title: A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data.
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 normal-cancer cell lines and real primary tumours.
authors.affiliation: Department of Statistics, University of Oxford, South Parks Road, Oxford, OX1 3TG, UK. yau@stats.ox.ac.uk
authors.forename: Christopher
authors.initials: C
authors.lastname: Yau
authors.forename: Dmitri
authors.initials: D
authors.lastname: Mouradov
authors.forename: Robert N
authors.initials: RN
authors.lastname: Jorissen
authors.forename: Stefano
authors.initials: S
authors.lastname: Colella
authors.forename: Ghazala
authors.initials: G
authors.lastname: Mirza
authors.forename: Graham
authors.initials: G
authors.lastname: Steers
authors.forename: Adrian
authors.initials: A
authors.lastname: Harris
authors.forename: Jiannis
authors.initials: J
authors.lastname: Ragoussis
authors.forename: Oliver
authors.initials: O
authors.lastname: Sieber
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Holmes
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D045744:Cell Line, Tumor; D059018:DNA Contamination; D056915:DNA Copy Number Variations; D003627:Data Interpretation, Statistical; D018740:Genetic Heterogeneity; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D046228:Microarray Analysis; D008957:Models, Genetic; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D011123:Polyploidy