Comparative genomics and experimental promoter analysis reveal functional liver-specific elements in mammalian hepatic lipase genes

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.     

A comparison study reveals important features of agreement and disagreement between summarized DNA and RNA data obtained from renal cell carcinoma

Cytogenetic abnormalities, such as DNA amplifications and deletions, often lead to significant changes in gene expression levels within a chromosomal region. Instead of generating additional DNA copy number data, one method to identify DNA copy number abnormalities has been to search existing gene expression data for regional perturbations in gene expression. However, it is not clear how well this surrogate method performs in the examination of individual tumors and how we can use both DNA and RNA data to identify candidate genes that may be mutated. Here we report a comparison study using summarized DNA and RNA data to identify chromosomal abnormalities in human samples. Forty-four tissue samples from patients diagnosed as having renal cell carcinoma (RCC) were collected, together with 15 normal kidney samples as controls, and for each sample the genome-wide DNA and RNA data were obtained for comparison using Affymetrix 100K SNP and HGU133plus2 gene expression chips, respectively. The DNA and RNA data was summarized by both chromosome arm and cytogenetic banding patterns and compared. The result of this analysis revealed that the two summarized data sets used to identify cytogenetic changes agreed well. However, some differences between the two were also identified. These differences of large-scale gene expression deregulation without evidence of the comparable DNA copy number alterations may be the result of known mechanisms, such as large-scale methylation or chromosome inactivation, or may be the result of some new mechanism of DNA-RNA translation. The usefulness of the combined data set for identifying regions of mutated genes is also discussed.     

Integration of DNA copy number alterations and transcriptional expression analysis in human gastric cancer

Background

Genomic instability with frequent DNA copy number alterations is one of the key hallmarks of carcinogenesis. The chromosomal regions with frequent DNA copy number gain and loss in human gastric cancer are still poorly defined. It remains unknown how the DNA copy number variations contributes to the changes of gene expression profiles, especially on the global level.

Principal Findings

We analyzed DNA copy number alterations in 64 human gastric cancer samples and 8 gastric cancer cell lines using bacterial artificial chromosome (BAC) arrays based comparative genomic hybridization (aCGH). Statistical analysis was applied to correlate previously published gene expression data obtained from cDNA microarrays with corresponding DNA copy number variation data to identify candidate oncogenes and tumor suppressor genes. We found that gastric cancer samples showed recurrent DNA copy number variations, including gains at 5p, 8q, 20p, 20q, and losses at 4q, 9p, 18q, 21q. The most frequent regions of amplification were 20q12 (7/72), 20q12–20q13.1 (12/72), 20q13.1–20q13.2 (11/72) and 20q13.2–20q13.3 (6/72). The most frequent deleted region was 9p21 (8/72). Correlating gene expression array data with aCGH identified 321 candidate oncogenes, which were overexpressed and showed frequent DNA copy number gains; and 12 candidate tumor suppressor genes which were down-regulated and showed frequent DNA copy number losses in human gastric cancers. Three networks of significantly expressed genes in gastric cancer samples were identified by ingenuity pathway analysis.

Conclusions

This study provides insight into DNA copy number variations and their contribution to altered gene expression profiles during human gastric cancer development. It provides novel candidate driver oncogenes or tumor suppressor genes for human gastric cancer, useful pathway maps for the future understanding of the molecular pathogenesis of this malignancy, and the construction of new therapeutic targets.     

Heterogeneity of 11q13 region rearrangements in laryngeal squamous cell carcinoma analyzed by microarray platforms and fluorescence in situ hybridization

We reinvestigated rearrangements occurring in region q13 of chromosome 11 aiming to: (i) describe heterogeneity of the observed structural alterations, (ii) estimate amplicon size and (iii) identify of oncogenes involved in laryngeal cancer progression as potential targets for therapy. The study included 17 cell lines derived from laryngeal cancers and 34 specimens from primary laryngeal tumors. The region 11q13 was analyzed by fluorescence in situ hybridization (FISH), array comparative genomic hybridization (aCGH) and gene expression microarray. Next, quantitative real time PCR was used for chosen genes to confirm results from aCGH and gene expression microarray. The observed pattern of aberrations allows to distinguish three ways, in which gain and amplification involving 11q13 region may occur: formation of a homogeneously staining region; breakpoints in/near 11q13, which lead to the three to sevenfold increase of the copy number of 11q13 region; the presence of additional copies of the whole chromosome 11. The minimal altered region of gain and/or amplification was limited to ~1.8 Mb (chr.11:69,395,184–71,209,568) and comprised mostly 11q13.3 band which contain 12 genes. Five, out of these genes (CCND1, ORAOV1, FADD, PPFIA1, CTTN) had higher expression levels in comparison to healthy controls. Apart from CCND1 gene, which has an established role in pathogenesis of head and neck cancers, CTTN, ORAOV1 and FADD genes appear to be oncogene-candidates in laryngeal cancers, while a function of PPFIA1 requires further studies.     

