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1.  Multisample aCGH Data Analysis via Total Variation and Spectral Regularization 
DNA copy number variation (CNV) accounts for a large proportion of genetic variation. One commonly used approach to detecting CNVs is array-based comparative genomic hybridization (aCGH). Although many methods have been proposed to analyze aCGH data, it is not clear how to combine information from multiple samples to improve CNV detection. In this paper, we propose to use a matrix to approximate the multisample aCGH data and minimize the total variation of each sample as well as the nuclear norm of the whole matrix. In this way, we can make use of the smoothness property of each sample and the correlation among multiple samples simultaneously in a convex optimization framework. We also developed an efficient and scalable algorithm to handle large-scale data. Experiments demonstrate that the proposed method outperforms the state-of-the-art techniques under a wide range of scenarios and it is capable of processing large data sets with millions of probes.
doi:10.1109/TCBB.2012.166
PMCID: PMC3715577  PMID: 23702561
CNV; aCGH; total variation; spectral regularization; convex optimization
2.  aCGHViewer: A Generic Visualization Tool For aCGH data 
Cancer Informatics  2007;2:36-43.
Array-Comparative Genomic Hybridization (aCGH) is a powerful high throughput technology for detecting chromosomal copy number aberrations (CNAs) in cancer, aiming at identifying related critical genes from the affected genomic regions. However, advancing from a dataset with thousands of tabular lines to a few candidate genes can be an onerous and time-consuming process. To expedite the aCGH data analysis process, we have developed a user-friendly aCGH data viewer (aCGHViewer) as a conduit between the aCGH data tables and a genome browser. The data from a given aCGH analysis are displayed in a genomic view comprised of individual chromosome panels which can be rapidly scanned for interesting features. A chromosome panel containing a feature of interest can be selected to launch a detail window for that single chromosome. Selecting a data point of interest in the detail window launches a query to the UCSC or NCBI genome browser to allow the user to explore the gene content in the chromosomal region. Additionally, aCGHViewer can display aCGH and expression array data concurrently to visually correlate the two. aCGHViewer is a stand alone Java visualization application that should be used in conjunction with separate statistical programs. It operates on all major computer platforms and is freely available at http://falcon.roswellpark.org/aCGHview/.
PMCID: PMC1847423  PMID: 17404607
array-CGH; CNA; gene expression; visualization
3.  aCGHViewer: A Generic Visualization Tool For aCGH data 
Cancer informatics  2006;2:36-43.
Array-Comparative Genomic Hybridization (aCGH) is a powerful high throughput technology for detecting chromosomal copy number aberrations (CNAs) in cancer, aiming at identifying related critical genes from the affected genomic regions. However, advancing from a dataset with thousands of tabular lines to a few candidate genes can be an onerous and time-consuming process. To expedite the aCGH data analysis process, we have developed a user-friendly aCGH data viewer (aCGHViewer) as a conduit between the aCGH data tables and a genome browser. The data from a given aCGH analysis are displayed in a genomic view comprised of individual chromosome panels which can be rapidly scanned for interesting features. A chromosome panel containing a feature of interest can be selected to launch a detail window for that single chromosome. Selecting a data point of interest in the detail window launches a query to the UCSC or NCBI genome browser to allow the user to explore the gene content in the chromosomal region. Additionally, aCGHViewer can display aCGH and expression array data concurrently to visually correlate the two. aCGHViewer is a stand alone Java visualization application that should be used in conjunction with separate statistical programs. It operates on all major computer platforms and is freely available at http://falcon.roswellpark.org/aCGHview/.
PMCID: PMC1847423  PMID: 17404607
array-CGH; CNA; gene expression; visualization
4.  Detection of divergent genes in microbial aCGH experiments 
BMC Bioinformatics  2006;7:181.
Background
Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to an index strain. Development of methods for analyzing aCGH data has primarily focused on copy number abberations in cancer research. In microbial aCGH analyses, genes are typically ranked by log-ratios, and classification into divergent or present is done by choosing a cutoff log-ratio, either manually or by statistics calculated from the log-ratio distribution. As experimental settings vary considerably, it is not possible to develop a classical discriminant or statistical learning approach.
Methods
We introduce a more efficient method for analyzing microbial aCGH data using a finite mixture model and a data rotation scheme. Using the average posterior probabilities from the model fitted to log-ratios before and after rotation, we get a score for each gene, and demonstrate its advantages for ranking and detecting divergent genes with enlarged specificity and sensitivity.
