generation sequencing cancer studies

Abstract

Abstract All large-scale cancer genome characterization efforts rely on the unprecedented throughput of massively parallel sequencing technologies, such as Illumina/Solexa, ABI/SOLiD, Roche/454, and others to generate short sequence reads. In turn, this has spurred the development of novel algorithms for sequence alignment and detection of DNA alterations. Particularly in cancer genome studies, these detection algorithms must be exceedingly sensitive since the events of interest are present in just a fraction of the observed data due to stromal contamination and tumor ploidy. At the same time they must be very specific, since the events are also extremely rare. Little has been published on tools to measure and control the quality of such data and in particular tools for identifying contamination of the sequenced DNA with DNA originating from different individuals than intended. In the case of cancer genome studies, contamination of this kind can lead to seemingly somatic events. We have developed and made available a novel tool, called ContEst, for estimating the level of contamination in next generation sequencing data of human samples. We demonstrate ContEst's accuracy across a range of contamination levels, sources and read depths using sequencing data that were mixed in-silico at known concentrations. Finally, we will show the effect of physical contamination by different individuals with regard to somatic mutation detection, and present strategies for compensating in the contaminated data. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 48. doi:10.1158/1538-7445.AM2011-48