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Isocitrate dehydrogenase (IDH) genes 1 and 2 are frequently mutated in acute myeloid leukaemia (AML), low-grade glioma, cholangiocarcinoma (CC) and chondrosarcoma (CS). For AML, low-grade glioma and CC, mutant IDH status is associated with a DNA hypermethylation phenotype, implicating altered epigenome dynamics in the aetiology of these cancers. Here we show that the IDH variants in CS are also associated with a hypermethylation phenotype and display increased production of the oncometabolite 2-hydroxyglutarate, supporting the role of mutant IDH-produced 2-hydroxyglutarate as an inhibitor of TET-mediated DNA demethylation. Meta-analysis of the acute myeloid leukaemia, low-grade glioma, cholangiocarcinoma and CS methylation data identifies cancer-specific effectors within the retinoic acid receptor activation pathway among the hypermethylated targets. By analysing sequence motifs surrounding hypermethylated sites across the four cancer types, and using chromatin immunoprecipitation and western blotting, we identify the transcription factor EBF1 (early B-cell factor 1) as an interaction partner for TET2, suggesting a sequence-specific mechanism for regulating DNA methylation., Cancer-associated mutations in isocitrate dehydrogenase are proposed to impair TET2-dependent DNA demethylation. By comparing the methylomes of IDH-mutant cancers, the authors identify the transcription factor EBF1 as a partner of TET2, suggesting a possible means for targeting TET2 to specific DNA sequences.

Epigenetic changes have been associated with ageing and cancer. Identifying and interpreting epigenetic changes associated with such phenotypes may benefit from integration with protein interactome models. We here develop and validate a novel integrative epigenome-interactome approach to identify differential methylation interactome hotspots associated with a phenotype of interest. We apply the algorithm to cancer and ageing, demonstrating the existence of hotspots associated with these phenotypes. Importantly, we discover tissue independent age-associated hotspots targeting stem-cell differentiation pathways, which we validate in independent DNA methylation data sets, encompassing over 1000 samples from different tissue types. We further show that these pathways would not have been discovered had we used a non-network based approach and that the use of the protein interaction network improves the overall robustness of the inference procedure. The proposed algorithm will be useful to any study seeking to identify interactome hotspots associated with common phenotypes.

Aberrant epigenetic regulation is a key cancer hallmark. Epigenetic changes observed in pre-neoplastic lesions and cancer also provides a promising avenue for the discovery of novel biomarkers for early detection, diagnosis and prognosis, as well as offering novel therapeutic opportunities. However, the biological interpretation and functional significance of the epigenetic changes in cancer is still unclear. This chapter describes an emerging computational systems framework for elucidating the observed epigenetic deregulation in cancer and other complex diseases. As we shall see, the novel graph-theoretical approach presented here provides a powerful framework for the identification of epigenetic biomarkers associated with common phenotypes. Moreover, it provides a convenient platform in which to perform integrative multi-dimensional analysis, allowing functional epigenetic modules driving disease to be identified. We illustrate the computational method with applications to ageing and the early detection of endometrial cancer. The methods and data presented here provide a concrete example of systems-medicine: the application of a systems-approach to identify a biomarker with great potential for clinical application.

TB filled in by Laureen Please see later in the article for the Editors' Summary, Background Endometrial cancer incidence is continuing to rise in the wake of the current ageing and obesity epidemics. Much of the risk for endometrial cancer development is influenced by the environment and lifestyle. Accumulating evidence suggests that the epigenome serves as the interface between the genome and the environment and that hypermethylation of stem cell polycomb group target genes is an epigenetic hallmark of cancer. The objective of this study was to determine the functional role of epigenetic factors in endometrial cancer development. Methods and Findings Epigenome-wide methylation analysis of >27,000 CpG sites in endometrial cancer tissue samples (n = 64) and control samples (n = 23) revealed that HAND2 (a gene encoding a transcription factor expressed in the endometrial stroma) is one of the most commonly hypermethylated and silenced genes in endometrial cancer. A novel integrative epigenome-transcriptome-interactome analysis further revealed that HAND2 is the hub of the most highly ranked differential methylation hotspot in endometrial cancer. These findings were validated using candidate gene methylation analysis in multiple clinical sample sets of tissue samples from a total of 272 additional women. Increased HAND2 methylation was a feature of premalignant endometrial lesions and was seen to parallel a decrease in RNA and protein levels. Furthermore, women with high endometrial HAND2 methylation in their premalignant lesions were less likely to respond to progesterone treatment. HAND2 methylation analysis of endometrial secretions collected using high vaginal swabs taken from women with postmenopausal bleeding specifically identified those patients with early stage endometrial cancer with both high sensitivity and high specificity (receiver operating characteristics area under the curve = 0.91 for stage 1A and 0.97 for higher than stage 1A). Finally, mice harbouring a Hand2 knock-out specifically in their endometrium were shown to develop precancerous endometrial lesions with increasing age, and these lesions also demonstrated a lack of PTEN expression. Conclusions HAND2 methylation is a common and crucial molecular alteration in endometrial cancer that could potentially be employed as a biomarker for early detection of endometrial cancer and as a predictor of treatment response. The true clinical utility of HAND2 DNA methylation, however, requires further validation in prospective studies. Please see later in the article for the Editors' Summary, Editors' Summary Background Cancer, which is responsible for 13% of global deaths, can develop anywhere in the body, but all cancers are characterized by uncontrolled cell growth and reduced cellular differentiation (the process by which unspecialized cells such as “stem” cells become specialized during development, tissue repair, and normal cell turnover). Genetic alterations—changes in the sequence of nucleotides (DNA's building blocks) in specific genes—are required for this cellular transformation and subsequent cancer development (carcinogenesis). However, recent evidence suggests that epigenetic modifications—reversible, heritable changes in gene function that occur in the absence of nucleotide sequence changes—may also be involved in carcinogenesis. For example, the addition of methyl groups to a set of genes called stem cell polycomb group target genes (PCGTs; polycomb genes control the expression of their target genes by modifying their DNA or associated proteins) is one of the earliest molecular changes in human cancer development, and increasing evidence suggests that hypermethylation of PCGTs is an epigenetic hallmark of cancer. Why Was This Study Done? The methylation of PCGTs, which is triggered by age and by environmental factors that are associated with cancer development, reduces cellular differentiation and leads to the accumulation of undifferentiated cells that are susceptible to cancer development. It is unclear, however, whether epigenetic modifications have a causal role in carcinogenesis. Here, the researchers investigate the involvement of epigenetic factors in the development of endometrial (womb) cancer. The risk of endometrial cancer (which affects nearly 50,000 women annually in the United States) is largely determined by environmental and lifestyle factors. Specifically, the risk of this cancer is increased in women in whom estrogen (a hormone that drives cell proliferation in the endometrium) is functionally dominant over progesterone (a hormone that inhibits endometrial proliferation and causes cell differentiation); obese women and women who have taken estrogen-only hormone replacement therapies fall into this category. Thus, endometrial cancer is an ideal model in which to study whether epigenetic mechanisms underlie carcinogenesis. What Did the Researchers Do and Find? The researchers collected data on genome-wide DNA methylation at cytosine- and guanine-rich sites in endometrial cancers and normal endometrium and integrated this information with the human interactome and transcriptome (all the physical interactions between proteins and all the genes expressed, respectively, in a cell) using an algorithm called Functional Epigenetic Modules (FEM). This analysis identified HAND2 as the hub of the most highly ranked differential methylation hotspot in endometrial cancer. HAND2 is a progesterone-regulated stem cell PCGT. It encodes a transcription factor that is expressed in the endometrial stroma (the connective tissue that lies below the epithelial cells in which most endometrial cancers develop) and that suppresses the production of the growth factors that mediate the growth-inducing effects of estrogen on the endometrial epithelium. The researchers hypothesized, therefore, that epigenetic deregulation of HAND2 could be a key step in endometrial cancer development. In support of this hypothesis, the researchers report that HAND2 methylation was increased in premalignant endometrial lesions (cancer-prone, abnormal-looking tissue) compared to normal endometrium, and was associated with suppression of HAND2 expression. Moreover, a high level of endometrial HAND2 methylation in premalignant lesions predicted a poor response to progesterone treatment (which stops the growth of some endometrial cancers), and analysis of HAND2 methylation in endometrial secretions collected from women with postmenopausal bleeding (a symptom of endometrial cancer) accurately identified individuals with early stage endometrial cancer. Finally, mice in which the Hand2 gene was specifically deleted in the endometrium developed precancerous endometrial lesions with age. What Do These Findings Mean? These and other findings identify HAND2 methylation as a common, key molecular alteration in endometrial cancer. These findings need to be confirmed in more women, and studies are needed to determine the immediate molecular and cellular consequences of HAND2 silencing in endometrial stromal cells. Nevertheless, these results suggest that HAND2 methylation could potentially be used as a biomarker for the early detection of endometrial cancer and for predicting treatment response. More generally, these findings support the idea that methylation of HAND2 (and, by extension, the methylation of other PCGTs) is not a passive epigenetic feature of cancer but is functionally involved in cancer development, and provide a framework for identifying other genes that are epigenetically regulated and functionally important in carcinogenesis. Additional Information Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001551 The US National Cancer Institute provides information on all aspects of cancer and has detailed information about endometrial cancer for patients and professionals (in English and Spanish) The not-for-profit organization American Cancer Society provides information on cancer and how it develops and specific information on endometrial cancer (in several languages) The UK National Health Service Choices website includes an introduction to cancer, a page on endometrial cancer, and a personal story about endometrial cancer The not-for-profit organization Cancer Research UK provides general information about cancer and specific information about endometrial cancer Wikipedia has a page on cancer epigenetics (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages) The Eve Appeal charity that supported this research provides useful information on gynecological cancers

