Integrated analysis of copy number alterations and gene expression: a bivariate assessment of equally directed abnormalities

CiberAMP: An R Package to Identify Differential mRNA Expression Linked to Somatic Copy Number Variations in Cancer Datasets

Simple Summary

The ability to establish accurate correlations between the number of copies of genes and the expression levels of their encoded transcripts remains a challenge despite the extensive progress made in the understanding of the genome of cancer cells. Here, we describe a new algorithm that does so by integrating both genomics and transcriptomics data from the Cancer Genome Atlas. In addition to explaining the step-by-step basis of this new method, we provide examples of how this new algorithm can help identify functionally meaningful gene copy alterations that are recurrently detected in cancer patients.

Abstract

Somatic copy number variations (SCNVs) are genetic alterations frequently found in cancer cells. These genetic alterations can lead to concomitant perturbations in the expression of the genes included in them and, as a result, promote a selective advantage to cancer cells. However, this is not always the case. Due to this, it is important to develop in silico tools to facilitate the accurate identification and functional cataloging of gene expression changes associated with SCNVs from pan-cancer data. Here, we present a new R-coded tool, designated as CiberAMP, which utilizes genomic and transcriptomic data contained in the Cancer Genome Atlas (TCGA) to identify such events. It also includes information on the genomic context in which such SCNVs take place. By doing so, CiberAMP provides clues about the potential functional relevance of each of the SCNV-associated gene expression changes found in the interrogated tumor samples. The main features and advantages of this new algorithm are illustrated using glioblastoma data from the TCGA database.

An Efficient Algorithm for Identifying Mutated Subnetworks Associated with Survival in Cancer

Protein-protein interaction (PPI) network models interconnections between protein-encoding genes. A group of proteins that perform similar functions are often connected to each other in the PPI network. The corresponding genes form pathways or functional modules. Mutation in protein-encoding genes affect behavior of pathways. This results in initiation, progression, and severity of diseases that propagates through pathways. In this work, we integrate mutation, survival information of patients, and PPI network to identify connected subnetworks associated with survival. We define the computational problem using a fitness function called log-rank statistic to score subnetworks. Log-rank statistic compares the survival between two populations. We propose a novel method, Survival Associated Mutated Subnetwork (SAMS) that adopts genetic algorithm strategy to find the connected subnetwork within the PPI network whose mutation yields highest log-rank statistic. We test on real cancer and synthetic datasets. SAMS generate solutions in negligible time while the state-of-art method in literature takes exponential time. Log-rank statistic of SAMS selected mutated subnetworks are comparable to the method. Our result genesets show significant overlap with well-known cancer driver genes derived from curated datasets and studies in literature, display high text-mining score in terms of number of citations combined with disease-specific keywords in PubMed, and identify pathways having high biological relevance.

Robust network-based analysis of the associations between (epi)genetic measurements

With its important biological implications, modeling the associations of gene expression (GE) and copy number variation (CNV) has been extensively conducted. Such analysis is challenging because of the high data dimensionality, lack of knowledge regulating CNVs for a specific GE, different behaviors of the cis-acting and trans-acting CNVs, possible long-tailed distributions and contamination of GE measurements, and correlations between CNVs. The existing methods fail to address one or more of these challenges. In this study, a new method is developed to model more effectively the GE-CNV associations. Specifically, for each GE, a partially linear model, with a nonlinear cis-acting CNV effect, is assumed. A robust loss function is adopted to accommodate long-tailed distributions and data contamination. We adopt penalization to accommodate the high dimensionality and identify relevant CNVs. A network structure is introduced to accommodate the correlations among CNVs. The proposed method comprehensively accommodates multiple challenging characteristics of GE-CNV modeling and effectively overcomes the limitations of existing methods. We develop an effective computational algorithm and rigorously establish the consistency properties. Simulation shows the superiority of the proposed method over alternatives. The TCGA (The Cancer Genome Atlas) data on the PCD (programmed cell death) pathway are analyzed, and the proposed method has improved prediction and stability and biologically plausible findings.

Integrative analysis of multiple genomic variables using a hierarchical Bayesian model

Motivation

Genes showing congruent differences in several genomic variables between two biological conditions are crucial to unravel causalities behind phenotypes of interest. Detecting such genes is important in biomedical research, e.g. when identifying genes responsible for cancer development. Small sample sizes common in next-generation sequencing studies are a key challenge, and there are still only very few statistical methods to analyze more than two genomic variables in an integrative, model-based way. Here, we present a novel bioinformatics approach to detect congruent differences between two biological conditions in a larger number of different measurements such as various epigenetic marks or mRNA transcript levels.

