A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations

DNA sequence copy number has been shown to be associated with cancer development and progression.Array-based comparative genomic hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome.Due to experimental and biological variations across chromosomes and hybridizations, current methods are limited to analyses of single chromosomes.We propose a more powerful approach that borrows strength across chromosomes and hybridizations.We assume a Gaussian mixture model, with a hidden Markov dependence structure and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation.For ease of computation, we base estimation on a pseudolikelihood function.The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation.We assess the characteristics of the method through simulation studies and analysis of a brain tumor aCGH data set.We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present.