Objective A noninvasive tool that allows individuals to be monitored who are at risk of developing a malignancy is an unmet need. Such a test would need to consist of a molecular signature that allows for gradual judgment to assess the efficacy of preventive strategies. Here we performed a proof-of-principle study to test whether a DNA methylation (DNAme) signature in fluid collected from the vagina is able to identify women with cervical or endometrial cancer. Materials and methods DNA from vaginal fluid samples from 111 women (30, 8, 73 with endometrial cancer, cervical cancer, and benign gynecological conditions, respectively) were analyzed for DNAme using the Illumina 450k DNA methylation bead array assay, which allows the assessment of DNAme at more than 480.000 CpG sites. We developed a cervical and an endometrial cancer DNAme signature by comparing normal and cancerous cervical and endometrial samples from the publicly available The Cancer Genome Atlas data and developed deviation scores to assess the potential of discriminating cancer from a control sample using a vaginal fluid DNAme signature. Results More than 60% of variations in DNAme in our vaginal fluid cannot be explained by those clinical or technical factors that we were aware of. Both the cervical and the endometrial cancer DNAme signature resulted in receiver operating characteristic area under the curve between 0.75 and 0.83 to discriminate controls and the cancers for which the signature has been designed for. Conclusions Whole DNAme signatures based on array technologies in body fluids are able to discriminate cancer cases from controls.

BMC Research Notes recently published a research article regarding the use of ligated DNA extracted from formalin-fixed paraffin embedded (FFPE) tissue on the Illumina Infinium methylation platform - “Interpretation of genome-wide infinium methylation data from ligated DNA in formalin-fixed, paraffin-embedded paired tumor and normal tissue” Jasmine et al. BMC Research Notes 2012, 5:117. This article repeatedly refers to our previous work and concludes that methylation data obtained from ligated FFPE extracted DNA should be used with great caution. In this Discussion we review the data analysis performed in Jasmine et al’s paper and suggest limitations which subsequently lead the authors to draw what we believe are incorrect conclusions. Moreover, we continue to analyse genome-wide methylation data from DNA extracted from FFPE tissue successfully on both the HumMeth27 and 450 K arrays.

Over the past decade it has become clear that cancer is an epigenetic and a genetic disease. While we have begun to understand the impact of variations in the DNA sequence on cancer development, it is only more recently that we have appreciated the significant contribution of the epigenome to carcinogenesis and cancer biology. Twin studies demonstrate that genetics makes little contribution to the development of women-specific cancers and that the 'epigenome,' the interphase between genome and environment, is likely to confer the largest component of risk. Epigenetic factors can therefore act as surrogate markers of disease risk that could potentially facilitate tailored treatment and refine preventive measures. This review focuses attention on DNA methylation-a core epigenetic mechanism that can be readily detected in body fluids and has the potential to substantially reform cancer screening and treatment.

Background The 27k Illumina Infinium Methylation Beadchip is a popular high-throughput technology that allows the methylation state of over 27,000 CpGs to be assayed. While feature selection and classification methods have been comprehensively explored in the context of gene expression data, relatively little is known as to how best to perform feature selection or classification in the context of Illumina Infinium methylation data. Given the rising importance of epigenomics in cancer and other complex genetic diseases, and in view of the upcoming epigenome wide association studies, it is critical to identify the statistical methods that offer improved inference in this novel context. Results Using a total of 7 large Illumina Infinium 27k Methylation data sets, encompassing over 1,000 samples from a wide range of tissues, we here provide an evaluation of popular feature selection, dimensional reduction and classification methods on DNA methylation data. Specifically, we evaluate the effects of variance filtering, supervised principal components (SPCA) and the choice of DNA methylation quantification measure on downstream statistical inference. We show that for relatively large sample sizes feature selection using test statistics is similar for M and β-values, but that in the limit of small sample sizes, M-values allow more reliable identification of true positives. We also show that the effect of variance filtering on feature selection is study-specific and dependent on the phenotype of interest and tissue type profiled. Specifically, we find that variance filtering improves the detection of true positives in studies with large effect sizes, but that it may lead to worse performance in studies with smaller yet significant effect sizes. In contrast, supervised principal components improves the statistical power, especially in studies with small effect sizes. We also demonstrate that classification using the Elastic Net and Support Vector Machine (SVM) clearly outperforms competing methods like LASSO and SPCA. Finally, in unsupervised modelling of cancer diagnosis, we find that non-negative matrix factorisation (NMF) clearly outperforms principal components analysis. Conclusions Our results highlight the importance of tailoring the feature selection and classification methodology to the sample size and biological context of the DNA methylation study. The Elastic Net emerges as a powerful classification algorithm for large-scale DNA methylation studies, while NMF does well in the unsupervised context. The insights presented here will be useful to any study embarking on large-scale DNA methylation profiling using Illumina Infinium beadarrays.

The role of transforming growth factor-beta (TGFβ) in the progression of different molecular subtypes of breast cancer has not been clarified. Here we show that TGFβ increases breast tumour-initiating cell (BTIC) numbers but only in claudin(low) breast cancer cell lines by orchestrating a specific gene signature enriched in stem cell processes that predicts worse clinical outcome in breast cancer patients. NEDD9, a member of the Cas family of integrin scaffold proteins, is necessary to mediate these TGFβ-specific effects through a positive feedback loop that integrates TGFβ/Smad and Rho-actin-SRF-dependent signals. In normal human mammary epithelium, TGFβ induces progenitor activity only in the basal/stem cell compartment, where claudin(low) cancers are presumed to arise. These data show opposing responses to TGFβ in both breast malignant cell subtypes and normal mammary epithelial cell subpopulations and suggest therapeutic strategies for a subset of human breast cancers.