Results
The procedure is tested and compared to other approaches on simulated data sets, as well as on four experimental validation data sets for aCGH analysis on fully sequenced strains of Staphylococcus aureus and Streptococcus pneumoniae.
Conclusion
When tested on simulated data as well as on four different experimental validation data sets from experiments with only fully sequenced strains, our procedure out-competes the standard procedures of using a simple log-ratio cutoff for classification into present and divergent genes.
doi:10.1186/1471-2105-7-181
PMCID: PMC1563484  PMID: 16573812
5.  A Bayesian Analysis for Identifying DNA Copy Number Variations Using a Compound Poisson Process 
To study chromosomal aberrations that may lead to cancer formation or genetic diseases, the array-based Comparative Genomic Hybridization (aCGH) technique is often used for detecting DNA copy number variants (CNVs). Various methods have been developed for gaining CNVs information based on aCGH data. However, most of these methods make use of the log-intensity ratios in aCGH data without taking advantage of other information such as the DNA probe (e.g., biomarker) positions/distances contained in the data. Motivated by the specific features of aCGH data, we developed a novel method that takes into account the estimation of a change point or locus of the CNV in aCGH data with its associated biomarker position on the chromosome using a compound Poisson process. We used a Bayesian approach to derive the posterior probability for the estimation of the CNV locus. To detect loci of multiple CNVs in the data, a sliding window process combined with our derived Bayesian posterior probability was proposed. To evaluate the performance of the method in the estimation of the CNV locus, we first performed simulation studies. Finally, we applied our approach to real data from aCGH experiments, demonstrating its applicability.
doi:10.1155/2010/268513
PMCID: PMC3171362  PMID: 20976296
6.  A probe-density-based analysis method for array CGH data: simulation, normalization and centralization 
Bioinformatics  2008;24(16):1749-1756.
Motivation: Genomic instability is one of the fundamental factors in tumorigenesis and tumor progression. Many studies have shown that copy-number abnormalities at the DNA level are important in the pathogenesis of cancer. Array comparative genomic hybridization (aCGH), developed based on expression microarray technology, can reveal the chromosomal aberrations in segmental copies at a high resolution. However, due to the nature of aCGH, many standard expression data processing tools, such as data normalization, often fail to yield satisfactory results.
Results: We demonstrated a novel aCGH normalization algorithm, which provides an accurate aCGH data normalization by utilizing the dependency of neighboring probe measurements in aCGH experiments. To facilitate the study, we have developed a hidden Markov model (HMM) to simulate a series of aCGH experiments with random DNA copy number alterations that are used to validate the performance of our normalization. In addition, we applied the proposed normalization algorithm to an aCGH study of lung cancer cell lines. By using the proposed algorithm, data quality and the reliability of experimental results are significantly improved, and the distinct patterns of DNA copy number alternations are observed among those lung cancer cell lines.
Contact: chuangey@ntu.edu.tw
Supplementary information: Source codes and.gures may be found at http://ntumaps.cgm.ntu.edu.tw/aCGH_supplementary
doi:10.1093/bioinformatics/btn321
PMCID: PMC2732214  PMID: 18603568
7.  DETECTING COPY NUMBER VARIATIONS FROM ARRAY CGH DATA BASED ON A CONDITIONAL RANDOM FIELD MODEL 
Array comparative genomic hybridization (aCGH) allows identification of copy number alterations across genomes. The key computational challenge in analyzing copy number variations (CNVs) using aCGH data or other similar data generated by a variety of array technologies is the detection of segment boundaries of copy number changes and inference of the copy number state for each segment. We have developed a novel statistical model based on the framework of conditional random fields (CRFs) that can effectively combine data smoothing, segmentation and copy number state decoding into one unified framework. Our approach (termed CRF-CNV) provides great flexibilities in defining meaningful feature functions. Therefore, it can effectively integrate local spatial information of arbitrary sizes into the model. For model parameter estimations, we have adopted the conjugate gradient (CG) method for likelihood optimization and developed efficient forward/backward algorithms within the CG framework. The method is evaluated using real data with known copy numbers as well as simulated data with realistic assumptions, and compared with two popular publicly available programs. Experimental results have demonstrated that CRF-CNV outperforms a Bayesian Hidden Markov Model-based approach on both datasets in terms of copy number assignments. Comparing to a non-parametric approach, CRF-CNV has achieved much greater precision while maintaining the same level of recall on the real data, and their performance on the simulated data is comparable.
