Micro-Scale Genomic DNA Copy Number Aberrations as Another Means of Mutagenesis in Breast Cancer

Introduction

In breast cancer, the basal-like subtype has high levels of genomic instability relative to other breast cancer subtypes with many basal-like-specific regions of aberration. There is evidence that this genomic instability extends to smaller scale genomic aberrations, as shown by a previously described micro-deletion event in the PTEN gene in the Basal-like SUM149 breast cancer cell line.

Methods

We sought to identify if small regions of genomic DNA copy number changes exist by using a high density, gene-centric Comparative Genomic Hybridizations (CGH) array on cell lines and primary tumors. A custom tiling array for CGH (244,000 probes, 200 bp tiling resolution) was created to identify small regions of genomic change, which was focused on previously identified basal-like-specific, and general cancer genes. Tumor genomic DNA from 94 patients and 2 breast cancer cell lines was labeled and hybridized to these arrays. Aberrations were called using SWITCHdna and the smallest 25% of SWITCHdna-defined genomic segments were called micro-aberrations (<64 contiguous probes, ∼ 15 kb).

Results

Our data showed that primary tumor breast cancer genomes frequently contained many small-scale copy number gains and losses, termed micro-aberrations, most of which are undetectable using typical-density genome-wide aCGH arrays. The basal-like subtype exhibited the highest incidence of these events. These micro-aberrations sometimes altered expression of the involved gene. We confirmed the presence of the PTEN micro-amplification in SUM149 and by mRNA-seq showed that this resulted in loss of expression of all exons downstream of this event. Micro-aberrations disproportionately affected the 5′ regions of the affected genes, including the promoter region, and high frequency of micro-aberrations was associated with poor survival.

Conclusion

Using a high-probe-density, gene-centric aCGH microarray, we present evidence of small-scale genomic aberrations that can contribute to gene inactivation. These events may contribute to tumor formation through mechanisms not detected using conventional DNA copy number analyses.     

Deep Sequencing of Target Linkage Assay-Identified Regions in Familial Breast Cancer: Methods,Analysis Pipeline and Troubleshooting

Background

The classical candidate-gene approach has failed to identify novel breast cancer susceptibility genes. Nowadays, massive parallel sequencing technology allows the development of studies unaffordable a few years ago. However, analysis protocols are not yet sufficiently developed to extract all information from the huge amount of data obtained.

Methodology/Principal Findings

In this study, we performed high throughput sequencing in two regions located on chromosomes 3 and 6, recently identified by linkage studies by our group as candidate regions for harbouring breast cancer susceptibility genes. In order to enrich for the coding regions of all described genes located in both candidate regions, a hybrid-selection method on tiling microarrays was performed.

Conclusions/Significance

We developed an analysis pipeline based on SOAP aligner to identify candidate variants with a high real positive confirmation rate (0.89), with which we identified eight variants considered candidates for functional studies. The results suggest that the present strategy might be a valid second step for identifying high penetrance genes.     

Application of Equilibrium Models of Solution Hybridization to Microarray Design and Analysis

Background

The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms.

Principal Findings

50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods.

Conclusions

Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe “percent bound” value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for short oligonucleotide arrays.     

Cross-platform microarray data normalisation for regulatory network inference

Background

Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.

Methods

We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.

Conclusions

Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.     

TiArA: A Virtual Appliance for the Analysis of Tiling Array Data

Background

Genomic tiling arrays have been described in the scientific literature since 2003, yet there is a shortage of user-friendly applications available for their analysis.

Methodology/Principal Findings

Tiling Array Analyzer (TiArA) is a software program that provides a user-friendly graphical interface for the background subtraction, normalization, and summarization of data acquired through the Affymetrix tiling array platform. The background signal is empirically measured using a group of nonspecific probes with varying levels of GC content and normalization is performed to enforce a common dynamic range.

Conclusions/Significance

TiArA is implemented as a standalone program for Linux systems and is available as a cross-platform virtual machine that will run under most modern operating systems using virtualization software such as Sun VirtualBox or VMware. The software is available as a Debian package or a virtual appliance at http://purl.org/NET/tiara.     

Methylomic analysis identifies frequent DNA methylation of zinc finger protein 582 (ZNF582) in cervical neoplasms

Background

Despite of the trend that the application of DNA methylation as a biomarker for cancer detection is promising, clinically applicable genes are few. Therefore, we looked for novel hypermethylated genes for cervical cancer screening.

Methods and Findings

At the discovery phase, we analyzed the methylation profiles of human cervical carcinomas and normal cervixes by methylated DNA immunoprecipitation coupled to promoter tiling arrays (MeDIP-on-chip). Methylation-specific PCR (MSP), quantitative MSP and bisulfite sequencing were used to verify the methylation status in cancer tissues and cervical scrapings from patients with different severities. Immunohistochemical staining of a cervical tissue microarray was used to confirm protein expression. We narrowed to three candidate genes: DBC1, PDE8B, and ZNF582; their methylation frequencies in tumors were 93%, 29%, and 100%, respectively. At the pre-validation phase, the methylation frequency of DBC1 and ZNF582 in cervical scraping correlated significantly with disease severity in an independent cohort (n = 330, both P<0.001). For the detection of cervical intraepithelial neoplasia 3 (CIN3) and worse, the area under the receiver operating characteristic curve (AUC) of ZNF582 was 0.82 (95% confidence interval  = 0.76–0.87).

