Identification of Recurrent DNA Copy Number Aberrations in Tumors

Genetic mutations and alterations are the hallmark of cancer. When these alterations change the expression or protein product of a gene, increased invasiveness into surrounding tissue can result from unchecked cell cycle progression and improper regulation of cell death. In turn, these can contribute to tumor genesis, development, and expansion. A variety of somatic mutations can occur in tumor tissue, including point mutations, changes in methylation status, and gains and losses of chromosomal regions. Here we will focus primarily on mutations of the last type, which are termed DNA copy number aberrations (CNAs). Many CNAs can arise due to general genomic instability, and occur sporadically in locations throughout the genome. A smaller subset of CNAs appears to be recurrent, occurring repeatedly in the same region across multiple individuals. Recurrent CNAs are thought to be due to regional chromosome structure, or to a selection effect in which gain or loss of important regions leads to increased tumor growth rate. The identification of true recurrent CNAs is important, because these regions may play a role in the initiation and progression of tumors, perhaps even highlighting individual genes for further study or targeted treatment. The detection of recurrent CNAs is largely a statistical problem, and a number of methods have been proposed to address this problem. In this chapter, we survey several methods for analyzing DNA copy number data in tumors, with the DiNAMIC approach [1] described in some detail. The nomenclature in the literature on DNA copy number mutations has sometimes been inconsistent, so we begin by providing relevant definitions. Next we discuss the biological changes that can lead to alterations in tumor DNA copy number, as well as how tumors can result from these changes. The analysis of DNA copy number relies crucially on genomic technologies, and we survey platforms for assaying copy number, noting some of the challenges associated with these data. In Sections 13.3 and 13.4, we survey some of the available methods for analyzing DNA copy number data. Methods for detecting recurrent CNAs share common features including computation of summary statistics in genomic regions, use of resampling in order to create "null" distributions, and adjustments for multiple comparisons. Some of these methods require specific preprocessing steps, and we describe these as well. Section 13.5 is devoted to DiNAMIC. This method uses a novel permutation scheme called cyclic shift to compute its null distribution, and we describe comparisons to other permutation schemes. Although some of the issues may seem technical, the simulation results of Walter et al. [1] suggest that DiNAMIC's cyclic shift procedure is attractive in comparison to other permutation schemes, and leads to proper control of error rates under a variety of realistic marker correlation structures. We conclude by introducing a confidence interval procedure for recurrent CNAs [2]. Publicly available tumor datasets were analyzed with DiNAMIC and the confidence interval procedure, and the results briefly surveyed here and in [2] have underlying biological support.