Oestrogen receptor-α (ER) is the defining and driving transcription factor in the majority of breast cancers and its target genes dictate cell growth and endocrine response, yet genomic understanding of ER function has been restricted to model systems. Here we map genome-wide ER-binding events, by chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq), in primary breast cancers from patients with different clinical outcomes and in distant ER-positive metastases. We find that drug-resistant cancers still recruit ER to the chromatin, but that ER binding is a dynamic process, with the acquisition of unique ER-binding regions in tumours from patients that are likely to relapse. The acquired ER regulatory regions associated with poor clinical outcome observed in primary tumours reveal gene signatures that predict clinical outcome in ER-positive disease exclusively. We find that the differential ER-binding programme observed in tumours from patients with poor outcome is not due to the selection of a rare subpopulation of cells, but is due to the FOXA1-mediated reprogramming of ER binding on a rapid timescale. The parallel redistribution of ER and FOXA1 binding events in drug-resistant cellular contexts is supported by histological co-expression of ER and FOXA1 in metastatic samples. By establishing transcription-factor mapping in primary tumour material, we show that there is plasticity in ER-binding capacity, with distinct combinations of cis-regulatory elements linked with the different clinical outcomes.

We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We firstly define a purely structural entropy obtained by computing the approximate entropy of the so called slide sequence. This is a surrogate of the degree sequence and it is suggested by the frequency partition of a graph. We examine this quantity for standard scale-free and Erd\H{o}s-R\'enyi networks. By using classical results of Pincus, we show that our entropy measure converges with network size to a certain binary Shannon entropy. On a second step, with specific attention to networks generated by dynamical processes, we investigate approximate entropy of horizontal visibility graphs. Visibility graphs permit to naturally associate to a network the notion of temporal correlations, therefore providing the measure a dynamical garment. We show that approximate entropy distinguishes visibility graphs generated by processes with different complexity. The result probes to a greater extent these networks for the study of dynamical systems. Applications to certain biological data arising in cancer genomics are finally considered in the light of both approaches., Comment: 11 pages, 5 EPS figures

Objective: A significant proportion of HNSCC is caused by human papillomavirus. Patients diagnosed with this subtype have a significantly better outcome. It is the aim of the UCL Head and Neck Cancer Epigenome Project to generate and analyze comprehensive, genome-wide profiles of epigenetic aberrations in this specific type of human cancer.Method: Whole-genome DNA methylation (methylome) analysis of 3 HPV positive and 3 HPV negative frozen HNSCC samples (obtained from the UCL Head and Neck Cancer Tissue Bank as part of a larger discovery set) was conducted using methylated DNA immunoprecipitation coupled with next-generation sequencing (MeDIP-Seq).Results: On average, each sample in the pilot phase generated 2 x 107 sequencing reads and 70% coverage of all CpG sites within the host genome. HPV type 16 was observed in all HPV positive samples and, interestingly, methylated regions of this virus were identified—most notably at genes E1 and L1 (previously shown to have been methylated in cervical cancer). We...

Motivation: A common difficulty in large-scale microarray studies is the presence of confounding factors, which may significantly skew estimates of statistical significance, cause unreliable feature selection and high false negative rates. To deal with these difficulties, an algorithmic framework known as Surrogate Variable Analysis (SVA) was recently proposed. Results: Based on the notion that data can be viewed as an interference pattern, reflecting the superposition of independent effects and random noise, we present a modified SVA, called Independent Surrogate Variable Analysis (ISVA), to identify features correlating with a phenotype of interest in the presence of potential confounding factors. Using simulated data, we show that ISVA performs well in identifying confounders as well as outperforming methods which do not adjust for confounding. Using four large-scale Illumina Infinium DNA methylation datasets subject to low signal to noise ratios and substantial confounding by beadchip effects and variable bisulfite conversion efficiency, we show that ISVA improves the identifiability of confounders and that this enables a framework for feature selection that is more robust to model misspecification and heterogeneous phenotypes. Finally, we demonstrate similar improvements of ISVA across four mRNA expression datasets. Thus, ISVA should be useful as a feature selection tool in studies that are subject to confounding. Availability: An R-package isva is available from www.cran.r-project.org. Contact: a.teschendorff@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Aberrant DNA methylation (DNAm) was first linked to cancer over 25 yr ago. Since then, many studies have associated hypermethylation of tumor suppressor genes and hypomethylation of oncogenes to the tumorigenic process. However, most of these studies have been limited to the analysis of promoters and CpG islands (CGIs). Recently, new technologies for whole-genome DNAm (methylome) analysis have been developed, enabling unbiased analysis of cancer methylomes. By using MeDIP-seq, we report a sequencing-based comparative methylome analysis of malignant peripheral nerve sheath tumors (MPNSTs), benign neurofibromas, and normal Schwann cells. Analysis of these methylomes revealed a complex landscape of DNAm alterations. In contrast to what has been reported for other tumor types, no significant global hypomethylation was observed in MPNSTs using methylome analysis by MeDIP-seq. However, a highly significant (P < 10−100) directional difference in DNAm was found in satellite repeats, suggesting these repeats to be the main target for hypomethylation in MPNSTs. Comparative analysis of the MPNST and Schwann cell methylomes identified 101,466 cancer-associated differentially methylated regions (cDMRs). Analysis showed these cDMRs to be significantly enriched for two satellite repeat types (SATR1 and ARLα) and suggests an association between aberrant DNAm of these sequences and transition from healthy cells to malignant disease. Significant enrichment of hypermethylated cDMRs in CGI shores (P < 10−60), non–CGI-associated promoters (P < 10−4) and hypomethylated cDMRs in SINE repeats (P < 10−100) was also identified. Integration of DNAm and gene expression data showed that the expression pattern of genes associated with CGI shore cDMRs was able to discriminate between disease phenotypes. This study establishes MeDIP-seq as an effective method to analyze cancer methylomes.

A substantial proportion of lymph node-negative patients who receive adjuvant chemotherapy do not derive any benefit from this aggressive and potentially toxic treatment. However, standard histopathological indices cannot reliably detect patients at low risk of relapse or distant metastasis. In the past few years several prognostic gene expression signatures have been developed and shown to potentially outperform histopathological factors in identifying low-risk patients in specific breast cancer subgroups with predictive values of around 90%, and therefore hold promise for clinical application. We envisage that further improvements and insights may come from integrative expression pathway analyses that dissect prognostic signatures into modules related to cancer hallmarks.

Recent multi-dimensional approaches to the study of complex disease have revealed powerful insights into how genetic and epigenetic factors may underlie their aetiopathogenesis. We examined genotype-epigenotype interactions in the context of Type 2 Diabetes (T2D), focussing on known regions of genomic susceptibility. We assayed DNA methylation in 60 females, stratified according to disease susceptibility haplotype using previously identified association loci. CpG methylation was assessed using methylated DNA immunoprecipitation on a targeted array (MeDIP-chip) and absolute methylation values were estimated using a Bayesian algorithm (BATMAN). Absolute methylation levels were quantified across LD blocks, and we identified increased DNA methylation on the FTO obesity susceptibility haplotype, tagged by the rs8050136 risk allele A (p = 9.40×10-4, permutation p = 1.0×10-3). Further analysis across the 46 kb LD block using sliding windows localised the most significant difference to be within a 7.7 kb region (p = 1.13×10-7). Sequence level analysis, followed by pyrosequencing validation, revealed that the methylation difference was driven by the co-ordinated phase of CpG-creating SNPs across the risk haplotype. This 7.7 kb region of haplotype-specific methylation (HSM), encapsulates a Highly Conserved Non-Coding Element (HCNE) that has previously been validated as a long-range enhancer, supported by the histone H3K4me1 enhancer signature. This study demonstrates that integration of Genome-Wide Association (GWA) SNP and epigenomic DNA methylation data can identify potential novel genotype-epigenotype interactions within diseaseassociated loci, thus providing a novel route to aid unravelling common complex diseases. © 2010 Bell et al.

Distinct molecular subtypes of breast carcinomas have been identified, but translation into clinical use has been limited. We have developed two platform-independent algorithms to explore genomic architectural distortion using array comparative genomic hybridization data to measure (i) whole-arm gains and losses [whole-arm aberration index (WAAI)] and (ii) complex rearrangements [complex arm aberration index (CAAI)]. By applying CAAI and WAAI to data from 595 breast cancer patients, we were able to separate the cases into eight subgroups with different distributions of genomic distortion. Within each subgroup data from expression analyses, sequencing and ploidy indicated that progression occurs along separate paths into more complex genotypes. Histological grade had prognostic impact only in the luminal-related groups, whereas the complexity identified by CAAI had an overall independent prognostic power. This study emphasizes the relation among structural genomic alterations, molecular subtype, and clinical behavior and shows that objective score of genomic complexity (CAAI) is an independent prognostic marker in breast cancer.

