Comparative Genomic Hybridization Data Research Articles

TIARA: a database for accurate analysis of multiple personal genomes based on cross-technology

High-throughput genomic technologies have been used to explore personal human genomes for the past few years. Although the integration of technologies is important for high-accuracy detection of personal genomic variations, no databases have been prepared to systematically archive genomes and to facilitate the comparison of personal genomic data sets prepared using a variety of experimental platforms. We describe here the Total Integrated Archive of Short-Read and Array (TIARA; http://tiara.gmi.ac.kr) database, which contains personal genomic information obtained from next generation sequencing (NGS) techniques and ultra-high-resolution comparative genomic hybridization (CGH) arrays. This database improves the accuracy of detecting personal genomic variations, such as SNPs, short indels and structural variants (SVs). At present, 36 individual genomes have been archived and may be displayed in the database. TIARA supports a user-friendly genome browser, which retrieves read-depths (RDs) and log2 ratios from NGS and CGH arrays, respectively. In addition, this database provides information on all genomic variants and the raw data, including short reads and feature-level CGH data, through anonymous file transfer protocol. More personal genomes will be archived as more individuals are analyzed by NGS or CGH array. TIARA provides a new approach to the accurate interpretation of personal genomes for genome research.

Down-regulation of tumor suppressor a kinase anchor protein 12 in human hepatocarcinogenesis by epigenetic mechanisms

The A kinase anchor protein 12 (AKAP12) is a central mediator of protein kinase A and protein kinase C signaling. Although AKAP12 has been described to act as a tumor suppressor and its expression is frequently down-regulated in several human malignancies, the underlying molecular mechanisms responsible for the AKAP12 reduction are poorly understood. We therefore analyzed the expression of AKAP12 and its genetic and epigenetic regulatory mechanisms in human hepatocarcinogenesis. Based on tissue microarray analyses (n = 388) and western immunoblotting, we observed a significant reduction of AKAP12 in cirrhotic liver (CL), premalignant lesions (DN), and hepatocellular carcinomas (HCCs) compared to histologically normal liver specimens (NL). Analyses of array comparative genomic hybridization data (aCGH) from human HCCs revealed chromosomal losses of AKAP12 in 36% of cases but suggested additional mechanisms underlying the observed reduction of AKAP12 expression in hepatocarcinogenesis. Quantitative methylation analysis by MassARRAY of NL, CL, DN, and HCC tissues, as well as of various tumorigenic and nontumorigenic liver cell lines revealed specific hypermethylation of the AKAP12α promoter but not of the AKAP12β promoter in HCC specimens and in HCC cell lines. Consequently, restoration experiments performed with 5-aza-2'deoxycytidine drastically increased AKAP12α mRNA levels in a HCC cell line (AKN1) paralleled by AKAP12α promoter demethylation. As hypermethylation is not observed in CL and DN, we investigated microRNA-mediated posttranscriptional regulation as an additional mechanism to explain reduced AKAP12 expression. We found that miR-183 and miR-186 are up-regulated in CL and DN and are able to target AKAP12. In addition to genetic alterations, epigenetic mechanisms are responsible for the reduction of the tumor suppressor gene AKAP12 in human hepatocarcinogenesis.

New insights in sarcoma oncogenesis: a comprehensive analysis of a large series of 160 soft tissue sarcomas with complex genomics

