Identification of tumor suppressors and oncogenes from genomic and epigenetic features in ovarian cancer

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatic analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We separately examined CNV and DNA methylation for 42 primary serous ovarian cancer samples using MOMA-ROMA assays and 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with altered copy number and correlated changes in expression. Among these genes CCNE1, POP4, UQCRB, PHF20L1 and C19orf2 were identified within both data sets. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic features for these modalities and perform a correlation analysis with expression. We predicted 611 potential oncogenes and tumor suppressors candidates by integrating these data types. Genes with a strong correlation for methylation dependent expression changes exhibited at varying copy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA, BOP1 and EIF2C3. We provide copy number variation and DNA methylation analysis for over 11,500 individual genes covering the genetic landscape of ovarian cancer tumors. We show the extent of genomic and epigenetic alterations for known tumor suppressors and oncogenes and also use these defined features to identify potential ovarian cancer gene candidates.     

Differential gene alteration among hepatoma cell lines demonstrated by cDNA microarray-based comparative genomic hybridization

We assayed chromosomal abnormalities in hepatoma cell lines using the microarray-based comparative genomic hybridization (array-CGH) method and investigated the relationship between genomic copy number alterations and expression profiles in these hepatoma cell lines. We modified a cDNA array-CGH assay to compare genomic DNAs from seven hepatoma cell lines, as well as DNA from two non-hepatoma cell lines and from normal cells. The mRNA expression of each sample was assayed in parallel by cDNA microarray. We identified small amplified or deleted chromosomal regions, as well as alterations in DNA copy number not previously described. We predominantly found alterations of apoptosis-related genes in Hep3B and HepG2, cell adhesion and receptor molecules in HLE, and cytokine-related genes in PLC/PRF/5. About 40% of the genes showing amplification or loss showed altered levels of mRNA (p < 0.05). Hierarchical clustering analysis showed that the expression of these genes allows differentiation between alpha-fetoprotein (AFP)-producing and AFP-negative cell lines. cDNA array-CGH is a sensitive method that can be used to detect alterations in genomic copy number in tumor cells. Differences in DNA copy alterations between AFP-producing and AFP-negative cells may lead to differential gene expression and may be related to the phenotype of these cells.     

Identification of Networks of Co-Occurring,Tumor-Related DNA Copy Number Changes Using a Genome-Wide Scoring Approach

Tumorigenesis is a multi-step process in which normal cells transform into malignant tumors following the accumulation of genetic mutations that enable them to evade the growth control checkpoints that would normally suppress their growth or result in apoptosis. It is therefore important to identify those combinations of mutations that collaborate in cancer development and progression. DNA copy number alterations (CNAs) are one of the ways in which cancer genes are deregulated in tumor cells. We hypothesized that synergistic interactions between cancer genes might be identified by looking for regions of co-occurring gain and/or loss. To this end we developed a scoring framework to separate truly co-occurring aberrations from passenger mutations and dominant single signals present in the data. The resulting regions of high co-occurrence can be investigated for between-region functional interactions. Analysis of high-resolution DNA copy number data from a panel of 95 hematological tumor cell lines correctly identified co-occurring recombinations at the T-cell receptor and immunoglobulin loci in T- and B-cell malignancies, respectively, showing that we can recover truly co-occurring genomic alterations. In addition, our analysis revealed networks of co-occurring genomic losses and gains that are enriched for cancer genes. These networks are also highly enriched for functional relationships between genes. We further examine sub-networks of these networks, core networks, which contain many known cancer genes. The core network for co-occurring DNA losses we find seems to be independent of the canonical cancer genes within the network. Our findings suggest that large-scale, low-intensity copy number alterations may be an important feature of cancer development or maintenance by affecting gene dosage of a large interconnected network of functionally related genes.     

Detection of low level genomic alterations by comparative genomic hybridization based on cDNA micro-arrays

MOTIVATION: The accumulation of genomic alterations is an important process in tumor formation and progression. Comparative genomic hybridization performed on cDNA arrays (cDNA aCGH) is a common method to investigate the genomic alterations on a genome-wide scale. However, when detecting low-level DNA copy number changes this technology requires the use of noise reduction strategies due to a low signal to noise ratio. RESULTS: Currently a running average smoothing filter is the most frequently used noise reduction strategy. We analyzed this strategy theoretically and experimentally and found that it is not sensitive to very low level genomic alterations. The presence of systematic errors in the data is one of the main reasons for this failure. We developed a novel algorithm which efficiently reduces systematic noise and allows for the detection of low-level genomic alterations. The algorithm is based on comparison of the biological relevant data to data from so-called self-self hybridizations, additional experiments which contain no biological information but contain systematic errors. We find that with our algorithm the effective resolution for +/-1 DNA copy number changes is about 2 Mb. For copy number changes larger than three the effective resolution is on the level of single genes.     

