Title Detection of recurrent rearrangement breakpoints from copy number data Permalink

Background: Copy number variants (CNVs), including deletions, amplifications, and other rearrangements, are common in human and cancer genomes. Copy number data from array comparative genome hybridization (aCGH) and next-generation DNA sequencing is widely used to measure copy number variants. Comparison of copy number data from multiple individuals reveals recurrent variants. Typically, the interior of a recurrent CNV is examined for genes or other loci associated with a phenotype. However, in some cases, such as gene truncations and fusion genes, the target of variant lies at the boundary of the variant. Results: We introduce Neighborhood Breakpoint Conservation (NBC), an algorithm for identifying rearrangement breakpoints that are highly conserved at the same locus in multiple individuals. NBC detects recurrent breakpoints at varying levels of resolution, including breakpoints whose location is exactly conserved and breakpoints whose location varies within a gene. NBC also identifies pairs of recurrent breakpoints such as those that result from fusion genes. We apply NBC to aCGH data from 36 primary prostate tumors and identify 12 novel rearrangements, one of which is the well-known TMPRSS2-ERG fusion gene. We also apply NBC to 227 glioblastoma tumors and predict 93 novel rearrangements which we further classify as gene truncations, germline structural variants, and fusion genes. A number of these variants involve the protein phosphatase PTPN12 suggesting that deregulation of PTPN12, via a variety of rearrangements, is common in glioblastoma. Conclusions: We demonstrate that NBC is useful for detection of recurrent breakpoints resulting from copy number variants or other structural variants, and in particular identifies recurrent breakpoints that result in gene truncations or fusion genes. Software is available at http://http. //cs.brown.edu/people/braphael/software.html. Background Copy number variants (CNVs) are genomic rearrangements that result in a different number of copies of a segment of the genome, and include deletions, amplifications, and unbalanced translocations. CNVs are common in the human genome, and CNVs have been associated with several diseases [1-3]. Similarly, CNVs (also referred to as copy number aberrations, or CNAs) are found in many cancer genomes [4,5]. Thus, detection of CNVs and characterization of the gene or genes that they affect is an important task. Array comparative genome hybridization (aCGH) [6-8] is a widely-used experimental technique for the measurement of copy number variants in genomes. aCGH involves the hybridization of differentially fluorescently labeled DNA fragments from a test genome and a reference genome to a set of genomic probes derived from the reference genome sequence. Measurements of the test:reference fluorescence ratio at each probe identify locations in the test genome that are present in lower, higher, or similar copy in the reference genome, producing a copy number profile of the test genome. Copy number profiles are typically compared across individuals to identify recurrent CNVs that are shared by multiple individuals. These recurrent CNVs may be germline polymorphisms, or in the case of cancer samples, recurrent somatic mutations. Large cohorts of aCGH data from cancer genomes (e.g. from The Cancer Genome Atlas (TCGA) [9]) provide the statistical power to identify numerous recurrent somatic CNVs. Several methods have been introduced to identify recurrent CNVs, including GISTIC [10], CoCoA [11], STAC [12], and CMDS [13]. These methods (with the exception of * Correspondence: aritz@cs.brown.edu; braphael@brown.edu Department of Computer Science, Brown University, Providence, RI, USA Full list of author information is available at the end of the article Ritz et al. BMC Bioinformatics 2011, 12:114 http://www.biomedcentral.com/1471-2105/12/114 © 2011 Ritz et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CMDS) first partition each copy number profile into regions (or segments) of equal copy number, producing a segmentation for each individual (see [14] for a survey of segmentation methods). Since a CNV alters the copy number of multiple adjacent probes, segmenting the copy number profile helps overcome experimental errors at each probe. These segmentations are then combined to identify aberrant intervals that are shared by multiple individuals. An implicit assumption of this approach is that the target of the CNV lies within the interval; this is the case for oncogenes that lie within amplifications or tumor suppressor genes that lie within deletions. Some recurrent rearrangements do not target a gene within the aberrant interval, but rather target a gene or locus at the boundary of the interval. A striking example is the TMPRSS2-ERG fusion gene in prostate cancer [15]. This fusion gene results from a 3 Mb deletion on chromosome 21, where the two endpoints (or breakpoints) of the deletion lie in the two partner genes of the fusion. More recently, next-generation DNA sequencing has shown other fusion genes that are located at the endpoints of the CNVs (cf. figure two (b) in [16]). These and other examples motivate the development of methods that discover recurrent breakpoints rather than recurrent intervals. We introduce a novel algorithm called Neighborhood Breakpoint Conservation (NBC) to identify recurrent breakpoints in copy number data. NBC computes the probability that a breakpoint occurs between each pair of adjacent probes over all possible segmentations of a single copy number profile and then combines these probabilities across multiple profiles to identify recurrent breakpoints. The probabilistic approach contrasts with the typical methods for aCGH analysis that compute only a single segmentation of a copy number profile. Consideration of a single segmentation is reasonable for identifying recurrent aberrations because large aberrations will typically overlap in different individuals as long as the segmentations reasonably approximate the true underlying copy number level. However, identification of recurrent breakpoints is more sensitive to the choice of segmentation. Due to measurement errors in individual probes, the optimal segmentation of each individual profile may not “align” across profiles. Thus it is necessary to consider multiple suboptimal segmentations. Moreover the probabilistic approach allows use to account for biological variability in the location of a breakpoint within a gene or other locus. We apply NBC to aCGH data from 36 primary prostate tumors and predict 12 CNVs, including one gene truncation and one fusion gene which is the well-known TMPRSS2ERG fusion gene. We also apply NBC to 227 glioblastoma (GBM) tumors and predict 91 CNVs, including 23 gene truncations and 33 fusion genes. Additionally, we predict 35 germline CNVs from 107 available matched blood samples from GBM patients. A number of the somatic CNV predictions in GBM involve the protein phosphatase PTPN12, suggesting that deregulation of PTPN12 via a variety of rearrangements is common in glioblastoma. We note that NBC is readily adapted to analyze copy number profiles obtained from next-generation DNA sequencing data [17,18]. Methods The Neighborhood Breakpoint Conservation (NBC) algorithm takes, as input, aCGH data from many individuals and identifies recurrent breakpoints and pairs of recurrent breakpoints in a subset of the individuals (Figure 1). The first step in NBC, as in most aCGH analysis, is to segment each copy number profile into intervals of equal copy number. While many existing methods produce a single segmentation for aCGH data [19-21], NBC uses a dynamic programming approach [22] to compute the probability P(X |A) of a copy number profile X given a segmentation A. NBC then employs a stochastic backtrace to compute the posterior probability P(A |X). Using this approach, one can derive the segmentation Â with maximum probability, but more importantly, one can compute the posterior probability of events of interest over all possible segmentations of the data. In particular, we compute the probability of a breakpoint between each pair of adjacent probes, as well as the probability of a breakpoint within a fixed interval or probes (e.g. from a gene region). The second step of NBC is to combine breakpoint probabilities in each individual to determine breakpoints that appear in multiple individuals. Similar to [11], we use a binomial order statistic [23] to compute a p-value for the event that k or more individuals share a breakpoint between two adjacent probes. We then extend this breakpoint score to consider pairs of breakpoints that are shared by multiple individuals. Finally, we also define a score for a breakpoint that may occur anywhere within an interval of adjacent probes (e.g. a gene) that is shared by multiple individuals. We detail each of these two steps in the following sections. A Probability Model for Segmentation and Breakpoint