Adult soft tissue sarcomas (STS) are rare tumours of mesenchymal lineage. Based on cytogenetic and comparative genomic hybridization (CGH) data, they can be divided into 'STS with simple genomics', displaying a characteristic genetic alteration, and 'STS with complex genomics' (SCG), where multiple genomic alterations occur. This latter group is mostly composed of leiomyosarcomas (LMS) and pleiomorphic undifferentiated tumours previously labelled as 'malignant fibrous histiocytomas' (MFH), corresponding in fact to myxofibrosarcomas (MFS), pleiomorphic liposarcomas/rhabdomyosarcomas (P-LPS, P-RMS), and undifferentiated pleiomorphic sarcomas (UPS). Their pathobiology is still not well understood, leading to challenges in diagnosis and therapeutic management. We report here a comprehensive study encompassing array-CGH and transcriptome analysis data of a large series of 160 SCG. Non-supervised clustering of transcriptome data led to the identification of five groups of tumours, one of them (group A) corresponding to well-differentiated LMS and the other four (B-E) to 'MFH' and poorly differentiated LMS. Welch analysis of transcriptome data in these groups allowed us to retrieve several genes of potential interest. Among them, RB1 alteration is a constant thread in SCG, often associated with RBL2 loss. PTEN tumour suppressor deletion would also stand out as a major recurrent event, especially in groups A, C, and D. The WNT canonical pathway could be potentially involved, as demonstrated by up-regulation of one of its inhibitors, DKK1, in groups D and E, whereas DKK1 is significantly down-regulated in groups A, B, and C. These data suggest a very complex interplay between pathways downstream of PTEN and the WNT canonical pathway, providing new hints about SCG pathobiology and their potential therapeutic targets.

EIF2C Is Overexpressed and Amplified in Head and Neck Squamous Cell Carcinoma

Aim: To discover putative oncogenes in head and neck squamous cell carcinoma (HNSCC) by integrating data from whole-genome comparison of array-based comparative genomic hybridization (CGH) and expression microarray analysis of HNSCC. Methods: We integrated published data defining regions of loss/gain identified from the profiling of 21 HNSCC using high-resolution (<1 Mb) CGH arrays and data from an mRNA expression microarray (approx. 12,000 genes) comparing 6 normal tissues and 8 HNSCC tumor tissues. Eukaryotic translation initiation factor 2C subunit 2 (EIF2C2) was found to be the most significantly overexpressed gene by mRNA expression array, and corresponded to the most common region of amplification found by the CGH array described by Sparano et al. We validated EIF2C2 overexpression in primary tissue, overexpression and amplification in HNSCC lines (JHU-011, JHU-012, FADU) relative to a minimally transformed oral keratinocyte cell line (OKF6) and performed knockdown experiments. Results: The tumor tissues had an average mRNA expression level of 123 (SD = 49) compared to the normal tissues (18.6, SD = 10) (p = 0.0005) by expression array. Quantitative RT-PCR validation of our expression arrays found that normal tissues had an average expression of 0.76 (SE = 0.08) and tumor tissues of 2.1 (SE = 0.35) (p = 0.0008). EIF2C2 was found to be amplified and overexpressed in 3 HNSCC cell lines. Knockdown of EIF2C2 in cell lines (JHU-012 and JHU-011) inhibited proliferation. Conclusion:EIF2C2 is amplified and overexpressed in HNSCC cell lines and primary tumors and functionally significant in cell lines.

Sticky Hidden Markov Modeling of Comparative Genomic Hybridization

We develop a sticky hidden Markov model (HMM) with a Dirichlet distribution (DD) prior, motivated by the problem of analyzing comparative genomic hybridization (CGH) data. As formulated the sticky DD-HMM prior is employed to infer the number of states in an HMM, while also imposing state persistence. The form of the proposed hierarchical model allows efficient variational Bayesian (VB) inference, of interest for large-scale CGH problems. We compare alternative formulations of the sticky HMM, while also examining the relative efficacy of VB and Markov chain Monte Carlo (MCMC) inference. To validate the formulation, example results are presented for an illustrative synthesized data set and our main application-CGH, for which we consider data for breast cancer. For the latter, we also make comparisons and partially validate the CGH analysis through factor analysis of associated (but distinct) gene-expression data.

Automation in an Array Comparative Genomic Hybridization Laboratory Improves Throughput and Data Quality

Array-based comparative genomic hybridization is rapidly becoming a standard assay for research and clinical cytogenetics laboratories. Certain automated workflows are used to achieve economies in running this relatively expensive assay. Here, we describe the ways in which our medium throughput laboratory has investigated automation at various points in our workflow to achieve economies and improve the resulting data for our investigators. We investigate an automated hybridization station to improve microarray hybridization, an autoloading scanner to facilitate the rapid scanning of multiple arrays, and a software package that allows for quick and easy access to many external resources to extend the analysis.