Results

We propose a coefficient quantifying the degree to which genes present consistent alterations in multiple (more than two) genomic variables when comparing samples presenting a condition of interest (e.g. cancer) to a reference group. A hierarchical Bayesian model is employed to assess uncertainty on a gene level, incorporating information on functional relationships between genes. We demonstrate the approach on different data sets containing RNA-seq gene transcripton and up to four ChIP-seq histone modification measurements. Both the coefficient-based ranking and the inference based on the model lead to a plausible prioritizing of candidate genes when analyzing multiple genomic variables.

Availability and implementation

BUGS code in the Supplement.

Contact

m.schaefer@uni-duesseldorf.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrative analysis of genome-wide gene copy number changes and gene expression in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) represents a genomically unstable cancer type with extensive copy number aberrations. The relationship of gene copy number alterations and subsequent mRNA levels has only fragmentarily been described. The aim of this study was to conduct a genome-wide analysis of gene copy number gains and corresponding gene expression levels in a clinically well annotated NSCLC patient cohort (n = 190) and their association with survival. While more than half of all analyzed gene copy number-gene expression pairs showed statistically significant correlations (10,296 of 18,756 genes), high correlations, with a correlation coefficient >0.7, were obtained only in a subset of 301 genes (1.6%), including KRAS, EGFR and MDM2. Higher correlation coefficients were associated with higher copy number and expression levels. Strong correlations were frequently based on few tumors with high copy number gains and correspondingly increased mRNA expression. Among the highly correlating genes, GO groups associated with posttranslational protein modifications were particularly frequent, including ubiquitination and neddylation. In a meta-analysis including 1,779 patients we found that survival associated genes were overrepresented among highly correlating genes (61 of the 301 highly correlating genes, FDR adjusted p<0.05). Among them are the chaperone CCT2, the core complex protein NUP107 and the ubiquitination and neddylation associated protein CAND1. In conclusion, in a comprehensive analysis we described a distinct set of highly correlating genes. These genes were found to be overrepresented among survival-associated genes based on gene expression in a large collection of publicly available datasets.

FocalScan: Scanning for altered genes in cancer based on coordinated DNA and RNA change

Somatic genomic copy-number alterations can lead to transcriptional activation or inactivation of tumor driver or suppressor genes, contributing to the malignant properties of cancer cells. Selection for such events may manifest as recurrent amplifications or deletions of size-limited (focal) regions. While methods have been developed to identify such focal regions, finding the exact targeted genes remains a challenge. Algorithms are also available that integrate copy number and RNA expression data, to aid in identifying individual targeted genes, but specificity is lacking. Here, we describe FocalScan, a tool designed to simultaneously uncover patterns of focal copy number alteration and coordinated expression change, thus combining both principles. The method outputs a ranking of tentative cancer drivers or suppressors. FocalScan works with RNA-seq data, and unlike other tools it can scan the genome unaided by a gene annotation, enabling identification of novel putatively functional elements including lncRNAs. Application on a breast cancer data set suggests considerably better performance than other DNA/RNA integration tools.

An Integrative Analysis to Identify Driver Genes in Esophageal Squamous Cell Carcinoma

Background

Few driver genes have been well established in esophageal squamous cell carcinoma (ESCC). Identification of the genomic aberrations that contribute to changes in gene expression profiles can be used to predict driver genes.

Methods

We searched for driver genes in ESCC by integrative analysis of gene expression microarray profiles and copy number data. To narrow down candidate genes, we performed survival analysis on expression data and tested the genetic vulnerability of each genes using public RNAi screening data. We confirmed the results by performing RNAi experiments and evaluating the clinical relevance of candidate genes in an independent ESCC cohort.

Results

We found 10 significantly recurrent copy number alterations accompanying gene expression changes, including loci 11q13.2, 7p11.2, 3q26.33, and 17q12, which harbored CCND1, EGFR, SOX2, and ERBB2, respectively. Analysis of survival data and RNAi screening data suggested that GRB7, located on 17q12, was a driver gene in ESCC. In ESCC cell lines harboring 17q12 amplification, knockdown of GRB7 reduced the proliferation, migration, and invasion capacities of cells. Moreover, siRNA targeting GRB7 had a synergistic inhibitory effect when combined with trastuzumab, an anti-ERBB2 antibody. Survival analysis of the independent cohort also showed that high GRB7 expression was associated with poor prognosis in ESCC.

Conclusion

Our integrative analysis provided important insights into ESCC pathogenesis. We identified GRB7 as a novel ESCC driver gene and potential new therapeutic target.

Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential

BACKGROUND

Development of human cancer can proceed through the accumulation of different genetic changes affecting the structure and function of the genome. Combined analyses of molecular data at multiple levels, such as DNA copy-number alteration, mRNA and miRNA expression, can clarify biological functions and pathways deregulated in cancer. The integrative methods that are used to investigate these data involve different fields, including biology, bioinformatics, and statistics.

RESULTS

These methodologies are presented in this review, and their implementation in breast cancer is discussed with a focus on integration strategies. We report current applications, recent studies and interesting results leading to the identification of candidate biomarkers for diagnosis, prognosis, and therapy in breast cancer by using both individual and combined analyses.

CONCLUSION

This review presents a state of art of the role of different technologies in breast cancer based on the integration of genetics and epigenetics, and shares some issues related to the new opportunities and challenges offered by the application of such integrative approaches.

Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach

MOTIVATION

Both gene expression levels (GEs) and copy number alterations (CNAs) have important biological implications. GEs are partly regulated by CNAs, and much effort has been devoted to understanding their relations. The regulation analysis is challenging with one gene expression possibly regulated by multiple CNAs and one CNA potentially regulating the expressions of multiple genes. The correlations among GEs and among CNAs make the analysis even more complicated. The existing methods have limitations and cannot comprehensively describe the regulation.

RESULTS

A sparse double Laplacian shrinkage method is developed. It jointly models the effects of multiple CNAs on multiple GEs. Penalization is adopted to achieve sparsity and identify the regulation relationships. Network adjacency is computed to describe the interconnections among GEs and among CNAs. Two Laplacian shrinkage penalties are imposed to accommodate the network adjacency measures. Simulation shows that the proposed method outperforms the competing alternatives with more accurate marker identification. The Cancer Genome Atlas data are analysed to further demonstrate advantages of the proposed method.

AVAILABILITY AND IMPLEMENTATION

R code is available at http://works.bepress.com/shuangge/49/.

Integrated DNA Copy Number and Gene Expression Regulatory Network Analysis of Non-small Cell Lung Cancer Metastasis

Integrative analysis of multi-level molecular profiles can distinguish interactions that cannot be revealed based on one kind of data in the analysis of cancer susceptibility and metastasis. DNA copy number variations (CNVs) are common in cancer cells, and their role in cell behaviors and relationship to gene expression (GE) is poorly understood. An integrative analysis of CNV and genome-wide mRNA expression can discover copy number alterations and their possible regulatory effects on GE. This study presents a novel framework to identify important genes and construct potential regulatory networks based on these genes. Using this approach, DNA copy number aberrations and their effects on GE in lung cancer progression were revealed. Specifically, this approach contains the following steps: (1) select a pool of candidate driver genes, which have significant CNV in lung cancer patient tumors or have a significant association with the clinical outcome at the transcriptional level; (2) rank important driver genes in lung cancer patients with good prognosis and poor prognosis, respectively, and use top-ranked driver genes to construct regulatory networks with the COpy Number and EXpression In Cancer (CONEXIC) method; (3) identify experimentally confirmed molecular interactions in the constructed regulatory networks using Ingenuity Pathway Analysis (IPA); and (4) visualize the refined regulatory networks with the software package Genatomy. The constructed CNV/mRNA regulatory networks provide important insights into potential CNV-regulated transcriptional mechanisms in lung cancer metastasis.

A meta-analysis of multiple matched copy number and transcriptomics data sets for inferring gene regulatory relationships

Inferring gene regulatory relationships from observational data is challenging. Manipulation and intervention is often required to unravel causal relationships unambiguously. However, gene copy number changes, as they frequently occur in cancer cells, might be considered natural manipulation experiments on gene expression. An increasing number of data sets on matched array comparative genomic hybridisation and transcriptomics experiments from a variety of cancer pathologies are becoming publicly available. Here we explore the potential of a meta-analysis of thirty such data sets. The aim of our analysis was to assess the potential of in silico inference of trans-acting gene regulatory relationships from this type of data. We found sufficient correlation signal in the data to infer gene regulatory relationships, with interesting similarities between data sets. A number of genes had highly correlated copy number and expression changes in many of the data sets and we present predicted potential trans-acted regulatory relationships for each of these genes. The study also investigates to what extent heterogeneity between cell types and between pathologies determines the number of statistically significant predictions available from a meta-analysis of experiments.

Loss of CDCP1 expression promotes invasiveness and poor prognosis in esophageal squamous cell carcinoma

BACKGROUND

Human CDCP1 gene, located on chromosome 3p21.3, is a transmembrane glycoprotein widely expressed in epithelial tissues, and its role in cancer remains to be understood.

