SIGMA2: a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Strategies for Integrated Analysis of Genetic, Epigenetic, and Gene Expression Variation in Cancer: Addressing the Challenges

The development and progression of cancer, a collection of diseases with complex genetic architectures, is facilitated by the interplay of multiple etiological factors. This complexity challenges the traditional single-platform study design and calls for an integrated approach to data analysis. However, integration of heterogeneous measurements of biological variation is a non-trivial exercise due to the diversity of the human genome and the variety of output data formats and genome coverage obtained from the commonly used molecular platforms. This review article will provide an introduction to integration strategies used for analyzing genetic risk factors for cancer. We critically examine the ability of these strategies to handle the complexity of the human genome and also accommodate information about the biological and functional interactions between the elements that have been measured-making the assessment of disease risk against a composite genomic factor possible. The focus of this review is to provide an overview and introduction to the main strategies and to discuss where there is a need for further development.

Identification of potential COPD genes based on multi-omics data at the functional level

A novel systematic approach MMMG (Methylation–MicroRNA–MRNA–GO) to identify potential COPD genes and their classifying performance evaluation.

The variation game: Cracking complex genetic disorders with NGS and omics data

Tremendous advances in Next Generation Sequencing (NGS) and high-throughput omics methods have brought us one step closer towards mechanistic understanding of the complex disease at the molecular level. In this review, we discuss four basic regulatory mechanisms implicated in complex genetic diseases, such as cancer, neurological disorders, heart disease, diabetes, and many others. The mechanisms, including genetic variations, copy-number variations, posttranscriptional variations, and epigenetic variations, can be detected using a variety of NGS methods. We propose that malfunctions detected in these mechanisms are not necessarily independent, since these malfunctions are often found associated with the same disease and targeting the same gene, group of genes, or functional pathway. As an example, we discuss possible rewiring effects of the cancer-associated genetic, structural, and posttranscriptional variations on the protein-protein interaction (PPI) network centered around P53 protein. The review highlights multi-layered complexity of common genetic disorders and suggests that integration of NGS and omics data is a critical step in developing new computational methods capable of deciphering this complexity.

Integrating and mining diverse data in human immunological studies

Bioanalysts and immunologists can interrogate the immune system with a variety of high-throughput technologies such as gene expression, multiplex bead arrays and flow cytometry. Conceptually, these assays support systems immunology studies, in which phenomena can be measured and correlated across biological compartments. First, however, the resulting high-dimensional data must be combined in a consistent fashion that supports analysis of the data as an integrated whole. Next, analytical methods must be applied to the hundreds or thousands of readouts. We recommend the use of a four-part analytical pipeline, consisting of data integration, hypothesis generation, prediction and hypothesis testing, and validation. We describe a variety of established methods appropriate for these integrated datasets, and highlight their application to human immunological studies. Our goal is to provide bioanalysts, immunologists and data analysts with a valuable perspective with which to approach the multiassay high-dimensional datasets generated by contemporary immunological studies.

Recurring DNA copy number gain at chromosome 9p13 plays a role in the activation of multiple candidate oncogenes in progressing oral premalignant lesions

Genomic alteration at chromosome 9p has been previously reported as a frequent and critical event in oral premalignancy. While this alteration is typically reported as a loss driven by selection for CDKN2A deactivation (at 9p21.3), we detect a recurrent DNA copy number gain of ∼2.49 Mbp at chromosome 9p13 in oral premalignant lesions (OPLs) that later progressed to invasive lesions. This recurrent alteration event has been validated using fluorescence in situ hybridization in an independent set of OPLs. Analysis of publicly available gene expression datasets aided in identifying three oncogene candidates that may have driven selection for DNA copy number increases in this region (VCP, DCTN3, and STOML2). We performed in vitro silencing and activation experiments for each of these genes in oral cancer cell lines and found that each gene is independently capable of upregulating proliferation and anchorage-independent growth. We next analyzed the activity of each of these genes in biopsies of varying histological grades that were obtained from a diseased oral tissue field in a single patient, finding further molecular evidence of parallel activation of VCP, DCTN3, and STOML2 during progression from normal healthy tissue to invasive oral carcinoma. Our results support the conclusion that DNA gain at 9p13 is important to the earliest stages of oral tumorigenesis and that this alteration event likely contributes to the activation of multiple oncogene candidates capable of governing oral cancer phenotypes.

