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Search Results: 1 - 10 of 182851 matches for " Andrew E Teschendorff "
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Increased entropy of signal transduction in the cancer metastasis phenotype
Andrew E Teschendorff, Simone Severini
BMC Systems Biology , 2010, DOI: 10.1186/1752-0509-4-104
Abstract: Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis and provide examples of de-novo discoveries of gene modules with known roles in apoptosis, immune-mediated tumour suppression, cell-cycle and tumour invasion. Importantly, we also identify a novel gene module within the insulin growth factor signalling pathway, alteration of which may predispose the tumour to metastasize.These results demonstrate that a metastatic cancer phenotype is characterised by an increase in the randomness of the local information flux patterns. Measures of local randomness in integrated protein interaction mRNA expression networks may therefore be useful for identifying genes and signalling pathways disrupted in one phenotype relative to another. Further exploration of the statistical properties of such integrated cancer expression and prot
A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer
Andrew E Teschendorff, Carlos Caldas
Breast Cancer Research , 2008, DOI: 10.1186/bcr2138
Abstract: 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.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.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.Oestrogen receptor (ER) negative (-) breast cancer accounts for about 30% of all breast cancer cases and generally has a worse prognosis compared with ER positive (+)disease [1,2]. Nevertheless, a significant proportion of ER- cases have shown a favourable outcome and could potentially benefit from a less aggressive course of therapy [3]. Reliable identification of such ER- patients with a good prognosis is, however, difficult and at present only possible through examining histopathological factors.Recently, attempts have been made to explain the observed clinical heterogeneity of ER- disease in terms of gene expression signatures [4-7]. However, most of these studies clearly indicated the difficulty of identifying a prognostic gene expression signature for ER- disease [4,6,7], unlike ER+ breast cancer where a multitude of alternative prognostic signatures have been identified [3,8-11]. Nevertheless, using an integrative analysis of gene expression microarray data from three untreated (no chemotherapy) ER- breast cancer cohorts (a total of 186 patients) [3,8,10] and a novel feature selection
The breast cancer somatic 'muta-ome': tackling the complexity
Andrew E Teschendorff, Carlos Caldas
Breast Cancer Research , 2009, DOI: 10.1186/bcr2236
Abstract: The most prominent feature in the breast cancer copy-number muta-ome is amplification of the HER2 locus, present in about 10% to 15% of all breast tumours. It is remarkable that since the discovery of this amplification no further ERBB2-like oncogene has been conclusively identified. Although two recent large-scale (145 and 171 tumours) genome-wide profiling studies combining high-resolution copy-number and matched gene expression data have confirmed candidate oncogenes in well-known regions of recurrent amplification (notably, 8p12, 8q24, 11q13-14, 17q21-24, and 20q13), none of these appears to be as frequently amplified as ERBB2 and they rarely exhibit amplification profiles that clearly point at a specific genomic location or target [1,2]. Instead, the amplification profiles are complex and multi-modal, suggesting that multiple targets may coexist within these regions. This identification problem is compounded by the fact that a relatively high proportion of variation at the gene expression level (approximately 20%) is driven by copy-number changes; thus, focusing on regions of expression bias that are driven by underlying amplifications (so-called 'hotspots') still leaves an unmanageably large number of targets. Nevertheless, by focusing within these hotspots on genes that are also druggable, Chin and colleagues [1] prioritised a smaller set of eight targets, FGFR1, IKBKB, PROSC, ADAM9, FNTA, ACACA, PNMT, and NR1D1, including ERBB2. Confirming the robustness of these findings, all of these were also found to reside in amplification hotspots in an independent breast cancer cohort [2] (A.E. Teschendorff and C. Caldas, unpublished data). In spite of this agreement, the two studies were discordant when hotspots were associated with clinical outcome, mirroring the disagreements of initial gene expression studies. Thus, whereas in [1] associations with survival and recurrence were restricted to the amplicons on 8p11-12 and 17q11-12, in [2] outcome-associated amplicons
A comparison of feature selection and classification methods in DNA methylation studies using the Illumina Infinium platform
Joanna Zhuang, Martin Widschwendter, Andrew E Teschendorff
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-59
Abstract: 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.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.DNA methylation (DNAm) is one
Prognostic gene network modules in breast cancer hold promise
Andrew E Teschendorff, Yan Jiao, Carlos Caldas
Breast Cancer Research , 2010, DOI: 10.1186/bcr2774
