Gerben J Schaaf

University of Amsterdam, Amsterdam, North Holland, Netherlands

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Publications (8)25.51 Total impact

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    ABSTRACT: RAS oncogenes are among the most frequently mutated genes in human cancer, but effective strategies for therapeutic inhibition of the RAS pathway have been elusive. Sprouty1 (SPRY1) is an upstream antagonist of RAS that is activated by extracellular signal-related kinase (ERK), providing a negative feedback loop for RAS signaling, and other evidence suggests that SPRY1 may have a tumor suppressor function. Studies of RAS status in the human childhood tumor rhabdomyosarcoma (RMS) indicated mutations in approximately half of the tumors of the embryonal rhabdomyosarcoma subtype (ERMS) but not the alveolar subtype (ARMS). ERMS tumors also showed overexpression of SPRY1, which was indeed upregulated by mutant RAS. However, we found that, in the presence of mutant RAS, the function of SPRY1 was changed from an antagonist to an agonist of RAS signaling. Thus, SPRY1 supported formation of activated ERK and mitogen-activated protein/ERK kinase and was essential for ERMS cell proliferation and survival. Conversely, silencing of SPRY1 in ERMS cells (but not ARMS cells) abolished their tumorigenicity in mice. Moreover, silencing of SPRY1 caused regression of established ERMS tumors (but not ARMS tumors) formed in xenograft settings. Our findings argue that SPRY1 inhibition can offer a therapeutic strategy to treat childhood RMS and possibly other tumors carrying oncogenic RAS mutations.
    Cancer Research 01/2010; 70(2):762-71. · 9.28 Impact Factor
  • Ejc Supplements - EJC SUPPL. 01/2010; 8(5):138-138.
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    ABSTRACT: Rhabdomyosarcomas (RMS) are the most common pediatric soft tissue sarcomas. They resemble developing skeletal muscle and are histologically divided into two main subtypes; alveolar and embryonal RMS. Characteristic genomic aberrations, including the PAX3- and PAX7-FOXO1 fusion genes in alveolar cases, have led to increased understanding of their molecular biology. Here, we determined the effect of genomic copy number on gene expression levels through array comparative genomic hybridization (CGH) analysis of 13 RMS cell lines, confirmed by multiplex ligation-dependent probe amplification copy number analyses, combined with their corresponding expression profiles. Genes altered at the transcriptional level by genomic imbalances were identified and the effect on expression was proportional to the level of genomic imbalance. Extrapolating to a public expression profiling dataset for 132 primary RMS identified features common to the cell lines and primary samples and associations with subtypes and fusion gene status. Genes identified such as CDK4 and MYCN are known to be amplified, overexpressed, and involved in RMS tumorigenesis. Of the many genes identified, those with likely functional relevance included CENPF, DTL, MYC, EYA2, and FGFR1. Copy number and expression of FGFR1 was validated in additional primary material and found amplified in 6 out of 196 cases and overexpressed relative to skeletal muscle and myoblasts, with significantly higher expression levels in the embryonal compared with alveolar subtypes. This illustrates the ability to identify genes of potential significance in tumor development through combining genomic and transcriptomic profiles from representative cell lines with publicly available expression profiling data from primary tumors.
    Genes Chromosomes and Cancer 03/2009; 48(6):455-67. · 3.55 Impact Factor
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    ABSTRACT: Serial analysis of gene expression (SAGE) and microarrays have found a widespread application, but much ambiguity exists regarding the amalgamation of the data resulting from these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce the need for duplicate experiments and facilitate a more extensive exchange of data within the research community. This requires a measure for the correspondence of the results from different gene expression platforms. To date, a number of cross-platform evaluations (including a few studies using SAGE and Affymetrix GeneChips) have been conducted showing a variable, but overall low, concordance using different overall correlation approaches, such as Up/Down classification, contingency tables, and correlation coefficients. Here, we demonstrate an approach to compare two platforms based on the calculation of the difference between expression ratios observed in each platform for each individual transcript. This approach results in a concordance measure per gene, as opposed to the commonly used overall concordance measures between platforms. This between-ratio difference is a filtering-independent measure for between-platform concordance. Moreover, the between-ratio difference per gene can be used to identify transcripts with similar regulation on both platforms.
    Methods in molecular biology (Clifton, N.J.) 02/2008; 387:169-83. · 1.29 Impact Factor
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    ABSTRACT: Several statistical tests have been introduced for the comparison of serial analysis of gene expression (SAGE) libraries to quantitatively analyze the differential expression of genes. As each SAGE library is only one measurement, the necessary information on biological variation or experimental precision is lacking. Therefore, each test includes its own approach to derive such a variance measure from the data set or a theoretical distribution. Because the confidence in tag counts depends on the library size, a test between two or more libraries should be based on original tag counts. When groups of libraries are compared, the test should determine that the proportion of a specific tag in all libraries is the same (null hypothesis), but also offer the possibility to detect specific differences between individual libraries and groups of libraries. The Z-test and the G-test encompass these characteristics and are described for the comparison of two libraries and (two or more) groups of libraries, respectively.
