Mark A van de Wiel

VU University Amsterdam, Amsterdamo, North Holland, Netherlands

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Publications (112)446.48 Total impact

  • Askar Obulkasim, Gerrit A Meijer, Mark A van de Wiel
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    ABSTRACT: Background In genomics, hierarchical clustering (HC) is a popular method for grouping similar samples based on a distance measure. HC algorithms do not actually create clusters, but compute a hierarchical representation of the data set. Usually, a fixed height on the HC tree is used, and each contiguous branch of samples below that height is considered a separate cluster. Due to the fixed-height cutting, those clusters may not unravel significant functional coherence hidden deeper in the tree. Besides that, most existing approaches do not make use of available clinical information to guide cluster extraction from the HC. Thus, the identified subgroups may be difficult to interpret in relation to that information.ResultsWe develop a novel framework for decomposing the HC tree into clusters by semi-supervised piecewise snipping. The framework, called guided piecewise snipping, utilizes both molecular data and clinical information to decompose the HC tree into clusters. It cuts the given HC tree at variable heights to find a partition (a set of non-overlapping clusters) which does not only represent a structure deemed to underlie the data from which HC tree is derived, but is also maximally consistent with the supplied clinical data. Moreover, the approach does not require the user to specify the number of clusters prior to the analysis. Extensive results on simulated and multiple medical data sets show that our approach consistently produces more meaningful clusters than the standard fixed-height cut and/or non-guided approaches.Conclusions The guided piecewise snipping approach features several novelties and advantages over existing approaches. The proposed algorithm is generic, and can be combined with other algorithms that operate on detected clusters. This approach represents an advancement in several regards: (1) a piecewise tree snipping framework that efficiently extracts clusters by snipping the HC tree possibly at variable heights while preserving the HC tree structure; (2) a flexible implementation allowing a variety of data types for both building and snipping the HC tree, including patient follow-up data like survival as auxiliary information.The data sets and R code are provided as supplementary files. The proposed method is available from Bioconductor as the R-package HCsnip.
    BMC Bioinformatics 01/2015; 16(1):15. DOI:10.1186/s12859-014-0448-1 · 2.67 Impact Factor
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    ABSTRACT: Osteoclasts and macrophages share progenitors that must receive decisive lineage signals driving them into their respective differentiation routes. Macrophage colony stimulation factor M-CSF is a common factor; bone is likely the stimulus for osteoclast differentiation. To elucidate the effect of both, shared mouse bone marrow precursor myeloid blast was pre-cultured with M-CSF on plastic and on bone. M-CSF priming prior to stimulation with M-CSF and osteoclast differentiation factor RANKL resulted in a complete loss of osteoclastogenic potential without bone. Such M-CSF primed cells expressed the receptor RANK, but lacked the crucial osteoclastogenic transcription factor NFATc1. This coincided with a steeply decreased expression of osteoclast genes TRACP and DC-STAMP, but an increased expression of the macrophage markers F4/80 and CD11b. Compellingly, M-CSF priming on bone accelerated the osteoclastogenic potential: M-CSF primed cells that had received only one day M-CSF and RANKL and were grown on bone already expressed an array of genes that are associated with osteoclast differentiation and these cells differentiated into osteoclasts within 2 days. Osteoclastogenesis-insensitive precursors grown in the absence of bone regained their osteoclastogenic potential when transferred to bone. This implies that adhesion to bone dictates the fate of osteoclast precursors. Common macrophage-osteoclast precursors may become insensitive to differentiate into osteoclasts and regain osteoclastogenesis when bound to bone or when in the vicinity of bone. © 2014 Wiley Periodicals, Inc.
    Journal of Cellular Physiology 01/2015; 230(1). DOI:10.1002/jcp.24702 · 3.87 Impact Factor
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    ABSTRACT: Response to drug therapy in individual colorectal cancer (CRC) patients is associated with tumour biology. Here we describe the genomic landscape of tumour samples of a homogeneous well-annotated series of patients with metastatic CRC (mCRC) of two phase III clinical trials, CAIRO and CAIRO2. DNA copy number aberrations of 349 patients are determined. Within three treatment arms, 194 chromosomal subregions are associated with progression-free survival (PFS; uncorrected single-test P-values <0.005). These subregions are filtered for effect on messenger RNA expression, using an independent data set from The Cancer Genome Atlas which returned 171 genes. Three chromosomal regions are associated with a significant difference in PFS between treatment arms with or without irinotecan. One of these regions, 6q16.1-q21, correlates in vitro with sensitivity to SN-38, the active metabolite of irinotecan. This genomic landscape of mCRC reveals a number of DNA copy number aberrations associated with response to drug therapy.
