Comparative Genomic Hybridization Data Research Articles

A model-based circular binary segmentation algorithm for the analysis of array CGH data

BackgroundCircular Binary Segmentation (CBS) is a permutation-based algorithm for array Comparative Genomic Hybridization (aCGH) data analysis. CBS accurately segments data by detecting change-points using a maximal-t test; but extensive computational burden is involved for evaluating the significance of change-points using permutations. A recent implementation utilizing a hybrid method and early stopping rules (hybrid CBS) to improve the performance in speed was subsequently proposed. However, a time analysis revealed that a major portion of computation time of the hybrid CBS was still spent on permutation. In addition, what the hybrid method provides is an approximation of the significance upper bound or lower bound, not an approximation of the significance of change-points itself.ResultsWe developed a novel model-based algorithm, extreme-value based CBS (eCBS), which limits permutations and provides robust results without loss of accuracy. Thousands of aCGH data under null hypothesis were simulated in advance based on a variety of non-normal assumptions, and the corresponding maximal-t distribution was modeled by the Generalized Extreme Value (GEV) distribution. The modeling results, which associate characteristics of aCGH data to the GEV parameters, constitute lookup tables (eXtreme model). Using the eXtreme model, the significance of change-points could be evaluated in a constant time complexity through a table lookup process.ConclusionsA novel algorithm, eCBS, was developed in this study. The current implementation of eCBS consistently outperforms the hybrid CBS 4× to 20× in computation time without loss of accuracy. Source codes, supplementary materials, supplementary figures, and supplementary tables can be found at http://ntumaps.cgm.ntu.edu.tw/eCBSsupplementary.

Array CGH Phylogeny: How accurate are Comparative Genomic Hybridization-based trees?

BackgroundArray-based Comparative Genomic Hybridization (CGH) data have been used to infer phylogenetic relationships. However, the reliability of array CGH analysis to determine evolutionary relationships has not been well established. In most CGH work, all species and strains are compared to a single reference species, whose genome was used to design the array. In the accompanying work, we critically evaluated CGH-based phylogeny using simulated competitive hybridization data. This work showed that a limited number of conditions, principally the tree topology and placement of the reference taxon in the tree, had a strong effect on the ability to recover the correct tree topology. Here, we add to our simulation study by testing the use of CGH as a phylogenetic tool with experimental CGH data from competitive hybridizations between N. crassa and other Neurospora species. In the discussion, we add to our empirical study of Neurospora by reanalyzing of data from a previous CGH phylogenetic analysis of the yeast sensu stricto complex.ResultsArray ratio data for Neurospora and related species were normalized with loess, robust spline, and linear ratio based methods, and then used to construct Neighbor-Joining and parsimony trees. These trees were compared to published phylogenetic analyses for Neurospora based on multilocus sequence analysis (MLSA). For the Neurospora dataset, the best combination of methods resulted in recovery of the MLSA tree topology less than half the time. Our reanalysis of a yeast dataset found that trees identical to established phylogeny were recovered only by pruning taxa - including the reference taxon - from the analysis.ConclusionOur results indicate that CGH data can be problematic for phylogenetic analysis. Success fluctuates based on the methods utilized to construct the tree and the taxa included. Selective pruning of the taxa improves the results - an impractical approach for normal phylogenetic analysis. From the more successful methods we make suggestions on the normalization and post-normalization methods that work best in estimating genetic distance between taxa.