Polycomb group proteins (PCGs) are involved in repression of genes that are required for stem cell differentiation. Recently, it was shown that promoters of PCG target genes (PCGTs) are 12-fold more likely to be methylated in cancer than non-PCGTs. Age is the most important demographic risk factor for cancer, and we hypothesized that its carcinogenic potential may be referred by irreversibly stabilizing stem cell features. To test this, we analyzed the methylation status of over 27,000 CpGs mapping to promoters of ∼14,000 genes in whole blood samples from 261 postmenopausal women. We demonstrate that stem cell PCGTs are far more likely to become methylated with age than non-targets (odds ratio = 5.3 [3.8–7.4],P< 10−10), independently of sex, tissue type, disease state, and methylation platform. We identified a specific subset of 69 PCGT CpGs that undergo hypermethylation with age and validated this methylation signature in seven independent data sets encompassing over 900 samples, including normal and cancer solid tissues and a population of bone marrow mesenchymal stem/stromal cells (P< 10−5). We find that the age-PCGT methylation signature is present in preneoplastic conditions and may drive gene expression changes associated with carcinogenesis. These findings shed substantial novel insights into the epigenetic effects of aging and support the view that age may predispose to malignant transformation by irreversibly stabilizing stem cell features.

Recent genome-wide association studies have identified a breast cancer susceptibility locus on 16q12 with an unknown biological basis. We used a set of single nucleotide polymorphism (SNP) markers to generate a fine-scale map and narrowed the region of association to a 133 kb DNA segment containing the largely uncharacterized hypothetical gene LOC643714, a short intergenic region and the 5' end of TOX3. Re-sequencing this segment in European subjects identified 293 common polymorphisms, including a set of 26 highly correlated candidate causal variants. By evaluation of these SNPs in five breast cancer case-control studies involving more than 23 000 subjects from populations of European and Southeast Asian ancestry, all but 14 variants could be excluded at odds of

Aberrant DNA methylation of promoter and other genomic regions can lead to changes in gene expression, such as over-expression of oncogenes and the silencing of tumour suppressor genes in the context of cancer. Ability to accurately assess the DNA methylation status is therefore of great importance in health and disease. Recently, various platforms for genome-wide analysis of promoter DNA methylation have been developed, including the Illumina Infinium platform. While some of these platforms can be used with formalin-fixed paraffin-embedded (FFPE) tissue, no protocol has yet been developed for the analysis of FFPE-derived DNA on the Infinium platform using the HumanMethylation27 BeadChip which interrogates 27,578 cytosine-guanine (CpG) dinucleotide sites, selected predominantly from the promoter regions of 14,000 annotated genes. As FFPE preservation has been the method of choice for the archiving of clinical samples, the ability to analyse such samples opens the possibility to study large numbers of clinically well annotated samples which can be expected to lead to more powerful and robust data, particularly for rare cancers. Here, we describe a protocol for the analysis of FFPE samples using the Infinium HumanMethylation27 BeadChip.

Although preliminary evidence suggests that germline variation in genes involved in steroid hormone metabolism may alter breast cancer prognosis, this has not been systematically evaluated. We examined associations between germline polymorphisms in 6 genes involved in the steroid hormone metabolism and signaling pathway (COMT, CYP19A1, ESR1, PGR, SULT1E1, STS) and survival among women with breast cancer participating in SEARCH, a population-based case-control study. Blood samples from up to 4,470 women were genotyped for 4 possible functional SNPs in CYP19A1 and 106 SNPs tagging the common variation in the remainder of the genes. The genotypes of each polymorphism were tested for association with survival after breast cancer diagnosis using Cox regression analysis. Significant evidence of an association was observed for a COMT polymorphism (rs4818 p = 0.016) under the codominant model. This SNP appeared to fit a dominant model better (HR = 0.80 95% CI: 0.69-0.95, p = 0.009); however, the result was only marginally significant after permutation analysis adjustment for multiple hypothesis tests (p = 0.047). To further evaluate this finding, somatic expression microarray data from 8 publicly available datasets were used to test the association between survival and tumor COMT gene expression; no statistically significant associations were observed. A correlated SNP in COMT, rs4860, has recently been associated with breast cancer prognosis in Chinese women in a dominant model. These results suggest that COMT rs4818, or a variant it tags, is associated with breast cancer prognosis. Further study of COMT and its putative association with breast cancer prognosis is warranted. (c) 2009 UICC

We have examined expression of the Myc target gene Misu (NSUN2) in breast cancer. There was extensive copy number gain, and increased mRNA and protein levels, of Misu in approximately one third of breast cancer cell lines and primary tumours examined, irrespective of tumour subtype. Genes on 5p15.31-33, where Misu is located, showed evolutionary synteny. siRNA-mediated knockdown of Misu reduced cell number in over half of the cell lines tested, irrespective of estrogen receptor status. We conclude that Misu is up-regulated in a substantial proportion of breast cancers and has therapeutic potential as a drug target.

INTRODUCTION: Several gene expression signatures have been proposed which have been demonstrated to be predictive of outcome in breast cancer. Here we address the following issues: 1) Do these signatures perform similarly? 2) Are there (common) molecular processes reported by these signatures? 3) Can better prognostic predictors be constructed based on these identified molecular processes? METHODS: We performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases. RESULTS: The classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a 'poor' or 'good' outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of 'poor' outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only 'good' outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and Immune modules resulted in a classifier with high prognostic performance on an independent validation set. CONCLUSIONS: This study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of Immunity and RNA-splicing processes in breast cancer

Lindsay A Brown1,2, Jeremy Hoog3, Suet-Feung Chin4,5, Yu Tao3, Abd Alnaser Zayed1,2, Koei Chin6, Andrew E Teschendorff4,5, John F Quackenbush7, John C Marioni8, Samuel Leung9,10, Charles M Perou11, Torsten O Neilsen9,10, Matthew Ellis3, Joe W Gray6, Philip S Bernard7, David G Huntsman1,2,9,10,12, and Carlos Caldas4,5,12 1Center for Translational and Applied Genomics, and the British Columbia Cancer Agency, Vancouver, British Columbia V5Z 4E6, Canada.

Background Human papilloma virus (HPV) load and physical status are considered useful parameters for clinical evaluation of cervical squamous cell neoplasia. However, the errors implicit in HPV gene quantification by PCR are not well documented. We have undertaken the first rigorous evaluation of the errors that can be expected when using SYBR green qPCR for quantification of HPV type 16 gene copy numbers. We assessed a modified method, in which external calibration curves were generated from a single construct containing HPV16 E2, HPV16 E6 and the host gene hydroxymethylbilane synthase in a 1:1:1 ratio. Results When testing dilutions of mixed HPV/host DNA in replicate runs, we observed errors in quantifying E2 and E6 amplicons of 5–40%, with greatest error at the lowest DNA template concentration (3 ng/μl). Errors in determining viral copy numbers per diploid genome were 13–53%. Nevertheless, in cervical keratinocyte cell lines we observed reasonable agreement between viral loads determined by qPCR and Southern blotting. The mean E2/E6 ratio in episome-only cells was 1.04, but with a range of 0.76–1.32. In three integrant-only lines the mean E2/E6 ratios were 0.20, 0.72 and 2.61 (values confirmed by gene-specific Southern blotting). When E2/E6 ratios in fourteen HPV16-positive cervical carcinomas were analysed, conclusions regarding viral physical state could only be made in three cases, where the E2/E6 ratio was ≤ 0.06. Conclusion Run-to-run variation in SYBR green qPCR produces unavoidable inaccuracies that should be allowed for when quantifying HPV gene copy number. While E6 copy numbers can be considered to provide a useable indication of viral loads, the E2/E6 ratio is of limited value. Previous studies may have overestimated the frequency of mixed episomal/integrant HPV infections.

