Assembly of microarrays for genome-wide measurement of DNA copy number

Molecular Karyotyping: Array CGH Quality Criteria for Constitutional Genetic Diagnosis

Array CGH (comparative genomic hybridization) enables the identification of chromosomal copy number changes. The availability of clone sets covering the human genome opens the possibility for the widespread use of array CGH for both research and diagnostic purposes. In this manuscript we report on the parameters that were critical for successful implementation of the technology, assess quality criteria, and discuss the potential benefits and pitfalls of the technology for improved pre- and postnatal constitutional genetic diagnosis. We propose to name the genome-wide array CGH “molecular karyotyping,” in analogy with conventional karyotyping that uses staining methods to visualize chromosomes.

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings.

A t(5;16) translocation is the likely driver of a syndrome with ambiguous genitalia, facial dysmorphism, intellectual disability, and speech delay

Genetic studies grounded on monogenic paradigms have accelerated both gene discovery and molecular diagnosis. At the same time, complex genomic rearrangements are also appreciated as potent drivers of disease pathology. Here, we report two male siblings with a dysmorphic face, ambiguous genitalia, intellectual disability, and speech delay. Through quad-based whole-exome sequencing and concomitant molecular cytogenetic testing, we identified two copy-number variants (CNVs) in both affected individuals likely arising from a balanced translocation: a 13.5-Mb duplication on Chromosome 16 (16q23.1 → 16qter) and a 7.7-Mb deletion on Chromosome 5 (5p15.31 → 5pter), as well as a hemizygous missense variant in CXorf36 (also known as DIA1R). The 5p terminal deletion has been associated previously with speech delay, whereas craniofacial dysmorphia and genital/urinary anomalies have been reported in patients with a terminal duplication of 16q. However, dosage changes in either genomic region alone could not account for the overall clinical presentation in our family; functional testing of CXorf36 in zebrafish did not induce defects in neurogenesis or the craniofacial skeleton. Notably, literature and database analysis revealed a similar dosage disruption in two siblings with extensive phenotypic overlap with our patients. Taken together, our data suggest that dosage perturbation of genes within the two chromosomal regions likely drives the syndromic manifestations of our patients and highlight how multiple genetic lesions can contribute to complex clinical pathologies.

Optimization of Signal Decomposition Matched Filtering (SDMF) for Improved Detection of Copy-Number Variations

We aim to improve the performance of the previously proposed signal decomposition matched filtering (SDMF) method [26] for the detection of copy-number variations (CNV) in the human genome. Through simulations, we show that the modified SDMF is robust even at high noise levels and outperforms the original SDMF method, which indirectly depends on CNV frequency. Simulations are also used to develop a systematic approach for selecting relevant parameter thresholds in order to optimize sensitivity, specificity and computational efficiency. We apply the modified method to array CGH data from normal samples in the cancer genome atlas (TCGA) and compare detected CNVs to those estimated using circular binary segmentation (CBS) [19], a hidden Markov model (HMM)-based approach [11] and a subset of CNVs in the Database of Genomic Variants. We show that a substantial number of previously identified CNVs are detected by the optimized SDMF, which also outperforms the other two methods.

Chromosomal Microarrays for the Prenatal Detection of Microdeletions and Microduplications

Chromosomal microarray analysis has replaced conventional G-banded karyotype in prenatal diagnosis as the first-tier test for the cytogenetic detection of copy number imbalances in fetuses with/without major structural abnormalities. This article reviews the basic technology of microarray; the value and clinical significance of the detection of microdeletions, microduplications, and other copy number variants; as well as the importance of genetic counseling for prenatal diagnosis. It also discusses the current status of noninvasive screening for some of these microdeletion and microduplication syndromes.

Sequential model selection-based segmentation to detect DNA copy number variation

Array-based CGH experiments are designed to detect genomic aberrations or regions of DNA copy-number variation that are associated with an outcome, typically a state of disease. Most of the existing statistical methods target on detecting DNA copy number variations in a single sample or array. We focus on the detection of group effect variation, through simultaneous study of multiple samples from multiple groups. Rather than using direct segmentation or smoothing techniques, as commonly seen in existing detection methods, we develop a sequential model selection procedure that is guided by a modified Bayesian information criterion. This approach improves detection accuracy by accumulatively utilizing information across contiguous clones, and has computational advantage over the existing popular detection methods. Our empirical investigation suggests that the performance of the proposed method is superior to that of the existing detection methods, in particular, in detecting small segments or separating neighboring segments with differential degrees of copy-number variation.

Application of DNA Microarray to Clinical Diagnostics

Microarray-based technology to conduct array comparative genomic hybridization (aCGH) has made a significant impact on the diagnosis of human genetic diseases. Such diagnoses, previously undetectable by traditional G-banding chromosome analysis, are now achieved by identifying genomic copy number variants (CNVs) using the microarray. Not only can hundreds of well-characterized genetic syndromes be detected in a single assay, but new genomic disorders and disease-causing genes can also be discovered through the utilization of aCGH technology. Although other platforms such as single nucleotide polymorphism (SNP) arrays can be used for detecting CNVs, in this chapter we focus on describing the methods for performing aCGH using Agilent oligonucleotide arrays for both prenatal (e.g., amniotic fluid and chorionic villus sample) and postnatal samples (e.g., blood).