Functional copy-number alterations in cancer

Understanding the molecular basis of cancer requires characterization of its genetic defects. DNA microarray technologies can provide detailed raw data about chromosomal aberrations in tumor samples. Computational analysis is needed (1) to deduce from raw array data actual amplification or deletion events for chromosomal fragments and (2) to distinguish causal chromosomal alterations from functionally neutral ones. We present a comprehensive computational approach, RAE, designed to robustly map chromosomal alterations in tumor samples and assess their functional importance in cancer. To demonstrate the methodology, we experimentally profile copy number changes in a clinically aggressive subtype of soft-tissue sarcoma, pleomorphic liposarcoma, and computationally derive a portrait of candidate oncogenic alterations and their target genes. Many affected genes are known to be involved in sarcomagenesis; others are novel, including mediators of adipocyte differentiation, and may include valuable therapeutic targets. Taken together, we present a statistically robust methodology applicable to high-resolution genomic data to assess the extent and function of copy-number alterations in cancer.     

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.     

The application of single nucleotide polymorphism microarrays in cancer research

The development of microarray technology has had a significant impact on the genetic analysis of human disease. The recently developed single nucleotide polymorphism (SNP) array can be used to measure both DNA polymorphism and dosage changes. Our laboratory has applied SNP microarray analysis to uncover frequent uniparental disomies and sub-microscopic genomic copy number gains and losses in different cancers. This review will focus on the wide range of applications of SNP microarray analysis to cancer research. SNP array genotyping can determine loss of heterozygosity, genomic copy number changes and DNA methylation alterations of cancer cells. The same technology can also be used to investigate allelic association in cancers. Therefore, it can be applied to the identification of cancer predisposition genes, oncogenes and tumor suppressor genes in specific types of tumors. As a consequence, they have potential in cancer risk assessment, diagnosis, prognosis and treatment selection.     

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.     

Progressive genomic instability in the FVB/Kras(LA2) mouse model of lung cancer

Alterations in DNA copy number contribute to the development and progression of cancers and are common in epithelial tumors. We have used array Comparative Genomic Hybridization (aCGH) to visualize DNA copy number alterations across the genomes of lung tumors in the Kras(LA2) model of lung cancer. Copy number gain involving the Kras locus, as focal amplification or whole chromosome gain, is the most common alteration in these tumors and with a prevalence that increased significantly with increasing tumor size. Furthermore, Kras amplification was the only major genomic event among the smallest lung tumors, suggesting that this alteration occurs early during the development of mutant Kras-driven lung cancers. Recurring gains and deletions of other chromosomes occur progressively more frequently among larger tumors. These results are in contrast to a previous aCGH analysis of lung tumors from Kras(LA2) mice on a mixed genetic background, in which relatively few DNA copy number alterations were observed regardless of tumor size. Our model features the Kras(LA2) allele on the inbred FVB/N mouse strain, and in this genetic background, there is a highly statistically significant increase in level of genomic instability with increasing tumor size. These data suggest that recurring DNA copy alterations are important for tumor progression in the Kras(LA2) model of lung cancer and that the requirement for these alterations may be dependent on the genetic background of the mouse strain.     

Digital karyotyping: an update of its applications in cancer

DNA copy number alterations, including entire chromosomal changes and small interstitial DNA amplifications and deletions, characterize the development of cancer. These changes usually affect the expression of target genes and subsequently the function of the target proteins. Since the completion of the human genome project, the capacity to comprehensively analyze the human cancer genome has expanded significantly. Techniques such as digital karyotyping have been developed to allow for the detection of DNA copy number alterations in cancer at the whole-genome scale. When compared with conventional methods such as spectral karyotyping, representational difference analysis, comparative genomic hybridization (CGH), or the more recent array CGH; digital karyotyping provides an evaluation of copy number of genetic material at higher resolution. Digital karyotyping has therefore promised to enhance our understanding of the cancer genome. This article provides an overview of digital karyotyping including the principle of the technology and its applications in identifying potential oncogenes and tumor suppressor genes.     

Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data

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.     