The recent whole-genome scan for breast cancer has revealed the FGFR2 (fibroblast growth factor receptor 2) gene as a locus associated with a small, but highly significant, increase in the risk of developing breast cancer. Using fine-scale genetic mapping of the region, it has been possible to narrow the causative locus to a haplotype of eight strongly linked single nucleotide polymorphisms (SNPs) spanning a region of 7.5 kilobases (kb) in the second intron of the FGFR2 gene. Here we describe a functional analysis to define the causative SNP, and we propose a model for a disease mechanism. Using gene expression microarray data, we observed a trend of increased FGFR2 expression in the rare homozygotes. This trend was confirmed using real-time (RT) PCR, with the difference between the rare and the common homozygotes yielding a Wilcox p-value of 0.028. To elucidate which SNPs might be responsible for this difference, we examined protein–DNA interactions for the eight most strongly disease-associated SNPs in different breast cell lines. We identify two cis-regulatory SNPs that alter binding affinity for transcription factors Oct-1/Runx2 and C/EBPβ, and we demonstrate that both sites are occupied in vivo. In transient transfection experiments, the two SNPs can synergize giving rise to increased FGFR2 expression. We propose a model in which the Oct-1/Runx2 and C/EBPβ binding sites in the disease-associated allele are able to lead to an increase in FGFR2 gene expression, thereby increasing the propensity for tumour formation., Author Summary Recently, a number of whole-genome association studies have identified genes that predispose individuals to common diseases such as cancer. The challenge now is to understand how the identified risk loci contribute to disease, since the majority of these loci are located within introns (which are discarded after transcription) and intergenic regions, and therefore do not change the coding region of nearby genes. This manuscript describes how two single–base pair changes in intron 2 of the FGFR2 (fibroblast growth factor receptor 2) gene, “the top hit” of the breast cancer susceptibility study, exert their function. We find that the changes alter the binding of two transcription factors and cause an increase in FGFR2 gene expression, thus providing a molecular explanation for the risk phenotype. This is the first functional study, to our knowledge, of the risk loci identified for breast cancer in a whole-genome scan and demonstrates that these studies can be used as valid starting points for studying the underlying biology of cancer., Recent whole-genome scans have identified novel risk genes for many common diseases, challenging researchers to determine how these genes contribute to disease. A new study provides molecular insights into a breast cancer risk factor.

Introduction Somatic alterations have been shown to correlate with breast cancer prognosis and survival, but less is known about the effects of common inherited genetic variation. Of particular interest are genes involved in cell cycle pathways, which regulate cell division. Methods We examined associations between common germline genetic variation in 13 genes involved in cell cycle control (CCND1, CCND2, CCND3, CCNE1, CDK2 [p33], CDK4, CDK6, CDKN1A [p21, Cip1], CDKN1B [p27, Kip1], CDKN2A [p16], CDKN2B [p15], CDKN2C [p18], and CDKN2D [p19]) and survival among women diagnosed with invasive breast cancer participating in the SEARCH (Studies of Epidemiology and Risk factors in Cancer Heredity) breast cancer study. DNA from up to 4,470 women was genotyped for 85 polymorphisms that tag the known common polymorphisms (minor allele frequency > 0.05) in the genes. The genotypes of each polymorphism were tested for association with survival using Cox regression analysis. Results The rare allele of the tagging single nucleotide polymorphism (SNP) rs2479717 is associated with an increased risk of death (hazard ratio = 1.26 per rare allele carried, 95% confidence interval: 1.12 to 1.42; P = 0.0001), which was not attenuated after adjusting for tumour stage, grade, and treatment. This SNP is part of a large linkage disequilibrium block, which contains CCND3, BYSL, TRFP, USP49, C6ofr49, FRS3, and PGC. We evaluated the association of survival and somatic expression of these genes in breast tumours using expression microarray data from seven published datasets. Elevated expression of the C6orf49 transcript was associated with breast cancer survival, adding biological interest to the finding. Conclusion It is possible that CCND3 rs2479717, or another variant it tags, is associated with prognosis after a diagnosis of breast cancer. Further study is required to validate this finding.

DNA microarrays provide such a huge amount of data that unsupervised methods are required to reduce the dimension of the data set and to extract meaningful biological information. This work shows that Independent Component Analysis (ICA) is a promising approach for the analysis of genome-wide transcriptomic data. The paper first presents an overview of the most popular algorithms to perform ICA. These algorithms are then applied on a microarray breast-cancer data set. Some issues about the application of ICA and the evaluation of biological relevance of the results are discussed. This study indicates that ICA significantly outperforms Principal Component Analysis (PCA).

INTRODUCTION: Patients with primary operable oestrogen receptor (ER) negative (-) breast cancer account for about 30% of all cases and generally have a worse prognosis than ER-positive (+) patients. Nevertheless, a significant proportion of ER- cases have favourable outcomes and could potentially benefit from a less aggressive course of therapy. However, identification of such patients with a good prognosis remains difficult and at present is only possible through examining histopathological factors. METHODS: Building on a previously identified seven-gene prognostic immune response module for ER- breast cancer, we developed a novel statistical tool based on Mixture Discriminant Analysis in order to build a classifier that could accurately identify ER- patients with a good prognosis. RESULTS: We report the construction of a seven-gene expression classifier that accurately predicts, across a training cohort of 183 ER- tumours and six independent test cohorts (a total of 469 ER- tumours), ER- patients of good prognosis (in test sets, average predictive value = 94% [range 85 to 100%], average hazard ratio = 0.15 [range 0.07 to 0.36] p < 0.000001) independently of lymph node status and treatment. CONCLUSIONS: This seven-gene classifier could be used in a polymerase chain reaction-based clinical assay to identify ER- patients with a good prognosis, who may therefore benefit from less aggressive treatment regimens.

DNA microarrays provide a huge amount of data and require therefore dimensionality reduction methods to extract meaningful biological information. Independent component analysis (ICA) was proposed by several authors as an interesting means. Unfortunately, experimental data are usually of poor quality because of noise, outliers and lack of samples. Robustness to these hurdles will thus be a key feature for an ICA algorithm. This paper identifies a robust contrast function and proposes a new ICA algorithm.

Epithelial cancers frequently have multiple amplifications, and particular amplicons tend to occur together. These co-amplifications have been suggested to result from amplification of pre-existing junctions between two chromosomes, that is, translocation junctions. We investigated this hypothesis for two amplifications frequent in breast cancer, at 8p12 and 11q13, which had been reported to be associated in Southern blot studies. We confirmed that both genomic amplification and expression of genes was correlated between the frequently-amplified regions of 8p and 11q, in array CGH and microarray expression data, supporting the importance of co-amplification. We examined by FISH the physical structure of co-amplifications that we had identified by array CGH, in five breast cancer cell lines (HCC1500, MDA-MB-134, MDA-MB-175, SUM44, and ZR-75-1), four breast tumors, and a pancreatic cancer cell line (SUIT2). We found a variety of arrangements: amplification of translocation junctions; entirely independent amplification of the two regions on separate chromosomes; and separate amplification of 8p and 11q sequences in distinct sites on the same rearranged chromosome. In this last arrangement, interphase nuclei often showed intermingling of FISH signals from 8p12 and 11q13, giving a false impression that the sequences were interdigitated. We conclude that co-amplification of the main 8p and 11q amplicons in breast tumors is not usually the result of a preceding translocation event but most likely reflects selection of clones that have amplified both loci. This article contains supplementary material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.