True 3q Chromosomal Amplification in Squamous Cell Lung Carcinoma by FISH and aCGH Molecular Analysis: Impact on Targeted Drugs

Squamous lung carcinoma lacks specific “ad hoc” therapies. Amplification of chromosome 3q is the most common genomic aberration and this region harbours genes having role as novel targets for therapeutics. There is no standard definition on how to score and report 3q amplification. False versus true 3q chromosomal amplification in squamous cell lung carcinoma may have tremendous impact on trials involving drugs which target DNA zones mapping on 3q. Forty squamous lung carcinomas were analyzed by FISH to assess chromosome 3q amplification. aCGH was performed as gold-standard to avoid false positive amplifications. Three clustered patterns of fluorescent signals were observed. Eight cases out of 40 (20%) showed ≥8 3q signals. Twenty out of 40 (50%) showed from 3 to 7 signals. The remaining showed two fluorescent signals (30%). When corrected by whole chromosome 3 signals, only cases with ≥8 signals maintained a LSI 3q/CEP3 ratio >2. Only the cases showing 3q amplification by aCGH (+3q25.3−3q27.3) showed ≥8 fluorescent signals at FISH evidencing a 3q/3 ratio >2. The remaining cases showed flat genomic portrait at aCGH on chromosome 3. We concluded that: 1) absolute copy number of 3q chromosomal region may harbour false positive interpretation of 3q amplification in squamous cell carcinoma; 2) a case results truly “amplified for chromosome 3q” when showing ≥8 fluorescent 3q signals; 3) trials involving drugs targeting loci on chromosome 3q in squamous lung carcinoma therapy have to consider false versus true 3q chromosomal amplification.     

Flexible and accurate detection of genomic copy-number changes from aCGH

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.     

Determining and interpreting new predictive rules for breast cancer familial inheritance

DNA copy number alterations have been discovered to be key genetic events in development and progression of cancer. No clear data of familial and sporadic breast cancer are available. We focused on looking for an independent platform as a tool to identify the chromosomal profile in familial versus sporadic breast cancer patients. A total of 124 breast cancer patients were studied utilizing aCGH. The dataset was analyzed using Gaussian Mixture Models to determine the thresholds in order to assess gene copy number changes and to minimize the impact of noise on further data analyses. The identification of regions of consistent aberration across samples was carried out with statistical approaches and machine learning tools to draw profiles for familial and sporadic groups. Familial and sporadic cases resulted with a chromosome imbalance of 15% [false discovery rate (FDR): q=718E-5] and 18% (FDR: q=632E-13), respectively. The differential map evidenced two cytogenetic bands (8p23 and 11q13-11q14) significantly altered in familial versus sporadic cases (FDR: q=7E-4). The application of a new bioinformatics tool that discovers fuzzy classification rules (IFRAIS) let to individualize association of genes alterations that identify familial or sporadic cases. These results are comparable to those of the other systems used and are consistent from the biological point of view.     

A fused lasso latent feature model for analyzing multi-sample aCGH data

Array-based comparative genomic hybridization (aCGH) enables the measurement of DNA copy number across thousands of locations in a genome. The main goals of analyzing aCGH data are to identify the regions of copy number variation (CNV) and to quantify the amount of CNV. Although there are many methods for analyzing single-sample aCGH data, the analysis of multi-sample aCGH data is a relatively new area of research. Further, many of the current approaches for analyzing multi-sample aCGH data do not appropriately utilize the additional information present in the multiple samples. We propose a procedure called the Fused Lasso Latent Feature Model (FLLat) that provides a statistical framework for modeling multi-sample aCGH data and identifying regions of CNV. The procedure involves modeling each sample of aCGH data as a weighted sum of a fixed number of features. Regions of CNV are then identified through an application of the fused lasso penalty to each feature. Some simulation analyses show that FLLat outperforms single-sample methods when the simulated samples share common information. We also propose a method for estimating the false discovery rate. An analysis of an aCGH data set obtained from human breast tumors, focusing on chromosomes 8 and 17, shows that FLLat and Significance Testing of Aberrant Copy number (an alternative, existing approach) identify similar regions of CNV that are consistent with previous findings. However, through the estimated features and their corresponding weights, FLLat is further able to discern specific relationships between the samples, for example, identifying 3 distinct groups of samples based on their patterns of CNV for chromosome 17.     