Polyclonality of Parathyroid Tumors in Neonatal Severe Hyperparathyroidism

Neonatal severe hyperparathyroidism (NSHPT) is a rare disorder characterized by major hypercalcemia, elevated parathyroid hormone levels, and marked enlargement of multiple parathyroid glands, usually associated with germline mutations in the calcium receptor gene CASR. However, little is known about the outgrowth of parathyroid tumors in NSHPT, including whether they represent monoclonal or polyclonal expansions. We sought to examine the clonality of parathyroid tissues resected from a patient with NSHPT and biallelic CASR mutations. DNA from two distinct parathyroid tumors resected from a girl with NSHPT, plus polyclonal/monoclonal control samples, were subjected to analyses of clonality by two independent methods, X-chromosome inactivation analysis at the androgen receptor locus (HUMARA) and BAC array comparative genomic hybridization (CGH). Both parathyroid tumor samples revealed polyclonal patterns by X-inactivation analysis, with polyclonal and monoclonal controls yielding the expected patterns. Similarly, by BAC array CGH, neither parathyroid sample contained monoclonal copy number changes and both appeared identical to the patient-matched polyclonal controls. Our observations provide direct experimental evidence that the markedly enlarged parathyroid tumors in the setting of NSHPT constitute polyclonal, generalized hyperplastic growths rather than monoclonal neoplasms.

Detection of Genomic Structural Variants from Next-Generation Sequencing Data

Structural variants are genomic rearrangements larger than 50 bp accounting for around 1% of the variation among human genomes. They impact on phenotypic diversity and play a role in various diseases including neurological/neurocognitive disorders and cancer development and progression. Dissecting structural variants from next-generation sequencing data presents several challenges and a number of approaches have been proposed in the literature. In this mini review, we describe and summarize the latest tools – and their underlying algorithms – designed for the analysis of whole-genome sequencing, whole-exome sequencing, custom captures, and amplicon sequencing data, pointing out the major advantages/drawbacks. We also report a summary of the most recent applications of third-generation sequencing platforms. This assessment provides a guided indication – with particular emphasis on human genetics and copy number variants – for researchers involved in the investigation of these genomic events.

"Down syndrome: an insight of the disease"

Down syndrome (DS) is one of the commonest disorders with huge medical and social cost. DS is associated with number of phenotypes including congenital heart defects, leukemia, Alzeihmer's disease, Hirschsprung disease etc. DS individuals are affected by these phenotypes to a variable extent thus understanding the cause of this variation is a key challenge. In the present review article, we emphasize an overview of DS, DS-associated phenotypes diagnosis and management of the disease. The genes or miRNA involved in Down syndrome associated Alzheimer's disease, congenital heart defects (AVSD), leukemia including AMKL and ALL, hypertension and Hirschprung disease are discussed in this article. Moreover, we have also reviewed various prenatal diagnostic method from karyotyping to rapid molecular methods -  MLPA, FISH, QF-PCR, PSQ, NGS and noninvasive prenatal diagnosis in detail.

Hybrid algorithms for multiple change-point detection in biological sequences

Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences in high resolution. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many complex diseases. In this study we propose two distinct hybrid algorithms that combine efficient sequential change-point detection procedures (the Shiryaev-Roberts procedure and the cumulative sum control chart (CUSUM) procedure) with the Cross-Entropy method, which is an evolutionary stochastic optimization technique to estimate both the number of change-points and their corresponding locations in aCGH data. The proposed hybrid algorithms are applied to both artificially generated data and real aCGH experimental data to illustrate their usefulness. Our results show that the proposed methodologies are effective in detecting multiple change-points in biological sequences of continuous measurements.

Iterative Potts and Blake-Zisserman minimization for the recovery of functions with discontinuities from indirect measurements

Signals with discontinuities appear in many problems in the applied sciences ranging from mechanics, electrical engineering to biology and medicine. The concrete data acquired are typically discrete, indirect and noisy measurements of some quantities describing the signal under consideration. The task is to restore the signal and, in particular, the discontinuities. In this respect, classical methods perform rather poor, whereas non-convex non-smooth variational methods seem to be the correct choice. Examples are methods based on Mumford-Shah and piecewise constant Mumford-Shah functionals and discretized versions which are known as Blake-Zisserman and Potts functionals. Owing to their non-convexity, minimization of such functionals is challenging. In this paper, we propose a new iterative minimization strategy for Blake-Zisserman as well as Potts functionals and a related jump-sparsity problem dealing with indirect, noisy measurements. We provide a convergence analysis and underpin our findings with numerical experiments.

