Statistical quantification of genomic tumoral alterations with a mixture model

International audienceThe study of genomic DNA alterations (recurrent regions of alteration, patterns of instability) contributes to tumor classification, and becomes of great importance for the personalization of cancer treatments. The use of Single-Nucleotide Polymorphism (SNP) arrays or of New Generation Sequences (NGS) techniques allows the simultaneous estimation of segmented copy number (CN) and B-allele frequency (BAF) profiles along the whole genome. In this context, Popova (2009) proposed the GAP method, based on pattern recognition with (BAF, CN) maps to detect genotype status of each segment in complex tumoral genome profiles. It takes into account the fact that the observations on these maps are necessarily placed on centers that depend --up to a proper scaling of the CN-- only on the unknown proportion of non tumoral tissue in the sample. Being deterministic and manually tuned, this method appears sensitive to noise. To overcome this drawback, we set a mixture model, allowing the automatic estimation of the proportion of non tumoral tissue and the test of genotype for each segment along the whole genome. We present the model, its estimation with an adapted EM algorithm and results on simulated data