PMCID: PMC3326659  PMID: 20401947
Array comparative genomic hybridization; copy number variations; conditional random fields
8.  A hidden Markov model-based algorithm for identifying tumour subtype using array CGH data 
BMC Genomics  2011;12(Suppl 5):S10.
Background
The recent advancement in array CGH (aCGH) research has significantly improved tumor identification using DNA copy number data. A number of unsupervised learning methods have been proposed for clustering aCGH samples. Two of the major challenges for developing aCGH sample clustering are the high spatial correlation between aCGH markers and the low computing efficiency. A mixture hidden Markov model based algorithm was developed to address these two challenges.
Results
The hidden Markov model (HMM) was used to model the spatial correlation between aCGH markers. A fast clustering algorithm was implemented and real data analysis on glioma aCGH data has shown that it converges to the optimal cluster rapidly and the computation time is proportional to the sample size. Simulation results showed that this HMM based clustering (HMMC) method has a substantially lower error rate than NMF clustering. The HMMC results for glioma data were significantly associated with clinical outcomes.
Conclusions
We have developed a fast clustering algorithm to identify tumor subtypes based on DNA copy number aberrations. The performance of the proposed HMMC method has been evaluated using both simulated and real aCGH data. The software for HMMC in both R and C++ is available in ND INBRE website http://ndinbre.org/programs/bioinformatics.php.
doi:10.1186/1471-2164-12-S5-S10
PMCID: PMC3287492  PMID: 22369459
9.  Gene-resolution analysis of DNA copy number variation using oligonucleotide expression microarrays 
BMC Genomics  2007;8:111.
Background
Array-based comparative genomic hybridization (aCGH) is a high-throughput method for measuring genome-wide DNA copy number changes. Current aCGH methods have limited resolution, sensitivity and reproducibility. Microarrays for aCGH are available only for a few organisms and combination of aCGH data with expression data is cumbersome.
Results
We present a novel method of using commercial oligonucleotide expression microarrays for aCGH, enabling DNA copy number measurements and expression profiles to be combined using the same platform. This method yields aCGH data from genomic DNA without complexity reduction at a median resolution of approximately 17,500 base pairs. Due to the well-defined nature of oligonucleotide probes, DNA amplification and deletion can be defined at the level of individual genes and can easily be combined with gene expression data.
Conclusion
A novel method of gene resolution analysis of copy number variation (graCNV) yields high-resolution maps of DNA copy number changes and is applicable to a broad range of organisms for which commercial oligonucleotide expression microarrays are available. Due to the standardization of oligonucleotide microarrays, graCNV results can reliably be compared between laboratories and can easily be combined with gene expression data using the same platform.
doi:10.1186/1471-2164-8-111
PMCID: PMC1868757  PMID: 17470268
10.  A Multi-Sample Based Method for Identifying Common CNVs in Normal Human Genomic Structure Using High-Resolution aCGH Data 
PLoS ONE  2011;6(10):e26975.
Background
It is difficult to identify copy number variations (CNV) in normal human genomic data due to noise and non-linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH) containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples. Normal human genome analysis often requires a joint approach across multiple samples. However, the majority of existing methods can only identify CNVs from a single sample.
Methodology and Principal Findings
We developed a multi-sample-based genomic variations detector (MGVD) that uses segmentation to identify common breakpoints across multiple samples and a k-means-based clustering strategy. Unlike previous methods, MGVD simultaneously considers multiple samples with different genomic intensities and identifies CNVs and CNV zones (CNVZs); CNVZ is a more precise measure of the location of a genomic variant than the CNV region (CNVR).
Conclusions and Significance
We designed a specialized algorithm to detect common CNVs from extremely high-resolution multi-sample aCGH data. MGVD showed high sensitivity and a low false discovery rate for a simulated data set, and outperformed most current methods when real, high-resolution HapMap datasets were analyzed. MGVD also had the fastest runtime compared to the other algorithms evaluated when actual, high-resolution aCGH data were analyzed. The CNVZs identified by MGVD can be used in association studies for revealing relationships between phenotypes and genomic aberrations. Our algorithm was developed with standard C++ and is available in Linux and MS Windows format in the STL library. It is freely available at: http://embio.yonsei.ac.kr/~Park/mgvd.php.
doi:10.1371/journal.pone.0026975
PMCID: PMC3205051  PMID: 22073121
11.  A robust penalized method for the analysis of noisy DNA copy number data 
BMC Genomics  2010;11:517.