Multiple Break-Points Detection in Array CGH Data via the Cross-Entropy Method

Array comparative genome hybridization (aCGH) is a widely used methodology to detect copy number variations of a genome in high resolution. Knowing the number of break-points and their corresponding locations in genomic sequences serves different biological needs. Primarily, it helps to identify disease-causing genes that have functional importance in characterizing genome wide diseases. For human autosomes the normal copy number is two, whereas at the sites of oncogenes it increases (gain of DNA) and at the tumour suppressor genes it decreases (loss of DNA). The majority of the current detection methods are deterministic in their set-up and use dynamic programming or different smoothing techniques to obtain the estimates of copy number variations. These approaches limit the search space of the problem due to different assumptions considered in the methods and do not represent the true nature of the uncertainty associated with the unknown break-points in genomic sequences. We propose the Cross-Entropy method, which is a model-based stochastic optimization technique as an exact search method, to estimate both the number and locations of the break-points in aCGH data. We model the continuous scale log-ratio data obtained by the aCGH technique as a multiple break-point problem. The proposed methodology is compared with well established publicly available methods using both artificially generated data and real data. Results show that the proposed procedure is an effective way of estimating number and especially the locations of break-points with high level of precision. Availability: The methods described in this article are implemented in the new R package breakpoint and it is available from the Comprehensive R Archive Network at http://CRAN.R-project.org/package=breakpoint.

[Current methods in genetic analysis : an approach for genetics-based preventive medicine]

Modern genetic analysis methods such as DNA arrays (gene chips) or high-throughput DNA sequencing of the next generation (Next Generation Sequencing, NGS) have once again accelerated the pace of innovation that has been powered by genome research over the past 10 years of the "post-genomic era". The present paper introduces array and NGS methods as two important innovation driving methods and provides examples for their application in large-scale scientific projects. However, a broad application of these very powerful technologies for genetic screening for the purpose of disease prevention is currently not yet in sight. The complexity of the interaction of genes, gene products and the environment has so far exceeded all expectations, suggesting that reliable statements about the medical relevance of common genetic variants can presently only be made in a few areas such as pharmacogenetics and oncology. We also discuss ethical issues raised by genetic population screening. The aim of this paper is to provide a brief outline of the development of methods in molecular genetics to the now dominant modern technologies and present their applications in research, in the diagnosis of rare diseases, and in terms of screening approaches.

DNA copy number analysis of fresh and formalin-fixed specimens by shallow whole-genome sequencing with identification and exclusion of problematic regions in the genome assembly

Detection of DNA copy number aberrations by shallow whole-genome sequencing (WGS) faces many challenges, including lack of completion and errors in the human reference genome, repetitive sequences, polymorphisms, variable sample quality, and biases in the sequencing procedures. Formalin-fixed paraffin-embedded (FFPE) archival material, the analysis of which is important for studies of cancer, presents particular analytical difficulties due to degradation of the DNA and frequent lack of matched reference samples. We present a robust, cost-effective WGS method for DNA copy number analysis that addresses these challenges more successfully than currently available procedures. In practice, very useful profiles can be obtained with ∼0.1× genome coverage. We improve on previous methods by first implementing a combined correction for sequence mappability and GC content, and second, by applying this procedure to sequence data from the 1000 Genomes Project in order to develop a blacklist of problematic genome regions. A small subset of these blacklisted regions was previously identified by ENCODE, but the vast majority are novel unappreciated problematic regions. Our procedures are implemented in a pipeline called QDNAseq. We have analyzed over 1000 samples, most of which were obtained from the fixed tissue archives of more than 25 institutions. We demonstrate that for most samples our sequencing and analysis procedures yield genome profiles with noise levels near the statistical limit imposed by read counting. The described procedures also provide better correction of artifacts introduced by low DNA quality than prior approaches and better copy number data than high-resolution microarrays at a substantially lower cost.

GENETICS FOR DIAGNOSTICS IN PREVENTIVE MEDICINE

The sequencing of first human genome followed by rapid development of technologies, that led to significant lowering of costs for genetic analyze and its fast performing, made possible a broad invention of genetic diagnostics methods into clinical practice. Contemporary methods of molecular genetics make possible to research on inherited factors on chromosome level with molecular cytogenetics methods, and on the level of local mutations with the use or polymeraze chain reaction, microchips and sequencing. Temps of the next generation sequencing methods provide the opportunity to predict soon inclusion in practice of the personalized medical analysis of large genetic data massive, that can be used for the disease outcome prediction, estimation of its course, and for the prescription and correction of pharmacotherapy. In this review, different (including novel) approaches to genetic diagnostics are explored for the rare as common diseases, their benefits and restrictions. 

Subgroup-Based Adaptive (SUBA) Designs for Multi-Arm Biomarker Trials

Targeted therapies based on biomarker profiling are becoming a mainstream direction of cancer research and treatment. Depending on the expression of specific prognostic biomarkers, targeted therapies assign different cancer drugs to subgroups of patients even if they are diagnosed with the same type of cancer by traditional means, such as tumor location. For example, Herceptin is only indicated for the subgroup of patients with HER2+ breast cancer, but not other types of breast cancer. However, subgroups like HER2+ breast cancer with effective targeted therapies are rare and most cancer drugs are still being applied to large patient populations that include many patients who might not respond or benefit. Also, the response to targeted agents in humans is usually unpredictable. To address these issues, we propose SUBA, subgroup-based adaptive designs that simultaneously search for prognostic subgroups and allocate patients adaptively to the best subgroup-specific treatments throughout the course of the trial. The main features of SUBA include the continuous reclassification of patient subgroups based on a random partition model and the adaptive allocation of patients to the best treatment arm based on posterior predictive probabilities. We compare the SUBA design with three alternative designs including equal randomization, outcome-adaptive randomization and a design based on a probit regression. In simulation studies we find that SUBA compares favorably against the alternatives.

