http://repub.eur.nl/res/aut/5201/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/25475/ 2009-01-01T00:00:00Z We examined the gene expression profiles of two independent cohorts of patients with acute myeloid leukemia [n=247 and n=214 (younger than or equal to 60 years)] to study the applicability of gene expression profiling as a single assay in prediction of acute myeloid leukemia-specific molecular subtypes. The favorable cytogenetic acute myeloid leukemia subtypes, i.e., acute myeloid leukemia with t(8;21), t(15;17) or inv(16), were predicted with maximum accuracy (positive and negative predictive value: 100%). Mutations in NPM1 and CEBPA were predicted less accurately (positive predictive value: 66% and 100%, and negative predictive value: 99% and 97% respectively). Various other characteristic molecular acute myeloid leukemia subtypes, i.e., mutant FLT3 and RAS, abnormalities involving 11q23, -5/5q-, -7/7q-, abnormalities involving 3q (abn3q) and t(9;22), could not be correctly predicted using gene expression profiling. In conclusion, gene expression profiling allows accurate prediction of certain acute myeloid leukemia subtypes, e.g. those characterized by expression of chimeric transcription factors. However, detection of mutations affecting signaling molecules and numerical abnormalities still requires alternative molecular methods. http://repub.eur.nl/res/pub/30367/ 2008-01-25T00:00:00Z Background: Accurate analyses of comprehensive genome-wide SNP genotyping and gene expression data sets is challenging for many researchers. In fact, obtaining an integrated view of both large scale SNP genotyping and gene expression is currently complicated since only a limited number of appropriate software tools are available. Results: We present SNPExpress, a software tool to accurately analyze Affymetrix and Illumina SNP genotype calls, copy numbers, polymorphic copy number variations (CNVs) and Affymetrix gene expression in a combinatorial and efficient way. In addition, SNPExpress allows concurrent interpretation of these items with Hidden-Markov Model (HMM) inferred Loss-of-Heterozygosity (LOH)- and copy number regions. Conclusion: The combined analyses with the easily accessible software tool SNPExpress will not only facilitate the recognition of recurrent genetic lesions, but also the identification of critical pathogenic genes. http://repub.eur.nl/res/pub/35102/ 2007-11-15T00:00:00Z Gene expression profiling of acute myeloid leukemia (AML) allows the discovery of previously unrecognized molecular entities. Here, we identified a specific subgroup of AML, defined by an expression profile resembling that of AMLs with mutations in the myeloid transcription factor CCAAT/enhancer-binding protein alpha (C/EBPα), while lacking such mutations. We found that in these leukemias, the CEBPA gene was silenced, which was associated with frequent promoter hypermethylation. The leukemias phenotypically showed aberrant expression of T-cell genes, of which CD7 was most consistent. We identified 2 mechanisms that may contribute to this phenotype. First, absence of Cebpa led to up-regulation of specific T-cell transcripts (ie, Cd7 and Lck) in hematopoietic stem cells isolated from conditional Cebpa knockout mice. Second, the enhanced expression of TRIB2, which we identify here as a direct target of the T-cell commitment factor NOTCH1, suggested aberrantly activated Notch signaling. Putatively activating NOTCH1 mutations were found in several specimens of the newly identified subgroup, while a large set of control AMLs was mutation negative. A gene expression prediction signature allowed the detection of similar cases of leukemia in independent series of AML. http://repub.eur.nl/res/pub/35205/ 2007-09-04T00:00:00Z Targeted disruption of a highly conserved distal enhancer reduces expression of the PU.1 transcription factor by 80% and leads to acute myeloid leukemia (AML) with frequent cytogenetic aberrations in mice. Here we identify a SNP within this element in humans that is more frequent in AML with a complex karyotype, leads to decreased enhancer activity, and reduces PU.1 expression in myeloid progenitors in a development-dependent manner. This SNP inhibits binding of the chromatin-remodeling transcriptional regulator special AT-rich sequence binding protein 1 (SATB1). Overexpression of SATB1 increased PU.1 expression, and siRNA inhibition of SATB1 downregulated PU.1 expression. Targeted disruption of the distal enhancer led to a loss of regulation of PU.1 by SATB1. Interestingly, disruption of SATB1 in mice led to a selective decrease of PU.1 RNA in specific progenitor types (granulocyte-macrophage and megakaryocyte-erythrocyte progenitors) and a similar effect was observed in AML samples harboring this SNP. Thus we have identified a SNP within a distal enhancer that is associated with a subtype of leukemia and exerts a deleterious effect through remote transcriptional dysregulation in specific progenitor subtypes. http://repub.eur.nl/res/pub/35952/ 2007-05-01T00:00:00Z The cooperation of stem cell factor (SCF) and erythropoietin (Epo) is required to induce renewal divisions in erythroid progenitors, whereas differentiation to mature erythrocytes requires the presence of Epo only. Epo and SCF activate common signaling