    Nature Communications 11/2014; 5:5457. DOI:10.1038/ncomms6457 · 10.74 Impact Factor
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    ABSTRACT: For many high-dimensional studies, additional information on the variables, like (genomic) annotation or external p-values, is available. In the context of binary and continuous prediction, we develop a method for adaptive group-regularized (logistic) ridge regression, which makes structural use of such 'co-data'. Here, 'groups' refer to a partition of the variables according to the co-data. We derive an empirical Bayes estimate of group-specific penalties, which possesses several nice properties: i) it is analytical; ii) it adapts to the informativeness of the co-data for the data at hand; iii) only one global penalty parameter requires tuning by cross-validation. In addition, the method allows use of multiple types of co-data at little extra computational effort. We show that the group-specific penalties may lead to a larger distinction between 'near-zero' and relatively large regression parameters, which facilitates post-hoc variable selection. The method, termed 'GRridge', is implemented in an easy-to-use R-package. It is demonstrated on two cancer genomics studies, which both concern the discrimination of precancerous cervical lesions from normal cervix tissues using methylation microarray data. For both examples, GRridge clearly improves the predictive performance of ordinary ridge regression. In addition, we show that for the second study the relatively good predictive performance is maintained when selecting only 35 variables.
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    ABSTRACT: To determine which changes in the host cell genome are crucial for cervical carcinogenesis, alongitudinal in vitro model system of HPV-transformed keratinocytes was profiled in a genomewidemanner. Four cell lines affected with either HPV16 or HPV18 were assayed at 8 sequential timepoints for gene expression (mRNA) and gene copy number (DNA) using high-resolution microarrays.Available methods for temporal differential expression analysis are not designed for integrativegenomic studies.
    BMC Bioinformatics 10/2014; 15(1):327. DOI:10.1186/1471-2105-15-327 · 2.67 Impact Factor
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    ABSTRACT: Background The disease course of patients with diffuse low-grade glioma is notoriously unpredictable. Temporal and spatially distinct samples may provide insight into the evolution of clinically relevant copy number aberrations (CNAs). The purpose of this study is to identify CNAs that are indicative of aggressive tumor behaviour and can thereby complement the prognostically favorable 1p/19q co-deletion.ResultsGenome-wide, 50 base pair single-end, sequencing was performed to detect CNAs in a clinically well-characterized cohort of 98 formalin-fixed paraffin-embedded low-grade gliomas. CNAs are correlated with overall survival as an endpoint. Seventy-five additional samples from spatially distinct regions and paired recurrent tumors of the discovery cohort were analysed to interrogate the intratumoral heterogeneity and spatial evolution. Loss of 10q25.2-qter is a frequent subclonal event and significantly correlates with an unfavorable prognosis. A significant correlation is furthermore observed in a validation set of 126 and confirmation set of 184 patients. Loss of 10q25.2-qter arises in a longitudinal manner in paired recurrent tumor specimens, whereas the prognostically favorable 1p/ 19q co-deletion is the only CNA that is stable across spatial regions and recurrent tumors.ConclusionsCNAs in low-grade gliomas display extensive intratumoral heterogeneity. Distal loss of 10q is a late onset event and a marker for reduced overall survival in low-grade glioma patients. Intratumoral heterogeneity and higher frequencies of distal 10q loss in recurrences suggest this event is involved in outgrowth to the recurrent tumor.