Cross-species comparison of aCGH data from mouse and human BRCA1- and BRCA2-mutated breast cancers

BackgroundGenomic gains and losses are a result of genomic instability in many types of cancers. BRCA1- and BRCA2-mutated breast cancers are associated with increased amounts of chromosomal aberrations, presumably due their functions in genome repair. Some of these genomic aberrations may harbor genes whose absence or overexpression may give rise to cellular growth advantage. So far, it has not been easy to identify the driver genes underlying gains and losses. A powerful approach to identify these driver genes could be a cross-species comparison of array comparative genomic hybridization (aCGH) data from cognate mouse and human tumors. Orthologous regions of mouse and human tumors that are commonly gained or lost might represent essential genomic regions selected for gain or loss during tumor development.MethodsTo identify genomic regions that are associated with BRCA1- and BRCA2-mutated breast cancers we compared aCGH data from 130 mouse Brca1Δ/Δ;p53Δ/Δ, Brca2Δ/Δ;p53Δ/Δ and p53Δ/Δ mammary tumor groups with 103 human BRCA1-mutated, BRCA2-mutated and non-hereditary breast cancers.ResultsOur genome-wide cross-species analysis yielded a complete collection of loci and genes that are commonly gained or lost in mouse and human breast cancer. Principal common CNAs were the well known MYC-associated gain and RB1/INTS6-associated loss that occurred in all mouse and human tumor groups, and the AURKA-associated gain occurred in BRCA2-related tumors from both species. However, there were also important differences between tumor profiles of both species, such as the prominent gain on chromosome 10 in mouse Brca2Δ/Δ;p53Δ/Δ tumors and the PIK3CA associated 3q gain in human BRCA1-mutated tumors, which occurred in tumors from one species but not in tumors from the other species. This disparity in recurrent aberrations in mouse and human tumors might be due to differences in tumor cell type or genomic organization between both species.ConclusionsThe selection of the oncogenome during mouse and human breast tumor development is markedly different, apart from the MYC gain and RB1-associated loss. These differences should be kept in mind when using mouse models for preclinical studies.

Segmentation of the mean of heteroscedastic data via cross-validation

This paper tackles the problem of detecting abrupt changes in the mean of a heteroscedastic signal by model selection, without knowledge on the variations of the noise. A new family of change-point detection procedures is proposed, showing that cross-validation methods can be successful in the heteroscedastic framework, whereas most existing procedures are not robust to heteroscedasticity. The robustness to heteroscedasticity of the proposed procedures is supported by an extensive simulation study, together with recent theoretical results. An application to Comparative Genomic Hybridization (CGH) data is provided, showing that robustness to heteroscedasticity can indeed be required for their analysis.

Genetic screening for infertility: When should it be done?

Depending on the clinical findings, the infertile male patient needs genetic evaluation. Karyotype analysis and Y-chromosomal microdeletion screening should be performed in patients with azoospermia or severe oligozoospermia in order to rule out structural chromosomal abnormalities, Klinefelter syndrome and Y chromosome microdeletions. Infertile patients with obstructive azoospermia need cystic fibrosis transmembrane receptor gene screening, while in patients with hypogonadotropic hypogonadism mutation screening may be performed according to clinical features. All genetic analyses should be accompanied by expert counseling by a clinical genetist both in male and female patients. Primary amenorrhea should be investigated by karyotype analysis and selected mutation screening according to the patient's clinical features. Karyotype analyses and FMR1 gene screening is recommended in cases of POF. At present the infertility of patients with POF cannot be restored if the diagnosis is made after complete follicular depletion, but in some cases, early diagnosis by genetic investigation may instead lead to the advice of early conception or oocyte harvesting and preservation. In addition, the accumulation and annotation of array comparative genomic hybridization data might, in the near future, lead to the identification of pathogenetic copy number variations and genes involved in POF. Karyotype analysis of both partners is recommended in all couples with recurrent pregnancy loss. No routine genetic test can be recommended so far in patients with PCOS.