Elevated expression of BIRC6 protein in non-small-cell lung cancers is associated with cancer recurrence and chemoresistance

INTRODUCTION

Non-small-cell lung cancer (NSCLC) is an aggressive, highly chemoresistant disease. Reliable prognostic assays and more effective treatments are critically required. BIRC6 (baculoviral inhibitors of apoptosis proteins repeat-containing 6) protein is a member of the inhibitors of apoptosis protein family thought to play an important role in the progression or chemoresistance of many cancers. In this study, we investigated whether BIRC6 expression can be used as a prognostic marker or potential therapeutic target for NSCLC.

METHODS

In a retrospective analysis, BIRC6 protein expression was determined for 78 resected primary NSCLCs and nine benign lung tissues. Twenty-nine chemoresistant or chemosensitive subrenal capsule NSCLC tissue xenografts were assessed for BIRC6 expression, using immunohistochemistry, and 13 of them for BIRC6 gene copy number, using array comparative genomic hybridization analysis. The effect of small interfering RNA-induced BIRC6 knockdown on the growth of human NSCLC cell cultures and apoptosis (in combination with cisplatin) was investigated.

RESULTS

Elevated BIRC6 protein expression in NSCLC tissues was associated with poor 3-year relapse-free patient survival, lymph node involvement, and advanced pathological tumor, node, metastasis stage. In patient-derived lung squamous cell carcinoma xenografts, chemoresistance was associated with elevated BIRC6 expression and increased gene copy number. Small interfering RNA-induced BIRC6 down-regulation inhibited growth of the NSCLC cells and sensitized the cells to cisplatin.

CONCLUSIONS

BIRC6 may play an important role in the malignant progression and chemoresistance of NSCLC. Elevated BIRC6 protein expression may serve as a predictive marker for chemoresistance of NSCLCs and a poor prognostic factor for NSCLC patients. Down-regulation of the BIRC6 gene as a therapeutic approach may be effective, especially in combination with conventional chemotherapeutics.

Applications of array-CGH for lung cancer

This chapter summarizes the current knowledge on gene copy number changes found in lung tumors, and their application in the diagnosis, prognostication, and prediction of response to chemotherapy. Examples of the identification of specific "driver" oncogenes within amplified DNA segments are described. A model of how array-CGH could be integrated clinically into the routine workup of lung cancers in clinical laboratory is proposed.

A predictive framework for integrating disparate genomic data types using sample-specific gene set enrichment analysis and multi-task learning

Understanding the root molecular and genetic causes driving complex traits is a fundamental challenge in genomics and genetics. Numerous studies have used variation in gene expression to understand complex traits, but the underlying genomic variation that contributes to these expression changes is not well understood. In this study, we developed a framework to integrate gene expression and genotype data to identify biological differences between samples from opposing complex trait classes that are driven by expression changes and genotypic variation. This framework utilizes pathway analysis and multi-task learning to build a predictive model and discover pathways relevant to the complex trait of interest. We simulated expression and genotype data to test the predictive ability of our framework and to measure how well it uncovered pathways with genes both differentially expressed and genetically associated with a complex trait. We found that the predictive performance of the multi-task model was comparable to other similar methods. Also, methods like multi-task learning that considered enrichment analysis scores from both data sets found pathways with both genetic and expression differences related to the phenotype. We used our framework to analyze differences between estrogen receptor (ER) positive and negative breast cancer samples. An analysis of the top 15 gene sets from the multi-task model showed they were all related to estrogen, steroids, cell signaling, or the cell cycle. Although our study suggests that multi-task learning does not enhance predictive accuracy, the models generated by our framework do provide valuable biological pathway knowledge for complex traits.

Dysfunctions associated with methylation, microRNA expression and gene expression in lung cancer

Integrating high-throughput data obtained from different molecular levels is essential for understanding the mechanisms of complex diseases such as cancer. In this study, we integrated the methylation, microRNA and mRNA data from lung cancer tissues and normal lung tissues using functional gene sets. For each Gene Ontology (GO) term, three sets were defined: the methylation set, the microRNA set and the mRNA set. The discriminating ability of each gene set was represented by the Matthews correlation coefficient (MCC), as evaluated by leave-one-out cross-validation (LOOCV). Next, the MCCs in the methylation sets, the microRNA sets and the mRNA sets were ranked. By comparing the MCC ranks of methylation, microRNA and mRNA for each GO term, we classified the GO sets into six groups and identified the dysfunctional methylation, microRNA and mRNA gene sets in lung cancer. Our results provide a systematic view of the functional alterations during tumorigenesis that may help to elucidate the mechanisms of lung cancer and lead to improved treatments for patients.

