TY - GEN
T1 - Accurate identification of significant aberrations in contaminated cancer genome
AU - Hou, Xuchu
AU - Yu, Guoqiang
AU - Yuan, Xiguo
AU - Zhang, Bai
AU - Shih, Ie Ming
AU - Zhang, Zhen
AU - Clarke, Robert
AU - Madhavan, Subha
PY - 2012
Y1 - 2012
N2 - Somatic Copy Number Alterations (CNAs) are quite common in human cancers. Identifying CNAs and Significant Copy number Aberrations (SCAs) in cancer genomes is a critical task in searching for cancer-associated genes. The advanced genomic technologies, such as SNP array technology, facilitate copy number study at a genome-wide scale with high resolution. However, in reality, due to normal tissue contamination, the observed intensity signals are actually the mixture of copy number signals contributed from both tumor cells and normal cells. This genetic heterogeneity could significantly affect the subsequent copy number analysis and SCAs detection. In order to accurately identify significant aberrations in contaminated cancer genome, we devise an approach including two major steps. We first use a statistical method, Bayesian Analysis of Copy number Mixtures (BACOM) to estimate the normal tissue contamination fraction and recover the "true" copy number profile. Then, based on the recovered profiles, we detect SCAs using Genome-wide Identification of Significant Aberrations in Cancer Genome (SAIC). We comprehensively evaluate the performance of the proposed algorithm on a large number of simulation data. The results show that the algorithm has higher detection power than peer methods including the most popular GISTIC. We then apply the method to the real copy number data of Glioblastoma Multiforme and successfully identified majority of SCAs reported by GISTIC, and some novel SCAs that contain some cancer-associated genes.
AB - Somatic Copy Number Alterations (CNAs) are quite common in human cancers. Identifying CNAs and Significant Copy number Aberrations (SCAs) in cancer genomes is a critical task in searching for cancer-associated genes. The advanced genomic technologies, such as SNP array technology, facilitate copy number study at a genome-wide scale with high resolution. However, in reality, due to normal tissue contamination, the observed intensity signals are actually the mixture of copy number signals contributed from both tumor cells and normal cells. This genetic heterogeneity could significantly affect the subsequent copy number analysis and SCAs detection. In order to accurately identify significant aberrations in contaminated cancer genome, we devise an approach including two major steps. We first use a statistical method, Bayesian Analysis of Copy number Mixtures (BACOM) to estimate the normal tissue contamination fraction and recover the "true" copy number profile. Then, based on the recovered profiles, we detect SCAs using Genome-wide Identification of Significant Aberrations in Cancer Genome (SAIC). We comprehensively evaluate the performance of the proposed algorithm on a large number of simulation data. The results show that the algorithm has higher detection power than peer methods including the most popular GISTIC. We then apply the method to the real copy number data of Glioblastoma Multiforme and successfully identified majority of SCAs reported by GISTIC, and some novel SCAs that contain some cancer-associated genes.
KW - copy number alterations
KW - normal tissue contamination
KW - significant copy number aberrations
UR - http://www.scopus.com/inward/record.url?scp=84877824971&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877824971&partnerID=8YFLogxK
U2 - 10.1109/GENSIPS.2012.6507730
DO - 10.1109/GENSIPS.2012.6507730
M3 - Conference contribution
AN - SCOPUS:84877824971
SN - 9781467352369
T3 - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
SP - 74
EP - 77
BT - Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012
T2 - 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012
Y2 - 2 December 2012 through 4 December 2012
ER -