Abstract
BackgroundCopy 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.ResultsWe 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.ConclusionsWe 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.
Highlights
Copy number variants (CNVs), including deletions, amplifications, and other rearrangements, are common in human and cancer genomes
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
Probabilistic segmentation approaches have been previously applied to CNV detection [21,24,25,26], but we found that these methods either: Input: array comparative genome hybridization (aCGH) data Patient 1
Summary
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. Array comparative genome hybridization (aCGH) [6,7,8] is a widely-used experimental technique for the measurement of copy number variants in genomes. 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 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
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