Genovar: a detection and visualization tool for genomic variants

BACKGROUND

Along with single nucleotide polymorphisms (SNPs), copy number variation (CNV) is considered an important source of genetic variation associated with disease susceptibility. Despite the importance of CNV, the tools currently available for its analysis often produce false positive results due to limitations such as low resolution of array platforms, platform specificity, and the type of CNV. To resolve this problem, spurious signals must be separated from true signals by visual inspection. None of the previously reported CNV analysis tools support this function and the simultaneous visualization of comparative genomic hybridization arrays (aCGH) and sequence alignment. The purpose of the present study was to develop a useful program for the efficient detection and visualization of CNV regions that enables the manual exclusion of erroneous signals.

RESULTS

A JAVA-based stand-alone program called Genovar was developed. To ascertain whether a detected CNV region is a novel variant, Genovar compares the detected CNV regions with previously reported CNV regions using the Database of Genomic Variants (DGV, http://projects.tcag.ca/variation) and the Single Nucleotide Polymorphism Database (dbSNP). The current version of Genovar is capable of visualizing genomic data from sources such as the aCGH data file and sequence alignment format files.

CONCLUSIONS

Genovar is freely accessible and provides a user-friendly graphic user interface (GUI) to facilitate the detection of CNV regions. The program also provides comprehensive information to help in the elimination of spurious signals by visual inspection, making Genovar a valuable tool for reducing false positive CNV results.

AVAILABILITY

http://genovar.sourceforge.net/.

Lessons from a decade of integrating cancer copy number alterations with gene expression profiles

Over the last decade, multiple functional genomic datasets studying chromosomal aberrations and their downstream effects on gene expression have accumulated for several cancer types. A vast majority of them are in the form of paired gene expression profiles and somatic copy number alterations (CNA) information on the same patients identified using microarray platforms. In response, many algorithms and software packages are available for integrating these paired data. Surprisingly, there has been no serious attempt to review the currently available methodologies or the novel insights brought using them. In this work, we discuss the quantitative relationships observed between CNA and gene expression in multiple cancer types and biological milestones achieved using the available methodologies. We discuss the conceptual evolution of both, the step-wise and the joint data integration methodologies over the last decade. We conclude by providing suggestions for building efficient data integration methodologies and asking further biological questions.

TRAF6 is an amplified oncogene bridging the RAS and NF-κB pathways in human lung cancer

Somatic mutations and copy number alterations (as a result of deletion or amplification of large portions of a chromosome) are major drivers of human lung cancers. Detailed analysis of lung cancer-associated chromosomal amplifications could identify novel oncogenes. By performing an integrative cytogenetic and gene expression analysis of non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) cell lines and tumors, we report here the identification of a frequently recurring amplification at chromosome 11 band p13. Within this region, only TNF receptor-associated factor 6 (TRAF6) exhibited concomitant mRNA overexpression and gene amplification in lung cancers. Inhibition of TRAF6 in human lung cancer cell lines suppressed NF-κB activation, anchorage-independent growth, and tumor formation. In these lung cancer cell lines, RAS required TRAF6 for its oncogenic capabilities. Furthermore, TRAF6 overexpression in NIH3T3 cells resulted in NF-κB activation, anchorage-independent growth, and tumor formation. Our findings show that TRAF6 is an oncogene that is important for RAS-mediated oncogenesis and provide a mechanistic explanation for the previously apparent importance of constitutive NF-κB activation in RAS-driven lung cancers.

FISH Oracle: a web server for flexible visualization of DNA copy number data in a genomic context

Background

The rapidly growing amount of array CGH data requires improved visualization software supporting the process of identifying candidate cancer genes. Optimally, such software should work across multiple microarray platforms, should be able to cope with data from different sources and should be easy to operate.

Results

We have developed a web-based software FISH Oracle to visualize data from multiple array CGH experiments in a genomic context. Its fast visualization engine and advanced web and database technology supports highly interactive use. FISH Oracle comes with a convenient data import mechanism, powerful search options for genomic elements (e.g. gene names or karyobands), quick navigation and zooming into interesting regions, and mechanisms to export the visualization into different high quality formats. These features make the software especially suitable for the needs of life scientists.