FACADE : a fast and sensitive algorithm for the segmentation and calling of high resolution array CGH data

The availability of high resolution array comparative genomic hybridization (CGH) platforms has led to increasing complexities in data analysis. Specifically, defining contiguous regions of alterations or segmentation can be computationally intensive and popular algorithms can take hours to days for the processing of arrays comprised of hundreds of thousands to millions of elements. Additionally, tumors tend to demonstrate subtle copy number alterations due to heterogeneity, ploidy and hybridization effects. Thus, there is a need for fast, sensitive array CGH segmentation and alteration calling algorithms. Here, we describe Fast Algorithm for Calling After Detection of Edges (FACADE), a highly sensitive and easy to use algorithm designed to rapidly segment and call high resolution array data.

Robust unmixing of tumor states in array comparative genomic hybridization data.

Motivation: Tumorigenesis is an evolutionary process by which tumor cells acquire sequences of mutations leading to increased growth, invasiveness and eventually metastasis. It is hoped that by identifying the common patterns of mutations underlying major cancer sub-types, we can better understand the molecular basis of tumor development and identify new diagnostics and therapeutic targets. This goal has motivated several attempts to apply evolutionary tree reconstruction methods to assays of tumor state. Inference of tumor evolution is in principle aided by the fact that tumors are heterogeneous, retaining remnant populations of different stages along their development along with contaminating healthy cell populations. In practice, though, this heterogeneity complicates interpretation of tumor data because distinct cell types are conflated by common methods for assaying the tumor state. We previously proposed a method to computationally infer cell populations from measures of tumor-wide gene expression through a geometric interpretation of mixture type separation, but this approach deals poorly with noisy and outlier data.Results: In the present work, we propose a new method to perform tumor mixture separation efficiently and robustly to an experimental error. The method builds on the prior geometric approach but uses a novel objective function allowing for robust fits that greatly reduces the sensitivity to noise and outliers. We further develop an efficient gradient optimization method to optimize this ‘soft geometric unmixing’ objective for measurements of tumor DNA copy numbers assessed by array comparative genomic hybridization (aCGH) data. We show, on a combination of semi-synthetic and real data, that the method yields fast and accurate separation of tumor states.Conclusions: We have shown a novel objective function and optimization method for the robust separation of tumor sub-types from aCGH data and have shown that the method provides fast, accurate reconstruction of tumor states from mixed samples. Better solutions to this problem can be expected to improve our ability to accurately identify genetic abnormalities in primary tumor samples and to infer patterns of tumor evolution.Contact: tolliver@cs.cmu.eduSupplementary information:Supplementary data are available at Bioinformatics online.

WHO grade-specific comparative genomic hybridization pattern of astrocytoma – A meta-analysis

To detect novel genetic alterations, many astrocytomas have been investigated by comparative genomic hybridization (CGH). To identify aberration profiles characteristic of World Health Organization (WHO) grade I, II, III, and IV astrocytoma, we performed a meta-analysis of detailed genome wide CGH data of all 467 cases published so far. After expansion of all given aberrations to the maximum of 850 GTG-band resolution, the frequencies of genetic imbalances were calculated for each chromosomal band, separately for all four WHO grades. Low-grade astrocytoma has already demonstrated one characteristic of glioblastoma multiforme, gain of chromosome 7 with a hot spot at 7q32, but without loss of chromosome 10. In anaplastic astrocytoma, a more complex aberration pattern emerges from diffuse genetic imbalances. Gains of 7q32–q36 and 7p12 become the most frequent aberrations at chromosome 7. In glioblastoma multiforme, coarse aberrations like +7, −9p, −10, and −13 represent the most frequent aberrations as a characteristic pattern. In contrast to lower tumor grades, glioblastoma multiforme demonstrates +7p12 as the most frequently affected band on chromosome 7. To quantify the gradual transition from WHO grade II–IV astrocytoma, we calculated the relative increase and decrease in frequency for each detected aberration of the tumor genome. The most pronounced and diverse changes of genetic material occur at the virtual transition from low-grade to anaplastic astrocytoma. Further transition to glioblastoma multiforme is characterized by gain of 1p, chromosome 7, and loss of chromosome 10. Summing up, the expansion of the CGH results to the 850 GTG-band resolution enabled a meta-analysis to visualize WHO grade-specific aberration profiles in astrocytoma.