MiR-101 DNA Copy Loss is a Prominent Subtype Specific Event in Lung Cancer

MicroRNA-101 (miR-101) is frequently downregulated in cancer and exhibits antitumorigenic properties, suggesting that miR-101 is a putative tumor suppressor. miR-101 is encoded at two loci in the human genome: 1p31.3 (miR-101-1) and 9p24.1 (miR-101-2). We sought to investigate miR-101 locus-specific deletions and genomic loss in the major subtypes of lung cancer. Analyses of high-resolution array comparative genomic hybridization and single-nucleotide polymorphism array data were performed to determine the DNA copy number status and deletion boundaries of miR-101-1 and miR-101-2 in seven independent cohorts that comprised 1,236 lung cancer specimens and 20 lung carcinoma in situ samples. miR-101 expression was also investigated in lung cancer cell lines and tumors. miR-101 loss at either genomic locus was identified in 29% of lung cancers analyzed and was associated with reduced miR-101 expression. Loss was more frequent at the 9p locus and occurred more often in NSCLC as opposed to small cell lung cancer. Lung carcinoma in situ also harbored miR-101 deletions, suggesting that genomic loss may be an early event in lung cancer development. Finally, miR-101 deletions on 9p were exclusive of CDKN2A deletions in several cases, providing evidence that loss of miR-101 is not merely a passenger of CDKN2A deletion. miR-101 genomic loss occurs frequently in NSCLC and may be an early event in lung tumorigenesis. DNA deletions likely represent a prominent molecular mechanism of miR-101 downregulation in NSCLC but not in small cell lung cancer.

A Bayesian approach to inference about a change point model with application to DNA copy number experimental data

In this paper, we study the change-point inference problem motivated by the genomic data that were collected for the purpose of monitoring DNA copy number changes. DNA copy number changes or copy number variations (CNVs) correspond to chromosomal aberrations and signify abnormality of a cell. Cancer development or other related diseases are usually relevant to DNA copy number changes on the genome. There are inherited random noises in such data, therefore, there is a need to employ an appropriate statistical model for identifying statistically significant DNA copy number changes. This type of statistical inference is evidently crucial in cancer researches, clinical diagnostic applications, and other related genomic researches. For the high-throughput genomic data resulting from DNA copy number experiments, a mean and variance change point model (MVCM) for detecting the CNVs is appropriate. We propose to use a Bayesian approach to study the MVCM for the cases of one change and propose to use a sliding window to search for all CNVs on a given chromosome. We carry out simulation studies to evaluate the estimate of the locus of the DNA copy number change using the derived posterior probability. These simulation results show that the approach is suitable for identifying copy number changes. The approach is also illustrated on several chromosomes from nine fibroblast cancer cell line data (array-based comparative genomic hybridization data). All DNA copy number aberrations that have been identified and verified by karyotyping are detected by our approach on these cell lines.

Volatile Compound Fingerprinting of Mixed-Culture Fermentations

With the advent of the -omics era, classical technology platforms, such as hyphenated mass spectrometry, are currently undergoing a transformation toward high-throughput application. These novel platforms yield highly detailed metabolite profiles in large numbers of samples. Such profiles can be used as fingerprints for the accurate identification and classification of samples as well as for the study of effects of experimental conditions on the concentrations of specific metabolites. Challenges for the application of these methods lie in the acquisition of high-quality data, data normalization, and data mining. Here, a high-throughput fingerprinting approach based on analysis of headspace volatiles using ultrafast gas chromatography coupled to time of flight mass spectrometry (ultrafast GC/TOF-MS) was developed and evaluated for classification and screening purposes in food fermentation. GC-MS mass spectra of headspace samples of milk fermented by different mixed cultures of lactic acid bacteria (LAB) were collected and preprocessed in MetAlign, a dedicated software package for the preprocessing and comparison of liquid chromatography (LC)-MS and GC-MS data. The Random Forest algorithm was used to detect mass peaks that discriminated combinations of species or strains used in fermentations. Many of these mass peaks originated from key flavor compounds, indicating that the presence or absence of individual strains or combinations of strains significantly influenced the concentrations of these components. We demonstrate that the approach can be used for purposes like the selection of strains from collections based on flavor characteristics and the screening of (mixed) cultures for the presence or absence of strains. In addition, we show that strain-specific flavor characteristics can be traced back to genetic markers when comparative genome hybridization (CGH) data are available.

Chromosomal imbalances in carcinoma showing thymus-like elements (CASTLE)

Carcinoma showing thymus-like elements (CASTLE) is a rare neoplasm of the thyroid gland resembling lymphoepithelioma-like and squamous cell carcinoma of the thymus and is thought to arise from ectopic thymic tissue within the thyroid gland or rudimentary branchial pouches along the thymic line. Using comparative genomic hybridization (CGH), chromosomal imbalances have been detected in several types of thymomas and thymic carcinomas. To evaluate whether there are hints of an underlying sequence in the pathogenesis of CASTLE analogue to those found in thymomas and thymic carcinomas, we evaluated four of these rare neoplasms for chromosomal imbalances using CGH. The most frequent gains were seen on chromosomal arm 1q (3/4), and losses were most frequently detected on 6p (4/4), 6q (3/4) and 16q (3/4). These CGH data show that CASTLE is characterized by chromosomal imbalances similar to those found in thymomas and thymic carcinomas and indicate a similar sequence in tumour development.