biomvRhsmm: genomic segmentation with hidden semi-Markov model

High-throughput technologies like tiling array and next-generation sequencing (NGS) generate continuous homogeneous segments or signal peaks in the genome that represent transcripts and transcript variants (transcript mapping and quantification), regions of deletion and amplification (copy number variation), or regions characterized by particular common features like chromatin state or DNA methylation ratio (epigenetic modifications). However, the volume and output of data produced by these technologies present challenges in analysis. Here, a hidden semi-Markov model (HSMM) is implemented and tailored to handle multiple genomic profile, to better facilitate genome annotation by assisting in the detection of transcripts, regulatory regions, and copy number variation by holistic microarray or NGS. With support for various data distributions, instead of limiting itself to one specific application, the proposed hidden semi-Markov model is designed to allow modeling options to accommodate different types of genomic data and to serve as a general segmentation engine. By incorporating genomic positions into the sojourn distribution of HSMM, with optional prior learning using annotation or previous studies, the modeling output is more biologically sensible. The proposed model has been compared with several other state-of-the-art segmentation models through simulation benchmarking, which shows that our efficient implementation achieves comparable or better sensitivity and specificity in genomic segmentation.

Genome-wide copy number analysis of cerebrospinal fluid tumor cells and their corresponding archival primary tumors

A debilitating complication of breast cancer is the metastatic spread of tumor cells to the leptomeninges or cerebrospinal fluid (CSF). Patients diagnosed with this aggressive clinical syndrome, known as leptomeningeal carcinomatosis, have very poor prognosis. Despite improvements in detecting cerebrospinal fluid tumor cells (CSFTCs), information regarding their molecular biology is extremely limited. In our recent work, we utilized a protocol previously used for circulating tumor cell isolation to purify tumor cells from the CSF. We then performed genomic characterization of CSFTCs as well as archival tumors from the same patient. Here, we describe the microarray data and quality controls associated with our study published in the Cancer Research journal in 2013 [1]. We also provide an R script containing code for quality control of microarray data and assessment of copy number calls. The microarray data has been deposited into Gene Expression Omnibus under accession # GSE46068.

Human molecular cytogenetics: From cells to nucleotides

The field of cytogenetics has focused on studying the number, structure, function and origin of chromosomal abnormalities and the evolution of chromosomes. The development of fluorescent molecules that either directly or via an intermediate molecule bind to DNA has led to the development of fluorescent in situ hybridization (FISH), a technology linking cytogenetics to molecular genetics. This technique has a wide range of applications that increased the dimension of chromosome analysis. The field of cytogenetics is particularly important for medical diagnostics and research as well as for gene ordering and mapping. Furthermore, the increased application of molecular biology techniques, such as array-based technologies, has led to improved resolution, extending the recognized range of microdeletion/microduplication syndromes and genomic disorders. In adopting these newly expanded methods, cytogeneticists have used a range of technologies to study the association between visible chromosome rearrangements and defects at the single nucleotide level. Overall, molecular cytogenetic techniques offer a remarkable number of potential applications, ranging from physical mapping to clinical and evolutionary studies, making a powerful and informative complement to other molecular and genomic approaches. This manuscript does not present a detailed history of the development of molecular cytogenetics; however, references to historical reviews and experiments have been provided whenever possible. Herein, the basic principles of molecular cytogenetics, the technologies used to identify chromosomal rearrangements and copy number changes, and the applications for cytogenetics in biomedical diagnosis and research are presented and discussed.

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.

Evolution of prenatal genetics: from point mutation testing to chromosomal microarray analysis

Molecular genetic testing involves DNA analysis using various methods for the purpose of diagnosing genetic disorders. In the prenatal DNA diagnostic setting, fetal DNA is usually tested for a specific single-gene disorder for which the fetal risk is 25% or more. In contrast, cytogenetic testing is often used to detect fetal chromosomal abnormalities in cases that involve a wider range of indications. Classic cytogenetic and DNA-based testing methods provide a range of aberrations detected with different levels of genomic resolution. More recently developed molecular cytogenetic methods provide powerful tools to bridge the technical divide between these related areas. One such hybrid method is microarray-based comparative genomic hybridization. Chromosomal microarray analysis has been applied to clinical testing for unbalanced gains or losses of genomic regions associated with genetic disorders. This technology is poised to have a substantial impact on clinical genetics, including prenatal genetic testing.

Applications of array technology: melanoma research and diagnosis

A complex set of genetic alterations occurs within a cell in order to permit neoplastic transformation. Human cancers undergo a continuous development from benign to malignant states, as most thoroughly documented in the mole-to-melanoma transition. Several specific genetic and transcriptional events correlate with the prolonged multistep sequence from early to late clinical stages of the disease. High-throughput microarrays are being used in expression profiling analyses with the aim of discovering genes and their pathways, functional characterization of genes and tumor subclassification. There are, however, many potential pitfalls in the use of microarrays that result in false leads and erroneous conclusions. This review summarizes the current status of the application of microarray technology in melanoma research. It also attempts to outline some of the steps needed to develop the key features to be observed in developing diagnostic and prognostic classification systems based upon gene expression profiling.

