Recurrent chromosomal copy number alterations in sporadic chordomas

The molecular events in chordoma pathogenesis have not been fully delineated, particularly with respect to copy number changes. Understanding copy number alterations in chordoma may reveal critical disease mechanisms that could be exploited for tumor classification and therapy. We report the copy number analysis of 21 sporadic chordomas using array comparative genomic hybridization (CGH). Recurrent copy changes were further evaluated with immunohistochemistry, methylation specific PCR, and quantitative real-time PCR. Similar to previous findings, large copy number losses, involving chromosomes 1p, 3, 4, 9, 10, 13, 14, and 18, were more common than copy number gains. Loss of CDKN2A with or without loss of CDKN2B on 9p21.3 was observed in 16/20 (80%) unique cases of which six (30%) showed homozygous deletions ranging from 76 kilobases to 4.7 megabases. One copy loss of the 10q23.31 region which encodes PTEN was found in 16/20 (80%) cases. Loss of CDKN2A and PTEN expression in the majority of cases was not attributed to promoter methylation. Our sporadic chordoma cases did not show hotspot point mutations in some common cancer gene targets. Moreover, most of these sporadic tumors are not associated with T (brachyury) duplication or amplification. Deficiency of CDKN2A and PTEN expression, although shared across many other different types of tumors, likely represents a key aspect of chordoma pathogenesis. Sporadic chordomas may rely on mechanisms other than copy number gain if they indeed exploit T/brachyury for proliferation.     

Modeling recurrent DNA copy number alterations in array CGH data

MOTIVATION: Recurrent DNA copy number alterations (CNA) measured with array comparative genomic hybridization (aCGH) reveal important molecular features of human genetics and disease. Studying aCGH profiles from a phenotypic group of individuals can determine important recurrent CNA patterns that suggest a strong correlation to the phenotype. Computational approaches to detecting recurrent CNAs from a set of aCGH experiments have typically relied on discretizing the noisy log ratios and subsequently inferring patterns. We demonstrate that this can have the effect of filtering out important signals present in the raw data. In this article we develop statistical models that jointly infer CNA patterns and the discrete labels by borrowing statistical strength across samples. RESULTS: We propose extending single sample aCGH HMMs to the multiple sample case in order to infer shared CNAs. We model recurrent CNAs as a profile encoded by a master sequence of states that generates the samples. We show how to improve on two basic models by performing joint inference of the discrete labels and providing sparsity in the output. We demonstrate on synthetic ground truth data and real data from lung cancer cell lines how these two important features of our model improve results over baseline models. We include standard quantitative metrics and a qualitative assessment on which to base our conclusions. AVAILABILITY: http://www.cs.ubc.ca/~sshah/acgh.     

Using high-density DNA methylation arrays to profile copy number alterations

The integration of genomic and epigenomic data is an increasingly popular approach for studying the complex mechanisms driving cancer development. We have developed a method for evaluating both methylation and copy number from high-density DNA methylation arrays. Comparing copy number data from Infinium HumanMethylation450 BeadChips and SNP arrays, we demonstrate that Infinium arrays detect copy number alterations with the sensitivity of SNP platforms. These results show that high-density methylation arrays provide a robust and economic platform for detecting copy number and methylation changes in a single experiment. Our method is available in the ChAMP Bioconductor package: http://www.bioconductor.org/packages/2.13/bioc/html/ChAMP.html.     

Pathogenic copy number variations are associated with foetal short femur length in a tertiary referral centre study

Shortened foetal femur length (FL) is a common abnormal phenotype that often causes anxiety in pregnant women, and standard clinical treatments remain unavailable. We investigated the clinical characteristics, genetic aetiology and obstetric pregnancy outcomes of foetuses with short FL and provided a reference for perinatal management of such cases. Chromosomal microarray analysis was used to analyse the copy number variations (CNV) in short FL foetuses. Of the 218 foetuses with short FL, 33 foetuses exhibited abnormal CNVs, including 19 with pathogenic CNVs and 14 with variations of uncertain clinical significance. Of the 19 foetuses with pathogenic CNVs, four had aneuploidy, 14 had deletions/duplications, and one had pathogenic uniparental diploidy. The 7q11.23 microdeletion was detected in three foetuses. The severity of short FL was not associated with the rate of pathogenic CNVs. The duration of short FL for the intrauterine ultrasound phenotype in foetuses carrying a pathogenic CNV was independent of the gestational age. Further, maternal age was not associated with the incidence of foetal pathogenic CNVs. Adverse pregnancy outcomes occurred in 77 cases, including termination of pregnancy in 63 cases, postnatal dwarfed foetuses with intellectual disability in 11 cases, and three deaths within 3 months of birth. Pathogenic CNVs closely related to foetal short FL were identified, among which the 7q11.23 microdeletion was highly associated with short FL development. This study provides a reference for the perinatal management of foetuses with short FL.     

A classification model for distinguishing copy number variants from cancer-related alterations

Background  

Both somatic copy number alterations (CNAs) and germline copy number variants (CNVs) that are prevalent in healthy individuals can appear as recurrent changes in comparative genomic hybridization (CGH) analyses of tumors. In order to identify important cancer genes CNAs and CNVs must be distinguished. Although the Database of Genomic Variants (DGV) contains a list of all known CNVs, there is no standard methodology to use the database effectively.     