Conclusions

FISH Oracle offers a fast and easy to use visualization tool for array CGH and SNP array data. It allows for the identification of genomic regions representing minimal common changes based on data from one or more experiments. FISH Oracle will be instrumental to identify candidate onco and tumor suppressor genes based on the frequency and genomic position of DNA copy number changes. The FISH Oracle application and an installed demo web server are available at http://www.zbh.uni-hamburg.de/fishoracle.

Tumor profiling: development of prognostic and predictive factors to guide brain tumor treatment

Primary brain tumors are a heterogeneous group of malignancies with highly variable outcomes, and diagnosis is largely based on the histological appearance of the tumors. However, the diversity of primary brain tumors has made prognostic determinations based purely on clinicopathologic variables difficult. There is an increasing body of data suggesting a significant amount of molecular diversity accounts for the heterogeneity of clinical observations, such as response to treatment and time to progression. The last decade has witnessed an explosive advance in our knowledge of the molecular genetics of brain tumors, due in large part to the availability of high-throughput profiling techniques and to the completion of the human genome sequencing project. The large amount of data generated by these efforts has enabled the identification of prognostic and predictive factors and helping to identify pathways which are driving tumor growth. Identification of biomarkers will enable better patient stratification and individualization of treatment.

MicroRNA gene dosage alterations and drug response in lung cancer

Chemotherapy resistance is a key contributor to the dismal prognoses for lung cancer patients. While the majority of studies have focused on sequence mutations and expression changes in protein-coding genes, recent reports have suggested that microRNA (miRNA) expression changes also play an influential role in chemotherapy response. However, the role of genetic alterations at miRNA loci in the context of chemotherapy response has yet to be investigated. In this study, we demonstrate the application of an integrative, multidimensional approach in order to identify miRNAs that are associated with chemotherapeutic resistance and sensitivity utilizing publicly available drug response, miRNA loci copy number, miRNA expression, and mRNA expression data from independent resources. By instigating a logical stepwise strategy, we have identified specific miRNAs that are associated with resistance to several chemotherapeutic agents and provide a proof of principle demonstration of how these various databases may be exploited to derive relevant pharmacogenomic results.

The use of global profiling in biomarker development for gliomas

The diffuse gliomas are a heterogeneous group of malignancies with highly variable outcomes and diagnosis is largely based on the histological appearance of the tumors. Tumor classification according to cello type and grade provides some prognostic information. However, the diversity of gliomas, within tumor type and grade categories, has made prognostic determinations based purely on clinicopathologic variables difficult. There is an increasing body of data suggesting a significant amount of molecular diversity accounts for the heterogeneity of clinical observations, such as response to treatment and time to progression. The last decade has witnessed an explosive advance in our knowledge of the molecular genetics of brain tumors, due in large part to the availability of high-throughput profiling techniques, including new sequencing methodologies as well as multidimensional profiling by the Cancer Genome Atlas project. The large amount of data generated by these efforts has enabled the identification of prognostic and predictive factors and helping to identify pathways that are driving tumor growth. Identification of biomarkers, especially when coupled to clinical trials of newer targeted therapies, will enable better patient stratification and individualization of treatment.

Deciphering squamous cell carcinoma using multidimensional genomic approaches

Squamous cell carcinomas (SqCCs) arise in a wide range of tissues including skin, lung, and oral mucosa. Although all SqCCs are epithelial in origin and share common nomenclature, these cancers differ greatly with respect to incidence, prognosis, and treatment. Current knowledge of genetic similarities and differences between SqCCs is insufficient to describe the biology of these cancers, which arise from diverse tissue origins. In this paper we provide a general overview of whole genome approaches for gene and pathway discovery and highlight the advancement of integrative genomics as a state-of-the-art technology in the study of SqCC genetics.

Expectations, validity, and reality in gene expression profiling

OBJECTIVE

To provide a critical overview of gene expression profiling methodology and discuss areas of future development.

RESULTS

Gene expression profiling has been used extensively in biological research and has resulted in significant advances in the understanding of the molecular mechanisms of complex disorders, including cancer, heart disease, and metabolic disorders. However, translating this technology into genomic medicine for use in diagnosis and prognosis faces many challenges. In addition, gene expression profile analysis is frequently controversial, because its conclusions often lack reproducibility and claims of effective dissemination into translational medicine have, in some cases, been remarkably unjustified. In the last decade, a large number of methodological and technical solutions have been offered to overcome the challenges.

