Genome-wide identification of significant aberrations in cancer genome
- Equal contributors
1 School of Computer Science and Technology, Xidian University, Xi'an, P. R. China
2 Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
3 Center for Sleep Sciences and Medicine, Stanford University School of Medicine, Palo Alto, CA, 94304, USA
4 Departments of Gynecology/Obstetrics and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA
5 Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC, 20057, USA
6 Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC, 20010, USA
7 The International Baccalaureate Magnet Diploma Program, Richard Montgomery High School, Rockville, MD, 20852, USA
8 Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, 21231, USA
Citation and License
BMC Genomics 2012, 13:342 doi:10.1186/1471-2164-13-342Published: 27 July 2012
Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme.
We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS) on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma). When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC) or tumor suppressor genes (e.g., CDKN2A/B). Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies.
Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes. Open–source and platform-independent SAIC software is implemented using C++, together with R scripts for data formatting and Perl scripts for user interfacing, and it is easy to install and efficient to use. The source code and documentation are freely available at http://www.cbil.ece.vt.edu/software.htm webcite.