http://repub.eur.nl/res/aut/9921/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/25468/ 2011-05-19T00:00:00Z Background: Overweight develops gradually as a result of a long term surplus on the balance between energy intake and energy expenditure. Aim of this study was to quantify the positive energy balance responsible for excess body weight gain (energy gap) in young overweight children. Methods. Reported data on weight and height were used of 2190 Dutch children participating in the PIAMA birth cohort study. Accumulated body energy was estimated from the weight gain observed between age 2 and age 5-7. Energy gap was calculated as the difference in positive energy balance between children with and without overweight assuming an energy efficiency of 50%. Results: Ten percent of the children were overweight at the age of 5-7 years. For these children, median weight gain during 4-years follow-up was 13.3 kg, as compared to 8.5 kg in the group of children who had a normal weight at the end of the study. A daily energy gap of 289-320 kJ (69-77 kcal) was responsible for the excess weight gain or weight maintenance in the majority of the children who were overweight at the age of 5-7 years. The increase in daily energy requirement to maintain the 4.8 kilograms excess weight gain among overweight children at the end of the study was approximately 1371 kJ. Conclusions: An energy gap of about 289-320 kJ per day over a number of years can make the difference between normal weight and overweight in young children. Closing the energy gap in overweight children can be achieved by relatively small behavior changes. However, much more effort is required to lose the excess weight gained. http://repub.eur.nl/res/pub/23051/ 2011-03-01T00:00:00Z DNA methylation is a hallmark in a subset of right-sided colorectal cancers. Methylation-based screening may improve prevention and survival rate for this type of cancer, which is often clinically asymptomatic in the early stages. We aimed to discover prognostic or diagnostic biomarkers for colon cancer by comparing DNA methylation profiles of right-sided colon tumours and paired normal colon mucosa using an 8.5 k CpG island microarray. We identified a diagnostic CpG-rich region, located in the first intron of the protein-tyrosine phosphatase gamma gene (PTPRG) gene, with altered methylation already in the adenoma stage, that is, before the carcinoma transition. Validation of this region in an additional cohort of 103 sporadic colorectal tumours and 58 paired normal mucosa tissue samples showed 94% sensitivity and 96% specificity. Interestingly, comparable results were obtained when screening a cohort of Lynch syndrome-associated cancers. Functional studies showed that PTPRG intron 1 methylation did not directly affect PTPRG expression, however, the methylated region overlapped with a binding site of the insulator protein CTCF. Chromatin immunoprecipitation (ChIP) showed that methylation of the locus was associated with absence of CTCF binding. Methylation-associated changes in CTCF binding to PTPRG intron 1 could have implications on tumour gene expression by enhancer blocking, chromosome loop formation or abrogation of its insulator function. The high sensitivity and specificity for the PTPRG intron 1 methylation in both sporadic and hereditary colon cancers support biomarker potential for early detection of colon cancer. http://repub.eur.nl/res/pub/28464/ 2010-09-07T00:00:00Z Background: In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used.Results: We show that a biased multiple testing correction results if non-differentially expressed probes are not filtered out with equal probability from the entire range of p-values. We illustrate our results using both a simulation study and an experimental dataset, where the FDR is shown to be biased mostly by filters that are associated with the hypothesis being tested, such as the fold change. Filters that induce little bias on the FDR yield less additional power of detecting differentially expressed genes. Finally, we propose a statistical test that can be used in practice to determine whether any chosen filter introduces bias on the FDR estimate used, given a general experimental setup.Conclusions: Filtering out of probes must be used with care as it may bias the multiple testing correction. Researchers can use our test for FDR bias to guide their choice of filter and amount of filtering in practice. http://repub.eur.nl/res/pub/25332/ 2009-12-24T00:00:00Z MLL-rearranged infant acute lymphoblastic leukemia (ALL) remains the most aggressive type of childhood leukemia, displaying a unique gene expression profile. Here we hypothesized that this characteristic gene expression signature may have been established by potentially reversible epigenetic modifications. To test this hypothesis, we used differential methylation hybridization to explore the DNA methylation patterns underlying MLL-rearranged ALL in infants. The obtained results were correlated with gene expression data to confirm gene silencing as a result of promoter hypermethylation. Distinct promoter CpG island methylation patterns separated different genetic subtypes of MLL-rearranged ALL in infants. MLL translocations t(4;11) and t(11;19) characterized extensively hypermethylated leukemias, whereas t(9;11)-positive infant ALL and infant ALL carrying wild-type MLL genes epigenetically resembled normal bone marrow. Furthermore, the degree of promoter hypermethylation among infant ALL patients carrying t(4; 11) or t(11;19) appeared to influence relapse-free survival, with patients displaying accentuated methylation being at high relapse risk. Finally, we show that the demethylating agent zebularine reverses aberrant DNA methylation and effectively induces apoptosis in MLL-rearranged ALL cells. Collectively these data suggest that aberrant DNA methylation occurs in the majority of MLL-rearranged