Chromosomal copy number analysis in melanoma diagnostics

The majority of melanocytic neoplasms can be correctly diagnosed using routine histopathologic analysis. However, a significant minority of tumors have ambiguous histopathologic attributes that overlap between melanocytic nevi and melanoma. Ancillary tests that assist in distinguishing potentially lethal melanomas from benign melanocytic nevi with atypical histopathologic features are available, but still need refining.Most melanomas have chromosomal copy number aberrations, frequently involving multiple chromosomes. With rare exceptions, such anomalies are not found in melanocytic nevi. This difference formed the basis to develop assays that can help distinguish melanoma from nevi by fluorescence in situ hybridization (FISH) and comparative genomic hybridization (CGH). FISH can detect chromosomal copy number changes of a limited number of loci within individual cells. By contrast, CGH assesses copy number across the entire genome, but typically is performed on bulk cell populations so that copy number changes in individual cells or subpopulations of cells can go undetected. Both FISH and CGH have been used to provide genomic information in histopathologically ambiguous melanocytic tumors that can assist pathologists make correct diagnoses.

Stability-based comparison of class discovery methods for DNA copy number profiles

MOTIVATION

Array-CGH can be used to determine DNA copy number, imbalances in which are a fundamental factor in the genesis and progression of tumors. The discovery of classes with similar patterns of array-CGH profiles therefore adds to our understanding of cancer and the treatment of patients. Various input data representations for array-CGH, dissimilarity measures between tumor samples and clustering algorithms may be used for this purpose. The choice between procedures is often difficult. An evaluation procedure is therefore required to select the best class discovery method (combination of one input data representation, one dissimilarity measure and one clustering algorithm) for array-CGH. Robustness of the resulting classes is a common requirement, but no stability-based comparison of class discovery methods for array-CGH profiles has ever been reported.

RESULTS

We applied several class discovery methods and evaluated the stability of their solutions, with a modified version of Bertoni's [Formula: see text]-based test [1]. Our version relaxes the assumption of independency required by original Bertoni's [Formula: see text]-based test. We conclude that Minimal Regions of alteration (a concept introduced by [2]) for input data representation, sim [3] or agree [4] for dissimilarity measure and the use of average group distance in the clustering algorithm produce the most robust classes of array-CGH profiles.

AVAILABILITY

The software is available from http://bioinfo.curie.fr/projects/cgh-clustering. It has also been partly integrated into "Visualization and analysis of array-CGH"(VAMP)[5]. The data sets used are publicly available from ACTuDB [6].

The novel myxofibrosarcoma cell line MUG-Myx1 expresses a tumourigenic stem-like cell population with high aldehyde dehydrogenase 1 activity

BACKGROUND

Myxofibrosarcoma comprises a spectrum of malignant neoplasms withprominent myxoid stromata, cellular pleomorphism, and distinct curvilinear vascular patterns. These neoplasms mainly affect patients in the sixth to eighth decades of life and the overall 5-year survival rate is 60-70%.

METHODS

After the establishment of the novel myxofibrosarcoma cell lines MUG-Myx1, cells were characterized using short tandem repeat (STR), copy number variation (CNV), and genotype/loss-of-heterozygosity (LOH) analyses. The growth behaviour of the cells was analyzed with the xCELLigence system and an MTS assay. The tumourigenicity of MUG-Myx1 was proved in NOD/SCID mice. Additionally, a stem-like cell population with high enzymatic activity of aldehyde dehydrogenase 1 (ALDH1(high)) was isolated for the first time from myxofibrosarcoma cells using the Aldefluor® assay followed by FACS analysis.

RESULTS

The frozen primary parental tumour tissue and the MUG-Myx1 cell line showed the same STR profile at the markers D3S1358, TH01, D21S11, D18S51, Penta E, D5S818, D13S317, D7S820, D16S539, CSF1PO, Penta D, Amelogenin, D8S1179, TPOX, and FGY. Typically, myxofibrosarcoma gain and/or amplification was mapped to 7p21.3-q31.1, q31.1-q31.33, q33-q36.2, p21.3, p21.2, p14.1-q11.23, q31.33-q33, p21.2-p14.1, q11.23-q21.3, q36.2-q36.3, which, respectively are known to harbour tumour-associated genes, including TIF, BRAF, MLL3, SMO, and MET. Typically an LOH for myxofibrosarcoma on chr5 q21 was found. In addition, MUG-Myx1 ALDH1(high) cells showed an upregulation of the ABC transporter ABCB1 and ABCG2; higher c-Myc, E-cadherin and SOX-2 expression; and a higher potential for tumourigenicity and proliferation levels.

CONCLUSION

The new myxofibrosarcoma cell line MUG-Myx1 was established to enrich the bank of publicly available cell lines, with respect to providing comprehensive genetic and epigenetic characterization. Furthermore, because of their tumourigenicity, the cell line is also suitable for in vivo experiments.