Use of BAC array CGH for evaluation of chromosomal stability of clinically used human mesenchymal stem cells and of cancer cell lines

Array-based comparative genomic hybridization (aCGH) using bacterial artificial chromosomes (BAC) is a powerful method to analyze DNA copy number aberrations of the entire human genome. In fact, CGH and aCGH have revealed various DNA copy number aberrations in numerous cancer cells and cancer cell lines examined so far. In this report, BAC aCGH was applied to evaluate the stability or instability of cell lines. Established cell lines have greatly contributed to advancements in not only biology but also medical science. However, cell lines have serious problems, such as alteration of biological properties during long-term cultivation. Firstly, we investigated two cancer cell lines, HeLa and Caco-2. HeLa cells, established from a cervical cancer, showed significantly increased DNA copy number alterations with passage time. Caco-2 cells, established from a colon cancer, showed no remarkable differences under various culture conditions. These results indicate that BAC aCGH can be used for the evaluation and validation of genomic stability of cultured cells. Secondly, BAC aCGH was applied to evaluate and validate the genomic stabilities of three patient's mesenchymal stem cells (MSCs), which were already used for their treatments. These three MSCs showed no significant differences in DNA copy number aberrations over their entire chromosomal regions. Therefore, BAC aCGH is highly recommended for use for a quality check of various cells before using them for any kind of biological investigation or clinical application.     

A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations

DNA sequence copy number has been shown to be associated with cancer development and progression. Array-based comparative genomic hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation. For ease of computation, we base estimation on a pseudolikelihood function. The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation. We assess the characteristics of the method through simulation studies and analysis of a brain tumor aCGH data set. We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present. Software for this approach is available at http://www.biostat.harvard.edu/ approximately betensky/papers.html.     

Inverted duplication pattern in anaphase bridges confirms the breakage-fusion-bridge (BFB) cycle model for 11q13 amplification

Conventional cytogenetic analyses and comparative genomic hybridization have revealed a complex and even chaotic nature of chromosomal aberrations in pleural malignant mesothelioma (MM). We set out to describe the complex gene copy number changes and screen for novel genetic aberrations using a high-density oligonucleotide microarray platform for comparative genomic hybridization (aCGH) of a series of 26 well-characterized MM tumor samples. The number of copy number changes varied from zero to 40 per sample. Gene copy number losses predominated over gains, and the most frequent region of loss was 9p21.3 (17/26 cases), the locus of CDKN2A and CDKN2B, both known to be commonly lost in MM. The most recurrent minimal regions of losses were 1p31.1--> p13.2, 3p22.1-->p14.2, 6q22.1, 9p21.3, 13cen-->q14.12, 14q22.1-->qter, and 22qcen-->q12.3. Previously unreported gains included 9p13.3, 7p22.3-->p22.2, 12q13.3, and 17q21.32-->qter. The results suggest that gene copy number losses are a major mechanism of MM carcinogenesis and reveal a recurrent pattern of copy number changes in MM.     

Identification of genes with correlated patterns of variations in DNA copy number and gene expression level in gastric cancer

To identify DNA copy number changes that had a direct influence on mRNA expression in gastric cancer, cDNA microarray-based comparative genomic hybridization (aCGH) and gene expression profiling were performed using 17 K cDNA microarrays. A set of 158 genes showing Pearson correlation coefficients over 0.6 between DNA copy number changes and mRNA expression level variations was selected. In an independent gene expression profiling of 60 tissue samples, the 158 genes were able to distinguish most of the normal and tumor tissues in an unsupervised hierarchical clustering, suggesting that the differential expression patterns displayed by this specific group of genes are most likely based on the gene copy number changes. Furthermore, 43 statistically significant (P<0.01) genes were selected that correctly distinguished all of the tissue samples. The copy number changes detected by aCGH can be verified by fluorescence in situ hybridization and real-time polymerase chain reaction. The selected genes include those that were previously identified as being tumor suppressors or deleted in various tumors, including GATA binding protein 4 (GATA4), monoamine oxidase A (MAOA), cyclin C (CCNC), and oncogenes including malignant fibrous histiocytoma amplified sequence 1 (MFHAS1/MASL1), high mobility group AT-hook 2 (HMGA2), PPAR binding protein (PPARBP), growth factor receptor-bound protein 7 (GRB7), and TBC1 (tre-2, BUB2, cdc16) domain family, member 1 (TBC1D1).     