Accumulation of Segmental Alterations Determines Progression in Neuroblastoma

Neuroblastoma is characterized by two distinct types of genetic profiles, consisting of either numerical or segmental chromosome alterations. The latter are associated with a higher risk of relapse, even when occurring together with numerical alterations. We explored the role of segmental alterations in tumor progression and the possibility of evolution from indolent to aggressive genomic types. Array-based comparative genomic hybridization data of 394 neuroblastoma samples were analyzed and linked to clinical data. Integration of ploidy and genomic data indicated that pseudotriploid tumors with mixed numerical and segmental profiles may be derived from pseudotriploid tumors with numerical alterations only. This was confirmed by the analysis of paired samples, at diagnosis and at relapse, as in tumors with a purely numerical profile at diagnosis additional segmental alterations at relapse were frequently observed. New segmental alterations at relapse were also seen in patients with segmental alterations at diagnosis. This was not linked to secondary effects of cytotoxic treatments since it occurred even in patients treated with surgery alone. A higher number of chromosome breakpoints were correlated with advanced age at diagnosis, advanced stage of disease, with a higher risk of relapse, and a poorer outcome. These data provide further evidence of the role of segmental alterations, suggesting that tumor progression is linked to the accumulation of segmental alterations in neuroblastoma. This possibility of genomic evolution should be taken into account in treatment strategies of low- and intermediate-risk neuroblastoma and should warrant biologic reinvestigation at the time of relapse.

Posterior Simulation in Countable Mixture Models for Large Datasets

Mixture models, or convex combinations of a countable number of probability distributions, offer an elegant framework for inference when the population of interest can be subdivided into latent clusters having random characteristics that are heterogeneous between, but homogeneous within, the clusters. Traditionally, the different kinds of mixture models have been motivated and analyzed from very different perspectives, and their common characteristics have not been fully appreciated. The inferential techniques developed for these models usually necessitate heavy computational burdens that make them difficult, if not impossible, to apply to the massive data sets increasingly encountered in real world studies. This paper introduces a flexible class of models called generalized Pólya urn (GPU) processes. Many common mixture models, including finite mixtures, hidden Markov models, and Dirichlet processes, are obtained as special cases of GPU processes. Other important special cases include finite-dimensional Dirichlet priors, infinite hidden Markov models, analysis of densities models, nested Chinese restaurant processes, hierarchical DP models, nonparametric density models, spatial Dirichlet processes, weighted mixtures of DP priors, and nested Dirichlet processes. An investigation of the theoretical properties of GPU processes offers new insight into asymptotics that form the basis of cost-effective Markov chain Monte Carlo (MCMC) strategies for large datasets. These MCMC techniques have the advantage of providing inferences from the posterior of interest, rather than an approximation, and are applicable to different mixture models. The versatility and impressive gains of the methodology are demonstrated by simulation studies and by a semiparametric Bayesian analysis of high-resolution comparative genomic hybridization data on lung cancer. The appendixes are available online as supplemental material.

CGHnormaliter: a Bioconductor package for normalization of array CGH data with many CNAs

CGHnormaliter is a package for normalization of array comparative genomic hybridization (aCGH) data. It uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). CGHnormaliter is integrated in the Bioconductor environment allowing a smooth link to visualization tools and further data analysis. The CGHnormaliter package is implemented in R and under GPL 3.0 license available at Bioconductor: http://www.bioconductor.org heringa@few.vu.nl

Abstract 2017: Key pathways that are altered at both gene copy number and transcript levels in ovarian cancer: An integrated analysis of TCGA data