Abstract: An outstanding problem in the clinical management of breast cancer is overtreatment. It is estimated that approximately 55 to 75% of breast cancer patients who receive adjuvant chemotherapy would do equally well without it [1], but identifying this low-risk population with a high enough predictive value (≥ 90%) is not possible using standard prognostic factors such as lymph node status or tumour size. Several recently developed gene expression classifiers have shown promise of achieving the required predictive values.One such classifer is Oncotype DX, a prognostic test based on the expression levels of 21 genes, which has been shown to identify low-risk patients with an accuracy of at least 90%, but is restricted to lymph node-negative oestrogen receptor-positive (ER+) breast cancer [2]. Another classifier is the 7 gene immune response (IR) module, which allows identification of low-risk patients in oestrogen receptor-negative (ER-) breast cancer [3]. Both of these signatures appear to be robust, demonstrating a high predictive value across many different breast cancer cohorts [2,3]. Gene Ontology (GO) analyses of prognostic signatures [2-6] have shown that specific biological processes play particularly important roles and that this is subgroup-specific. Thus, while cell-proliferation is strongly prognostic in ER+ breast cancer [6], the clinical heterogeneity of ER- breast cancers appears to be explained mainly by differential expression of genes related to immune response pathways, highlighting the need to conduct survival analysis within specific breast cancer subgroups [7-10].In line with this, Li and colleagues [11] have recently conducted a novel bioinformatic analysis of existing breast cancer expression data sets in order to identify gene expression modules that may predict patients at low risk of distant metastasis in specific breast cancer sub-groups. A common difficulty in identifying robust prognostic gene signatures is the presence of noise and spurious
Network Transfer Entropy and Metric Space for Causality Inference
Christopher R. S. Banerji,Simone Severini,Andrew E. Teschendorff
Physics , 2013, DOI: 10.1103/PhysRevE.87.052814
Abstract: A measure is derived to quantify directed information transfer between pairs of vertices in a weighted network, over paths of a specified maximal length. Our approach employs a general, probabilistic model of network traffic, from which the informational distance between dynamics on two weighted networks can be naturally expressed as a Jensen Shannon Divergence (JSD). Our network transfer entropy measure is shown to be able to distinguish and quantify causal relationships between network elements, in applications to simple synthetic networks and a biological signalling network. We conclude with a theoretical extension of our framework, in which the square root of the JSD induces a metric on the space of dynamics on weighted networks. We prove a convergence criterion, demonstrating that a form of convergence in the structure of weighted networks in a family of matrix metric spaces implies convergence of their dynamics with respect to the square root JSD metric.
Comments on: Interpretation of genome-wide infinium methylation data from ligated DNA in formalin-fixed paraffin-embedded paired tumor and normal tissue
Christina Thirlwell, Andrew Feber, Matthias Lechner, Andrew E Teschendorff, Stephan Beck
BMC Research Notes , 2012, DOI: 10.1186/1756-0500-5-631
Abstract: We are writing in response to the recently published research article “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 [1].Throughout the article reference is made to our previously published paper describing a novel method for the analysis of formalin-fixed, paraffin embedded (FFPE) extracted DNA on the Illumina HumMeth27 DNA methylation array [2]. We feel that there are at least four major limitations concerning the statistical analysis implemented in Jasmine et al’s paper, which lead to the conclusions drawn to be overly pessimistic.One issue which Jasmine et al. acknowledge in their Discussion is that of a potential batch effect confounding their analysis. This batch phenomenon has been described in the analysis of HumMeth27 data by Teschendorff et al.[3] and Leek JT et al.[4]. As pointed out in Leek JT et al., it is not uncommon that over 50 to 80% of measured probes may be subject to confounding by batch effects. In Jasmine’s paper, all of the fresh frozen (FF) samples were analysed on a separate date/batch to the FFPE samples, hence it is not entirely surprising that the largest variation is associated with batch, and that consequently the correlations between the matched FF and FFPE samples is lower than expected. Moreover, since batch and FF/FFPE status are completely confounded (in our view a fundamental limitation of their study), it is simply wrong to draw the conclusion that the differences seen are entirely driven by FF/FFPE status.Jasmine et al. imply that among the top 50 DMLs from the FFs and FFPEs there are “only” 7 in common. This is without determining whether 7 is significant or not. In fact, a simple binomial test shows that 7 is greater than would be expected by random chance: under the null, the expected number of overlaps would be approximately 50*(50/27000)?=?~0.1+/?0.6, that is, on average we would exp
Elucidating the Altered Transcriptional Programs in Breast Cancer using Independent Component Analysis
Andrew E Teschendorff ,Michel Journée ,Pierre A Absil,Rodolphe Sepulchre,Carlos Caldas
PLOS Computational Biology , 2007, DOI: 10.1371/journal.pcbi.0030161
Abstract: 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.
An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer
Andrew E Teschendorff, Ahmad Miremadi, Sarah E Pinder, Ian O Ellis, Carlos Caldas
Genome Biology , 2007, DOI: 10.1186/gb-2007-8-8-r157
Abstract: 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.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.It is widely recognized that estrogen receptor (ER)-positive (ER+) and ER-negative (ER-) breast cancers are two different disease entities. Generally, ER- tumours tend to be of high grade, are more frequently p53 mutated, and have worse prognosis compared with ER+ disease. Moreover, while ER+ disease can be treated with hormone therapy, the only targeted therapy available for ER- patients is a monoclonal antibody that binds to the ERBB2 receptor and that is effective only for those ER- tumours with HER2/ERBB2 over-expression.In spite of these clinical advances, ER+ and ER- breast cancers remain heterogeneous diseases, and little is known re
Corruption of the Intra-Gene DNA Methylation Architecture Is a Hallmark of Cancer
Thomas E. Bartlett, Alexey Zaikin, Sofia C. Olhede, James West, Andrew E. Teschendorff, Martin Widschwendter
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0068285
Abstract: 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 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.
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