A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data

被引：114

作者：

Yau, Christopher ^{[1
]}

Mouradov, Dmitri ^{[2
]}

Jorissen, Robert N. ^{[2
]}

Colella, Stefano ^{[3
]}

Mirza, Ghazala ^{[3
]}

Steers, Graham ^{[4
]}

Harris, Adrian ^{[4
]}

Ragoussis, Jiannis ^{[3
]}

Sieber, Oliver ^{[2
]}

Holmes, Christopher C. ^{[1
,5
]}

机构：

[1] Univ Oxford, Dept Stat, Oxford OX1 3TG, England

[2] Royal Melbourne Hosp, Ludwig Inst Canc Res, Ludwig Colon Canc Initiat Lab, Melbourne, Vic 3050, Australia

[3] Univ Oxford, Wellcome Trust Ctr Human Genet, Oxford OX3 7BN, England

[4] Univ Oxford, Weatherall Inst Mol Med, Dept Med Oncol, Mol Oncol Labs, Oxford OX3 9DS, England

[5] MRC Harwell, Didcot OX11 0RD, Oxon, England

来源：

GENOME BIOLOGY | 2010年 / 11卷 / 09期

基金：

英国惠康基金; 英国医学研究理事会; 英国工程与自然科学研究理事会;

关键词：

HIDDEN-MARKOV MODEL; DENSITY SNP ARRAYS; COPY-NUMBER; CANCER GENOME; CHROMOSOMAL-ABERRATIONS; REARRANGEMENT; MICROARRAYS; SIGNATURES;

D O I：

10.1186/gb-2010-11-9-r92

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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 normal-cancer cell lines and real primary tumours.

引用

页数：15

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