MiRNA expression profiles in myelodysplastic syndromes reveal Epstein–Barr virus miR-BART13 dysregulation

Recently, the microRNA (miRNA) signature has been used for better characterization and understanding of the pathogenesis of different malignancies, including myelodysplastic syndromes (MDS). MDS are a heterogeneous group of stem cell disorders in which the genetic and molecular defects are not well defined. In the present study, we applied array based miRNA profiling to study 19 bone marrow cell samples of de novo MDS compared with eight healthy individuals. In addition, integration of the miRNA profiling data with our previous array comparative genomic hybridization data, from the same cohort of patients, was performed. We observed up-regulation of hsa-miR-720 and hsa-miR-21, and down-regulation of hsa-miR-671-5p and one human virus miRNA (Epstein–Barr virus miR-BART13) in MDS samples compared with normal samples. In our study, the copy number alteration harboring miRNA was not affecting miRNA expression, but a distinct microRNA expression pattern was observed, not only in MDS compared with controls, but also between MDS entities.

Bayesian–frequentist hybrid model with application to the analysis of gene copy number changes

Gene copy number (GCN) changes are common characteristics of many genetic diseases. Comparative genomic hybridization (CGH) is a new technology widely used today to screen the GCN changes in mutant cells with high resolution genome-wide. Statistical methods for analyzing such CGH data have been evolving. Existing methods are either frequentist's or full Bayesian. The former often has computational advantage, while the latter can incorporate prior information into the model, but could be misleading when one does not have sound prior information. In an attempt to take full advantages of both approaches, we develop a Bayesian-frequentist hybrid approach, in which a subset of the model parameters is inferred by the Bayesian method, while the rest parameters by the frequentist's. This new hybrid approach provides advantages over those of the Bayesian or frequentist's method used alone. This is especially the case when sound prior information is available on part of the parameters, and the sample size is relatively small. Spatial dependence and false discovery rate are also discussed, and the parameter estimation is efficient. As an illustration, we used the proposed hybrid approach to analyze a real CGH data.

Approximation algorithms for speeding up dynamic programming and denoising aCGH data

The development of cancer is largely driven by the gain or loss of subsets of the genome, promoting uncontrolled growth or disabling defenses against it. Denoising array-based Comparative Genome Hybridization (aCGH) data is an important computational problem central to understanding cancer evolution. In this article, we propose a new formulation of the denoising problem that we solve with a “vanilla” dynamic programming algorithm, which runs in O ( n 2 ) units of time. Then, we propose two approximation techniques. Our first algorithm reduces the problem into a well-studied geometric problem, namely halfspace emptiness queries, and provides an ϵ additive approximation to the optimal objective value in Õ( n 4/3;+Δ log (U/ϵ)) time, where Δ is an arbitrarily small positive constant and U = max{#8730;C,(| P i |) i =1,…, n } ( P =( P 1 , P 2 , …, P n ), P i ∈ ℝ, is the vector of the noisy aCGH measurements, C a normalization constant). The second algorithm provides a (1 ± ϵ) approximation (multiplicative error) and runs in O ( n log n /ϵ) time. The algorithm decomposes the initial problem into a small (logarithmic) number of Monge optimization subproblems that we can solve in linear time using existing techniques. Finally, we validate our model on synthetic and real cancer datasets. Our method consistently achieves superior precision and recall to leading competitors on the data with ground truth. In addition, it finds several novel markers not recorded in the benchmarks but supported in the oncology literature.