METHODS

Using microarray profiles of gene expression and copy number data from 69 esophageal squamous cell carcinoma (ESCC) samples, we performed informatics analyses to reveal the significance of CDCP1 expression. We also performed migration and invasion assays of siRNA-targeted CDCP1-transfected cells and CDCP1-overexpressing cell in vitro. Moreover, we evaluated the clinical magnitude of CDCP1 expression in esophageal squamous cell cancer cases.

RESULTS

Allelic loss of chromosome 3p was confirmed by copy number analysis. The expression level of CDCP1 in tumor tissue was significantly lower than that in corresponding normal tissue. siRNA targeting of CDCP1 promoted the migratory and invasive abilities of esophageal cancer cell lines, whereas both abilities were reduced in CDCP1-overexpressing cells. Gene set enrichment analysis showed that expression levels of CDCP1 were associated with tumor differentiation and metastasis, consistent with the result of clinicopathologic analyses. Finally, multivariate analysis revealed that the expression level of CDCP1 was an independent prognostic factor for survival.

CONCLUSIONS

Loss of CDCP1 expression may be a novel indicator for biological aggressiveness in ESCC.

Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models

MOTIVATION

Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification.

RESULTS

We introduce a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays and demonstrate that a proper normalization of ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different types of distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene transcription and histone modification. The method is applied to different datasets, and its superiority to a naive separate analysis of both data types is demonstrated.

AVAILABILITY AND IMPLEMENTATION

R/Bioconductor package epigenomix.

CONTACT

h.klein@uni-muenster.de Supplementary information: Supplementary data are available at Bioinformatics online.

Integrative genomics with mediation analysis in a survival context

DNA copy number aberrations (DCNA) and subsequent altered gene expression profiles may have a major impact on tumor initiation, on development, and eventually on recurrence and cancer-specific mortality. However, most methods employed in integrative genomic analysis of the two biological levels, DNA and RNA, do not consider survival time. In the present note, we propose the adoption of a survival analysis-based framework for the integrative analysis of DCNA and mRNA levels to reveal their implication on patient clinical outcome with the prerequisite that the effect of DCNA on survival is mediated by mRNA levels. The specific aim of the paper is to offer a feasible framework to test the DCNA-mRNA-survival pathway. We provide statistical inference algorithms for mediation based on asymptotic results. Furthermore, we illustrate the applicability of the method in an integrative genomic analysis setting by using a breast cancer data set consisting of 141 invasive breast tumors. In addition, we provide implementation in R.

Detection of candidate tumor driver genes using a fully integrated Bayesian approach

DNA copy number alterations (CNAs), including amplifications and deletions, can result in significant changes in gene expression and are closely related to the development and progression of many diseases, especially cancer. For example, CNA-associated expression changes in certain genes (called candidate tumor driver genes) can alter the expression levels of many downstream genes through transcription regulation and cause cancer. Identification of such candidate tumor driver genes leads to discovery of novel therapeutic targets for personalized treatment of cancers. Several approaches have been developed for this purpose by using both copy number and gene expression data. In this study, we propose a Bayesian approach to identify candidate tumor driver genes, in which the copy number and gene expression data are modeled together, and the dependency between the two data types is modeled through conditional probabilities. The proposed joint modeling approach can identify CNA and differentially expressed genes simultaneously, leading to improved detection of candidate tumor driver genes and comprehensive understanding of underlying biological processes. We evaluated the proposed method in simulation studies, and then applied to a head and neck squamous cell carcinoma data set. Both simulation studies and data application show that the joint modeling approach can significantly improve the performance in identifying candidate tumor driver genes, when compared with other existing approaches.

Structural and genic characterization of stable genomic regions in breast cancer: relevance to chemotherapy

BACKGROUND

Cancer genomes accumulate frequent and diverse chromosomal abnormalities as well as gene mutations but must maintain the ability to survive in vivo. We hypothesize that genetic selection acts to maintain tumour survival by preserving copy number of specific genes and genomic regions. Genomic regions and genes that remain unaltered in copy number and expression, respectively, may be essential for maintaining tumour survival.

METHODS

We analyzed copy number data of 243 previously reported breast tumours and computationally derived stable copy number regions. To identify genes in stable copy number regions with nominal changes in expression, datasets for tumour and normal samples were compared. Results were replicated by analysis of a series of independent copy number, expression and genomic sequencing studies. A subset of stable regions, including stable paralogous regions, were confirmed by quantitative PCR and fluorescence in situ hybridization (FISH) in 5 breast cancer cell lines. We deduced a comprehensive set of dually stable genes (i.e. maintaining nominal copy number and expression) which were categorized according to pathway and ontology assignments. The stability of genes encoding therapeutic drug targets was also assessed.