Background
Deletions and amplifications of the human genomic DNA copy number are the causes of numerous diseases, such as, various forms of cancer. Therefore, the detection of DNA copy number variations (CNV) is important in understanding the genetic basis of many diseases. Various techniques and platforms have been developed for genome-wide analysis of DNA copy number, such as, array-based comparative genomic hybridization (aCGH) and high-resolution mapping with high-density tiling oligonucleotide arrays. Since complicated biological and experimental processes are often associated with these platforms, data can be potentially contaminated by outliers.
Results
We propose a penalized LAD regression model with the adaptive fused lasso penalty for detecting CNV. This method contains robust properties and incorporates both the spatial dependence and sparsity of CNV into the analysis. Our simulation studies and real data analysis indicate that the proposed method can correctly detect the numbers and locations of the true breakpoints while appropriately controlling the false positives.
Conclusions
The proposed method has three advantages for detecting CNV change points: it contains robustness properties; incorporates both spatial dependence and sparsity; and estimates the true values at each marker accurately.
doi:10.1186/1471-2164-11-517
PMCID: PMC3247090  PMID: 20868505
12.  Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH 
PLoS Computational Biology  2007;3(6):e122.
Genomic DNA copy-number alterations (CNAs) are associated with complex diseases, including cancer: CNAs are indeed related to tumoral grade, metastasis, and patient survival. CNAs discovered from array-based comparative genomic hybridization (aCGH) data have been instrumental in identifying disease-related genes and potential therapeutic targets. To be immediately useful in both clinical and basic research scenarios, aCGH data analysis requires accurate methods that do not impose unrealistic biological assumptions and that provide direct answers to the key question, “What is the probability that this gene/region has CNAs?” Current approaches fail, however, to meet these requirements. Here, we introduce reversible jump aCGH (RJaCGH), a new method for identifying CNAs from aCGH; we use a nonhomogeneous hidden Markov model fitted via reversible jump Markov chain Monte Carlo; and we incorporate model uncertainty through Bayesian model averaging. RJaCGH provides an estimate of the probability that a gene/region has CNAs while incorporating interprobe distance and the capability to analyze data on a chromosome or genome-wide basis. RJaCGH outperforms alternative methods, and the performance difference is even larger with noisy data and highly variable interprobe distance, both commonly found features in aCGH data. Furthermore, our probabilistic method allows us to identify minimal common regions of CNAs among samples and can be extended to incorporate expression data. In summary, we provide a rigorous statistical framework for locating genes and chromosomal regions with CNAs with potential applications to cancer and other complex human diseases.
Author Summary
As a consequence of problems during cell division, the number of copies of a gene in a chromosome can either increase or decrease. These copy-number alterations (CNAs) can play a crucial role in the emergence of complex multigenic diseases. For example, in cancer, amplification of oncogenes can drive tumor activation, and CNAs are associated with metastasis development and patient survival. Studies on the relationship between CNAs and disease have been recently fueled by the widespread use of array-based comparative genomic hybridization (aCGH), a technique with much finer resolution than previous experimental approaches. Detection of CNAs from these data depends on methods of analysis that do not impose biologically unrealistic assumptions and that provide direct answers to fundamental research questions. We have developed a statistical method, using a Bayesian approach, that returns estimates of the probabilities of CNAs from aCGH data, the most direct and valuable answer to the key biological question: “What is the probability that this gene/region has an altered copy number?” The output of the method can therefore be immediately used in different settings from clinical to basic research scenarios, and is applicable over a wide variety of aCGH technologies.
doi:10.1371/journal.pcbi.0030122
PMCID: PMC1894821  PMID: 17590078
13.  Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH 
PLoS Computational Biology  2007;3(6):e122.