pathways such as the activation of protein kinase B (PKB) and the subsequent phosphorylation and inactivation of Foxo3a. In contrast, only Epo activates Stat5. Both Foxo3a and Stat5 promote erythroid differentiation. To understand the interplay of SCF and Epo in maintaining the balance between renewal and differentiation during erythroid development, we investigated differential Foxo3a target regulation by Epo and SCF. Expression profiling revealed that a subset of Foxo3a targets was not inhibited but was activated by Epo. One of these genes was Cited2. Transcriptional control of Epo/Foxo3a-induced Cited2 was studied and compared with that of the Epo-repressed Foxo3a target Btg1. We show that in response to Epo, the allegedly growth-inhibitory factor Foxo3a associates with the allegedly growth-stimulatory factor Stat5 in the nucleus, which is required for Epo-induced Cited2 expression. In contrast, Btg1 expression is controlled by the cooperation of Foxo3a with cyclic AMP- and Jun kinase-dependent Creb family members. Thus, Foxo3a not only is an effector of PKB but also integrates distinct signals to regulate gene expression in erythropoiesis. Copyright http://repub.eur.nl/res/pub/8124/ 2006-11-24T00:00:00Z Hematopoïese, of de vorming van functionele bloedcellen, is een proces wat plaats vindt in het beenmerg. Hematopoïetische stamcellen ondergaan cycli van deling en differentiatie waarin de functionele eindcellen, zoals rode bloedcellen, bloedplaatjes en witte bloedcellen, worden gevormd. Leukemie is een ziekte waarbij de stamcellen abnormale processen van deling in combinatie met een stop van de differentiatie ondergaan, waardoor er de vorming van functionele eindcellen wordt belemmerd. In het geval van acute myeloïde leukemie (AML) is er een afwijking in de tak van bloedcelvorming waar onder andere rode bloedcellen, bloedplaatjes en granulocyten worden gevormd. De ontsporing van hematopoïetische stamcellen met AML als gevolg wordt veroorzaakt door abnormaliteiten in het genoom, zoals chromosomale fusies, deleties en mutaties. De klinische prognose wordt momenteel bepaald aan de hand van de aan- of afwezigheid van (combinaties van) abnormaliteiten. Het belangrijkste gevolg van genomische afwijkingen is de abnormale transcriptie van genen naar mRNA. Met behulpvan gen expressie profilering, door middel van microarrays, kunnen de transcriptie niveaus van duizenden genen simultaan worden bepaald. In hoofdstuk 2 is een onderzoek beschreven waarin met gen expressie profilering is toegepast op 285 beenmerg monsters van de novo AML patiënten, voor het bepalen van prognose. Verschillende bekende prognostische groepen, zoals t(8;21) en inv(16) konden worden geidentificeerd, alsmede een nieuwe prognostisch relevante groep van patiënten met een relatief slechte prognose (cluster 10).Hoofdstuk 2 laat zien dat gen expressie profilering in staat is om de huidige technieken voor het bepalen van prognose te vervangen, en prognose te verbeteren. http://repub.eur.nl/res/pub/14018/ 2006-07-12T00:00:00Z BACKGROUND: Accurate interpretation of data obtained by unsupervised analysis of large scale expression profiling studies is currently frequently performed by visually combining sample-gene heatmaps and sample characteristics. This method is not optimal for comparing individual samples or groups of samples. Here, we describe an approach to visually integrate the results of unsupervised and supervised cluster analysis using a correlation plot and additional sample metadata. RESULTS: We have developed a tool called the HeatMapper that provides such visualizations in a dynamic and flexible manner and is available from http://www.erasmusmc.nl/hematologie/heatmapper/. CONCLUSION: The HeatMapper allows an accessible and comprehensive visualization of the results of gene expression profiling and cluster analysis. http://repub.eur.nl/res/pub/13984/ 2006-03-02T00:00:00Z BACKGROUND: Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers. RESULTS: Using two publicly available datasets, i.e., gene-expression data of 285 patients with Acute Myeloid Leukemia (AML, Affymetrix HG-U133A GeneChip) and 42 samples of tumor tissue of the embryonal central nervous system (CNS, Affymetrix HuGeneFL GeneChip), we tested the effect of the four pre-processing strategies mentioned above, on (1) expression level measurements, (2) detection of differential expression, (3) cluster analysis and (4) classification of samples. In most cases, the effect of pre-processing is relatively small compared to other choices made in an analysis for the AML dataset, but has a more profound effect on the outcome of the CNS dataset. Analyses on individual probe sets, such as testing for differential expression, are affected most; supervised, multivariate analyses such as classification are far less sensitive to pre-processing. CONCLUSION: Using two experimental datasets, we show that the choice of pre-processing method is of relatively minor influence on the final analysis outcome of large microarray studies whereas it can have important effects on the results of a smaller study. The data source (platform, tissue homogeneity, RNA quality) is potentially of bigger importance than the choice of pre-processing method. 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.