Identification of somatic mutations in cancer through Bayesian-based analysis of sequenced genome pairs

BMC Genomics

Methodology Article

Daniel D Von Hoff¹  John D Carpten²  Michael J Demeure³  David W Craig⁴  Alexis Christoforides⁵  Glen J Weiss⁶ 

[1] Clinical Translational Research Division, Translational Genomics Research Institute, 85259, Scottsdale, AZ, USA;Translational Genomics Research Institute, Integrated Cancer Genomics Division, 85004, Phoenix, AZ, USA;Translational Genomics Research Institute, Integrated Cancer Genomics Division, 85004, Phoenix, AZ, USA;Virginia G Piper Cancer Center, 85258, Scottsdale, AZ, USA;Translational Genomics Research Institute, Neurogenomics Division, 85004, Phoenix, AZ, USA;Translational Genomics Research Institute, Neurogenomics Division, 85004, Phoenix, AZ, USA;Department of Biomedical Informatics, Arizona State University, 85284, Tempe, AZ, USA;Translational Genomics Research Institute, Neurogenomics Division, 85004, Phoenix, AZ, USA;Virginia G Piper Cancer Center, 85258, Scottsdale, AZ, USA;

关键词: Cancer genomics;    Next generation sequencing;    Somatic mutation detection;   

DOI  :  10.1186/1471-2164-14-302

 received in 2012-09-15, accepted in 2013-04-13,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundThe field of cancer genomics has rapidly adopted next-generation sequencing (NGS) in order to study and characterize malignant tumors with unprecedented resolution. In particular for cancer, one is often trying to identify somatic mutations – changes specific to a tumor and not within an individual’s germline. However, false positive and false negative detections often result from lack of sufficient variant evidence, contamination of the biopsy by stromal tissue, sequencing errors, and the erroneous classification of germline variation as tumor-specific.ResultsWe have developed a generalized Bayesian analysis framework for matched tumor/normal samples with the purpose of identifying tumor-specific alterations such as single nucleotide mutations, small insertions/deletions, and structural variation. We describe our methodology, and discuss its application to other types of paired-tissue analysis such as the detection of loss of heterozygosity as well as allelic imbalance. We also demonstrate the high level of sensitivity and specificity in discovering simulated somatic mutations, for various combinations of a) genomic coverage and b) emulated heterogeneity.ConclusionWe present a Java-based implementation of our methods named Seurat, which is made available for free academic use. We have demonstrated and reported on the discovery of different types of somatic change by applying Seurat to an experimentally-derived cancer dataset using our methods; and have discussed considerations and practices regarding the accurate detection of somatic events in cancer genomes. Seurat is available at https://sites.google.com/site/seuratsomatic.

【 授权许可】

Unknown   
© Christoforides et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.