tailieunhanh - Báo cáo y học: "A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data. | Yau et al. Genome Biology 2010 11 R92 http content 11 9 R92 Genome Biology METHOD Open Access A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data f I fl fl I 1 f 11 I 11 Ì f-ff I f Ì l I n1 I f-J m Í2 D n 1-1 1 f I I n I 11 11 3 6 K T71 I -ì l I I f fl 3 1-1 -1 f-ff c 1 f r 4 Christopher Yau Dmitri Mouradov Robert N Jorissen Stetano Colella Ghazala Mirza Graham Steers A f-J n in I_11 r4 I 11 fl fl I n Di n n1 I r r I 3 I I f c I fl ti 1 f 2 ti f i I Hn fl 1-1 1 f I_I n1 m If1 5 Adrian Harris Jiannis Ragoussis Oliver Sieber Christopher C Holmes Abstract We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normalcancer cell lines and real primary tumours. Background Single nucleotide polymorphism SNP genotyping microarrays provide a relatively low-cost high-throughput platform for genome-wide pro ling of DNA copy number alterations CNAs and loss-of-heterozygosity LOH in cancer genomes. These arrays have enabled the discovery of genomic aberrations associated with cancer development or prognosis 1-4 and two recent studies in particular have examined 746 cancer cell lines 5 and 26 cancer types 6 revealing much about the landscape of the cancer genome. However whilst numerous robust computational methods are available for the detection of copy number variants CNVs in normal genomes 7-11 the approaches applied to cancers are often sub-optimal due to data properties that are unique or more pronounced in cancer. Potential difficulties in the analysis of SNP data from cancers have been considered .