Abstract 2256: Novel prognostic targets identified using integrated analysis of copy number and expression data 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 less than 20%. Understanding the genomic aberrations and biology of this cancer may enhance disease interventions. Aims: i) To identify novel molecular targets with clinical relevance using an integrated approach in analyzing genome-wide copy number and expression data of EAC. ii) To correlate reduced expression of molecular targets with promoter hypermethylation in EAC. Methods: 30K oligonucleotide array comparative genomic hybridization (CGH) and gene expression microarray profiling were performed on 56 chemo-naive EAC resection samples with long-term clinical follow-up data (median follow up: 20 months, range 0.5-137) to identify regions of copy number changes leading to differential expression levels. Fluorescence in situ hybridization (FISH) assays were carried out on touch-imprint slides of frozen tumor samples to confirm amplification status. Immunohistochemistry (IHC) assays on tissuemicroarrays (TMAs) on the same (n=56) and an independent (n=371; median follow-up: 17 months, range 0.5-193) cohort of EAC samples were used for subsequent validation at the protein level. Kaplan-Meier survival analyses were performed based on IHC data to determine prognostic significance of novel molecular targets. An Illumina Infinium Array was used to investigate the methylation status of 24 independent EAC tumors, compared with 24 premalignant Barrett's Esophagus (BE) samples. Results: Integrated array CGH data with matched gene expression microarray data highlighted 4 genes with copy number gains (log2 ratio>0.32) and increased expression (fold-change>6.5): EGFR, WT1, NEIL2 and MTMR9. FISH assays confirmed amplifications of these genes (more than 6 copies/centromere) in 11% of EAC samples and IHC assays (n=371 independent cases) demonstrated protein over-expression (IHC scores above 2): EGFR (10%), WT1 (20%), NEIL2 (14%) and MTMR9 (25%), corroborating findings from the integrated analysis. Each of these targets individually had prognostic significance in our cohort (P<0.060). Interestingly, reduced expressions of WT1 (P=0.037) and NEIL2 (P=0.051) were associated with poorer prognosis in EAC. In order to investigate the mechanism of reduced expression of these targets which might have contributed to a worse prognosis, methylation array data was utilized from an independent dataset. This showed increased methylation of WT1 in EAC (63%) compared to BE (55%; t-test P=0.028) and normal controls (50%), but no difference in methylation status of NEIL2 between disease states. Conclusion: Integration of array CGH and gene expression data identified novel molecular targrets in EAC and reduced expression of WT1 may be secondary to methylation. These genes have prognostic potential and require further study. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2256. doi:10.1158/1538-7445.AM2011-2256

Abstract 44: Towards novel marker discovery from phylogenetic analysis of heterogeneous tumor samples

Abstract Computational methods based on phylogenetics, or the inference of evolutionary trees, have been useful for inferring likely pathways of tumor progression, but their use has been limited by our inability to precisely characterize the mutational states of individual cells in heterogeneous tumor samples. We describe a novel approach to tumor phylogenetics using computationally inferred sub-populations of cells derived from whole-genome data on heterogeneous tumor samples. We previously proposed a strategy for computationally separating cell types from heterogeneous tumor samples and inferring genome-wide profiles of RNA expression or DNA copy number for these discrete cell populations from profiles of the full tumor samples. In the present work, we extend this method by showing how one can use these computationally inferred cell states to identify markers informative of progression state and use them in phylogenetic analysis of tumor samples. We demonstrate the approach on a publicly available data set of array comparative genome hybridization (aCGH) data profiles applied to sectioned ductal breast carcinomas (Navin et al., 2010; NCBI GEO GSE16672). Our approach begins by applying our previously described method for cell type separation to generate inferred profiles of six cell types, and computationally inferring virtual aCGH profiles for these types. We then scan over sliding windows of 20 consecutive probes per window, identifying windows for which probe values are significantly aberrant relative to diploid by a modified chi-squared test (p-value 0.001 with Bonferroni correction), and merging overlapping windows to identify phylogenetically informative amplicons. We then identify, for each amplicon, those components that have significantly aberrant copy number by Gaussian test with diploid mean (p-value 0.001). Finally, the identified amplicon states for the inferred cell types, as well as an additional all-diploid normal type, are used as input for a maximum parsimony phylogenetic tree reconstruction using the PAUP package with 300 bootstrap replicates. The method applied to the Navin et al. data identified 15 amplicons phylogenetically informative for breast tumor progression. Bootstrap replicates from the phylogenetic tree reconstruction reveal a robust grouping of cell states into three distinct subgroups and suggest several possible progression pathways by which the identified amplicons may successively accumulate in particular breast cancer sub-types. Continuing work will examine robustness of the inferred markers and pathways to independent data sources. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 44. doi:10.1158/1538-7445.AM2011-44