Increased expression of matrix extracellular phosphoglycoprotein (MEPE) in cortical bone of the rat tibia after mechanical loading: identification by oligonucleotide microarray

Skeletal integrity in humans and animals is maintained by daily mechanical loading. It has been widely accepted that osteocytes function as mechanosensors. Many biochemical signaling molecules are involved in the response of osteocytes to mechanical stimulation. The aim of this study was to identify genes involved in the translation of mechanical stimuli into bone formation. The four-point bending model was used to induce a single period of mechanical loading on the right tibia, while the contra lateral left tibia served as control. Six hours after loading, the effects of mechanical loading on gene-expression were determined with microarray analysis. Protein expression of differentially regulated genes was evaluated with immunohistochemistry. Nine genes were found to exhibit a significant differential gene expression in LOAD compared to control. MEPE, Garnl1, V2R2B, and QFG-TN1 olfactory receptor were up-regulated, and creatine kinase (muscle form), fibrinogen-B beta-polypeptide, monoamine oxidase A, troponin-C and kinesin light chain-C were down-regulated. Validation with real-time RT-PCR analysis confirmed the up-regulation of MEPE and the down-regulation of creatine kinase (muscle form) and troponin-C in the loaded tibia. Immunohistochemistry showed that the increase of MEPE protein expression was already detectable six hours after mechanical loading. In conclusion, these genes probably play a role during translation of mechanical stimuli six hours after mechanical loading. The modulation of MEPE expression may indicate a connection between bone mineralization and bone formation after mechanical stimulation.

Recurrent inactivation of STAG2 in bladder cancer is not associated with aneuploidy

Urothelial bladder cancer (UBC) is heterogeneous at the clinical, pathological, and genetic levels. Tumor invasiveness (T) and grade (G) are the main factors associated with outcome and determine patient management (1). A discovery exome sequencing screen (n=17), followed by a prevalence screen (n=60), identified new genes mutated in this tumor coding for proteins involved in chromatin modification (MLL2, ASXL2, BPTF), cell division (STAG2, SMC1A, SMC1B), and DNA repair (ATM, ERCC2, FANCA). STAG2, a subunit of cohesin, was significantly and commonly mutated/lost in UBC, mainly in tumors of low stage/grade, and its loss was associated with improved outcome. Loss of expression was often observed in chromosomally-stable tumors and STAG2 knockdown in bladder cancer cells did not increase aneuploidy. STAG2 reintroduction in non-expressing cells led to reduced colony formation. Our findings indicate that STAG2 is a novel UBC tumor suppressor acting through mechanisms that are different from its role to prevent aneuploidy.

Use of autocorrelation scanning in DNA copy number analysis

MOTIVATION

Data quality is a critical issue in the analyses of DNA copy number alterations obtained from microarrays. It is commonly assumed that copy number alteration data can be modeled as piecewise constant and the measurement errors of different probes are independent. However, these assumptions do not always hold in practice. In some published datasets, we find that measurement errors are highly correlated between probes that interrogate nearby genomic loci, and the piecewise-constant model does not fit the data well. The correlated errors cause problems in downstream analysis, leading to a large number of DNA segments falsely identified as having copy number gains and losses.

METHOD

We developed a simple tool, called autocorrelation scanning profile, to assess the dependence of measurement error between neighboring probes.

RESULTS

Autocorrelation scanning profile can be used to check data quality and refine the analysis of DNA copy number data, which we demonstrate in some typical datasets.

CONTACT

lzhangli@mdanderson.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.

A review of advanced genetic testing for clinical prognostication in uveal melanoma

Uveal melanoma (UM) has a strong propensity to metastasize and the prognosis for metastatic disease is very poor. It has been suggested that occult micrometastases are already present, but undetectable, in many patients at the time when the primary ocular tumor is diagnosed and treated. To identify high-risk patients for close monitoring and early intervention with prophylactic adjuvant systemic therapy, an accurate predictive system is necessary for stratifying those patients at risk of developing metastatic disease. To date, many clinical and histopathological features, molecular pathway characteristics, and genetic fingerprints of UM have been suggested for disease prognostication. Among the newest of them, tumor genetics has received the most attention in demonstrating promise as a prognostic tool. Because of the plethora of recent developments, we summarize and compare in this review the important standard and more advanced cytogenetic prognostic markers. We further describe the variety of genetic tests available for prognostication of UM, and provide a critical assessment of the respective advantages and disadvantages of these tools.

Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data

We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regression conveniently allows us to include additional sample specific covariates such as biological conditions and clinical outcomes. The two developed methods are aimed respectively to make inference on differential behaviour of genes in patients showing different subtypes of breast cancer and to predict the pathological complete response (pCR) of patients borrowing strength across the genomic platforms. Posterior inference is carried out via MCMC simulations. We demonstrate the proposed methodology using a published data set consisting of 121 breast cancer patients.

CNVinspector: a web-based tool for the interactive evaluation of copy number variations in single patients and in cohorts

OBJECTIVES

Many genetic disorders are caused by copy number variations (CNVs) in the human genome. However, the large number of benign CNV polymorphisms makes it difficult to delineate causative variants for a certain disease phenotype. Hence, we set out to create software that accumulates and visualises locus-specific knowledge and enables clinicians to study their own CNVs in the context of known polymorphisms and disease variants.

METHODS

CNV data from healthy cohorts (Database of Genomic Variants) and from disease-related databases (DECIPHER) were integrated into a joint resource. Data are presented in an interactive web-based application that allows inspection, evaluation and filtering of CNVs in single individuals or in entire cohorts.