STUDY DESIGN AND SETTING

We consider the strengths, limitations, and appropriate applications of gene expression profiling techniques, with particular reference to the clinical relevance.

CONCLUSION

Some studies have demonstrated the ability and clinical utility of gene expression profiling for use as diagnostic, prognostic, and predictive molecular markers. The challenges of gene expression profiling lie with the standardization of analytic approaches and the evaluation of the clinical merit in broader heterogeneous populations by prospective clinical trials.

An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer

Background

Genomics has substantially changed our approach to cancer research. Gene expression profiling, for example, has been utilized to delineate subtypes of cancer, and facilitated derivation of predictive and prognostic signatures. The emergence of technologies for the high resolution and genome-wide description of genetic and epigenetic features has enabled the identification of a multitude of causal DNA events in tumors. This has afforded the potential for large scale integration of genome and transcriptome data generated from a variety of technology platforms to acquire a better understanding of cancer.

Results

Here we show how multi-dimensional genomics data analysis would enable the deciphering of mechanisms that disrupt regulatory/signaling cascades and downstream effects. Since not all gene expression changes observed in a tumor are causal to cancer development, we demonstrate an approach based on multiple concerted disruption (MCD) analysis of genes that facilitates the rational deduction of aberrant genes and pathways, which otherwise would be overlooked in single genomic dimension investigations.

Conclusions

Notably, this is the first comprehensive study of breast cancer cells by parallel integrative genome wide analyses of DNA copy number, LOH, and DNA methylation status to interpret changes in gene expression pattern. Our findings demonstrate the power of a multi-dimensional approach to elucidate events which would escape conventional single dimensional analysis and as such, reduce the cohort sample size for cancer gene discovery.

Integrating the multiple dimensions of genomic and epigenomic landscapes of cancer

Advances in high-throughput, genome-wide profiling technologies have allowed for an unprecedented view of the cancer genome landscape. Specifically, high-density microarrays and sequencing-based strategies have been widely utilized to identify genetic (such as gene dosage, allelic status, and mutations in gene sequence) and epigenetic (such as DNA methylation, histone modification, and microRNA) aberrations in cancer. Although the application of these profiling technologies in unidimensional analyses has been instrumental in cancer gene discovery, genes affected by low-frequency events are often overlooked. The integrative approach of analyzing parallel dimensions has enabled the identification of (a) genes that are often disrupted by multiple mechanisms but at low frequencies by any one mechanism and (b) pathways that are often disrupted at multiple components but at low frequencies at individual components. These benefits of using an integrative approach illustrate the concept that the whole is greater than the sum of its parts. As efforts have now turned toward parallel and integrative multidimensional approaches for studying the cancer genome landscape in hopes of obtaining a more insightful understanding of the key genes and pathways driving cancer cells, this review describes key findings disseminating from such high-throughput, integrative analyses, including contributions to our understanding of causative genetic events in cancer cell biology.

Transcriptome profiles of carcinoma-in-situ and invasive non-small cell lung cancer as revealed by SAGE

Background

Non-small cell lung cancer (NSCLC) presents as a progressive disease spanning precancerous, preinvasive, locally invasive, and metastatic lesions. Identification of biological pathways reflective of these progressive stages, and aberrantly expressed genes associated with these pathways, would conceivably enhance therapeutic approaches to this devastating disease.

Methodology/Principal Findings

Through the construction and analysis of SAGE libraries, we have determined transcriptome profiles for preinvasive carcinoma-in-situ (CIS) and invasive squamous cell carcinoma (SCC) of the lung, and compared these with expression profiles generated from both bronchial epithelium, and precancerous metaplastic and dysplastic lesions using Ingenuity Pathway Analysis. Expression of genes associated with epidermal development, and loss of expression of genes associated with mucociliary biology, are predominant features of CIS, largely shared with precancerous lesions. Additionally, expression of genes associated with xenobiotic metabolism/detoxification is a notable feature of CIS, and is largely maintained in invasive cancer. Genes related to tissue fibrosis and acute phase immune response are characteristic of the invasive SCC phenotype. Moreover, the data presented here suggests that tissue remodeling/fibrosis is initiated at the early stages of CIS. Additionally, this study indicates that alteration in copy-number status represents a plausible mechanism for differential gene expression in CIS and invasive SCC.