infant ALL cases and guides clinical outcome. Therefore, inhibition of aberrant DNA methylation may be an important novel therapeutic strategy for MLL-rearranged ALL in infants. http://repub.eur.nl/res/pub/24940/ 2009-09-17T00:00:00Z Background: With the increasing number of expression profiling technologies, researchers today are confronted with choosing the technology that has sufficient power with minimal sample size, in order to reduce cost and time. These depend on data variability, partly determined by sample type, preparation and processing. Objective measures that help experimental design, given own pilot data, are thus fundamental. Results: Relative power and sample size analysis were performed on two distinct data sets. The first set consisted of Affymetrix array data derived from a nutrigenomics experiment in which weak, intermediate and strong PPARα agonists were administered to wild-type and PPARα-null mice. Our analysis confirms the hierarchy of PPARα-activating compounds previously reported and the general idea that larger effect sizes positively contribute to the average power of the experiment. A simulation experiment was performed that mimicked the effect sizes seen in the first data set. The relative power was predicted but the estimates were slightly conservative. The second, more challenging, data set describes a microarray platform comparison study using hippocampal δC-doublecortin-like kinase transgenic mice that were compared to wild-type mice, which was combined with results from Solexa/Illumina deep sequencing runs. As expected, the choice of technology greatly influences the performance of the experiment. Solexa/Illumina deep sequencing has the highest overall power followed by the microarray platforms Agilent and Affymetrix. Interestingly, Solexa/Illumina deep sequencing displays comparable power across all intensity ranges, in contrast with microarray platforms that have decreased power in the low intensity range due to background noise. This means that deep sequencing technology is especially more powerful in detecting differences in the low intensity range, compared to microarray platforms. Conclusion: Power and sample size analysis based on pilot data give valuable information on the performance of the experiment and can thereby guide further decisions on experimental design. Solexa/Illumina deep sequencing is the technology of choice if interest lies in genes expressed in the low-intensity range. Researchers can get guidance on experimental design using our approach on their own pilot data implemented as a BioConductor package, SSPA http://bioconductor.org/packages/release/bioc/html/SSPA.html. http://repub.eur.nl/res/pub/24937/ 2009-06-29T00:00:00Z Background: Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy number and gene expression jointly. Furthermore, some copy number changes extend over larger chromosomal regions affecting the expression levels of multiple resident genes. Results: We propose to analyse copy number and expression array data using gene sets, rather than individual genes. The proposed model is robust and sensitive. We re-analysed two publicly available datasets as illustration. These two independent breast cancer datasets yielded similar patterns of association between gene dosage and gene expression levels, in spite of different platforms having been used. Our comparisons show a clear advantage to using sets of genes' expressions to detect associations with long-spanning, low-amplitude copy number aberrations. In addition, our model allows for using additional explanatory variables and does not require mapping between copy number and expression probes. Conclusion: We developed a general and flexible tool for integration of multiple microarray data sets, and showed how the identification of genes whose expression is affected by copy number aberrations provides a powerful approach to prioritize putative targets for functional validation. http://repub.eur.nl/res/pub/28961/ 2008-09-01T00:00:00Z Cholesteryl ester transfer protein (CETP) and hepatic lipase (HL) are two HDL modifying proteins that have both pro- and anti-atherogenic properties. We hypothesized that CETP and HL synergistically affect HDL cholesterol and atherosclerotic risk. To test our hypothesis, we analysed the genotype frequencies of CETP Taq1B (rs708272) and LIPC-514C/T (rs1800588) polymorphisms in male coronary artery disease patients (CAD; n = 792) and non-symptomatic controls (n = 539). Cases and controls had similar allele frequencies, but the occurrence of the combined genotypes differed (p = 0.027). In CAD patients, 1.3% had the CETP-B2B2/LIPC-TT genotype, with only 0.2% in controls (p = 0.033). The presence of the CETP lowering B2 allele and the HL lowering LIPC-T allele synergistically increased HDL cholesterol from 0.87 ± 0.19 mmol/L in the B1B1/CC (n = 183) to 1.21 ± 0.25 mmol/L in the B2B2/TT carriers (n = 10). The B1B1/CC carriers had an increased CAD risk (OR 1.4; p = 0.025). Despite their high HDL cholesterol, the B2B2/TT individuals also had an increased CAD risk (OR 3.7; p = 0.033). In a 2-year follow up, the loss of coronary artery lumen diameter in these patients was higher than in all other patients combined (0.34 ± 0.70 versus 0.10 ± 0.29 mm; p = 0.044). We conclude that a high HDL cholesterol does not protect against coronary artery disease when associated with combined CETP- and HL-lowering gene variants. http://repub.eur.nl/res/pub/30327/ 2008-03-10T00:00:00Z Background: The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle. Results: Gene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms. Quantitative RT-PCR analysis confirmed 20 out of 28 of the genes detected by two or more platforms and 8 out of 15 of the genes detected by Agilent only. We observed improved correlations between platforms when ranking the genes based on the significance level than with a fixed