A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data.
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.
Yau
Christopher
C
Department of Statistics, University of Oxford, South Parks Road, Oxford, OX1 3TG, UK. yau@stats.ox.ac.uk
Mouradov
Dmitri
D
Jorissen
Robert N
RN
Colella
Stefano
S
Mirza
Ghazala
G
Steers
Graham
G
Harris
Adrian
A
Ragoussis
Jiannis
J
Sieber
Oliver
O
Holmes
Christopher C
CC
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