Following integration of human papillomavirus (HPV) into the host genome, overexpression of the viral oncogenes E6 and E7 requires loss of the transcriptional repressor functions of E2. A key step in HPV-related carcinogenesis is therefore clearance of residual viral episomes, which encode E2. As spontaneous loss of HPV-16 episomes in vitro is associated with increased expression of antiviral genes inducible by type I interferon (IFN), we used the W12 model to examine the effects of exogenous IFN-beta on cervical keratinocytes containing HPV-16 episomes as a result of 'natural' infection in vivo. In contrast to studies of cells transfected with HPV-31 or bovine papillomavirus, IFN-beta caused rapid reduction in numbers of HPV-16 episomes. This was associated with the emergence of cells bearing previously latent integrants, in which there was increased expression of E6 and E7. Our data indicate that integrated HPV-16 can exist in a minority of cells in a mixed population without exerting a selective advantage until episome numbers are reduced. The kinetics of cell death and changes in viral transcription and translation that we observed support a model where integrants are initially present in cells also containing episomes, with generalized episome clearance by IFN-beta resulting in integrant de-repression. We conclude that IFN-beta can hasten the transition from episomal to integrated HPV-16 in naturally infected cervical keratinocytes. Greater emphasis should be placed on episome loss in models of HPV-related carcinogenesis. We provide the strongest evidence to date that treating HPV-16 lesions by inducing an IFN response may cause clinical progression.

Prognostic signatures in breast cancer derived from microarray expression profiling have been reported by two independent groups. These signatures, however, have not been validated in external studies, making clinical application problematic. We performed microarray expression profiling of 135 early-stage tumors, from a cohort representative of the demographics of breast cancer. Using a recently proposed semisupervised method, we identified a prognostic signature of 70 genes that significantly correlated with survival (hazard ratio (HR): 5.97, 95% confidence interval: 3.0-11.9, P = 2.7e-07). In multivariate analysis, the signature performed independently of other standard prognostic classifiers such as the Nottingham Prognostic Index and the 'Adjuvant!' software. Using two different prognostic classification schemes and measures, nearest centroid (HR) and risk ordering (D-index), the 70-gene classifier was also found to be prognostic in two independent external data sets. Overall, the 70-gene set was prognostic in our study and the two external studies which collectively include 715 patients. In contrast, we found that the two previously described prognostic gene sets performed less optimally in external validation. Finally, a common prognostic module of 29 genes that associated with survival in both our cohort and the two external data sets was identified. In spite of these results, further studies that profile larger cohorts using a single microarray platform, will be needed before prospective clinical use of molecular classifiers can be contemplated.

A consensus prognostic classifier for estrogen receptor positive breast tumors has been developed and shown to be valid in nearly 900 samples across different microarray platforms., Background A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer. Results Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation. Conclusion The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors.

BackgroundPost-translational modification of histones resulting in chromatin remodelling plays a key role in the regulation of gene expression. Here we report characteristic patterns of expression of 12 members of 3 classes of chromatin modifier genes in 6 different cancer types: histone acetyltransferases (HATs)-EP300, CREBBP, andPCAF; histone deacetylases (HDACs)-HDAC1, HDAC2, HDAC4, HDAC5, HDAC7A, andSIRT1; and histone methyltransferases (HMTs)-SUV39H1andSUV39H2. Expression of each gene in 225 samples (135 primary tumours, 47 cancer cell lines, and 43 normal tissues) was analysedby QRT-PCR, normalized with 8 housekeeping genes, and given as a ratio by comparison with a universal reference RNA.ResultsThis involved a total of 13,000 PCR assays allowing for rigorous analysis by fitting a linear regression model to the data. Mutation analysis ofHDAC1, HDAC2, SUV39H1, andSUV39H2revealed only two out of 181 cancer samples (both cell lines) with significant coding-sequence alterations. Supervised analysis and Independent Component Analysis showed that expression of many of these genes was able to discriminate tumour samples from their normal counterparts. Clustering based on the normalized expression ratios of the 12 genes also showed that most samples were grouped according to tissue type. Using a linear discriminant classifier and internal cross-validation revealed that with as few as 5 of the 12 genes,SIRT1, CREBBP, HDAC7A, HDAC5andPCAF, most samples were correctly assigned.ConclusionThe expression patterns of HATs, HDACs, and HMTs suggest these genes are important in neoplastic transformation and have characteristic patterns of expression depending on tissue of origin, with implications for potential clinical application.

Amplification of 8p11–12 is a well-known alteration in human breast cancers but the driving oncogene has not been identified. We have developed a high-resolution comparative genomic hybridization array covering 8p11–12 and analysed 33 primary breast tumors, 20 primary ovarian tumors and 27 breast cancer cell lines. Expression analysis of the genes in the region was carried out by using real-time quantitative PCR and/or oligo-microarray profiling. In all, 24% (8/33) of the breast tumors, 5% (1/20) of the ovary tumors and 15% (4/27) of the cell lines showed 8p11–12 amplification. We identified a 1 Mb segment of common amplification that excludes previously proposed candidate genes. Some of the amplified genes did not show overexpression, whereas for others, overexpression was not specifically attributable to amplification. The genes FLJ14299, C8orf2, BRF2 and RAB11FIP, map within the 8p11–12 minimal amplicon, two have a putative function consistent with an oncogenic role, these four genes showed a strong correlation between amplification and overexpression and are therefore the best candidate driver oncogenes at 8p12.

While a number of key signalling pathways (e.g. mTOR signalling) and biological processes (e.g. telomere attrition) affecting lifespan have been identified [1,2], other theories have argued that aging results mainly from accumulated molecular damage [1]. Most likely, aging is determined by a complex cross-talk between multiple biological effects. Molecular damage itself can take many forms, including somatic DNA mutations and copy-number changes. The advent of novel biotechnologies, allowing routine genome-wide quantitative measurement of epigenetic marks, specially DNA methylation, have recently demonstrated that age-associated changes in DNA methylation, a phenomenon now known as “epigenetic drift”, may play an equally important role in contributing to the aging phenotype [3,4]. Indeed, like telomere attrition, epigenetic drift has been associated with stem cell dysfunction [5], disease risk factors and common age-related diseases, such as cancer and Alzheimer's [3]. Apart from extensive experimental work supporting a role for DNA methylation in aging, computational network biology approaches have recently shed further light into the potential role of epigenetic drift. For instance, one study has shown that drift appears to target WNT signalling, a key pathway in stem-cell differentiation and already known to be deregulated with age [3]. A more recent study mapped epigenetic drift occurring in gene promoters onto a human protein interactome and observed that most of the changes happen at genes which occupy peripheral network positions, i.e. those of relatively low connectivity [6]. Although developmental transcription factors make up a significant proportion of “drift genes”, the observed topological effect was not entirely driven by this enrichment. Crucially, the topological properties of genes undergoing epigenetic drift were highly distinctive from those which have been associated with modulating longevity, those undergoing age-related changes in expression, or those somatically mutated in age-related diseases like cancer (Figure ​(Figure1).1). Moreover, essential housekeeping genes, many of which occupy highly central positions in the interactome, appear protected from epigenetic drift. Although the overall functional significance of epigenetic drift remains to be established, a few instances of epigenetic drift causing silencing of key transcription factors have already been reported [5]. Thus, it is plausible that epigenetic drift may gradually affect differentiation programs through functional deregulation of key lineage determining transcription factors, leading to well-known observations such as myeloid skewing of the aged hematopoietic system [5]. Epigenetic drift affecting key transcription factors may further increase predisposition to age-related diseases like cancer, by locking stem cells into states of self-renewal, and causing tissues to exhibit an increased cellular plasticity and diversity, a likely prerequisite for neoplastic formation [3]. The distinctive network topology of epigenetic drift is striking for another reason. A number of recent studies have observed that epigenetic drift kicks in straight after birth and appears to be particularly prominent during pre-puberty [3,7]. Thus, in stark contrast to age-related gene expression and cancer related mutational changes, which affect mainly genes of relatively high connectivity and which have been inferred from adult populations, epigenetic drift occurs throughout life. It is therefore plausible that if functional changes caused by epigenetic drift, occurring prior to and during reproductive age, were to affect essential, highly connected genes in the network that these would be weeded out by natural selection. If true, this could explain the enrichment of epigenetic drift at peripheral network positions and is reminiscent of the observation made by Goh et al that most non-essential heritable disease genes occupy peripheral network positions, since the associated heritable mutations are less likely to compromise viability [8]. In conclusion, given that a number of other studies have also indicated that the epigenome is particularly sensitive to environmental factors (e.g. nutrient deprivation) during early life, it could well be that epigenetic drift plays a key role, not only in determining the biological age of an individual, but also in evolution. Figure 1 Epigenetic drift occurs throughout life, including pediatric populations and pre-puberty, targeting genes which occupy peripheral positions in the human interactome. Interactions have been suppressed and connectivity is approximated by the radial distance ...