Genomic DNA copy-number alterations (CNAs) are associated with complex diseases, including cancer: CNAs are indeed related to tumoral grade, metastasis, and patient survival. CNAs discovered from array-based comparative genomic hybridization (aCGH) data have been instrumental in identifying disease-related genes and potential therapeutic targets. To be immediately useful in both clinical and basic research scenarios, aCGH data analysis requires accurate methods that do not impose unrealistic biological assumptions and that provide direct answers to the key question, “What is the probability that this gene/region has CNAs?” Current approaches fail, however, to meet these requirements. Here, we introduce reversible jump aCGH (RJaCGH), a new method for identifying CNAs from aCGH; we use a nonhomogeneous hidden Markov model fitted via reversible jump Markov chain Monte Carlo; and we incorporate model uncertainty through Bayesian model averaging. RJaCGH provides an estimate of the probability that a gene/region has CNAs while incorporating interprobe distance and the capability to analyze data on a chromosome or genome-wide basis. RJaCGH outperforms alternative methods, and the performance difference is even larger with noisy data and highly variable interprobe distance, both commonly found features in aCGH data. Furthermore, our probabilistic method allows us to identify minimal common regions of CNAs among samples and can be extended to incorporate expression data. In summary, we provide a rigorous statistical framework for locating genes and chromosomal regions with CNAs with potential applications to cancer and other complex human diseases.
Author Summary
As a consequence of problems during cell division, the number of copies of a gene in a chromosome can either increase or decrease. These copy-number alterations (CNAs) can play a crucial role in the emergence of complex multigenic diseases. For example, in cancer, amplification of oncogenes can drive tumor activation, and CNAs are associated with metastasis development and patient survival. Studies on the relationship between CNAs and disease have been recently fueled by the widespread use of array-based comparative genomic hybridization (aCGH), a technique with much finer resolution than previous experimental approaches. Detection of CNAs from these data depends on methods of analysis that do not impose biologically unrealistic assumptions and that provide direct answers to fundamental research questions. We have developed a statistical method, using a Bayesian approach, that returns estimates of the probabilities of CNAs from aCGH data, the most direct and valuable answer to the key biological question: “What is the probability that this gene/region has an altered copy number?” The output of the method can therefore be immediately used in different settings from clinical to basic research scenarios, and is applicable over a wide variety of aCGH technologies.
doi:10.1371/journal.pcbi.0030122
PMCID: PMC1894821  PMID: 17590078
14.  CGHnormaliter: an iterative strategy to enhance normalization of array CGH data with imbalanced aberrations 
BMC Genomics  2009;10:401.
Background
Array comparative genomic hybridization (aCGH) is a popular technique for detection of genomic copy number imbalances. These play a critical role in the onset of various types of cancer. In the analysis of aCGH data, normalization is deemed a critical pre-processing step. In general, aCGH normalization approaches are similar to those used for gene expression data, albeit both data-types differ inherently. A particular problem with aCGH data is that imbalanced copy numbers lead to improper normalization using conventional methods.
Results
In this study we present a novel method, called CGHnormaliter, which addresses this issue by means of an iterative normalization procedure. First, provisory balanced copy numbers are identified and subsequently used for normalization. These two steps are then iterated to refine the normalization. We tested our method on three well-studied tumor-related aCGH datasets with experimentally confirmed copy numbers. Results were compared to a conventional normalization approach and two more recent state-of-the-art aCGH normalization strategies. Our findings show that, compared to these three methods, CGHnormaliter yields a higher specificity and precision in terms of identifying the 'true' copy numbers.
Conclusion
We demonstrate that the normalization of aCGH data can be significantly enhanced using an iterative procedure that effectively eliminates the effect of imbalanced copy numbers. This also leads to a more reliable assessment of aberrations. An R-package containing the implementation of CGHnormaliter is available at .
doi:10.1186/1471-2164-10-401
PMCID: PMC2748095  PMID: 19709427
15.  The development of a mini-array for estimating the disease state of gastric adenocarcinoma by array CGH 
BMC Cancer  2008;8:393.
Background
The treatment strategy usually depends on the disease state in the individual patient. However, it is difficult to estimate the disease state before treatment in many patients. The purpose of this study was to develop a BAC (bacterial artificial chromosome) mini-array allowing for the estimation of node metastasis, liver metastasis, peritoneal dissemination and the depth of tumor invasion in gastric cancers.
Methods
Initially, the DNA copy number aberrations (DCNAs) were analyzed by array-based comparative genomic hybridization (aCGH) in 83 gastric adenocarcinomas as a training-sample set. Next, two independent analytical methods were applied to the aCGH data to identify the BAC clones with DNA copy number aberrations that were linked with the disease states. One of the methods, a decision-tree model classifier, identified 6, 4, 4, 4, and 7 clones for estimating lymph node metastasis, liver metastasis, peritoneal dissemination, depth of tumor invasion, and histological type, respectively. In the other method, a clone-by-clone comparison of the frequency of the DNA copy number aberrations selected 26 clones to estimate the disease states.