RESULTS AND CONCLUSION

Tumour genome analysis revealed 766 unstable (amplified and/or deleted) and 812 stable contiguous genomic regions. Replication analysis of an independent set of 171 breast tumours confirmed copy number stability of 1.3 Gb of the genome. We found that 5804 of these genes were dually stable. The composition of this gene set remained essentially unchanged (<2% reduction) after accounting for commonly mutated breast cancer genes found by sequencing and differential expression. The stable breast cancer genome is enriched for cellular metabolism, regulation of gene expression, DNA packaging (chromatin and nucleosome assembly), and regulation of apoptosis functions. Stable genes participating in multiple essential pathways were consistently found to be targets of chemotherapies. Preservation of stable, essential genes may be related to the effectiveness of certain chemotherapeutic agents that act on multiple gene products in this set.

Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses

Background

An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.

Results

We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.

Conclusions

Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.

Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review

A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.

Amplifications of NCOA3 gene in colorectal cancers in a Chinese population

AIM: To investigate the copy number variation of NACO3 gene in colorectal cancer (CRC) and its correlation with tumor progression.

METHODS: A total of 142 samples of case-matched CRC tissues and adjacent normal tissues were obtained from patients undergoing bowel resection. Quantitative real-time polymerase chain reaction method was used to investigate the copy number variations of NCOA3 as well as gene expression in the collected tissues.

RESULTS: Copy number gains of NCOA3 were detected in 39 CRC samples (27.5%) and were correlated with tumor progression (χ² = 6.42, P = 0.0112). Moreover, there was a positive correlation between copy number gain and mRNA over-expression of NCOA3 in CRCs (P = 0.0023). Expression level of NCOA3 mRNA was also enhanced in the CRC samples with unaltered copy numbers (3.85 ± 1.23 vs 2.71 ± 0.64, P < 0.01).

CONCLUSION: Sporadic colorectal cancers exhibit different mechanisms of NCOA3 regulation.

Comparative analysis of algorithms for integration of copy number and expression data

Chromosomal instability is a hallmark of cancer, and genes that display abnormal expression in aberrant chromosomal regions are likely to be key players in tumor progression. Identifying such driver genes reliably requires computational methods that can integrate genome-scale data from several sources. We compared the performance of ten algorithms that integrate copy-number and transcriptomics data from 15 head and neck squamous cell carcinoma cell lines, 129 lung squamous cell carcinoma primary tumors and simulated data. Our results revealed clear differences between the methods in terms of sensitivity and specificity as well as in their performance in small and large sample sizes. Results of the comparison are available at http://csbi.ltdk.helsinki.fi/cn2gealgo/.

Integrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements

The analysis of different variations in genomics, transcriptomics, epigenomics, and proteomics has increased considerably in recent years. This is especially due to the success of microarray and, more recently, sequencing technology. Apart from understanding mechanisms of disease pathogenesis on a molecular basis, for example in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important also for the validation of new sequencing technologies with maximum resolution. For this purpose, a methodological framework for their comparison with microarray techniques in the context of smallest sample sizes, which result from the high costs of experiments, is proposed in this contribution. Based on an adaptation of the externally centered correlation coefficient ( Schäfer et al. 2009 ), it is demonstrated how a Bayesian mixture model can be applied to compare and classify measurements of histone acetylation that stem from chromatin immunoprecipitation combined with either microarray (ChIP-chip) or sequencing techniques (ChIP-seq) for the identification of DNA fragments. Here, the murine hematopoietic cell line 32D, which was transduced with the oncogene BCR-ABL, the hallmark of chronic myeloid leukemia, was characterized. Cells were compared to mock-transduced cells as control. Activation or inhibition of other genes by histone modifications induced by the oncogene is considered critical in such a context for the understanding of the disease.

Integrated analysis of gene expression and copy number data on gene shaving using independent component analysis

DNA microarray gene expression and microarray-based comparative genomic hybridization (aCGH) have been widely used for biomedical discovery. Because of the large number of genes and the complex nature of biological networks, various analysis methods have been proposed. One such method is "gene shaving," a procedure which identifies subsets of the genes with coherent expression patterns and large variation across samples. Since combining genomic information from multiple sources can improve classification and prediction of diseases, in this paper we proposed a new method, "ICA gene shaving" (ICA, independent component analysis), for jointly analyzing gene expression and copy number data. First we used ICA to analyze joint measurements, gene expression and copy number, of a biological system and project the data onto statistically independent biological processes. Next, we used these results to identify patterns of variation in the data and then applied an iterative shaving method. We investigated the properties of our proposed method by analyzing both simulated and real data. We demonstrated that the robustness of our method to noise using simulated data. Using breast cancer data, we showed that our method is superior to the Generalized Singular Value Decomposition (GSVD) gene shaving method for identifying genes associated with breast cancer.