The precise mechanisms underlying dissections, especially those without connective tissue diseases or congenital vascular diseases, are incompletely understood. This study attempted to identify both the expression profile of the dissected ascending aorta and the interactome hotspots associated with the disease, using microarray technology and gene regulatory network analysis. There were 2,737 genes differentially expressed between patients with acute Stanford type A aortic dissection and controls. Eight interactome hotspots significantly associated with aortic dissection were identified by an integrative network algorithm. In particular, we identified a JAK2-centered expression module, which was validated in an independent gene expression microarray data set, and which was characterized by over-expressed cytokines and receptors in acute aortic dissection cases, indicating that JAK2 may play a key role in the inflammatory process, which potentially contributes to the occurrence of acute aortic dissection. Overall, the analytical strategy used in this study offered the possibility to identify functional relevant network modules and subsequently facilitated the biological interpretation in the complicated disease.

The integration of genomic and epigenomic data is an increasingly popular approach for studying the complex mechanisms driving cancer development. We have developed a method for evaluating both methylation and copy number from high-density DNA methylation arrays. Comparing copy number data from Infinium HumanMethylation450 BeadChips and SNP arrays, we demonstrate that Infinium arrays detect copy number alterations with the sensitivity of SNP platforms. These results show that high-density methylation arrays provide a robust and economic platform for detecting copy number and methylation changes in a single experiment. Our method is available in the ChAMP Bioconductor package: http://www.bioconductor.org/packages/2.13/bioc/html/ChAMP.html.

Background Human papillomavirus-positive (HPV+) head and neck squamous cell carcinoma (HNSCC) represents a distinct clinical and epidemiological condition compared with HPV-negative (HPV-) HNSCC. To test the possible involvement of epigenetic modulation by HPV in HNSCC, we conducted a genome-wide DNA-methylation analysis. Methods Using laser-capture microdissection of 42 formalin-fixed paraffin wax-embedded (FFPE) HNSCCs, we generated DNA-methylation profiles of 18 HPV+ and 14 HPV- samples, using Infinium 450 k BeadArray technology. Methylation data were validated in two sets of independent HPV+/HPV- HNSCC samples (fresh-frozen samples and cell lines) using two independent methods (Infinium 450 k and whole-genome methylated DNA immunoprecipitation sequencing (MeDIP-seq)). For the functional analysis, an HPV- HNSCC cell line was transduced with lentiviral constructs containing the two HPV oncogenes (E6 and E7), and effects on methylation were assayed using the Infinium 450 k technology. Results and discussion Unsupervised clustering over the methylation variable positions (MVPs) with greatest variation showed that samples segregated in accordance with HPV status, but also that HPV+ tumors are heterogeneous. MVPs were significantly enriched at transcriptional start sites, leading to the identification of a candidate CpG island methylator phenotype in a sub-group of the HPV+ tumors. Supervised analysis identified a strong preponderance (87%) of MVPs towards hypermethylation in HPV+ HNSCC. Meta-analysis of our HNSCC and publicly available methylation data in cervical and lung cancers confirmed the observed DNA-methylation signature to be HPV-specific and tissue-independent. Grouping of MVPs into functionally more significant differentially methylated regions identified 43 hypermethylated promoter DMRs, including for three cadherins of the Polycomb group target genes. Integration with independent expression data showed strong negative correlation, especially for the cadherin gene-family members. Combinatorial ectopic expression of the two HPV oncogenes (E6 and E7) in an HPV- HNSCC cell line partially phenocopied the hypermethylation signature seen in HPV+ HNSCC tumors, and established E6 as the main viral effector gene. Conclusions Our data establish that archival FFPE tissue is very suitable for this type of methylome analysis, and suggest that HPV modulates the HNSCC epigenome through hypermethylation of Polycomb repressive complex 2 target genes such as cadherins, which are implicated in tumor progression and metastasis.

Epigenetic processes--including DNA methylation--are increasingly seen as having a fundamental role in chronic diseases like cancer. It is well known that methylation levels at particular genes or loci differ between normal and diseased tissue. Here we investigate whether the intra-gene methylation architecture is corrupted in cancer and whether the variability of levels of methylation of individual CpGs within a defined gene is able to discriminate cancerous from normal tissue, and is associated with heterogeneous tumour phenotype, as defined by gene expression. We analysed 270985 CpGs annotated to 18272 genes, in 3284 cancerous and 681 normal samples, corresponding to 14 different cancer types. In doing so, we found novel differences in intra-gene methylation pattern across phenotypes, particularly in those genes which are crucial for stem cell biology; our measures of intra-gene methylation architecture are a better determinant of phenotype than measures based on mean methylation level alone (K-S test [Formula: see text] in all 14 diseases tested). These per-gene methylation measures also represent a considerable reduction in complexity, compared to conventional per-CpG beta-values. Our findings strongly support the view that intra-gene methylation architecture has great clinical potential for the development of DNA-based cancer biomarkers.

Background: Recently, it has been proposed that epigenetic variation may contribute to the risk of complex genetic diseases like cancer. We aimed to demonstrate that epigenetic changes in normal cells, collected years in advance of the first signs of morphological transformation, can predict the risk of such transformation. Methods: We analyzed DNA methylation (DNAm) profiles of over 27,000 CpGs in cytologically normal cells of the uterine cervix from 152 women in a prospective nested case-control study. We used statistics based on differential variability to identify CpGs associated with the risk of transformation and a novel statistical algorithm called EVORA (Epigenetic Variable Outliers for Risk prediction Analysis) to make predictions. Results: We observed many CpGs that were differentially variable between women who developed a non-invasive cervical neoplasia within 3 years of sample collection and those that remained disease-free. These CpGs exhibited heterogeneous outlier methylation profiles and overlapped strongly with CpGs undergoing age-associated DNA methylation changes in normal tissue. Using EVORA, we demonstrate that the risk of cervical neoplasia can be predicted in blind test sets (AUC = 0.66 (0.58 to 0.75)), and that assessment of DNAm variability allows more reliable identification of risk-associated CpGs than statistics based on differences in mean methylation levels. In independent data, EVORA showed high sensitivity and specificity to detect pre-invasive neoplasia and cervical cancer (AUC = 0.93 (0.86 to 1) and AUC = 1, respectively). Conclusions: We demonstrate that the risk of neoplastic transformation can be predicted from DNA methylation profiles in the morphologically normal cell of origin of an epithelial cancer. Having profiled only 0.1% of CpGs in the human genome, studies of wider coverage are likely to yield improved predictive and diagnostic models with the accuracy needed for clinical application. Trial registration: The ARTISTIC trial is registered with the International Standard Randomised Controlled Trial Number ISRCTN25417821.

BACKGROUND:Recent studies have shown that DNA methylation (DNAm) markers in peripheral blood may hold promise as diagnostic or early detection/risk markers for epithelial cancers. However, to date no study has evaluated the diagnostic and predictive potential of such markers in a large case control cohort and on a genome-wide basis. PRINCIPAL FINDINGS:By performing genome-wide DNAm profiling of a large ovarian cancer case control cohort, we here demonstrate that active ovarian cancer has a significant impact on the DNAm pattern in peripheral blood. Specifically, by measuring the methylation levels of over 27,000 CpGs in blood cells from 148 healthy individuals and 113 age-matched pre-treatment ovarian cancer cases, we derive a DNAm signature that can predict the presence of active ovarian cancer in blind test sets with an AUC of 0.8 (95% CI (0.74-0.87)). We further validate our findings in another independent set of 122 post-treatment cases (AUC = 0.76 (0.72-0.81)). In addition, we provide evidence for a significant number of candidate risk or early detection markers for ovarian cancer. Furthermore, by comparing the pattern of methylation with gene expression data from major blood cell types, we here demonstrate that age and cancer elicit common changes in the composition of peripheral blood, with a myeloid skewing that increases with age and which is further aggravated in the presence of ovarian cancer. Finally, we show that most cancer and age associated methylation variability is found at CpGs located outside of CpG islands. SIGNIFICANCE:Our results underscore the potential of DNAm profiling in peripheral blood as a tool for detection or risk-prediction of epithelial cancers, and warrants further in-depth and higher CpG coverage studies to further elucidate this role.