Conclusions

Our study shows ZNF582 is frequently methylated in CIN3 and worse lesions, and it is demonstrated as a potential biomarker for the molecular screening of cervical cancer.     

Genome-Wide Mapping Indicates That p73 and p63 Co-Occupy Target Sites and Have Similar DNA-Binding Profiles In Vivo

Background

The p53 homologs, p63 and p73, share ∼85% amino acid identity in their DNA-binding domains, but they have distinct biological functions.

Principal Findings

Using chromatin immunoprecipitation and high-resolution tiling arrays covering the human genome, we identify p73 DNA binding sites on a genome-wide level in ME180 human cervical carcinoma cells. Strikingly, the p73 binding profile is indistinguishable from the previously described binding profile for p63 in the same cells. Moreover, the p73∶p63 binding ratio is similar at all genomic loci tested, suggesting that there are few, if any, targets that are specific for one of these factors. As assayed by sequential chromatin immunoprecipitation, p63 and p73 co-occupy DNA target sites in vivo, suggesting that p63 and p73 bind primarily as heterotetrameric complexes in ME180 cells.

Conclusions

The observation that p63 and p73 associate with the same genomic targets suggest that their distinct biological functions are due to cell-type specific expression and/or protein domains that involve functions other than DNA binding.     

Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data

Background

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.

Methodology/Results

We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.

Conclusions

Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.     

Mutation spectrum of Drosophila CNVs revealed by breakpoint sequencing

Background

The detailed study of breakpoints associated with copy number variants (CNVs) can elucidate the mutational mechanisms that generate them and the comparison of breakpoints across species can highlight differences in genomic architecture that may lead to lineage-specific differences in patterns of CNVs. Here, we provide a detailed analysis of Drosophila CNV breakpoints and contrast it with similar analyses recently carried out for the human genome.

Results

By applying split-read methods to a total of 10x coverage of 454 shotgun sequence across nine lines of D. melanogaster and by re-examining a previously published dataset of CNVs detected using tiling arrays, we identified the precise breakpoints of more than 600 insertions, deletions, and duplications. Contrasting these CNVs with those found in humans showed that in both taxa CNV breakpoints fall into three classes: blunt breakpoints; simple breakpoints associated with microhomology; and breakpoints with additional nucleotides inserted/deleted and no microhomology. In both taxa CNV breakpoints are enriched with non-B DNA sequence structures, which may impair DNA replication and/or repair. However, in contrast to human genomes, non-allelic homologous-recombination (NAHR) plays a negligible role in CNV formation in Drosophila. In flies, non-homologous repair mechanisms are responsible for simple, recurrent, and complex CNVs, including insertions of de novo sequence as large as 60 bp.

Conclusions

Humans and Drosophila differ considerably in the importance of homology-based mechanisms for the formation of CNVs, likely as a consequence of the differences in the abundance and distribution of both segmental duplications and transposable elements between the two genomes.     

Disease Gene Characterization through Large-Scale Co-Expression Analysis

Background

In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).

Results

Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.

Discussion

UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis.     

Use of Whole Genome Sequencing to Determine the Microevolution of Mycobacterium tuberculosis during an Outbreak

Rationale

Current tools available to study the molecular epidemiology of tuberculosis do not provide information about the directionality and sequence of transmission for tuberculosis cases occurring over a short period of time, such as during an outbreak. Recently, whole genome sequencing has been used to study molecular epidemiology of Mycobacterium tuberculosis over short time periods.

Objective

To describe the microevolution of M. tuberculosis during an outbreak caused by one drug-susceptible strain.

Method and Measurements

We included 9 patients with tuberculosis diagnosed during a period of 22 months, from a population-based study of the molecular epidemiology in San Francisco. Whole genome sequencing was performed using Illumina’s sequencing by synthesis technology. A custom program written in Python was used to determine single nucleotide polymorphisms which were confirmed by PCR product Sanger sequencing.

Main results

We obtained an average of 95.7% (94.1–96.9%) coverage for each isolate and an average fold read depth of 73 (1 to 250). We found 7 single nucleotide polymorphisms among the 9 isolates. The single nucleotide polymorphisms data confirmed all except one known epidemiological link. The outbreak strain resulted in 5 bacterial variants originating from the index case A1 with 0–2 mutations per transmission event that resulted in a secondary case.

Conclusions

Whole genome sequencing analysis from a recent outbreak of tuberculosis enabled us to identify microevolutionary events observable during transmission, to determine 0–2 single nucleotide polymorphisms per transmission event that resulted in a secondary case, and to identify new epidemiologic links in the chain of transmission.