    Genome Biology 09/2014; 15(9):471. DOI:10.1186/PREACCEPT-1419175304135559 · 10.47 Impact Factor
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    ABSTRACT: Detection of DNA copy number aberrations by shallow whole-genome sequencing (WGS) faces many challenges including lack of completion and errors in the human reference genome, repetitive sequences, polymorphisms, variable sample quality, and biases in the sequencing procedures. Formalin-fixed paraffin-embedded (FFPE) archival material, the analysis of which is important for studies of cancer, presents particular analytical difficulties due to degradation of the DNA and frequent lack of matched reference samples. We present a robust, cost-effective WGS method for DNA copy number analysis that addresses these challenges more successfully than currently available procedures. In practice, very useful profiles can be obtained with ~0.1x genome coverage. We improve on previous methods by; first, implementing a combined correction for sequence mappability and GC content, and second, applying this procedure to sequence data from the 1000 Genomes Project in order to develop a blacklist of problematic genome regions. A small subset of these blacklisted regions were previously identified by ENCODE, but the vast majority are novel unappreciated problematic regions. Our procedures are implemented in a pipeline called QDNAseq. We have analyzed over 1,000 samples, most of which were obtained from the fixed tissue archives of over 25 institutions. We demonstrate that for most samples our sequencing and analysis procedures yield genome profiles with noise levels near the statistical limit imposed by read counting. The described procedures also provide better correction of artifacts introduced by low DNA quality than prior approaches, and better copy number data than high-resolution microarrays at substantially lower cost.
    Genome Research 09/2014; 24(12). DOI:10.1101/gr.175141.114 · 13.85 Impact Factor
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    ABSTRACT: Creatine transporter (SLC6A8) deficiency is the most common cause of cerebral creatine syndromes, and is characterized by depletion of creatine in the brain. Manifestations of this X-linked disorder include intellectual disability, speech/language impairment, behavior abnormalities, and seizures. At the moment no effective treatment is available. In order to investigate the molecular pathophysiology of this disorder we performed RNA sequencing on fibroblasts derived from patients. The transcriptomes of fibroblast cells from eight unrelated individuals with SLC6A8 deficiency and three wild type controls were sequenced. SLC6A8 mutations with different effects on the protein product resulted in different gene expression profiles. Differential gene expression analysis followed by gene ontology term enrichment analysis revealed that especially the expression of genes encoding components of the extracellular matrix and cytoskeleton are altered in SLC6A8 deficiency, such as collagens, keratins, integrins, and cadherins. This suggests an important novel role for creatine in the structural development and maintenance of cells. It is likely that the (extracellular) structure of brain cells is also impaired in SLC6A8 deficient patients, and future studies are necessary to confirm this and to reveal the true functions of creatine in the brain. This article is protected by copyright. All rights reserved.
    Human Mutation 09/2014; 35(9). DOI:10.1002/humu.22609 · 5.05 Impact Factor
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    ABSTRACT: Complex designs are common in (observational) clinical studies. Sequencing data for such studies are produced more and more often, implying challenges for the analysis, such as excess of zeros, presence of random effects and multi-parameter inference. Moreover, when sample sizes are small, inference is likely to be too liberal when, in a Bayesian setting, applying a non-appropriate prior or to lack power when not carefully borrowing information across features. We show on microRNA sequencing data from a clinical cancer study how our software ShrinkBayes tackles the aforementioned challenges. In addition, we illustrate its comparatively good performance on multi-parameter inference for groups using a data-based simulation. Finally, in the small sample size setting, we demonstrate its high power and improved FDR estimation by use of Gaussian mixture priors that include a point mass. ShrinkBayes is a versatile software package for the analysis of count-based sequencing data, which is particularly useful for studies with small sample sizes or complex designs.
    BMC Bioinformatics 04/2014; 15(1):116. DOI:10.1186/1471-2105-15-116 · 2.67 Impact Factor
  • Wessel N van Wieringen, Mark A van de Wiel
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    ABSTRACT: Abstract Through integration of genomic data from multiple sources, we may obtain a more accurate and complete picture of the molecular mechanisms underlying tumorigenesis. We discuss the integration of DNA copy number and mRNA gene expression data from an observational integrative genomics study involving cancer patients. The two molecular levels involved are linked through the central dogma of molecular biology. DNA copy number aberrations abound in the cancer cell. Here we investigate how these aberrations affect gene expression levels within a pathway using observational integrative genomics data of cancer patients. In particular, we aim to identify differential edges between regulatory networks of two groups involving these molecular levels. Motivated by the rate equations, the regulatory mechanism between DNA copy number aberrations and gene expression levels within a pathway is modeled by a simultaneous-equations model, for the one- and two-group case. The latter facilitates the identification of differential interactions between the two groups. Model parameters are estimated by penalized least squares using the lasso (L1) penalty to obtain a sparse pathway topology. Simulations show that the inclusion of DNA copy number data benefits the discovery of gene-gene interactions. In addition, the simulations reveal that cis-effects tend to be over-estimated in a univariate (single gene) analysis. In the application to real data from integrative oncogenomic studies we show that inclusion of prior information on the regulatory network architecture benefits the reproducibility of all edges. Furthermore, analyses of the TP53 and TGFb signaling pathways between ER+ and ER- samples from an integrative genomics breast cancer study identify reproducible differential regulatory patterns that corroborate with existing literature.