Novel prognostic targets identified using integrated analysis of copy number and expression data in oesophageal adenocarcinoma

IntroductionThe incidence of oesophageal adenocarcinoma (OAC) has increased sixfold in the west over the last 30 years, with a dismal survival rate of less than 20%. Understanding the genomic aberrations...

ACGH.Spline—an R package for aCGH dye bias normalization

The careful normalization of array-based comparative genomic hybridization (aCGH) data is of critical importance for the accurate detection of copy number changes. The difference in labelling affinity between the two fluorophores used in aCGH-usually Cy5 and Cy3-can be observed as a bias within the intensity distributions. If left unchecked, this bias is likely to skew data interpretation during downstream analysis and lead to an increased number of false discoveries. In this study, we have developed aCGH.Spline, a natural cubic spline interpolation method followed by linear interpolation of outlier values, which is able to remove a large portion of the dye bias from large aCGH datasets in a quick and efficient manner. We have shown that removing this bias and reducing the experimental noise has a strong positive impact on the ability to detect accurately both copy number variation (CNV) and copy number alterations (CNA).

Integrated Genomic Analysis of Nodular Tissue in Macronodular Adrenocortical Hyperplasia: Progression of Tumorigenesis in a Disorder Associated with Multiple Benign Lesions

Massive macronodular adrenocortical disease or ACTH-independent macronodular adrenal hyperplasia (AIMAH) is a clinically and genetically heterogeneous disorder. Whole-genome expression profiling and oligonucleotide array comparative genomic hybridization changes were analyzed in samples of different nodules from the same patients with AIMAH. Quantitative RT-PCR and staining were employed to validate the mRNA array data. Chromosomal gains were more frequent in larger nodules when compared with smaller nodules from the same patients. Among the 50 most overexpressed genes, 50% had a chromosomal locus that was amplified in the comparative genomic hybridization data. Although the list of most over- and underexpressed genes was similar between the nodules of different size, the gene set enrichment analysis identified different pathways associated with AIMAH that corresponded to the size; the smaller nodules were mainly enriched for metabolic pathways, whereas p53 signaling and cancer genes were enriched in larger nodules. Confirmatory studies demonstrated that BCL2, E2F1, EGF, c-KIT, MYB, PRKCA, and CTNNB1 were overexpressed in the larger nodules at messenger and/or protein levels. Chromosomal enrichment analysis showed that chromosomes 20q13 and 14q23 might be involved in progression of AIMAH from smaller to larger tumors. Integrated transcriptomic and genomic data for AIMAH provides supporting evidence to the hypothesis that larger adrenal lesions, in the context of this chronic, polyclonal hyperplasia, accumulate an increased number of genomic and, subsequently, transcript abnormalities. The latter shows that the disease appears to start with mainly tissue metabolic derangements, as suggested by the study of the smaller nodules, but larger lesions showed aberrant expression of oncogenic pathways.

Joint segmentation, calling, and normalization of multiple CGH profiles

The statistical analysis of array comparative genomic hybridization (CGH) data has now shifted to the joint assessment of copy number variations at the cohort level. Considering multiple profiles gives the opportunity to correct for systematic biases observed on single profiles, such as probe GC content or the so-called "wave effect." In this article, we extend the segmentation model developed in the univariate case to the joint analysis of multiple CGH profiles. Our contribution is multiple: we propose an integrated model to perform joint segmentation, normalization, and calling for multiple array CGH profiles. This model shows great flexibility, especially in the modeling of the wave effect that gives a likelihood framework to approaches proposed by others. We propose a new dynamic programming algorithm for break point positioning, as well as a model selection criterion based on a modified bayesian information criterion proposed in the univariate case. The performance of our method is assessed using simulated and real data sets. Our method is implemented in the R package cghseg.

