A Hidden Markov Model to estimate population mixture and allelic copy-numbers in cancers using Affymetrix SNP arrays

Background: Affymetrix SNP arrays can interrogate thousands of SNPs at the same time. This allows us to look at the genomic content of cancer cells and to investigate the underlying events leading to cancer. Genomic copy-numbers are today routinely derived from SNP array data, but the proposed algorithms for this task most often disregard the genotype information available from germline cells in paired germline-tumour samples. Including this information may deepen our understanding of the "true" biological situation e.g. by enabling analysis of allele specific copynumbers. Here we rely on matched germline-tumour samples and have developed a Hidden Markov Model (HMM) to estimate allelic copy-number changes in tumour cells. Further with this approach we are able to estimate the proportion of normal cells in the tumour (mixture proportion). Results: We show that our method is able to recover the underlying copy-number changes in simulated data sets with high accuracy (above 97....

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Philippe Lamy, Claus L. Andersen, Lars Dyrskjot, Niels Torring, Carsten Wiuf
• Keywords: BMCBI 2007, Cancer, Mixture Proportion, SNP Array