Detection of DNA copy number alterations using penalized least squares regression

MOTIVATION: Genomic DNA copy number alterations are characteristic of many human diseases including cancer. Various techniques and platforms have been proposed to allow researchers to partition the whole genome into segments where copy numbers change between contiguous segments, and subsequently to quantify DNA copy number alterations. In this paper, we incorporate the spatial dependence of DNA copy number data into a regression model and formalize the detection of DNA copy number alterations as a penalized least squares regression problem. In addition, we use a stationary bootstrap approach to estimate the statistical significance and false discovery rate. RESULTS: The proposed method is studied by simulations and illustrated by an application to an extensively analyzed dataset in the literature. The results show that the proposed method can correctly detect the numbers and locations of the true breakpoints while appropriately controlling the false positives. AVAILABILITY: http://bioinformatics.med.yale.edu/DNACopyNumber CONTACT: hongyu.zhao@yale.edu SUPPLEMENTARY INFORMATION: http://bioinformatics.med.yale.edu/DNACopyNumber.     

Genomic organization of low copy number sequences that are associated with deca-satellite DNA in the monkey genome.

A previously described segment of African green monkey DNA (cloned in phage lambda MkA) contains deca-satellite linked to DNA sequences that are estimated to occur once per genome. Sequences homologous to the low copy number sequences in lambda MkA are also associated with species-specific satellite DNAs in the human and mouse genomes. A second clone, lambda Mk8, contains a monkey DNA region that is colinear and homologous to a portion of the low copy number sequences in lambda MkA, but no satellite sequences. The two cloned segments are markedly different starting at a point proximal to the satellite DNA region in lambda MkA. DNA-blotting experiments indicate that lambda Mk8 but not lambda MkA represents the typical genomic organization and that the low copy number segments occur only once per haploid genome. The data suggest that rearrangements such as deletions or inversions occurring in monkey cells account in part for the structure of lambda MkA. Additional rearrangements may have occurred during cloning in E. coli. This unique chromosomal region may be particularly susceptible to recombination.     

Functional profiling and gene expression analysis of chromosomal copy number alterations

Contrarily to the traditional view in which only one or a few key genes were supposed to be the causative factors of diseases, we discuss the importance of considering groups of functionally related genes in the study of pathologies characterised by chromosomal copy number alterations. Recent observations have reported the existence of regions in higher eukaryotic chromosomes (including humans) containing genes of related function that show a high degree of coregulation. Copy number alterations will consequently affect to clusters of functionally related genes, which will be the final causative agents of the diseased phenotype, in many cases. Therefore, we propose that the functional profiling of the regions affected by copy number alterations must be an important aspect to take into account in the understanding of this type of pathologies. To illustrate this, we present an integrated study of DNA copy number variations, gene expression along with the functional profiling of chromosomal regions in a case of multiple myeloma.     

Lessons from a decade of integrating cancer copy number alterations with gene expression profiles

Over the last decade, multiple functional genomic datasets studying chromosomal aberrations and their downstream effects on gene expression have accumulated for several cancer types. A vast majority of them are in the form of paired gene expression profiles and somatic copy number alterations (CNA) information on the same patients identified using microarray platforms. In response, many algorithms and software packages are available for integrating these paired data. Surprisingly, there has been no serious attempt to review the currently available methodologies or the novel insights brought using them. In this work, we discuss the quantitative relationships observed between CNA and gene expression in multiple cancer types and biological milestones achieved using the available methodologies. We discuss the conceptual evolution of both, the step-wise and the joint data integration methodologies over the last decade. We conclude by providing suggestions for building efficient data integration methodologies and asking further biological questions.     

Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization

SUMMARY: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs. AVAILABILITY: Source code and sample data are available at http://bioinfo-out.curie.fr/projects/freec/.     

Transgene copy number can be positively or negatively associated with transgene expression

Two different types of T-DNA insert were found in tobacco plants transformed with Agrobacterium tumefaciens. High-expressing (H) types had one copy of the T-DNA at a locus and produced high expression of the transgene uidA, as measured by uidA RNA levels and -glucuronidase activity; low-expressing (L) types had inverted repeats of the T-DNA at a locus and produced low uidA expression. H-types from different transformants acted additively, and cross-fertilization between two different homozygous transformants with H-type inserts produced F₁ plants with GUS activity that equalled the parents and individual F₂ plants with 50%, 100%, 150% and 200% of parental values. However, the L-type inserts worked in trans to suppress uidA expression from H-type inserts when both were present in the same genome. Hence when a transformant homozygous for the L-type insert was crossed to one homozygous for the H-type, all plants in the F₁ and F₂ generations with both types of insert had low GUS activity while F₂ segregants that only had the H-type inserts had high GUS activity again. Suppression of the H-type gene was associated with increased methylation of the insert. Particle acceleration was used to introduce further copies of uidA into tissues of the transformants. Regardless of the promoter used, those plants with endogenous L-type inserts showed none of the distinct loci of GUS activity readily visible in material with no inserts, showing that L-type inserts could suppress not only the uidA expression of genomic homologues, but also of copies added in vitro.     

