Comparative Analysis of Methods for Identifying Recurrent Copy Number Alterations in Cancer

Abstract

Recurrent copy number alterations (CNAs) play an important role in cancer genesis. While a number of computational methods have been proposed for identifying such CNAs, their relative merits remain largely unknown in practice since very few efforts have been focused on comparative analysis of the methods. To facilitate studies of recurrent CNA identification in cancer genome, it is imperative to conduct a comprehensive comparison of performance and limitations among existing methods. In this paper, six representative methods proposed in the latest six years are compared. These include one-stage and two-stage approaches, working with raw intensity ratio data and discretized data respectively. They are based on various techniques such as kernel regression, correlation matrix diagonal segmentation, semi-parametric permutation and cyclic permutation schemes. We explore multiple criteria including type I error rate, detection power, Receiver Operating Characteristics (ROC) curve and the area under curve (AUC), and computational complexity, to evaluate performance of the methods under multiple simulation scenarios. We also characterize their abilities on applications to two real datasets obtained from cancers with lung adenocarcinoma and glioblastoma. This comparison study reveals general characteristics of the existing methods for identifying recurrent CNAs, and further provides new insights into their strengths and weaknesses. It is believed helpful to accelerate the development of novel and improved methods.

Comprehensive analysis of copy number aberrations in microsatellite stable colon cancer in view of stromal component

“…Focal regions were analysed with the GAIA package ( Morganella et al , 2011 ; Yuan et al , 2012 ), which allowed using different cut-offs according to the proportion of stroma. GAIA uses a statistical framework based on a conservative permutation test that estimates the null probability distribution of CNA based on the observed data.…”

rCGH: a comprehensive array-based genomic profile platform for precision medicine

“…In precision medicine, the decision-making regarding a therapeutic orientation relies on the actionable gene’s status, defined with respect to gain/loss thresholds and alteration lengths. Defining such thresholds is often arbitrary: (i) there is no consensus on which LRR values correspond to biologically relevant CNAs, (ii) focal alterations, possibly referring to significantly recurrent alterations within a cohort ( Mermel et al , 2011 ; Yuan et al , 2012 ), are not clearly defined when transposed to the interpretation of unique profiles. rCGH provides an interactive visualization tool that allows the user to visualize and manipulate genomic profiles from within a web interface ( Fig.…”

Integrating Omics Data

“…To address this challenge, it is important to perform joint analysis of the somatic genomic alteration profiles across many tumors. Several algorithms [53] are designed for identifying those regions with aberrations that occur significantly more often than would be expected by chance, using permutation tests that are based on the overall pattern of aberrations seen across the genome. In the current study, we used two wellestablished algorithms, GISTIC [54,55] and RAE [56], to identify the regions that harbor recurrent SCNAs by using SNP and aCGH data, respectively.…”