    Statistical Applications in Genetics and Molecular Biology 02/2014; 13(2). DOI:10.1515/sagmb-2013-0020 · 1.52 Impact Factor
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    ABSTRACT: In order to identify somatic focal copy number aberrations (CNAs) in cancer specimens and to distinguish them from germ-line copy number variations (CNVs), we developed the software package FocalCall. FocalCall enables user-defined size cutoffs to recognize focal aberrations and builds on established array comparative genomic hybridization segmentation and calling algorithms. To distinguish CNAs from CNVs, the algorithm uses matched patient normal signals as references or, if this is not available, a list with known CNVs in a population. Furthermore, FocalCall differentiates between homozygous and heterozygous deletions as well as between gains and amplifications and is applicable to high-resolution array and sequencing data.
    Cancer informatics 01/2014; 13:153-6. DOI:10.4137/CIN.S19519
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    Askar Obulkasim, Mark A van de Wiel
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    ABSTRACT: This paper presents the R/Bioconductor package stepwiseCM, which classifies cancer samples using two heterogeneous data sets in an efficient way. The algorithm is able to capture the distinct classification power of two given data types without actually combining them. This package suits for classification problems where two different types of data sets on the same samples are available. One of these data types has measurements on all samples and the other one has measurements on some samples. One is easy to collect and/or relatively cheap (eg, clinical covariates) compared to the latter (high-dimensional data, eg, gene expression). One additional application for which stepwiseCM is proven to be useful as well is the combination of two high-dimensional data types, eg, DNA copy number and mRNA expression. The package includes functions to project the neighborhood information in one data space to the other to determine a potential group of samples that are likely to benefit most by measuring the second type of covariates. The two heterogeneous data spaces are connected by indirect mapping. The crucial difference between the stepwise classification strategy implemented in this package and the existing packages is that our approach aims to be cost-efficient by avoiding measuring additional covariates, which might be expensive or patient-unfriendly, for a potentially large subgroup of individuals. Moreover, in diagnosis for these individuals test, results would be quickly available, which may lead to reduced waiting times and hence lower the patients' distress. The improvement described remedies the key limitations of existing packages, and facilitates the use of the stepwiseCM package in diverse applications.
    Cancer informatics 01/2014; 13:1-11. DOI:10.4137/CIN.S13075
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    ABSTRACT: We recently identified a DNA copy number aberration (CNA)-based classifier, including changes at 3p26.3-p11.1, 3q26.2-29, and 6p25.3-24.3, as a risk predictor for cancer in individuals presenting with endobronchial squamous metaplasia. The current study was set out to validate the prediction accuracy of this classifier in an independent series of endobronchial squamous metaplastic and dysplastic lesions.The study included 36 high-risk subjects who had endobronchial lesions of various histological grades that were identified and biopsied by autofluorescence bronchoscopy and were subjected to arrayCGH in a nested case-control design. Of the 36 patients, 12 had a carcinoma in situ or invasive carcinoma at the same site at follow-up (median 11 months, range 4-24), while 24 controls remained cancer free (78 months, range 21-142).The previously defined CNA-based classifier demonstrated 92% (95% CI 77% to 98%) accuracy for cancer (in situ) prediction. All nine subjects with CNA-based classifier-positive endobronchial lesions at baseline experienced cancer outcome, whereas all 24 controls and 3 cases were classified as being low risk.In conclusion, CNAs prove to be a highly accurate biomarker for assessing the progression risk of endobronchial squamous metaplastic and dysplastic lesions. This classifier could assist in selecting subjects with endobronchial lesions who might benefit from more aggressive therapeutic intervention or surveillance.