statistical cut-off. We demonstrate significant overlap in the affected gene sets identified by the different platforms, although biological processes were represented by only partially overlapping sets of genes. Aberrances in GABA-ergic signalling in the transgenic mice were consistently found by all platforms. Conclusion: The different microarray platforms give partially complementary views on biological processes affected. Our data indicate that when analyzing samples with only subtle differences in gene expression the use of two different platforms might be more attractive than increasing the number of replicates. Commercial two-color platforms seem to have higher power for finding differentially expressed genes between groups with small differences in expression. http://repub.eur.nl/res/pub/13533/ 2004-01-01T00:00:00Z Comparative genomic hybridization by means of BAC microarrays (array CGH) allows high-resolution profiling of copy-number aberrations in tumor DNA. However, specific genetic lesions associated with small but clinically relevant tumor areas may pass undetected due to intra-tumor heterogeneity and/or the presence of contaminating normal cells. Here, we show that the combination of laser capture microdissection, phi29 DNA polymerase-mediated isothermal genomic DNA amplification, and array CGH allows genomic profiling of very limited numbers of cells. Moreover, by means of simple statistical models, we were able to bypass the exclusion of amplification distortions and variability prone areas, and to detect tumor-specific chromosomal gains and losses. We applied this new combined experimental and analytical approach to the genomic profiling of colorectal adenomatous polyps and demonstrated our ability to accurately detect single copy gains and losses affecting either whole chromosomes or small genomic regions from as little as 2 ng of DNA or 1000 microdissected cells. http://repub.eur.nl/res/pub/8461/ 2004-01-01T00:00:00Z BACKGROUND: In patients with acute myeloid leukemia (AML) a combination of methods must be used to classify the disease, make therapeutic decisions, and determine the prognosis. However, this combined approach provides correct therapeutic and prognostic information in only 50 percent of cases. METHODS: We determined the gene-expression profiles in samples of peripheral blood or bone marrow from 285 patients with AML using Affymetrix U133A GeneChips containing approximately 13,000 unique genes or expression-signature tags. Data analyses were carried out with Omniviz, significance analysis of microarrays, and prediction analysis of microarrays software. Statistical analyses were performed to determine the prognostic significance of cases of AML with specific molecular signatures. RESULTS: Unsupervised cluster analyses identified 16 groups of patients with AML on the basis of molecular signatures. We identified the genes that defined these clusters and determined the minimal numbers of genes needed to identify prognostically important clusters with a high degree of accuracy. The clustering was driven by the presence of chromosomal lesions (e.g., t(8;21), t(15;17), and inv(16)), particular genetic mutations (CEBPA), and abnormal oncogene expression (EVI1). We identified several novel clusters, some consisting of specimens with normal karyotypes. A unique cluster with a distinctive gene-expression signature included cases of AML with a poor treatment outcome. CONCLUSIONS: Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis. http://repub.eur.nl/res/pub/18189/ 1997-09-03T00:00:00Z Chromosome translocations are cytogenetically visible genetic abnormalities that are often associated with specific tumors. Characterization of the genes at the chromosome breakpoints can give insights into the processes that transform normal cells to tumor cells. The (6;9) translocation, associated with acute myeloid leukemia and myelodysplastic syndrome, fuses the DEK gene to CAN and results in the expression of a chimeric DEK-CAN gene. In a second chromosome rearrangement, found in one patient with acute undifferentiated leukemia, CAN is fused to SET. Knowledge of the normal functions of the proteins encoded by the fusion partners is indispensable in understanding the mechanisms by which DEK-CAN and SET-CAN contribute to leukemogenesis. The aim of the research described here is to define the functions of CAN, which was identified as a nuclear pore complex component (nucleoporin), and of the CAN-derived fusion proteins. This thesis starts out with an introduction about chromosome aberrations in hematopoietic malignancies, that result in the generation of fusion genes. The DEK, SET, and CAN proteins are introduced, and a number of molecular mechanisms in oncogenic transformation by fusion proteins are highlighted with examples (Chapter 1). The chapters that follow describe our experimental work concerning the functions of CAN and DEK-CAN. First, we studied the consequences of loss of CAN function after disruption of the mouse CAN gene by homologous recombination (Chapter 2). Second, the effects of CAN and DEK-CAN expression on the growth, differentiation and survival of myeloid precursor cells were investigated (Chapter 3). Third, we studied the localization of the CAN protein within the nuclear pore complex using immunoelectron microscopy (Chapter 4). An additional approach to unravel the function of CAN was to identify CAN-interacting proteins by coimmunoprecipitation (Chapter 5). Lastly, sensitive molecular detection of the (6;9) translocation was applied towards the diagnosis and follow-up of an acute myeloid leukemia patient (Chapter 6). CAN emerges from these studies as an essential factor involved in both nuclear protein import and mRNA export through the nuclear pore. These findings have implications for the possible molecular mechanism of leukemogenic transformation by the CAN-derived fusion proteins (Chapter 7).