Segmented regression, a versatile tool to analyze mRNA levels in relation to DNA copy number aberrations

DNA copy number aberrations (CNA) and subsequent altered gene expression profiles (mRNA levels) are characteristic features of cancerous cells. Integrative genomic analysis aims to identify recurrent CNA that may have a potential role in cancer development, assuming that gene amplification is accompanied by overexpression, while deletions give rise to downregulation of gene expression. We propose a segmented regression-based approach to identify CNA-driven alteration of gene expression profiles. Segmented regression allows to fit piecewise linear models in different domains of CNA joined by a change-point, where the mRNA-CNA relationship undergoes structural changes. Here, we illustrate the implementation and applicability of the proposed model using 1,161 chromosome fragments detected as DNA CNA in primary tumors from 97 breast cancer patients. We identified significant CNA-driven changes in gene expression levels for 341 chromosome fragments, of which 72 showed a nonlinear relationship to CNA. For 59 of 72 chromosome fragments (82%), we observed an initial increase in mRNA levels due to changes in CNA. After the change-point was passed, the mRNA levels reached a plateau, and a further increase in DNA copy numbers did not induce further elevation in mRNA levels. In contrast, for 13 chromosome fragments, the change-point marked the point where mRNA production accelerated. We conclude that segmented regression modeling may provide valuable insights into the impact CNA have on gene expression in cancer cells.

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.

Copy number variations of EphA3 are associated with multiple types of hematologic malignancies

BACKGROUND

EphA3 is a component of the Eph receptor family, the largest subgroup of the receptor tyrosine kinase (RTK) family. A recent array-based study implicated the presence of copy-number variations (CNVs) of EphA3 in the genomes of acute myelogenous leukemia. CNVs are present in the general population at varying degrees, and have been found to associate with various types of diseases including hematologic malignancies. However, most of the current studies focused on the genome-wide screening of CNVs, and the functional impact of such regions needs to be extensively investigated in large number of clinical samples.

PATIENTS AND METHODS

In our study, we collected 617 bone marrow samples from multiple types of hematologic malignancies as well as healthy controls. DNA copy numbers and mRNA levels of EphA3 in these samples were examined.

RESULTS

We found significant association between the CNVs of EphA3 and these hematologic malignancies including acute lymphoblastic leukemia (ALL), acute myelogenous leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), multiple myeloma (MM), and myelodysplastic syndrome (MDS). We also observed a positive correlation between the relative mRNA level and gene dosage of EphA3.

CONCLUSION

The CNVs of EphA3 were associated with multiple types of hematologic malignancies including ALL, AML, CLL, CML, MM, and MDS.

Penalized regression elucidates aberration hotspots mediating subtype-specific transcriptional responses in breast cancer

MOTIVATION

Copy number alterations (CNAs) associated with cancer are known to contribute to genomic instability and gene deregulation. Integrating CNAs with gene expression helps to elucidate the mechanisms by which CNAs act and to identify the transcriptional downstream targets of CNAs. Such analyses can help to sort functional driver events from the many accompanying passenger alterations. However, the way CNAs affect gene expression can vary in different cellular contexts, for example between different subtypes of the same cancer. Thus, it is important to develop computational approaches capable of inferring differential connectivity of regulatory networks in different cellular contexts.

RESULTS

We propose a statistical deregulation model that integrates copy number and expression data of different disease subtypes to jointly model common and differential regulatory relationships. Our model not only identifies CNAs driving gene expression changes, but at the same time also predicts differences in regulation that distinguish one cancer subtype from the other. We implement our model in a penalized regression framework and demonstrate in a simulation study the feasibility and accuracy of our approach. Subsequently, we show that this model can identify both known and novel aspects of cross-talk between the ER and NOTCH pathways in ER-negative-specific deregulations, when compared with ER-positive breast cancer. This flexible model can be applied on other modalities such as methylation or microRNA and expression to disentangle cancer signaling pathways.

AVAILABILITY

The Bioconductor-compliant R package DANCE is available from www.markowetzlab.org/software/

CONTACT

yinyin.yuan@cancer.org.uk; florian.markowetz@cancer.org.uk.