Results
By spotting these 50 clones together with 26 frequently or rarely involved clones and 62 reference clones, a mini-array was made to estimate the above parameters, and the diagnostic performance of the mini-array was evaluated for an independent set of 30 gastric cancers (blinded – sample set). In comparison to the clinicopathological features, the overall accuracy was 66.7% for node metastasis, 86.7% for liver metastasis, 86.7% for peritoneal dissemination, and 96.7% for depth of tumor invasion. The intratumoral heterogeneity barely affected the diagnostic performance of the mini-array.
Conclusion
These results suggest that the mini-array makes it possible to determine an optimal treatment for each of the patients with gastric adenocarcinoma.
doi:10.1186/1471-2407-8-393
PMCID: PMC2637883  PMID: 19115996
16.  Multiple samples aCGH analysis for rare CNVs detection 
Background
DNA copy number variations (CNV) constitute an important source of genetic variability. The standard method used for CNV detection is array comparative genomic hybridization (aCGH).
Results
We propose a novel multiple sample aCGH analysis methodology aiming in rare CNVs detection. In contrast to the majority of previous approaches, which deal with cancer datasets, we focus on constitutional genomic abnormalities identified in a diverse spectrum of diseases in human. Our method is tested on exon targeted aCGH array of 366 patients affected with developmental delay/intellectual disability, epilepsy, or autism. The proposed algorithms can be applied as a post–processing filtering to any given segmentation method.
Conclusions
Thanks to the additional information obtained from multiple samples, we could efficiently detect significant segments corresponding to rare CNVs responsible for pathogenic changes. The robust statistical framework applied in our method enables to eliminate the influence of widespread technical artifact termed ‘waves’.
doi:10.1186/2043-9113-3-12
PMCID: PMC3691624  PMID: 23758813
17.  Comparison of chromosomal and array-based comparative genomic hybridization for the detection of genomic imbalances in primary prostate carcinomas 
Molecular Cancer  2006;5:33.
Background
In order to gain new insights into the molecular mechanisms involved in prostate cancer, we performed array-based comparative genomic hybridization (aCGH) on a series of 46 primary prostate carcinomas using a 1 Mbp whole-genome coverage platform. As chromosomal comparative genomic hybridization (cCGH) data was available for these samples, we compared the sensitivity and overall concordance of the two methodologies, and used the combined information to infer the best of three different aCGH scoring approaches.
Results
Our data demonstrate that the reliability of aCGH in the analysis of primary prostate carcinomas depends to some extent on the scoring approach used, with the breakpoint estimation method being the most sensitive and reliable. The pattern of copy number changes detected by aCGH was concordant with that of cCGH, but the higher resolution technique detected 2.7 times more aberrations and 15.2% more carcinomas with genomic imbalances. We additionally show that several aberrations were consistently overlooked using cCGH, such as small deletions at 5q, 6q, 12p, and 17p. The latter were validated by fluorescence in situ hybridization targeting TP53, although only one carcinoma harbored a point mutation in this gene. Strikingly, homozygous deletions at 10q23.31, encompassing the PTEN locus, were seen in 58% of the cases with 10q loss.
Conclusion
We conclude that aCGH can significantly improve the detection of genomic aberrations in cancer cells as compared to previously established whole-genome methodologies, although contamination with normal cells may influence the sensitivity and specificity of some scoring approaches. Our work delineated recurrent copy number changes and revealed novel amplified loci and frequent homozygous deletions in primary prostate carcinomas, which may guide future work aimed at identifying the relevant target genes. In particular, biallelic loss seems to be a frequent mechanism of inactivation of the PTEN gene in prostate carcinogenesis.
doi:10.1186/1476-4598-5-33
PMCID: PMC1570364  PMID: 16952311
18.  Whole-Genome Array CGH Evaluation for Replacing Prenatal Karyotyping in Hong Kong 
PLoS ONE  2014;9(2):e87988.
Objective
To evaluate the effectiveness of whole-genome array comparative genomic hybridization (aCGH) in prenatal diagnosis in Hong Kong.
Methods
Array CGH was performed on 220 samples recruited prospectively as the first-tier test study. In addition 150 prenatal samples with abnormal fetal ultrasound findings found to have normal karyotypes were analyzed as a ‘further-test’ study using NimbleGen CGX-135K oligonucleotide arrays.