Genetic diversity of Streptococcus suis isolates as determined by comparative genome hybridization

BackgroundStreptococcus suis is a zoonotic pathogen that causes infections in young piglets. S. suis is a heterogeneous species. Thirty-three different capsular serotypes have been described, that differ in virulence between as well as within serotypes.ResultsIn this study, the correlation between gene content, serotype, phenotype and virulence among 55 S. suis strains was studied using Comparative Genome Hybridization (CGH). Clustering of CGH data divided S. suis isolates into two clusters, A and B. Cluster A isolates could be discriminated from cluster B isolates based on the protein expression of extracellular factor (EF). Cluster A contained serotype 1 and 2 isolates that were correlated with virulence. Cluster B mainly contained serotype 7 and 9 isolates. Genetic similarity was observed between serotype 7 and serotype 2 isolates that do not express muramidase released protein (MRP) and EF (MRP-EF-), suggesting these isolates originated from a common founder. Profiles of 25 putative virulence-associated genes of S. suis were determined among the 55 isolates. Presence of all 25 genes was shown for cluster A isolates, whereas cluster B isolates lacked one or more putative virulence genes. Divergence of S. suis isolates was further studied based on the presence of 39 regions of difference. Conservation of genes was evaluated by the definition of a core genome that contained 78% of all ORFs in P1/7.ConclusionsIn conclusion, we show that CGH is a valuable method to study distribution of genes or gene clusters among isolates in detail, yielding information on genetic similarity, and virulence traits of S. suis isolates.

MYBL2 (B-MYB) in Cervical Cancer: Putative Biomarker

Cervical cancer is the second most common cancer affecting women worldwide. It is characterized by chromosomal aberrations and alteration in the expression levels of many cell cycle regulatory proteins. MYBL2 is a member of the MYB proto-oncogene family that encodes DNA binding proteins. These proteins are involved in cell proliferation and control of cellular differentiation. Four established cervical cancer cell lines were examined and compared with normal cervix using gene expression profiling and comparative genomic hybridization, and results were correlated to identify potential novel cervical cancer biomarkers. Results were validated using TaqMan polymerase chain reaction, and the potential role of MYBL2 as a clinical biomarker was then evaluated by immunohistochemistry on 30 tissue samples. MYBL2 was found to be overexpressed in the cervical cancer cell lines by gene expression profiling, and this result was confirmed using TaqMan polymerase chain reaction. Analysis of comparative genomic hybridization data indicated that chromosome 20q13.1, which encodes the MYBL2 gene, was amplified in the human papillomavirus (HPV) type 16-positive CaSki and SiHa cell lines but not in the HPV-18-positive HeLa or HPV-negative C33A cell lines. Although MYBL2 staining was predominantly absent in normal cervical epithelium, strong staining (score of 2 or 3) was identified in all cases of cervical intraepithelial neoplasia, cervical glandular intraepithelial neoplasia, and invasive cancer on immunohistochemistry. In addition, strong staining of a population of diffusely scattered single cells is identified. We postulate that these may represent so-called cancer stem-like cells.

−8p12–23 and +20q Are Predictors of Subtypes and Metastatic Pathways in Colorectal Cancer: Construction of Tree Models Using Comparative Genomic Hybridization Data

A substantial body of evidence suggests the genetic heterogeneous pattern and multiple pathways in colorectal cancer initiation and progression. In this study, we construct a branching tree and multiple distance-based tree models to elucidate these genetic patterns and pathways in colorectal cancer by using a data set comprised of 244 cases of comparative genomic hybridization. We identify the six most common gains of chromosomal regions of 7p (37.0%), 7q11-32 (34.8%), 8q (48.3%), 13q (49.1%), 20p (36.1%), and 20q (50.4%), and the nine most common losses of 1p13-36 (30.9%), 4p15 (24.3%), 4q33-34 (24.3%), 8p12-23 (50.9%), 15q13-14 (23.5%), 15q24-25 (24.3%), 17p (34.8%), 18p (36.5%), and 18q (61.7%) in colorectal cancer. We classify colorectal cancer into two distinct groups: one preceding with -8p12-23, and the other with +20q. The sample-based classification tree also demonstrates that colorectal cancer can be classified into multiple subtypes marked by -8p12-23 and +20q. By comparing chromosomal abnormalities between primary and metastatic colorectal cancer, we identify five potential metastatic pathways: (-18q, -18p), (-8p12-23, -4p15, -4q33-34), (+20q, +20p), (+20q, +7p, +7q11-32), and +8q. -8p12-23 and +20q are inferred to be the two marker events of colorectal cancer metastasis. The current oncogenetic tree models may contribute to our understanding towards molecular genetics in colorectal cancer. Particularly, the metastatic pathways we describe may provide pivotal clues for metastatic candidate genes, and thus impact on the prediction and intervention of metastatic colorectal cancer.