Integrative epigenetic and genetic pan-cancer somatic alteration portraits

Genetic and epigenetic alterations are required for carcinogenesis and the mutation burden across tumor types has been investigated. Here, we investigate epigenetic alterations with a novel measure of global DNA methylation dysregulation, the methylation dysregulation index (MDI), across 14 cancer types in The Cancer Genome Atlas (TCGA) database. DNA methylation data—obtained using Illumina HumanMethylation450 BeadChip—was accessed from TCGA. We calculated the MDI in 14 tumor types (n = 5,592 tumors), using adjacent normal tissues (n = 701) from each tumor site. Copy number alteration, and mutation burden were retrieved from cBioportal (n = 5,152). We tested the relation of subject MDI across tumors and with age, gender, tumor stage, estimated tumor purity, and copy number alterations for both overall MDI and genomic-context-specific MDI. We also investigated the top most dysregulated loci shared across tumor types. There was a broad range of extent in methylation dysregulation across tumor types (P < 2.2E-16). However, a consistent pattern of methylation dysregulation stratified by genomic context was observed across tumor types where the highest dysregulation occurred at non-CpG island regions. Considering other summary measures of somatic alteration, MDI was correlated with copy number alterations but not with mutation burden. Using the top dysregulated CpG sites in common across tumors, 4 classes of cancer types were observed, and the functional consequences of these alterations to gene expression were confirmed. This work identified the global DNA methylation dysregulation patterns across 14 cancer types showing a higher impact for the non-CpG island areas. The most dysregulated loci across cancer types identified common clusters across cancer types that may have implications for future treatment and prevention measures.     

Estimation of Copy Number Alterations from Exome Sequencing Data

Exome sequencing constitutes an important technology for the study of human hereditary diseases and cancer. However, the ability of this approach to identify copy number alterations in primary tumor samples has not been fully addressed. Here we show that somatic copy number alterations can be reliably estimated using exome sequencing data through a strategy that we have termed exome2cnv. Using data from 86 paired normal and primary tumor samples, we identified losses and gains of complete chromosomes or large genomic regions, as well as smaller regions affecting a minimum of one gene. Comparison with high-resolution comparative genomic hybridization (CGH) arrays revealed a high sensitivity and a low number of false positives in the copy number estimation between both approaches. We explore the main factors affecting sensitivity and false positives with real data, and provide a side by side comparison with CGH arrays. Together, these results underscore the utility of exome sequencing to study cancer samples by allowing not only the identification of substitutions and indels, but also the accurate estimation of copy number alterations.     

Array-CGH für die Analyse von Tumorgenomen

Array comparative genomic hybridization (array CGH) allows the genome-wide analysis of copy number changes at a high resolution. In the last decade, such copy number aberrations have been found frequently and in large quantities in tumor genomes. Alterations in the array CGH profile of tumor DNA indicate the location of tumor suppressor or proto-oncogenes, thereby enabling identification of cancer-relevant genes. In addition, patterns of aberrations have been detected that allow the molecular subclassification of certain tumor types with diagnostic significance. Array CGH analyses have also been instrumental in identifying new prognostic markers. In the future, data evaluation by integrated approaches, including other molecular levels and the selective use of chromosome and tumor-specific microarrays, will be of particular importance.     

Global regulation on microRNA in hepatitis B virus-associated hepatocellular carcinoma

Recent work has revealed the causative links between deregulation of microRNAs (miRNAs) and cancer development. In hepatocellular carcinoma (HCC), aberrant expression of miRNAs has been observed, but the molecular mechanisms that contribute to such changes remains to be elucidated. Here, we reported the analysis of miRNA expression in 94 pairs of tumor and adjacent nontumor tissues from HBV-associated HCC in Chinese patients. We found miRNAs were aberrantly expressed in HCC tissues. To investigate the cause of such deregulation, we detected changes in DNA copy number by measuring locus-specific hybridization intensity, and found changes in expression of several miRNAs are correlated with genomic amplification or deletion. For example, the genomic regions of miR-30d and miR-151 were amplified in ～50% of HCC tumor tissues, and the expressions of these miRNAs are significantly correlated with DNA copy number. We also employed cDNA microarray data, and provide evidence that key regulators of the miRNA biosynthetic pathway, including DROSHA, DGCR8, AGO1, and AGO2, are frequently overexpressed in HCC. This study provides molecular clues that may contribute to the global changes of miRNA expression in HCC.     

