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A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data |
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| Authors: | Christopher Yau Dmitri Mouradov Robert N Jorissen Stefano Colella Ghazala Mirza Graham Steers Adrian Harris Jiannis Ragoussis Oliver Sieber Christopher C Holmes |
| Affiliation: | (1) Department of Statistics, University of Oxford, South Parks Road, Oxford, OX1 3TG, UK;(2) Ludwig Colon Cancer Initiative Laboratory, Ludwig Institute for Cancer Research, Royal Melbourne Hospital, Victoria, 3050, Australia;(3) Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK;(4) Molecular Oncology Laboratories, Department of Medical Oncology, University of Oxford, Weatherall institute of Molecular Medicine, Headington, Oxford, OX3 9DS, UK;(5) MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK;(6) UMR203 INRA INSA-Lyon BF2I, Biologie Fonctionnelle Insectes et Interactions, Bat. L. Pasteur, 20 ave. A. Einstein, F-69621 Villeurbanne Cedex, France |
| 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. |
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