Results
Array CGH findings were concordant with conventional cytogenetic results with the exception of one case of triploidy. It was found in the first-tier test study that aCGH detected 20% (44/220) clinically significant copy number variants (CNV), of which 21 were common aneuploidies and 23 had other chromosomal imbalances. There were 3.2% (7/220) samples with CNVs detected by aCGH but not by conventional cytogenetics. In the ‘further-test’ study, the additional diagnostic yield of detecting chromosome imbalance was 6% (9/150). The overall detection for CNVs of unclear clinical significance was 2.7% (10/370) with 0.9% found to be de novo. Eleven loci of common CNVs were found in the local population.
Conclusion
Whole-genome aCGH offered a higher resolution diagnostic capacity than conventional karyotyping for prenatal diagnosis either as a first-tier test or as a ‘further-test’ for pregnancies with fetal ultrasound anomalies. We propose replacing conventional cytogenetics with aCGH for all pregnancies undergoing invasive diagnostic procedures after excluding common aneuploidies and triploidies by quantitative fluorescent PCR. Conventional cytogenetics can be reserved for visualization of clinically significant CNVs.
doi:10.1371/journal.pone.0087988
PMCID: PMC3914896  PMID: 24505343
19.  Automated array-CGH optimized for archival formalin-fixed, paraffin-embedded tumor material 
BMC Cancer  2007;7:43.
Background
Array Comparative Genomic Hybridization (aCGH) is a rapidly evolving technology that still lacks complete standardization. Yet, it is of great importance to obtain robust and reproducible data to enable meaningful multiple hybridization comparisons. Special difficulties arise when aCGH is performed on archival formalin-fixed, paraffin-embedded (FFPE) tissue due to its variable DNA quality. Recently, we have developed an effective DNA quality test that predicts suitability of archival samples for BAC aCGH.
Methods
In this report, we first used DNA from a cancer cell-line (SKBR3) to optimize the aCGH protocol for automated hybridization, and subsequently optimized and validated the procedure for FFPE breast cancer samples. We aimed for highest throughput, accuracy, and reproducibility applicable to FFPE samples, which can also be important in future diagnostic use.
Results
Our protocol of automated array-CGH on archival FFPE ULS-labeled DNA showed very similar results compared with published data and our previous manual hybridization method.
Conclusion
This report combines automated aCGH on unamplified archival FFPE DNA using non-enzymatic ULS labeling, and describes an optimized protocol for this combination resulting in improved quality and reproducibility.
doi:10.1186/1471-2407-7-43
PMCID: PMC1829401  PMID: 17343727
20.  Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data 
Nucleic Acids Research  2008;36(2):e13.
Tumor formation is in part driven by DNA copy number alterations (CNAs), which can be measured using microarray-based Comparative Genomic Hybridization (aCGH). Multiexperiment analysis of aCGH data from tumors allows discovery of recurrent CNAs that are potentially causal to cancer development. Until now, multiexperiment aCGH data analysis has been dependent on discretization of measurement data to a gain, loss or no-change state. Valuable biological information is lost when a heterogeneous system such as a solid tumor is reduced to these states. We have developed a new approach which inputs nondiscretized aCGH data to identify regions that are significantly aberrant across an entire tumor set. Our method is based on kernel regression and accounts for the strength of a probe's signal, its local genomic environment and the signal distribution across multiple tumors. In an analysis of 89 human breast tumors, our method showed enrichment for known cancer genes in the detected regions and identified aberrations that are strongly associated with breast cancer subtypes and clinical parameters. Furthermore, we identified 18 recurrent aberrant regions in a new dataset of 19 p53-deficient mouse mammary tumors. These regions, combined with gene expression microarray data, point to known cancer genes and novel candidate cancer genes.
doi:10.1093/nar/gkm1143
PMCID: PMC2241875  PMID: 18187509
21.  Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data 
Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. We propose a hierarchical Bayesian random segmentation approach for modeling aCGH data that utilizes information across arrays from a common population to yield segments of shared copy number changes. These changes characterize the underlying population and allow us to compare different population aCGH profiles to assess which regions of the genome have differential alterations. Our method, referred to as BDSAcgh (Bayesian Detection of Shared Aberrations in aCGH), is based on a unified Bayesian hierarchical model that allows us to obtain probabilities of alteration states as well as probabilities of differential alteration that correspond to local false discovery rates. We evaluate the operating characteristics of our method via simulations and an application using a lung cancer aCGH data set.
doi:10.1198/jasa.2010.ap09250
PMCID: PMC3079218  PMID: 21512611
Bayesian methods; Comparative Genomic Hybridization; Copy number; Functional data analysis; Mixed Models; Mixture Models
22.  Clinical use of array comparative genomic hybridization (aCGH) for prenatal diagnosis in 300 cases† 
Prenatal diagnosis  2009;29(1):29-39.