    Thorax 11/2013; 69(5). DOI:10.1136/thoraxjnl-2013-203821 · 8.56 Impact Factor
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    ABSTRACT: Objective The folate antagonist methotrexate (MTX) is an anchor drug in the treatment of rheumatoid arthritis (RA), but its mechanism of action with regard to the impact on folate metabolism remains elusive. The aim of the present study was to investigate the cellular pharmacologic impact of MTX on peripheral blood cells, by comparing MTX-treated RA patients to MTX-naive RA patients and healthy controls. Methods Gene expression microarray data were used to investigate the expression of 17 folate pathway genes by peripheral blood cells from a cohort of 25 RA patients treated with MTX, 10 MTX-naive RA patients starting treatment with MTX, and 15 healthy controls (test cohort). Multiplex real-time polymerase chain reaction was used to validate the results in an independent cohort, consisting of 151 RA patients treated with MTX, 28 MTX-naive RA patients starting treatment with MTX, and 24 healthy controls (validation cohort). ResultsMultiple folate metabolism-related genes were consistently and significantly altered between the 3 groups in both cohorts. Concurrent with evidence of an immune-activation gene signature in MTX-naive RA patients, significant up-regulation of the folate-metabolizing enzymes -glutamyl hydrolase and dihydrofolate reductase, as well as the MTX/folate efflux transporters ABCC2 and ABCC5, was observed in the MTX-naive RA group compared to healthy controls. Strikingly, MTX treatment of RA patients normalized these differential gene expression levels to the levels observed in healthy controls. Conclusion These results suggest that under inflammatory conditions, basal folate metabolism in the peripheral blood cells of RA patients is markedly up-regulated, and treatment with MTX restores folate metabolism to normal levels. Identification of this novel gene signature provides insight into the mechanism of action of MTX, thus paving the way for development of novel folate metabolism-targeted therapies.
    Arthritis & Rheumatology 11/2013; 65(11). DOI:10.1002/art.38094 · 7.87 Impact Factor
  • Cancer Research 08/2013; 73(8 Supplement):3073-3073. DOI:10.1158/1538-7445.AM2013-3073 · 9.28 Impact Factor
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    ABSTRACT: Abstract In the design of microarray or next-generation sequencing experiments it is crucial to choose the appropriate number of biological replicates. As often the number of differentially expressed genes and their effect sizes are small and too few replicates will lead to insufficient power to detect these. On the other hand, too many replicates unnecessary leads to high experimental costs. Power and sample size analysis can guide experimentalist in choosing the appropriate number of biological replicates. Several methods for power and sample size analysis have recently been proposed for microarray data. However, most of these are restricted to two group comparisons and require user-defined effect sizes. Here we propose a pilot-data based method for power and sample size analysis which can handle more general experimental designs and uses pilot-data to obtain estimates of the effect sizes. The method can also handle χ2 distributed test statistics which enables power and sample size calculations for a much wider class of models, including high-dimensional generalized linear models which are used, e.g., for RNA-seq data analysis. The performance of the method is evaluated using simulated and experimental data from several microarray and next-generation sequencing experiments. Furthermore, we compare our proposed method for estimation of the density of effect sizes from pilot data with a recent proposed method specific for two group comparisons.
    Statistical Applications in Genetics and Molecular Biology 08/2013; 12(4):449-67. DOI:10.1515/sagmb-2012-0046 · 1.52 Impact Factor
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    ABSTRACT: DNA copy number and mRNA expression are widely used data types in cancer studies, which combined provide more insight than separately. Whereas in existing literature the form of the relationship between these two types of markers is fixed a priori, in this paper we model their association. We employ piecewise linear regression splines (PLRS), which combine good interpretation with sufficient flexibility to identify any plausible type of relationship. The specification of the model leads to estimation and model selection in a constrained, nonstandard setting. We provide methodology for testing the effect of DNA on mRNA and choosing the appropriate model. Furthermore, we present a novel approach to obtain reliable confidence bands for constrained PLRS, which incorporates model uncertainty. The procedures are applied to colorectal and breast cancer data. Common assumptions are found to be potentially misleading for biologically relevant genes. More flexible models may bring more insight in the interaction between the two markers.
    The Annals of Applied Statistics 06/2013; 7(2). DOI:10.1214/12-AOAS605 · 1.69 Impact Factor
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    ABSTRACT: High-throughput (HT) RNA interference (RNAi) screens are increasingly used for reverse genetics and drug discovery. These experiments are laborious and costly, hence sample sizes are often very small. Powerful statistical techniques to detect siRNAs that potentially enhance treatment are currently lacking, because they do not optimally use the amount of data in the other dimension, the feature dimension. We introduce ShrinkHT, a Bayesian method for shrinking multiple parameters in a statistical model, where 'shrinkage' refers to borrowing information across features. ShrinkHT is very flexible in fitting the effect size distribution for the main parameter of interest, thereby accommodating skewness that naturally occurs when siRNAs are compared with controls. In addition, it naturally down-weights the impact of nuisance parameters (e.g. assay-specific effects) when these tend to have little effects across siRNAs. We show that these properties lead to better ROC-curves than with the popular limma software. Moreover, in a 3 + 3 treatment vs control experiment with 'assay' as an additional nuisance factor, ShrinkHT is able to detect three (out of 960) significant siRNAs with stronger enhancement effects than the positive control. These were not detected by limma. In the context of gene-targeted (conjugate) treatment, these are interesting candidates for further research.