Abstract Background One of the three cancer types that have been systematically examined at genomic and epigenomic levels by The Cancer Genome Atlas (TCGA) program is ovarian cancer, which represents a major health issue for women world-wide. Currently, a major challenge is to interrogate the vast amount of raw data to reveal fundamental knowledge regarding the etiology of these cancers. One of our goals is to identify genes and pathways that are jointly altered in terms of both gene copy number and transcript levels. We hypothesize that these genes and pathways represent the critical genomic and transcriptomic events that are important for cancer maintenance. Method Paired array Comparative Genomic Hybridization (aCGH) and transcriptome data from 345 ovarian cancer samples were obtained from TCGA. The majority of these tumors (263) are grade 3 serous adenocarcinoma. Both expression and aCGH data were lowess normalized and aCGH data further subjected to the circular binary segmentation (CBS) algorithm. Spearman's rank correlation (Rho) was computed on 15,553 genes detected for both aCGH and expression array to investigate the association between copy number and expression in the 345 ovarian tumor samples. The genes were then ranked based on Rho and significantly enriched pathways were indentified with highly ranked genes using gene set enrichment analysis (GSEA) algorithm in 380 pathways from Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta databases. Result Among the 15,553 genes, 5,486 genes in the cytobands of 19p13.11-p13.2, 8q24.3, 4p16.3, 16p13.3, 15q22.31, 12q24.31 and 10q24.32 exhibited high gene dosage-expression correlations (FDR &amp;lt; 0.1%, Rho &amp;gt; 0.4). Among the 5,486 dosage sensitive genes, 142 were pre-defined as cancer genes in Cancer Gene Census database. These genes include BRCA2 (Correlation P&amp;lt; 5.65E-18), PIK3CA (P&amp;lt;5.44E-37), PTEN (P&amp;lt;1.30E-24), AKT1 (P&amp;lt;1.33E-33), AKT2 (P&amp;lt;6.78E-17), MSH6 (P&amp;lt;1.18E-16), STK11 (P&amp;lt;6.47E-18), MLH1 (P&amp;lt;1.23E-24), MSH2 (P&amp;lt;2.63E-20), KRAS (P &amp;lt; 8.33E-39), and NF1 (P&amp;lt;2.56E-19). Further GSEA pathway analysis revealed a number of cancer related pathways that were highly enriched with dosage sensitive genes. These pathways included PI3K/Akt/mTOR pathway, Wnt-signal pathway, MAPK pathway, VEGF pathway, RAS pathway, and insulin signaling pathway. Conclusion Our integrated analysis of gene copy number and transcriptome data of ovarian cancer data in TCGA revealed key pathways that may be critical for ovarian cancer development and maintenance. Further analyses incorporating other information, including clinical data, will shed further light on the genetic and molecular basis of ovarian cancer pathophysiology and help identify the key targets for therapeutic intervention. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2017.

Abstract 1189: Chromosomal aberrations reveal novel genes with prognostic significance in esophageal adenocarcinoma

Abstract INTRODUCTION: The incidence of esophageal adenocarcinoma (EAC) has increased 6-fold in the west over the last 30 years, with a dismal survival rate of about 10%. Understanding the genomic aberrations and molecular pathogenesis in this cancer may enhance disease interventions. AIMS: To identify novel molecular targets with prognostic significance and to better characterize EAC by analyzing chromosomal aberrations across the whole genome. METHODS: An in-house oligonucleotide array comparative genomic hybridization (CGH) was performed on 89 chemo-naive EAC resection samples with long-term clinical follow-up data to identify regions of copy number gains/losses: good quality data obtained for 56 samples. Illumina Human Cyto Single-nucleotide Polymorphism (SNP)-12 Beadarray was carried out to map genomic boundaries of regions with aberrations. Touch-imprinting fluorescence in situ hybridization (FISH) assays were carried out using bacterial artificial chromosome probes on interphase nuclei of frozen samples to confirm findings. Regions with potential homozygous deletions (HDs) were investigated by multiplex nested PCR using microdissected DNA of frozen samples. The degree and pattern of genomic gains were studied using different FISH probes. RESULTS: 19 minimal regions with gains and 12 regions with losses (cut off=2 standard deviations (SD) above/below mean log2 ratios) were identified. Potential HDs (cut off=3 SD) were identified in 4 samples (7.1%) at 9p21. Nested multiplex PCR and FISH (quantitative: 100 nuclei per sample counted) confirmed a p16 deletion in all 4 samples. SNP analysis suggested another region of potential HD that involves a novel gene, ELAVL2. Amplifications (cut off=3 SD) in 6 samples (10.7%) at 8p23.1 were confirmed by analyses from the SNP array, which narrowed down the width of amplicon to 2 regions involving: (a) SOX7/PINX1 (5/6 samples) and (b) GATA4, NEIL2, FDFT1 and CTSB (4/6 samples). FISH assays confirmed amplifications of GATA4, NEIL2, FDFT1, CTSB, SOX7 (6 or more copies/centromere) and gains of PINX1 (3-6 copies/centromere). Unsupervised K means clustering (50 iterations) of the array CGH data generated 5 groups and survival analyses identified a group (32.1% of cohort) with significantly worse prognosis (median survival=500 days, p=0.0149). Modified T test with adjusted Bonferroni correction identified 17 clones with different log2 ratios (p&amp;lt;0.05), implicating 4 regions of gains and including 6 novel genes, between this group and the other 4 combined. The only clinical difference between this group and the others was an increased male:female ratio (Fishers exact test; p=0.01). CONCLUSIONS: 9p deletions and 8p amplifications are present in 7-11% of samples. The use of array CGH has identified copy number gains with prognostic significance in EAC. Further work is required to determine whether the genes involved have a causative role. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1189.