RESULTS

CNVinspector provides simple interfaces to upload CNV data, compare them with own or published control data and visualise the results in graphical interfaces. Beyond choosing control data from different public studies, platforms and methods, dedicated filter options allow the detection of CNVs that are either enriched in patients or depleted in controls. Alternatively, a search can be restricted to those CNVs that appear in individuals of similar clinical phenotype. For each gene of interest within a CNV, we provide a link to NCBI, ENSEMBL and the GeneDistiller search engine to browse for potential disease-associated genes.

CONCLUSIONS

With its user-friendly handling, the integration of control data and the filtering options, CNVinspector will facilitate the daily work of clinical geneticists and accelerate the delineation of new syndromes and gene functions. CNVinspector is freely accessible under http://www.cnvinspector.org.

Copy number variants in locally raised Chinese chicken genomes determined using array comparative genomic hybridization

Background

Copy number variants contribute to genetic variation in birds. Analyses of copy number variants in chicken breeds had focused primarily on those from commercial varieties with nothing known about the occurrence and diversity of copy number variants in locally raised Chinese chicken breeds. To address this deficiency, we characterized copy number variants in 11 chicken breeds and compared the variation among these breeds.

Results

We presented a detailed analysis of the copy number variants in locally raised Chinese chicken breeds identified using a customized comparative genomic hybridization array. We identified 833 copy number variants contained within 308 copy number variant regions. The median and mean sizes of the copy number variant regions were 14.6 kb and 35.1 kb, respectively. Of the copy number variant regions, 138 (45%) involved gain of DNA, 159 (52%) involved loss of DNA, and 11 (3%) involved both gain and loss of DNA. Principal component analysis and agglomerative hierarchical clustering revealed the close relatedness of the four locally raised chicken breeds, Shek-Ki, Langshan, Qingyuan partridge, and Wenchang. Biological process enrichment analysis of the copy number variant regions confirmed the greater variation among the four aforementioned varieties than among the seven other breeds studied.

Conclusion

Our description of the distribution of the copy number variants and comparison of the differences among the copy number variant regions of the 11 chicken breeds supplemented the information available concerning the copy number variants of other Chinese chicken breeds. In addition to its relevance for functional analysis, our results provided the first insight into how chicken breeds can be clustered on the basis of their genomic copy number variation.

ASS1 as a novel tumor suppressor gene in myxofibrosarcomas: aberrant loss via epigenetic DNA methylation confers aggressive phenotypes, negative prognostic impact, and therapeutic relevance

PURPOSE

The principal goals were to identify and validate targetable metabolic drivers relevant to myxofibrosarcoma pathogenesis using a published transcriptome.

EXPERIMENTAL DESIGN

As the most significantly downregulated gene regulating amino acid metabolism, argininosuccinate synthetase (ASS1) was selected for further analysis by methylation-specific PCR, pyrosequencing, and immunohistochemistry of myxofibrosarcoma samples. The roles of ASS1 in tumorigenesis and the therapeutic relevance of the arginine-depriving agent pegylated arginine deiminase (ADI-PEG20) were elucidated in ASS1-deficient myxofibrosarcoma cell lines and xenografts with and without stable ASS1 reexpression.

RESULTS

ASS1 promoter hypermethylation was detected in myxofibrosarcoma samples and cell lines and was strongly linked to ASS1 protein deficiency. The latter correlated with increased tumor grade and stage and independently predicted a worse survival. ASS1-deficient cell lines were auxotrophic for arginine and susceptible to ADI-PEG20 treatment, with dose-dependent reductions in cell viability and tumor growth attributable to cell-cycle arrest in the S-phase. ASS1 expression was restored in 2 of 3 ASS1-deficient myxofibrosarcoma cell lines by 5-aza-2'-deoxycytidine, abrogating the inhibitory effect of ADI-PEG20. Conditioned media following ASS1 reexpression attenuated HUVEC tube-forming capability, which was associated with suppression of MMP-9 and an antiangiogenic effect in corresponding myxofibrosarcoma xenografts. In addition to delayed wound closure and fewer invading cells in a Matrigel assay, ASS1 reexpression reduced tumor cell proliferation, induced G1-phase arrest, and downregulated cyclin E with corresponding growth inhibition in soft agar and xenograft assays.

CONCLUSIONS

Our findings highlight ASS1 as a novel tumor suppressor in myxofibrosarcomas, with loss of expression linked to promoter methylation, clinical aggressiveness, and sensitivity to ADI-PEG20.

Array-based comparative genomic hybridization-guided identification of reference genes for normalization of real-time quantitative polymerase chain reaction assay data for lymphomas, histiocytic sarcomas, and osteosarcomas of dogs

OBJECTIVE

To identify suitable reference genes for normalization of real-time quantitative PCR (RT-qPCR) assay data for common tumors of dogs.

SAMPLE

Malignant lymph node (n = 8), appendicular osteosarcoma (9), and histiocytic sarcoma (12) samples and control samples of various nonneoplastic canine tissues.

PROCEDURES

Array-based comparative genomic hybridization (aCGH) data were used to guide selection of 9 candidate reference genes. Expression stability of candidate reference genes and 4 commonly used reference genes was determined for tumor samples with RT-qPCR assays and 3 software programs.