Acquired somatic mutations are responsible for approximately 90% of breast tumours. However, only one somatic aberration, amplification of the HER2 locus, is currently used to define a clinical subtype, one that accounts for approximately 10% to 15% of breast tumours. In recent years, a number of mutational profiling studies have attempted to further identify clinically relevant mutations. While these studies have confirmed the oncogenic or tumour suppressor role of many known suspects, they have exposed complexity as a main feature of the breast cancer mutational landscape (the 'muta-ome'). The two defining features of this complexity are (a) a surprising richness of low-frequency mutants contrasting with the relative rarity of high-frequency events and (b) the relatively large number of somatic genomic aberrations (approximately 20 to 50) driving an average tumour. Structural features of this complex landscape have begun to emerge from follow-up studies that have tackled the complexity by integrating the spectrum of genomic mutations with a variety of complementary biological knowledge databases. Among these structural features are the growing links between somatic gene disruptions and those conferring breast cancer risk, mutually exclusive coexistence and synergistic mutational patterns, and a clearly non-random distribution of mutations implicating specific molecular pathways in breast tumour initiation and progression. Recognising that a shift from a gene-centric to a pathway-centric approach is necessary, we envisage that further progress in identifying clinically relevant genomic aberration patterns and associated breast cancer subtypes will require not only multi-dimensional integrative analyses that combine mutational and functional profiles, but also larger profiling studies that use second- and third-generation sequencing technologies in order to fill out the important gaps in the current mutational landscape.

Abstract #2026 To determine the prognosis of breast cancer patients, clinical and pathological factors are currently employed. Gene expression micro-arrays offer new opportunities to determine individual prognosis. Publications have raised concerns about micro-arrays studies who have the potential to preclude their use in clinical routine. To improve the understanding of gene-expression classifiers we addressed the following issues: 1) Is the performance similar between independent classifiers? 2) Is proliferation a common biological theme that represents various signatures? 3) Are there other enriched pathways among signatures with prognostic ability? Methods: On 6 public datasets we applied the 76-gene signature; the Molecular subtypes; the Chromosomal Instability Signature; the Wound Signature; the Invasiveness Gene Signature; the Molecular Prognosis Index; and the Genomic Grade Index. Survival, predictive accuracy and overlap analyses were performed. We created enlarged signatures by including all probes with significant correlation to at least one of the genes in the original signatures. We gathered a collection of gene sets from four databases (GO, KEGG, Reactome, MSDB). For each signature, we evaluated whether specific gene sets (modules) are overrepresented. We tested the prognosis ability of each of them. Results: The survival and predictive accuracy analyses gave similar results for each of the 9 signatures. They all added significant information to a multivariate model including standard pathological and clinical criteria. Nevertheless, we showed that none of these signatures were able to identify good and poor prognosis patients when applied to samples with intrinsically poor prognosis features (Positive Lymph Node, Negative Estrogen Receptor, High Grade). Conversely they identified good and poor prognosis patients when applied to samples with intrinsically good prognosis features (Negative LN, Positive ER Low Grade). The overlap analysis showed a low agreement between the signatures. 50% of the samples had almost one discordant classification result out of the 9 classifiers tested. The intersection of the signatures revealed a set of proliferation genes. The signatures were build on 10 different gene ontology modules with prognostic ability. Conclusion: This study underlines the need of large prospective validation studies of gene expression signatures. Further computational intelligence and system biology studies would be held to determine the best way to use these classifiers in clinical routine. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2026.

The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes. Independent Component Analysis (ICA) is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data in an attempt to understand phenotype differences in terms of changes in the activation/inhibition patterns of biological pathways. While the ICA model has been shown to outperform other linear representations of the data such as Principal Components Analysis (PCA), a validation using explicit pathway and regulatory element information has not yet been performed. We apply a range of popular ICA algorithms to six of the largest microarray cancer datasets and use pathway-knowledge and regulatory-element databases for validation. We show that ICA outperforms PCA and clustering-based methods in that ICA components map closer to known cancer-related pathways, regulatory modules, and cancer phenotypes. Furthermore, we identify cancer signalling and oncogenic pathways and regulatory modules that play a prominent role in breast cancer and relate the differential activation patterns of these to breast cancer phenotypes. Importantly, we find novel associations linking immune response and epithelial–mesenchymal transition pathways with estrogen receptor status and histological grade, respectively. In addition, we find associations linking the activity levels of biological pathways and transcription factors (NF1 and NFAT) with clinical outcome in breast cancer. ICA provides a framework for a more biologically relevant interpretation of genomewide transcriptomic data. Adopting ICA as the analysis tool of choice will help understand the phenotype–pathway relationship and thus help elucidate the molecular taxonomy of heterogeneous cancers and of other complex genetic diseases., Author Summary The amount of a given transcript or protein in a cell is determined by a balance of expression and repression in a complex network of biological processes. This delicate balance is compromised in complex genetic diseases such as cancer by alterations in the activation patterns of functionally important biological processes known as pathways. Over the last years, a large number of microarray experiments profiling the expression levels of more than 20,000 human genes in hundreds of tumor samples have shown that most cancer types are heterogeneous diseases, each characterized by many different expression subtypes. The biological and clinical goal is to explain the observed tumor and clinical heterogeneity in terms of specific patterns of altered pathways. The bioinformatic challenge is therefore to devise mathematical tools that explicitly attempt to infer these altered pathways. To this end, we applied a signal processing tool in a meta-analysis of breast cancer, encompassing more than 800 tumor specimens derived from four different patient cohorts, and showed that this algorithm significantly outperforms popular standard bioinformatics tools in identifying altered pathways underlying breast cancer. These results show that the same tool could be applied to other complex human genetic diseases to better elucidate the underlying altered pathways.

A feature selection method was used in an analysis of three major microarray expression datasets to identify molecular subclasses and prognostic markers in estrogen receptor-negative breast cancer, showing that it is a heterogeneous disease with at least four main subtypes., Background Estrogen receptor (ER)-negative breast cancer specimens are predominantly of high grade, have frequent p53 mutations, and are broadly divided into HER2-positive and basal subtypes. Although ER-negative disease has overall worse prognosis than does ER-positive breast cancer, not all ER-negative breast cancer patients have poor clinical outcome. Reliable identification of ER-negative tumors that have a good prognosis is not yet possible. Results We apply a recently proposed feature selection method in an integrative analysis of three major microarray expression datasets to identify molecular subclasses and prognostic markers in ER-negative breast cancer. We find a subclass of basal tumors, characterized by over-expression of immune response genes, which has a better prognosis than the rest of ER-negative breast cancers. Moreover, we show that, in contrast to ER-positive tumours, the majority of prognostic markers in ER-negative breast cancer are over-expressed in the good prognosis group and are associated with activation of complement and immune response pathways. Specifically, we identify an immune response related seven-gene module and show that downregulation of this module confers greater risk for distant metastasis (hazard ratio 2.02, 95% confidence interval 1.2-3.4; P = 0.009), independent of lymph node status and lymphocytic infiltration. Furthermore, we validate the immune response module using two additional independent datasets. Conclusion We show that ER-negative basal breast cancer is a heterogeneous disease with at least four main subtypes. Furthermore, we show that the heterogeneity in clinical outcome of ER-negative breast cancer is related to the variability in expression levels of complement and immune response pathway genes, independent of lymphocytic infiltration.