Discovery of common Asian copy number variants using integrated high-resolution array CGH and massively parallel DNA sequencing

Copy number variants (CNVs) account for the majority of human genomic diversity in terms of base coverage. Here, we have developed and applied a new method to combine high-resolution array comparative genomic hybridization (CGH) data with whole-genome DNA sequencing data to obtain a comprehensive catalog of common CNVs in Asian individuals. The genomes of 30 individuals from three Asian populations (Korean, Chinese and Japanese) were interrogated with an ultra-high-resolution array CGH platform containing 24 million probes. Whole-genome sequencing data from a reference genome (NA10851, with 28.3x coverage) and two Asian genomes (AK1, with 27.8x coverage and AK2, with 32.0x coverage) were used to transform the relative copy number information obtained from array CGH experiments into absolute copy number values. We discovered 5,177 CNVs, of which 3,547 were putative Asian-specific CNVs. These common CNVs in Asian populations will be a useful resource for subsequent genetic studies in these populations, and the new method of calling absolute CNVs will be essential for applying CNV data to personalized medicine.

An integrated study of aberrant gene copy number and gene expression in GIST and LMS.

Increased chromosomal instability that alters the gene copy numbers throughout the genome is known to have a role in molecular pathogenesis of tumors. The impact of gene dosage effect to the expression levels of genes in GIST and LMS is unknown. In this paper, we used a combination of array comparative genomic hybridization (aCGH) and gene expression data to gain insights into the interplay of structural and functional changes of the genome in GIST and LMSs. We identified specific target genes that change their expression due to the gene dosage effect. Statistical analysis identified four chromosomal regions, 1p, 14q, 15q, and 22q, where both copy number and mRNA expression were significantly different between the tumor types. Multi-dimensional scaling (MDS) analysis showed that the gene expression profiles of these four regions accurately distinguish GIST and LMS. In addition, the gene dosage sensitive genes in these regions are differently involved in several tumor growth promoting pathways, implying that there are different mechanisms underlying the GIST and LMS carcinogenesis. Integration of aCGH and gene expression data has not only provided insights into how DNA copy number variations affect the gene expression patterns in these cancers, but also proves to be a promising method to choose biologically relevant biomarkers.

Prognostic classification of patients with acute lymphoblastic leukemia by using gene copy number profiles identified from array-based comparative genomic hybridization data

The development of risk-adapted therapy has improved the treatment results of acute lymphoblastic leukemia (ALL) especially in children. However, more accurate risk classifiers are warranted. In this study we aimed at defining a prognostic classifier based on DNA copy number alterations of adolescent and young adult (AYA) (10–25 yrs) ALL patients ( n = 60) determined by microarray CGH and the relapse status of the patients. As a result of prognostic model identification procedure, we got a model of four genes: BAK1, CDKN2C, GSTM1, and MT1F, the copy number profile combinations of which differentiated AYA ALL patients at diagnosis depending on their risk of relapse. The performance of the model was poorer on other age groups. We suggest that this kind of approach produces models simple and accurate enough for potential use in ALL routine classification.