Conclusions/Significance

This study is the first report of large-scale expression profiling of CIS of the lung. Unbiased expression profiling of these preinvasive and invasive lesions provides a platform for further investigations into the molecular genetic events relevant to early stages of squamous NSCLC development. Additionally, up-regulated genes detected at extreme differences between CIS and invasive cancer may have potential to serve as biomarkers for early detection.

Methylation analysis by DNA immunoprecipitation

DNA methylation regulates gene expression primarily through modification of chromatin structure. Global methylation studies have revealed biologically relevant patterns of DNA methylation in the human genome affecting sequences such as gene promoters, gene bodies, and repetitive elements. Disruption of normal methylation patterns and subsequent gene expression changes have been observed in several diseases especially in human cancers. Immunoprecipitation (IP)-based methods to evaluate methylation status of DNA have been instrumental in such genome-wide methylation studies. This review describes techniques commonly used to identify and quantify methylated DNA with emphasis on IP based platforms. In an effort to consolidate the wealth of information and highlight critical aspects of methylated DNA analysis, sample considerations, experimental and bioinformatic approaches for analyzing genome-wide methylation profiles, and the benefit of integrating DNA methylation data with complementary dimensions of genomic data are discussed.

CHESS (CgHExpreSS): a comprehensive analysis tool for the analysis of genomic alterations and their effects on the expression profile of the genome

Background

Genomic alterations frequently occur in many cancer patients and play important mechanistic roles in the pathogenesis of cancer. Furthermore, they can modify the expression level of genes due to altered copy number in the corresponding region of the chromosome. An accumulating body of evidence supports the possibility that strong genome-wide correlation exists between DNA content and gene expression. Therefore, more comprehensive analysis is needed to quantify the relationship between genomic alteration and gene expression. A well-designed bioinformatics tool is essential to perform this kind of integrative analysis. A few programs have already been introduced for integrative analysis. However, there are many limitations in their performance of comprehensive integrated analysis using published software because of limitations in implemented algorithms and visualization modules.

Results

To address this issue, we have implemented the Java-based program CHESS to allow integrative analysis of two experimental data sets: genomic alteration and genome-wide expression profile. CHESS is composed of a genomic alteration analysis module and an integrative analysis module. The genomic alteration analysis module detects genomic alteration by applying a threshold based method or SW-ARRAY algorithm and investigates whether the detected alteration is phenotype specific or not. On the other hand, the integrative analysis module measures the genomic alteration's influence on gene expression. It is divided into two separate parts. The first part calculates overall correlation between comparative genomic hybridization ratio and gene expression level by applying following three statistical methods: simple linear regression, Spearman rank correlation and Pearson's correlation. In the second part, CHESS detects the genes that are differentially expressed according to the genomic alteration pattern with three alternative statistical approaches: Student's t-test, Fisher's exact test and Chi square test. By successive operations of two modules, users can clarify how gene expression levels are affected by the phenotype specific genomic alterations. As CHESS was developed in both Java application and web environments, it can be run on a web browser or a local machine. It also supports all experimental platforms if a properly formatted text file is provided to include the chromosomal position of probes and their gene identifiers.

Conclusions

CHESS is a user-friendly tool for investigating disease specific genomic alterations and quantitative relationships between those genomic alterations and genome-wide gene expression profiling.

Integrated analysis of DNA copy number and gene expression microarray data using gene sets

BACKGROUND

Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy number and gene expression jointly. Furthermore, some copy number changes extend over larger chromosomal regions affecting the expression levels of multiple resident genes.

RESULTS

We propose to analyse copy number and expression array data using gene sets, rather than individual genes. The proposed model is robust and sensitive. We re-analysed two publicly available datasets as illustration. These two independent breast cancer datasets yielded similar patterns of association between gene dosage and gene expression levels, in spite of different platforms having been used. Our comparisons show a clear advantage to using sets of genes' expressions to detect associations with long-spanning, low-amplitude copy number aberrations. In addition, our model allows for using additional explanatory variables and does not require mapping between copy number and expression probes.

CONCLUSION

We developed a general and flexible tool for integration of multiple microarray data sets, and showed how the identification of genes whose expression is affected by copy number aberrations provides a powerful approach to prioritize putative targets for functional validation.