RESULTS

LOC611555 was the candidate reference gene with the highest expression stability among the 3 tumor types. Of the commonly used reference genes, expression stability of HPRT was high in histiocytic sarcoma samples, and expression stability of Ubi and RPL32 was high in osteosarcoma samples. Some of the candidate reference genes had higher expression stability than did the commonly used reference genes.

CONCLUSIONS AND CLINICAL RELEVANCE

Data for constitutively expressed genes with high expression stability are required for normalization of RT-qPCR assay results. Without such data, accurate quantification of gene expression in tumor tissue samples is difficult. Results of the present study indicated LOC611555 may be a useful RT-qPCR assay reference gene for multiple tissue types. Some commonly used reference genes may be suitable for normalization of gene expression data for tumors of dogs, such as lymphomas, osteosarcomas, or histiocytic sarcomas.

Fabrication of oligonucleotide and protein arrays on rigid and flexible substrates coated with reactive polymer multilayers

We report a top-down approach to the fabrication of oligonucleotide and protein arrays on surfaces coated with ultrathin, amine-reactive polymer multilayers fabricated by the covalent "layer-by-layer" (LbL) assembly of polyethyleneimine (PEI) and the amine-reactive, azlactone-functionalized polymer poly(2-vinyl-4,4-dimethylazlactone) (PVDMA). Manual spotting of amine-terminated oligonucleotide probe sequences on planar glass slides coated with PEI/PVDMA multilayers (~35 nm thick) yielded arrays of immobilized probes that hybridized fluorescently labeled complementary sequences with high signal intensities, high signal-to-noise ratios, and high sequence specificity. Treatment of residual azlactone functionality with the nonfouling small-molecule amine d-glucamine resulted in regions between the features of these arrays that resisted adsorption of protein and permitted hybridization in complex media containing up to 10 mg/mL protein. The residual azlactone groups in these films were also exploited to immobilize proteins on film-coated surfaces and fabricate functional arrays of proteins and enzymes. The ability to deposit PEI/PVDMA multilayers on substrates of arbitrary size, shape, and composition permitted the fabrication of arrays of oligonucleotides on the surfaces of multilayer-coated sheets of poly(ethylene terephthalate) and heat-shrinkable polymer film. Arrays fabricated on these flexible plastic substrates can be bent, cut, resized, and manipulated physically in ways that are difficult using more conventional rigid substrates. This approach could thus contribute to the development of new assay formats and new applications of biomolecule arrays. The methods described here are straightforward to implement, do not require access to specialized equipment, and should also be compatible with automated liquid-handling methods used to fabricate higher-density arrays of oligonucleotides and proteins on more traditional surfaces.

Array comparative genomic hybridization: an overview of protocols, applications, and technology trends

From the earliest observations of human chromosomes in the late 1800s to modern day next generation sequencing technologies, much has been learned about human cancers by the vigorous application of the techniques of the day. In general, resolution has improved tremendously, and correspondingly the size of the datasets generated has grown exponentially such that computational methods required to handle massive datasets have had to be devised. This chapter provides a brief synopsis of the evolution of such techniques as an introduction to the subsequent chapters that provide methods and applications, relevant to research, and clinical diagnostics.

Genomic profiling of mantle cell lymphoma

Genomic profiling of mantle cell lymphoma (MCL) cells has enabled a better understanding of the complex mechanisms underlying the pathogenesis of disease. Besides the t(11;14)(q13;q32) leading to cyclin D1 overexpression, MCL exhibits a characteristic pattern of DNA copy number aberrations that differs from those detected in other B-cell lymphomas. These genomic changes disrupt selected oncogenes and suppressor genes that are required for lymphoma development and progression, many of which are components of cell cycle, DNA damage response and repair, apoptosis, and cell-signaling pathways. Additionally, some of them may represent effective therapeutic targets. A number of genomic and molecular abnormalities have been correlated with the clinical outcome of patients with MCL and are considered prognostic factors. However, only a few genomic markers have been shown to predict the response to current or novel targeted therapies. One representative example is the high-level amplification of the BCL2 gene, which predicts a good response to pro-apoptotic BH3 mimetic drugs. In summary, genomic analyses have contributed to the substantial advances made in the comprehension of the pathogenesis of MCL, providing a solid basis for the identification of optimal therapeutic targets and for the design of new molecular therapies aiming to cure this fatal disease.

Array CGH in brain tumors

Alterations in the copy number of the cancer genome are frequently observed in brain tumors especially gliomas. Some pertinent examples include amplification of the EGFR locus in chromosome 7p and loss of the PTEN locus in 10q in glioblastoma. Meningiomas are often associated with loss of the NF2 locus in 22q. Array CGH or aCGH probes provide a reliable, consistent, and economical method of profiling genome-wide copy number alterations (CNAs) of cancer specimens at fairly robust resolution. This has allowed for the systematic assessment of brain tumors for recurrent genomic CNAs. In addition, recent technical advancements have increased the robustness of this technique to accommodate DNA derived from formalin-fixed paraffin-embedded (FFPE) tissue. Lastly, novel technologies such as next-generation sequencing and multiplex digital gene counting technology such as NanoString will expand the -repertoire of techniques for assessing CNAs in brain tumors.