    BMC Medical Genomics 05/2013; 6(2). DOI:10.1186/1755-8794-6-S2-S1 · 3.91 Impact Factor
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    ABSTRACT: Background:Few studies have attempted to characterise genomic changes occurring in hereditary epithelial ovarian carcinomas (EOCs) and inconsistent results have been obtained. Given the relevance of DNA copy number alterations in ovarian oncogenesis and growing clinical implications of the BRCA-gene status, we aimed to characterise the genomic profiles of hereditary and sporadic ovarian tumours.Methods:High-resolution array Comparative Genomic Hybridisation profiling of 53 familial (21 BRCA1, 6 BRCA2 and 26 non-BRCA1/2) and 15 sporadic tumours in combination with supervised and unsupervised analysis was used to define common and/or specific copy number features.Results:Unsupervised hierarchical clustering did not stratify tumours according to their familial or sporadic condition or to their BRCA1/2 mutation status. Common recurrent changes, spanning genes potentially fundamental for ovarian carcinogenesis, regardless of BRCA mutations, and several candidate subtype-specific events were defined. Despite similarities, greater contribution of losses was revealed to be a hallmark of BRCA1 and BRCA2 tumours.Conclusion:Somatic alterations occurring in the development of familial EOCs do not differ substantially from the ones occurring in sporadic carcinomas. However, some specific features like extensive genomic loss observed in BRCA1/2 tumours may be of clinical relevance helping to identify BRCA-related patients likely to respond to PARP inhibitors.British Journal of Cancer advance online publication, 4 April 2013; doi:10.1038/bjc.2013.141 www.bjcancer.com.
    British Journal of Cancer 04/2013; 108(8). DOI:10.1038/bjc.2013.141 · 4.82 Impact Factor
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    ABSTRACT: To characterize the promoterome of caudate and putamen regions (striatum), frontal and temporal cortices, and hippocampi from aged human brains, we used high-throughput cap analysis of gene expression to profile the transcription start sites and to quantify the differences in gene expression across the 5 brain regions. We also analyzed the extent to which methylation influenced the observed expression profiles. We sequenced more than 71 million cap analysis of gene expression tags corresponding to 70,202 promoter regions and 16,888 genes. More than 7000 transcripts were differentially expressed, mainly because of differential alternative promoter usage. Unexpectedly, 7% of differentially expressed genes were neurodevelopmental transcription factors. Functional pathway analysis on the differentially expressed genes revealed an overrepresentation of several signaling pathways (e.g., fibroblast growth factor and wnt signaling) in hippocampus and striatum. We also found that although 73% of methylation signals mapped within genes, the influence of methylation on the expression profile was small. Our study underscores alternative promoter usage as an important mechanism for determining the regional differences in gene expression at old age.
    Neurobiology of aging 02/2013; 34(7). DOI:10.1016/j.neurobiolaging.2013.01.005 · 4.85 Impact Factor

Publication Stats

2k Citations
446.48 Total Impact Points

Institutions

  • 2004–2015
    • VU University Amsterdam
      • • Mathematics Department
      • • Department of Clinical Chemistry
      Amsterdamo, North Holland, Netherlands
  • 2006–2014
    • VU University Medical Center
      • • Department of Pathology
      • • Department of Epidemiology and Biostatistics
      Amsterdamo, North Holland, Netherlands
    • Friedrich-Alexander Universität Erlangen-Nürnberg
      Erlangen, Bavaria, Germany
  • 2013
    • Leiden University
      Leyden, South Holland, Netherlands
  • 2010
    • Pierre and Marie Curie University - Paris 6
      Lutetia Parisorum, Île-de-France, France
  • 1998–2006
    • Technische Universiteit Eindhoven
      • Department of Mathematics and Computer Science
      Eindhoven, North Brabant, Netherlands