Accurate detection of aneuploidies in array CGH and gene expression microarray data

MOTIVATION: Chromosomal copy number changes (aneuploidies) are common in cell populations that undergo multiple cell divisions including yeast strains, cell lines and tumor cells. Identification of aneuploidies is critical in evolutionary studies, where changes in copy number serve an adaptive purpose, as well as in cancer studies, where amplifications and deletions of chromosomal regions have been identified as a major pathogenetic mechanism. Aneuploidies can be studied on whole-genome level using array CGH (a microarray-based method that measures the DNA content), but their presence also affects gene expression. In gene expression microarray analysis, identification of copy number changes is especially important in preventing aberrant biological conclusions based on spurious gene expression correlation or masked phenotypes that arise due to aneuploidies. Previously suggested approaches for aneuploidy detection from microarray data mostly focus on array CGH, address only whole-chromosome or whole-arm copy number changes, and rely on thresholds or other heuristics, making them unsuitable for fully automated general application to gene expression datasets. There is a need for a general and robust method for identification of aneuploidies of any size from both array CGH and gene expression microarray data. RESULTS: We present ChARM (Chromosomal Aberration Region Miner), a robust and accurate expectation-maximization based method for identification of segmental aneuploidies (partial chromosome changes) from gene expression and array CGH microarray data. Systematic evaluation of the algorithm on synthetic and biological data shows that the method is robust to noise, aneuploidal segment size and P-value cutoff. Using our approach, we identify known chromosomal changes and predict novel potential segmental aneuploidies in commonly used yeast deletion strains and in breast cancer. ChARM can be routinely used to identify aneuploidies in array CGH datasets and to screen gene expression data for aneuploidies or array biases. Our methodology is sensitive enough to detect statistically significant and biologically relevant aneuploidies even when expression or DNA content changes are subtle as in mixed populations of cells. AVAILABILITY: Code available by request from the authors and on Web supplement at http://function.cs.princeton.edu/ChARM/     

Array-based comparative genomic hybridization and copy number variation in cancer research

Array-based comparative genomic hybridization (aCGH) is a molecular cytogenetic technique used in detecting and mapping DNA copy number alterations. aCGH is able to interrogate the entire genome at a previously unattainable, high resolution and has directly led to the recent appreciation of a novel class of genomic variation: copy number variation (CNV) in mammalian genomes. All forms of DNA variation/polymorphism are important for studying the basis of phenotypic diversity among individuals. CNV research is still at its infancy, requiring careful collation and annotation of accumulating CNV data that will undoubtedly be useful for accurate interpretation of genomic imbalances identified during cancer research.     

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.     

Identification of large-scale human-specific copy number differences by inter-species array comparative genomic hybridization

Copy number differences (CNDs), and the concomitant differences in gene number, have contributed significantly to the genomic divergence between humans and other primates. To assess its relative importance, the genomes of human, common chimpanzee, bonobo, gorilla, orangutan and macaque were compared by comparative genomic hybridization using a high-resolution human BAC array (aCGH). In an attempt to avoid potential interference from frequent intra-species polymorphism, pooled DNA samples were used from each species. A total of 322 sites of large-scale inter-species CND were identified. Most CNDs were lineage-specific but frequencies differed considerably between the lineages; the highest CND frequency among hominoids was observed in gorilla. The conserved nature of the orangutan genome has already been noted by karyotypic studies and our findings suggest that this degree of conservation may extend to the sub-microscopic level. Of the 322 CND sites identified, 14 human lineage-specific gains were observed. Most of these human-specific copy number gains span regions previously identified as segmental duplications (SDs) and our study demonstrates that SDs are major sites of CND between the genomes of humans and other primates. Four of the human-specific CNDs detected by aCGH map close to the breakpoints of human-specific karyotypic changes [e.g., the human-specific inversion of chromosome 1 and the polymorphic inversion inv(2)(p11.2q13)], suggesting that human-specific duplications may have predisposed to chromosomal rearrangement. The association of human-specific copy number gains with chromosomal breakpoints emphasizes their potential importance in mediating karyotypic evolution as well as in promoting human genomic diversity. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

Integrating copy number polymorphisms into array CGH analysis using a robust HMM

MOTIVATION: Array comparative genomic hybridization (aCGH) is a pervasive technique used to identify chromosomal aberrations in human diseases, including cancer. Aberrations are defined as regions of increased or decreased DNA copy number, relative to a normal sample. Accurately identifying the locations of these aberrations has many important medical applications. Unfortunately, the observed copy number changes are often corrupted by various sources of noise, making the boundaries hard to detect. One popular current technique uses hidden Markov models (HMMs) to divide the signal into regions of constant copy number called segments; a subsequent classification phase labels each segment as a gain, a loss or neutral. Unfortunately, standard HMMs are sensitive to outliers, causing over-segmentation, where segments erroneously span very short regions. RESULTS: We propose a simple modification that makes the HMM robust to such outliers. More importantly, this modification allows us to exploit prior knowledge about the likely location of "outliers", which are often due to copy number polymorphisms (CNPs). By "explaining away" these outliers with prior knowledge about the locations of CNPs, we can focus attention on the more clinically relevant aberrated regions. We show significant improvements over the current state of the art technique (DNAcopy with MergeLevels) on previously published data from mantle cell lymphoma cell lines, and on published benchmark synthetic data augmented with outliers. AVAILABILITY: Source code written in Matlab is available from http://www.cs.ubc.ca/~sshah/acgh.