Objective
To evaluate the use of array comparative genomic hybridization (aCGH) for prenatal diagnosis, including assessment of variants of uncertain significance, and the ability to detect abnormalities not detected by karyotype, and vice versa.
Methods
Women undergoing amniocentesis or chorionic villus sampling (CVS) for karyotype were offered aCGH analysis using a targeted microarray. Parental samples were obtained concurrently to exclude maternal cell contamination and determine if copy number variants (CNVs) were de novo, or inherited prior to issuing a report.
Results
We analyzed 300 samples, most were amniotic fluid (82%) and CVS (17%). The most common indications were advanced maternal age (N = 123) and abnormal ultrasound findings (N = 84). We detected 58 CNVs (19.3%). Of these, 40 (13.3%) were interpreted as likely benign, 15 (5.0%) were of defined pathological significance, while 3 (1.0%) were of uncertain clinical significance. For seven (~2.3% or 1/43), aCGH contributed important new information. For two of these (1% or ~1/150), the abnormality would not have been detected without aCGH analysis.
Conclusion
Although aCGH-detected benign inherited variants in 13.3% of cases, these did not present major counseling difficulties, and the procedure is an improved diagnostic tool for prenatal detection of chromosomal abnormalities.
doi:10.1002/pd.2127
PMCID: PMC3665952  PMID: 19012303
aCGH; chromosomal abnormality; chromosomal microarray analysis; prenatal; copy number variants; CVS; amniotic fluid
23.  Down-Regulation of ECRG4, a Candidate Tumor Suppressor Gene, in Human Breast Cancer 
PLoS ONE  2011;6(11):e27656.
Introduction
ECRG4/C2ORF40 is a potential tumor suppressor gene (TSG) recently identified in esophageal carcinoma. Its expression, gene copy number and prognostic value have never been explored in breast cancer.
Methods
Using DNA microarray and array-based comparative genomic hybridization (aCGH), we examined ECRG4 mRNA expression and copy number alterations in 353 invasive breast cancer samples and normal breast (NB) samples. A meta-analysis was done on a large public retrospective gene expression dataset (n = 1,387) in search of correlations between ECRG4 expression and histo-clinical features including survival.
Results
ECRG4 was underexpressed in 94.3% of cancers when compared to NB. aCGH data revealed ECRG4 loss in 18% of tumors, suggesting that DNA loss is not the main mechanism of underexpression. Meta-analysis showed that ECRG4 expression was significantly higher in tumors displaying earlier stage, smaller size, negative axillary lymph node status, lower grade, and normal-like subtype. Higher expression was also associated with disease-free survival (DFS; HR = 0.84 [0.76–0.92], p = 0.0002) and overall survival (OS; HR = 0.72 [0.63–0.83], p = 5.0E-06). In multivariate analysis including the other histo-clinical prognostic features, ECRG4 expression remained the only prognostic factor for DFS and OS.
Conclusions
Our data suggest that ECRG4 is a candidate TSG in breast cancer, the expression of which may help improve the prognostication. If functional analyses confirm this TSG role, restoring ECRG4 expression in the tumor may represent a promising therapeutic approach.
doi:10.1371/journal.pone.0027656
PMCID: PMC3218004  PMID: 22110708
24.  Accuracy of CNV Detection from GWAS Data 
PLoS ONE  2011;6(1):e14511.
Several computer programs are available for detecting copy number variants (CNVs) using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites—Birdsuite, Partek, HelixTree, and PennCNV-Affy—in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH) followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS) data set (1001 bipolar cases and 1033 controls, all of European ancestry) as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR), while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two “gold standards,” the sequence data of Kidd et al., and aCGH data of Conrad et al. Algorithms for calling CNVs especially common ones need substantial improvement, and a “gold standard” for detection of CNVs remains to be established.
doi:10.1371/journal.pone.0014511
PMCID: PMC3020939  PMID: 21249187
25.  Genome-Wide Mapping of Copy Number Variation in Humans: Comparative Analysis of High Resolution Array Platforms 
PLoS ONE  2011;6(11):e27859.
Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications.
doi:10.1371/journal.pone.0027859
PMCID: PMC3227574  PMID: 22140474

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