Glioblastoma stem-like cells give rise to tumour endothelium

Glioblastoma (GBM) is among the most aggressive of human cancers. A key feature of GBMs is the extensive network of abnormal vasculature characterized by glomeruloid structures and endothelial hyperplasia. Yet the mechanisms of angiogenesis and the origin of tumour endothelial cells remain poorly defined. Here we demonstrate that a subpopulation of endothelial cells within glioblastomas harbour the same somatic mutations identified within tumour cells, such as amplification of EGFR and chromosome 7. We additionally demonstrate that the stem-cell-like CD133(+) fraction includes a subset of vascular endothelial-cadherin (CD144)-expressing cells that show characteristics of endothelial progenitors capable of maturation into endothelial cells. Extensive in vitro and in vivo lineage analyses, including single cell clonal studies, further show that a subpopulation of the CD133(+) stem-like cell fraction is multipotent and capable of differentiation along tumour and endothelial lineages, possibly via an intermediate CD133(+)/CD144(+) progenitor cell. The findings are supported by genetic studies of specific exons selected from The Cancer Genome Atlas, quantitative FISH and comparative genomic hybridization data that demonstrate identical genomic profiles in the CD133(+) tumour cells, their endothelial progenitor derivatives and mature endothelium. Exposure to the clinical anti-angiogenesis agent bevacizumab or to a γ-secretase inhibitor as well as knockdown shRNA studies demonstrate that blocking VEGF or silencing VEGFR2 inhibits the maturation of tumour endothelial progenitors into endothelium but not the differentiation of CD133(+) cells into endothelial progenitors, whereas γ-secretase inhibition or NOTCH1 silencing blocks the transition into endothelial progenitors. These data may provide new perspectives on the mechanisms of failure of anti-angiogenesis inhibitors currently in use. The lineage plasticity and capacity to generate tumour vasculature of the putative cancer stem cells within glioblastoma are novel findings that provide new insight into the biology of gliomas and the definition of cancer stemness, as well as the mechanisms of tumour neo-angiogenesis.

KC-SMARTR: An R package for detection of statistically significant aberrations in multi-experiment aCGH data

BackgroundMost approaches used to find recurrent or differential DNA Copy Number Alterations (CNA) in array Comparative Genomic Hybridization (aCGH) data from groups of tumour samples depend on the discretization of the aCGH data to gain, loss or no-change states. This causes loss of valuable biological information in tumour samples, which are frequently heterogeneous. We have previously developed an algorithm, KC-SMART, that bases its estimate of the magnitude of the CNA at a given genomic location on kernel convolution (Klijn et al., 2008). This accounts for the intensity of the probe signal, its local genomic environment and the signal distribution across multiple samples.ResultsHere we extend the approach to allow comparative analyses of two groups of samples and introduce the R implementation of these two approaches. The comparative module allows for a supervised analysis to be performed, to enable the identification of regions that are differentially aberrated between two user-defined classes.We analyzed data from a series of B- and T-cell lymphomas and were able to retrieve all positive control regions (VDJ regions) in addition to a number of new regions. A t-test employing segmented data, that we implemented, was also able to locate all the positive control regions and a number of new regions but these regions were highly fragmented.ConclusionsKC-SMARTR offers recurrent CNA and class specific CNA detection, at different genomic scales, in a single package without the need for additional segmentation. It is memory efficient and runs on a wide range of machines. Most importantly, it does not rely on data discretization and therefore maximally exploits the biological information in the aCGH data.The program is freely available from the Bioconductor website http://www.bioconductor.org/ under the terms of the GNU General Public License.