Comparative study of MLPA-FISH to determine DNA copy number alterations in neuroblastic tumors

Neuroblastoma tumor cells show complex combinations of genetic aberrations, and to date many different methods have been used for their detection. To apply genome-wide techniques, such as Multiplex Ligation-dependent Probe Amplification (MLPA), in routine diagnosis their validation is appropriate and necessary. DNA copy number alterations in 129 cases of neuroblastic tumors were detected using MPLA, and the results validated by Fluorescence In Situ Hybridization (FISH) (MYCN gene, 1p36, 11q and 17q). Kappa index values showed very good concordance between the two techniques in detecting homogeneous MYCN amplification (1); 11q deletion (0.908) and 17q gain (0.922). The validation results showed that MLPA is a highly efficient technique for diagnosis based on the genetic aberrations in relevant regions in neuroblastoma, showing a high concordance with FISH.     

rSW-seq: Algorithm for detection of copy number alterations in deep sequencing data

Background  

Recent advances in sequencing technologies have enabled generation of large-scale genome sequencing data. These data can be used to characterize a variety of genomic features, including the DNA copy number profile of a cancer genome. A robust and reliable method for screening chromosomal alterations would allow a detailed characterization of the cancer genome with unprecedented accuracy.     

Integrative analysis of gene expression and copy number alterations using canonical correlation analysis

Background  

With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia.     

Sparse representation and Bayesian detection of genome copy number alterations from microarray data

MOTIVATION: Genomic instability in cancer leads to abnormal genome copy number alterations (CNA) that are associated with the development and behavior of tumors. Advances in microarray technology have allowed for greater resolution in detection of DNA copy number changes (amplifications or deletions) across the genome. However, the increase in number of measured signals and accompanying noise from the array probes present a challenge in accurate and fast identification of breakpoints that define CNA. This article proposes a novel detection technique that exploits the use of piece wise constant (PWC) vectors to represent genome copy number and sparse Bayesian learning (SBL) to detect CNA breakpoints. METHODS: First, a compact linear algebra representation for the genome copy number is developed from normalized probe intensities. Second, SBL is applied and optimized to infer locations where copy number changes occur. Third, a backward elimination (BE) procedure is used to rank the inferred breakpoints; and a cut-off point can be efficiently adjusted in this procedure to control for the false discovery rate (FDR). RESULTS: The performance of our algorithm is evaluated using simulated and real genome datasets and compared to other existing techniques. Our approach achieves the highest accuracy and lowest FDR while improving computational speed by several orders of magnitude. The proposed algorithm has been developed into a free standing software application (GADA, Genome Alteration Detection Algorithm). AVAILABILITY: http://biron.usc.edu/~piquereg/GADA     

Recurrent patterns of DNA copy number alterations in tumors reflect metabolic selection pressures

Copy number alteration (CNA) profiling of human tumors has revealed recurrent patterns of DNA amplifications and deletions across diverse cancer types. These patterns are suggestive of conserved selection pressures during tumor evolution but cannot be fully explained by known oncogenes and tumor suppressor genes. Using a pan‐cancer analysis of CNA data from patient tumors and experimental systems, here we show that principal component analysis‐defined CNA signatures are predictive of glycolytic phenotypes, including ¹⁸F‐fluorodeoxy‐glucose (FDG) avidity of patient tumors, and increased proliferation. The primary CNA signature is enriched for p53 mutations and is associated with glycolysis through coordinate amplification of glycolytic genes and other cancer‐linked metabolic enzymes. A pan‐cancer and cross‐species comparison of CNAs highlighted 26 consistently altered DNA regions, containing 11 enzymes in the glycolysis pathway in addition to known cancer‐driving genes. Furthermore, exogenous expression of hexokinase and enolase enzymes in an experimental immortalization system altered the subsequent copy number status of the corresponding endogenous loci, supporting the hypothesis that these metabolic genes act as drivers within the conserved CNA amplification regions. Taken together, these results demonstrate that metabolic stress acts as a selective pressure underlying the recurrent CNAs observed in human tumors, and further cast genomic instability as an enabling event in tumorigenesis and metabolic evolution.     

Mitochondrial NADH:ubiquinone oxidoreductase alterations are associated with endometriosis

Genetic alterations and aberrant expression of ‘mitochondrial membrane complex I’ (MMC-I) underlie several complex human disorders, but no reports are documented to date in endometriosis. Sequencing of mitochondrially encoded MMC-I subunits revealed 72 mutations of which 2 missense (G10398A; A13603A/G) mutations and 1 synonymous (T10400C) mutation showed higher prevalence in patients. In silico functional analysis predicted A13603A/G, a novel heteroplasmy as a ‘damaging variant’. Our results indicate higher endometriosis risk for haplotype ‘10398A/10400C/13603AG’ and haplogroup ‘N’. Immunohistochemical analysis revealed elevated MMC-I expression in eutopic endometria of patients compared to controls. In conclusion, MMC-I alterations may constitute an inheritable risk factor for endometriosis.