Genomic Heterogeneity of Osteosarcoma - Shift from Single Candidates to Functional Modules

Osteosarcoma (OS), a bone tumor, exhibit a complex karyotype. On the genomic level a highly variable degree of alterations in nearly all chromosomal regions and between individual tumors is observable. This hampers the identification of common drivers in OS biology. To identify the common molecular mechanisms involved in the maintenance of OS, we follow the hypothesis that all the copy number-associated differences between the patients are intercepted on the level of the functional modules. The implementation is based on a network approach utilizing copy number associated genes in OS, paired expression data and protein interaction data. The resulting functional modules of tightly connected genes were interpreted regarding their biological functions in OS and their potential prognostic significance. We identified an osteosarcoma network assembling well-known and lesser-known candidates. The derived network shows a significant connectivity and modularity suggesting that the genes affected by the heterogeneous genetic alterations share the same biological context. The network modules participate in several critical aspects of cancer biology like DNA damage response, cell growth, and cell motility which is in line with the hypothesis of specifically deregulated but functional modules in cancer. Further, we could deduce genes with possible prognostic significance in OS for further investigation (e.g. EZR, CDKN2A, MAP3K5). Several of those module genes were located on chromosome 6q. The given systems biological approach provides evidence that heterogeneity on the genomic and expression level is ordered by the biological system on the level of the functional modules. Different genomic aberrations are pointing to the same cellular network vicinity to form vital, but already neoplastically altered, functional modules maintaining OS. This observation, exemplarily now shown for OS, has been under discussion already for a longer time, but often in a hypothetical manner, and can here be exemplified for OS.     

CNAmet: an R package for integrating copy number, methylation and expression data

SUMMARY: Gene copy number and DNA methylation alterations are key regulators of gene expression in cancer. Accordingly, genes that show simultaneous methylation, copy number and expression alterations are likely to have a key role in tumor progression. We have implemented a novel software package (CNAmet) for integrative analysis of high-throughput copy number, DNA methylation and gene expression data. To demonstrate the utility of CNAmet, we use copy number, DNA methylation and gene expression data from 50 glioblastoma multiforme and 188 ovarian cancer primary tumor samples. Our results reveal a synergistic effect of DNA methylation and copy number alterations on gene expression for several known oncogenes as well as novel candidate oncogenes. AVAILABILITY: CNAmet R-package and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/CNAmet.     

Impact on disease development, genomic location and biological function of copy number alterations in non-small cell lung cancer

Lung cancer, of which more than 80% is non-small cell, is the leading cause of cancer-related death in the United States. Copy number alterations (CNAs) in lung cancer have been shown to be positionally clustered in certain genomic regions. However, it remains unclear whether genes with copy number changes are functionally clustered. Using a dense single nucleotide polymorphism array, we performed genome-wide copy number analyses of a large collection of non-small cell lung tumors (n = 301). We proposed a formal statistical test for CNAs between different groups (e.g., non-involved lung vs. tumors, early vs. late stage tumors). We also customized the gene set enrichment analysis (GSEA) algorithm to investigate the overrepresentation of genes with CNAs in predefined biological pathways and gene sets (i.e., functional clustering). We found that CNAs events increase substantially from germline, early stage to late stage tumor. In addition to genomic position, CNAs tend to occur away from the gene locations, especially in germline, non-involved tissue and early stage tumors. Such tendency decreases from germline to early stage and then to late stage tumors, suggesting a relaxation of selection during tumor progression. Furthermore, genes with CNAs in non-small cell lung tumors were enriched in certain gene sets and biological pathways that play crucial roles in oncogenesis and cancer progression, demonstrating the functional aspect of CNAs in the context of biological pathways that were overlooked previously. We conclude that CNAs increase with disease progression and CNAs are both positionally and functionally clustered. The potential functional capabilities acquired via CNAs may be sufficient for normal cells to transform into malignant cells.     

Development of a DNA-labeling system for array-based comparative genomic hybridization.

Chromosomal amplifications and deletions are critical components of tumorigenesis and DNA copy-number variations also correlate with changes in mRNA expression levels. Genome-wide microarray comparative genomic hybridization (CGH) has become an important method for detecting and mapping chromosomal changes in tumors. Thus, the ability to detect twofold differences in fluorescent intensity between samples on microarrays depends on the generation of high-quality labeled probes. To enhance array-based CGH analysis, a random prime genomic DNA labeling method optimized for improved sensitivity, signal-to-noise ratios, and reproducibility has been developed. The labeling system comprises formulated random primers, nucleotide mixtures, and notably a high concentration of the double mutant exo-large fragment of DNA polymerase I (exo-Klenow). Microarray analyses indicate that the genomic DNA-labeled templates yield hybridization signals with higher fluorescent intensities and greater signal-to-noise ratios and detect more positive features than the standard random prime and conventional nick translation methods. Also, templates generated by this system have detected twofold differences in gene copy number between male and female genomic DNA and identified amplification and deletions from the BT474 breast cancer cell line in microarray hybridizations. Moreover, alterations in gene copy number were routinely detected with 0.5 microg of genomic DNA starting sample. The method is flexible and performs efficiently with different fluorescently labeled nucleotides. Application of the optimized CGH labeling system may enhance the resolution and sensitivity of array-based CGH analysis in cancer and medical genetic studies.