The association between the copy-number variations of ZMAT4 and hematological malignancy

Copy-number variations (CNVs) have been found in association with various types of diseases, including hematological malignancies. A recent array-based study implicated the presence of CNVs of ZMAT4 in the genome of acute myelogenous leukemia. In our study, we collected 617 bone marrow samples from multitypes of hematological malignancies as well as healthy controls. We found significant association between the CNVs of ZMAT4 and these hematological malignancies, including acute lymphoblastic leukemia, acute myelogenous leukemia, chronic lymphocytic leukemia, chronic myelogenous leukemia, multiple myeloma, and myelodysplastic syndrome. We also examined the expression of ZMAT4 mRNA in the samples with 1 or 2 copies of DNA, and observed a weak yet positive correlation between the relative expression level and gene dosage. In conclusion, the CNVs of ZMAT4 have the potential to serve as a diagnostic indicator, alone or in combination with other markers, for hematological malignancies.

PREDA: an R-package to identify regional variations in genomic data

SUMMARY

Chromosomal patterns of genomic signals represent molecular fingerprints that may reveal how the local structural organization of a genome impacts the functional control mechanisms. Thus, the integrative analysis of multiple sources of genomic data and information deepens the resolution and enhances the interpretation of stand-alone high-throughput data. In this note, we present PREDA (Position RElated Data Analysis), an R package for detecting regional variations in genomics data. PREDA identifies relevant chromosomal patterns in high-throughput data using a smoothing approach that accounts for distance and density variability of genomics features. Custom-designed data structures allow efficiently managing diverse signals in different genomes. A variety of smoothing functions and statistics empower flexible and robust workflows. The modularity of package design allows an easy deployment of custom analytical pipelines. Tabular and graphical representations facilitate downstream biological interpretation of results.

AVAILABILITY

PREDA is available in Bioconductor and at http://www.xlab.unimo.it/PREDA.

CONTACT

silvio.bicciato@unimore.it

SUPPLEMENTARY INFORMATION

Supplementary information is available at Bioinformatics online.

Linear and non-linear dependencies between copy number aberrations and mRNA expression reveal distinct molecular pathways in breast cancer

Background

Elucidating the exact relationship between gene copy number and expression would enable identification of regulatory mechanisms of abnormal gene expression and biological pathways of regulation. Most current approaches either depend on linear correlation or on nonparametric tests of association that are insensitive to the exact shape of the relationship. Based on knowledge of enzyme kinetics and gene regulation, we would expect the functional shape of the relationship to be gene dependent and to be related to the gene regulatory mechanisms involved. Here, we propose a statistical approach to investigate and distinguish between linear and nonlinear dependences between DNA copy number alteration and mRNA expression.

Results

We applied the proposed method to DNA copy numbers derived from Illumina 109 K SNP-CGH arrays (using the log R values) and expression data from Agilent 44 K mRNA arrays, focusing on commonly aberrated genomic loci in a collection of 102 breast tumors. Regression analysis was used to identify the type of relationship (linear or nonlinear), and subsequent pathway analysis revealed that genes displaying a linear relationship were overall associated with substantially different biological processes than genes displaying a nonlinear relationship. In the group of genes with a linear relationship, we found significant association to canonical pathways, including purine and pyrimidine metabolism (for both deletions and amplifications) as well as estrogen metabolism (linear amplification) and BRCA-related response to damage (linear deletion). In the group of genes displaying a nonlinear relationship, the top canonical pathways were specific pathways like PTEN and PI13K/AKT (nonlinear amplification) and Wnt(B) and IL-2 signalling (nonlinear deletion). Both amplifications and deletions pointed to the same affected pathways and identified cancer as the top significant disease and cell cycle, cell signaling and cellular development as significant networks.

Conclusions

This paper presents a novel approach to assessing the validity of the dependence of expression data on copy number data, and this approach may help in identifying the drivers of carcinogenesis.

ERBB2 gene amplification in oral squamous cell malignancies: a correlation with tumor progression and gene expression

OBJECTIVES

Chromosomal instability is hallmark of carcinoma. Amplification of chromosome 17q11-q12 is present in some oral squamous cell cancer (OSCC) cases. In this study, we investigated the copy number variations of ERBB2 gene, which is located at this locus in collected OSCC samples and their correlation with tumor progression and gene expression.

STUDY DESIGN

Quantitative real-time polymerase chain reaction was performed to detect the copy number of ERBB2 gene and the mRNA expression in 92 OSCC samples with matched adjacent normal tissues (ANTs). Proportional odds regression and 2-way repeated measurement analysis of variance were used to analyze the association between copy number variations and mRNA expression of the targeted gene.

RESULTS

Copy number gains of ERBB2 were detected in some of the OSCCs (19.6%, 18/92) and correlated with tumor stage (P < .001). Copy number gains of ERBB2 also showed a positive correlation with mRNA overexpression in OSCCs (P < .001). However, enhanced ERBB2 mRNA expression was also detected in a group of OSCC samples with unaltered copy number of ERBB2 gene (P < .05).