    Methods in molecular biology (Clifton, N.J.) 02/2008; 387:151-68. · 1.29 Impact Factor
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    ABSTRACT: Several statistical tests have been introduced for the comparison of serial analysis of gene expression (SAGE) libraries to quantitatively analyze the differential expression of genes. As each SAGE library is only one measurement, the necessary information on biological variation or experimental precision is lacking. Therefore, each test includes its own approach to derive such a variance measure from the data set or a theoretical distribution. Because the confidence in tag counts depends on the library size, a test between two or more libraries should be based on original tag counts. When groups of libraries are compared, the test should determine that the proportion of a specific tag in all libraries is the same (null hypothesis), but also offer the possibility to detect specific differences between individual libraries and groups of libraries. The Z-test and the G-test encompass these characteristics and are described for the comparison of two libraries and (two or more) groups of libraries, respectively. Key WordsSAGE-statistics–Z-test–binomial distribution–G-test–log–likelihood ratio–multinomial distribution–null hypothesis
    12/2007: pages 151-168;
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    ABSTRACT: Rhabdomyosarcoma (RMS) is the most frequent soft tissue sarcoma in children. Improved treatment strategies have increased overall survival, but the response of approximately one-third of the patients is still poor. To increase the knowledge of RMS pathogenesis, we performed the first full transcriptome analysis of RMS using serial analysis of gene expression (SAGE). With a G-test for the simultaneous comparison of subsets of SAGE libraries of normal skeletal muscle, embryonal (ERMS) and alveolar (ARMS) RMS, we identified 251 differentially expressed genes. A literature-mining procedure demonstrated that 158 of these genes have not previously been associated with RMS or normal muscle. Gene Ontology (GO) analysis assigned 198 of the 251 genes to muscle-specific classes, including those involved in normal myogenic development, as well as tumor-related classes. Prominent GO classes were those associated with proliferation and actin reorganization, which are processes that play roles during early muscle development, muscle function, and tumor progression. Using custom microarrays, we confirmed the (up- or down-) regulation of 80% of 98 differentially expressed genes. Another SAGE library of 19- to 22-week-old fetal skeletal muscle was compared with the RMS and normal muscle transcriptomes. Cluster analysis showed that the RMS and fetal muscle SAGE libraries formed one cluster distinct from normal muscle samples. Moreover, the expression profile of 86% of the differentially expressed genes between normal muscle and RMS was highly similar in fetal muscle and RMS. In conclusion, the G-test is a robust tool for analyzing groups of SAGE libraries and correctly identifies genes marking the difference between fully differentiated skeletal muscle and RMS. This study not only substantiates the close association between embryonic myogenesis and RMS development but also provides a rich source of candidate genes to further elucidate the etiology of RMS or to identify diagnostic and/or prognostic markers.
    The FASEB Journal 04/2005; 19(3):404-6. · 5.70 Impact Factor
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    ABSTRACT: Serial Analysis of Gene Expression (SAGE) and microarrays have found a widespread application, but much ambiguity exists regarding the evaluation of these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce the need for duplicate experiments and facilitate a more extensive exchange of data within the research community. This requires a measure for the correspondence of the different gene expression platforms. To date, a number of cross-platform evaluations (including a few studies using SAGE and Affymetrix GeneChips) have been conducted showing a variable, but overall low, concordance. This study evaluates these overall measures and introduces the between-ratio difference as a concordance measure pergene. In this study, gene expression measurements of Unigene clusters represented by both Affymetrix GeneChips HG-U133A and SAGE were compared using two independent RNA samples. After matching of the data sets the final comparison contains a small data set of 1094 unique Unigene clusters, which is unbiased with respect to expression level. Different overall correlation approaches, like Up/Down classification, contingency tables and correlation coefficients were used to compare both platforms. In addition, we introduce a novel approach to compare two platforms based on the calculation of differences between expression ratios observed in each platform for each individual transcript. This approach results in a concordance measure per gene (with statistical probability value), as opposed to the commonly used overall concordance measures between platforms. We can conclude that intra-platform correlations are generally good, but that overall agreement between the two platforms is modest. This might be due to the binomially distributed sampling variation in SAGE tag counts, SAGE annotation errors and the intensity variation between probe sets of a single gene in Affymetrix GeneChips. We cannot identify or advice which platform performs better since both have their (dis)-advantages. Therefore it is strongly recommended to perform follow-up studies of interesting genes using additional techniques. The newly introduced between-ratio difference is a filtering-independent measure for between-platform concordance. Moreover, the between-ratio difference per gene can be used to detect transcripts with similar regulation on both platforms.
    BMC Genomics 02/2005; 6:91. · 4.40 Impact Factor