CONCLUSIONS

Copy number increase of ERBB2 is observed in OSCCs and correlates with gene overexpression in these tumors. In addition, overexpression of ERBB2 is also observed in some OSCCs that lack copy number changes, indicating involvement of another mechanism.

Copy number increase of aurora kinase A in colorectal cancers: a correlation with tumor progression

The centrosome-associated kinase aurora A (AURKA) is involved in genetic instability and is over-expressed in several human carcinomas including colorectal cancer (CRC). The choromosome locus of AURKA, 20q13, is frequently amplified in CRC, and the functional impact of such regions needs to be extensively investigated in large amount of clinical samples. Case-matched tissues of colorectal adenocarcinomas and adjacent normal epithelium (n= 134) were included in this study. Quantitative PCR was carried out to examine the copy number and mRNA level of AURKA in CRC. Our results showed that copy number gains of AUKRA were detected in a relative high percentage of CRC samples (32.4%, 43 of 134). There was a positive correlation between copy number increase of AURKA and tumor progression. And copy number gains of AURKA also showed a positive correlation with mRNA over-expression in CRC. However, the expression level of AURKA mRNA was also enhanced in the group of CRC samples with unaltered copy numbers. These findings indicated that sporadic colorectal cancers exhibit different mechanisms of aurora A regulation and this may impact the efficacy of aurora-targeted therapies.

Investigation of copy-number variations of C8orf4 in hematological malignancies

C8orf4, thyroid cancer-1 (TC1), was first identified in papillary thyroid carcinoma and encodes a nucleus-localized protein. A recent array-based study implicated the presence of copy-number variations (CNVs) of C8orf4 in the genomes of acute myelogenous leukemia. However, the functional impact of such regions needs to be extensively investigated in large amount of clinical samples. The purpose of this study is to confirm the relationship between C8orf4 CNVs and hematological malignancies. In our study, we collected bone marrow samples from 515 hematological malignancies and 102 healthy controls. And the CNVs of C8orf4 were detected by real-time PCR. We found significant association between the copy-number deletions of C8orf4 and the risk of these hematological malignancies including acute lymphoblastic leukemia (ALL), acute myelogenous leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), multiple myeloma (MM), and myelodysplastic syndrome (MDS). We also found that the expression of C8orf4 mRNA was relatively lower in the samples with 1 copy of DNA than those with 2 copies of DNA. The CNVs of C8orf4 were associated with the risk of hematological malignancies.

Coexistence of copy number increases of ZNF217 and CYP24A1 in colorectal cancers in a Chinese population

Evidence suggests that the amplification of chromosome 20q13 is common in colorectal cancers (CRCs). Certain candidate oncogenes located in this region are reported to be associated with tumorigenesis of the gastrointestinal tract. The functional impact of such regions should be extensively investigated in a large number of clinical samples. In this study, 145 CRC samples with matched adjacent normal tissues were collected from a Chinese population for copy number variation (CNV) analysis. Our results showed that both the copy numbers of 25-hydroxy vitamin D3 24-hydroxylase (CYP24A1) and zinc-finger protein 217 (ZNF217) were amplified in a relatively high percentage of CRC samples (51.1 and 60%, respectively). The mRNA expression levels of both CYP24A1 and ZNF217 were found to have increased in the collected CRC samples as compared to the matched adjacent normal tissues. ZNF217, but not CYP24A1, showed a positive correlation between copy number increases and mRNA overexpression. These findings suggest the potential role of CNVs of certain oncogenes in CRCs.

Copy-number increase of AURKA in gastric cancers in a Chinese population: a correlation with tumor progression

The centrosome-associated kinase aurora A (AURKA) has been shown to be involved in genetic instability and to be over-expressed in several human carcinomas including gastric cancers (GCs). The chromosome locus of AURKA, 20q13, is frequently amplified in GCs, and the functional impact of such regions needs to be extensively investigated in large amount of clinical samples. Case-matched tissues of gastric carcinomas and adjacent normal epithelium (n=141) were included in this study. Quantitative PCR was carried out to examine the copy number and mRNA expression of AURKA in GCs. Our results showed copy-number gains of AUKRA were detected in a relative high percentage of GC samples (30.5%, 43 out of 141). There was a positive correlation between copy-number increase of AURKA and tumor progression. And copy-number gains of AURKA also showed a positive correlation with mRNA over-expression in GCs. However, expression level of AURKA mRNA was also enhanced in the group of GC samples with unaltered copy numbers. These findings indicated that sporadic gastric cancers exhibit different mechanisms of AURKA regulation and that this may impact the efficacy of aurora-targeted therapies.